diff --git a/CW1/460cw1_2020.ipynb b/CW1/460cw1_2020.ipynb new file mode 100644 index 0000000..a3074ce --- /dev/null +++ b/CW1/460cw1_2020.ipynb @@ -0,0 +1,12189 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "HgGeQMItJMVo" + }, + "source": [ + "# Coursework1: Convolutional Neural Networks " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Iz5hs-NtJMVq" + }, + "source": [ + "## instructions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FF6miJ0pJMVr" + }, + "source": [ + "Please submit a version of this notebook containing your answers **together with your trained model** on CATe as CW2.zip. Write your answers in the cells below each question." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OTd4PWOJJMVr" + }, + "source": [ + "### Setting up working environment " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aawBRqNYJMVs" + }, + "source": [ + "For this coursework you will need to train a large network, therefore we recommend you work with Google Colaboratory, which provides free GPU time. You will need a Google account to do so. \n", + "\n", + "Please log in to your account and go to the following page: https://colab.research.google.com. Then upload this notebook.\n", + "\n", + "For GPU support, go to \"Edit\" -> \"Notebook Settings\", and select \"Hardware accelerator\" as \"GPU\".\n", + "\n", + "You will need to install pytorch by running the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "99KzLz6ZJMVs", + "outputId": "168b2724-f1a7-42d7-ce15-3ffa8c6c3082" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: torch in c:\\users\\yunmao\\.conda\\envs\\ml\\lib\\site-packages (1.7.1)\n", + "Requirement already satisfied: torchvision in c:\\users\\yunmao\\.conda\\envs\\ml\\lib\\site-packages (0.8.2)\n", + "Requirement already satisfied: typing-extensions in c:\\users\\yunmao\\.conda\\envs\\ml\\lib\\site-packages (from torch) (3.7.4.3)\n", + "Requirement already satisfied: numpy in c:\\users\\yunmao\\.conda\\envs\\ml\\lib\\site-packages (from torch) (1.19.5)\n", + "Requirement already satisfied: pillow>=4.1.1 in c:\\users\\yunmao\\.conda\\envs\\ml\\lib\\site-packages (from torchvision) (8.1.0)\n" + ] + } + ], + "source": [ + "!pip install torch torchvision" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PzhJatU5JMVt" + }, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3XhBM-YJMVt" + }, + "source": [ + "For this coursework you will implement one of the most commonly used model for image recognition tasks, the Residual Network. The architecture is introduced in 2015 by Kaiming He, et al. in the paper [\"Deep residual learning for image recognition\"](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf). \n", + "
\n", + "\n", + "In a residual network, each block contains some convolutional layers, plus \"skip\" connections, which allow the activations to by pass a layer, and then be summed up with the activations of the skipped layer. The image below illustrates a building block in residual networks." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mgFPNJz4JMVt" + }, + "source": [ + "![resnet-block](utils/resnet-block.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nsER7qvIJMVt" + }, + "source": [ + "Depending on the number of building blocks, resnets can have different architectures, for example ResNet-50, ResNet-101 and etc. Here you are required to build ResNet-18 to perform classification on the CIFAR-10 dataset, therefore your network will have the following architecture:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TjqXBo_KJMVu" + }, + "source": [ + "![resnet](utils/resnet.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ellLCBc6JMVu" + }, + "source": [ + "## Part 1 (40 points)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4BBu-gVCJMVu" + }, + "source": [ + "In this part, you will use basic pytorch operations to define the 2D convolution, max pooling operation, linear layer as well as 2d batch normalization. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "60FUTQkaJMVu" + }, + "source": [ + "### YOUR TASK" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "quBpjSufJMVu" + }, + "source": [ + "- implement the forward pass for Conv2D, MaxPool2D, Linear and BatchNorm2d\n", + "- You are **NOT** allowed to use the torch.nn modules" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "88OnDTEcJMVv" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class Conv2d(nn.Module):\n", + " def __init__(self,\n", + " in_channels,\n", + " out_channels,\n", + " kernel_size,\n", + " stride=1,\n", + " padding=0,\n", + " bias=True):\n", + "\n", + " super(Conv2d, self).__init__()\n", + " \"\"\"\n", + " An implementation of a convolutional layer.\n", + "\n", + " The input consists of N data points, each with C channels, height H and\n", + " width W. We convolve each input with F different filters, where each filter\n", + " spans all C channels and has height HH and width WW.\n", + "\n", + " Parameters:\n", + " - w: Filter weights of shape (F, C, HH, WW)\n", + " - b: Biases, of shape (F,)\n", + " - kernel_size: Size of the convolving kernel\n", + " - stride: The number of pixels between adjacent receptive fields in the\n", + " horizontal and vertical directions.\n", + " - padding: The number of pixels that will be used to zero-pad the input.\n", + " \"\"\"\n", + "\n", + " ########################################################################\n", + " # TODO: Define the parameters used in the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " self.in_channels = in_channels\n", + " self.out_channels = out_channels\n", + " # self.stride = stride\n", + " \n", + " self.padding = padding\n", + "\n", + " if type(kernel_size) is tuple :\n", + " self.kernel_size = kernel_size\n", + " else:\n", + " self.kernel_size = (kernel_size, kernel_size)\n", + "\n", + " if type(stride) is tuple :\n", + " self.stride = stride\n", + " else:\n", + " self.stride = (stride, stride)\n", + "\n", + " # Good practice is to start your weights in the range of [-y, y] where y=1/sqrt(n) (n is the number of inputs to a given neuron).\n", + " self.w = torch.randn((out_channels, in_channels, self.kernel_size[0], self.kernel_size[1]), requires_grad = True) \\\n", + " * torch.sqrt(torch.tensor(1.0/in_channels))\n", + "\n", + " if bias:\n", + " self.b = torch.randn(out_channels, requires_grad = True) * torch.sqrt(torch.tensor(1.0/in_channels))\n", + " else:\n", + " self.b = None\n", + "\n", + " self.bias = bias\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Input:\n", + " - x: Input data of shape (N, C, H, W)\n", + " Output:\n", + " - out: Output data, of shape (N, F, H', W').\n", + " \"\"\"\n", + "\n", + " ########################################################################\n", + " # TODO: Implement the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " #Calculate the output shape\n", + " out_H = (x.shape[2] + 2*self.padding - self.kernel_size[0]) // self.stride[0] + 1\n", + " out_W = (x.shape[3] + 2*self.padding - self.kernel_size[1]) // self.stride[1] + 1\n", + "\n", + " x_unfold = F.unfold(x, kernel_size = self.kernel_size, padding = self.padding, stride = self.stride)\n", + "\n", + " if self.bias:\n", + " out_unfold = x_unfold.permute(0, 2, 1).matmul(self.w.view(self.w.size(0), -1).t()).permute(0, 2, 1) + self.b.view(-1, 1)\n", + " else:\n", + " out_unfold = x_unfold.permute(0, 2, 1).matmul(self.w.view(self.w.size(0), -1).t()).permute(0, 2, 1)\n", + " out = out_unfold.view(x.shape[0], self.out_channels, out_H, out_W)\n", + "\n", + " # for testing the function\n", + " # out_test = nn.functional.conv2d(x, self.w, self.b, padding=1)\n", + " # print((out_test - out).abs().max())\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " return out\n", + "# for testing the function\n", + "# inputs = torch.rand(3, 3, 24, 24)\n", + "# conv2 = Conv2d(in_channels=3, out_channels=3, kernel_size=(3, 3),stride=1, padding=1)\n", + "# out = conv2(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cGf6Da9iJMVv" + }, + "outputs": [], + "source": [ + "class MaxPool2d(nn.Module):\n", + " def __init__(self, kernel_size):\n", + " super(MaxPool2d, self).__init__()\n", + " \"\"\"\n", + " An implementation of a max-pooling layer.\n", + "\n", + " Parameters:\n", + " - kernel_size: the size of the window to take a max over\n", + " \"\"\"\n", + " ########################################################################\n", + " # TODO: Define the parameters used in the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " if type(kernel_size) is tuple :\n", + " self.kernel_size = kernel_size\n", + " else:\n", + " self.kernel_size = (kernel_size, kernel_size)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Input:\n", + " - x: Input data of shape (N, C, H, W)\n", + " Output:\n", + " - out: Output data, of shape (N, F, H', W').\n", + " \"\"\"\n", + " ########################################################################\n", + " # TODO: Implement the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " H_k, W_k = self.kernel_size\n", + "\n", + " x_unfold = F.unfold(x, kernel_size = self.kernel_size, stride = self.kernel_size)\n", + "\n", + " x_reshape = x_unfold.view(x.shape[0], x.shape[1], self.kernel_size[0] * self.kernel_size[1], -1)\n", + "\n", + " x_max = x_reshape.max(axis = 2)[0]\n", + "\n", + " H_out = x.shape[2] // H_k\n", + " W_out = x.shape[3] // W_k\n", + "\n", + " out = x_max.view(x.shape[0], x.shape[1], H_out, W_out)\n", + " # print(out == nn.functional.max_pool2d(x, kernel_size = self.kernel_size))\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " return out\n", + "# inputs = torch.rand(2, 2, 3, 4)\n", + "# maxpool = MaxPool2d(kernel_size=2)\n", + "# print(maxpool.forward(inputs).shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aIR9IY5iJMVw" + }, + "outputs": [], + "source": [ + "class Linear(nn.Module):\n", + " def __init__(self, in_channels, out_channels, bias=True):\n", + " super(Linear, self).__init__()\n", + " \"\"\"\n", + " An implementation of a Linear layer.\n", + "\n", + " Parameters:\n", + " - weight: the learnable weights of the module of shape (in_channels, out_channels).\n", + " - bias: the learnable bias of the module of shape (out_channels).\n", + " \"\"\"\n", + " ########################################################################\n", + " # TODO: Define the parameters used in the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " self.weight = torch.randn((in_channels, out_channels), requires_grad = True) * torch.sqrt(torch.tensor(1.0/in_channels))\n", + "\n", + " if bias:\n", + " self.bias = torch.randn(out_channels, requires_grad = True) * torch.sqrt(torch.tensor(1.0/in_channels))\n", + " else:\n", + " self.bias = None\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " Input:\n", + " - x: Input data of shape (N, *, H) where * means any number of additional\n", + " dimensions and H = in_channels\n", + " Output:\n", + " - out: Output data of shape (N, *, H') where * means any number of additional\n", + " dimensions and H' = out_channels\n", + " \"\"\"\n", + " ########################################################################\n", + " # TODO: Implement the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " if self.bias != None:\n", + " out = torch.matmul(x, self.weight) + self.bias\n", + " else:\n", + " out = torch.matmul(x, self.weight)\n", + " # nn.functional.linear(x,self.weight, self.bias)\n", + " # print(out == nn.functional.linear(x,self.weight.t(), self.bias))\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " return out\n", + "# inputs = torch.rand(3, 3, 23, 23)\n", + "# linear = Linear(in_channels=23, out_channels = 4)\n", + "# out2 = linear.forward(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "qMpvG3jBJMVx" + }, + "outputs": [], + "source": [ + "class BatchNorm2d(nn.Module):\n", + " def __init__(self, num_features, eps=1e-05, momentum=0.1):\n", + " super(BatchNorm2d, self).__init__()\n", + " \"\"\"\n", + " An implementation of a Batch Normalization over a mini-batch of 2D inputs.\n", + "\n", + " The mean and standard-deviation are calculated per-dimension over the\n", + " mini-batches and gamma and beta are learnable parameter vectors of\n", + " size num_features.\n", + "\n", + " Parameters:\n", + " - num_features: C from an expected input of size (N, C, H, W).\n", + " - eps: a value added to the denominator for numerical stability. Default: 1e-5\n", + " - momentum: momentum – the value used for the running_mean and running_var\n", + " computation. Default: 0.1\n", + " - gamma: the learnable weights of shape (num_features).\n", + " - beta: the learnable bias of the module of shape (num_features).\n", + " \"\"\"\n", + " ########################################################################\n", + " # TODO: Define the parameters used in the forward pass #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " self.num_features = num_features\n", + " self.eps = eps\n", + " self.momentum = momentum\n", + " self.gamma = torch.ones(num_features, requires_grad = True)\n", + " self.beta = torch.zeros(num_features, requires_grad = True)\n", + " self.running_mean = torch.zeros(num_features)\n", + " self.running_var = torch.ones(num_features)\n", + " self.training = True\n", + "\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " During training this layer keeps running estimates of its computed mean and\n", + " variance, which are then used for normalization during evaluation.\n", + " Input:\n", + " - x: Input data of shape (N, C, H, W)\n", + " Output:\n", + " - out: Output data of shape (N, C, H, W) (same shape as input)\n", + " \"\"\"\n", + " ########################################################################\n", + " # TODO: Implement the forward pass #\n", + " # (be aware of the difference for training and testing) #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " if self.training:\n", + " mean = x.mean([0, 2, 3])\n", + " var = x.var([0, 2, 3])\n", + " self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean\n", + " self.running_var = (1 - self.momentum) * self.running_var + self.momentum * var\n", + " var = x.var([0, 2, 3], unbiased = False)\n", + " else:\n", + " mean = self.running_mean\n", + " var = self.running_var\n", + " \n", + " x = (x - mean.view(1, -1, 1, 1)) / torch.sqrt(var + self.eps).view(1, -1, 1, 1) * self.gamma.view(1, -1, 1, 1) \\\n", + " + self.beta.view(1, -1, 1, 1)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + "\n", + " return x\n", + "# inputs = torch.rand(3, 3, 23, 23)\n", + "# batch = BatchNorm2d(3)\n", + "# test = nn.BatchNorm2d(3)\n", + "\n", + "# for i in range(3):\n", + "# out = test.forward(inputs)\n", + "# out2 = batch.forward(inputs)\n", + "# # test.training = False\n", + "# # batch.training = False\n", + "# out = test.forward(inputs)\n", + "# out2 = batch.forward(inputs)\n", + "# for i in range(5):\n", + "# out = test.forward(inputs)\n", + "# out2 = batch.forward(inputs)\n", + "# print(out)\n", + "# print(out2)\n", + "# print(test.running_var)\n", + "# print(batch.running_var)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nKR54x28JMVy" + }, + "source": [ + "## Part 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DQmghXtJJMVy" + }, + "source": [ + "In this part, you will train a ResNet-18 defined on the CIFAR-10 dataset. Code for training and evaluation are provided. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LSRwh-ASJMVz" + }, + "source": [ + "### Your Task" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SVijmjCLJMVz" + }, + "source": [ + "1. Train your network to achieve the best possible test set accuracy after a maximum of 10 epochs of training.\n", + "\n", + "2. You can use techniques such as optimal hyper-parameter searching, data pre-processing\n", + "\n", + "3. If necessary, you can also use another optimizer\n", + "\n", + "4. **Answer the following question:**\n", + "Given such a network with a large number of trainable parameters, and a training set of a large number of data, what do you think is the best strategy for hyperparameter searching? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**YOUR ANSWER FOR PART 2.4 HERE**\n", + "\n", + "A: In my view, Bayesian Optimisation can be one of the best strategies to find the quit good hyperparameters. As we know, the time and power for training can be quit huge under large number of data if you just search the hyperparameters with grid search, worsely you even cannot get the good ones. While, you can find a better hyperparameter combination with a very small number of steps with Bayesian Optimisation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "aPWE0kvLJMVz" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.nn import Conv2d, MaxPool2d\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BawVv_AcJMVz" + }, + "source": [ + "Next, we define ResNet-18:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "rn847LqAJMVz" + }, + "outputs": [], + "source": [ + "# define resnet building blocks\n", + "\n", + "class ResidualBlock(nn.Module): \n", + " def __init__(self, inchannel, outchannel, stride=1): \n", + " \n", + " super(ResidualBlock, self).__init__() \n", + " \n", + " self.left = nn.Sequential(Conv2d(inchannel, outchannel, kernel_size=3, \n", + " stride=stride, padding=1, bias=False), \n", + " nn.BatchNorm2d(outchannel), \n", + " nn.ReLU(inplace=True), \n", + " Conv2d(outchannel, outchannel, kernel_size=3, \n", + " stride=1, padding=1, bias=False), \n", + " nn.BatchNorm2d(outchannel)) \n", + " \n", + " self.shortcut = nn.Sequential() \n", + " \n", + " if stride != 1 or inchannel != outchannel: \n", + " \n", + " self.shortcut = nn.Sequential(Conv2d(inchannel, outchannel, \n", + " kernel_size=1, stride=stride, \n", + " padding = 0, bias=False), \n", + " nn.BatchNorm2d(outchannel) ) \n", + " \n", + " def forward(self, x): \n", + " \n", + " out = self.left(x) \n", + " \n", + " out += self.shortcut(x) \n", + " \n", + " out = F.relu(out) \n", + " \n", + " return out\n", + "\n", + "\n", + " \n", + " # define resnet\n", + "\n", + "class ResNet(nn.Module):\n", + " \n", + " def __init__(self, ResidualBlock, num_classes = 10):\n", + " \n", + " super(ResNet, self).__init__()\n", + " \n", + " self.inchannel = 64\n", + " self.conv1 = nn.Sequential(Conv2d(3, 64, kernel_size = 3, stride = 1,\n", + " padding = 1, bias = False), \n", + " nn.BatchNorm2d(64), \n", + " nn.ReLU())\n", + " \n", + " self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride = 1)\n", + " self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride = 2)\n", + " self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride = 2)\n", + " self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride = 2)\n", + " self.maxpool = MaxPool2d(4)\n", + " self.fc = nn.Linear(512, num_classes)\n", + " \n", + " \n", + " def make_layer(self, block, channels, num_blocks, stride):\n", + " \n", + " strides = [stride] + [1] * (num_blocks - 1)\n", + " \n", + " layers = []\n", + " \n", + " for stride in strides:\n", + " \n", + " layers.append(block(self.inchannel, channels, stride))\n", + " \n", + " self.inchannel = channels\n", + " \n", + " return nn.Sequential(*layers)\n", + " \n", + " \n", + " def forward(self, x):\n", + " \n", + " x = self.conv1(x)\n", + " \n", + " x = self.layer1(x)\n", + " x = self.layer2(x)\n", + " x = self.layer3(x)\n", + " x = self.layer4(x)\n", + " \n", + " x = self.maxpool(x)\n", + " \n", + " x = x.view(x.size(0), -1)\n", + " \n", + " x = self.fc(x)\n", + " \n", + " return x\n", + " \n", + " \n", + "def ResNet18():\n", + " return ResNet(ResidualBlock)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a5mpaI_FJMVz" + }, + "source": [ + "### Loading dataset\n", + "We will import images from the [torchvision.datasets](https://pytorch.org/docs/stable/torchvision/datasets.html) library
\n", + "First, we need to define the alterations (transforms) we want to perform to our images - given that transformations are applied when importing the data.
\n", + "Define the following transforms using the torchvision.datasets library -- you can read the transforms documentation [here](https://pytorch.org/docs/stable/torchvision/transforms.html):
\n", + "1. Convert images to tensor\n", + "2. Normalize mean and std of images with values:mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "IVd9nxxcJMV0" + }, + "outputs": [], + "source": [ + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader\n", + "from torch.utils.data import sampler\n", + "\n", + "import torchvision.datasets as dset\n", + "\n", + "import numpy as np\n", + "\n", + "import torchvision.transforms as T\n", + "\n", + "##############################################################\n", + "# YOUR CODE HERE # \n", + "##############################################################\n", + "\n", + "transform_aug = T.Compose([\n", + " T.RandomRotation(10),\n", + " T.RandomCrop(size=32, padding=4),\n", + " T.RandomHorizontalFlip(),\n", + " T.ToTensor(),\n", + " T.Normalize(mean = [0.4914, 0.4822, 0.4465], std = [0.2023, 0.1994, 0.2010])\n", + "])\n", + "\n", + "transform = T.Compose([\n", + " T.ToTensor(),\n", + " T.Normalize(mean = [0.4914, 0.4822, 0.4465], std = [0.2023, 0.1994, 0.2010])\n", + "])\n", + "\n", + "##############################################################\n", + "# END OF YOUR CODE #\n", + "##############################################################\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3ZT5uKAKJMV0" + }, + "source": [ + "Now load the dataset using the transform you defined above, with batch_size = 64
\n", + "You can check the documentation [here](https://pytorch.org/docs/stable/torchvision/datasets.html).\n", + "Then create data loaders (using DataLoader from torch.utils.data) for the training and test set" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 116, + "referenced_widgets": [ + "9e14695099e542dc902486ab289de00e", + "706ee09960624d38af0ba789aee88e61", + "83b2ceb8db1545a2beda4af4a1c5557f", + "bab34fa6fd4f4bed93471e276768f77a", + "0cb3b424ec58490bbf97d3ee9bed7d68", + "11c7e207eabc4e4ca00f47c84f96223d", + "4f9a65395946462fb463cabcb9479d2e", + "0fff04cf382444deaa00dd4583b9e782" + ] + }, + "id": "LgUDdV7vJMV0", + "outputId": "230a5465-eb65-437e-927f-ada66218895a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "\n", + "##############################################################\n", + "# YOUR CODE HERE # \n", + "##############################################################\n", + "\n", + "data_dir = './data'\n", + "\n", + "cifar10_train_data = dset.CIFAR10(data_dir, train = True, download = True, transform = transform_aug)\n", + "loader_train = DataLoader(cifar10_train_data, batch_size=64, sampler=sampler.SubsetRandomSampler(range(49000)))\n", + "\n", + "cifar10_val_data = dset.CIFAR10(data_dir, train = True, download = True, transform = transform)\n", + "loader_val = DataLoader(cifar10_val_data, batch_size=64, sampler=sampler.SubsetRandomSampler(range(49000, 50000)))\n", + "\n", + "cifar10_test = dset.CIFAR10(data_dir, train = False, download = True, transform = transform)\n", + "loader_test = DataLoader(cifar10_test, batch_size=64)\n", + "##############################################################\n", + "# END OF YOUR CODE # \n", + "##############################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BkaYT4ahJMV0", + "outputId": "cc8787f6-ac4b-4a61-c252-b1c80b04ca0e" + }, + "outputs": [], + "source": [ + "USE_GPU = True\n", + "dtype = torch.float32 \n", + "\n", + "if USE_GPU and torch.cuda.is_available():\n", + " device = torch.device('cuda')\n", + "else:\n", + " device = torch.device('cpu')\n", + "\n", + "print_every = 100\n", + "def check_accuracy(loader, model):\n", + " # function for test accuracy on validation and test set\n", + " \n", + " if loader.dataset.train:\n", + " print('Checking accuracy on validation set')\n", + " else:\n", + " print('Checking accuracy on test set') \n", + " num_correct = 0\n", + " num_samples = 0\n", + " model.eval() # set model to evaluation mode\n", + " with torch.no_grad():\n", + " for x, y in loader:\n", + " x = x.to(device=device, dtype=dtype) # move to device\n", + " y = y.to(device=device, dtype=torch.long)\n", + " scores = model(x)\n", + " _, preds = scores.max(1)\n", + " num_correct += (preds == y).sum()\n", + " num_samples += preds.size(0)\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f)' % (num_correct, num_samples, 100 * acc))\n", + " return acc # add by Tianyu Dai\n", + " \n", + "\n", + "def train_part(model, optimizer, epochs=1):\n", + " \"\"\"\n", + " Train a model on CIFAR-10 using the PyTorch Module API.\n", + " \n", + " Inputs:\n", + " - model: A PyTorch Module giving the model to train.\n", + " - optimizer: An Optimizer object we will use to train the model\n", + " - epochs: (Optional) A Python integer giving the number of epochs to train for\n", + " \n", + " Returns: Nothing, but prints model accuracies during training.\n", + " \"\"\"\n", + " model = model.to(device=device) # move the model parameters to CPU/GPU\n", + " for e in range(epochs):\n", + " print(len(loader_train))\n", + " for t, (x, y) in enumerate(loader_train):\n", + " model.train() # put model to training mode\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.long)\n", + "\n", + " scores = model(x)\n", + " loss = F.cross_entropy(scores, y)\n", + "\n", + " # Zero out all of the gradients for the variables which the optimizer\n", + " # will update.\n", + " optimizer.zero_grad()\n", + "\n", + " loss.backward()\n", + "\n", + " # Update the parameters of the model using the gradients\n", + " optimizer.step()\n", + "\n", + " if t % print_every == 0:\n", + " print('Epoch: %d, Iteration %d, loss = %.4f' % (e, t, loss.item()))\n", + " # check_accuracy(loader_val, model)\n", + " print()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# code for optimising your network performance\n", + "\n", + "##############################################################\n", + "# YOUR CODE HERE # \n", + "##############################################################\n", + "\n", + "## I have used two method to optimising my net work, one without learn rate scheduler, while other with it.\n", + "# for the function train_part2, the scheduler parameter is added.\n", + "\n", + "def train_part2(model, optimizer, epochs=1, scheduler = None):\n", + " \"\"\"\n", + " Train a model on CIFAR-10 using the PyTorch Module API.\n", + " \n", + " Inputs:\n", + " - model: A PyTorch Module giving the model to train.\n", + " - optimizer: An Optimizer object we will use to train the model\n", + " - epochs: (Optional) A Python integer giving the number of epochs to train for\n", + " \n", + " Returns: Nothing, but prints model accuracies during training.\n", + " \"\"\"\n", + " model = model.to(device=device) # move the model parameters to CPU/GPU\n", + " for e in range(epochs):\n", + " print(len(loader_train))\n", + " for t, (x, y) in enumerate(loader_train):\n", + " model.train() # put model to training mode\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.long)\n", + "\n", + " scores = model(x)\n", + " loss = F.cross_entropy(scores, y)\n", + "\n", + " # Zero out all of the gradients for the variables which the optimizer\n", + " # will update.\n", + " optimizer.zero_grad()\n", + "\n", + " loss.backward()\n", + "\n", + " # Update the parameters of the model using the gradients\n", + " optimizer.step()\n", + "\n", + " if t % print_every == 0:\n", + " print('Epoch: %d, Iteration %d, loss = %.4f' % (e, t, loss.item()))\n", + " check_accuracy(loader_val, model)\n", + " print()\n", + " scheduler.step()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2RzkDweiJMV0", + "outputId": "2cf53caa-accc-49d9-eb0a-c8d7c4b93037" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting GPyopt\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/52/be/669d505416d7e465b2aef7df3b58d590f56468c4f7dc50c91fe91b8a78d9/GPyOpt-1.2.6.tar.gz (56kB)\n", + "\u001b[K |████████████████████████████████| 61kB 6.6MB/s \n", + "\u001b[?25hRequirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from GPyopt) (1.19.5)\n", + "Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.6/dist-packages (from GPyopt) (1.4.1)\n", + "Collecting GPy>=1.8\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/67/95/976598f98adbfa918a480cb2d643f93fb555ca5b6c5614f76b69678114c1/GPy-1.9.9.tar.gz (995kB)\n", + "\u001b[K |████████████████████████████████| 1.0MB 21.8MB/s \n", + "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from GPy>=1.8->GPyopt) (1.15.0)\n", + "Collecting paramz>=0.9.0\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/d8/37/4abbeb78d30f20d3402887f46e6e9f3ef32034a9dea65d243654c82c8553/paramz-0.9.5.tar.gz (71kB)\n", + "\u001b[K |████████████████████████████████| 71kB 11.2MB/s \n", + "\u001b[?25hRequirement already satisfied: decorator>=4.0.10 in /usr/local/lib/python3.6/dist-packages (from paramz>=0.9.0->GPy>=1.8->GPyopt) (4.4.2)\n", + "Building wheels for collected packages: GPyopt, GPy, paramz\n", + " Building wheel for GPyopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for GPyopt: filename=GPyOpt-1.2.6-cp36-none-any.whl size=83623 sha256=5db6858d32c213a07b1f1924813580754150a4c4a7894107adbc7f32977a4b48\n", + " Stored in directory: /root/.cache/pip/wheels/b2/00/69/cfa967a125cf25e66f644be6193ad6f0edf231147879ad714f\n", + " Building wheel for GPy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for GPy: filename=GPy-1.9.9-cp36-cp36m-linux_x86_64.whl size=2633961 sha256=2106a6e2d1a2e6f68018fb879721db2d5fa9eec5001a5116076fc30743971ca3\n", + " Stored in directory: /root/.cache/pip/wheels/5d/36/66/2b58860c84c9f2b51615da66bfd6feeddbc4e04d887ff96dfa\n", + " Building wheel for paramz (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for paramz: filename=paramz-0.9.5-cp36-none-any.whl size=102552 sha256=60318d6e522d1e31f8c4e3258dc400d55ea6e67145312874cb7f218c26528451\n", + " Stored in directory: /root/.cache/pip/wheels/c8/4a/0e/6e0dc85541825f991c431619e25b870d4b812c911214690cf8\n", + "Successfully built GPyopt GPy paramz\n", + "Installing collected packages: paramz, GPy, GPyopt\n", + "Successfully installed GPy-1.9.9 GPyopt-1.2.6 paramz-0.9.5\n" + ] + } + ], + "source": [ + "!pip install GPyopt" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4-FQLRSbJMV1", + "outputId": "7d8af593-7a22-4b41-c472-f504fab4a566" + }, + "source": [ + "import GPyOpt\n", + "def Bayes_Opt(parameters):\n", + " lr = parameters[0, 0]\n", + " weight_decay = parameters[0, 1]\n", + " print(\"---- New lr = %.5f and weight_decay = %.5f ----\"% (lr, weight_decay))\n", + " model = ResNet18()\n", + " optimizer = optim.Adam(model.parameters(),lr = lr, weight_decay = weight_decay)\n", + " train_part(model, optimizer, epochs = 10)\n", + " return check_accuracy(loader_val, model)\n", + "\n", + "domain = [ {'name' : 'lr', 'type': 'continuous', 'domain': (0.0005, 0.005)},\n", + " {'name': 'weight_decay', 'type': 'continuous', 'domain': (0.0001, 0.001)}, ]\n", + "opt = GPyOpt.methods.BayesianOptimization(f = Bayes_Opt,domain = domain,acquisition_type ='LCB',acquisition_weight = 0.1,maximize = True)\n", + "\n", + "\n", + "opt.run_optimization( max_iter = 10)\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "---- New lr = 0.00212 and weight_decay = 0.00013 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.6991\n", + "Checking accuracy on validation set\n", + "Got 107 / 1000 correct (10.70)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.6612\n", + "Checking accuracy on validation set\n", + "Got 313 / 1000 correct (31.30)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6770\n", + "Checking accuracy on validation set\n", + "Got 340 / 1000 correct (34.00)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.5844\n", + "Checking accuracy on validation set\n", + "Got 384 / 1000 correct (38.40)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.7507\n", + "Checking accuracy on validation set\n", + "Got 372 / 1000 correct (37.20)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5577\n", + "Checking accuracy on validation set\n", + "Got 471 / 1000 correct (47.10)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.6247\n", + "Checking accuracy on validation set\n", + "Got 422 / 1000 correct (42.20)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4275\n", + "Checking accuracy on validation set\n", + "Got 500 / 1000 correct (50.00)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.5315\n", + "Checking accuracy on validation set\n", + "Got 538 / 1000 correct (53.80)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.4147\n", + "Checking accuracy on validation set\n", + "Got 585 / 1000 correct (58.50)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.1238\n", + "Checking accuracy on validation set\n", + "Got 572 / 1000 correct (57.20)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.2450\n", + "Checking accuracy on validation set\n", + "Got 555 / 1000 correct (55.50)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.0476\n", + "Checking accuracy on validation set\n", + "Got 580 / 1000 correct (58.00)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.9857\n", + "Checking accuracy on validation set\n", + "Got 611 / 1000 correct (61.10)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.3515\n", + "Checking accuracy on validation set\n", + "Got 598 / 1000 correct (59.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0223\n", + "Checking accuracy on validation set\n", + "Got 556 / 1000 correct (55.60)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9896\n", + "Checking accuracy on validation set\n", + "Got 617 / 1000 correct (61.70)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.8825\n", + "Checking accuracy on validation set\n", + "Got 621 / 1000 correct (62.10)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.9283\n", + "Checking accuracy on validation set\n", + "Got 661 / 1000 correct (66.10)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.8696\n", + "Checking accuracy on validation set\n", + "Got 652 / 1000 correct (65.20)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9148\n", + "Checking accuracy on validation set\n", + "Got 624 / 1000 correct (62.40)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.8600\n", + "Checking accuracy on validation set\n", + "Got 668 / 1000 correct (66.80)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.9862\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.1166\n", + "Checking accuracy on validation set\n", + "Got 674 / 1000 correct (67.40)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 1.0123\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.8012\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.0722\n", + "Checking accuracy on validation set\n", + "Got 708 / 1000 correct (70.80)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 1.1618\n", + "Checking accuracy on validation set\n", + "Got 703 / 1000 correct (70.30)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6213\n", + "Checking accuracy on validation set\n", + "Got 710 / 1000 correct (71.00)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.7957\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.9643\n", + "Checking accuracy on validation set\n", + "Got 702 / 1000 correct (70.20)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 1.0311\n", + "Checking accuracy on validation set\n", + "Got 709 / 1000 correct (70.90)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.8642\n", + "Checking accuracy on validation set\n", + "Got 711 / 1000 correct (71.10)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 1.0074\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.8587\n", + "Checking accuracy on validation set\n", + "Got 743 / 1000 correct (74.30)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.6553\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.9331\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6007\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.9240\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6366\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6410\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.8375\n", + "Checking accuracy on validation set\n", + "Got 766 / 1000 correct (76.60)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.6668\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.7622\n", + "Checking accuracy on validation set\n", + "Got 741 / 1000 correct (74.10)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 1.1154\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.6868\n", + "Checking accuracy on validation set\n", + "Got 756 / 1000 correct (75.60)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.7558\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5453\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.6800\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7670\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4405\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.9417\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.8832\n", + "Checking accuracy on validation set\n", + "Got 756 / 1000 correct (75.60)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 1.0137\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.6195\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.6126\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.5029\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.6902\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.7581\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.6064\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.6673\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5918\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.6958\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5946\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.8255\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.6452\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.6256\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.7987\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4277\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.5055\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5609\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.7767\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.5631\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.6253\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4565\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.6071\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.7066\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.4473\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.6348\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.5920\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "---- New lr = 0.00436 and weight_decay = 0.00043 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.4620\n", + "Checking accuracy on validation set\n", + "Got 87 / 1000 correct (8.70)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.1996\n", + "Checking accuracy on validation set\n", + "Got 255 / 1000 correct (25.50)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 2.1659\n", + "Checking accuracy on validation set\n", + "Got 257 / 1000 correct (25.70)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.9238\n", + "Checking accuracy on validation set\n", + "Got 329 / 1000 correct (32.90)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.5040\n", + "Checking accuracy on validation set\n", + "Got 356 / 1000 correct (35.60)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.6353\n", + "Checking accuracy on validation set\n", + "Got 367 / 1000 correct (36.70)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.7766\n", + "Checking accuracy on validation set\n", + "Got 459 / 1000 correct (45.90)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.3713\n", + "Checking accuracy on validation set\n", + "Got 361 / 1000 correct (36.10)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.6148\n", + "Checking accuracy on validation set\n", + "Got 381 / 1000 correct (38.10)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.4392\n", + "Checking accuracy on validation set\n", + "Got 436 / 1000 correct (43.60)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.4399\n", + "Checking accuracy on validation set\n", + "Got 474 / 1000 correct (47.40)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.3040\n", + "Checking accuracy on validation set\n", + "Got 500 / 1000 correct (50.00)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.3870\n", + "Checking accuracy on validation set\n", + "Got 474 / 1000 correct (47.40)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.2443\n", + "Checking accuracy on validation set\n", + "Got 501 / 1000 correct (50.10)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.2861\n", + "Checking accuracy on validation set\n", + "Got 535 / 1000 correct (53.50)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1665\n", + "Checking accuracy on validation set\n", + "Got 551 / 1000 correct (55.10)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.2621\n", + "Checking accuracy on validation set\n", + "Got 542 / 1000 correct (54.20)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.1407\n", + "Checking accuracy on validation set\n", + "Got 565 / 1000 correct (56.50)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.3779\n", + "Checking accuracy on validation set\n", + "Got 574 / 1000 correct (57.40)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.2618\n", + "Checking accuracy on validation set\n", + "Got 555 / 1000 correct (55.50)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 1.0655\n", + "Checking accuracy on validation set\n", + "Got 555 / 1000 correct (55.50)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.2842\n", + "Checking accuracy on validation set\n", + "Got 599 / 1000 correct (59.90)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.9316\n", + "Checking accuracy on validation set\n", + "Got 546 / 1000 correct (54.60)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.9524\n", + "Checking accuracy on validation set\n", + "Got 609 / 1000 correct (60.90)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 1.1476\n", + "Checking accuracy on validation set\n", + "Got 618 / 1000 correct (61.80)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.9909\n", + "Checking accuracy on validation set\n", + "Got 606 / 1000 correct (60.60)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.3002\n", + "Checking accuracy on validation set\n", + "Got 636 / 1000 correct (63.60)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 1.1747\n", + "Checking accuracy on validation set\n", + "Got 577 / 1000 correct (57.70)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.8237\n", + "Checking accuracy on validation set\n", + "Got 646 / 1000 correct (64.60)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8466\n", + "Checking accuracy on validation set\n", + "Got 648 / 1000 correct (64.80)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 1.0334\n", + "Checking accuracy on validation set\n", + "Got 680 / 1000 correct (68.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 1.0465\n", + "Checking accuracy on validation set\n", + "Got 676 / 1000 correct (67.60)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.9823\n", + "Checking accuracy on validation set\n", + "Got 587 / 1000 correct (58.70)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 1.2976\n", + "Checking accuracy on validation set\n", + "Got 666 / 1000 correct (66.60)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 1.2033\n", + "Checking accuracy on validation set\n", + "Got 637 / 1000 correct (63.70)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 1.0674\n", + "Checking accuracy on validation set\n", + "Got 644 / 1000 correct (64.40)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 1.0377\n", + "Checking accuracy on validation set\n", + "Got 676 / 1000 correct (67.60)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 1.0306\n", + "Checking accuracy on validation set\n", + "Got 652 / 1000 correct (65.20)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7871\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.9151\n", + "Checking accuracy on validation set\n", + "Got 690 / 1000 correct (69.00)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 1.0005\n", + "Checking accuracy on validation set\n", + "Got 648 / 1000 correct (64.80)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.9758\n", + "Checking accuracy on validation set\n", + "Got 675 / 1000 correct (67.50)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.9337\n", + "Checking accuracy on validation set\n", + "Got 685 / 1000 correct (68.50)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 1.1043\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 1.0914\n", + "Checking accuracy on validation set\n", + "Got 608 / 1000 correct (60.80)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.9031\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 1.0843\n", + "Checking accuracy on validation set\n", + "Got 652 / 1000 correct (65.20)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.8896\n", + "Checking accuracy on validation set\n", + "Got 692 / 1000 correct (69.20)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 1.0099\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.9278\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 1.1078\n", + "Checking accuracy on validation set\n", + "Got 671 / 1000 correct (67.10)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.7642\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.8321\n", + "Checking accuracy on validation set\n", + "Got 726 / 1000 correct (72.60)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.6917\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.8216\n", + "Checking accuracy on validation set\n", + "Got 660 / 1000 correct (66.00)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 1.0368\n", + "Checking accuracy on validation set\n", + "Got 679 / 1000 correct (67.90)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.8373\n", + "Checking accuracy on validation set\n", + "Got 692 / 1000 correct (69.20)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 1.0273\n", + "Checking accuracy on validation set\n", + "Got 696 / 1000 correct (69.60)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.7657\n", + "Checking accuracy on validation set\n", + "Got 698 / 1000 correct (69.80)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.9009\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.8699\n", + "Checking accuracy on validation set\n", + "Got 714 / 1000 correct (71.40)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 1.0327\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.7949\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.8411\n", + "Checking accuracy on validation set\n", + "Got 682 / 1000 correct (68.20)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.7828\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.6952\n", + "Checking accuracy on validation set\n", + "Got 721 / 1000 correct (72.10)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.9176\n", + "Checking accuracy on validation set\n", + "Got 745 / 1000 correct (74.50)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.7200\n", + "Checking accuracy on validation set\n", + "Got 739 / 1000 correct (73.90)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.6784\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.7591\n", + "Checking accuracy on validation set\n", + "Got 723 / 1000 correct (72.30)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5839\n", + "Checking accuracy on validation set\n", + "Got 714 / 1000 correct (71.40)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.8639\n", + "Checking accuracy on validation set\n", + "Got 707 / 1000 correct (70.70)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.7626\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.9066\n", + "Checking accuracy on validation set\n", + "Got 717 / 1000 correct (71.70)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.6524\n", + "Checking accuracy on validation set\n", + "Got 745 / 1000 correct (74.50)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.8014\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.8328\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.6587\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.6628\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.8611\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 661 / 1000 correct (66.10)\n", + "---- New lr = 0.00054 and weight_decay = 0.00075 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.4717\n", + "Checking accuracy on validation set\n", + "Got 136 / 1000 correct (13.60)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.6679\n", + "Checking accuracy on validation set\n", + "Got 339 / 1000 correct (33.90)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.3465\n", + "Checking accuracy on validation set\n", + "Got 441 / 1000 correct (44.10)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.2794\n", + "Checking accuracy on validation set\n", + "Got 468 / 1000 correct (46.80)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.0497\n", + "Checking accuracy on validation set\n", + "Got 472 / 1000 correct (47.20)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5951\n", + "Checking accuracy on validation set\n", + "Got 562 / 1000 correct (56.20)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1339\n", + "Checking accuracy on validation set\n", + "Got 524 / 1000 correct (52.40)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.1411\n", + "Checking accuracy on validation set\n", + "Got 504 / 1000 correct (50.40)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 0.9967\n", + "Checking accuracy on validation set\n", + "Got 634 / 1000 correct (63.40)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2501\n", + "Checking accuracy on validation set\n", + "Got 617 / 1000 correct (61.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.3029\n", + "Checking accuracy on validation set\n", + "Got 559 / 1000 correct (55.90)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.3562\n", + "Checking accuracy on validation set\n", + "Got 672 / 1000 correct (67.20)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9250\n", + "Checking accuracy on validation set\n", + "Got 675 / 1000 correct (67.50)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0387\n", + "Checking accuracy on validation set\n", + "Got 611 / 1000 correct (61.10)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8259\n", + "Checking accuracy on validation set\n", + "Got 674 / 1000 correct (67.40)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8010\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.7967\n", + "Checking accuracy on validation set\n", + "Got 595 / 1000 correct (59.50)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.6788\n", + "Checking accuracy on validation set\n", + "Got 699 / 1000 correct (69.90)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.9476\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.7392\n", + "Checking accuracy on validation set\n", + "Got 739 / 1000 correct (73.90)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9760\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9528\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.6238\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.9815\n", + "Checking accuracy on validation set\n", + "Got 743 / 1000 correct (74.30)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.5655\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7785\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7119\n", + "Checking accuracy on validation set\n", + "Got 755 / 1000 correct (75.50)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.7810\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6422\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 1.1456\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.9161\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.7744\n", + "Checking accuracy on validation set\n", + "Got 772 / 1000 correct (77.20)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.8108\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7126\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.4532\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5126\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.8368\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5903\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.4587\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6397\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6492\n", + "Checking accuracy on validation set\n", + "Got 765 / 1000 correct (76.50)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4773\n", + "Checking accuracy on validation set\n", + "Got 751 / 1000 correct (75.10)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.7213\n", + "Checking accuracy on validation set\n", + "Got 777 / 1000 correct (77.70)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.3517\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4851\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4831\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6262\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5309\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5612\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.8659\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4710\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.7906\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6560\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5261\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.6829\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.6867\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4425\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3692\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.7967\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4009\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5452\n", + "Checking accuracy on validation set\n", + "Got 835 / 1000 correct (83.50)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.6414\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4052\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4581\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3370\n", + "Checking accuracy on validation set\n", + "Got 816 / 1000 correct (81.60)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.5719\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.6107\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3562\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.5714\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.7531\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.7050\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5880\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.4624\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2769\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4989\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3623\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.5116\n", + "Checking accuracy on validation set\n", + "Got 845 / 1000 correct (84.50)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.6583\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5442\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4197\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "---- New lr = 0.00237 and weight_decay = 0.00025 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 4.1793\n", + "Checking accuracy on validation set\n", + "Got 118 / 1000 correct (11.80)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.5484\n", + "Checking accuracy on validation set\n", + "Got 303 / 1000 correct (30.30)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.9622\n", + "Checking accuracy on validation set\n", + "Got 315 / 1000 correct (31.50)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.7700\n", + "Checking accuracy on validation set\n", + "Got 370 / 1000 correct (37.00)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.8527\n", + "Checking accuracy on validation set\n", + "Got 388 / 1000 correct (38.80)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.6491\n", + "Checking accuracy on validation set\n", + "Got 462 / 1000 correct (46.20)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.7105\n", + "Checking accuracy on validation set\n", + "Got 434 / 1000 correct (43.40)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.5549\n", + "Checking accuracy on validation set\n", + "Got 459 / 1000 correct (45.90)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3154\n", + "Checking accuracy on validation set\n", + "Got 447 / 1000 correct (44.70)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2250\n", + "Checking accuracy on validation set\n", + "Got 482 / 1000 correct (48.20)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.5368\n", + "Checking accuracy on validation set\n", + "Got 423 / 1000 correct (42.30)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.4170\n", + "Checking accuracy on validation set\n", + "Got 541 / 1000 correct (54.10)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.1212\n", + "Checking accuracy on validation set\n", + "Got 514 / 1000 correct (51.40)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0606\n", + "Checking accuracy on validation set\n", + "Got 573 / 1000 correct (57.30)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.2285\n", + "Checking accuracy on validation set\n", + "Got 465 / 1000 correct (46.50)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0321\n", + "Checking accuracy on validation set\n", + "Got 585 / 1000 correct (58.50)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.2234\n", + "Checking accuracy on validation set\n", + "Got 589 / 1000 correct (58.90)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.1061\n", + "Checking accuracy on validation set\n", + "Got 590 / 1000 correct (59.00)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.9904\n", + "Checking accuracy on validation set\n", + "Got 539 / 1000 correct (53.90)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.9322\n", + "Checking accuracy on validation set\n", + "Got 550 / 1000 correct (55.00)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 1.4275\n", + "Checking accuracy on validation set\n", + "Got 647 / 1000 correct (64.70)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.0921\n", + "Checking accuracy on validation set\n", + "Got 616 / 1000 correct (61.60)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8230\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.3125\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.8059\n", + "Checking accuracy on validation set\n", + "Got 649 / 1000 correct (64.90)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 1.2662\n", + "Checking accuracy on validation set\n", + "Got 652 / 1000 correct (65.20)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.4342\n", + "Checking accuracy on validation set\n", + "Got 671 / 1000 correct (67.10)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 1.0389\n", + "Checking accuracy on validation set\n", + "Got 610 / 1000 correct (61.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.8884\n", + "Checking accuracy on validation set\n", + "Got 653 / 1000 correct (65.30)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.9431\n", + "Checking accuracy on validation set\n", + "Got 584 / 1000 correct (58.40)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.9420\n", + "Checking accuracy on validation set\n", + "Got 694 / 1000 correct (69.40)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 1.0104\n", + "Checking accuracy on validation set\n", + "Got 658 / 1000 correct (65.80)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6480\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.9509\n", + "Checking accuracy on validation set\n", + "Got 724 / 1000 correct (72.40)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.8382\n", + "Checking accuracy on validation set\n", + "Got 680 / 1000 correct (68.00)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8911\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.8223\n", + "Checking accuracy on validation set\n", + "Got 706 / 1000 correct (70.60)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.8344\n", + "Checking accuracy on validation set\n", + "Got 748 / 1000 correct (74.80)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.9187\n", + "Checking accuracy on validation set\n", + "Got 726 / 1000 correct (72.60)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.7117\n", + "Checking accuracy on validation set\n", + "Got 710 / 1000 correct (71.00)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.9971\n", + "Checking accuracy on validation set\n", + "Got 723 / 1000 correct (72.30)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5312\n", + "Checking accuracy on validation set\n", + "Got 755 / 1000 correct (75.50)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.6531\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.9101\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.9794\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.8032\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6739\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5911\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.8268\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.8238\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.6990\n", + "Checking accuracy on validation set\n", + "Got 742 / 1000 correct (74.20)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.7243\n", + "Checking accuracy on validation set\n", + "Got 766 / 1000 correct (76.60)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.8468\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5584\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.7031\n", + "Checking accuracy on validation set\n", + "Got 753 / 1000 correct (75.30)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.8523\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.6473\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.7900\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.7323\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.7542\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3939\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.9694\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.7124\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.6660\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.6534\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4023\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.6430\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.7326\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4063\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.6452\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.7223\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.6381\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.5030\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.6153\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.5535\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.7405\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3113\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.4472\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4203\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.6771\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "---- New lr = 0.00120 and weight_decay = 0.00080 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.2614\n", + "Checking accuracy on validation set\n", + "Got 117 / 1000 correct (11.70)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9088\n", + "Checking accuracy on validation set\n", + "Got 337 / 1000 correct (33.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6255\n", + "Checking accuracy on validation set\n", + "Got 395 / 1000 correct (39.50)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4188\n", + "Checking accuracy on validation set\n", + "Got 426 / 1000 correct (42.60)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.4744\n", + "Checking accuracy on validation set\n", + "Got 478 / 1000 correct (47.80)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5330\n", + "Checking accuracy on validation set\n", + "Got 333 / 1000 correct (33.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.6294\n", + "Checking accuracy on validation set\n", + "Got 453 / 1000 correct (45.30)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.5761\n", + "Checking accuracy on validation set\n", + "Got 471 / 1000 correct (47.10)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3879\n", + "Checking accuracy on validation set\n", + "Got 526 / 1000 correct (52.60)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.3661\n", + "Checking accuracy on validation set\n", + "Got 540 / 1000 correct (54.00)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.2735\n", + "Checking accuracy on validation set\n", + "Got 524 / 1000 correct (52.40)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.2293\n", + "Checking accuracy on validation set\n", + "Got 577 / 1000 correct (57.70)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.2795\n", + "Checking accuracy on validation set\n", + "Got 581 / 1000 correct (58.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.2490\n", + "Checking accuracy on validation set\n", + "Got 595 / 1000 correct (59.50)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9781\n", + "Checking accuracy on validation set\n", + "Got 589 / 1000 correct (58.90)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0093\n", + "Checking accuracy on validation set\n", + "Got 568 / 1000 correct (56.80)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.0968\n", + "Checking accuracy on validation set\n", + "Got 573 / 1000 correct (57.30)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.1275\n", + "Checking accuracy on validation set\n", + "Got 546 / 1000 correct (54.60)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8807\n", + "Checking accuracy on validation set\n", + "Got 592 / 1000 correct (59.20)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.2455\n", + "Checking accuracy on validation set\n", + "Got 613 / 1000 correct (61.30)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9981\n", + "Checking accuracy on validation set\n", + "Got 643 / 1000 correct (64.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.1209\n", + "Checking accuracy on validation set\n", + "Got 666 / 1000 correct (66.60)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 1.0208\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.0614\n", + "Checking accuracy on validation set\n", + "Got 578 / 1000 correct (57.80)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.8487\n", + "Checking accuracy on validation set\n", + "Got 581 / 1000 correct (58.10)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 1.0686\n", + "Checking accuracy on validation set\n", + "Got 693 / 1000 correct (69.30)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.9531\n", + "Checking accuracy on validation set\n", + "Got 641 / 1000 correct (64.10)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.7356\n", + "Checking accuracy on validation set\n", + "Got 695 / 1000 correct (69.50)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 1.0272\n", + "Checking accuracy on validation set\n", + "Got 581 / 1000 correct (58.10)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.9614\n", + "Checking accuracy on validation set\n", + "Got 674 / 1000 correct (67.40)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8424\n", + "Checking accuracy on validation set\n", + "Got 705 / 1000 correct (70.50)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8609\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.8225\n", + "Checking accuracy on validation set\n", + "Got 716 / 1000 correct (71.60)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7704\n", + "Checking accuracy on validation set\n", + "Got 677 / 1000 correct (67.70)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.9049\n", + "Checking accuracy on validation set\n", + "Got 705 / 1000 correct (70.50)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.9314\n", + "Checking accuracy on validation set\n", + "Got 707 / 1000 correct (70.70)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.9410\n", + "Checking accuracy on validation set\n", + "Got 698 / 1000 correct (69.80)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 1.0104\n", + "Checking accuracy on validation set\n", + "Got 727 / 1000 correct (72.70)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.8121\n", + "Checking accuracy on validation set\n", + "Got 744 / 1000 correct (74.40)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.8204\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6797\n", + "Checking accuracy on validation set\n", + "Got 748 / 1000 correct (74.80)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6976\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.6350\n", + "Checking accuracy on validation set\n", + "Got 733 / 1000 correct (73.30)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.8498\n", + "Checking accuracy on validation set\n", + "Got 732 / 1000 correct (73.20)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.6859\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5634\n", + "Checking accuracy on validation set\n", + "Got 772 / 1000 correct (77.20)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6001\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5672\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.6694\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7682\n", + "Checking accuracy on validation set\n", + "Got 727 / 1000 correct (72.70)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5734\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.6905\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6748\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5193\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.6373\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.7484\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.7486\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.7751\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4654\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 1.0860\n", + "Checking accuracy on validation set\n", + "Got 772 / 1000 correct (77.20)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.7464\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5870\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.7671\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.7933\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4610\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.6934\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.6043\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.5587\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.6358\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.7137\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4559\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.7269\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.7253\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.7316\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.5183\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.5698\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.6955\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.7105\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.6265\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.6090\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "---- New lr = 0.00050 and weight_decay = 0.00082 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.3882\n", + "Checking accuracy on validation set\n", + "Got 96 / 1000 correct (9.60)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9832\n", + "Checking accuracy on validation set\n", + "Got 383 / 1000 correct (38.30)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.7399\n", + "Checking accuracy on validation set\n", + "Got 374 / 1000 correct (37.40)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4217\n", + "Checking accuracy on validation set\n", + "Got 405 / 1000 correct (40.50)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 0.9706\n", + "Checking accuracy on validation set\n", + "Got 516 / 1000 correct (51.60)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5235\n", + "Checking accuracy on validation set\n", + "Got 559 / 1000 correct (55.90)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.4245\n", + "Checking accuracy on validation set\n", + "Got 504 / 1000 correct (50.40)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.1195\n", + "Checking accuracy on validation set\n", + "Got 628 / 1000 correct (62.80)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.0691\n", + "Checking accuracy on validation set\n", + "Got 630 / 1000 correct (63.00)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.3584\n", + "Checking accuracy on validation set\n", + "Got 626 / 1000 correct (62.60)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 0.8176\n", + "Checking accuracy on validation set\n", + "Got 637 / 1000 correct (63.70)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1158\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.8554\n", + "Checking accuracy on validation set\n", + "Got 653 / 1000 correct (65.30)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.8557\n", + "Checking accuracy on validation set\n", + "Got 742 / 1000 correct (74.20)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9247\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1115\n", + "Checking accuracy on validation set\n", + "Got 687 / 1000 correct (68.70)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9358\n", + "Checking accuracy on validation set\n", + "Got 690 / 1000 correct (69.00)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7606\n", + "Checking accuracy on validation set\n", + "Got 735 / 1000 correct (73.50)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.7134\n", + "Checking accuracy on validation set\n", + "Got 687 / 1000 correct (68.70)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.8810\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.8457\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.8176\n", + "Checking accuracy on validation set\n", + "Got 765 / 1000 correct (76.50)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8160\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.6686\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7775\n", + "Checking accuracy on validation set\n", + "Got 745 / 1000 correct (74.50)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.8526\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7031\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.5623\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.8627\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.6818\n", + "Checking accuracy on validation set\n", + "Got 682 / 1000 correct (68.20)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.6608\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8219\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6142\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.5636\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.8941\n", + "Checking accuracy on validation set\n", + "Got 765 / 1000 correct (76.50)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.6417\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.4629\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.3687\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7354\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5455\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6358\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.8770\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.7338\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.5864\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.6946\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5171\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6001\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.7053\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5662\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7583\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.8706\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.4611\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5987\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4876\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.5553\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.3599\n", + "Checking accuracy on validation set\n", + "Got 848 / 1000 correct (84.80)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.8698\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4808\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.3942\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5623\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4901\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4920\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.5605\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4478\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4168\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.6236\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3991\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.6085\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.6161\n", + "Checking accuracy on validation set\n", + "Got 826 / 1000 correct (82.60)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.7398\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4308\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5133\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3946\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.5042\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4650\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.4590\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4735\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3961\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3924\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4914\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "---- New lr = 0.00053 and weight_decay = 0.00074 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 2.8960\n", + "Checking accuracy on validation set\n", + "Got 89 / 1000 correct (8.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7372\n", + "Checking accuracy on validation set\n", + "Got 349 / 1000 correct (34.90)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5385\n", + "Checking accuracy on validation set\n", + "Got 467 / 1000 correct (46.70)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.7382\n", + "Checking accuracy on validation set\n", + "Got 514 / 1000 correct (51.40)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3829\n", + "Checking accuracy on validation set\n", + "Got 524 / 1000 correct (52.40)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.3320\n", + "Checking accuracy on validation set\n", + "Got 553 / 1000 correct (55.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.4322\n", + "Checking accuracy on validation set\n", + "Got 505 / 1000 correct (50.50)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 0.9005\n", + "Checking accuracy on validation set\n", + "Got 597 / 1000 correct (59.70)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.1869\n", + "Checking accuracy on validation set\n", + "Got 603 / 1000 correct (60.30)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.0709\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 0.8830\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0941\n", + "Checking accuracy on validation set\n", + "Got 668 / 1000 correct (66.80)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9058\n", + "Checking accuracy on validation set\n", + "Got 661 / 1000 correct (66.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.7797\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8978\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.9699\n", + "Checking accuracy on validation set\n", + "Got 709 / 1000 correct (70.90)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.7268\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.6918\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.9680\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.6771\n", + "Checking accuracy on validation set\n", + "Got 735 / 1000 correct (73.50)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.8356\n", + "Checking accuracy on validation set\n", + "Got 745 / 1000 correct (74.50)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7316\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7657\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.6672\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7681\n", + "Checking accuracy on validation set\n", + "Got 733 / 1000 correct (73.30)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7143\n", + "Checking accuracy on validation set\n", + "Got 775 / 1000 correct (77.50)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.6585\n", + "Checking accuracy on validation set\n", + "Got 745 / 1000 correct (74.50)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6900\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.5632\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.6221\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.6540\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.9284\n", + "Checking accuracy on validation set\n", + "Got 766 / 1000 correct (76.60)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6393\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.5543\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5578\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.6493\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.6937\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6973\n", + "Checking accuracy on validation set\n", + "Got 739 / 1000 correct (73.90)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6218\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6870\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.7695\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.9082\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5385\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4258\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5241\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4779\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.7324\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.7170\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5928\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7935\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5279\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5025\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.7199\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.6331\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.6008\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.5692\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.6853\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4740\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.8133\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5928\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.6576\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5687\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3314\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.6263\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.6491\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.5072\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3904\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.4085\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.5357\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.4488\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.7515\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.6092\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.5596\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.5594\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.5287\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.7262\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.5378\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.5358\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4880\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.5292\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "---- New lr = 0.00051 and weight_decay = 0.00085 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 4.1503\n", + "Checking accuracy on validation set\n", + "Got 102 / 1000 correct (10.20)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9966\n", + "Checking accuracy on validation set\n", + "Got 375 / 1000 correct (37.50)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6229\n", + "Checking accuracy on validation set\n", + "Got 425 / 1000 correct (42.50)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.2188\n", + "Checking accuracy on validation set\n", + "Got 469 / 1000 correct (46.90)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.5325\n", + "Checking accuracy on validation set\n", + "Got 507 / 1000 correct (50.70)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.4456\n", + "Checking accuracy on validation set\n", + "Got 533 / 1000 correct (53.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1287\n", + "Checking accuracy on validation set\n", + "Got 533 / 1000 correct (53.30)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.0200\n", + "Checking accuracy on validation set\n", + "Got 580 / 1000 correct (58.00)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.1188\n", + "Checking accuracy on validation set\n", + "Got 637 / 1000 correct (63.70)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 0.9769\n", + "Checking accuracy on validation set\n", + "Got 629 / 1000 correct (62.90)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 0.9254\n", + "Checking accuracy on validation set\n", + "Got 635 / 1000 correct (63.50)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 0.9253\n", + "Checking accuracy on validation set\n", + "Got 644 / 1000 correct (64.40)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.7224\n", + "Checking accuracy on validation set\n", + "Got 707 / 1000 correct (70.70)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.8657\n", + "Checking accuracy on validation set\n", + "Got 699 / 1000 correct (69.90)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8441\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0471\n", + "Checking accuracy on validation set\n", + "Got 709 / 1000 correct (70.90)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.8703\n", + "Checking accuracy on validation set\n", + "Got 727 / 1000 correct (72.70)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7444\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8265\n", + "Checking accuracy on validation set\n", + "Got 753 / 1000 correct (75.30)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.6313\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.7653\n", + "Checking accuracy on validation set\n", + "Got 748 / 1000 correct (74.80)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.5917\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.5945\n", + "Checking accuracy on validation set\n", + "Got 751 / 1000 correct (75.10)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.5390\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.6110\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.5770\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7545\n", + "Checking accuracy on validation set\n", + "Got 715 / 1000 correct (71.50)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.7257\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6690\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.6368\n", + "Checking accuracy on validation set\n", + "Got 707 / 1000 correct (70.70)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8973\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6669\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.7403\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7201\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5322\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8529\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.9235\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6760\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5609\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6591\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5439\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.7734\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4753\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6923\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5035\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5697\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5093\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.3828\n", + "Checking accuracy on validation set\n", + "Got 772 / 1000 correct (77.20)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4392\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4809\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5452\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.6074\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5006\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4080\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4281\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.7381\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4305\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.5672\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5379\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.6583\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.6010\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5061\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.5041\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.6369\n", + "Checking accuracy on validation set\n", + "Got 816 / 1000 correct (81.60)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4875\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4042\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3034\n", + "Checking accuracy on validation set\n", + "Got 835 / 1000 correct (83.50)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.6222\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.2876\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.4746\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4563\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.6177\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3782\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.4504\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4213\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.6403\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4950\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.5395\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5900\n", + "Checking accuracy on validation set\n", + "Got 816 / 1000 correct (81.60)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.7208\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "---- New lr = 0.00050 and weight_decay = 0.00074 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 2.9900\n", + "Checking accuracy on validation set\n", + "Got 93 / 1000 correct (9.30)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.2026\n", + "Checking accuracy on validation set\n", + "Got 385 / 1000 correct (38.50)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5419\n", + "Checking accuracy on validation set\n", + "Got 441 / 1000 correct (44.10)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.3920\n", + "Checking accuracy on validation set\n", + "Got 434 / 1000 correct (43.40)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.5053\n", + "Checking accuracy on validation set\n", + "Got 525 / 1000 correct (52.50)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2482\n", + "Checking accuracy on validation set\n", + "Got 492 / 1000 correct (49.20)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1716\n", + "Checking accuracy on validation set\n", + "Got 580 / 1000 correct (58.00)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.1843\n", + "Checking accuracy on validation set\n", + "Got 585 / 1000 correct (58.50)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.2650\n", + "Checking accuracy on validation set\n", + "Got 631 / 1000 correct (63.10)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.3987\n", + "Checking accuracy on validation set\n", + "Got 623 / 1000 correct (62.30)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.1273\n", + "Checking accuracy on validation set\n", + "Got 661 / 1000 correct (66.10)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1902\n", + "Checking accuracy on validation set\n", + "Got 651 / 1000 correct (65.10)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9114\n", + "Checking accuracy on validation set\n", + "Got 655 / 1000 correct (65.50)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0766\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8231\n", + "Checking accuracy on validation set\n", + "Got 667 / 1000 correct (66.70)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1207\n", + "Checking accuracy on validation set\n", + "Got 698 / 1000 correct (69.80)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.0396\n", + "Checking accuracy on validation set\n", + "Got 716 / 1000 correct (71.60)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.8301\n", + "Checking accuracy on validation set\n", + "Got 744 / 1000 correct (74.40)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.5622\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.9594\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.7337\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.6436\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8499\n", + "Checking accuracy on validation set\n", + "Got 723 / 1000 correct (72.30)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.6876\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.6217\n", + "Checking accuracy on validation set\n", + "Got 756 / 1000 correct (75.60)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7299\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.5290\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6067\n", + "Checking accuracy on validation set\n", + "Got 775 / 1000 correct (77.50)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6803\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.4996\n", + "Checking accuracy on validation set\n", + "Got 748 / 1000 correct (74.80)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.5941\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.7592\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5923\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6799\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5093\n", + "Checking accuracy on validation set\n", + "Got 699 / 1000 correct (69.90)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.6285\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5999\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.8450\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6552\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6284\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6110\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4533\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.6417\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6912\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5965\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5194\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5114\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.7473\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5990\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7416\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5837\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5577\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5457\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.6175\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.6260\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.3134\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.3607\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.5121\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.9030\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.6064\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5213\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.7041\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3822\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5971\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3462\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.2971\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.5118\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.4683\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4492\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.5109\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5005\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.3625\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.6624\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.5690\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.7475\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.7934\n", + "Checking accuracy on validation set\n", + "Got 852 / 1000 correct (85.20)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4027\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.5645\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5980\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.5367\n", + "Checking accuracy on validation set\n", + "Got 845 / 1000 correct (84.50)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 835 / 1000 correct (83.50)\n", + "---- New lr = 0.00051 and weight_decay = 0.00075 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.0983\n", + "Checking accuracy on validation set\n", + "Got 108 / 1000 correct (10.80)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7051\n", + "Checking accuracy on validation set\n", + "Got 420 / 1000 correct (42.00)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5716\n", + "Checking accuracy on validation set\n", + "Got 464 / 1000 correct (46.40)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.3148\n", + "Checking accuracy on validation set\n", + "Got 456 / 1000 correct (45.60)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.0571\n", + "Checking accuracy on validation set\n", + "Got 536 / 1000 correct (53.60)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2714\n", + "Checking accuracy on validation set\n", + "Got 611 / 1000 correct (61.10)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.0131\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4530\n", + "Checking accuracy on validation set\n", + "Got 561 / 1000 correct (56.10)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.2481\n", + "Checking accuracy on validation set\n", + "Got 556 / 1000 correct (55.60)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.1219\n", + "Checking accuracy on validation set\n", + "Got 654 / 1000 correct (65.40)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.1604\n", + "Checking accuracy on validation set\n", + "Got 606 / 1000 correct (60.60)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 0.9005\n", + "Checking accuracy on validation set\n", + "Got 694 / 1000 correct (69.40)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.8375\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0693\n", + "Checking accuracy on validation set\n", + "Got 669 / 1000 correct (66.90)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.0741\n", + "Checking accuracy on validation set\n", + "Got 676 / 1000 correct (67.60)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8250\n", + "Checking accuracy on validation set\n", + "Got 721 / 1000 correct (72.10)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.8854\n", + "Checking accuracy on validation set\n", + "Got 719 / 1000 correct (71.90)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.9415\n", + "Checking accuracy on validation set\n", + "Got 708 / 1000 correct (70.80)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.7790\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.7506\n", + "Checking accuracy on validation set\n", + "Got 704 / 1000 correct (70.40)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.5354\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.6427\n", + "Checking accuracy on validation set\n", + "Got 707 / 1000 correct (70.70)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.9203\n", + "Checking accuracy on validation set\n", + "Got 706 / 1000 correct (70.60)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.6959\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.8380\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.6226\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.5761\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6658\n", + "Checking accuracy on validation set\n", + "Got 742 / 1000 correct (74.20)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.5191\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.5908\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.6046\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6939\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5852\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6786\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5774\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5768\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.7820\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6224\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5407\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5748\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6400\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4682\n", + "Checking accuracy on validation set\n", + "Got 777 / 1000 correct (77.70)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5524\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6756\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5301\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5190\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5214\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4764\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5956\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7601\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5999\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.6388\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6397\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.6234\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4905\n", + "Checking accuracy on validation set\n", + "Got 826 / 1000 correct (82.60)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4038\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.5944\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.5494\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5485\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.6186\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5281\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5067\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4757\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5740\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4340\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.7337\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4004\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.5963\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3859\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.4326\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.8595\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5769\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.6562\n", + "Checking accuracy on validation set\n", + "Got 752 / 1000 correct (75.20)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.6340\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.5386\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.4806\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4095\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.5529\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5816\n", + "Checking accuracy on validation set\n", + "Got 845 / 1000 correct (84.50)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4187\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "---- New lr = 0.00051 and weight_decay = 0.00097 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.1594\n", + "Checking accuracy on validation set\n", + "Got 119 / 1000 correct (11.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.5933\n", + "Checking accuracy on validation set\n", + "Got 339 / 1000 correct (33.90)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6613\n", + "Checking accuracy on validation set\n", + "Got 453 / 1000 correct (45.30)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4392\n", + "Checking accuracy on validation set\n", + "Got 489 / 1000 correct (48.90)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3757\n", + "Checking accuracy on validation set\n", + "Got 533 / 1000 correct (53.30)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2997\n", + "Checking accuracy on validation set\n", + "Got 555 / 1000 correct (55.50)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.3592\n", + "Checking accuracy on validation set\n", + "Got 592 / 1000 correct (59.20)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.1877\n", + "Checking accuracy on validation set\n", + "Got 568 / 1000 correct (56.80)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.0762\n", + "Checking accuracy on validation set\n", + "Got 598 / 1000 correct (59.80)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.1156\n", + "Checking accuracy on validation set\n", + "Got 605 / 1000 correct (60.50)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.0227\n", + "Checking accuracy on validation set\n", + "Got 605 / 1000 correct (60.50)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0193\n", + "Checking accuracy on validation set\n", + "Got 594 / 1000 correct (59.40)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9157\n", + "Checking accuracy on validation set\n", + "Got 644 / 1000 correct (64.40)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0235\n", + "Checking accuracy on validation set\n", + "Got 619 / 1000 correct (61.90)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9720\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1894\n", + "Checking accuracy on validation set\n", + "Got 665 / 1000 correct (66.50)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.7986\n", + "Checking accuracy on validation set\n", + "Got 704 / 1000 correct (70.40)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.6674\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.9065\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.8165\n", + "Checking accuracy on validation set\n", + "Got 691 / 1000 correct (69.10)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.6364\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7128\n", + "Checking accuracy on validation set\n", + "Got 724 / 1000 correct (72.40)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7546\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7555\n", + "Checking accuracy on validation set\n", + "Got 755 / 1000 correct (75.50)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.9789\n", + "Checking accuracy on validation set\n", + "Got 717 / 1000 correct (71.70)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.6425\n", + "Checking accuracy on validation set\n", + "Got 682 / 1000 correct (68.20)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.4944\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.8772\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6425\n", + "Checking accuracy on validation set\n", + "Got 735 / 1000 correct (73.50)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8637\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.5974\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.5531\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5518\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6001\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5977\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8598\n", + "Checking accuracy on validation set\n", + "Got 741 / 1000 correct (74.10)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.6167\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5918\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5196\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5925\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5271\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6188\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.7416\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6546\n", + "Checking accuracy on validation set\n", + "Got 722 / 1000 correct (72.20)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5793\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.7126\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.7926\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5117\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5511\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.5697\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.6638\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5790\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5849\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.8372\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4263\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4212\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.5369\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3156\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.7106\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.6470\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4659\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4866\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.5374\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5527\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.5973\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.7957\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3198\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.6724\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.5941\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.7354\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.6024\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5895\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.6464\n", + "Checking accuracy on validation set\n", + "Got 835 / 1000 correct (83.50)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.4896\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3090\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2924\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4450\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.4467\n", + "Checking accuracy on validation set\n", + "Got 826 / 1000 correct (82.60)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4059\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.5869\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "---- New lr = 0.00050 and weight_decay = 0.00091 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.5292\n", + "Checking accuracy on validation set\n", + "Got 99 / 1000 correct (9.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7849\n", + "Checking accuracy on validation set\n", + "Got 401 / 1000 correct (40.10)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5000\n", + "Checking accuracy on validation set\n", + "Got 441 / 1000 correct (44.10)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.1252\n", + "Checking accuracy on validation set\n", + "Got 483 / 1000 correct (48.30)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.2558\n", + "Checking accuracy on validation set\n", + "Got 527 / 1000 correct (52.70)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2677\n", + "Checking accuracy on validation set\n", + "Got 512 / 1000 correct (51.20)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1492\n", + "Checking accuracy on validation set\n", + "Got 555 / 1000 correct (55.50)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 0.9287\n", + "Checking accuracy on validation set\n", + "Got 597 / 1000 correct (59.70)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 0.9018\n", + "Checking accuracy on validation set\n", + "Got 601 / 1000 correct (60.10)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.0772\n", + "Checking accuracy on validation set\n", + "Got 642 / 1000 correct (64.20)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.2542\n", + "Checking accuracy on validation set\n", + "Got 645 / 1000 correct (64.50)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0525\n", + "Checking accuracy on validation set\n", + "Got 654 / 1000 correct (65.40)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.0601\n", + "Checking accuracy on validation set\n", + "Got 659 / 1000 correct (65.90)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.8084\n", + "Checking accuracy on validation set\n", + "Got 685 / 1000 correct (68.50)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9537\n", + "Checking accuracy on validation set\n", + "Got 724 / 1000 correct (72.40)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.7780\n", + "Checking accuracy on validation set\n", + "Got 700 / 1000 correct (70.00)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9292\n", + "Checking accuracy on validation set\n", + "Got 702 / 1000 correct (70.20)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7438\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.0062\n", + "Checking accuracy on validation set\n", + "Got 709 / 1000 correct (70.90)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.6028\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9864\n", + "Checking accuracy on validation set\n", + "Got 698 / 1000 correct (69.80)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7675\n", + "Checking accuracy on validation set\n", + "Got 703 / 1000 correct (70.30)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8466\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.8401\n", + "Checking accuracy on validation set\n", + "Got 756 / 1000 correct (75.60)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.9811\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.4526\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.6802\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.5794\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.8341\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8173\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8851\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8478\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6617\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7000\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6212\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.7232\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.8025\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6613\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6481\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.9401\n", + "Checking accuracy on validation set\n", + "Got 753 / 1000 correct (75.30)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5713\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6121\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.6714\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.5078\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.7692\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.7845\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5401\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.3791\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5391\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.5360\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.3402\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.8290\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6662\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5482\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.7195\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.5386\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4818\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4594\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.6278\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4539\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.7451\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5179\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.5149\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5291\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.8229\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4574\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4412\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.6149\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.5153\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.5199\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5540\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.3660\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.5438\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3687\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3749\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.4254\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3552\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3540\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4388\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4890\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "---- New lr = 0.00050 and weight_decay = 0.00082 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.9432\n", + "Checking accuracy on validation set\n", + "Got 93 / 1000 correct (9.30)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7274\n", + "Checking accuracy on validation set\n", + "Got 361 / 1000 correct (36.10)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6559\n", + "Checking accuracy on validation set\n", + "Got 465 / 1000 correct (46.50)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4980\n", + "Checking accuracy on validation set\n", + "Got 463 / 1000 correct (46.30)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3796\n", + "Checking accuracy on validation set\n", + "Got 529 / 1000 correct (52.90)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.4555\n", + "Checking accuracy on validation set\n", + "Got 507 / 1000 correct (50.70)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.2790\n", + "Checking accuracy on validation set\n", + "Got 588 / 1000 correct (58.80)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.1431\n", + "Checking accuracy on validation set\n", + "Got 564 / 1000 correct (56.40)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.1642\n", + "Checking accuracy on validation set\n", + "Got 588 / 1000 correct (58.80)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.1614\n", + "Checking accuracy on validation set\n", + "Got 596 / 1000 correct (59.60)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 0.9741\n", + "Checking accuracy on validation set\n", + "Got 625 / 1000 correct (62.50)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0658\n", + "Checking accuracy on validation set\n", + "Got 656 / 1000 correct (65.60)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9641\n", + "Checking accuracy on validation set\n", + "Got 656 / 1000 correct (65.60)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.2149\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9369\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.9825\n", + "Checking accuracy on validation set\n", + "Got 669 / 1000 correct (66.90)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.0409\n", + "Checking accuracy on validation set\n", + "Got 691 / 1000 correct (69.10)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0830\n", + "Checking accuracy on validation set\n", + "Got 707 / 1000 correct (70.70)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8835\n", + "Checking accuracy on validation set\n", + "Got 719 / 1000 correct (71.90)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.1544\n", + "Checking accuracy on validation set\n", + "Got 672 / 1000 correct (67.20)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.4849\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.6828\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.9992\n", + "Checking accuracy on validation set\n", + "Got 748 / 1000 correct (74.80)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.0991\n", + "Checking accuracy on validation set\n", + "Got 716 / 1000 correct (71.60)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7112\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.6186\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.0166\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.4893\n", + "Checking accuracy on validation set\n", + "Got 766 / 1000 correct (76.60)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6277\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.5451\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.7836\n", + "Checking accuracy on validation set\n", + "Got 772 / 1000 correct (77.20)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8119\n", + "Checking accuracy on validation set\n", + "Got 753 / 1000 correct (75.30)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6852\n", + "Checking accuracy on validation set\n", + "Got 743 / 1000 correct (74.30)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6550\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5477\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5926\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.8370\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5467\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.4604\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.7892\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.7822\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.8198\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5756\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6554\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.7153\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.6718\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5048\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.6823\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.6810\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4934\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.7071\n", + "Checking accuracy on validation set\n", + "Got 741 / 1000 correct (74.10)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5266\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6158\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5155\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4234\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.3946\n", + "Checking accuracy on validation set\n", + "Got 826 / 1000 correct (82.60)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4830\n", + "Checking accuracy on validation set\n", + "Got 777 / 1000 correct (77.70)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.6948\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5233\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5337\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5247\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5005\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4929\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.6678\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.7033\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4644\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3878\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.5264\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.5193\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.6293\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5938\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.3853\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.2763\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3056\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4894\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.5017\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4624\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.7533\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3735\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4570\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "---- New lr = 0.00055 and weight_decay = 0.00098 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.3854\n", + "Checking accuracy on validation set\n", + "Got 116 / 1000 correct (11.60)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.6567\n", + "Checking accuracy on validation set\n", + "Got 363 / 1000 correct (36.30)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.4229\n", + "Checking accuracy on validation set\n", + "Got 389 / 1000 correct (38.90)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.5334\n", + "Checking accuracy on validation set\n", + "Got 471 / 1000 correct (47.10)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.4760\n", + "Checking accuracy on validation set\n", + "Got 506 / 1000 correct (50.60)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.1962\n", + "Checking accuracy on validation set\n", + "Got 533 / 1000 correct (53.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.2452\n", + "Checking accuracy on validation set\n", + "Got 558 / 1000 correct (55.80)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.2508\n", + "Checking accuracy on validation set\n", + "Got 583 / 1000 correct (58.30)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3188\n", + "Checking accuracy on validation set\n", + "Got 548 / 1000 correct (54.80)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 0.9639\n", + "Checking accuracy on validation set\n", + "Got 615 / 1000 correct (61.50)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.0628\n", + "Checking accuracy on validation set\n", + "Got 647 / 1000 correct (64.70)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0704\n", + "Checking accuracy on validation set\n", + "Got 665 / 1000 correct (66.50)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.7760\n", + "Checking accuracy on validation set\n", + "Got 661 / 1000 correct (66.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.9646\n", + "Checking accuracy on validation set\n", + "Got 671 / 1000 correct (67.10)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.0256\n", + "Checking accuracy on validation set\n", + "Got 611 / 1000 correct (61.10)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8378\n", + "Checking accuracy on validation set\n", + "Got 649 / 1000 correct (64.90)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9279\n", + "Checking accuracy on validation set\n", + "Got 674 / 1000 correct (67.40)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7689\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.0087\n", + "Checking accuracy on validation set\n", + "Got 713 / 1000 correct (71.30)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.8847\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9505\n", + "Checking accuracy on validation set\n", + "Got 703 / 1000 correct (70.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9368\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.9765\n", + "Checking accuracy on validation set\n", + "Got 677 / 1000 correct (67.70)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7841\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7116\n", + "Checking accuracy on validation set\n", + "Got 742 / 1000 correct (74.20)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7271\n", + "Checking accuracy on validation set\n", + "Got 737 / 1000 correct (73.70)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.6250\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.7341\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6676\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.7619\n", + "Checking accuracy on validation set\n", + "Got 716 / 1000 correct (71.60)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.5638\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6864\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5565\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7806\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6519\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8548\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 1.0225\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5555\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6534\n", + "Checking accuracy on validation set\n", + "Got 752 / 1000 correct (75.20)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6509\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.4907\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.8136\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4548\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6562\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.7857\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.7260\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5587\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5603\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.3230\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.6212\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5422\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5094\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5657\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5565\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.5747\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4881\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4859\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4174\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.3539\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5127\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5854\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5352\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3682\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5187\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.8194\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.6189\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.5894\n", + "Checking accuracy on validation set\n", + "Got 833 / 1000 correct (83.30)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.5716\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3859\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.4780\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5495\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5138\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3479\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3710\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3846\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3355\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.5888\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.5702\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5300\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4486\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "---- New lr = 0.00052 and weight_decay = 0.00093 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 4.8097\n", + "Checking accuracy on validation set\n", + "Got 120 / 1000 correct (12.00)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.0077\n", + "Checking accuracy on validation set\n", + "Got 401 / 1000 correct (40.10)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5969\n", + "Checking accuracy on validation set\n", + "Got 431 / 1000 correct (43.10)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.2957\n", + "Checking accuracy on validation set\n", + "Got 419 / 1000 correct (41.90)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3770\n", + "Checking accuracy on validation set\n", + "Got 504 / 1000 correct (50.40)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.6065\n", + "Checking accuracy on validation set\n", + "Got 533 / 1000 correct (53.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1166\n", + "Checking accuracy on validation set\n", + "Got 583 / 1000 correct (58.30)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4100\n", + "Checking accuracy on validation set\n", + "Got 592 / 1000 correct (59.20)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3371\n", + "Checking accuracy on validation set\n", + "Got 618 / 1000 correct (61.80)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2688\n", + "Checking accuracy on validation set\n", + "Got 579 / 1000 correct (57.90)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.2119\n", + "Checking accuracy on validation set\n", + "Got 621 / 1000 correct (62.10)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0653\n", + "Checking accuracy on validation set\n", + "Got 676 / 1000 correct (67.60)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.0130\n", + "Checking accuracy on validation set\n", + "Got 634 / 1000 correct (63.40)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.9105\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8772\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8092\n", + "Checking accuracy on validation set\n", + "Got 655 / 1000 correct (65.50)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9544\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.2294\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.7566\n", + "Checking accuracy on validation set\n", + "Got 715 / 1000 correct (71.50)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.1225\n", + "Checking accuracy on validation set\n", + "Got 688 / 1000 correct (68.80)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.7678\n", + "Checking accuracy on validation set\n", + "Got 766 / 1000 correct (76.60)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.5276\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 1.1735\n", + "Checking accuracy on validation set\n", + "Got 744 / 1000 correct (74.40)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.9342\n", + "Checking accuracy on validation set\n", + "Got 699 / 1000 correct (69.90)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7137\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7256\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.0187\n", + "Checking accuracy on validation set\n", + "Got 732 / 1000 correct (73.20)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.9083\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.5523\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.7612\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.7845\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.5943\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.8133\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.8084\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.8139\n", + "Checking accuracy on validation set\n", + "Got 777 / 1000 correct (77.70)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.6193\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.4547\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5483\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7278\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6827\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.7242\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6623\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.3890\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.5336\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5161\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4266\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5401\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.6615\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4929\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.7394\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5043\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5000\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.8214\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5762\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.7195\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.7007\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.6578\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.5217\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4287\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4335\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4230\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.6537\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.5097\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5066\n", + "Checking accuracy on validation set\n", + "Got 829 / 1000 correct (82.90)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.5852\n", + "Checking accuracy on validation set\n", + "Got 831 / 1000 correct (83.10)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4786\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.6132\n", + "Checking accuracy on validation set\n", + "Got 756 / 1000 correct (75.60)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.6842\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.6220\n", + "Checking accuracy on validation set\n", + "Got 833 / 1000 correct (83.30)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.5882\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.6863\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.4889\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.4778\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.5545\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3108\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2996\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.5607\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.6222\n", + "Checking accuracy on validation set\n", + "Got 826 / 1000 correct (82.60)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5995\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4971\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 384 + }, + "id": "pLjNXL95UUyb", + "outputId": "18901bab-90fb-43c2-f48a-72b2de205b23" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Hyperparameters: [0.00051311 0.00097364]\n", + "Best Accuracy: -0.853\n" + ] + } + ], + "source": [ + "opt.plot_convergence()\n", + "print(\"Best Hyperparameters: \", opt.x_opt)\n", + "print(\"Best Accuracy: \", opt.fx_opt)\n", + "##############################################################\n", + "# END OF YOUR CODE #\n", + "##############################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sCHAl38M7-RF", + "outputId": "b0e03e73-397d-4939-ab6e-536b821cf99d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "766\n", + "Epoch: 0, Iteration 0, loss = 3.8242\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.6075\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5011\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.3821\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.2997\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.1808\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.6881\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.0739\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.2621\n", + "\n", + "Epoch: 1, Iteration 100, loss = 0.9631\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.3792\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1428\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9323\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.7955\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.0677\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.9228\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.8093\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7533\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8682\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.8569\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.7559\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.6908\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.6042\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.5876\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.9688\n", + "\n", + "Epoch: 3, Iteration 100, loss = 1.1008\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7724\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6468\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.9111\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8497\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.6558\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8008\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6815\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6311\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5132\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5730\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5668\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.4968\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6016\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.8137\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5700\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6260\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5672\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6930\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.6346\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5215\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6199\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.3816\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.7697\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.6668\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.6334\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.7414\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5848\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4268\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.7574\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.6004\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.5996\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.6203\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5860\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.3861\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4512\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.6834\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.5661\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4902\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4905\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.5776\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4862\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.7179\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4977\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3454\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5161\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5944\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3990\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.4425\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4498\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.7687\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4281\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3201\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.6686\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4585\n", + "\n", + "Checking accuracy on test set\n", + "Got 8381 / 10000 correct (83.81)\n" + ] + } + ], + "source": [ + "# define and train the network\n", + "model = ResNet18()\n", + "optimizer = optim.Adam(model.parameters(),lr = 0.000513, weight_decay = 0.000973)\n", + "\n", + "train_part(model, optimizer, epochs = 10)\n", + "\n", + "\n", + "# report test set accuracy\n", + "\n", + "check_accuracy(loader_test, model)\n", + "\n", + "\n", + "# save the model\n", + "torch.save(model.state_dict(), 'model(83.8).pt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 1 Result\n", + "\n", + "Method 1 Accuracy is 83.81 %.\n", + "\n", + "Then I add learning rate scheduler trying to get a better model." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LTuPrzN08xSj", + "outputId": "f95c9ddd-f8cf-4625-a465-5571e5da9c52" + }, + "source": [ + "import GPyOpt\r\n", + "def Bayes_Opt2(parameters):\r\n", + " lr = parameters[0, 0]\r\n", + " gamma = parameters[0, 1]\r\n", + " print(\"---- New lr = %.5f and gamma = %.3f ----\"% (lr, gamma))\r\n", + " model = ResNet18()\r\n", + " optimizer = optim.Adam(model.parameters(),lr = lr)\r\n", + " scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5, 9], gamma=gamma)\r\n", + " train_part2(model, optimizer, epochs = 10, scheduler = scheduler)\r\n", + " return check_accuracy(loader_val, model)\r\n", + "\r\n", + "domain = [ {'name' : 'lr', 'type': 'continuous', 'domain': (0.0001, 0.005)},\r\n", + " {'name': 'gamma', 'type': 'continuous', 'domain': (0.1, 0.2)}, ]\r\n", + "opt2 = GPyOpt.methods.BayesianOptimization(f = Bayes_Opt2,domain = domain,acquisition_type ='LCB', acquisition_weight = 0.1, maximize = True)\r\n", + "\r\n", + "\r\n", + "opt2.run_optimization( max_iter = 10)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "---- New lr = 0.00491 and gamma = 0.103 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.4908\n", + "Checking accuracy on validation set\n", + "Got 87 / 1000 correct (8.70)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9779\n", + "Checking accuracy on validation set\n", + "Got 261 / 1000 correct (26.10)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.8863\n", + "Checking accuracy on validation set\n", + "Got 278 / 1000 correct (27.80)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 2.0314\n", + "Checking accuracy on validation set\n", + "Got 397 / 1000 correct (39.70)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.5723\n", + "Checking accuracy on validation set\n", + "Got 372 / 1000 correct (37.20)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5974\n", + "Checking accuracy on validation set\n", + "Got 421 / 1000 correct (42.10)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.5917\n", + "Checking accuracy on validation set\n", + "Got 452 / 1000 correct (45.20)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.5624\n", + "Checking accuracy on validation set\n", + "Got 490 / 1000 correct (49.00)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.4231\n", + "Checking accuracy on validation set\n", + "Got 475 / 1000 correct (47.50)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.3586\n", + "Checking accuracy on validation set\n", + "Got 486 / 1000 correct (48.60)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.6096\n", + "Checking accuracy on validation set\n", + "Got 525 / 1000 correct (52.50)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.2357\n", + "Checking accuracy on validation set\n", + "Got 563 / 1000 correct (56.30)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.1306\n", + "Checking accuracy on validation set\n", + "Got 572 / 1000 correct (57.20)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.4168\n", + "Checking accuracy on validation set\n", + "Got 583 / 1000 correct (58.30)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8847\n", + "Checking accuracy on validation set\n", + "Got 616 / 1000 correct (61.60)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.3069\n", + "Checking accuracy on validation set\n", + "Got 648 / 1000 correct (64.80)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9494\n", + "Checking accuracy on validation set\n", + "Got 600 / 1000 correct (60.00)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0113\n", + "Checking accuracy on validation set\n", + "Got 642 / 1000 correct (64.20)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.1501\n", + "Checking accuracy on validation set\n", + "Got 677 / 1000 correct (67.70)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.9163\n", + "Checking accuracy on validation set\n", + "Got 643 / 1000 correct (64.30)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 1.1084\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.1930\n", + "Checking accuracy on validation set\n", + "Got 681 / 1000 correct (68.10)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8687\n", + "Checking accuracy on validation set\n", + "Got 652 / 1000 correct (65.20)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.8604\n", + "Checking accuracy on validation set\n", + "Got 690 / 1000 correct (69.00)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.8512\n", + "Checking accuracy on validation set\n", + "Got 713 / 1000 correct (71.30)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 1.0329\n", + "Checking accuracy on validation set\n", + "Got 663 / 1000 correct (66.30)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.9070\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.7286\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.9888\n", + "Checking accuracy on validation set\n", + "Got 719 / 1000 correct (71.90)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.6579\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.9349\n", + "Checking accuracy on validation set\n", + "Got 685 / 1000 correct (68.50)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.5492\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.7818\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.5941\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6460\n", + "Checking accuracy on validation set\n", + "Got 741 / 1000 correct (74.10)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8928\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.6071\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.8448\n", + "Checking accuracy on validation set\n", + "Got 767 / 1000 correct (76.70)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5390\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6977\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.7904\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5483\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5887\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4090\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4997\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.6901\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5862\n", + "Checking accuracy on validation set\n", + "Got 834 / 1000 correct (83.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5732\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5505\n", + "Checking accuracy on validation set\n", + "Got 848 / 1000 correct (84.80)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.6043\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.6065\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5492\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6615\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4306\n", + "Checking accuracy on validation set\n", + "Got 852 / 1000 correct (85.20)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4407\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.5397\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4170\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.5742\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4968\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5642\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3527\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4670\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4621\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5971\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3926\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.2852\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.5670\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3422\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3374\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.5653\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.3743\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.4100\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.6381\n", + "Checking accuracy on validation set\n", + "Got 856 / 1000 correct (85.60)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3007\n", + "Checking accuracy on validation set\n", + "Got 862 / 1000 correct (86.20)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3193\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3022\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3831\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.5464\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5951\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4095\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "---- New lr = 0.00328 and gamma = 0.110 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.7827\n", + "Checking accuracy on validation set\n", + "Got 112 / 1000 correct (11.20)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.1240\n", + "Checking accuracy on validation set\n", + "Got 306 / 1000 correct (30.60)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6856\n", + "Checking accuracy on validation set\n", + "Got 374 / 1000 correct (37.40)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 2.0650\n", + "Checking accuracy on validation set\n", + "Got 377 / 1000 correct (37.70)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.4062\n", + "Checking accuracy on validation set\n", + "Got 414 / 1000 correct (41.40)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5591\n", + "Checking accuracy on validation set\n", + "Got 460 / 1000 correct (46.00)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.6131\n", + "Checking accuracy on validation set\n", + "Got 461 / 1000 correct (46.10)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.3579\n", + "Checking accuracy on validation set\n", + "Got 485 / 1000 correct (48.50)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.5374\n", + "Checking accuracy on validation set\n", + "Got 502 / 1000 correct (50.20)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.5503\n", + "Checking accuracy on validation set\n", + "Got 459 / 1000 correct (45.90)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.5506\n", + "Checking accuracy on validation set\n", + "Got 516 / 1000 correct (51.60)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.7249\n", + "Checking accuracy on validation set\n", + "Got 529 / 1000 correct (52.90)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.2105\n", + "Checking accuracy on validation set\n", + "Got 492 / 1000 correct (49.20)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.9550\n", + "Checking accuracy on validation set\n", + "Got 629 / 1000 correct (62.90)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.3709\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0338\n", + "Checking accuracy on validation set\n", + "Got 600 / 1000 correct (60.00)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.2700\n", + "Checking accuracy on validation set\n", + "Got 607 / 1000 correct (60.70)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0416\n", + "Checking accuracy on validation set\n", + "Got 668 / 1000 correct (66.80)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.2548\n", + "Checking accuracy on validation set\n", + "Got 645 / 1000 correct (64.50)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.2854\n", + "Checking accuracy on validation set\n", + "Got 654 / 1000 correct (65.40)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 1.1765\n", + "Checking accuracy on validation set\n", + "Got 631 / 1000 correct (63.10)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9371\n", + "Checking accuracy on validation set\n", + "Got 676 / 1000 correct (67.60)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7726\n", + "Checking accuracy on validation set\n", + "Got 689 / 1000 correct (68.90)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.8469\n", + "Checking accuracy on validation set\n", + "Got 688 / 1000 correct (68.80)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7750\n", + "Checking accuracy on validation set\n", + "Got 704 / 1000 correct (70.40)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 1.0219\n", + "Checking accuracy on validation set\n", + "Got 688 / 1000 correct (68.80)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7910\n", + "Checking accuracy on validation set\n", + "Got 722 / 1000 correct (72.20)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.8571\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.7197\n", + "Checking accuracy on validation set\n", + "Got 703 / 1000 correct (70.30)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 1.0258\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.5649\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.7147\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.7924\n", + "Checking accuracy on validation set\n", + "Got 755 / 1000 correct (75.50)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6863\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6870\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.7050\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.7424\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.7331\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5645\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.8589\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.8163\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6936\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.3809\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6409\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4780\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4866\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6618\n", + "Checking accuracy on validation set\n", + "Got 856 / 1000 correct (85.60)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.5149\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.6829\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4299\n", + "Checking accuracy on validation set\n", + "Got 856 / 1000 correct (85.60)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4458\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.5115\n", + "Checking accuracy on validation set\n", + "Got 862 / 1000 correct (86.20)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4966\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.6518\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.3672\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4238\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4353\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3724\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5217\n", + "Checking accuracy on validation set\n", + "Got 856 / 1000 correct (85.60)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4400\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5062\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5645\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4511\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4194\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.5277\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4581\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.2421\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3511\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.5542\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3182\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4455\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.4730\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3804\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2445\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.6847\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2880\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4479\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2952\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3778\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2819\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "---- New lr = 0.00029 and gamma = 0.157 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.5967\n", + "Checking accuracy on validation set\n", + "Got 124 / 1000 correct (12.40)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.8172\n", + "Checking accuracy on validation set\n", + "Got 377 / 1000 correct (37.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5712\n", + "Checking accuracy on validation set\n", + "Got 449 / 1000 correct (44.90)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4319\n", + "Checking accuracy on validation set\n", + "Got 449 / 1000 correct (44.90)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.2919\n", + "Checking accuracy on validation set\n", + "Got 492 / 1000 correct (49.20)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.7838\n", + "Checking accuracy on validation set\n", + "Got 510 / 1000 correct (51.00)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.4226\n", + "Checking accuracy on validation set\n", + "Got 578 / 1000 correct (57.80)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.1799\n", + "Checking accuracy on validation set\n", + "Got 568 / 1000 correct (56.80)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3552\n", + "Checking accuracy on validation set\n", + "Got 611 / 1000 correct (61.10)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2660\n", + "Checking accuracy on validation set\n", + "Got 646 / 1000 correct (64.60)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.0093\n", + "Checking accuracy on validation set\n", + "Got 638 / 1000 correct (63.80)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0531\n", + "Checking accuracy on validation set\n", + "Got 702 / 1000 correct (70.20)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.8785\n", + "Checking accuracy on validation set\n", + "Got 634 / 1000 correct (63.40)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.1385\n", + "Checking accuracy on validation set\n", + "Got 691 / 1000 correct (69.10)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.2019\n", + "Checking accuracy on validation set\n", + "Got 714 / 1000 correct (71.40)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1474\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9164\n", + "Checking accuracy on validation set\n", + "Got 692 / 1000 correct (69.20)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.8589\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8384\n", + "Checking accuracy on validation set\n", + "Got 735 / 1000 correct (73.50)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.9310\n", + "Checking accuracy on validation set\n", + "Got 741 / 1000 correct (74.10)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.7051\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7350\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.6549\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7526\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7654\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7137\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7581\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.5569\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.4707\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.5827\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.4977\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.5013\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6611\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7710\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6295\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8087\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.6450\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.3922\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.4490\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.4873\n", + "Checking accuracy on validation set\n", + "Got 835 / 1000 correct (83.50)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5894\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.2466\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.3827\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4328\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.3347\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.3762\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.3710\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4074\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.2718\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.6100\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.2685\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.4034\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4388\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.3998\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4225\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.2468\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4635\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.2883\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.3579\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.2532\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4276\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.3791\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4328\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3544\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.2344\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4699\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4633\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.5331\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.2811\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3937\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.3763\n", + "Checking accuracy on validation set\n", + "Got 894 / 1000 correct (89.40)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.2790\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.2548\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.4839\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2110\n", + "Checking accuracy on validation set\n", + "Got 894 / 1000 correct (89.40)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2991\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4862\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2107\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3681\n", + "Checking accuracy on validation set\n", + "Got 894 / 1000 correct (89.40)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2887\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "---- New lr = 0.00212 and gamma = 0.106 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.1316\n", + "Checking accuracy on validation set\n", + "Got 79 / 1000 correct (7.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.1216\n", + "Checking accuracy on validation set\n", + "Got 267 / 1000 correct (26.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.7866\n", + "Checking accuracy on validation set\n", + "Got 372 / 1000 correct (37.20)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 2.0431\n", + "Checking accuracy on validation set\n", + "Got 255 / 1000 correct (25.50)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.6054\n", + "Checking accuracy on validation set\n", + "Got 462 / 1000 correct (46.20)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.9709\n", + "Checking accuracy on validation set\n", + "Got 455 / 1000 correct (45.50)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.6421\n", + "Checking accuracy on validation set\n", + "Got 455 / 1000 correct (45.50)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4080\n", + "Checking accuracy on validation set\n", + "Got 431 / 1000 correct (43.10)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.2347\n", + "Checking accuracy on validation set\n", + "Got 483 / 1000 correct (48.30)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2290\n", + "Checking accuracy on validation set\n", + "Got 575 / 1000 correct (57.50)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.4810\n", + "Checking accuracy on validation set\n", + "Got 534 / 1000 correct (53.40)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0287\n", + "Checking accuracy on validation set\n", + "Got 546 / 1000 correct (54.60)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.3628\n", + "Checking accuracy on validation set\n", + "Got 588 / 1000 correct (58.80)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.1625\n", + "Checking accuracy on validation set\n", + "Got 576 / 1000 correct (57.60)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.0348\n", + "Checking accuracy on validation set\n", + "Got 575 / 1000 correct (57.50)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.9100\n", + "Checking accuracy on validation set\n", + "Got 565 / 1000 correct (56.50)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.1923\n", + "Checking accuracy on validation set\n", + "Got 638 / 1000 correct (63.80)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0303\n", + "Checking accuracy on validation set\n", + "Got 645 / 1000 correct (64.50)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8979\n", + "Checking accuracy on validation set\n", + "Got 623 / 1000 correct (62.30)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.3692\n", + "Checking accuracy on validation set\n", + "Got 634 / 1000 correct (63.40)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9920\n", + "Checking accuracy on validation set\n", + "Got 635 / 1000 correct (63.50)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9063\n", + "Checking accuracy on validation set\n", + "Got 673 / 1000 correct (67.30)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7681\n", + "Checking accuracy on validation set\n", + "Got 697 / 1000 correct (69.70)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.9267\n", + "Checking accuracy on validation set\n", + "Got 699 / 1000 correct (69.90)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.9494\n", + "Checking accuracy on validation set\n", + "Got 701 / 1000 correct (70.10)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.8988\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.8179\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6385\n", + "Checking accuracy on validation set\n", + "Got 694 / 1000 correct (69.40)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.9039\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.6882\n", + "Checking accuracy on validation set\n", + "Got 703 / 1000 correct (70.30)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.7006\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6911\n", + "Checking accuracy on validation set\n", + "Got 657 / 1000 correct (65.70)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6731\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.8246\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.9289\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.4502\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.8024\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.7689\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 1.0922\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6704\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.4867\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4918\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4816\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4710\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.6817\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5950\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.4737\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4765\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4118\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.5081\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4249\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.4300\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6371\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4426\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.3462\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.2572\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4955\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3551\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5904\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4397\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3383\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4422\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4094\n", + "Checking accuracy on validation set\n", + "Got 862 / 1000 correct (86.20)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3240\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4988\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.2640\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4774\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.2791\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3446\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3399\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4702\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.3868\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3312\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.4338\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2987\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.4251\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4932\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3390\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4782\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.5245\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "---- New lr = 0.00173 and gamma = 0.150 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.4751\n", + "Checking accuracy on validation set\n", + "Got 119 / 1000 correct (11.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9029\n", + "Checking accuracy on validation set\n", + "Got 300 / 1000 correct (30.00)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.8446\n", + "Checking accuracy on validation set\n", + "Got 384 / 1000 correct (38.40)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.9407\n", + "Checking accuracy on validation set\n", + "Got 394 / 1000 correct (39.40)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.8898\n", + "Checking accuracy on validation set\n", + "Got 407 / 1000 correct (40.70)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.9139\n", + "Checking accuracy on validation set\n", + "Got 448 / 1000 correct (44.80)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.4882\n", + "Checking accuracy on validation set\n", + "Got 452 / 1000 correct (45.20)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4849\n", + "Checking accuracy on validation set\n", + "Got 493 / 1000 correct (49.30)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.4339\n", + "Checking accuracy on validation set\n", + "Got 512 / 1000 correct (51.20)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.5095\n", + "Checking accuracy on validation set\n", + "Got 517 / 1000 correct (51.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.3235\n", + "Checking accuracy on validation set\n", + "Got 536 / 1000 correct (53.60)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1312\n", + "Checking accuracy on validation set\n", + "Got 569 / 1000 correct (56.90)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.2431\n", + "Checking accuracy on validation set\n", + "Got 582 / 1000 correct (58.20)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.9272\n", + "Checking accuracy on validation set\n", + "Got 587 / 1000 correct (58.70)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.0039\n", + "Checking accuracy on validation set\n", + "Got 646 / 1000 correct (64.60)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.2936\n", + "Checking accuracy on validation set\n", + "Got 620 / 1000 correct (62.00)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.1144\n", + "Checking accuracy on validation set\n", + "Got 597 / 1000 correct (59.70)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.3048\n", + "Checking accuracy on validation set\n", + "Got 637 / 1000 correct (63.70)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8679\n", + "Checking accuracy on validation set\n", + "Got 645 / 1000 correct (64.50)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.2361\n", + "Checking accuracy on validation set\n", + "Got 660 / 1000 correct (66.00)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.6617\n", + "Checking accuracy on validation set\n", + "Got 667 / 1000 correct (66.70)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.0003\n", + "Checking accuracy on validation set\n", + "Got 696 / 1000 correct (69.60)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 1.1792\n", + "Checking accuracy on validation set\n", + "Got 684 / 1000 correct (68.40)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7483\n", + "Checking accuracy on validation set\n", + "Got 693 / 1000 correct (69.30)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.8898\n", + "Checking accuracy on validation set\n", + "Got 704 / 1000 correct (70.40)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.8265\n", + "Checking accuracy on validation set\n", + "Got 721 / 1000 correct (72.10)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7055\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.8557\n", + "Checking accuracy on validation set\n", + "Got 723 / 1000 correct (72.30)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.7421\n", + "Checking accuracy on validation set\n", + "Got 740 / 1000 correct (74.00)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 1.2978\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.6813\n", + "Checking accuracy on validation set\n", + "Got 696 / 1000 correct (69.60)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8648\n", + "Checking accuracy on validation set\n", + "Got 717 / 1000 correct (71.70)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6326\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.8266\n", + "Checking accuracy on validation set\n", + "Got 728 / 1000 correct (72.80)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.8983\n", + "Checking accuracy on validation set\n", + "Got 751 / 1000 correct (75.10)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.8363\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.7216\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.7817\n", + "Checking accuracy on validation set\n", + "Got 746 / 1000 correct (74.60)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.8122\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.7207\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.7926\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5898\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4040\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6447\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4707\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.3317\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.4963\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.7133\n", + "Checking accuracy on validation set\n", + "Got 839 / 1000 correct (83.90)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.5046\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4456\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.3964\n", + "Checking accuracy on validation set\n", + "Got 856 / 1000 correct (85.60)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3075\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4242\n", + "Checking accuracy on validation set\n", + "Got 845 / 1000 correct (84.50)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4490\n", + "Checking accuracy on validation set\n", + "Got 862 / 1000 correct (86.20)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.5258\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.5954\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.5758\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4514\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5223\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.3769\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.5434\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4132\n", + "Checking accuracy on validation set\n", + "Got 866 / 1000 correct (86.60)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4822\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3455\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3441\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.3791\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4796\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3259\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3054\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3345\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.3371\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5068\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.5104\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2198\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3055\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.5864\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2931\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2711\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4161\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.4150\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "---- New lr = 0.00224 and gamma = 0.200 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.3400\n", + "Checking accuracy on validation set\n", + "Got 79 / 1000 correct (7.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.0673\n", + "Checking accuracy on validation set\n", + "Got 264 / 1000 correct (26.40)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6844\n", + "Checking accuracy on validation set\n", + "Got 351 / 1000 correct (35.10)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4802\n", + "Checking accuracy on validation set\n", + "Got 391 / 1000 correct (39.10)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.7347\n", + "Checking accuracy on validation set\n", + "Got 420 / 1000 correct (42.00)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.3526\n", + "Checking accuracy on validation set\n", + "Got 443 / 1000 correct (44.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 2.0143\n", + "Checking accuracy on validation set\n", + "Got 384 / 1000 correct (38.40)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4733\n", + "Checking accuracy on validation set\n", + "Got 455 / 1000 correct (45.50)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.6008\n", + "Checking accuracy on validation set\n", + "Got 449 / 1000 correct (44.90)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.4484\n", + "Checking accuracy on validation set\n", + "Got 517 / 1000 correct (51.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.4111\n", + "Checking accuracy on validation set\n", + "Got 501 / 1000 correct (50.10)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.3083\n", + "Checking accuracy on validation set\n", + "Got 554 / 1000 correct (55.40)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.4190\n", + "Checking accuracy on validation set\n", + "Got 570 / 1000 correct (57.00)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0279\n", + "Checking accuracy on validation set\n", + "Got 620 / 1000 correct (62.00)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.0156\n", + "Checking accuracy on validation set\n", + "Got 618 / 1000 correct (61.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0270\n", + "Checking accuracy on validation set\n", + "Got 631 / 1000 correct (63.10)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.7607\n", + "Checking accuracy on validation set\n", + "Got 648 / 1000 correct (64.80)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0502\n", + "Checking accuracy on validation set\n", + "Got 637 / 1000 correct (63.70)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.2713\n", + "Checking accuracy on validation set\n", + "Got 648 / 1000 correct (64.80)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.9643\n", + "Checking accuracy on validation set\n", + "Got 655 / 1000 correct (65.50)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 1.0713\n", + "Checking accuracy on validation set\n", + "Got 665 / 1000 correct (66.50)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9282\n", + "Checking accuracy on validation set\n", + "Got 714 / 1000 correct (71.40)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8041\n", + "Checking accuracy on validation set\n", + "Got 721 / 1000 correct (72.10)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.0561\n", + "Checking accuracy on validation set\n", + "Got 679 / 1000 correct (67.90)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7627\n", + "Checking accuracy on validation set\n", + "Got 702 / 1000 correct (70.20)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.9045\n", + "Checking accuracy on validation set\n", + "Got 671 / 1000 correct (67.10)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.8641\n", + "Checking accuracy on validation set\n", + "Got 659 / 1000 correct (65.90)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.8158\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.9471\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.7963\n", + "Checking accuracy on validation set\n", + "Got 719 / 1000 correct (71.90)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 1.0158\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 1.1738\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.7115\n", + "Checking accuracy on validation set\n", + "Got 744 / 1000 correct (74.40)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.8255\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6834\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5801\n", + "Checking accuracy on validation set\n", + "Got 787 / 1000 correct (78.70)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5836\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6806\n", + "Checking accuracy on validation set\n", + "Got 776 / 1000 correct (77.60)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7339\n", + "Checking accuracy on validation set\n", + "Got 775 / 1000 correct (77.50)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.8451\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6451\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4476\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.2803\n", + "Checking accuracy on validation set\n", + "Got 833 / 1000 correct (83.30)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4305\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4481\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.3994\n", + "Checking accuracy on validation set\n", + "Got 845 / 1000 correct (84.50)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.4655\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.7997\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.3839\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.6031\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5790\n", + "Checking accuracy on validation set\n", + "Got 852 / 1000 correct (85.20)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.4635\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4370\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4937\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4296\n", + "Checking accuracy on validation set\n", + "Got 841 / 1000 correct (84.10)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4188\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.3501\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4365\n", + "Checking accuracy on validation set\n", + "Got 862 / 1000 correct (86.20)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.2846\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5365\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4087\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.3588\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3979\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4056\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.2877\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.3343\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.5482\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3078\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4083\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.4440\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.3905\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.5507\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.4986\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3605\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4014\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2548\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2906\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2563\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3032\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.3767\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "---- New lr = 0.00399 and gamma = 0.200 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.3441\n", + "Checking accuracy on validation set\n", + "Got 98 / 1000 correct (9.80)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.0383\n", + "Checking accuracy on validation set\n", + "Got 237 / 1000 correct (23.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.9783\n", + "Checking accuracy on validation set\n", + "Got 320 / 1000 correct (32.00)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.7798\n", + "Checking accuracy on validation set\n", + "Got 387 / 1000 correct (38.70)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.9845\n", + "Checking accuracy on validation set\n", + "Got 403 / 1000 correct (40.30)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.6205\n", + "Checking accuracy on validation set\n", + "Got 423 / 1000 correct (42.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.4477\n", + "Checking accuracy on validation set\n", + "Got 404 / 1000 correct (40.40)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4039\n", + "Checking accuracy on validation set\n", + "Got 458 / 1000 correct (45.80)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.6623\n", + "Checking accuracy on validation set\n", + "Got 467 / 1000 correct (46.70)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.3914\n", + "Checking accuracy on validation set\n", + "Got 521 / 1000 correct (52.10)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.5173\n", + "Checking accuracy on validation set\n", + "Got 470 / 1000 correct (47.00)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.4042\n", + "Checking accuracy on validation set\n", + "Got 521 / 1000 correct (52.10)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.3632\n", + "Checking accuracy on validation set\n", + "Got 561 / 1000 correct (56.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.2489\n", + "Checking accuracy on validation set\n", + "Got 594 / 1000 correct (59.40)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8850\n", + "Checking accuracy on validation set\n", + "Got 568 / 1000 correct (56.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1263\n", + "Checking accuracy on validation set\n", + "Got 628 / 1000 correct (62.80)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.1349\n", + "Checking accuracy on validation set\n", + "Got 656 / 1000 correct (65.60)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0982\n", + "Checking accuracy on validation set\n", + "Got 594 / 1000 correct (59.40)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.1382\n", + "Checking accuracy on validation set\n", + "Got 651 / 1000 correct (65.10)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.0693\n", + "Checking accuracy on validation set\n", + "Got 663 / 1000 correct (66.30)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.9793\n", + "Checking accuracy on validation set\n", + "Got 650 / 1000 correct (65.00)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9308\n", + "Checking accuracy on validation set\n", + "Got 668 / 1000 correct (66.80)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 1.1331\n", + "Checking accuracy on validation set\n", + "Got 717 / 1000 correct (71.70)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.1032\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7419\n", + "Checking accuracy on validation set\n", + "Got 680 / 1000 correct (68.00)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 1.3553\n", + "Checking accuracy on validation set\n", + "Got 710 / 1000 correct (71.00)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.8757\n", + "Checking accuracy on validation set\n", + "Got 725 / 1000 correct (72.50)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.8168\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.8909\n", + "Checking accuracy on validation set\n", + "Got 705 / 1000 correct (70.50)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8957\n", + "Checking accuracy on validation set\n", + "Got 733 / 1000 correct (73.30)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8328\n", + "Checking accuracy on validation set\n", + "Got 674 / 1000 correct (67.40)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.9733\n", + "Checking accuracy on validation set\n", + "Got 722 / 1000 correct (72.20)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6503\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.8042\n", + "Checking accuracy on validation set\n", + "Got 756 / 1000 correct (75.60)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.7708\n", + "Checking accuracy on validation set\n", + "Got 748 / 1000 correct (74.80)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.7363\n", + "Checking accuracy on validation set\n", + "Got 663 / 1000 correct (66.30)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 1.0033\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.8366\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.8341\n", + "Checking accuracy on validation set\n", + "Got 731 / 1000 correct (73.10)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.6722\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5519\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.6087\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5493\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4529\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4955\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5173\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6498\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4708\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.3194\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4300\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.3447\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.2981\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.3843\n", + "Checking accuracy on validation set\n", + "Got 840 / 1000 correct (84.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4544\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.5354\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4964\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.5417\n", + "Checking accuracy on validation set\n", + "Got 845 / 1000 correct (84.50)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4589\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.5187\n", + "Checking accuracy on validation set\n", + "Got 852 / 1000 correct (85.20)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.2976\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3887\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4260\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4061\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3330\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.4170\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.4547\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4136\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3343\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3868\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3472\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5305\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.4608\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3239\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2941\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2810\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2725\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2608\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.4872\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.2028\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.3407\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "---- New lr = 0.00235 and gamma = 0.200 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 4.3808\n", + "Checking accuracy on validation set\n", + "Got 119 / 1000 correct (11.90)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7424\n", + "Checking accuracy on validation set\n", + "Got 287 / 1000 correct (28.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6634\n", + "Checking accuracy on validation set\n", + "Got 408 / 1000 correct (40.80)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.6876\n", + "Checking accuracy on validation set\n", + "Got 414 / 1000 correct (41.40)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.5370\n", + "Checking accuracy on validation set\n", + "Got 413 / 1000 correct (41.30)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.7662\n", + "Checking accuracy on validation set\n", + "Got 423 / 1000 correct (42.30)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.5063\n", + "Checking accuracy on validation set\n", + "Got 458 / 1000 correct (45.80)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.3563\n", + "Checking accuracy on validation set\n", + "Got 544 / 1000 correct (54.40)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3317\n", + "Checking accuracy on validation set\n", + "Got 479 / 1000 correct (47.90)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2209\n", + "Checking accuracy on validation set\n", + "Got 527 / 1000 correct (52.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.1528\n", + "Checking accuracy on validation set\n", + "Got 537 / 1000 correct (53.70)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1037\n", + "Checking accuracy on validation set\n", + "Got 592 / 1000 correct (59.20)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.3538\n", + "Checking accuracy on validation set\n", + "Got 596 / 1000 correct (59.60)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.0600\n", + "Checking accuracy on validation set\n", + "Got 646 / 1000 correct (64.60)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 1.2763\n", + "Checking accuracy on validation set\n", + "Got 605 / 1000 correct (60.50)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0419\n", + "Checking accuracy on validation set\n", + "Got 635 / 1000 correct (63.50)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9005\n", + "Checking accuracy on validation set\n", + "Got 666 / 1000 correct (66.60)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.1208\n", + "Checking accuracy on validation set\n", + "Got 621 / 1000 correct (62.10)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.8994\n", + "Checking accuracy on validation set\n", + "Got 700 / 1000 correct (70.00)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.1205\n", + "Checking accuracy on validation set\n", + "Got 602 / 1000 correct (60.20)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.8429\n", + "Checking accuracy on validation set\n", + "Got 656 / 1000 correct (65.60)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7491\n", + "Checking accuracy on validation set\n", + "Got 716 / 1000 correct (71.60)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 1.0532\n", + "Checking accuracy on validation set\n", + "Got 706 / 1000 correct (70.60)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.8063\n", + "Checking accuracy on validation set\n", + "Got 716 / 1000 correct (71.60)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 1.0231\n", + "Checking accuracy on validation set\n", + "Got 711 / 1000 correct (71.10)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.6705\n", + "Checking accuracy on validation set\n", + "Got 719 / 1000 correct (71.90)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.1768\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6276\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6319\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8928\n", + "Checking accuracy on validation set\n", + "Got 742 / 1000 correct (74.20)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8681\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6440\n", + "Checking accuracy on validation set\n", + "Got 735 / 1000 correct (73.50)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6910\n", + "Checking accuracy on validation set\n", + "Got 745 / 1000 correct (74.50)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7559\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.6109\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.7437\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5569\n", + "Checking accuracy on validation set\n", + "Got 789 / 1000 correct (78.90)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5803\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5346\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.4527\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.7097\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5696\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4183\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4147\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5540\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4827\n", + "Checking accuracy on validation set\n", + "Got 848 / 1000 correct (84.80)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5464\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.6117\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.3822\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.5609\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.7965\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.4345\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.5589\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4401\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4306\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.3084\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.2984\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.7324\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4246\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.2730\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3995\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.3483\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3674\n", + "Checking accuracy on validation set\n", + "Got 866 / 1000 correct (86.60)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4345\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3267\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.2322\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4576\n", + "Checking accuracy on validation set\n", + "Got 862 / 1000 correct (86.20)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3227\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3256\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.2883\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5129\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.6178\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.5951\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2867\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2024\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3340\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3103\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3712\n", + "Checking accuracy on validation set\n", + "Got 895 / 1000 correct (89.50)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.4006\n", + "Checking accuracy on validation set\n", + "Got 901 / 1000 correct (90.10)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.3149\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "---- New lr = 0.00170 and gamma = 0.200 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.5384\n", + "Checking accuracy on validation set\n", + "Got 114 / 1000 correct (11.40)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7893\n", + "Checking accuracy on validation set\n", + "Got 307 / 1000 correct (30.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.7394\n", + "Checking accuracy on validation set\n", + "Got 363 / 1000 correct (36.30)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.5583\n", + "Checking accuracy on validation set\n", + "Got 388 / 1000 correct (38.80)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.6195\n", + "Checking accuracy on validation set\n", + "Got 435 / 1000 correct (43.50)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.5559\n", + "Checking accuracy on validation set\n", + "Got 455 / 1000 correct (45.50)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.5190\n", + "Checking accuracy on validation set\n", + "Got 485 / 1000 correct (48.50)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4212\n", + "Checking accuracy on validation set\n", + "Got 490 / 1000 correct (49.00)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.3042\n", + "Checking accuracy on validation set\n", + "Got 511 / 1000 correct (51.10)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.2058\n", + "Checking accuracy on validation set\n", + "Got 450 / 1000 correct (45.00)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.6638\n", + "Checking accuracy on validation set\n", + "Got 595 / 1000 correct (59.50)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1053\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.1026\n", + "Checking accuracy on validation set\n", + "Got 601 / 1000 correct (60.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.1718\n", + "Checking accuracy on validation set\n", + "Got 600 / 1000 correct (60.00)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9558\n", + "Checking accuracy on validation set\n", + "Got 654 / 1000 correct (65.40)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.1013\n", + "Checking accuracy on validation set\n", + "Got 639 / 1000 correct (63.90)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.8864\n", + "Checking accuracy on validation set\n", + "Got 623 / 1000 correct (62.30)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0272\n", + "Checking accuracy on validation set\n", + "Got 680 / 1000 correct (68.00)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.0119\n", + "Checking accuracy on validation set\n", + "Got 685 / 1000 correct (68.50)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.7593\n", + "Checking accuracy on validation set\n", + "Got 700 / 1000 correct (70.00)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.6974\n", + "Checking accuracy on validation set\n", + "Got 713 / 1000 correct (71.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.0174\n", + "Checking accuracy on validation set\n", + "Got 680 / 1000 correct (68.00)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 1.0014\n", + "Checking accuracy on validation set\n", + "Got 711 / 1000 correct (71.10)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.8271\n", + "Checking accuracy on validation set\n", + "Got 631 / 1000 correct (63.10)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.9375\n", + "Checking accuracy on validation set\n", + "Got 705 / 1000 correct (70.50)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7072\n", + "Checking accuracy on validation set\n", + "Got 721 / 1000 correct (72.10)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 1.0054\n", + "Checking accuracy on validation set\n", + "Got 699 / 1000 correct (69.90)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 1.0299\n", + "Checking accuracy on validation set\n", + "Got 727 / 1000 correct (72.70)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.8531\n", + "Checking accuracy on validation set\n", + "Got 714 / 1000 correct (71.40)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.8423\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.7333\n", + "Checking accuracy on validation set\n", + "Got 724 / 1000 correct (72.40)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6610\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.8058\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7237\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.8137\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.7372\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5923\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5019\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7827\n", + "Checking accuracy on validation set\n", + "Got 777 / 1000 correct (77.70)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5961\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5461\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5473\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4395\n", + "Checking accuracy on validation set\n", + "Got 838 / 1000 correct (83.80)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.6015\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5580\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.3356\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.5910\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4744\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.2364\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.3937\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5067\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.6344\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4568\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5547\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.5102\n", + "Checking accuracy on validation set\n", + "Got 866 / 1000 correct (86.60)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.5799\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4153\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.1640\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4151\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4356\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3498\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4946\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4006\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.7344\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3042\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.3870\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.2733\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.2195\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3259\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.6561\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.5186\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.2954\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.2349\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2311\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4278\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2860\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3067\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3143\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.2801\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.1832\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "---- New lr = 0.00028 and gamma = 0.157 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 4.3223\n", + "Checking accuracy on validation set\n", + "Got 126 / 1000 correct (12.60)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9050\n", + "Checking accuracy on validation set\n", + "Got 363 / 1000 correct (36.30)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.8971\n", + "Checking accuracy on validation set\n", + "Got 454 / 1000 correct (45.40)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.2633\n", + "Checking accuracy on validation set\n", + "Got 505 / 1000 correct (50.50)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3635\n", + "Checking accuracy on validation set\n", + "Got 534 / 1000 correct (53.40)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.6452\n", + "Checking accuracy on validation set\n", + "Got 539 / 1000 correct (53.90)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.2916\n", + "Checking accuracy on validation set\n", + "Got 596 / 1000 correct (59.60)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.0500\n", + "Checking accuracy on validation set\n", + "Got 591 / 1000 correct (59.10)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.0049\n", + "Checking accuracy on validation set\n", + "Got 624 / 1000 correct (62.40)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.0560\n", + "Checking accuracy on validation set\n", + "Got 643 / 1000 correct (64.30)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 0.6617\n", + "Checking accuracy on validation set\n", + "Got 658 / 1000 correct (65.80)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0707\n", + "Checking accuracy on validation set\n", + "Got 698 / 1000 correct (69.80)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.8459\n", + "Checking accuracy on validation set\n", + "Got 681 / 1000 correct (68.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.7423\n", + "Checking accuracy on validation set\n", + "Got 712 / 1000 correct (71.20)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9377\n", + "Checking accuracy on validation set\n", + "Got 700 / 1000 correct (70.00)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.7620\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.6666\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.8997\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.5957\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.0742\n", + "Checking accuracy on validation set\n", + "Got 758 / 1000 correct (75.80)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.6781\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7096\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8003\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7257\n", + "Checking accuracy on validation set\n", + "Got 724 / 1000 correct (72.40)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.8673\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.5970\n", + "Checking accuracy on validation set\n", + "Got 817 / 1000 correct (81.70)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.4027\n", + "Checking accuracy on validation set\n", + "Got 771 / 1000 correct (77.10)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6468\n", + "Checking accuracy on validation set\n", + "Got 783 / 1000 correct (78.30)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.7374\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.7456\n", + "Checking accuracy on validation set\n", + "Got 826 / 1000 correct (82.60)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8067\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.3778\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5895\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7542\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.4184\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5718\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5560\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.3611\n", + "Checking accuracy on validation set\n", + "Got 835 / 1000 correct (83.50)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7504\n", + "Checking accuracy on validation set\n", + "Got 830 / 1000 correct (83.00)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.3494\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5284\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5334\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4535\n", + "Checking accuracy on validation set\n", + "Got 855 / 1000 correct (85.50)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4226\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4338\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5324\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.3418\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.3848\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.3739\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4791\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.3140\n", + "Checking accuracy on validation set\n", + "Got 866 / 1000 correct (86.60)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3110\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.6300\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.3704\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.3339\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.3635\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.3913\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.4652\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.2616\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.3416\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4999\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.2048\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.4366\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.2647\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.2614\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.2582\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3439\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3465\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4282\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.2055\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4031\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.2775\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.4034\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3101\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2920\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3061\n", + "Checking accuracy on validation set\n", + "Got 894 / 1000 correct (89.40)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3022\n", + "Checking accuracy on validation set\n", + "Got 896 / 1000 correct (89.60)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3115\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.2317\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.3365\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 897 / 1000 correct (89.70)\n", + "---- New lr = 0.00024 and gamma = 0.157 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.2529\n", + "Checking accuracy on validation set\n", + "Got 92 / 1000 correct (9.20)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 2.0868\n", + "Checking accuracy on validation set\n", + "Got 386 / 1000 correct (38.60)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.5951\n", + "Checking accuracy on validation set\n", + "Got 422 / 1000 correct (42.20)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.2398\n", + "Checking accuracy on validation set\n", + "Got 483 / 1000 correct (48.30)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3089\n", + "Checking accuracy on validation set\n", + "Got 521 / 1000 correct (52.10)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2073\n", + "Checking accuracy on validation set\n", + "Got 541 / 1000 correct (54.10)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.3923\n", + "Checking accuracy on validation set\n", + "Got 563 / 1000 correct (56.30)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.4038\n", + "Checking accuracy on validation set\n", + "Got 617 / 1000 correct (61.70)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.1073\n", + "Checking accuracy on validation set\n", + "Got 576 / 1000 correct (57.60)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.0259\n", + "Checking accuracy on validation set\n", + "Got 653 / 1000 correct (65.30)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.0741\n", + "Checking accuracy on validation set\n", + "Got 633 / 1000 correct (63.30)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.1376\n", + "Checking accuracy on validation set\n", + "Got 653 / 1000 correct (65.30)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.1907\n", + "Checking accuracy on validation set\n", + "Got 659 / 1000 correct (65.90)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.9305\n", + "Checking accuracy on validation set\n", + "Got 708 / 1000 correct (70.80)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.6608\n", + "Checking accuracy on validation set\n", + "Got 710 / 1000 correct (71.00)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.7672\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 1.0357\n", + "Checking accuracy on validation set\n", + "Got 722 / 1000 correct (72.20)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7181\n", + "Checking accuracy on validation set\n", + "Got 729 / 1000 correct (72.90)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.6848\n", + "Checking accuracy on validation set\n", + "Got 733 / 1000 correct (73.30)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.5542\n", + "Checking accuracy on validation set\n", + "Got 755 / 1000 correct (75.50)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.6745\n", + "Checking accuracy on validation set\n", + "Got 723 / 1000 correct (72.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.6984\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.6557\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.8747\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.6127\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7351\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.8717\n", + "Checking accuracy on validation set\n", + "Got 768 / 1000 correct (76.80)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.5615\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.7351\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.4598\n", + "Checking accuracy on validation set\n", + "Got 816 / 1000 correct (81.60)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.4240\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.5211\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6100\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.5908\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5752\n", + "Checking accuracy on validation set\n", + "Got 804 / 1000 correct (80.40)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5732\n", + "Checking accuracy on validation set\n", + "Got 805 / 1000 correct (80.50)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.6937\n", + "Checking accuracy on validation set\n", + "Got 761 / 1000 correct (76.10)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.8447\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6927\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5873\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.8131\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4744\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4309\n", + "Checking accuracy on validation set\n", + "Got 851 / 1000 correct (85.10)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.2917\n", + "Checking accuracy on validation set\n", + "Got 860 / 1000 correct (86.00)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.5513\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.3585\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.2208\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.1595\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4061\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.5259\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4616\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3907\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.3680\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4195\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4059\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.5927\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.3025\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.2992\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.3503\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.4944\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.2631\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4459\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.2598\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4285\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3171\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.3878\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4071\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3048\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.2519\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.4327\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.2929\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.4176\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.2854\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.3639\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2505\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3558\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2580\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2672\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3460\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2265\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "---- New lr = 0.00040 and gamma = 0.156 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.0041\n", + "Checking accuracy on validation set\n", + "Got 113 / 1000 correct (11.30)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.5552\n", + "Checking accuracy on validation set\n", + "Got 387 / 1000 correct (38.70)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.7383\n", + "Checking accuracy on validation set\n", + "Got 417 / 1000 correct (41.70)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4862\n", + "Checking accuracy on validation set\n", + "Got 470 / 1000 correct (47.00)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.4768\n", + "Checking accuracy on validation set\n", + "Got 527 / 1000 correct (52.70)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.4985\n", + "Checking accuracy on validation set\n", + "Got 505 / 1000 correct (50.50)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.2593\n", + "Checking accuracy on validation set\n", + "Got 505 / 1000 correct (50.50)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.0072\n", + "Checking accuracy on validation set\n", + "Got 598 / 1000 correct (59.80)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.0785\n", + "Checking accuracy on validation set\n", + "Got 626 / 1000 correct (62.60)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 0.8680\n", + "Checking accuracy on validation set\n", + "Got 611 / 1000 correct (61.10)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.1844\n", + "Checking accuracy on validation set\n", + "Got 641 / 1000 correct (64.10)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 0.8719\n", + "Checking accuracy on validation set\n", + "Got 639 / 1000 correct (63.90)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.8579\n", + "Checking accuracy on validation set\n", + "Got 668 / 1000 correct (66.80)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 1.1585\n", + "Checking accuracy on validation set\n", + "Got 682 / 1000 correct (68.20)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9654\n", + "Checking accuracy on validation set\n", + "Got 711 / 1000 correct (71.10)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8875\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.8991\n", + "Checking accuracy on validation set\n", + "Got 730 / 1000 correct (73.00)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.7952\n", + "Checking accuracy on validation set\n", + "Got 762 / 1000 correct (76.20)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.9639\n", + "Checking accuracy on validation set\n", + "Got 721 / 1000 correct (72.10)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.7576\n", + "Checking accuracy on validation set\n", + "Got 759 / 1000 correct (75.90)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.8674\n", + "Checking accuracy on validation set\n", + "Got 773 / 1000 correct (77.30)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 1.1443\n", + "Checking accuracy on validation set\n", + "Got 775 / 1000 correct (77.50)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7992\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.6768\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.6827\n", + "Checking accuracy on validation set\n", + "Got 782 / 1000 correct (78.20)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.3276\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.6353\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6028\n", + "Checking accuracy on validation set\n", + "Got 797 / 1000 correct (79.70)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.9329\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.7284\n", + "Checking accuracy on validation set\n", + "Got 801 / 1000 correct (80.10)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.8372\n", + "Checking accuracy on validation set\n", + "Got 769 / 1000 correct (76.90)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8380\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5812\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.4398\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5337\n", + "Checking accuracy on validation set\n", + "Got 755 / 1000 correct (75.50)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.4700\n", + "Checking accuracy on validation set\n", + "Got 809 / 1000 correct (80.90)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.3529\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.8105\n", + "Checking accuracy on validation set\n", + "Got 828 / 1000 correct (82.80)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.5171\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.7219\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.4878\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.3926\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4768\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4254\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4326\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4648\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.3744\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4712\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4701\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.2807\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4050\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3388\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.3830\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.1982\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.4662\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4765\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4249\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3685\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4917\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.2847\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4112\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.2419\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3290\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3393\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.2138\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.3025\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.2061\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.4583\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.4827\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3224\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.2801\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.2194\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3916\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.1828\n", + "Checking accuracy on validation set\n", + "Got 894 / 1000 correct (89.40)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2793\n", + "Checking accuracy on validation set\n", + "Got 900 / 1000 correct (90.00)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3837\n", + "Checking accuracy on validation set\n", + "Got 898 / 1000 correct (89.80)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.3561\n", + "Checking accuracy on validation set\n", + "Got 900 / 1000 correct (90.00)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2297\n", + "Checking accuracy on validation set\n", + "Got 901 / 1000 correct (90.10)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.3085\n", + "Checking accuracy on validation set\n", + "Got 901 / 1000 correct (90.10)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2498\n", + "Checking accuracy on validation set\n", + "Got 901 / 1000 correct (90.10)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 900 / 1000 correct (90.00)\n", + "---- New lr = 0.00046 and gamma = 0.156 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 4.6236\n", + "Checking accuracy on validation set\n", + "Got 106 / 1000 correct (10.60)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.6401\n", + "Checking accuracy on validation set\n", + "Got 408 / 1000 correct (40.80)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.4711\n", + "Checking accuracy on validation set\n", + "Got 453 / 1000 correct (45.30)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.5340\n", + "Checking accuracy on validation set\n", + "Got 523 / 1000 correct (52.30)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.5806\n", + "Checking accuracy on validation set\n", + "Got 539 / 1000 correct (53.90)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2615\n", + "Checking accuracy on validation set\n", + "Got 515 / 1000 correct (51.50)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1840\n", + "Checking accuracy on validation set\n", + "Got 545 / 1000 correct (54.50)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.0978\n", + "Checking accuracy on validation set\n", + "Got 616 / 1000 correct (61.60)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 0.9700\n", + "Checking accuracy on validation set\n", + "Got 588 / 1000 correct (58.80)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.1454\n", + "Checking accuracy on validation set\n", + "Got 667 / 1000 correct (66.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 0.8481\n", + "Checking accuracy on validation set\n", + "Got 577 / 1000 correct (57.70)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.5319\n", + "Checking accuracy on validation set\n", + "Got 630 / 1000 correct (63.00)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.8632\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.7830\n", + "Checking accuracy on validation set\n", + "Got 704 / 1000 correct (70.40)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.8413\n", + "Checking accuracy on validation set\n", + "Got 678 / 1000 correct (67.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8573\n", + "Checking accuracy on validation set\n", + "Got 700 / 1000 correct (70.00)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.7530\n", + "Checking accuracy on validation set\n", + "Got 744 / 1000 correct (74.40)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.6313\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.6904\n", + "Checking accuracy on validation set\n", + "Got 722 / 1000 correct (72.20)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.8144\n", + "Checking accuracy on validation set\n", + "Got 757 / 1000 correct (75.70)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.5430\n", + "Checking accuracy on validation set\n", + "Got 752 / 1000 correct (75.20)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.9902\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7443\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 1.0529\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.7189\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.5933\n", + "Checking accuracy on validation set\n", + "Got 811 / 1000 correct (81.10)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.5509\n", + "Checking accuracy on validation set\n", + "Got 796 / 1000 correct (79.60)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.7412\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.5749\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.4909\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.4808\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.5429\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.4815\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.7286\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5562\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.3004\n", + "Checking accuracy on validation set\n", + "Got 813 / 1000 correct (81.30)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5608\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5760\n", + "Checking accuracy on validation set\n", + "Got 808 / 1000 correct (80.80)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.4156\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5689\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.5317\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4237\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.2851\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4276\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.3796\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4269\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.3337\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.1934\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.2748\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.3886\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4500\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.2769\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.3774\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.2222\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.2495\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4428\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.3482\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3109\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4540\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.3117\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3851\n", + "Checking accuracy on validation set\n", + "Got 871 / 1000 correct (87.10)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.2866\n", + "Checking accuracy on validation set\n", + "Got 875 / 1000 correct (87.50)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.2683\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3366\n", + "Checking accuracy on validation set\n", + "Got 870 / 1000 correct (87.00)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.3422\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.1588\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4600\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.3872\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.2750\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3156\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.3842\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.2145\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.2250\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2751\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.3624\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3043\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.4484\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.3253\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.5018\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2125\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "---- New lr = 0.00035 and gamma = 0.157 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 2.7029\n", + "Checking accuracy on validation set\n", + "Got 98 / 1000 correct (9.80)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.9425\n", + "Checking accuracy on validation set\n", + "Got 415 / 1000 correct (41.50)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.6426\n", + "Checking accuracy on validation set\n", + "Got 461 / 1000 correct (46.10)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4405\n", + "Checking accuracy on validation set\n", + "Got 466 / 1000 correct (46.60)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3201\n", + "Checking accuracy on validation set\n", + "Got 462 / 1000 correct (46.20)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.1107\n", + "Checking accuracy on validation set\n", + "Got 579 / 1000 correct (57.90)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1979\n", + "Checking accuracy on validation set\n", + "Got 579 / 1000 correct (57.90)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.0425\n", + "Checking accuracy on validation set\n", + "Got 626 / 1000 correct (62.60)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.2576\n", + "Checking accuracy on validation set\n", + "Got 571 / 1000 correct (57.10)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.3173\n", + "Checking accuracy on validation set\n", + "Got 596 / 1000 correct (59.60)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.2307\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.2469\n", + "Checking accuracy on validation set\n", + "Got 620 / 1000 correct (62.00)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.1848\n", + "Checking accuracy on validation set\n", + "Got 691 / 1000 correct (69.10)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.7335\n", + "Checking accuracy on validation set\n", + "Got 715 / 1000 correct (71.50)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.6982\n", + "Checking accuracy on validation set\n", + "Got 708 / 1000 correct (70.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8714\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.7631\n", + "Checking accuracy on validation set\n", + "Got 747 / 1000 correct (74.70)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.6958\n", + "Checking accuracy on validation set\n", + "Got 736 / 1000 correct (73.60)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.0073\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 1.0252\n", + "Checking accuracy on validation set\n", + "Got 741 / 1000 correct (74.10)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.7239\n", + "Checking accuracy on validation set\n", + "Got 780 / 1000 correct (78.00)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.5252\n", + "Checking accuracy on validation set\n", + "Got 738 / 1000 correct (73.80)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.7515\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7093\n", + "Checking accuracy on validation set\n", + "Got 775 / 1000 correct (77.50)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.6625\n", + "Checking accuracy on validation set\n", + "Got 774 / 1000 correct (77.40)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.8363\n", + "Checking accuracy on validation set\n", + "Got 750 / 1000 correct (75.00)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.7760\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6631\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.7225\n", + "Checking accuracy on validation set\n", + "Got 791 / 1000 correct (79.10)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.3829\n", + "Checking accuracy on validation set\n", + "Got 814 / 1000 correct (81.40)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.9196\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6149\n", + "Checking accuracy on validation set\n", + "Got 785 / 1000 correct (78.50)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.5346\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.6300\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.4231\n", + "Checking accuracy on validation set\n", + "Got 818 / 1000 correct (81.80)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.4898\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.4371\n", + "Checking accuracy on validation set\n", + "Got 832 / 1000 correct (83.20)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.4678\n", + "Checking accuracy on validation set\n", + "Got 812 / 1000 correct (81.20)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.7011\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.4516\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6017\n", + "Checking accuracy on validation set\n", + "Got 836 / 1000 correct (83.60)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.4426\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4200\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.4160\n", + "Checking accuracy on validation set\n", + "Got 864 / 1000 correct (86.40)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4623\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4303\n", + "Checking accuracy on validation set\n", + "Got 866 / 1000 correct (86.60)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.3417\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4889\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.2908\n", + "Checking accuracy on validation set\n", + "Got 867 / 1000 correct (86.70)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4103\n", + "Checking accuracy on validation set\n", + "Got 880 / 1000 correct (88.00)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.5861\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3779\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.2607\n", + "Checking accuracy on validation set\n", + "Got 883 / 1000 correct (88.30)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.5548\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.2579\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.2752\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.2895\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.3754\n", + "Checking accuracy on validation set\n", + "Got 884 / 1000 correct (88.40)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4808\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5442\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.3742\n", + "Checking accuracy on validation set\n", + "Got 896 / 1000 correct (89.60)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.3223\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3975\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.5793\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.2515\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.3516\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.4826\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.4186\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.2660\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3845\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.2973\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.3523\n", + "Checking accuracy on validation set\n", + "Got 886 / 1000 correct (88.60)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.2452\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.1537\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2971\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3346\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2982\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.1645\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.2138\n", + "Checking accuracy on validation set\n", + "Got 895 / 1000 correct (89.50)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.3958\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "---- New lr = 0.00033 and gamma = 0.157 ----\n", + "766\n", + "Epoch: 0, Iteration 0, loss = 3.5074\n", + "Checking accuracy on validation set\n", + "Got 124 / 1000 correct (12.40)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.7763\n", + "Checking accuracy on validation set\n", + "Got 401 / 1000 correct (40.10)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.2754\n", + "Checking accuracy on validation set\n", + "Got 435 / 1000 correct (43.50)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.4828\n", + "Checking accuracy on validation set\n", + "Got 500 / 1000 correct (50.00)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.4539\n", + "Checking accuracy on validation set\n", + "Got 495 / 1000 correct (49.50)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.1752\n", + "Checking accuracy on validation set\n", + "Got 555 / 1000 correct (55.50)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.3656\n", + "Checking accuracy on validation set\n", + "Got 606 / 1000 correct (60.60)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 0.8323\n", + "Checking accuracy on validation set\n", + "Got 603 / 1000 correct (60.30)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.0346\n", + "Checking accuracy on validation set\n", + "Got 615 / 1000 correct (61.50)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 0.9869\n", + "Checking accuracy on validation set\n", + "Got 607 / 1000 correct (60.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.4071\n", + "Checking accuracy on validation set\n", + "Got 661 / 1000 correct (66.10)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 1.0342\n", + "Checking accuracy on validation set\n", + "Got 686 / 1000 correct (68.60)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 1.0959\n", + "Checking accuracy on validation set\n", + "Got 649 / 1000 correct (64.90)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.7572\n", + "Checking accuracy on validation set\n", + "Got 691 / 1000 correct (69.10)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.9704\n", + "Checking accuracy on validation set\n", + "Got 708 / 1000 correct (70.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 1.0783\n", + "Checking accuracy on validation set\n", + "Got 681 / 1000 correct (68.10)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9335\n", + "Checking accuracy on validation set\n", + "Got 705 / 1000 correct (70.50)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 1.0284\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 1.0286\n", + "Checking accuracy on validation set\n", + "Got 734 / 1000 correct (73.40)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.6474\n", + "Checking accuracy on validation set\n", + "Got 720 / 1000 correct (72.00)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.8375\n", + "Checking accuracy on validation set\n", + "Got 708 / 1000 correct (70.80)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.8079\n", + "Checking accuracy on validation set\n", + "Got 760 / 1000 correct (76.00)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8748\n", + "Checking accuracy on validation set\n", + "Got 763 / 1000 correct (76.30)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7010\n", + "Checking accuracy on validation set\n", + "Got 764 / 1000 correct (76.40)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.6751\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.7962\n", + "Checking accuracy on validation set\n", + "Got 766 / 1000 correct (76.60)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.8568\n", + "Checking accuracy on validation set\n", + "Got 792 / 1000 correct (79.20)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.5213\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.4303\n", + "Checking accuracy on validation set\n", + "Got 777 / 1000 correct (77.70)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.5026\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.7166\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.6212\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.6888\n", + "Checking accuracy on validation set\n", + "Got 770 / 1000 correct (77.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.5512\n", + "Checking accuracy on validation set\n", + "Got 784 / 1000 correct (78.40)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.5945\n", + "Checking accuracy on validation set\n", + "Got 803 / 1000 correct (80.30)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.5159\n", + "Checking accuracy on validation set\n", + "Got 821 / 1000 correct (82.10)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5250\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.5521\n", + "Checking accuracy on validation set\n", + "Got 788 / 1000 correct (78.80)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6096\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.7480\n", + "Checking accuracy on validation set\n", + "Got 816 / 1000 correct (81.60)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.6211\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.3448\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.4602\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.5082\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.3498\n", + "Checking accuracy on validation set\n", + "Got 874 / 1000 correct (87.40)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.4001\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.3749\n", + "Checking accuracy on validation set\n", + "Got 868 / 1000 correct (86.80)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.4356\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4176\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.3800\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.3230\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3620\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4409\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.4637\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.3763\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.4510\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.4608\n", + "Checking accuracy on validation set\n", + "Got 876 / 1000 correct (87.60)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.2564\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.4005\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.3845\n", + "Checking accuracy on validation set\n", + "Got 878 / 1000 correct (87.80)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4020\n", + "Checking accuracy on validation set\n", + "Got 873 / 1000 correct (87.30)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.4942\n", + "Checking accuracy on validation set\n", + "Got 872 / 1000 correct (87.20)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3363\n", + "Checking accuracy on validation set\n", + "Got 887 / 1000 correct (88.70)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.3673\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.2784\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.1946\n", + "Checking accuracy on validation set\n", + "Got 891 / 1000 correct (89.10)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.3110\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.2592\n", + "Checking accuracy on validation set\n", + "Got 888 / 1000 correct (88.80)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3613\n", + "Checking accuracy on validation set\n", + "Got 885 / 1000 correct (88.50)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.2231\n", + "Checking accuracy on validation set\n", + "Got 881 / 1000 correct (88.10)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.2508\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.4067\n", + "Checking accuracy on validation set\n", + "Got 879 / 1000 correct (87.90)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.3836\n", + "Checking accuracy on validation set\n", + "Got 882 / 1000 correct (88.20)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2747\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.4442\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.2703\n", + "Checking accuracy on validation set\n", + "Got 892 / 1000 correct (89.20)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2722\n", + "Checking accuracy on validation set\n", + "Got 893 / 1000 correct (89.30)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.4033\n", + "Checking accuracy on validation set\n", + "Got 894 / 1000 correct (89.40)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.2632\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2515\n", + "Checking accuracy on validation set\n", + "Got 889 / 1000 correct (88.90)\n", + "\n", + "Checking accuracy on validation set\n", + "Got 897 / 1000 correct (89.70)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 384 + }, + "id": "KKt9CMwk9R6A", + "outputId": "f58d263d-26ca-4a99-d118-a4edc7745001" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Hyperparameters: [0.00039521 0.15641885]\n", + "Best Accuracy: -0.9\n" + ] + } + ], + "source": [ + "opt2.plot_convergence()\n", + "print(\"Best Hyperparameters: \", opt2.x_opt)\n", + "print(\"Best Accuracy: \", opt2.fx_opt)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AdrH2dYdmMIS", + "outputId": "1457f3e2-8ce6-4351-9d14-cdcbda53fe78" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "766\n", + "Epoch: 0, Iteration 0, loss = 3.5337\n", + "Checking accuracy on validation set\n", + "Got 111 / 1000 correct (11.10)\n", + "\n", + "Epoch: 0, Iteration 100, loss = 1.6355\n", + "Checking accuracy on validation set\n", + "Got 404 / 1000 correct (40.40)\n", + "\n", + "Epoch: 0, Iteration 200, loss = 1.4058\n", + "Checking accuracy on validation set\n", + "Got 476 / 1000 correct (47.60)\n", + "\n", + "Epoch: 0, Iteration 300, loss = 1.3713\n", + "Checking accuracy on validation set\n", + "Got 450 / 1000 correct (45.00)\n", + "\n", + "Epoch: 0, Iteration 400, loss = 1.3050\n", + "Checking accuracy on validation set\n", + "Got 533 / 1000 correct (53.30)\n", + "\n", + "Epoch: 0, Iteration 500, loss = 1.2080\n", + "Checking accuracy on validation set\n", + "Got 580 / 1000 correct (58.00)\n", + "\n", + "Epoch: 0, Iteration 600, loss = 1.1986\n", + "Checking accuracy on validation set\n", + "Got 622 / 1000 correct (62.20)\n", + "\n", + "Epoch: 0, Iteration 700, loss = 1.2054\n", + "Checking accuracy on validation set\n", + "Got 574 / 1000 correct (57.40)\n", + "\n", + "766\n", + "Epoch: 1, Iteration 0, loss = 1.0314\n", + "Checking accuracy on validation set\n", + "Got 604 / 1000 correct (60.40)\n", + "\n", + "Epoch: 1, Iteration 100, loss = 1.0442\n", + "Checking accuracy on validation set\n", + "Got 647 / 1000 correct (64.70)\n", + "\n", + "Epoch: 1, Iteration 200, loss = 1.0622\n", + "Checking accuracy on validation set\n", + "Got 667 / 1000 correct (66.70)\n", + "\n", + "Epoch: 1, Iteration 300, loss = 0.7683\n", + "Checking accuracy on validation set\n", + "Got 691 / 1000 correct (69.10)\n", + "\n", + "Epoch: 1, Iteration 400, loss = 0.9047\n", + "Checking accuracy on validation set\n", + "Got 706 / 1000 correct (70.60)\n", + "\n", + "Epoch: 1, Iteration 500, loss = 0.8421\n", + "Checking accuracy on validation set\n", + "Got 702 / 1000 correct (70.20)\n", + "\n", + "Epoch: 1, Iteration 600, loss = 0.7493\n", + "Checking accuracy on validation set\n", + "Got 718 / 1000 correct (71.80)\n", + "\n", + "Epoch: 1, Iteration 700, loss = 0.8387\n", + "Checking accuracy on validation set\n", + "Got 751 / 1000 correct (75.10)\n", + "\n", + "766\n", + "Epoch: 2, Iteration 0, loss = 0.9612\n", + "Checking accuracy on validation set\n", + "Got 677 / 1000 correct (67.70)\n", + "\n", + "Epoch: 2, Iteration 100, loss = 0.8154\n", + "Checking accuracy on validation set\n", + "Got 752 / 1000 correct (75.20)\n", + "\n", + "Epoch: 2, Iteration 200, loss = 0.6409\n", + "Checking accuracy on validation set\n", + "Got 779 / 1000 correct (77.90)\n", + "\n", + "Epoch: 2, Iteration 300, loss = 0.9355\n", + "Checking accuracy on validation set\n", + "Got 786 / 1000 correct (78.60)\n", + "\n", + "Epoch: 2, Iteration 400, loss = 0.8536\n", + "Checking accuracy on validation set\n", + "Got 781 / 1000 correct (78.10)\n", + "\n", + "Epoch: 2, Iteration 500, loss = 0.7979\n", + "Checking accuracy on validation set\n", + "Got 775 / 1000 correct (77.50)\n", + "\n", + "Epoch: 2, Iteration 600, loss = 0.8413\n", + "Checking accuracy on validation set\n", + "Got 749 / 1000 correct (74.90)\n", + "\n", + "Epoch: 2, Iteration 700, loss = 0.7854\n", + "Checking accuracy on validation set\n", + "Got 754 / 1000 correct (75.40)\n", + "\n", + "766\n", + "Epoch: 3, Iteration 0, loss = 0.5875\n", + "Checking accuracy on validation set\n", + "Got 753 / 1000 correct (75.30)\n", + "\n", + "Epoch: 3, Iteration 100, loss = 0.5632\n", + "Checking accuracy on validation set\n", + "Got 793 / 1000 correct (79.30)\n", + "\n", + "Epoch: 3, Iteration 200, loss = 0.5337\n", + "Checking accuracy on validation set\n", + "Got 800 / 1000 correct (80.00)\n", + "\n", + "Epoch: 3, Iteration 300, loss = 0.6515\n", + "Checking accuracy on validation set\n", + "Got 807 / 1000 correct (80.70)\n", + "\n", + "Epoch: 3, Iteration 400, loss = 0.6055\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 3, Iteration 500, loss = 0.6261\n", + "Checking accuracy on validation set\n", + "Got 798 / 1000 correct (79.80)\n", + "\n", + "Epoch: 3, Iteration 600, loss = 0.6996\n", + "Checking accuracy on validation set\n", + "Got 778 / 1000 correct (77.80)\n", + "\n", + "Epoch: 3, Iteration 700, loss = 0.8325\n", + "Checking accuracy on validation set\n", + "Got 820 / 1000 correct (82.00)\n", + "\n", + "766\n", + "Epoch: 4, Iteration 0, loss = 0.4648\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 4, Iteration 100, loss = 0.8418\n", + "Checking accuracy on validation set\n", + "Got 799 / 1000 correct (79.90)\n", + "\n", + "Epoch: 4, Iteration 200, loss = 0.4759\n", + "Checking accuracy on validation set\n", + "Got 810 / 1000 correct (81.00)\n", + "\n", + "Epoch: 4, Iteration 300, loss = 0.7682\n", + "Checking accuracy on validation set\n", + "Got 794 / 1000 correct (79.40)\n", + "\n", + "Epoch: 4, Iteration 400, loss = 0.5442\n", + "Checking accuracy on validation set\n", + "Got 819 / 1000 correct (81.90)\n", + "\n", + "Epoch: 4, Iteration 500, loss = 0.6703\n", + "Checking accuracy on validation set\n", + "Got 795 / 1000 correct (79.50)\n", + "\n", + "Epoch: 4, Iteration 600, loss = 0.6319\n", + "Checking accuracy on validation set\n", + "Got 802 / 1000 correct (80.20)\n", + "\n", + "Epoch: 4, Iteration 700, loss = 0.5755\n", + "Checking accuracy on validation set\n", + "Got 822 / 1000 correct (82.20)\n", + "\n", + "766\n", + "Epoch: 5, Iteration 0, loss = 0.4065\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "Epoch: 5, Iteration 100, loss = 0.5689\n", + "Checking accuracy on validation set\n", + "Got 827 / 1000 correct (82.70)\n", + "\n", + "Epoch: 5, Iteration 200, loss = 0.5039\n", + "Checking accuracy on validation set\n", + "Got 806 / 1000 correct (80.60)\n", + "\n", + "Epoch: 5, Iteration 300, loss = 0.5279\n", + "Checking accuracy on validation set\n", + "Got 790 / 1000 correct (79.00)\n", + "\n", + "Epoch: 5, Iteration 400, loss = 0.4655\n", + "Checking accuracy on validation set\n", + "Got 842 / 1000 correct (84.20)\n", + "\n", + "Epoch: 5, Iteration 500, loss = 0.5494\n", + "Checking accuracy on validation set\n", + "Got 824 / 1000 correct (82.40)\n", + "\n", + "Epoch: 5, Iteration 600, loss = 0.6118\n", + "Checking accuracy on validation set\n", + "Got 823 / 1000 correct (82.30)\n", + "\n", + "Epoch: 5, Iteration 700, loss = 0.3999\n", + "Checking accuracy on validation set\n", + "Got 825 / 1000 correct (82.50)\n", + "\n", + "766\n", + "Epoch: 6, Iteration 0, loss = 0.4940\n", + "Checking accuracy on validation set\n", + "Got 846 / 1000 correct (84.60)\n", + "\n", + "Epoch: 6, Iteration 100, loss = 0.4116\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 6, Iteration 200, loss = 0.4020\n", + "Checking accuracy on validation set\n", + "Got 833 / 1000 correct (83.30)\n", + "\n", + "Epoch: 6, Iteration 300, loss = 0.3090\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 6, Iteration 400, loss = 0.4268\n", + "Checking accuracy on validation set\n", + "Got 850 / 1000 correct (85.00)\n", + "\n", + "Epoch: 6, Iteration 500, loss = 0.3099\n", + "Checking accuracy on validation set\n", + "Got 847 / 1000 correct (84.70)\n", + "\n", + "Epoch: 6, Iteration 600, loss = 0.5355\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 6, Iteration 700, loss = 0.3724\n", + "Checking accuracy on validation set\n", + "Got 815 / 1000 correct (81.50)\n", + "\n", + "766\n", + "Epoch: 7, Iteration 0, loss = 0.3103\n", + "Checking accuracy on validation set\n", + "Got 853 / 1000 correct (85.30)\n", + "\n", + "Epoch: 7, Iteration 100, loss = 0.5241\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 7, Iteration 200, loss = 0.2575\n", + "Checking accuracy on validation set\n", + "Got 837 / 1000 correct (83.70)\n", + "\n", + "Epoch: 7, Iteration 300, loss = 0.5466\n", + "Checking accuracy on validation set\n", + "Got 861 / 1000 correct (86.10)\n", + "\n", + "Epoch: 7, Iteration 400, loss = 0.4297\n", + "Checking accuracy on validation set\n", + "Got 859 / 1000 correct (85.90)\n", + "\n", + "Epoch: 7, Iteration 500, loss = 0.5571\n", + "Checking accuracy on validation set\n", + "Got 863 / 1000 correct (86.30)\n", + "\n", + "Epoch: 7, Iteration 600, loss = 0.3736\n", + "Checking accuracy on validation set\n", + "Got 856 / 1000 correct (85.60)\n", + "\n", + "Epoch: 7, Iteration 700, loss = 0.4913\n", + "Checking accuracy on validation set\n", + "Got 857 / 1000 correct (85.70)\n", + "\n", + "766\n", + "Epoch: 8, Iteration 0, loss = 0.5584\n", + "Checking accuracy on validation set\n", + "Got 858 / 1000 correct (85.80)\n", + "\n", + "Epoch: 8, Iteration 100, loss = 0.2475\n", + "Checking accuracy on validation set\n", + "Got 844 / 1000 correct (84.40)\n", + "\n", + "Epoch: 8, Iteration 200, loss = 0.6618\n", + "Checking accuracy on validation set\n", + "Got 849 / 1000 correct (84.90)\n", + "\n", + "Epoch: 8, Iteration 300, loss = 0.5299\n", + "Checking accuracy on validation set\n", + "Got 866 / 1000 correct (86.60)\n", + "\n", + "Epoch: 8, Iteration 400, loss = 0.3968\n", + "Checking accuracy on validation set\n", + "Got 854 / 1000 correct (85.40)\n", + "\n", + "Epoch: 8, Iteration 500, loss = 0.3674\n", + "Checking accuracy on validation set\n", + "Got 877 / 1000 correct (87.70)\n", + "\n", + "Epoch: 8, Iteration 600, loss = 0.4814\n", + "Checking accuracy on validation set\n", + "Got 843 / 1000 correct (84.30)\n", + "\n", + "Epoch: 8, Iteration 700, loss = 0.3654\n", + "Checking accuracy on validation set\n", + "Got 865 / 1000 correct (86.50)\n", + "\n", + "766\n", + "Epoch: 9, Iteration 0, loss = 0.4867\n", + "Checking accuracy on validation set\n", + "Got 869 / 1000 correct (86.90)\n", + "\n", + "Epoch: 9, Iteration 100, loss = 0.2796\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 200, loss = 0.2874\n", + "Checking accuracy on validation set\n", + "Got 890 / 1000 correct (89.00)\n", + "\n", + "Epoch: 9, Iteration 300, loss = 0.3900\n", + "Checking accuracy on validation set\n", + "Got 904 / 1000 correct (90.40)\n", + "\n", + "Epoch: 9, Iteration 400, loss = 0.2860\n", + "Checking accuracy on validation set\n", + "Got 901 / 1000 correct (90.10)\n", + "\n", + "Epoch: 9, Iteration 500, loss = 0.2704\n", + "Checking accuracy on validation set\n", + "Got 903 / 1000 correct (90.30)\n", + "\n", + "Epoch: 9, Iteration 600, loss = 0.2639\n", + "Checking accuracy on validation set\n", + "Got 898 / 1000 correct (89.80)\n", + "\n", + "Epoch: 9, Iteration 700, loss = 0.2255\n", + "Checking accuracy on validation set\n", + "Got 899 / 1000 correct (89.90)\n", + "\n", + "Checking accuracy on test set\n", + "Got 8958 / 10000 correct (89.58)\n" + ] + } + ], + "source": [ + "model = ResNet18()\n", + "optimizer = optim.Adam(model.parameters(),lr = 0.000395)\n", + "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[9, 10], gamma=0.1564)\n", + "train_part2(model, optimizer, epochs = 10, scheduler = scheduler)\n", + "\n", + "check_accuracy(loader_test, model)\n", + "\n", + "\n", + "# save the model\n", + "torch.save(model.state_dict(), 'model.pt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Method 2 Result\n", + "\n", + "Method 2 Accuracy is 89.58 %." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6IQdUdufJMV1" + }, + "source": [ + "## Part 3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e3GOnhsiJMV1" + }, + "source": [ + "The code provided below will allow you to visualise the feature maps computed by different layers of your network. Run the code (install matplotlib if necessary) and **answer the following questions**: \n", + "\n", + "1. Compare the feature maps from low-level layers to high-level layers, what do you observe? \n", + "\n", + "2. Use the training log, reported test set accuracy and the feature maps, analyse the performance of your network. If you think the performance is sufficiently good, explain why; if not, what might be the problem and how can you improve the performance?\n", + "\n", + "3. What are the other possible ways to analyse the performance of your network?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kb1vxCf9JMV1" + }, + "source": [ + "**YOUR ANSWER FOR PART 3 HERE**\n", + "\n", + "A:\n", + "\n", + "Q1 :\n", + "\n", + "I choose the 35th image of the dataset, which looks like a kind of fish. On the lower-level layers, you can see outline of them, however with the higher-level layer, we can only see some details of images. In other words, on the higher level layers, you cannot distinguish what it is, there are only some abstract objects on the images.\n", + "\n", + "Q2 :\n", + "\n", + "In the Part2, I have used two methods to train the data. As for the Method 1, I just use data augmemtation, which helps generalisation of the network, and using finally, the accuracy is 83.81%, I think this result is not so bad.\n", + "\n", + "However, in the Method 2, I added learning rate schedule, which can help reaching the global minimum and not getting overshooting when minimising the loss. It can be quite helpful for the accuracy can be up to 89.58%.\n", + "\n", + "Actually, if we train more epochs or get more training datas or use dropout, the performance can be much better.\n", + "\n", + "Q3 :\n", + "\n", + "We can use confusion matrix. Actually, we can not only pay attention on the accuracy, for example, the recall, precison, f-score are all helpful to let us know the performance of my network. Maybe graphs of error rates can be used to show something like overfitting much easier.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "SNcmV4BEJMV1", + "outputId": "201a8799-9b77-405e-91d6-50c2535305f7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "#!pip install matplotlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.tight_layout()\n", + "# model = ResNet18()\n", + "# model.load_state_dict(torch.load('model.pt'))\n", + "# model.to(device)\n", + "# model.eval()\n", + "\n", + "activation = {}\n", + "def get_activation(name):\n", + " def hook(model, input, output):\n", + " activation[name] = output.detach()\n", + " return hook\n", + "\n", + "vis_labels = ['conv1', 'layer1', 'layer2', 'layer3', 'layer4']\n", + "\n", + "for l in vis_labels:\n", + "\n", + " getattr(model, l).register_forward_hook(get_activation(l))\n", + " \n", + " \n", + "data, _ = cifar10_test[34]\n", + "data = data.unsqueeze_(0).to(device = device, dtype = dtype)\n", + "\n", + "output = model(data)\n", + "\n", + "\n", + "\n", + "for idx, l in enumerate(vis_labels):\n", + "\n", + " act = activation[l].squeeze()\n", + "\n", + " if idx < 2:\n", + " ncols = 8\n", + " else:\n", + " ncols = 32\n", + " \n", + " nrows = act.size(0) // ncols\n", + " \n", + " fig, axarr = plt.subplots(nrows, ncols)\n", + " fig.suptitle(l)\n", + "\n", + "\n", + " for i in range(nrows):\n", + " for j in range(ncols):\n", + " axarr[i, j].imshow(act[i * nrows + j].cpu())\n", + " axarr[i, j].axis('off')" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "460cw1_2020_my.ipynb（副本）", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1-final" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0cb3b424ec58490bbf97d3ee9bed7d68": { + "model_module": 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A version of this notebook containing your answers. Write your answers in the cells below each question. **Please deliver the notebook including the outputs of the cells below.**\n", + "2. Your trained VAE model as *VAE_model.pth*\n", + "\n", + "*GAN.zip* containing your trained Generator and Discriminator: *DCGAN_model_D.pth and DCGAN_model_G.pth*\n", + "\n", + "Please avoid using markdown headings (# ## etc.) as these will affect the ToC. Instead use html headings if you want emphasis.\n", + "\n", + "Similarly to the previous coursework, we recommend that you use Google Colaboratory in order to train the required networks.\n", + "\n", + "TAs will run a testing cell (at the end of this notebook), so you are required to copy your transform and denorm functions to a cell near the bottom of the document (it is demarkated).\n", + "\n", + "**The deadline for submission is 19:00, Thursday 19th February, 2021** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1oqY55OLpxDm" + }, + "source": [ + "## Setting up working environment\n", + "\n", + "For this coursework you, will need to train a large network, therefore we recommend you work with Google Colaboratory, which provides free GPU time. You will need a Google account to do so.\n", + "\n", + "Please log in to your account and go to the following page: https://colab.research.google.com. Then upload this notebook.\n", + "\n", + "For GPU support, go to \"Edit\" -> \"Notebook Settings\", and select \"Hardware accelerator\" as \"GPU\".\n", + "\n", + "You will need to install pytorch and import some utilities by running the following cell:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FJg7ozC_q3HF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4edef2cd-79ba-4a8d-a1b5-ae1ceea1da3e" + }, + "source": [ + "!pip install -q torch torchvision\n", + "!git clone -q https://github.com/afspies/icl_dl_cw2_utils\n", + "from icl_dl_cw2_utils.utils.plotting import plot_tsne\n", + "%load_ext google.colab.data_table" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "fatal: destination path 'icl_dl_cw2_utils' already exists and is not an empty directory.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oEyMm16MoegE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "40c8bcb3-a749-4894-8594-a9e78c2f1b3d" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive') # Outputs will be saved in your google drive" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ezLSfB6IqAzK" + }, + "source": [ + "## Introduction\n", + "\n", + "For this coursework, you are asked to implement two commonly used generative models:\n", + "1. A **Variational Autoencoder (VAE)**\n", + "2. A **Deep Convolutional Generative Adversarial Network (DCGAN)**\n", + "\n", + "For the first part you will the MNIST dataset https://en.wikipedia.org/wiki/MNIST_database and for the second the CIFAR-10 (https://www.cs.toronto.edu/~kriz/cifar.html).\n", + "\n", + "Each part is worth 50 points. \n", + "\n", + "The emphasis of both parts lies in understanding how the models behave and learn, however, some points will be available for getting good results with your GAN (though you should not spend too long on this)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "75mICbvzqQyx" + }, + "source": [ + "# Part 1 - Variational Autoencoder\n", + "\n", + "## Part 1.1 (25 points)\n", + "**Your Task:**\n", + "\n", + "a. Implement the VAE architecture with accompanying hyperparameters. Experiment with Feedforward and Convolutional Layers to see which gives better results.\n", + "\n", + "b. Design an appropriate loss function and train the model.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ym5l5RmmJtLw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "33d92050-a092-4eb4-856e-e37663a74e1d" + }, + "source": [ + "import os\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader, sampler\n", + "from torchvision import datasets, transforms\n", + "from torchvision.utils import save_image, make_grid\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def show(img):\n", + " npimg = img.cpu().numpy()\n", + " plt.imshow(np.transpose(npimg, (1,2,0)))\n", + "\n", + "if not os.path.exists('/content/drive/MyDrive/icl_dl_cw2/CW_VAE/'):\n", + " os.makedirs('/content/drive/MyDrive/icl_dl_cw2/CW_VAE/')\n", + "\n", + "# We set a random seed to ensure that your results are reproducible.\n", + "if torch.cuda.is_available():\n", + " torch.backends.cudnn.deterministic = True\n", + "torch.manual_seed(0)\n", + "\n", + "GPU = True # Choose whether to use GPU\n", + "if GPU:\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + "print(f'Using {device}')" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using cuda\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hqT7sdGzJtLy" + }, + "source": [ + "---\n", + "## Part 1.1a: Implement VAE (25 Points)\n", + "###Hyper-parameter selection\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZVPM6pgqJtLz" + }, + "source": [ + "# Necessary Hyperparameters \n", + "num_epochs = 25\n", + "learning_rate = 0.001\n", + "batch_size = 128\n", + "latent_dim = 16 # Choose a value for the size of the latent space\n", + "\n", + "# Additional Hyperparameters \n", + "conv1_c = 32\n", + "conv2_c = 64\n", + "leaky_relu = 0.2\n", + "beta = 2.5\n", + "\n", + "# (Optionally) Modify transformations on input\n", + "transform = transforms.Compose([\n", + " transforms.ToTensor(),\n", + "])\n", + "\n", + "# (Optionally) Modify the network's output for visualizing your images\n", + "def denorm(x):\n", + " return x" + ], + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iN5aL7sdJtL2" + }, + "source": [ + "### Data loading\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VOKdAZa2JtL3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 427, + "referenced_widgets": [ + "f5b45a4d4cd040cfb5b5c9b0034f2879", + "51b95cf998e7427c9ad7b96d3e6bd1ee", + "5e08e45a0cba4dd0b8ce21959c673933", + "c7ccdfb21c14421c9316a8b7aee926e8", + "7d98adb1baa54d50bed2fd291b3b61d7", + "271afb66e8984130bd29bc0352162918", + "03f0a5d734424182a9561f1b0894e722", + "08ef0e4e350348f8a51dcd641b261c48", + "7d3df8e5a7e24ba68a159d8e42b3f087", + "dd38e332f62442e3974aed66affbbae7", + "2f3367c024ed4e2a944ff8c6f416e82f", + "56fa7685cee643a18df229f4128509c0", + "23868dc9379a4f6cb90d67364d00172f", + "3801c55d57344820952ad82035e6127c", + "2d1279a7f86d4ce297375f262036fb33", + "f0059cf109384a259c024d4dc560428f", + "977a64a542a940edbd8a711e62a8896d", + "eee9e7dba76249169ec3d54a1eb0ec9a", + "71b3dc664d5348f6bc121ff33194074c", + "b59b7d5861194d3284c183880fb38983", + "d41970da65bd471fbf1fdbfe05eb4b31", + "440eb9e1709c482dadb1936c7233d2b4", + "12bba14540ac4f0494af76bc3ce8409c", + "836b8f0466fd4e26b3791248dee03bf0", + "800e985bb7db4f7f9368d8464b6b1dfc", + "1f5492e0a5d542adb296b39d078c0e7e", + "88bd758103cd47128998a9b430df1c05", + "78dc4f4d1fef45d49f90acadf971f393", + "89854090e47c4da8baf33730638b211f", + "33abc99f932c4879a5701637e7f1ded4", + "a895cb2fe4ab484d8982586a52062fe6", + "d55fea12d6bd48efaad78efd0dadf7ed" + ] + }, + "outputId": "3e27eb0b-6b97-418a-ba1b-55ff88d477ab" + }, + "source": [ + "train_dat = datasets.MNIST(\n", + " \"data/\", train=True, download=True, transform=transform\n", + ")\n", + "test_dat = datasets.MNIST(\"data/\", train=False, transform=transform)\n", + "\n", + "loader_train = DataLoader(train_dat, batch_size, shuffle=True)\n", + "loader_test = DataLoader(test_dat, batch_size, shuffle=False)\n", + "\n", + "# Don't change \n", + "sample_inputs, _ = next(iter(loader_test))\n", + "fixed_input = sample_inputs[:32, :, :, :]\n", + "save_image(fixed_input, '/content/drive/MyDrive/icl_dl_cw2/CW_VAE/image_original.png')" + ], + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LiQDXD24JtL7" + }, + "source": [ + "### Model Definition\n", + "\n", + "
\n", + " \n", + "
\n", + " Fig.1 - VAE Diagram (with a Guassian prior), taken from 1.\n", + "
\n", + "
\n", + "\n", + "\n", + "You will need to define:\n", + "* The hyperparameters\n", + "* The constructor\n", + "* encode\n", + "* reparametrize\n", + "* decode\n", + "* forward\n", + "\n", + "\n", + "\n", + "Hints:\n", + "- It is common practice to encode the log of the variance, rather than the variance\n", + "- You might try using BatchNorm" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wDlll3BUJtL8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4ef5c6a7-48af-4db9-833d-72a9775662f8" + }, + "source": [ + "# *CODE FOR PART 1.1a IN THIS CELL*\n", + "\n", + "class VAE(nn.Module):\n", + " def __init__(self, latent_dim):\n", + " super(VAE, self).__init__()\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " self.conv1 = nn.Sequential( nn.Conv2d(1, conv1_c, kernel_size = 4, stride = 2, padding = 1),\n", + " nn.BatchNorm2d(conv1_c),\n", + " nn.LeakyReLU(leaky_relu,inplace=True))\n", + "\n", + " self.conv2 = nn.Sequential( nn.Conv2d(conv1_c, conv2_c, kernel_size = 4, stride = 2, padding = 1),\n", + " nn.BatchNorm2d(conv2_c),\n", + " nn.LeakyReLU(leaky_relu,inplace=True))\n", + "\n", + " self.conv3 = nn.Sequential( nn.Conv2d(conv2_c, conv1_c, kernel_size = 3, stride = 1, padding = 1),\n", + " nn.BatchNorm2d(conv1_c),\n", + " nn.LeakyReLU(leaky_relu,inplace=True))\n", + "\n", + " self.fcl1 = nn.Sequential(nn.Linear(conv1_c * 7 * 7, 128),\n", + " nn.LeakyReLU(leaky_relu,inplace=True)) \n", + "\n", + " self.fc12_1 = nn.Linear(128, latent_dim)\n", + " self.fc12_2 = nn.Linear(128, latent_dim)\n", + "\n", + "\n", + " self.fcld1 = nn.Sequential(nn.Linear(latent_dim, 128)) \n", + " self.fcld2 = nn.Sequential(nn.Linear(128, conv1_c * 7 * 7),\n", + " nn.ReLU()) \n", + "\n", + " # self.dropoutd = nn.Dropout(p = dropout)\n", + "\n", + " # self.deconv1 = nn.Sequential(nn.ConvTranspose2d(conv1_c, conv2_c, kernel_size=3, stride=1, padding = 1),\n", + " # nn.BatchNorm2d(conv2_c),\n", + " # nn.ReLU())\n", + " self.deconv2 = nn.Sequential(nn.ConvTranspose2d(conv1_c, conv1_c, kernel_size=4, stride=2, padding = 1),\n", + " nn.BatchNorm2d(conv1_c),\n", + " nn.ReLU())\n", + "\n", + " self.deconv3 = nn.Sequential(nn.ConvTranspose2d(conv1_c, 1, kernel_size=4, stride=2, padding = 1),\n", + " nn.Sigmoid())\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " def encode(self, x):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " out = self.conv1(x)\n", + " out = self.conv2(out)\n", + " out = self.conv3(out)\n", + " out = self.fcl1(out.view(out.size(0),-1))\n", + " return self.fc12_1(out),self.fc12_2(out)\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " def reparametrize(self, mu, logvar):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " std = torch.exp(logvar / 2)\n", + " epsilon = torch.randn_like(std)\n", + "\n", + " return mu + epsilon * std\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + "\n", + " def decode(self, z):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " out = self.fcld1(z)\n", + " out = self.fcld2(out)\n", + " # print(out.shape)\n", + " out = out.view(z.size(0), conv1_c, 7, 7)\n", + " # out = self.deconv1(out)\n", + " out = self.deconv2(out)\n", + " out = self.deconv3(out)\n", + " # out = self.deconv3(out)\n", + " return out\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " def forward(self, x):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " mu, logvar = self.encode(x)\n", + " out = self.reparametrize(mu, logvar)\n", + " out = self.decode(out)\n", + " return out, mu, logvar\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + "\n", + "model = VAE(latent_dim).to(device)\n", + "params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "print(\"Total number of parameters is: {}\".format(params))\n", + "print(model)\n", + "# optimizer\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total number of parameters is: 478497\nVAE(\n (conv1): Sequential(\n (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.2, inplace=True)\n )\n (conv2): Sequential(\n (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.2, inplace=True)\n )\n (conv3): Sequential(\n (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): LeakyReLU(negative_slope=0.2, inplace=True)\n )\n (fcl1): Sequential(\n (0): Linear(in_features=1568, out_features=128, bias=True)\n (1): LeakyReLU(negative_slope=0.2, inplace=True)\n )\n (fc12_1): Linear(in_features=128, out_features=16, bias=True)\n (fc12_2): Linear(in_features=128, out_features=16, bias=True)\n (fcld1): Sequential(\n (0): Linear(in_features=16, out_features=128, bias=True)\n )\n (fcld2): Sequential(\n (0): Linear(in_features=128, out_features=1568, bias=True)\n (1): ReLU()\n )\n (deconv2): Sequential(\n (0): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n (2): ReLU()\n )\n (deconv3): Sequential(\n (0): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n (1): Sigmoid()\n )\n)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aeSX6RZhJtMB" + }, + "source": [ + "--- \n", + "\n", + "## Part 1.1b: Training the Model (5 Points)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JN-Pc0mvq-7_" + }, + "source": [ + "### Defining a Loss\n", + "Recall the Beta VAE loss, with an encoder $q$ and decoder $p$:\n", + "$$ \\mathcal{L}=\\mathbb{E}_{q_\\phi(z \\mid X)}[\\log p_\\theta(X \\mid z)]-\\beta D_{K L}[q_\\phi(z \\mid X) \\| p_\\theta(z)]$$\n", + "\n", + "In order to implement this loss you will need to think carefully about your model's outputs and the choice of prior.\n", + "\n", + "There are multiple accepted solutions. Explain your design choices based on the assumptions you make regarding the distribution of your data.\n", + "\n", + "* Hint: this refers to the log likelihood as mentioned in the tutorial. Make sure these assumptions reflect on the values of your input data, i.e. depending on your choice you might need to do a simple preprocessing step.\n", + "\n", + "* You are encouraged to experiment with the weighting coefficient $\\beta$ and observe how it affects your training" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "F6CeeS9CJtMC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67b756e3-644f-4bdd-f01a-5622a8b38e19" + }, + "source": [ + "# *CODE FOR PART 1.1b IN THIS CELL*\n", + "\n", + "def loss_function_VAE(recon_x, x, mu, logvar, beta):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " bce = F.binary_cross_entropy(recon_x, x, reduction='sum') / batch_size\n", + " kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) / batch_size\n", + "\n", + " return bce, kld * beta\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + "\n", + "model.train()\n", + "#######################################################################\n", + "# ** START OF YOUR CODE **\n", + "#######################################################################\n", + "total_loss_train = []\n", + "kl_loss_train = []\n", + "recon_loss_train = []\n", + "\n", + "total_loss_test = []\n", + "kl_loss_test = []\n", + "recon_loss_test = []\n", + "#######################################################################\n", + "# ** END OF YOUR CODE **\n", + "####################################################################### \n", + "\n", + "for epoch in range(num_epochs): \n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " model.train()\n", + " total_loss_train_epoch = 0\n", + " kl_loss_train_epoch = 0\n", + " recon_loss_train_epoch = 0\n", + "\n", + " for batch_idx, (data, _) in enumerate(loader_train):\n", + " data = data.to(device)\n", + " model.zero_grad()\n", + "\n", + "\n", + " recon_x, mu, logvar = model(data)\n", + " recon_loss, kl_loss = loss_function_VAE(recon_x, data, mu, logvar, beta)\n", + " total_loss = recon_loss + kl_loss\n", + " total_loss_train_epoch += total_loss.item()\n", + " kl_loss_train_epoch += kl_loss.item() / beta\n", + " recon_loss_train_epoch += recon_loss.item()\n", + "\n", + "\n", + " total_loss.backward()\n", + " optimizer.step()\n", + "\n", + " total_loss_train.append(total_loss_train_epoch / len(loader_train.dataset))\n", + " kl_loss_train.append(kl_loss_train_epoch / len(loader_train.dataset))\n", + " recon_loss_train.append(recon_loss_train_epoch / len(loader_train.dataset))\n", + "\n", + " print('epoch [{}/{}], train loss:{:.4f}'.format(epoch + 1, num_epochs, total_loss_train_epoch / len(loader_train.dataset)))\n", + " \n", + " model.eval()\n", + " total_loss_test_epoch = 0\n", + " kl_loss_test_epoch = 0\n", + " recon_loss_test_epoch = 0\n", + "\n", + " with torch.no_grad():\n", + " for batch_idx, (data, _) in enumerate(loader_test):\n", + " data = data.to(device)\n", + " recon_x, mu, logvar = model(data)\n", + " recon_loss, kl_loss = loss_function_VAE(recon_x, data, mu, logvar, beta)\n", + " total_loss = recon_loss + kl_loss\n", + " total_loss_test_epoch += total_loss.item()\n", + " kl_loss_test_epoch += kl_loss.item() / beta\n", + " recon_loss_test_epoch += recon_loss.item()\n", + " \n", + " total_loss_test.append(total_loss_test_epoch / len(loader_test.dataset))\n", + " kl_loss_test.append(kl_loss_test_epoch / len(loader_test.dataset))\n", + " recon_loss_test.append(recon_loss_test_epoch / len(loader_test.dataset))\n", + "\n", + " print('epoch [{}/{}], test loss:{:.4f}'.format(epoch + 1, num_epochs, total_loss_test_epoch / len(loader_test.dataset)))\n", + "\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " # save the model\n", + " if epoch == num_epochs - 1:\n", + " with torch.no_grad():\n", + " torch.jit.save(torch.jit.trace(model, (data), check_trace=False),\n", + " '/content/drive/MyDrive/icl_dl_cw2/CW_VAE/VAE_model.pth')\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "epoch [1/25], train loss:1.3808\n", + "epoch [1/25], test loss:1.0844\n", + "epoch [2/25], train loss:1.0679\n", + "epoch [2/25], test loss:1.0416\n", + "epoch [3/25], train loss:1.0409\n", + "epoch [3/25], test loss:1.0301\n", + "epoch [4/25], train loss:1.0276\n", + "epoch [4/25], test loss:1.0139\n", + "epoch [5/25], train loss:1.0168\n", + "epoch [5/25], test loss:1.0078\n", + "epoch [6/25], train loss:1.0119\n", + "epoch [6/25], test loss:1.0026\n", + "epoch [7/25], train loss:1.0069\n", + "epoch [7/25], test loss:1.0009\n", + "epoch [8/25], train loss:1.0021\n", + "epoch [8/25], test loss:0.9941\n", + "epoch [9/25], train loss:0.9988\n", + "epoch [9/25], test loss:0.9944\n", + "epoch [10/25], train loss:0.9959\n", + "epoch [10/25], test loss:0.9893\n", + "epoch [11/25], train loss:0.9930\n", + "epoch [11/25], test loss:0.9868\n", + "epoch [12/25], train loss:0.9911\n", + "epoch [12/25], test loss:0.9838\n", + "epoch [13/25], train loss:0.9884\n", + "epoch [13/25], test loss:0.9837\n", + "epoch [14/25], train loss:0.9867\n", + "epoch [14/25], test loss:0.9795\n", + "epoch [15/25], train loss:0.9853\n", + "epoch [15/25], test loss:0.9780\n", + "epoch [16/25], train loss:0.9832\n", + "epoch [16/25], test loss:0.9811\n", + "epoch [17/25], train loss:0.9811\n", + "epoch [17/25], test loss:0.9755\n", + "epoch [18/25], train loss:0.9801\n", + "epoch [18/25], test loss:0.9771\n", + "epoch [19/25], train loss:0.9793\n", + "epoch [19/25], test loss:0.9735\n", + "epoch [20/25], train loss:0.9775\n", + "epoch [20/25], test loss:0.9727\n", + "epoch [21/25], train loss:0.9767\n", + "epoch [21/25], test loss:0.9729\n", + "epoch [22/25], train loss:0.9754\n", + "epoch [22/25], test loss:0.9726\n", + "epoch [23/25], train loss:0.9748\n", + "epoch [23/25], test loss:0.9707\n", + "epoch [24/25], train loss:0.9736\n", + "epoch [24/25], test loss:0.9680\n", + "epoch [25/25], train loss:0.9727\n", + "epoch [25/25], test loss:0.9697\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vF6B26_oJtMF" + }, + "source": [ + "### Loss Explanation\n", + "Explain your choice of loss and how this relates to:\n", + "\n", + "* The VAE Prior\n", + "* The output data domain\n", + "* Disentanglement in the latent space\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DUqWwUvlrYnH" + }, + "source": [ + "# Any code for your explanation here" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dhjE07mrB7Zs" + }, + "source": [ + "**YOUR ANSWER**\n", + "\n", + "The VAE loss function have two terms: one is aimed to maximises the reconstruction likelihood, and the other is designed to make the approximation of the posterior $q_\\phi(z \\mid X) $ becomes closer to the prior distribution $ p_θ(z) $.\n", + "\n", + "1. As for $D_{K L}[q_\\phi(z \\mid X) \\| p_\\theta(z)]$, we assume that:\n", + "\n", + "* $ p_θ(z) \\sim N\\left(0, I\\right)$, so $ p_θ(z) $ has no parameter, so it can be writen as $ p(z) $.\n", + "* $q_\\phi(z \\mid X) \\sim N\\left(\\mu, \\Sigma ; x^{(i)}\\right)$\n", + "\n", + "Then, we can get:\n", + "\n", + "$$\\begin{aligned} & D_{K L}\\left(q_{\\phi}\\left(z \\mid x^{(i)}\\right)_{d} \\| p_{\\theta}(z)_{d}\\right) \\\\=& K L\\left(N\\left(\\mu_{d}, \\sigma_{d}^{2}\\right) \\| N(0,1)\\right) \\\\=& \\frac{1}{2}\\left(-\\log \\sigma_{d}^{2}+\\mu_{d}^{2}+\\sigma_{d}^{2}-1\\right) \\end{aligned}$$\n", + "\n", + "So, the python code is `kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())`\n", + "\n", + "2. As for $\\mathbb{E}_{q_\\phi(z \\mid X)}[\\log p_\\theta(X \\mid z)]$, we know $\\mathbb{E}_{z}\\left[\\log p_{\\theta}\\left(x^{(i)} \\mid z\\right)\\right] \\approx \\log p_{\\theta}\\left(x^{(i)} \\mid z\\right)$, and we assume $p_{\\theta}(x \\mid z) \\sim$ Bernoulli distribution, which corresponds to a binary value $X$ and a vector with $Q$ independent dimensions $\\left[\\rho_{1}, \\rho_{2}, \\ldots, \\rho_{Q}\\right]$. Then we can get $\\rho(z)=\\operatorname{dec}_{\\theta}(z)$. Now, we can calculate the reconstruction likelihood $\\log p_{\\theta}\\left(x^{(i)} \\mid z\\right)=\\sum_{q=1}^{Q}\\left(x_{q}^{(i)} \\log \\left[\\rho_{q}(z)\\right]+\\left(1-x_{q}^{(i)}\\right) \\log \\left[1-\\rho_{q}(z)\\right]\\right)$.\n", + "\n", + "So, we designed the sigmoid as activation function for the last layer, and use binary cross entropy as loss function. `bce = F.binary_cross_entropy(recon_x, x, reduction='sum') / batch_size`\n", + "\n", + "3. As for $\\beta$, it will reduce information of $z$, however improve the ability of disentanglement." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ez5nlMi1JtMF" + }, + "source": [ + "

Part 1.2 (9 points)

\n", + "\n", + "a. Plot your loss curves\n", + "\n", + "b. Show reconstructions and samples\n", + "\n", + "c. Discuss your results from parts (a) and (b)\n", + "\n", + "## Part 1.2a: Loss Curves (3 Points)\n", + "Plot your loss curves (6 in total, 3 for the training set and 3 for the test set): total loss, reconstruction log likelihood loss, KL loss (x-axis: epochs, y-axis: loss). If you experimented with different values of $\\beta$, you may wish to display multiple plots (worth 1 point)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fNA778FdhGFo" + }, + "source": [ + "# *CODE FOR PART 1.2a IN THIS CELL*\r\n", + "\r\n", + "# before running this code, we should make sure beta == 2.5\r\n", + "if beta == 2.5:\r\n", + " total_loss_train_beta25 = total_loss_train\r\n", + " total_loss_test_beta25 = total_loss_test\r\n", + " recon_loss_train_beta25 = recon_loss_train\r\n", + " recon_loss_test_beta25 = recon_loss_test\r\n", + " kl_loss_train_beta25 = kl_loss_train\r\n", + " kl_loss_test_beta25 = kl_loss_test" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "C0zWo_cMiq-j" + }, + "source": [ + "if beta == 1:\r\n", + " total_loss_train_beta1 = total_loss_train\r\n", + " total_loss_test_beta1 = total_loss_test\r\n", + " recon_loss_train_beta1 = recon_loss_train\r\n", + " recon_loss_test_beta1 = recon_loss_test\r\n", + " kl_loss_train_beta1 = kl_loss_train\r\n", + " kl_loss_test_beta1 = kl_loss_test" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "A4UJqR3qkd-c" + }, + "source": [ + "if beta == 4:\r\n", + " total_loss_train_beta4 = total_loss_train\r\n", + " total_loss_test_beta4 = total_loss_test\r\n", + " recon_loss_train_beta4 = recon_loss_train\r\n", + " recon_loss_test_beta4 = recon_loss_test\r\n", + " kl_loss_train_beta4 = kl_loss_train\r\n", + " kl_loss_test_beta4 = kl_loss_test" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AADYspqtJtMG", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 896 + }, + "outputId": "c44c557c-3922-47fe-a3b4-a3dd9b638049" + }, + "source": [ + "\r\n", + "\r\n", + "fig, axs = plt.subplots(3, 1, figsize=(15,15))\r\n", + "axs[0].plot(list(range(1, 1 + len(total_loss_train_beta25))), total_loss_train_beta25)\r\n", + "axs[0].plot(list(range(1, 1 + len(total_loss_test_beta25))), total_loss_test_beta25)\r\n", + "axs[0].plot(list(range(1, 1 + len(total_loss_train_beta1))), total_loss_train_beta1)\r\n", + "axs[0].plot(list(range(1, 1 + len(total_loss_test_beta1))), total_loss_test_beta1)\r\n", + "axs[0].plot(list(range(1, 1 + len(total_loss_train_beta4))), total_loss_train_beta4)\r\n", + "axs[0].plot(list(range(1, 1 + len(total_loss_test_beta4))), total_loss_test_beta4)\r\n", + "axs[0].legend(['Train loss(beta=2.5)', 'Test loss(beta=2.5)','Train loss(beta=1)', 'Test loss(beta=1)','Train loss(beta=4)', 'Test loss(beta=4)'])\r\n", + "axs[0].set_title('Total Loss')\r\n", + "\r\n", + "axs[1].plot(list(range(1, 1 + len(recon_loss_train_beta25))), recon_loss_train_beta25)\r\n", + "axs[1].plot(list(range(1, 1 + len(recon_loss_test_beta25))), recon_loss_test_beta25)\r\n", + "axs[1].plot(list(range(1, 1 + len(recon_loss_train_beta1))), recon_loss_train_beta1)\r\n", + "axs[1].plot(list(range(1, 1 + len(recon_loss_test_beta1))), recon_loss_test_beta1)\r\n", + "axs[1].plot(list(range(1, 1 + len(recon_loss_train_beta4))), recon_loss_train_beta4)\r\n", + "axs[1].plot(list(range(1, 1 + len(recon_loss_test_beta4))), recon_loss_test_beta4)\r\n", + "axs[1].legend(['Train loss(beta=2.5)', 'Test loss(beta=2.5)','Train loss(beta=1)', 'Test loss(beta=1)','Train loss(beta=4)', 'Test loss(beta=4)'])\r\n", + "axs[1].set_title('Log Likelihood Loss')\r\n", + "\r\n", + "axs[2].plot(list(range(1, 1 + len(kl_loss_train_beta25))), kl_loss_train_beta25)\r\n", + "axs[2].plot(list(range(1, 1 + len(kl_loss_test_beta25))), kl_loss_test_beta25)\r\n", + "axs[2].plot(list(range(1, 1 + len(kl_loss_train_beta1))), kl_loss_train_beta1)\r\n", + "axs[2].plot(list(range(1, 1 + len(kl_loss_test_beta1))), kl_loss_test_beta1)\r\n", + "axs[2].plot(list(range(1, 1 + len(kl_loss_train_beta4))), kl_loss_train_beta4)\r\n", + "axs[2].plot(list(range(1, 1 + len(kl_loss_test_beta4))), kl_loss_test_beta4)\r\n", + "axs[2].legend(['Train loss(beta=2.5)', 'Test loss(beta=2.5)','Train loss(beta=1)', 'Test loss(beta=1)','Train loss(beta=4)', 'Test loss(beta=4)'])\r\n", + "axs[2].set_title('KL Divergence Loss')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'KL Divergence Loss')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B7wp4MzqsjjZ" + }, + "source": [ + "## Part 1.2b: Samples and Reconstructions (6 Points)\n", + "Visualize a subset of the images of the test set and their reconstructions **as well as** a few generated samples. Most of the code for this part is provided. You only need to call the forward pass of the model for the given inputs (might vary depending on your implementation)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wu9CWtqoJtMK", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 997 + }, + "outputId": "a5fe3fba-e263-48ad-fe14-6004356a7b8a" + }, + "source": [ + "# *CODE FOR PART 1.2b IN THIS CELL*\n", + "\n", + "# load the model\n", + "print('Input images')\n", + "print('-'*50)\n", + "\n", + "sample_inputs, _ = next(iter(loader_test))\n", + "fixed_input = sample_inputs[0:32, :, :, :]\n", + "# visualize the original images of the last batch of the test set\n", + "img = make_grid(denorm(fixed_input), nrow=8, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + "plt.figure()\n", + "show(img)\n", + "\n", + "print('Reconstructed images')\n", + "print('-'*50)\n", + "with torch.no_grad():\n", + " # visualize the reconstructed images of the last batch of test set\n", + " \n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " fixed_input = fixed_input.to(device)\n", + " recon_batch, _, _ = model(fixed_input)\n", + " recon_batch = recon_batch.view(-1, 1, 28, 28)\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " recon_batch = recon_batch.cpu()\n", + " recon_batch = make_grid(denorm(recon_batch), nrow=8, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + " plt.figure()\n", + " show(recon_batch)\n", + "\n", + "print('Generated Images') \n", + "print('-'*50)\n", + "model.eval()\n", + "n_samples = 256\n", + "z = torch.randn(n_samples,latent_dim).to(device)\n", + "with torch.no_grad():\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " samples = model.decode(z).view(-1, 1, 28, 28)\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " samples = samples.cpu()\n", + " samples = make_grid(denorm(samples), nrow=16, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + " plt.figure(figsize = (8,8))\n", + " show(samples)\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Input images\n", + "--------------------------------------------------\n", + "Reconstructed images\n", + "--------------------------------------------------\n", + "Generated Images\n", + "--------------------------------------------------\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IfZJRgj1rudZ" + }, + "source": [ + "### Discussion\n", + "Provide a brief analysis of your loss curves and reconstructions: \n", + "* What do you observe in the behaviour of the log-likelihood loss and the KL loss (increasing/decreasing)?\n", + "* Can you intuitively explain if this behaviour is desirable? Have you observed posterior collapse during traing (i.e. when the KL is too small during the early stages of training)? \n", + " * If yes, how did you mitigate it? How did this phenomenon reflect on your output samples?\n", + " * If no, why do you think that is?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vy4KKp2UJtMJ" + }, + "source": [ + "**YOUR ANSWER**\n", + "\n", + "During training, the log-likelihood loss decreases with increasing epochs and then stabilises, and if we increase the beta, it will converge to a higher value. This behaviour is desirable, for we can get a better network to sample outputs which can be similar to the training data.\n", + "\n", + "While KL loss first increases with more epochs and then stabilises, and if we use a higher beta, it will converge to a lower value. Because we performed gradient descent the sign of KL loss in the loss function is flipped, so an increase is desirable.\n", + "\n", + "To combat posterior collapse, which was observed during initial training, first we increased the epochs, and also decrease the dimension of z, while if it is not enough, we can get little factors.\n", + "\n", + "As for beta, the higher beta, the generated image can be more desirable, however, the T-SNE result can be quite terrible. So we need to find a balance between them.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JTprojS7sLP8" + }, + "source": [ + "---\n", + "

Part 1.3 (11 points)

\n", + "\n", + "Qualitative analysis of the learned representations\n", + "\n", + "In this question you are asked to qualitatively assess the representations that your model has learned. In particular:\n", + "\n", + "a. Dimensionality Reduction of learned embeddings\n", + "\n", + "b. Interpolating in the latent space\n", + "\n", + "## Part 1.3a: T-SNE on Embeddings (7 Points)\n", + "Extract the latent representations of the test set and visualize them using [T-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) [(see implementation)](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html). \n", + "\n", + "We've provided a function to visualize a subset of the data, but you are encouraged to also produce a matplotlib plot (please use different colours for each digit class)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xl4xZOg7s0ke" + }, + "source": [ + "# *CODE FOR PART 1.3a IN THIS CELL\n", + "\n", + "from sklearn.manifold import TSNE\n", + "import numpy as np\n", + "\n", + "z_batches = []\n", + "label_batches = []\n", + "for i, (data, label) in enumerate(loader_test):\n", + " data = data.to(device)\n", + " mu, logvar = model.encode(data)\n", + " z = model.reparametrize(mu, logvar).detach().cpu().numpy()\n", + " z_batches.append(z)\n", + " label_batches.append(label.detach().cpu().numpy())\n", + "\n", + "zs = []\n", + "labels = []\n", + "for z, label in zip(z_batches, label_batches):\n", + " for i in z:\n", + " zs.append(i)\n", + " for l in label:\n", + " labels.append(l)\n", + "\n", + "z_embedded = TSNE(n_components=2).fit_transform(np.array(zs))" + ], + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-M_EI2ZnnXHZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 442 + }, + "outputId": "5350416e-de11-4a8d-de84-34c7626ba11e" + }, + "source": [ + "# Interactive Visualization - Code Provided\n", + "test_dataloader = DataLoader(test_dat, 10000, shuffle=False)\n", + "\"\"\" Inputs to the function are\n", + " z_embedded - X, Y positions for every point in test_dataloader\n", + " test_dataloader - dataloader with batchsize set to 10000\n", + " num_points - number of points plotted (will slow down with >1k)\n", + "\"\"\"\n", + "plot_tsne(z_embedded, test_dataloader, num_points=1000, darkmode=False)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "alt.HConcatChart(...)" + ], + "text/html": [ + "\n", + "
\n", + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xvQvtlDzIB3M", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "outputId": "39afce13-8a16-45df-ab7a-ee08b5a0de78" + }, + "source": [ + "# Custom Visualizations\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "data_dict = {'x': z_embedded[:, 0], 'y': z_embedded[:, 1], 'labels': labels}\n", + "df = pd.DataFrame(data_dict)\n", + "\n", + "plt.figure(figsize = (10,10))\n", + "sns.scatterplot(x='x', y='y', data=df, hue='labels', legend='full', palette='Paired')\n", + "plt.show()\n", + "\n", + "zero_x=np.array(df.x[df[\"labels\"]==0])\n", + "zero_y=np.array(df.y[df[\"labels\"]==0])\n", + "one_x=np.array(df.x[df[\"labels\"]==1])\n", + "one_y=np.array(df.y[df[\"labels\"]==1])\n", + "two_x=np.array(df.x[df[\"labels\"]==2])\n", + "two_y=np.array(df.y[df[\"labels\"]==2])\n", + "three_x=np.array(df.x[df[\"labels\"]==3])\n", + "three_y=np.array(df.y[df[\"labels\"]==3])\n", + "four_x=np.array(df.x[df[\"labels\"]==4])\n", + "four_y=np.array(df.y[df[\"labels\"]==4])\n", + "five_x=np.array(df.x[df[\"labels\"]==5])\n", + "five_y=np.array(df.y[df[\"labels\"]==5])\n", + "six_x=np.array(df.x[df[\"labels\"]==6])\n", + "six_y=np.array(df.y[df[\"labels\"]==6])\n", + "siven_x=np.array(df.x[df[\"labels\"]==7])\n", + "siven_y=np.array(df.y[df[\"labels\"]==7])\n", + "eight_x=np.array(df.x[df[\"labels\"]==8])\n", + "eight_y=np.array(df.y[df[\"labels\"]==8])\n", + "night_x=np.array(df.x[df[\"labels\"]==9])\n", + "night_y=np.array(df.y[df[\"labels\"]==9])\n", + "\n", + "data=[zero_x,zero_y,one_x,one_y,two_x,two_y,three_x,three_y,four_x,four_y,five_x,five_y,six_x,six_y,siven_x,siven_y,eight_x,eight_y,night_x,night_y]\n", + "plt.figure(figsize = (10,10))\n", + "plt.boxplot(data)\n", + "plt.show()" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6HiAHb0ztTW8" + }, + "source": [ + "### Discussion\n", + "What do you observe? Discuss the structure of the visualized representations. \n", + "* What do you observe? What role do the KL loss term and $\\beta$ have, if any, in what you observe (multiple matplotlib plots may be desirable here)?\n", + " * Consider Outliers\n", + " * Counsider Boundaries\n", + " * Consider Clusters\n", + "* Is T-SNE reliable? What happens if you change the parameters (don't worry about being particularly thorough). [This link](https://distill.pub/2016/misread-tsne/) may be helpful." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u0_3QlEYteYk" + }, + "source": [ + "**YOUR ANSWER**\n", + "\n", + "Actually, when seeing the boxplot, some labels have quite huge outliers in either x or y, but not in both, so it looks quite good in scatterplot. We can observe that points in the 2D embedding are similar to their neighbours, so the clusters and boundaries are obviously.\n", + "\n", + "We also observe relatively smooth boundaries of points, which is the effect of the reconstruction loss and the KL loss. The KL loss make the network to learn more or less latent distributions, and the trade off can be infulenced by $\\beta$.\n", + "\n", + "The label 2 is really spetial for having two clusters, and it may be due to two different styles of writing.\n", + "\n", + "T-SNE is reliable, for it succeeded in reducing dimensions in the latent sapce, whilst it managed to distinguish one digit from an other in the 2-d space. The perplexity is a very important parameter, if it is too small we can get too many clusters, while if it is too big, we will find all the points are in the same cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uCtbTLv4thEH" + }, + "source": [ + "## Part 1.3b: Interpolating in $z$ (4 Points)\n", + "Perform a linear interpolation in the latent space of the autoencoder by choosing any two digits from the test set. What do you observe regarding the transition from on digit to the other?\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MVk7GUIxtgiF" + }, + "source": [ + "# CODE FOR PART 1.3b IN THIS CELL\n", + "\n", + "vae = torch.jit.load('./model/VAE_model.pth')\n", + "model.load_state_dict(vae.state_dict())\n", + "# model.eval()\n", + "test_data, test_labels = next(iter(loader_test))\n", + "\n", + "x1 = torch.FloatTensor(test_data[100]).reshape(1,1,28,28)\n", + "x2 = torch.FloatTensor(test_data[10]).reshape(1,1,28,28)\n", + "\n", + "with torch.no_grad():\n", + " x1 = x1.to(device)\n", + " x2 = x2.to(device)\n", + " mu1, var1 = model.encode(x1)\n", + " mu2, var2 = model.encode(x2)\n", + " z1 = model.reparametrize(mu1, var1)\n", + " z2 = model.reparametrize(mu2, var2)\n", + " a = np.linspace(0,1,num=11)\n", + " inter = []\n", + " for i in range(len(a)):\n", + " Z = a[i] * z1 + (1 - a[i]) * z2 \n", + " x_hat = model.decode(Z)\n", + " inter.append(x_hat)\n", + "\n", + "fig, axs = plt.subplots(1, 11, figsize=(20, 20))\n", + "for i in range(11):\n", + " y = inter[i].view(28,28).squeeze().squeeze().cpu().numpy()\n", + " axs[i].imshow(y, cmap='gray')\n", + " axs[i].axis('off')" + ], + "execution_count": 59, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gdk6yyrittNx" + }, + "source": [ + "### Discussion\n", + "What did you observe in the interpolation? Is this what you expected?\n", + "* Can you relate the interpolation to your T-SNE visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZF2jUHWHtt3V" + }, + "source": [ + "**YOUR ANSWER**\n", + "\n", + "From the above picture, we can see the change from 0 to 6. On the most left picture, we can see a clear 0, and on the right we can see a clear 6, while in the middle it looks like both 0 and 6.\n", + "\n", + "I guess, maybe the interpolation is just like one point have a linear movement from one cluster to another." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EG68ntJ2qfIC" + }, + "source": [ + "# Part 2 - Deep Convolutional GAN\n", + "\n", + "In this task, your main objective is to train a DCGAN (https://arxiv.org/abs/1511.06434) on the CIFAR-10 dataset. You should experiment with different architectures, tricks for stability in training (such as using different activation functions, batch normalization, different values for the hyper-parameters, etc.). In the end, you should provide us with: \n", + "\n", + "- your best trained model (which we will be able to run), \n", + "- some generations for the fixed latent vectors $\\mathbf{z}\\sim \\mathcal{N}\\left(\\mathbf{0}, \\mathbf{I}\\right)$ we have provided you with (train for a number of epochs and make sure there is no mode collapse), \n", + "- plots with the losses for the discriminator $D$ and the generator $G$ as the training progresses and explain whether your produced plots are theoretically sensible and why this is (or not) the case. \n", + "- a discussion on whether you noticed any mode collapse, where this behaviour may be attributed to, and explanations of what you did in order to cope with mode collapse. \n", + "\n", + "## Part 2.1 (30 points)\n", + "**Your Task**: \n", + "\n", + "a. Implement the DCGAN architecture. \n", + "\n", + "b. Define a loss and implement the Training Loop\n", + "\n", + "c. Visualize images sampled from your best model's generator (\"Extension\" Assessed on quality)\n", + "\n", + "d. Discuss the experimentations which led to your final architecture. You can plot losses or generated results by other architectures that you tested to back your arguments (but this is not necessary to get full marks).\n", + "\n", + "\n", + "_Clarification: You should not be worrying too much about getting an \"optimal\" performance on your trained GAN. We want you to demonstrate to us that you experimented with different types of DCGAN variations, report what difficulties transpired throughout the training process, etc. In other words, if we see that you provided us with a running implementation, that you detail different experimentations that you did before providing us with your best one, and that you have grapsed the concepts, you can still get good marks. The attached model does not have to be perfect, and the extension marks for performance are only worth 10 points._" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uFEt7wGXP_aE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3735a195-7da1-4091-97ac-53bcc099f8c4" + }, + "source": [ + "import os\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader\n", + "from torch.utils.data import sampler\n", + "from torchvision import datasets, transforms\n", + "from torchvision.utils import save_image, make_grid\n", + "from torch.optim.lr_scheduler import StepLR, MultiStepLR\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def denorm(x, channels=None, w=None ,h=None, resize = False):\n", + " x = 0.5 * (x + 1)\n", + " x = x.clamp(0, 1)\n", + " if resize:\n", + " if channels is None or w is None or h is None:\n", + " print('Number of channels, width and height must be provided for resize.')\n", + " x = x.view(x.size(0), channels, w, h)\n", + " return x\n", + "\n", + "def show(img):\n", + " npimg = img.cpu().numpy()\n", + " plt.imshow(np.transpose(npimg, (1,2,0)))\n", + "\n", + "if not os.path.exists('/content/drive/MyDrive/icl_dl_cw2/GAN2'):\n", + " os.makedirs('/content/drive/MyDrive/icl_dl_cw2/GAN2')\n", + "\n", + "GPU = True # Choose whether to use GPU\n", + "if GPU:\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + "print(f'Using {device}')\n", + "\n", + "# We set a random seed to ensure that your results are reproducible.\n", + "if torch.cuda.is_available():\n", + " torch.backends.cudnn.deterministic = True\n", + "torch.manual_seed(0)" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Using cuda\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VosOpcpfGvWO" + }, + "source": [ + "### Part 2.1a: Implement DCGAN (8 Points)\n", + "Fill in the missing parts in the cells below in order to complete the Generator and Discriminator classes. You will need to define:\n", + "\n", + "- The hyperparameters\n", + "- The constructors\n", + "- `decode`\n", + "- `discriminator`\n", + "\n", + "Recomendations for experimentation:\n", + "- use the architecture that you implemented for the Autoencoder of Part 1 (encoder as discriminator, decoder as generator).\n", + "- use the architecture desribed in the DCGAN paper (https://arxiv.org/abs/1511.06434).\n", + "\n", + "Some general reccomendations:\n", + "- add several convolutional layers (3-4).\n", + "- accelerate training with batch normalization after every convolutional layer.\n", + "- use the appropriate activation functions. \n", + "- Generator module: the upsampling can be done with various methods, such as nearest neighbor upsampling (`torch.nn.Upsample`) or transposed convolutions(`torch.nn.ConvTranspose2d`). \n", + "- Discriminator module: Experiment with batch normalization (`torch.nn.BatchNorm2d`) and leaky relu (`torch.nn.LeakyReLu`) units after each convolutional layer.\n", + "\n", + "Try to follow the common practices for CNNs (e.g small receptive fields, max pooling, RELU activations), in order to narrow down your possible choices.\n", + "\n", + "**Your model should not have more than 25 Million Parameters**\n", + "\n", + "The number of epochs that will be needed in order to train the network will vary depending on your choices. As an advice, we recommend that while experimenting you should allow around 20 epochs and if the loss doesn't sufficiently drop, restart the training with a more powerful architecture. You don't need to train the network to an extreme if you don't have the time." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pOi_Q_jleQJq" + }, + "source": [ + "#### Data loading" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "__ENlW2aeQJr", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a75d07a3-bc07-40b7-f1a0-d05a1947c020" + }, + "source": [ + "batch_size = 128 # change that\n", + "\n", + "transform = transforms.Compose([\n", + " transforms.Resize(64),\n", + " transforms.RandomHorizontalFlip(p=0.5), \n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),), \n", + "])\n", + "\n", + "transform_test = transforms.Compose([\n", + " transforms.Resize(64), \n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),), \n", + "])\n", + "\n", + "data_dir = './datasets'\n", + "\n", + "cifar10_train = datasets.CIFAR10(data_dir, train=True, download=True, transform=transform)\n", + "cifar10_test = datasets.CIFAR10(data_dir, train=False, download=True, transform=transform_test)\n", + "loader_train = DataLoader(cifar10_train, batch_size=batch_size)\n", + "loader_test = DataLoader(cifar10_test, batch_size=batch_size)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-TmrRudFRhOB" + }, + "source": [ + "We'll visualize a subset of the test set: " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CY2ka775Rfxm", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 846 + }, + "outputId": "c090672a-1bd3-4381-d40a-099638b9c312" + }, + "source": [ + "samples, _ = next(iter(loader_test))\n", + "\n", + "samples = samples.cpu()\n", + "samples = make_grid(denorm(samples), nrow=8, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + "plt.figure(figsize = (15,15))\n", + "plt.axis('off')\n", + "show(samples)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XSkveP0mZ-Pd" + }, + "source": [ + "#### Model Definition\n", + "Define hyperparameters and the model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FBoxMTihZ-Pd" + }, + "source": [ + "# *CODE FOR PART 2.1 IN THIS CELL*\n", + "\n", + "# Choose the number of epochs, the learning rate\n", + "# and the size of the Generator's input noise vetor.\n", + "\n", + "num_epochs = 100\n", + "learning_rate = 0.0002\n", + "latent_vector_size = 100\n", + "\n", + "# Other hyperparams\n", + "num_gen_features = 64\n", + "num_disc_features = 64" + ], + "execution_count": 44, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ATTUAhCDZ-Pg" + }, + "source": [ + "# *CODE FOR PART 2.1 IN THIS CELL*\n", + "\n", + "\n", + "class Generator(nn.Module):\n", + " def __init__(self):\n", + " super(Generator, self).__init__()\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " #input 100*1*1\n", + " self.layer1 = nn.Sequential(nn.ConvTranspose2d(latent_vector_size,num_gen_features*8,4,1,0,bias = False),\n", + " nn.BatchNorm2d(num_gen_features*8),\n", + " nn.ReLU(True))\n", + "\n", + " #input 512*4*4\n", + " self.layer2 = nn.Sequential(nn.ConvTranspose2d(num_gen_features*8,num_gen_features*4,4,2,1,bias = False),\n", + " nn.BatchNorm2d(num_gen_features*4),\n", + " nn.ReLU(True))\n", + " #input 256*8*8\n", + " self.layer3 = nn.Sequential(nn.ConvTranspose2d(num_gen_features*4,num_gen_features*2,4,2,1,bias = False),\n", + " nn.BatchNorm2d(num_gen_features*2),\n", + " nn.ReLU(True))\n", + " #input 128*16*16\n", + " self.layer4 = nn.Sequential(nn.ConvTranspose2d(num_gen_features*2,num_gen_features,4,2,1,bias = False),\n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(True))\n", + " #input 64*32*32\n", + " self.layer5 = nn.Sequential(nn.ConvTranspose2d(num_gen_features,3,4,2,1,bias = False),\n", + " nn.Tanh())\n", + " #output 3*64*64\n", + " \n", + " self.embedding = nn.Embedding(10, latent_vector_size)\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + "\n", + "\n", + " def forward(self, z, label):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " label_embedding = self.embedding(label)\n", + " z = z.view(-1, latent_vector_size)\n", + " out = torch.mul(z, label_embedding)\n", + " out = out.view(-1, latent_vector_size, 1, 1)\n", + " out = self.layer1(out)\n", + " out = self.layer2(out)\n", + " out = self.layer3(out)\n", + " out = self.layer4(out)\n", + " out = self.layer5(out)\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " #######################################################################\n", + " return out\n", + "\n", + "\n", + "class Discriminator(nn.Module):\n", + " def __init__(self):\n", + " super(Discriminator, self).__init__()\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " #input 3*64*64\n", + " self.layer1 = nn.Sequential(nn.Conv2d(3,num_disc_features,4,2,1,bias = False),\n", + " nn.BatchNorm2d(num_disc_features),\n", + " nn.LeakyReLU(0.2,True),\n", + " nn.Dropout2d(0.5))\n", + " \n", + " #input 64*32*32\n", + " self.layer2 = nn.Sequential(nn.Conv2d(num_disc_features,num_disc_features*2,4,2,1,bias = False),\n", + " nn.BatchNorm2d(num_disc_features*2),\n", + " nn.LeakyReLU(0.2,True),\n", + " nn.Dropout2d(0.5))\n", + " #input 128*16*16\n", + " self.layer3 = nn.Sequential(nn.Conv2d(num_disc_features*2,num_disc_features*4,4,2,1,bias = False),\n", + " nn.BatchNorm2d(num_disc_features*4),\n", + " nn.LeakyReLU(0.2,True),\n", + " nn.Dropout2d(0.5))\n", + " #input 256*8*8\n", + " self.layer4 = nn.Sequential(nn.Conv2d(num_disc_features*4,num_disc_features*8,4,2,1,bias = False),\n", + " nn.BatchNorm2d(num_disc_features*8),\n", + " nn.LeakyReLU(0.2,True))\n", + " #input 512*4*4\n", + " self.out_layer = nn.Sequential(nn.Conv2d(num_disc_features*8,1,4,1,0,bias = False),\n", + " nn.Sigmoid())\n", + " \n", + " self.label_layer = nn.Sequential(nn.Conv2d(num_disc_features*8,11,4,1,0,bias = False),\n", + " nn.LogSoftmax(dim = 1))\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " def forward(self, x):\n", + " #######################################################################\n", + " # ** START OF YOUR CODE **\n", + " #######################################################################\n", + " x = self.layer1(x)\n", + " x = self.layer2(x)\n", + " x = self.layer3(x)\n", + " x = self.layer4(x)\n", + " out = self.out_layer(x)\n", + " label = self.label_layer(x)\n", + " \n", + " out = out.view(-1)\n", + " label = label.view(-1,11)\n", + " #######################################################################\n", + " # ** END OF YOUR CODE **\n", + " ####################################################################### \n", + " \n", + " return out, label\n" + ], + "execution_count": 45, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o8YDyYf8Z-Pi" + }, + "source": [ + "

Initialize Model and print number of parameters

" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xh3NpfD_Z-Pj" + }, + "source": [ + "You can use method `weights_init` to initialize the weights of the Generator and Discriminator networks. Otherwise, implement your own initialization, or do not use at all. You will not be penalized for not using initialization." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JAVpgpmUZ-Pk" + }, + "source": [ + "# custom weights initialization called on netG and netD\n", + "def weights_init(m):\n", + " classname = m.__class__.__name__\n", + " if classname.find('Conv') != -1:\n", + " m.weight.data.normal_(0.0, 0.02)\n", + " elif classname.find('BatchNorm') != -1:\n", + " m.weight.data.normal_(1.0, 0.02)\n", + " m.bias.data.fill_(0)" + ], + "execution_count": 46, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ew-OdvNJZ-Pm", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6e4ae1a9-f288-4b7b-91e6-90d56cb92fe1" + }, + "source": [ + "use_weights_init = True\n", + "\n", + "model_G = Generator().to(device)\n", + "if use_weights_init:\n", + " model_G.apply(weights_init)\n", + "params_G = sum(p.numel() for p in model_G.parameters() if p.requires_grad)\n", + "print(\"Total number of parameters in Generator is: {}\".format(params_G))\n", + "print(model_G)\n", + "print('\\n')\n", + "\n", + "model_D = Discriminator().to(device)\n", + "if use_weights_init:\n", + " model_D.apply(weights_init)\n", + "params_D = sum(p.numel() for p in model_D.parameters() if p.requires_grad)\n", + "print(\"Total number of parameters in Discriminator is: {}\".format(params_D))\n", + "print(model_D)\n", + "print('\\n')\n", + "\n", + "print(\"Total number of parameters is: {}\".format(params_G + params_D))" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Total number of parameters in Generator is: 3577704\n", + "Generator(\n", + " (layer1): Sequential(\n", + " (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU(inplace=True)\n", + " )\n", + " (layer2): Sequential(\n", + " (0): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU(inplace=True)\n", + " )\n", + " (layer3): Sequential(\n", + " (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU(inplace=True)\n", + " )\n", + " (layer4): Sequential(\n", + " (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU(inplace=True)\n", + " )\n", + " (layer5): Sequential(\n", + " (0): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): Tanh()\n", + " )\n", + " (embedding): Embedding(10, 100)\n", + ")\n", + "\n", + "\n", + "Total number of parameters in Discriminator is: 2855808\n", + "Discriminator(\n", + " (layer1): Sequential(\n", + " (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2, inplace=True)\n", + " (3): Dropout2d(p=0.5, inplace=False)\n", + " )\n", + " (layer2): Sequential(\n", + " (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2, inplace=True)\n", + " (3): Dropout2d(p=0.5, inplace=False)\n", + " )\n", + " (layer3): Sequential(\n", + " (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2, inplace=True)\n", + " (3): Dropout2d(p=0.5, inplace=False)\n", + " )\n", + " (layer4): Sequential(\n", + " (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2, inplace=True)\n", + " )\n", + " (out_layer): Sequential(\n", + " (0): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n", + " (1): Sigmoid()\n", + " )\n", + " (label_layer): Sequential(\n", + " (0): Conv2d(512, 11, kernel_size=(4, 4), stride=(1, 1), bias=False)\n", + " (1): LogSoftmax(dim=1)\n", + " )\n", + ")\n", + "\n", + "\n", + "Total number of parameters is: 6433512\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "by_TNUPXJamb" + }, + "source": [ + "### Part 2.1b: Training the Model (12 Points)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "00wgs1VNZ-Pp" + }, + "source": [ + "#### Defining a Loss" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gPlxaL_cZ-Pq" + }, + "source": [ + "criterion = nn.BCELoss()\n", + "def loss_function(out, label):\n", + " loss = criterion(out, label)\n", + " return loss" + ], + "execution_count": 48, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GrgmhlSXZ-Ps" + }, + "source": [ + "

Choose and initialize optimizers

" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pFM8iI24Z-Pt" + }, + "source": [ + "# setup optimizer\n", + "# You are free to add a scheduler or change the optimizer if you want. We chose one for you for simplicity.\n", + "beta1 = 0.5\n", + "optimizerD = torch.optim.Adam(model_D.parameters(), lr=learning_rate, betas=(beta1, 0.999))\n", + "optimizerG = torch.optim.Adam(model_G.parameters(), lr=learning_rate, betas=(beta1, 0.999))\n", + "schedD = torch.optim.lr_scheduler.MultiStepLR(optimizerD, milestones=[50], gamma= 0.1)\n", + "schedG = torch.optim.lr_scheduler.MultiStepLR(optimizerG, milestones=[50, 75], gamma= 0.1)" + ], + "execution_count": 49, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qZ311RPlZ-Pv" + }, + "source": [ + "

Define fixed input vectors to monitor training and mode collapse.

" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EGB_9A1UZ-Pw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "39f99938-2190-4a28-ae1f-b44408824d4b" + }, + "source": [ + "fixed_noise = torch.randn(batch_size, latent_vector_size, 1, 1, device=device)\n", + "fixed_labels = torch.randint(0,10,(batch_size,),dtype = torch.long,device = device)\n", + "print(fixed_noise.size())\n", + "print(batch_size)\n", + "real_labels = 0.7 + 0.5 * torch.rand(10, device = device)\n", + "fake_labels = 0.3 * torch.rand(10, device = device)" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "text": [ + "torch.Size([80, 100, 1, 1])\n", + "80\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gD9S_3yZZ-Py" + }, + "source": [ + "#### Training Loop" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "w5vD--X6Z-Pz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "tags": [], + "outputId": "4d40b520-9d18-4351-b572-e809db57196e" + }, + "source": [ + "train_losses_G = []\n", + "train_losses_D = []\n", + "\n", + "for epoch in range(num_epochs):\n", + " train_loss_D = 0\n", + " train_loss_G = 0\n", + " for i, data in enumerate(loader_train, 0):\n", + " ############################\n", + " # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n", + " ###########################device\n", + " # train with real\n", + "\n", + " model_D.zero_grad()\n", + " real_cpu = data[0].to(device)\n", + " labels = data[1].to(device)\n", + " real_label = real_labels[i % 10]\n", + " fake_label = fake_labels[i % 10]\n", + " batch_size = real_cpu.size(0)\n", + " fake_class_labels = 10 * torch.ones((batch_size,), dtype = torch.long, device = device)\n", + "\n", + " if i % 30 == 0:\n", + " real_label, fake_label = fake_label, real_label\n", + " label = torch.full((batch_size,), real_label, device=device)\n", + "\n", + "\n", + " output, out_labels = model_D(real_cpu)\n", + " errD_real = loss_function(output, label) + F.nll_loss(out_labels, labels)\n", + " errD_real.backward()\n", + " D_x = output.mean().item()\n", + "\n", + " # train with fake\n", + " noise = torch.randn(batch_size, latent_vector_size, 1, 1, device=device)\n", + " sample_labels = torch.randint(0,10,(batch_size,), dtype = torch.long, device = device)\n", + "\n", + " fake = model_G(noise, sample_labels)\n", + "\n", + " label.fill_(fake_label)\n", + "\n", + " output, out_labels = model_D(fake.detach())\n", + " errD_fake = loss_function(output, label) + F.nll_loss(out_labels, fake_class_labels)\n", + " errD_fake.backward()\n", + "\n", + " D_G_z1 = output.mean().item()\n", + " errD = errD_real + errD_fake\n", + " train_loss_D += errD.item()\n", + " optimizerD.step()\n", + "\n", + "\n", + " ############################\n", + " # (2) Update G network: maximize log(D(G(z)))\n", + " ###########################\n", + " model_G.zero_grad()\n", + " label.fill_(1)\n", + " output, outlabels = model_D(fake)\n", + "\n", + " errG = loss_function(output, label) + F.nll_loss(outlabels, sample_labels)\n", + " errG.backward()\n", + " D_G_z2 = output.mean().item()\n", + " train_loss_G += errG.item()\n", + " optimizerG.step()\n", + "\n", + " print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'\n", + " % (epoch+1, num_epochs, i+1, len(loader_train),\n", + " errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n", + "\n", + " schedD.step()\n", + " schedG.step()\n", + " if epoch == 0:\n", + " save_image(denorm(real_cpu.cpu()).float(), '/content/drive/MyDrive/icl_dl_cw2/GAN2/real_samples.png')\n", + " with torch.no_grad():\n", + " fake = model_G(fixed_noise, fixed_labels)\n", + " save_image(denorm(fake.cpu()).float(), '/content/drive/MyDrive/icl_dl_cw2/GAN2/fake_samples_epoch_%03d.png' % epoch)\n", + " train_losses_D.append(train_loss_D / len(loader_train))\n", + " train_losses_G.append(train_loss_G / len(loader_train))" + ], + "execution_count": 51, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n", + "[88/100][84/391] Loss_D: 3.5527 Loss_G: 3.0607 D(x): 0.5631 D(G(z)): 0.5151 / 0.3833\n", + "[88/100][85/391] Loss_D: 2.7218 Loss_G: 2.4398 D(x): 0.7468 D(G(z)): 0.4465 / 0.4565\n", + "[88/100][86/391] Loss_D: 3.3780 Loss_G: 2.6007 D(x): 0.6515 D(G(z)): 0.5268 / 0.4394\n", + "[88/100][87/391] Loss_D: 3.3666 Loss_G: 3.6614 D(x): 0.6080 D(G(z)): 0.4639 / 0.3211\n", + "[88/100][88/391] Loss_D: 3.1745 Loss_G: 3.7921 D(x): 0.5723 D(G(z)): 0.4475 / 0.3165\n", + "[88/100][89/391] Loss_D: 2.7192 Loss_G: 2.3323 D(x): 0.6846 D(G(z)): 0.4141 / 0.4812\n", + "[88/100][90/391] Loss_D: 3.1284 Loss_G: 1.8367 D(x): 0.6773 D(G(z)): 0.5080 / 0.5611\n", + "[88/100][91/391] Loss_D: 3.4666 Loss_G: 2.4045 D(x): 0.6386 D(G(z)): 0.4810 / 0.5057\n", + "[88/100][92/391] Loss_D: 2.6873 Loss_G: 2.5932 D(x): 0.7031 D(G(z)): 0.4250 / 0.4516\n", + "[88/100][93/391] Loss_D: 3.2966 Loss_G: 3.1624 D(x): 0.6746 D(G(z)): 0.5215 / 0.3739\n", + "[88/100][94/391] Loss_D: 2.6231 Loss_G: 3.1163 D(x): 0.6292 D(G(z)): 0.3555 / 0.3754\n", + "[88/100][95/391] Loss_D: 2.8173 Loss_G: 2.9865 D(x): 0.6864 D(G(z)): 0.4105 / 0.3959\n", + "[88/100][96/391] Loss_D: 2.8343 Loss_G: 2.8137 D(x): 0.6016 D(G(z)): 0.3192 / 0.4180\n", + "[88/100][97/391] Loss_D: 3.2312 Loss_G: 3.0118 D(x): 0.6646 D(G(z)): 0.4846 / 0.3979\n", + "[88/100][98/391] Loss_D: 3.2750 Loss_G: 3.8746 D(x): 0.5898 D(G(z)): 0.4793 / 0.2909\n", + "[88/100][99/391] Loss_D: 2.6684 Loss_G: 2.6387 D(x): 0.6640 D(G(z)): 0.3467 / 0.4464\n", + "[88/100][100/391] Loss_D: 3.0999 Loss_G: 2.3298 D(x): 0.5729 D(G(z)): 0.4041 / 0.4809\n", + "[88/100][101/391] Loss_D: 3.5016 Loss_G: 2.2815 D(x): 0.6777 D(G(z)): 0.5642 / 0.5073\n", + "[88/100][102/391] Loss_D: 2.9385 Loss_G: 2.7786 D(x): 0.6895 D(G(z)): 0.4379 / 0.4212\n", + "[88/100][103/391] Loss_D: 2.8947 Loss_G: 2.1295 D(x): 0.6183 D(G(z)): 0.4014 / 0.5127\n", + "[88/100][104/391] Loss_D: 3.3214 Loss_G: 2.7851 D(x): 0.6135 D(G(z)): 0.5039 / 0.4189\n", + "[88/100][105/391] Loss_D: 2.7190 Loss_G: 2.8076 D(x): 0.6852 D(G(z)): 0.3674 / 0.4119\n", + "[88/100][106/391] Loss_D: 2.5153 Loss_G: 2.6725 D(x): 0.7196 D(G(z)): 0.3984 / 0.4225\n", + "[88/100][107/391] Loss_D: 3.7164 Loss_G: 2.5476 D(x): 0.5260 D(G(z)): 0.4774 / 0.4489\n", + "[88/100][108/391] Loss_D: 3.0308 Loss_G: 3.0837 D(x): 0.6343 D(G(z)): 0.4478 / 0.3638\n", + "[88/100][109/391] Loss_D: 3.4944 Loss_G: 1.8024 D(x): 0.5745 D(G(z)): 0.5108 / 0.5883\n", + "[88/100][110/391] Loss_D: 3.0668 Loss_G: 2.5326 D(x): 0.7147 D(G(z)): 0.5295 / 0.4618\n", + "[88/100][111/391] Loss_D: 2.7107 Loss_G: 2.2804 D(x): 0.6808 D(G(z)): 0.3873 / 0.4851\n", + "[88/100][112/391] Loss_D: 2.9741 Loss_G: 2.8283 D(x): 0.6093 D(G(z)): 0.4248 / 0.4144\n", + "[88/100][113/391] Loss_D: 3.0972 Loss_G: 2.7351 D(x): 0.6109 D(G(z)): 0.3998 / 0.4192\n", + "[88/100][114/391] Loss_D: 2.9475 Loss_G: 2.3050 D(x): 0.6462 D(G(z)): 0.4812 / 0.4806\n", + "[88/100][115/391] Loss_D: 3.0661 Loss_G: 2.6235 D(x): 0.6497 D(G(z)): 0.4459 / 0.4389\n", + "[88/100][116/391] Loss_D: 3.4674 Loss_G: 2.0054 D(x): 0.5807 D(G(z)): 0.5184 / 0.5116\n", + "[88/100][117/391] Loss_D: 2.9985 Loss_G: 2.0659 D(x): 0.6966 D(G(z)): 0.4530 / 0.5278\n", + "[88/100][118/391] Loss_D: 2.5373 Loss_G: 2.8538 D(x): 0.7624 D(G(z)): 0.4887 / 0.4154\n", + "[88/100][119/391] Loss_D: 2.5330 Loss_G: 2.8244 D(x): 0.7768 D(G(z)): 0.4312 / 0.4134\n", + "[88/100][120/391] Loss_D: 2.5793 Loss_G: 3.1359 D(x): 0.7209 D(G(z)): 0.4174 / 0.4027\n", + "[88/100][121/391] Loss_D: 3.6358 Loss_G: 2.6066 D(x): 0.5909 D(G(z)): 0.5135 / 0.4603\n", + "[88/100][122/391] Loss_D: 2.5295 Loss_G: 3.5637 D(x): 0.7227 D(G(z)): 0.3735 / 0.3334\n", + "[88/100][123/391] Loss_D: 2.9649 Loss_G: 2.5778 D(x): 0.6157 D(G(z)): 0.3708 / 0.4475\n", + "[88/100][124/391] Loss_D: 2.7467 Loss_G: 2.9711 D(x): 0.6282 D(G(z)): 0.3563 / 0.3901\n", + "[88/100][125/391] Loss_D: 3.9871 Loss_G: 3.1296 D(x): 0.5333 D(G(z)): 0.5364 / 0.3752\n", + "[88/100][126/391] Loss_D: 2.9049 Loss_G: 2.6806 D(x): 0.5907 D(G(z)): 0.3860 / 0.4340\n", + "[88/100][127/391] Loss_D: 2.6803 Loss_G: 3.0630 D(x): 0.7241 D(G(z)): 0.3747 / 0.3902\n", + "[88/100][128/391] Loss_D: 2.1101 Loss_G: 2.9855 D(x): 0.7424 D(G(z)): 0.3602 / 0.3992\n", + "[88/100][129/391] Loss_D: 2.6992 Loss_G: 3.0225 D(x): 0.6837 D(G(z)): 0.4243 / 0.4017\n", + "[88/100][130/391] Loss_D: 3.2083 Loss_G: 2.2376 D(x): 0.6321 D(G(z)): 0.4608 / 0.5094\n", + "[88/100][131/391] Loss_D: 2.9858 Loss_G: 2.4283 D(x): 0.6676 D(G(z)): 0.4555 / 0.4697\n", + "[88/100][132/391] Loss_D: 2.9828 Loss_G: 2.4314 D(x): 0.6254 D(G(z)): 0.4383 / 0.4638\n", + "[88/100][133/391] Loss_D: 2.9985 Loss_G: 2.0369 D(x): 0.6404 D(G(z)): 0.4886 / 0.5347\n", + "[88/100][134/391] Loss_D: 3.0950 Loss_G: 3.2619 D(x): 0.5742 D(G(z)): 0.4239 / 0.3598\n", + "[88/100][135/391] Loss_D: 2.9127 Loss_G: 2.3863 D(x): 0.6312 D(G(z)): 0.4253 / 0.4635\n", + "[88/100][136/391] Loss_D: 3.3264 Loss_G: 3.1541 D(x): 0.6807 D(G(z)): 0.5423 / 0.3844\n", + "[88/100][137/391] Loss_D: 3.0097 Loss_G: 2.3201 D(x): 0.5787 D(G(z)): 0.3584 / 0.4830\n", + "[88/100][138/391] Loss_D: 2.7457 Loss_G: 2.5216 D(x): 0.6953 D(G(z)): 0.4941 / 0.4345\n", + "[88/100][139/391] Loss_D: 2.9406 Loss_G: 2.3891 D(x): 0.6435 D(G(z)): 0.3906 / 0.4788\n", + "[88/100][140/391] Loss_D: 3.3459 Loss_G: 2.6409 D(x): 0.6044 D(G(z)): 0.4979 / 0.4294\n", + "[88/100][141/391] Loss_D: 2.9299 Loss_G: 2.2932 D(x): 0.6834 D(G(z)): 0.4213 / 0.4968\n", + "[88/100][142/391] Loss_D: 2.9993 Loss_G: 2.6459 D(x): 0.7269 D(G(z)): 0.5212 / 0.4411\n", + "[88/100][143/391] Loss_D: 2.8873 Loss_G: 2.0892 D(x): 0.6250 D(G(z)): 0.3851 / 0.5145\n", + "[88/100][144/391] Loss_D: 3.1384 Loss_G: 2.5540 D(x): 0.6716 D(G(z)): 0.5284 / 0.4523\n", + "[88/100][145/391] Loss_D: 3.0436 Loss_G: 3.1029 D(x): 0.6661 D(G(z)): 0.4326 / 0.3794\n", + "[88/100][146/391] Loss_D: 2.9119 Loss_G: 3.1975 D(x): 0.6420 D(G(z)): 0.3796 / 0.3550\n", + "[88/100][147/391] Loss_D: 2.6076 Loss_G: 3.3534 D(x): 0.7791 D(G(z)): 0.4181 / 0.3529\n", + "[88/100][148/391] Loss_D: 3.4869 Loss_G: 2.8858 D(x): 0.5293 D(G(z)): 0.4629 / 0.4122\n", + "[88/100][149/391] Loss_D: 2.8906 Loss_G: 2.8080 D(x): 0.6901 D(G(z)): 0.4507 / 0.4270\n", + "[88/100][150/391] Loss_D: 3.3469 Loss_G: 2.6155 D(x): 0.7072 D(G(z)): 0.5716 / 0.4594\n", + "[88/100][151/391] Loss_D: 3.6237 Loss_G: 2.0681 D(x): 0.6283 D(G(z)): 0.4297 / 0.5413\n", + "[88/100][152/391] Loss_D: 3.3564 Loss_G: 2.9075 D(x): 0.5838 D(G(z)): 0.5105 / 0.4096\n", + "[88/100][153/391] Loss_D: 3.2188 Loss_G: 2.5257 D(x): 0.6029 D(G(z)): 0.4431 / 0.4849\n", + "[88/100][154/391] Loss_D: 3.2626 Loss_G: 2.3240 D(x): 0.5400 D(G(z)): 0.4171 / 0.4978\n", + "[88/100][155/391] Loss_D: 3.3651 Loss_G: 2.5369 D(x): 0.5975 D(G(z)): 0.4159 / 0.4516\n", + "[88/100][156/391] Loss_D: 2.9869 Loss_G: 2.5590 D(x): 0.5943 D(G(z)): 0.3744 / 0.4570\n", + "[88/100][157/391] Loss_D: 3.6233 Loss_G: 2.6560 D(x): 0.5924 D(G(z)): 0.5151 / 0.4157\n", + "[88/100][158/391] Loss_D: 3.2094 Loss_G: 3.0221 D(x): 0.5709 D(G(z)): 0.4124 / 0.3849\n", + "[88/100][159/391] Loss_D: 2.5474 Loss_G: 1.9693 D(x): 0.7565 D(G(z)): 0.4260 / 0.5427\n", + "[88/100][160/391] Loss_D: 2.8850 Loss_G: 1.9493 D(x): 0.7150 D(G(z)): 0.4900 / 0.5493\n", + "[88/100][161/391] Loss_D: 2.8603 Loss_G: 2.4506 D(x): 0.6760 D(G(z)): 0.4153 / 0.4633\n", + "[88/100][162/391] Loss_D: 2.9063 Loss_G: 2.6111 D(x): 0.6354 D(G(z)): 0.4450 / 0.4393\n", + "[88/100][163/391] Loss_D: 2.8634 Loss_G: 2.2318 D(x): 0.6529 D(G(z)): 0.4519 / 0.4950\n", + "[88/100][164/391] Loss_D: 2.4432 Loss_G: 3.1943 D(x): 0.6750 D(G(z)): 0.3500 / 0.3824\n", + "[88/100][165/391] Loss_D: 2.8595 Loss_G: 2.7609 D(x): 0.6209 D(G(z)): 0.3987 / 0.4203\n", + "[88/100][166/391] Loss_D: 3.0905 Loss_G: 2.5281 D(x): 0.6408 D(G(z)): 0.4711 / 0.4541\n", + "[88/100][167/391] Loss_D: 2.8504 Loss_G: 3.2215 D(x): 0.7013 D(G(z)): 0.4000 / 0.3565\n", + "[88/100][168/391] Loss_D: 3.0777 Loss_G: 2.6194 D(x): 0.6052 D(G(z)): 0.3675 / 0.4549\n", + "[88/100][169/391] Loss_D: 3.3837 Loss_G: 2.7942 D(x): 0.6584 D(G(z)): 0.5734 / 0.4198\n", + "[88/100][170/391] Loss_D: 3.4212 Loss_G: 2.2132 D(x): 0.7510 D(G(z)): 0.6086 / 0.4981\n", + "[88/100][171/391] Loss_D: 3.4010 Loss_G: 2.7737 D(x): 0.6304 D(G(z)): 0.5241 / 0.4312\n", + "[88/100][172/391] Loss_D: 3.1207 Loss_G: 2.3737 D(x): 0.7349 D(G(z)): 0.5519 / 0.4951\n", + "[88/100][173/391] Loss_D: 2.9022 Loss_G: 2.3895 D(x): 0.5921 D(G(z)): 0.3228 / 0.4778\n", + "[88/100][174/391] Loss_D: 3.0972 Loss_G: 3.0734 D(x): 0.5954 D(G(z)): 0.4562 / 0.3916\n", + "[88/100][175/391] Loss_D: 3.1669 Loss_G: 2.4423 D(x): 0.6566 D(G(z)): 0.4919 / 0.4834\n", + "[88/100][176/391] Loss_D: 2.9655 Loss_G: 2.6714 D(x): 0.6033 D(G(z)): 0.3264 / 0.4228\n", + "[88/100][177/391] Loss_D: 3.1158 Loss_G: 3.5796 D(x): 0.6329 D(G(z)): 0.4685 / 0.3313\n", + "[88/100][178/391] Loss_D: 3.1749 Loss_G: 2.3403 D(x): 0.5950 D(G(z)): 0.4803 / 0.4868\n", + "[88/100][179/391] Loss_D: 2.7621 Loss_G: 2.0422 D(x): 0.6949 D(G(z)): 0.4193 / 0.5401\n", + "[88/100][180/391] Loss_D: 2.6892 Loss_G: 2.0212 D(x): 0.7633 D(G(z)): 0.4499 / 0.5444\n", + "[88/100][181/391] Loss_D: 3.5513 Loss_G: 2.9390 D(x): 0.6753 D(G(z)): 0.3618 / 0.3939\n", + "[88/100][182/391] Loss_D: 2.6900 Loss_G: 2.7709 D(x): 0.6468 D(G(z)): 0.3897 / 0.4185\n", + "[88/100][183/391] Loss_D: 3.0993 Loss_G: 2.4856 D(x): 0.6721 D(G(z)): 0.5244 / 0.4598\n", + "[88/100][184/391] Loss_D: 3.0283 Loss_G: 3.7996 D(x): 0.6233 D(G(z)): 0.4073 / 0.3043\n", + "[88/100][185/391] Loss_D: 2.9653 Loss_G: 3.8930 D(x): 0.6295 D(G(z)): 0.4154 / 0.2912\n", + "[88/100][186/391] Loss_D: 2.9971 Loss_G: 3.3011 D(x): 0.6769 D(G(z)): 0.4847 / 0.3534\n", + "[88/100][187/391] Loss_D: 2.8881 Loss_G: 2.1916 D(x): 0.6706 D(G(z)): 0.4350 / 0.5022\n", + "[88/100][188/391] Loss_D: 3.5642 Loss_G: 3.1417 D(x): 0.5921 D(G(z)): 0.5603 / 0.3793\n", + "[88/100][189/391] Loss_D: 2.8689 Loss_G: 2.5676 D(x): 0.6253 D(G(z)): 0.3776 / 0.4517\n", + "[88/100][190/391] Loss_D: 3.1398 Loss_G: 2.4429 D(x): 0.6513 D(G(z)): 0.4920 / 0.4677\n", + "[88/100][191/391] Loss_D: 3.0719 Loss_G: 2.9148 D(x): 0.6741 D(G(z)): 0.4576 / 0.3943\n", + "[88/100][192/391] Loss_D: 3.1609 Loss_G: 2.7979 D(x): 0.5672 D(G(z)): 0.4383 / 0.4284\n", + "[88/100][193/391] Loss_D: 2.5416 Loss_G: 2.6886 D(x): 0.6834 D(G(z)): 0.3997 / 0.4304\n", + "[88/100][194/391] Loss_D: 3.2965 Loss_G: 2.7708 D(x): 0.5460 D(G(z)): 0.3902 / 0.4202\n", + "[88/100][195/391] Loss_D: 2.8057 Loss_G: 2.5197 D(x): 0.6783 D(G(z)): 0.4307 / 0.4422\n", + "[88/100][196/391] Loss_D: 3.4339 Loss_G: 1.7968 D(x): 0.5607 D(G(z)): 0.5345 / 0.5676\n", + "[88/100][197/391] Loss_D: 2.9132 Loss_G: 2.2329 D(x): 0.7043 D(G(z)): 0.4528 / 0.4851\n", + "[88/100][198/391] Loss_D: 3.2224 Loss_G: 2.0588 D(x): 0.5595 D(G(z)): 0.3738 / 0.5161\n", + "[88/100][199/391] Loss_D: 2.9884 Loss_G: 1.7462 D(x): 0.6401 D(G(z)): 0.4601 / 0.5863\n", + "[88/100][200/391] Loss_D: 2.6613 Loss_G: 2.9026 D(x): 0.7359 D(G(z)): 0.4535 / 0.4053\n", + "[88/100][201/391] Loss_D: 3.5694 Loss_G: 2.0888 D(x): 0.6946 D(G(z)): 0.5782 / 0.5185\n", + "[88/100][202/391] Loss_D: 2.6668 Loss_G: 2.2803 D(x): 0.6994 D(G(z)): 0.4396 / 0.5011\n", + "[88/100][203/391] Loss_D: 2.5929 Loss_G: 3.6629 D(x): 0.7258 D(G(z)): 0.3937 / 0.3055\n", + "[88/100][204/391] Loss_D: 2.2985 Loss_G: 2.2967 D(x): 0.7218 D(G(z)): 0.4068 / 0.4989\n", + "[88/100][205/391] Loss_D: 2.5001 Loss_G: 2.4790 D(x): 0.6973 D(G(z)): 0.3707 / 0.4597\n", + "[88/100][206/391] Loss_D: 3.0130 Loss_G: 2.9073 D(x): 0.6384 D(G(z)): 0.4638 / 0.4048\n", + "[88/100][207/391] Loss_D: 3.0214 Loss_G: 3.2025 D(x): 0.6128 D(G(z)): 0.4102 / 0.3730\n", + "[88/100][208/391] Loss_D: 2.8561 Loss_G: 3.1630 D(x): 0.5894 D(G(z)): 0.3837 / 0.3819\n", + "[88/100][209/391] Loss_D: 3.3133 Loss_G: 1.9995 D(x): 0.5505 D(G(z)): 0.4399 / 0.5485\n", + "[88/100][210/391] Loss_D: 2.7221 Loss_G: 1.9917 D(x): 0.6524 D(G(z)): 0.3703 / 0.5420\n", + "[88/100][211/391] Loss_D: 3.7302 Loss_G: 1.5868 D(x): 0.6125 D(G(z)): 0.3392 / 0.5986\n", + "[88/100][212/391] Loss_D: 2.9443 Loss_G: 2.1335 D(x): 0.7070 D(G(z)): 0.4812 / 0.5046\n", + "[88/100][213/391] Loss_D: 3.0689 Loss_G: 2.4270 D(x): 0.7104 D(G(z)): 0.5245 / 0.4698\n", + "[88/100][214/391] Loss_D: 2.3532 Loss_G: 2.3282 D(x): 0.6971 D(G(z)): 0.3617 / 0.4804\n", + "[88/100][215/391] Loss_D: 2.9081 Loss_G: 3.9604 D(x): 0.5867 D(G(z)): 0.3461 / 0.2825\n", + "[88/100][216/391] Loss_D: 2.6740 Loss_G: 2.3688 D(x): 0.7228 D(G(z)): 0.4306 / 0.4740\n", + "[88/100][217/391] Loss_D: 3.1548 Loss_G: 2.6026 D(x): 0.6000 D(G(z)): 0.4441 / 0.4445\n", + "[88/100][218/391] Loss_D: 2.5768 Loss_G: 2.3307 D(x): 0.7519 D(G(z)): 0.4850 / 0.4845\n", + "[88/100][219/391] Loss_D: 2.7823 Loss_G: 2.5495 D(x): 0.6992 D(G(z)): 0.4435 / 0.4582\n", + "[88/100][220/391] Loss_D: 3.2040 Loss_G: 2.3595 D(x): 0.6858 D(G(z)): 0.5346 / 0.4783\n", + "[88/100][221/391] Loss_D: 3.1674 Loss_G: 3.4162 D(x): 0.6640 D(G(z)): 0.5162 / 0.3458\n", + "[88/100][222/391] Loss_D: 3.0514 Loss_G: 2.6536 D(x): 0.6188 D(G(z)): 0.4578 / 0.4384\n", + "[88/100][223/391] Loss_D: 2.5609 Loss_G: 2.4868 D(x): 0.7333 D(G(z)): 0.3781 / 0.4546\n", + "[88/100][224/391] Loss_D: 2.9630 Loss_G: 3.4387 D(x): 0.6135 D(G(z)): 0.4293 / 0.3490\n", + "[88/100][225/391] Loss_D: 3.2990 Loss_G: 2.9620 D(x): 0.5579 D(G(z)): 0.4018 / 0.3894\n", + "[88/100][226/391] Loss_D: 2.7806 Loss_G: 2.4489 D(x): 0.6528 D(G(z)): 0.4341 / 0.4561\n", + "[88/100][227/391] Loss_D: 3.0047 Loss_G: 2.8624 D(x): 0.6254 D(G(z)): 0.4447 / 0.3970\n", + "[88/100][228/391] Loss_D: 3.5874 Loss_G: 2.2119 D(x): 0.5539 D(G(z)): 0.5153 / 0.5133\n", + "[88/100][229/391] Loss_D: 2.8490 Loss_G: 2.5416 D(x): 0.6792 D(G(z)): 0.4477 / 0.4614\n", + "[88/100][230/391] Loss_D: 2.6575 Loss_G: 3.3070 D(x): 0.7165 D(G(z)): 0.4694 / 0.3651\n", + "[88/100][231/391] Loss_D: 2.7332 Loss_G: 1.9146 D(x): 0.7031 D(G(z)): 0.4316 / 0.5517\n", + "[88/100][232/391] Loss_D: 2.8604 Loss_G: 3.5083 D(x): 0.6587 D(G(z)): 0.4135 / 0.3366\n", + "[88/100][233/391] Loss_D: 2.6995 Loss_G: 3.1690 D(x): 0.6532 D(G(z)): 0.3624 / 0.3781\n", + "[88/100][234/391] Loss_D: 3.1279 Loss_G: 2.7503 D(x): 0.6103 D(G(z)): 0.4775 / 0.4208\n", + "[88/100][235/391] Loss_D: 2.7979 Loss_G: 2.7326 D(x): 0.6188 D(G(z)): 0.4090 / 0.4249\n", + "[88/100][236/391] Loss_D: 2.8753 Loss_G: 3.1815 D(x): 0.7083 D(G(z)): 0.4404 / 0.3712\n", + "[88/100][237/391] Loss_D: 2.9215 Loss_G: 2.5077 D(x): 0.6757 D(G(z)): 0.4608 / 0.4498\n", + "[88/100][238/391] Loss_D: 3.1250 Loss_G: 2.7361 D(x): 0.6431 D(G(z)): 0.5171 / 0.4302\n", + "[88/100][239/391] Loss_D: 2.7180 Loss_G: 2.3622 D(x): 0.7313 D(G(z)): 0.4478 / 0.4880\n", + "[88/100][240/391] Loss_D: 3.8743 Loss_G: 2.1819 D(x): 0.4673 D(G(z)): 0.4276 / 0.5056\n", + "[88/100][241/391] Loss_D: 3.7894 Loss_G: 2.4681 D(x): 0.6048 D(G(z)): 0.3364 / 0.4884\n", + "[88/100][242/391] Loss_D: 3.0736 Loss_G: 2.3449 D(x): 0.7350 D(G(z)): 0.5457 / 0.4800\n", + "[88/100][243/391] Loss_D: 2.9383 Loss_G: 2.6455 D(x): 0.6188 D(G(z)): 0.4258 / 0.4292\n", + "[88/100][244/391] Loss_D: 3.1865 Loss_G: 2.3503 D(x): 0.6634 D(G(z)): 0.5183 / 0.4692\n", + "[88/100][245/391] Loss_D: 3.2795 Loss_G: 2.8987 D(x): 0.6504 D(G(z)): 0.5160 / 0.4007\n", + "[88/100][246/391] Loss_D: 2.7898 Loss_G: 3.2558 D(x): 0.6286 D(G(z)): 0.3659 / 0.3739\n", + "[88/100][247/391] Loss_D: 3.2198 Loss_G: 2.5193 D(x): 0.5734 D(G(z)): 0.4390 / 0.4339\n", + "[88/100][248/391] Loss_D: 2.7722 Loss_G: 2.0651 D(x): 0.6738 D(G(z)): 0.4214 / 0.5320\n", + "[88/100][249/391] Loss_D: 3.1523 Loss_G: 2.0997 D(x): 0.6673 D(G(z)): 0.5137 / 0.5307\n", + "[88/100][250/391] Loss_D: 2.6082 Loss_G: 1.9338 D(x): 0.6927 D(G(z)): 0.3707 / 0.5637\n", + "[88/100][251/391] Loss_D: 3.3811 Loss_G: 3.0467 D(x): 0.6786 D(G(z)): 0.5444 / 0.3902\n", + "[88/100][252/391] Loss_D: 4.1063 Loss_G: 2.7371 D(x): 0.6525 D(G(z)): 0.6332 / 0.4391\n", + "[88/100][253/391] Loss_D: 3.0761 Loss_G: 2.7582 D(x): 0.6637 D(G(z)): 0.4782 / 0.4376\n", + "[88/100][254/391] Loss_D: 2.6116 Loss_G: 2.6435 D(x): 0.5874 D(G(z)): 0.2906 / 0.4436\n", + "[88/100][255/391] Loss_D: 3.3151 Loss_G: 3.3666 D(x): 0.5696 D(G(z)): 0.4353 / 0.3606\n", + "[88/100][256/391] Loss_D: 3.3240 Loss_G: 2.2230 D(x): 0.5679 D(G(z)): 0.4175 / 0.5102\n", + "[88/100][257/391] Loss_D: 3.8344 Loss_G: 2.6252 D(x): 0.5367 D(G(z)): 0.4923 / 0.4475\n", + "[88/100][258/391] Loss_D: 2.6911 Loss_G: 2.2461 D(x): 0.7039 D(G(z)): 0.4623 / 0.5015\n", + "[88/100][259/391] Loss_D: 3.0443 Loss_G: 2.3660 D(x): 0.6447 D(G(z)): 0.4611 / 0.4905\n", + "[88/100][260/391] Loss_D: 2.5456 Loss_G: 2.3960 D(x): 0.7720 D(G(z)): 0.4460 / 0.4722\n", + "[88/100][261/391] Loss_D: 3.1408 Loss_G: 2.7714 D(x): 0.6776 D(G(z)): 0.4863 / 0.4291\n", + "[88/100][262/391] Loss_D: 3.5089 Loss_G: 2.6829 D(x): 0.5609 D(G(z)): 0.4530 / 0.4474\n", + "[88/100][263/391] Loss_D: 2.7188 Loss_G: 2.4919 D(x): 0.7336 D(G(z)): 0.3766 / 0.4596\n", + "[88/100][264/391] Loss_D: 3.3523 Loss_G: 2.6088 D(x): 0.5487 D(G(z)): 0.4727 / 0.4491\n", + "[88/100][265/391] Loss_D: 2.5904 Loss_G: 2.0232 D(x): 0.6694 D(G(z)): 0.3419 / 0.5203\n", + "[88/100][266/391] Loss_D: 3.3414 Loss_G: 2.5322 D(x): 0.6520 D(G(z)): 0.4917 / 0.4479\n", + "[88/100][267/391] Loss_D: 2.8486 Loss_G: 2.7717 D(x): 0.6597 D(G(z)): 0.4274 / 0.4075\n", + "[88/100][268/391] Loss_D: 2.5775 Loss_G: 2.1413 D(x): 0.7072 D(G(z)): 0.4586 / 0.5246\n", + "[88/100][269/391] Loss_D: 3.3472 Loss_G: 3.2413 D(x): 0.6355 D(G(z)): 0.5221 / 0.3675\n", + "[88/100][270/391] Loss_D: 2.6755 Loss_G: 2.3990 D(x): 0.6719 D(G(z)): 0.3781 / 0.4537\n", + "[88/100][271/391] Loss_D: 3.7365 Loss_G: 2.9589 D(x): 0.5783 D(G(z)): 0.3598 / 0.4098\n", + "[88/100][272/391] Loss_D: 2.8261 Loss_G: 2.2654 D(x): 0.6621 D(G(z)): 0.4179 / 0.5056\n", + "[88/100][273/391] Loss_D: 3.2519 Loss_G: 2.5939 D(x): 0.6519 D(G(z)): 0.5091 / 0.4397\n", + "[88/100][274/391] Loss_D: 3.8922 Loss_G: 3.1431 D(x): 0.5748 D(G(z)): 0.5472 / 0.3807\n", + "[88/100][275/391] Loss_D: 2.9898 Loss_G: 2.7211 D(x): 0.5940 D(G(z)): 0.3807 / 0.4325\n", + "[88/100][276/391] Loss_D: 3.0105 Loss_G: 2.8220 D(x): 0.6584 D(G(z)): 0.4278 / 0.4156\n", + "[88/100][277/391] Loss_D: 3.0019 Loss_G: 2.3182 D(x): 0.7179 D(G(z)): 0.4835 / 0.4792\n", + "[88/100][278/391] Loss_D: 3.0694 Loss_G: 2.1298 D(x): 0.6044 D(G(z)): 0.4744 / 0.5241\n", + "[88/100][279/391] Loss_D: 3.4288 Loss_G: 2.8319 D(x): 0.5954 D(G(z)): 0.4963 / 0.4132\n", + "[88/100][280/391] Loss_D: 2.7519 Loss_G: 2.6489 D(x): 0.7100 D(G(z)): 0.4237 / 0.4440\n", + "[88/100][281/391] Loss_D: 3.1406 Loss_G: 2.8647 D(x): 0.7101 D(G(z)): 0.5138 / 0.4147\n", + "[88/100][282/391] Loss_D: 3.5904 Loss_G: 3.4424 D(x): 0.5908 D(G(z)): 0.5067 / 0.3591\n", + "[88/100][283/391] Loss_D: 3.8643 Loss_G: 2.8543 D(x): 0.4741 D(G(z)): 0.4062 / 0.3993\n", + "[88/100][284/391] Loss_D: 3.7360 Loss_G: 2.6616 D(x): 0.5899 D(G(z)): 0.5652 / 0.4356\n", + "[88/100][285/391] Loss_D: 3.1921 Loss_G: 2.3206 D(x): 0.5993 D(G(z)): 0.4433 / 0.5066\n", + "[88/100][286/391] Loss_D: 2.8682 Loss_G: 3.6479 D(x): 0.6543 D(G(z)): 0.4201 / 0.3214\n", + "[88/100][287/391] Loss_D: 3.2564 Loss_G: 2.8943 D(x): 0.5977 D(G(z)): 0.4356 / 0.4031\n", + "[88/100][288/391] Loss_D: 2.8728 Loss_G: 2.5555 D(x): 0.7071 D(G(z)): 0.5256 / 0.4663\n", + "[88/100][289/391] Loss_D: 3.1925 Loss_G: 3.4295 D(x): 0.6217 D(G(z)): 0.4828 / 0.3521\n", + "[88/100][290/391] Loss_D: 3.1837 Loss_G: 3.8836 D(x): 0.6674 D(G(z)): 0.5171 / 0.3032\n", + "[88/100][291/391] Loss_D: 2.6178 Loss_G: 2.1918 D(x): 0.6786 D(G(z)): 0.3549 / 0.5078\n", + "[88/100][292/391] Loss_D: 3.0553 Loss_G: 2.4097 D(x): 0.6341 D(G(z)): 0.4209 / 0.4790\n", + "[88/100][293/391] Loss_D: 2.8360 Loss_G: 3.0772 D(x): 0.6311 D(G(z)): 0.3750 / 0.3898\n", + "[88/100][294/391] Loss_D: 2.7480 Loss_G: 2.2272 D(x): 0.6447 D(G(z)): 0.4272 / 0.5135\n", + "[88/100][295/391] Loss_D: 3.2269 Loss_G: 2.1911 D(x): 0.6216 D(G(z)): 0.4716 / 0.5027\n", + "[88/100][296/391] Loss_D: 3.5130 Loss_G: 2.4463 D(x): 0.5998 D(G(z)): 0.5412 / 0.4642\n", + "[88/100][297/391] Loss_D: 2.8168 Loss_G: 2.9135 D(x): 0.7364 D(G(z)): 0.4502 / 0.4072\n", + "[88/100][298/391] Loss_D: 3.6499 Loss_G: 2.1789 D(x): 0.6322 D(G(z)): 0.5854 / 0.5049\n", + "[88/100][299/391] Loss_D: 2.7609 Loss_G: 3.0448 D(x): 0.6873 D(G(z)): 0.4384 / 0.3901\n", + "[88/100][300/391] Loss_D: 2.9301 Loss_G: 2.1461 D(x): 0.6377 D(G(z)): 0.4011 / 0.5137\n", + "[88/100][301/391] Loss_D: 3.6536 Loss_G: 2.7420 D(x): 0.6072 D(G(z)): 0.4131 / 0.4292\n", + "[88/100][302/391] Loss_D: 2.8973 Loss_G: 2.4944 D(x): 0.6640 D(G(z)): 0.4433 / 0.4624\n", + "[88/100][303/391] Loss_D: 3.0437 Loss_G: 2.4703 D(x): 0.6591 D(G(z)): 0.4859 / 0.4598\n", + "[88/100][304/391] Loss_D: 2.3098 Loss_G: 2.4906 D(x): 0.7148 D(G(z)): 0.4270 / 0.4735\n", + "[88/100][305/391] Loss_D: 3.4507 Loss_G: 3.1717 D(x): 0.5468 D(G(z)): 0.4471 / 0.3854\n", + "[88/100][306/391] Loss_D: 2.9949 Loss_G: 2.3187 D(x): 0.6708 D(G(z)): 0.4384 / 0.4842\n", + "[88/100][307/391] Loss_D: 3.0084 Loss_G: 2.5033 D(x): 0.6031 D(G(z)): 0.4075 / 0.4516\n", + "[88/100][308/391] Loss_D: 2.3938 Loss_G: 3.2590 D(x): 0.6874 D(G(z)): 0.2940 / 0.3607\n", + "[88/100][309/391] Loss_D: 3.4616 Loss_G: 2.3992 D(x): 0.5862 D(G(z)): 0.5264 / 0.4886\n", + "[88/100][310/391] Loss_D: 3.0577 Loss_G: 2.7764 D(x): 0.7398 D(G(z)): 0.5246 / 0.4428\n", + "[88/100][311/391] Loss_D: 2.6113 Loss_G: 2.7517 D(x): 0.7249 D(G(z)): 0.3662 / 0.4258\n", + "[88/100][312/391] Loss_D: 2.5169 Loss_G: 2.9207 D(x): 0.7039 D(G(z)): 0.3660 / 0.3959\n", + "[88/100][313/391] Loss_D: 3.3550 Loss_G: 4.0037 D(x): 0.5959 D(G(z)): 0.5317 / 0.2909\n", + "[88/100][314/391] Loss_D: 2.1147 Loss_G: 3.3272 D(x): 0.7205 D(G(z)): 0.3613 / 0.3602\n", + "[88/100][315/391] Loss_D: 2.8591 Loss_G: 3.1776 D(x): 0.6860 D(G(z)): 0.4204 / 0.3883\n", + "[88/100][316/391] Loss_D: 2.6213 Loss_G: 3.9345 D(x): 0.7063 D(G(z)): 0.3792 / 0.2912\n", + "[88/100][317/391] Loss_D: 3.1167 Loss_G: 2.9970 D(x): 0.6689 D(G(z)): 0.5034 / 0.3979\n", + "[88/100][318/391] Loss_D: 2.4384 Loss_G: 3.2169 D(x): 0.6957 D(G(z)): 0.4054 / 0.3810\n", + "[88/100][319/391] Loss_D: 3.0779 Loss_G: 3.5325 D(x): 0.6031 D(G(z)): 0.4175 / 0.3356\n", + "[88/100][320/391] Loss_D: 3.3720 Loss_G: 3.1267 D(x): 0.5531 D(G(z)): 0.3907 / 0.3880\n", + "[88/100][321/391] Loss_D: 2.9563 Loss_G: 2.8849 D(x): 0.6646 D(G(z)): 0.4425 / 0.4171\n", + "[88/100][322/391] Loss_D: 2.6827 Loss_G: 1.9435 D(x): 0.6694 D(G(z)): 0.4141 / 0.5245\n", + "[88/100][323/391] Loss_D: 3.6162 Loss_G: 3.0193 D(x): 0.6081 D(G(z)): 0.5268 / 0.3926\n", + "[88/100][324/391] Loss_D: 2.6176 Loss_G: 2.1816 D(x): 0.7033 D(G(z)): 0.4493 / 0.5096\n", + "[88/100][325/391] Loss_D: 2.7843 Loss_G: 1.9022 D(x): 0.7103 D(G(z)): 0.4068 / 0.5490\n", + "[88/100][326/391] Loss_D: 3.3189 Loss_G: 2.8475 D(x): 0.5796 D(G(z)): 0.4738 / 0.4071\n", + "[88/100][327/391] Loss_D: 2.8786 Loss_G: 2.9885 D(x): 0.6497 D(G(z)): 0.4502 / 0.3808\n", + "[88/100][328/391] Loss_D: 3.4841 Loss_G: 3.3220 D(x): 0.5566 D(G(z)): 0.4979 / 0.3508\n", + "[88/100][329/391] Loss_D: 2.6705 Loss_G: 2.7366 D(x): 0.7242 D(G(z)): 0.4235 / 0.4271\n", + "[88/100][330/391] Loss_D: 2.9454 Loss_G: 2.3292 D(x): 0.7048 D(G(z)): 0.4892 / 0.4890\n", + "[88/100][331/391] Loss_D: 3.4077 Loss_G: 2.4350 D(x): 0.5868 D(G(z)): 0.4269 / 0.4873\n", + "[88/100][332/391] Loss_D: 3.3015 Loss_G: 2.5969 D(x): 0.5749 D(G(z)): 0.4696 / 0.4383\n", + "[88/100][333/391] Loss_D: 3.5628 Loss_G: 3.3957 D(x): 0.5962 D(G(z)): 0.4959 / 0.3452\n", + "[88/100][334/391] Loss_D: 2.6447 Loss_G: 2.5080 D(x): 0.6678 D(G(z)): 0.4316 / 0.4520\n", + "[88/100][335/391] Loss_D: 2.9054 Loss_G: 2.9207 D(x): 0.6851 D(G(z)): 0.4564 / 0.3990\n", + "[88/100][336/391] Loss_D: 3.0782 Loss_G: 2.4719 D(x): 0.6425 D(G(z)): 0.4256 / 0.4596\n", + "[88/100][337/391] Loss_D: 2.8472 Loss_G: 2.8912 D(x): 0.6828 D(G(z)): 0.4201 / 0.4026\n", + "[88/100][338/391] Loss_D: 3.0215 Loss_G: 2.7933 D(x): 0.6559 D(G(z)): 0.4837 / 0.4257\n", + "[88/100][339/391] Loss_D: 2.8269 Loss_G: 3.1742 D(x): 0.7400 D(G(z)): 0.5114 / 0.3773\n", + "[88/100][340/391] Loss_D: 2.9462 Loss_G: 3.2316 D(x): 0.6687 D(G(z)): 0.4465 / 0.3817\n", + "[88/100][341/391] Loss_D: 3.1979 Loss_G: 2.7931 D(x): 0.5633 D(G(z)): 0.4058 / 0.4298\n", + "[88/100][342/391] Loss_D: 3.6494 Loss_G: 2.3255 D(x): 0.4768 D(G(z)): 0.4122 / 0.4948\n", + "[88/100][343/391] Loss_D: 3.5457 Loss_G: 2.7264 D(x): 0.5706 D(G(z)): 0.4592 / 0.4252\n", + "[88/100][344/391] Loss_D: 3.6642 Loss_G: 1.8664 D(x): 0.5794 D(G(z)): 0.5219 / 0.5436\n", + "[88/100][345/391] Loss_D: 2.6845 Loss_G: 2.3811 D(x): 0.6576 D(G(z)): 0.3828 / 0.4850\n", + "[88/100][346/391] Loss_D: 3.2705 Loss_G: 1.8426 D(x): 0.6019 D(G(z)): 0.4867 / 0.5594\n", + "[88/100][347/391] Loss_D: 3.1515 Loss_G: 2.5188 D(x): 0.6366 D(G(z)): 0.4635 / 0.4479\n", + "[88/100][348/391] Loss_D: 2.5448 Loss_G: 1.8526 D(x): 0.6868 D(G(z)): 0.4200 / 0.5770\n", + "[88/100][349/391] Loss_D: 2.9398 Loss_G: 2.1016 D(x): 0.6617 D(G(z)): 0.4449 / 0.5140\n", + "[88/100][350/391] Loss_D: 2.7152 Loss_G: 2.6869 D(x): 0.7109 D(G(z)): 0.4547 / 0.4452\n", + "[88/100][351/391] Loss_D: 3.6075 Loss_G: 3.1826 D(x): 0.6483 D(G(z)): 0.5516 / 0.3806\n", + "[88/100][352/391] Loss_D: 3.0212 Loss_G: 3.1515 D(x): 0.6967 D(G(z)): 0.5200 / 0.3796\n", + "[88/100][353/391] Loss_D: 2.8329 Loss_G: 2.2712 D(x): 0.6801 D(G(z)): 0.4031 / 0.4934\n", + "[88/100][354/391] Loss_D: 2.6404 Loss_G: 1.9337 D(x): 0.6313 D(G(z)): 0.3468 / 0.5483\n", + "[88/100][355/391] Loss_D: 2.8859 Loss_G: 3.3384 D(x): 0.6596 D(G(z)): 0.4489 / 0.3569\n", + "[88/100][356/391] Loss_D: 2.7355 Loss_G: 3.1505 D(x): 0.6766 D(G(z)): 0.4279 / 0.3662\n", + "[88/100][357/391] Loss_D: 3.7136 Loss_G: 2.1861 D(x): 0.4952 D(G(z)): 0.4684 / 0.5015\n", + "[88/100][358/391] Loss_D: 3.2736 Loss_G: 1.9986 D(x): 0.5794 D(G(z)): 0.4544 / 0.5398\n", + "[88/100][359/391] Loss_D: 2.8898 Loss_G: 1.9455 D(x): 0.6225 D(G(z)): 0.4226 / 0.5480\n", + "[88/100][360/391] Loss_D: 3.3524 Loss_G: 2.0573 D(x): 0.6707 D(G(z)): 0.5391 / 0.5148\n", + "[88/100][361/391] Loss_D: 3.5729 Loss_G: 2.5449 D(x): 0.7116 D(G(z)): 0.4349 / 0.4584\n", + "[88/100][362/391] Loss_D: 2.8763 Loss_G: 2.8784 D(x): 0.6615 D(G(z)): 0.4207 / 0.4181\n", + "[88/100][363/391] Loss_D: 3.2286 Loss_G: 3.0764 D(x): 0.5694 D(G(z)): 0.4382 / 0.3972\n", + "[88/100][364/391] Loss_D: 3.1732 Loss_G: 2.7718 D(x): 0.6328 D(G(z)): 0.4616 / 0.4285\n", + "[88/100][365/391] Loss_D: 2.6079 Loss_G: 2.6515 D(x): 0.7157 D(G(z)): 0.4038 / 0.4408\n", + "[88/100][366/391] Loss_D: 2.8585 Loss_G: 1.7829 D(x): 0.5739 D(G(z)): 0.3572 / 0.5690\n", + "[88/100][367/391] Loss_D: 2.8536 Loss_G: 1.9592 D(x): 0.6758 D(G(z)): 0.4091 / 0.5412\n", + "[88/100][368/391] Loss_D: 3.2123 Loss_G: 1.9994 D(x): 0.6625 D(G(z)): 0.5301 / 0.5353\n", + "[88/100][369/391] Loss_D: 2.9311 Loss_G: 2.4461 D(x): 0.6592 D(G(z)): 0.4669 / 0.4690\n", + "[88/100][370/391] Loss_D: 2.6715 Loss_G: 2.5330 D(x): 0.7280 D(G(z)): 0.4431 / 0.4669\n", + "[88/100][371/391] Loss_D: 3.0011 Loss_G: 2.9646 D(x): 0.6857 D(G(z)): 0.5043 / 0.4091\n", + "[88/100][372/391] Loss_D: 2.8513 Loss_G: 2.2423 D(x): 0.5949 D(G(z)): 0.3709 / 0.5220\n", + "[88/100][373/391] Loss_D: 3.9373 Loss_G: 2.6487 D(x): 0.5779 D(G(z)): 0.5935 / 0.4379\n", + "[88/100][374/391] Loss_D: 2.4410 Loss_G: 2.6389 D(x): 0.7051 D(G(z)): 0.3855 / 0.4484\n", + "[88/100][375/391] Loss_D: 3.1205 Loss_G: 3.0797 D(x): 0.6570 D(G(z)): 0.4807 / 0.4042\n", + "[88/100][376/391] Loss_D: 3.0809 Loss_G: 3.6812 D(x): 0.6671 D(G(z)): 0.4484 / 0.3181\n", + "[88/100][377/391] Loss_D: 2.9573 Loss_G: 3.3410 D(x): 0.5894 D(G(z)): 0.3168 / 0.3592\n", + "[88/100][378/391] Loss_D: 3.2591 Loss_G: 1.7249 D(x): 0.6089 D(G(z)): 0.5301 / 0.5755\n", + "[88/100][379/391] Loss_D: 2.8428 Loss_G: 2.1425 D(x): 0.6610 D(G(z)): 0.4493 / 0.5233\n", + "[88/100][380/391] Loss_D: 2.6921 Loss_G: 3.0378 D(x): 0.6966 D(G(z)): 0.4176 / 0.3960\n", + "[88/100][381/391] Loss_D: 3.5769 Loss_G: 3.2316 D(x): 0.6221 D(G(z)): 0.5700 / 0.3705\n", + "[88/100][382/391] Loss_D: 3.1707 Loss_G: 3.0777 D(x): 0.6425 D(G(z)): 0.5094 / 0.3880\n", + "[88/100][383/391] Loss_D: 2.6344 Loss_G: 2.2552 D(x): 0.6399 D(G(z)): 0.3161 / 0.4959\n", + "[88/100][384/391] Loss_D: 2.6360 Loss_G: 2.5018 D(x): 0.6458 D(G(z)): 0.4181 / 0.4650\n", + "[88/100][385/391] Loss_D: 3.2748 Loss_G: 2.7515 D(x): 0.6622 D(G(z)): 0.5254 / 0.4289\n", + "[88/100][386/391] Loss_D: 3.2011 Loss_G: 2.0104 D(x): 0.6123 D(G(z)): 0.4462 / 0.5284\n", + "[88/100][387/391] Loss_D: 2.8122 Loss_G: 2.5022 D(x): 0.6605 D(G(z)): 0.3957 / 0.4559\n", + "[88/100][388/391] Loss_D: 2.7328 Loss_G: 2.3364 D(x): 0.6732 D(G(z)): 0.4546 / 0.4849\n", + "[88/100][389/391] Loss_D: 3.2061 Loss_G: 3.1179 D(x): 0.6334 D(G(z)): 0.4942 / 0.3898\n", + "[88/100][390/391] Loss_D: 2.8821 Loss_G: 2.3474 D(x): 0.6235 D(G(z)): 0.4169 / 0.4965\n", + "[88/100][391/391] Loss_D: 3.7222 Loss_G: 2.5341 D(x): 0.5909 D(G(z)): 0.3710 / 0.4581\n", + "[89/100][1/391] Loss_D: 3.6653 Loss_G: 2.3071 D(x): 0.6886 D(G(z)): 0.5351 / 0.5074\n", + "[89/100][2/391] Loss_D: 2.9445 Loss_G: 2.0054 D(x): 0.6139 D(G(z)): 0.4150 / 0.5436\n", + "[89/100][3/391] Loss_D: 2.9577 Loss_G: 1.9354 D(x): 0.7096 D(G(z)): 0.4902 / 0.5502\n", + "[89/100][4/391] Loss_D: 2.5650 Loss_G: 2.6334 D(x): 0.6443 D(G(z)): 0.3767 / 0.4312\n", + "[89/100][5/391] Loss_D: 3.1824 Loss_G: 2.3975 D(x): 0.6318 D(G(z)): 0.4625 / 0.4781\n", + "[89/100][6/391] Loss_D: 2.6913 Loss_G: 3.3833 D(x): 0.6711 D(G(z)): 0.3603 / 0.3386\n", + "[89/100][7/391] Loss_D: 3.3261 Loss_G: 2.3597 D(x): 0.6295 D(G(z)): 0.4981 / 0.4698\n", + "[89/100][8/391] Loss_D: 3.1401 Loss_G: 2.9709 D(x): 0.5236 D(G(z)): 0.3842 / 0.3889\n", + "[89/100][9/391] Loss_D: 3.2891 Loss_G: 1.9146 D(x): 0.6466 D(G(z)): 0.5367 / 0.5651\n", + "[89/100][10/391] Loss_D: 2.9085 Loss_G: 2.6244 D(x): 0.6795 D(G(z)): 0.4693 / 0.4591\n", + "[89/100][11/391] Loss_D: 3.0021 Loss_G: 3.0627 D(x): 0.6234 D(G(z)): 0.4433 / 0.3795\n", + "[89/100][12/391] Loss_D: 3.4654 Loss_G: 2.3018 D(x): 0.6512 D(G(z)): 0.5584 / 0.4886\n", + "[89/100][13/391] Loss_D: 3.6100 Loss_G: 3.2411 D(x): 0.5629 D(G(z)): 0.4938 / 0.3638\n", + "[89/100][14/391] Loss_D: 3.0311 Loss_G: 2.5859 D(x): 0.5649 D(G(z)): 0.3880 / 0.4545\n", + "[89/100][15/391] Loss_D: 3.4540 Loss_G: 3.0865 D(x): 0.5251 D(G(z)): 0.4413 / 0.3778\n", + "[89/100][16/391] Loss_D: 3.2941 Loss_G: 3.1891 D(x): 0.6764 D(G(z)): 0.4940 / 0.3826\n", + "[89/100][17/391] Loss_D: 3.1455 Loss_G: 2.4870 D(x): 0.5767 D(G(z)): 0.4429 / 0.4582\n", + "[89/100][18/391] Loss_D: 2.4755 Loss_G: 2.4401 D(x): 0.7021 D(G(z)): 0.3870 / 0.4711\n", + "[89/100][19/391] Loss_D: 2.7003 Loss_G: 2.6559 D(x): 0.6969 D(G(z)): 0.4541 / 0.4310\n", + "[89/100][20/391] Loss_D: 2.8785 Loss_G: 1.8706 D(x): 0.6289 D(G(z)): 0.3958 / 0.5474\n", + "[89/100][21/391] Loss_D: 2.0952 Loss_G: 2.5570 D(x): 0.7766 D(G(z)): 0.3214 / 0.4392\n", + "[89/100][22/391] Loss_D: 3.4806 Loss_G: 2.7414 D(x): 0.5714 D(G(z)): 0.4840 / 0.4133\n", + "[89/100][23/391] Loss_D: 2.9036 Loss_G: 2.7029 D(x): 0.6901 D(G(z)): 0.5002 / 0.4307\n", + "[89/100][24/391] Loss_D: 2.8389 Loss_G: 2.6557 D(x): 0.7758 D(G(z)): 0.5087 / 0.4320\n", + "[89/100][25/391] Loss_D: 3.0114 Loss_G: 2.7816 D(x): 0.6200 D(G(z)): 0.4150 / 0.4049\n", + "[89/100][26/391] Loss_D: 2.7125 Loss_G: 3.4493 D(x): 0.6768 D(G(z)): 0.4000 / 0.3648\n", + "[89/100][27/391] Loss_D: 3.8211 Loss_G: 2.5964 D(x): 0.5296 D(G(z)): 0.4422 / 0.4281\n", + "[89/100][28/391] Loss_D: 2.6672 Loss_G: 2.8055 D(x): 0.7120 D(G(z)): 0.4653 / 0.4170\n", + "[89/100][29/391] Loss_D: 2.9153 Loss_G: 2.2625 D(x): 0.6878 D(G(z)): 0.4554 / 0.4940\n", + "[89/100][30/391] Loss_D: 3.1863 Loss_G: 2.9123 D(x): 0.5820 D(G(z)): 0.4795 / 0.4056\n", + "[89/100][31/391] Loss_D: 3.6571 Loss_G: 2.5713 D(x): 0.6727 D(G(z)): 0.4003 / 0.4729\n", + "[89/100][32/391] Loss_D: 3.0110 Loss_G: 2.7738 D(x): 0.6833 D(G(z)): 0.4919 / 0.4330\n", + "[89/100][33/391] Loss_D: 3.1771 Loss_G: 2.2788 D(x): 0.6364 D(G(z)): 0.4466 / 0.4786\n", + "[89/100][34/391] Loss_D: 3.0508 Loss_G: 2.9585 D(x): 0.5864 D(G(z)): 0.4120 / 0.4039\n", + "[89/100][35/391] Loss_D: 3.4515 Loss_G: 2.8073 D(x): 0.5505 D(G(z)): 0.4649 / 0.4159\n", + "[89/100][36/391] Loss_D: 3.2996 Loss_G: 2.4878 D(x): 0.6296 D(G(z)): 0.4771 / 0.4627\n", + "[89/100][37/391] Loss_D: 3.0323 Loss_G: 2.6253 D(x): 0.6257 D(G(z)): 0.4337 / 0.4329\n", + "[89/100][38/391] Loss_D: 2.8148 Loss_G: 2.2478 D(x): 0.6919 D(G(z)): 0.4973 / 0.4880\n", + "[89/100][39/391] Loss_D: 2.9959 Loss_G: 2.3364 D(x): 0.6679 D(G(z)): 0.4524 / 0.4764\n", + "[89/100][40/391] Loss_D: 3.0706 Loss_G: 3.7701 D(x): 0.6604 D(G(z)): 0.4322 / 0.3011\n", + "[89/100][41/391] Loss_D: 3.1104 Loss_G: 2.5389 D(x): 0.6574 D(G(z)): 0.4799 / 0.4726\n", + "[89/100][42/391] Loss_D: 2.6201 Loss_G: 3.3497 D(x): 0.6738 D(G(z)): 0.3780 / 0.3646\n", + "[89/100][43/391] Loss_D: 3.5153 Loss_G: 2.6948 D(x): 0.6855 D(G(z)): 0.5467 / 0.4303\n", + "[89/100][44/391] Loss_D: 2.6437 Loss_G: 2.6664 D(x): 0.6586 D(G(z)): 0.4133 / 0.4350\n", + "[89/100][45/391] Loss_D: 2.9994 Loss_G: 3.0728 D(x): 0.6104 D(G(z)): 0.3907 / 0.3926\n", + "[89/100][46/391] Loss_D: 2.8846 Loss_G: 2.7900 D(x): 0.6686 D(G(z)): 0.4408 / 0.4181\n", + "[89/100][47/391] Loss_D: 2.9009 Loss_G: 2.3801 D(x): 0.6169 D(G(z)): 0.4138 / 0.4572\n", + "[89/100][48/391] Loss_D: 2.7539 Loss_G: 2.2822 D(x): 0.6465 D(G(z)): 0.3557 / 0.4623\n", + "[89/100][49/391] Loss_D: 2.8531 Loss_G: 1.7424 D(x): 0.6401 D(G(z)): 0.4516 / 0.5875\n", + "[89/100][50/391] Loss_D: 3.4071 Loss_G: 2.0312 D(x): 0.5349 D(G(z)): 0.4351 / 0.5361\n", + "[89/100][51/391] Loss_D: 3.7337 Loss_G: 2.8271 D(x): 0.6685 D(G(z)): 0.5997 / 0.4076\n", + "[89/100][52/391] Loss_D: 2.5288 Loss_G: 2.8002 D(x): 0.7386 D(G(z)): 0.4159 / 0.4343\n", + "[89/100][53/391] Loss_D: 2.4873 Loss_G: 2.5962 D(x): 0.7533 D(G(z)): 0.4258 / 0.4502\n", + "[89/100][54/391] Loss_D: 2.3871 Loss_G: 2.8004 D(x): 0.7378 D(G(z)): 0.3957 / 0.4190\n", + "[89/100][55/391] Loss_D: 3.0163 Loss_G: 2.4887 D(x): 0.6716 D(G(z)): 0.4899 / 0.4608\n", + "[89/100][56/391] Loss_D: 3.0809 Loss_G: 2.8649 D(x): 0.5961 D(G(z)): 0.3960 / 0.4109\n", + "[89/100][57/391] Loss_D: 3.1953 Loss_G: 2.5582 D(x): 0.5732 D(G(z)): 0.4049 / 0.4480\n", + "[89/100][58/391] Loss_D: 2.9947 Loss_G: 2.3274 D(x): 0.6164 D(G(z)): 0.4174 / 0.4714\n", + "[89/100][59/391] Loss_D: 3.1392 Loss_G: 2.8174 D(x): 0.5600 D(G(z)): 0.3582 / 0.4282\n", + "[89/100][60/391] Loss_D: 2.6158 Loss_G: 2.8455 D(x): 0.7476 D(G(z)): 0.4134 / 0.4224\n", + "[89/100][61/391] Loss_D: 3.6816 Loss_G: 2.0336 D(x): 0.6391 D(G(z)): 0.5187 / 0.5390\n", + "[89/100][62/391] Loss_D: 2.7989 Loss_G: 2.4867 D(x): 0.7975 D(G(z)): 0.5234 / 0.4546\n", + "[89/100][63/391] Loss_D: 2.7144 Loss_G: 3.1826 D(x): 0.7164 D(G(z)): 0.4611 / 0.3744\n", + "[89/100][64/391] Loss_D: 2.4084 Loss_G: 3.5888 D(x): 0.6662 D(G(z)): 0.3338 / 0.3230\n", + "[89/100][65/391] Loss_D: 2.7064 Loss_G: 2.8724 D(x): 0.6632 D(G(z)): 0.3730 / 0.4037\n", + "[89/100][66/391] Loss_D: 2.6173 Loss_G: 2.1511 D(x): 0.6597 D(G(z)): 0.3620 / 0.5026\n", + "[89/100][67/391] Loss_D: 3.2252 Loss_G: 2.9756 D(x): 0.6470 D(G(z)): 0.4475 / 0.3915\n", + "[89/100][68/391] Loss_D: 2.6942 Loss_G: 3.0253 D(x): 0.6255 D(G(z)): 0.3611 / 0.4016\n", + "[89/100][69/391] Loss_D: 3.0847 Loss_G: 2.1831 D(x): 0.7420 D(G(z)): 0.5706 / 0.5206\n", + "[89/100][70/391] Loss_D: 2.9208 Loss_G: 3.2141 D(x): 0.6952 D(G(z)): 0.4400 / 0.3716\n", + "[89/100][71/391] Loss_D: 3.4549 Loss_G: 2.3966 D(x): 0.6009 D(G(z)): 0.4729 / 0.4835\n", + "[89/100][72/391] Loss_D: 2.5234 Loss_G: 2.4865 D(x): 0.6844 D(G(z)): 0.3682 / 0.4672\n", + "[89/100][73/391] Loss_D: 2.1941 Loss_G: 2.3180 D(x): 0.7383 D(G(z)): 0.3037 / 0.4984\n", + "[89/100][74/391] Loss_D: 2.7261 Loss_G: 2.5623 D(x): 0.6561 D(G(z)): 0.3996 / 0.4508\n", + "[89/100][75/391] Loss_D: 3.3006 Loss_G: 2.6075 D(x): 0.6587 D(G(z)): 0.5128 / 0.4671\n", + "[89/100][76/391] Loss_D: 3.4551 Loss_G: 3.4184 D(x): 0.6040 D(G(z)): 0.5195 / 0.3404\n", + "[89/100][77/391] Loss_D: 2.9187 Loss_G: 2.2976 D(x): 0.6584 D(G(z)): 0.3789 / 0.5066\n", + "[89/100][78/391] Loss_D: 2.9022 Loss_G: 2.0575 D(x): 0.6504 D(G(z)): 0.4464 / 0.5283\n", + "[89/100][79/391] Loss_D: 2.4596 Loss_G: 3.4838 D(x): 0.7163 D(G(z)): 0.4106 / 0.3488\n", + "[89/100][80/391] Loss_D: 3.2585 Loss_G: 2.9781 D(x): 0.6313 D(G(z)): 0.4617 / 0.3960\n", + "[89/100][81/391] Loss_D: 2.4804 Loss_G: 2.7953 D(x): 0.7079 D(G(z)): 0.3820 / 0.4158\n", + "[89/100][82/391] Loss_D: 3.1355 Loss_G: 3.3281 D(x): 0.6688 D(G(z)): 0.4862 / 0.3532\n", + "[89/100][83/391] Loss_D: 2.8010 Loss_G: 2.7367 D(x): 0.7097 D(G(z)): 0.4257 / 0.4220\n", + "[89/100][84/391] Loss_D: 3.1175 Loss_G: 2.8223 D(x): 0.5897 D(G(z)): 0.4656 / 0.4042\n", + "[89/100][85/391] Loss_D: 2.8632 Loss_G: 2.7687 D(x): 0.6922 D(G(z)): 0.4671 / 0.4122\n", + "[89/100][86/391] Loss_D: 3.2269 Loss_G: 3.4115 D(x): 0.6428 D(G(z)): 0.4806 / 0.3501\n", + "[89/100][87/391] Loss_D: 2.8399 Loss_G: 3.2070 D(x): 0.7062 D(G(z)): 0.4432 / 0.3655\n", + "[89/100][88/391] Loss_D: 3.3599 Loss_G: 3.5453 D(x): 0.5944 D(G(z)): 0.5289 / 0.3371\n", + "[89/100][89/391] Loss_D: 3.4159 Loss_G: 3.7023 D(x): 0.5644 D(G(z)): 0.4701 / 0.3284\n", + "[89/100][90/391] Loss_D: 2.8887 Loss_G: 2.5940 D(x): 0.6578 D(G(z)): 0.4311 / 0.4373\n", + "[89/100][91/391] Loss_D: 3.5951 Loss_G: 2.6789 D(x): 0.5740 D(G(z)): 0.3761 / 0.4440\n", + "[89/100][92/391] Loss_D: 2.8773 Loss_G: 2.4787 D(x): 0.7113 D(G(z)): 0.4901 / 0.4751\n", + "[89/100][93/391] Loss_D: 2.8126 Loss_G: 3.1740 D(x): 0.6609 D(G(z)): 0.4105 / 0.3662\n", + "[89/100][94/391] Loss_D: 3.3080 Loss_G: 3.5952 D(x): 0.6081 D(G(z)): 0.4972 / 0.3217\n", + "[89/100][95/391] Loss_D: 3.3680 Loss_G: 2.7688 D(x): 0.6082 D(G(z)): 0.4726 / 0.4145\n", + "[89/100][96/391] Loss_D: 3.0160 Loss_G: 2.4799 D(x): 0.6974 D(G(z)): 0.4827 / 0.4718\n", + "[89/100][97/391] Loss_D: 3.0810 Loss_G: 2.7200 D(x): 0.6871 D(G(z)): 0.4587 / 0.4293\n", + "[89/100][98/391] Loss_D: 2.7731 Loss_G: 2.7979 D(x): 0.6493 D(G(z)): 0.3815 / 0.4217\n", + "[89/100][99/391] Loss_D: 2.8669 Loss_G: 3.6035 D(x): 0.6348 D(G(z)): 0.3794 / 0.3273\n", + "[89/100][100/391] Loss_D: 2.8739 Loss_G: 2.2293 D(x): 0.6811 D(G(z)): 0.4492 / 0.5210\n", + "[89/100][101/391] Loss_D: 3.1572 Loss_G: 3.0505 D(x): 0.5662 D(G(z)): 0.4038 / 0.3846\n", + "[89/100][102/391] Loss_D: 3.0622 Loss_G: 3.2998 D(x): 0.5641 D(G(z)): 0.3908 / 0.3596\n", + "[89/100][103/391] Loss_D: 3.6002 Loss_G: 2.3957 D(x): 0.5851 D(G(z)): 0.5317 / 0.4780\n", + "[89/100][104/391] Loss_D: 3.1863 Loss_G: 2.7203 D(x): 0.7345 D(G(z)): 0.5488 / 0.4328\n", + "[89/100][105/391] Loss_D: 2.9208 Loss_G: 2.6764 D(x): 0.5967 D(G(z)): 0.3466 / 0.4354\n", + "[89/100][106/391] Loss_D: 2.6804 Loss_G: 3.0148 D(x): 0.6580 D(G(z)): 0.4002 / 0.4041\n", + "[89/100][107/391] Loss_D: 2.9540 Loss_G: 2.5824 D(x): 0.6903 D(G(z)): 0.4517 / 0.4245\n", + "[89/100][108/391] Loss_D: 3.0666 Loss_G: 1.8392 D(x): 0.6622 D(G(z)): 0.4827 / 0.5655\n", + "[89/100][109/391] Loss_D: 3.0096 Loss_G: 2.8989 D(x): 0.6471 D(G(z)): 0.4717 / 0.4077\n", + "[89/100][110/391] Loss_D: 3.1262 Loss_G: 2.1188 D(x): 0.6601 D(G(z)): 0.4855 / 0.5240\n", + "[89/100][111/391] Loss_D: 3.1191 Loss_G: 2.9670 D(x): 0.6757 D(G(z)): 0.4949 / 0.4050\n", + "[89/100][112/391] Loss_D: 3.4929 Loss_G: 3.5429 D(x): 0.5878 D(G(z)): 0.5142 / 0.3382\n", + "[89/100][113/391] Loss_D: 3.3555 Loss_G: 1.9246 D(x): 0.5811 D(G(z)): 0.4545 / 0.5683\n", + "[89/100][114/391] Loss_D: 2.9727 Loss_G: 2.3410 D(x): 0.6842 D(G(z)): 0.5003 / 0.4877\n", + "[89/100][115/391] Loss_D: 2.7152 Loss_G: 2.6780 D(x): 0.7091 D(G(z)): 0.4220 / 0.4486\n", + "[89/100][116/391] Loss_D: 2.8840 Loss_G: 3.4427 D(x): 0.5728 D(G(z)): 0.3098 / 0.3450\n", + "[89/100][117/391] Loss_D: 3.3048 Loss_G: 2.4714 D(x): 0.5601 D(G(z)): 0.3994 / 0.4517\n", + "[89/100][118/391] Loss_D: 2.9100 Loss_G: 3.0025 D(x): 0.6909 D(G(z)): 0.4833 / 0.3993\n", + "[89/100][119/391] Loss_D: 3.0536 Loss_G: 2.7478 D(x): 0.6956 D(G(z)): 0.5159 / 0.4108\n", + "[89/100][120/391] Loss_D: 3.0333 Loss_G: 2.7820 D(x): 0.6042 D(G(z)): 0.4071 / 0.4227\n", + "[89/100][121/391] Loss_D: 3.5272 Loss_G: 2.4281 D(x): 0.6958 D(G(z)): 0.4768 / 0.4792\n", + "[89/100][122/391] Loss_D: 3.0491 Loss_G: 2.8267 D(x): 0.5950 D(G(z)): 0.3844 / 0.4108\n", + "[89/100][123/391] Loss_D: 2.9317 Loss_G: 2.5783 D(x): 0.7264 D(G(z)): 0.4759 / 0.4603\n", + "[89/100][124/391] Loss_D: 3.2481 Loss_G: 2.7640 D(x): 0.7150 D(G(z)): 0.5725 / 0.4248\n", + "[89/100][125/391] Loss_D: 2.9279 Loss_G: 2.8353 D(x): 0.6304 D(G(z)): 0.3939 / 0.4120\n", + "[89/100][126/391] Loss_D: 2.9861 Loss_G: 3.3869 D(x): 0.6532 D(G(z)): 0.4718 / 0.3527\n", + "[89/100][127/391] Loss_D: 3.0327 Loss_G: 2.9566 D(x): 0.6093 D(G(z)): 0.3913 / 0.4160\n", + "[89/100][128/391] Loss_D: 2.6870 Loss_G: 2.5823 D(x): 0.6308 D(G(z)): 0.3912 / 0.4481\n", + "[89/100][129/391] Loss_D: 2.5841 Loss_G: 3.0546 D(x): 0.6868 D(G(z)): 0.4013 / 0.4037\n", + "[89/100][130/391] Loss_D: 3.2472 Loss_G: 2.4048 D(x): 0.6924 D(G(z)): 0.5213 / 0.4771\n", + "[89/100][131/391] Loss_D: 2.8135 Loss_G: 2.0836 D(x): 0.6676 D(G(z)): 0.4163 / 0.5498\n", + "[89/100][132/391] Loss_D: 3.2014 Loss_G: 2.9765 D(x): 0.6900 D(G(z)): 0.5346 / 0.4073\n", + "[89/100][133/391] Loss_D: 2.4968 Loss_G: 2.6357 D(x): 0.6949 D(G(z)): 0.3805 / 0.4262\n", + "[89/100][134/391] Loss_D: 2.8304 Loss_G: 2.9417 D(x): 0.6073 D(G(z)): 0.3974 / 0.3988\n", + "[89/100][135/391] Loss_D: 3.3617 Loss_G: 2.4390 D(x): 0.5166 D(G(z)): 0.4032 / 0.4597\n", + "[89/100][136/391] Loss_D: 2.8509 Loss_G: 2.2099 D(x): 0.6633 D(G(z)): 0.4219 / 0.4998\n", + "[89/100][137/391] Loss_D: 3.2400 Loss_G: 2.8158 D(x): 0.5852 D(G(z)): 0.4683 / 0.4167\n", + "[89/100][138/391] Loss_D: 2.9507 Loss_G: 1.6059 D(x): 0.6040 D(G(z)): 0.3770 / 0.6049\n", + "[89/100][139/391] Loss_D: 3.1484 Loss_G: 3.4425 D(x): 0.7099 D(G(z)): 0.5294 / 0.3402\n", + "[89/100][140/391] Loss_D: 2.8678 Loss_G: 2.7565 D(x): 0.7345 D(G(z)): 0.5208 / 0.4279\n", + "[89/100][141/391] Loss_D: 2.6807 Loss_G: 2.2958 D(x): 0.6968 D(G(z)): 0.3733 / 0.4841\n", + "[89/100][142/391] Loss_D: 2.4377 Loss_G: 2.5013 D(x): 0.7598 D(G(z)): 0.4122 / 0.4822\n", + "[89/100][143/391] Loss_D: 2.7017 Loss_G: 3.1810 D(x): 0.6885 D(G(z)): 0.3846 / 0.3774\n", + "[89/100][144/391] Loss_D: 2.6398 Loss_G: 3.6541 D(x): 0.7040 D(G(z)): 0.4608 / 0.3278\n", + "[89/100][145/391] Loss_D: 3.0278 Loss_G: 2.6525 D(x): 0.6328 D(G(z)): 0.4282 / 0.4457\n", + "[89/100][146/391] Loss_D: 3.0794 Loss_G: 2.3825 D(x): 0.6586 D(G(z)): 0.4327 / 0.4659\n", + "[89/100][147/391] Loss_D: 2.9827 Loss_G: 3.4129 D(x): 0.6559 D(G(z)): 0.4087 / 0.3392\n", + "[89/100][148/391] Loss_D: 2.4878 Loss_G: 2.6028 D(x): 0.6548 D(G(z)): 0.3431 / 0.4468\n", + "[89/100][149/391] Loss_D: 2.9221 Loss_G: 2.6119 D(x): 0.6519 D(G(z)): 0.4018 / 0.4632\n", + "[89/100][150/391] Loss_D: 2.7878 Loss_G: 2.2370 D(x): 0.6679 D(G(z)): 0.3849 / 0.5126\n", + "[89/100][151/391] Loss_D: 3.6616 Loss_G: 2.1919 D(x): 0.7383 D(G(z)): 0.4377 / 0.4973\n", + "[89/100][152/391] Loss_D: 2.7487 Loss_G: 3.3191 D(x): 0.6115 D(G(z)): 0.3712 / 0.3504\n", + "[89/100][153/391] Loss_D: 3.1036 Loss_G: 2.3596 D(x): 0.6743 D(G(z)): 0.5003 / 0.4814\n", + "[89/100][154/391] Loss_D: 2.7881 Loss_G: 2.3353 D(x): 0.5871 D(G(z)): 0.3479 / 0.4812\n", + "[89/100][155/391] Loss_D: 2.9352 Loss_G: 2.7173 D(x): 0.7335 D(G(z)): 0.4746 / 0.4345\n", + "[89/100][156/391] Loss_D: 3.1211 Loss_G: 3.3426 D(x): 0.6281 D(G(z)): 0.4517 / 0.3509\n", + "[89/100][157/391] Loss_D: 3.1285 Loss_G: 3.2782 D(x): 0.6684 D(G(z)): 0.4839 / 0.3472\n", + "[89/100][158/391] Loss_D: 3.0320 Loss_G: 2.4155 D(x): 0.6251 D(G(z)): 0.4593 / 0.4705\n", + "[89/100][159/391] Loss_D: 3.2054 Loss_G: 2.7592 D(x): 0.5986 D(G(z)): 0.4203 / 0.4188\n", + "[89/100][160/391] Loss_D: 3.4599 Loss_G: 2.3993 D(x): 0.6210 D(G(z)): 0.5306 / 0.4713\n", + "[89/100][161/391] Loss_D: 3.3806 Loss_G: 2.8444 D(x): 0.7166 D(G(z)): 0.5651 / 0.4305\n", + "[89/100][162/391] Loss_D: 3.0358 Loss_G: 2.2398 D(x): 0.6181 D(G(z)): 0.4381 / 0.5101\n", + "[89/100][163/391] Loss_D: 3.4003 Loss_G: 3.0425 D(x): 0.5922 D(G(z)): 0.4967 / 0.3833\n", + "[89/100][164/391] Loss_D: 2.8738 Loss_G: 3.0920 D(x): 0.6243 D(G(z)): 0.3968 / 0.3950\n", + "[89/100][165/391] Loss_D: 3.0690 Loss_G: 3.2177 D(x): 0.6377 D(G(z)): 0.4627 / 0.3536\n", + "[89/100][166/391] Loss_D: 3.0599 Loss_G: 2.3111 D(x): 0.6296 D(G(z)): 0.4261 / 0.4846\n", + "[89/100][167/391] Loss_D: 2.8423 Loss_G: 3.1629 D(x): 0.6975 D(G(z)): 0.4493 / 0.3564\n", + "[89/100][168/391] Loss_D: 2.6975 Loss_G: 2.3247 D(x): 0.7311 D(G(z)): 0.4503 / 0.4832\n", + "[89/100][169/391] Loss_D: 3.2025 Loss_G: 2.4839 D(x): 0.5514 D(G(z)): 0.4222 / 0.4345\n", + "[89/100][170/391] Loss_D: 2.9109 Loss_G: 2.3224 D(x): 0.6321 D(G(z)): 0.4070 / 0.4947\n", + "[89/100][171/391] Loss_D: 3.3221 Loss_G: 1.9685 D(x): 0.6499 D(G(z)): 0.5053 / 0.5530\n", + "[89/100][172/391] Loss_D: 3.1770 Loss_G: 2.6634 D(x): 0.6642 D(G(z)): 0.5121 / 0.4274\n", + "[89/100][173/391] Loss_D: 2.9104 Loss_G: 3.4332 D(x): 0.6379 D(G(z)): 0.4019 / 0.3414\n", + "[89/100][174/391] Loss_D: 3.2152 Loss_G: 3.7149 D(x): 0.6421 D(G(z)): 0.5181 / 0.3194\n", + "[89/100][175/391] Loss_D: 2.8506 Loss_G: 2.6795 D(x): 0.6400 D(G(z)): 0.3557 / 0.4529\n", + "[89/100][176/391] Loss_D: 3.1234 Loss_G: 2.4467 D(x): 0.6218 D(G(z)): 0.4370 / 0.4594\n", + "[89/100][177/391] Loss_D: 2.7423 Loss_G: 2.6357 D(x): 0.6488 D(G(z)): 0.3673 / 0.4317\n", + "[89/100][178/391] Loss_D: 2.6491 Loss_G: 2.5916 D(x): 0.6747 D(G(z)): 0.4033 / 0.4470\n", + "[89/100][179/391] Loss_D: 3.2723 Loss_G: 1.7876 D(x): 0.6309 D(G(z)): 0.4931 / 0.5702\n", + "[89/100][180/391] Loss_D: 3.0140 Loss_G: 2.6444 D(x): 0.6243 D(G(z)): 0.4002 / 0.4332\n", + "[89/100][181/391] Loss_D: 3.6104 Loss_G: 2.7552 D(x): 0.6898 D(G(z)): 0.4933 / 0.4431\n", + "[89/100][182/391] Loss_D: 2.7758 Loss_G: 2.9257 D(x): 0.6726 D(G(z)): 0.4440 / 0.4011\n", + "[89/100][183/391] Loss_D: 2.6049 Loss_G: 2.7210 D(x): 0.6950 D(G(z)): 0.4189 / 0.4203\n", + "[89/100][184/391] Loss_D: 3.4409 Loss_G: 2.4432 D(x): 0.6413 D(G(z)): 0.5419 / 0.4732\n", + "[89/100][185/391] Loss_D: 3.1657 Loss_G: 1.6305 D(x): 0.5664 D(G(z)): 0.3595 / 0.5947\n", + "[89/100][186/391] Loss_D: 3.2539 Loss_G: 3.1105 D(x): 0.7038 D(G(z)): 0.5672 / 0.3734\n", + "[89/100][187/391] Loss_D: 2.5861 Loss_G: 3.7320 D(x): 0.7096 D(G(z)): 0.3367 / 0.2995\n", + "[89/100][188/391] Loss_D: 3.1211 Loss_G: 2.5372 D(x): 0.6075 D(G(z)): 0.4729 / 0.4485\n", + "[89/100][189/391] Loss_D: 2.9650 Loss_G: 2.2114 D(x): 0.6952 D(G(z)): 0.4617 / 0.4980\n", + "[89/100][190/391] Loss_D: 2.8282 Loss_G: 2.9281 D(x): 0.6763 D(G(z)): 0.4615 / 0.4072\n", + "[89/100][191/391] Loss_D: 2.9365 Loss_G: 2.5241 D(x): 0.6807 D(G(z)): 0.4146 / 0.4531\n", + "[89/100][192/391] Loss_D: 3.2615 Loss_G: 2.6180 D(x): 0.5954 D(G(z)): 0.4910 / 0.4362\n", + "[89/100][193/391] Loss_D: 3.1624 Loss_G: 3.0670 D(x): 0.5700 D(G(z)): 0.4115 / 0.3829\n", + "[89/100][194/391] Loss_D: 2.6622 Loss_G: 2.9273 D(x): 0.6869 D(G(z)): 0.4263 / 0.3973\n", + "[89/100][195/391] Loss_D: 2.7035 Loss_G: 2.4391 D(x): 0.6797 D(G(z)): 0.3935 / 0.4683\n", + "[89/100][196/391] Loss_D: 2.7033 Loss_G: 3.0069 D(x): 0.6315 D(G(z)): 0.4152 / 0.3905\n", + "[89/100][197/391] Loss_D: 2.7693 Loss_G: 2.0478 D(x): 0.7005 D(G(z)): 0.3989 / 0.5158\n", + "[89/100][198/391] Loss_D: 3.0844 Loss_G: 1.4830 D(x): 0.5892 D(G(z)): 0.3748 / 0.6325\n", + "[89/100][199/391] Loss_D: 2.8899 Loss_G: 2.7257 D(x): 0.6213 D(G(z)): 0.3995 / 0.4357\n", + "[89/100][200/391] Loss_D: 3.1198 Loss_G: 1.9745 D(x): 0.6875 D(G(z)): 0.5084 / 0.5460\n", + "[89/100][201/391] Loss_D: 3.2478 Loss_G: 2.9915 D(x): 0.7106 D(G(z)): 0.5424 / 0.3971\n", + "[89/100][202/391] Loss_D: 2.4281 Loss_G: 2.9537 D(x): 0.7543 D(G(z)): 0.4234 / 0.4035\n", + "[89/100][203/391] Loss_D: 3.6116 Loss_G: 3.2137 D(x): 0.5733 D(G(z)): 0.5229 / 0.3641\n", + "[89/100][204/391] Loss_D: 2.8417 Loss_G: 2.4884 D(x): 0.6492 D(G(z)): 0.4584 / 0.4635\n", + "[89/100][205/391] Loss_D: 2.6647 Loss_G: 2.9454 D(x): 0.6384 D(G(z)): 0.3707 / 0.4109\n", + "[89/100][206/391] Loss_D: 3.0249 Loss_G: 2.9917 D(x): 0.7146 D(G(z)): 0.5132 / 0.4038\n", + "[89/100][207/391] Loss_D: 3.1351 Loss_G: 2.6646 D(x): 0.6081 D(G(z)): 0.4320 / 0.4180\n", + "[89/100][208/391] Loss_D: 2.7967 Loss_G: 2.7262 D(x): 0.6505 D(G(z)): 0.4621 / 0.4269\n", + "[89/100][209/391] Loss_D: 3.0051 Loss_G: 2.7966 D(x): 0.6350 D(G(z)): 0.4406 / 0.4265\n", + "[89/100][210/391] Loss_D: 2.9396 Loss_G: 3.5726 D(x): 0.6016 D(G(z)): 0.3885 / 0.3429\n", + "[89/100][211/391] Loss_D: 3.6520 Loss_G: 3.1320 D(x): 0.6718 D(G(z)): 0.4310 / 0.3871\n", + "[89/100][212/391] Loss_D: 2.9057 Loss_G: 2.4841 D(x): 0.6247 D(G(z)): 0.4044 / 0.4718\n", + "[89/100][213/391] Loss_D: 3.0567 Loss_G: 3.3419 D(x): 0.6252 D(G(z)): 0.4187 / 0.3494\n", + "[89/100][214/391] Loss_D: 2.6295 Loss_G: 3.6554 D(x): 0.7222 D(G(z)): 0.4680 / 0.3272\n", + "[89/100][215/391] Loss_D: 3.0150 Loss_G: 2.9156 D(x): 0.6107 D(G(z)): 0.3791 / 0.4119\n", + "[89/100][216/391] Loss_D: 3.1733 Loss_G: 2.5304 D(x): 0.5539 D(G(z)): 0.4024 / 0.4565\n", + "[89/100][217/391] Loss_D: 3.0678 Loss_G: 3.2993 D(x): 0.6603 D(G(z)): 0.5120 / 0.3529\n", + "[89/100][218/391] Loss_D: 2.8224 Loss_G: 3.0122 D(x): 0.6923 D(G(z)): 0.5048 / 0.4031\n", + "[89/100][219/391] Loss_D: 2.9818 Loss_G: 2.8844 D(x): 0.7046 D(G(z)): 0.5103 / 0.4061\n", + "[89/100][220/391] Loss_D: 2.7444 Loss_G: 3.0048 D(x): 0.6668 D(G(z)): 0.3938 / 0.4044\n", + "[89/100][221/391] Loss_D: 2.8091 Loss_G: 2.8347 D(x): 0.6587 D(G(z)): 0.4359 / 0.4410\n", + "[89/100][222/391] Loss_D: 2.6683 Loss_G: 3.4523 D(x): 0.6806 D(G(z)): 0.4253 / 0.3456\n", + "[89/100][223/391] Loss_D: 3.0359 Loss_G: 2.5579 D(x): 0.6843 D(G(z)): 0.5021 / 0.4632\n", + "[89/100][224/391] Loss_D: 2.5229 Loss_G: 2.7370 D(x): 0.6731 D(G(z)): 0.3859 / 0.4240\n", + "[89/100][225/391] Loss_D: 2.8154 Loss_G: 2.3007 D(x): 0.6575 D(G(z)): 0.4128 / 0.4825\n", + "[89/100][226/391] Loss_D: 2.7688 Loss_G: 2.4328 D(x): 0.6439 D(G(z)): 0.4182 / 0.4764\n", + "[89/100][227/391] Loss_D: 2.6498 Loss_G: 2.6115 D(x): 0.6766 D(G(z)): 0.3596 / 0.4395\n", + "[89/100][228/391] Loss_D: 3.3161 Loss_G: 2.3054 D(x): 0.5272 D(G(z)): 0.2759 / 0.4892\n", + "[89/100][229/391] Loss_D: 3.0877 Loss_G: 2.5210 D(x): 0.6854 D(G(z)): 0.5027 / 0.4584\n", + "[89/100][230/391] Loss_D: 2.2466 Loss_G: 2.7856 D(x): 0.7203 D(G(z)): 0.3768 / 0.4258\n", + "[89/100][231/391] Loss_D: 2.7877 Loss_G: 2.3160 D(x): 0.6649 D(G(z)): 0.4059 / 0.4977\n", + "[89/100][232/391] Loss_D: 2.9306 Loss_G: 2.5886 D(x): 0.7275 D(G(z)): 0.4850 / 0.4568\n", + "[89/100][233/391] Loss_D: 2.9737 Loss_G: 3.0710 D(x): 0.7387 D(G(z)): 0.5343 / 0.3775\n", + "[89/100][234/391] Loss_D: 2.9085 Loss_G: 3.2363 D(x): 0.6272 D(G(z)): 0.4507 / 0.3792\n", + "[89/100][235/391] Loss_D: 2.9084 Loss_G: 2.5283 D(x): 0.6309 D(G(z)): 0.4263 / 0.4709\n", + "[89/100][236/391] Loss_D: 2.7248 Loss_G: 2.7242 D(x): 0.6677 D(G(z)): 0.3728 / 0.4272\n", + "[89/100][237/391] Loss_D: 2.8419 Loss_G: 2.6587 D(x): 0.6629 D(G(z)): 0.4169 / 0.4424\n", + "[89/100][238/391] Loss_D: 3.2631 Loss_G: 2.7484 D(x): 0.5478 D(G(z)): 0.3997 / 0.4234\n", + "[89/100][239/391] Loss_D: 3.5961 Loss_G: 2.5545 D(x): 0.5685 D(G(z)): 0.5128 / 0.4494\n", + "[89/100][240/391] Loss_D: 2.7198 Loss_G: 2.3572 D(x): 0.7489 D(G(z)): 0.4440 / 0.4939\n", + "[89/100][241/391] Loss_D: 3.7325 Loss_G: 2.0879 D(x): 0.6654 D(G(z)): 0.3859 / 0.5310\n", + "[89/100][242/391] Loss_D: 2.7738 Loss_G: 2.9588 D(x): 0.7460 D(G(z)): 0.4885 / 0.4088\n", + "[89/100][243/391] Loss_D: 2.9005 Loss_G: 2.3167 D(x): 0.6412 D(G(z)): 0.4155 / 0.4918\n", + "[89/100][244/391] Loss_D: 2.1907 Loss_G: 2.8539 D(x): 0.7073 D(G(z)): 0.3108 / 0.4057\n", + "[89/100][245/391] Loss_D: 2.5424 Loss_G: 2.5594 D(x): 0.6775 D(G(z)): 0.3504 / 0.4510\n", + "[89/100][246/391] Loss_D: 3.1472 Loss_G: 2.4258 D(x): 0.6523 D(G(z)): 0.4727 / 0.4660\n", + "[89/100][247/391] Loss_D: 2.9729 Loss_G: 3.1134 D(x): 0.7451 D(G(z)): 0.5150 / 0.3757\n", + "[89/100][248/391] Loss_D: 3.1162 Loss_G: 2.3550 D(x): 0.5629 D(G(z)): 0.3628 / 0.4728\n", + "[89/100][249/391] Loss_D: 3.0487 Loss_G: 1.8626 D(x): 0.6500 D(G(z)): 0.4888 / 0.5651\n", + "[89/100][250/391] Loss_D: 2.6129 Loss_G: 2.7780 D(x): 0.7170 D(G(z)): 0.3687 / 0.4345\n", + "[89/100][251/391] Loss_D: 2.9242 Loss_G: 2.5389 D(x): 0.6944 D(G(z)): 0.4275 / 0.4726\n", + "[89/100][252/391] Loss_D: 2.8227 Loss_G: 2.8745 D(x): 0.6274 D(G(z)): 0.3671 / 0.3900\n", + "[89/100][253/391] Loss_D: 3.3067 Loss_G: 2.7162 D(x): 0.5968 D(G(z)): 0.4799 / 0.4281\n", + "[89/100][254/391] Loss_D: 3.3758 Loss_G: 2.5022 D(x): 0.6586 D(G(z)): 0.5661 / 0.4746\n", + "[89/100][255/391] Loss_D: 3.6855 Loss_G: 3.3638 D(x): 0.5799 D(G(z)): 0.5319 / 0.3597\n", + "[89/100][256/391] Loss_D: 2.7675 Loss_G: 2.7307 D(x): 0.6807 D(G(z)): 0.4363 / 0.4396\n", + "[89/100][257/391] Loss_D: 3.0930 Loss_G: 2.9698 D(x): 0.6228 D(G(z)): 0.4142 / 0.3964\n", + "[89/100][258/391] Loss_D: 2.8418 Loss_G: 2.4077 D(x): 0.6439 D(G(z)): 0.4163 / 0.4808\n", + "[89/100][259/391] Loss_D: 3.0744 Loss_G: 2.6572 D(x): 0.6403 D(G(z)): 0.4441 / 0.4498\n", + "[89/100][260/391] Loss_D: 3.3916 Loss_G: 3.0146 D(x): 0.5570 D(G(z)): 0.4587 / 0.4064\n", + "[89/100][261/391] Loss_D: 3.2120 Loss_G: 2.3352 D(x): 0.6553 D(G(z)): 0.4849 / 0.4924\n", + "[89/100][262/391] Loss_D: 2.8918 Loss_G: 2.7713 D(x): 0.6852 D(G(z)): 0.4312 / 0.4217\n", + "[89/100][263/391] Loss_D: 3.3145 Loss_G: 2.4004 D(x): 0.6133 D(G(z)): 0.4540 / 0.4630\n", + "[89/100][264/391] Loss_D: 3.0437 Loss_G: 2.4200 D(x): 0.6151 D(G(z)): 0.4847 / 0.4730\n", + "[89/100][265/391] Loss_D: 2.8844 Loss_G: 1.9429 D(x): 0.6393 D(G(z)): 0.4251 / 0.5546\n", + "[89/100][266/391] Loss_D: 3.8614 Loss_G: 2.6902 D(x): 0.5909 D(G(z)): 0.5556 / 0.4463\n", + "[89/100][267/391] Loss_D: 2.9247 Loss_G: 2.6677 D(x): 0.6551 D(G(z)): 0.4359 / 0.4392\n", + "[89/100][268/391] Loss_D: 2.9988 Loss_G: 2.2148 D(x): 0.6532 D(G(z)): 0.5038 / 0.5187\n", + "[89/100][269/391] Loss_D: 2.7348 Loss_G: 2.7292 D(x): 0.6799 D(G(z)): 0.3718 / 0.4296\n", + "[89/100][270/391] Loss_D: 2.7525 Loss_G: 2.3492 D(x): 0.6793 D(G(z)): 0.3934 / 0.4996\n", + "[89/100][271/391] Loss_D: 3.9085 Loss_G: 2.7983 D(x): 0.5065 D(G(z)): 0.5617 / 0.4222\n", + "[89/100][272/391] Loss_D: 3.8416 Loss_G: 2.7416 D(x): 0.6507 D(G(z)): 0.5964 / 0.4108\n", + "[89/100][273/391] Loss_D: 2.5913 Loss_G: 2.5172 D(x): 0.7213 D(G(z)): 0.4335 / 0.4674\n", + "[89/100][274/391] Loss_D: 2.7937 Loss_G: 3.0798 D(x): 0.6561 D(G(z)): 0.4078 / 0.3936\n", + "[89/100][275/391] Loss_D: 3.4294 Loss_G: 2.3019 D(x): 0.5310 D(G(z)): 0.4070 / 0.4797\n", + "[89/100][276/391] Loss_D: 3.0521 Loss_G: 3.0457 D(x): 0.6311 D(G(z)): 0.4408 / 0.4033\n", + "[89/100][277/391] Loss_D: 3.7174 Loss_G: 2.8273 D(x): 0.5952 D(G(z)): 0.5473 / 0.4147\n", + "[89/100][278/391] Loss_D: 2.9874 Loss_G: 2.4210 D(x): 0.6354 D(G(z)): 0.4585 / 0.4842\n", + "[89/100][279/391] Loss_D: 3.2679 Loss_G: 2.5338 D(x): 0.6338 D(G(z)): 0.5028 / 0.4708\n", + "[89/100][280/391] Loss_D: 2.9813 Loss_G: 2.5474 D(x): 0.6472 D(G(z)): 0.4609 / 0.4691\n", + "[89/100][281/391] Loss_D: 3.0673 Loss_G: 2.8429 D(x): 0.6823 D(G(z)): 0.4821 / 0.4294\n", + "[89/100][282/391] Loss_D: 3.3923 Loss_G: 2.6797 D(x): 0.6560 D(G(z)): 0.5502 / 0.4533\n", + "[89/100][283/391] Loss_D: 3.9778 Loss_G: 2.6957 D(x): 0.4728 D(G(z)): 0.4765 / 0.4319\n", + "[89/100][284/391] Loss_D: 2.7601 Loss_G: 3.2266 D(x): 0.6093 D(G(z)): 0.3352 / 0.3692\n", + "[89/100][285/391] Loss_D: 3.7332 Loss_G: 2.7624 D(x): 0.6229 D(G(z)): 0.5937 / 0.4200\n", + "[89/100][286/391] Loss_D: 2.9568 Loss_G: 2.3427 D(x): 0.6299 D(G(z)): 0.4252 / 0.4744\n", + "[89/100][287/391] Loss_D: 3.7537 Loss_G: 2.7826 D(x): 0.5803 D(G(z)): 0.5409 / 0.4222\n", + "[89/100][288/391] Loss_D: 2.6778 Loss_G: 3.0916 D(x): 0.6686 D(G(z)): 0.4486 / 0.4088\n", + "[89/100][289/391] Loss_D: 2.6442 Loss_G: 2.9162 D(x): 0.7319 D(G(z)): 0.4254 / 0.3999\n", + "[89/100][290/391] Loss_D: 3.0510 Loss_G: 2.3859 D(x): 0.5455 D(G(z)): 0.3199 / 0.4771\n", + "[89/100][291/391] Loss_D: 2.8427 Loss_G: 3.4765 D(x): 0.6918 D(G(z)): 0.4497 / 0.3423\n", + "[89/100][292/391] Loss_D: 2.8663 Loss_G: 2.2531 D(x): 0.7058 D(G(z)): 0.4284 / 0.4998\n", + "[89/100][293/391] Loss_D: 2.7271 Loss_G: 3.3040 D(x): 0.6979 D(G(z)): 0.4380 / 0.3702\n", + "[89/100][294/391] Loss_D: 2.7100 Loss_G: 2.6242 D(x): 0.6221 D(G(z)): 0.4162 / 0.4414\n", + "[89/100][295/391] Loss_D: 2.6779 Loss_G: 3.2835 D(x): 0.7511 D(G(z)): 0.4320 / 0.3664\n", + "[89/100][296/391] Loss_D: 3.1756 Loss_G: 3.3187 D(x): 0.6337 D(G(z)): 0.4643 / 0.3565\n", + "[89/100][297/391] Loss_D: 3.0572 Loss_G: 2.8633 D(x): 0.6535 D(G(z)): 0.3936 / 0.4288\n", + "[89/100][298/391] Loss_D: 3.1385 Loss_G: 2.1056 D(x): 0.6067 D(G(z)): 0.4388 / 0.5205\n", + "[89/100][299/391] Loss_D: 2.7893 Loss_G: 2.4736 D(x): 0.6733 D(G(z)): 0.4198 / 0.4728\n", + "[89/100][300/391] Loss_D: 3.2512 Loss_G: 2.8398 D(x): 0.6567 D(G(z)): 0.5184 / 0.4287\n", + "[89/100][301/391] Loss_D: 3.7546 Loss_G: 3.5139 D(x): 0.7903 D(G(z)): 0.4345 / 0.3553\n", + "[89/100][302/391] Loss_D: 2.7924 Loss_G: 2.3853 D(x): 0.6148 D(G(z)): 0.3794 / 0.4801\n", + "[89/100][303/391] Loss_D: 3.5197 Loss_G: 2.5073 D(x): 0.6380 D(G(z)): 0.5601 / 0.4659\n", + "[89/100][304/391] Loss_D: 2.9014 Loss_G: 2.0065 D(x): 0.6090 D(G(z)): 0.4754 / 0.5358\n", + "[89/100][305/391] Loss_D: 3.0384 Loss_G: 2.7957 D(x): 0.6712 D(G(z)): 0.4743 / 0.4157\n", + "[89/100][306/391] Loss_D: 3.1358 Loss_G: 3.0218 D(x): 0.5360 D(G(z)): 0.3472 / 0.3880\n", + "[89/100][307/391] Loss_D: 3.0261 Loss_G: 2.9799 D(x): 0.7258 D(G(z)): 0.5238 / 0.4040\n", + "[89/100][308/391] Loss_D: 3.0145 Loss_G: 2.4166 D(x): 0.5919 D(G(z)): 0.3386 / 0.4670\n", + "[89/100][309/391] Loss_D: 2.9639 Loss_G: 2.3634 D(x): 0.6440 D(G(z)): 0.4544 / 0.4784\n", + "[89/100][310/391] Loss_D: 3.8458 Loss_G: 2.4979 D(x): 0.6429 D(G(z)): 0.5970 / 0.4783\n", + "[89/100][311/391] Loss_D: 3.1252 Loss_G: 2.5209 D(x): 0.6439 D(G(z)): 0.4467 / 0.4605\n", + "[89/100][312/391] Loss_D: 2.4551 Loss_G: 3.2723 D(x): 0.7270 D(G(z)): 0.3799 / 0.3640\n", + "[89/100][313/391] Loss_D: 2.7015 Loss_G: 2.6688 D(x): 0.6713 D(G(z)): 0.3941 / 0.4487\n", + "[89/100][314/391] Loss_D: 3.0953 Loss_G: 2.4652 D(x): 0.5987 D(G(z)): 0.4432 / 0.4696\n", + "[89/100][315/391] Loss_D: 2.7422 Loss_G: 2.9895 D(x): 0.6603 D(G(z)): 0.3815 / 0.4055\n", + "[89/100][316/391] Loss_D: 3.0827 Loss_G: 2.6808 D(x): 0.6437 D(G(z)): 0.4418 / 0.4405\n", + "[89/100][317/391] Loss_D: 3.4895 Loss_G: 2.3207 D(x): 0.6334 D(G(z)): 0.5436 / 0.4817\n", + "[89/100][318/391] Loss_D: 2.7056 Loss_G: 2.4384 D(x): 0.6096 D(G(z)): 0.3420 / 0.4731\n", + "[89/100][319/391] Loss_D: 2.9356 Loss_G: 2.6770 D(x): 0.6110 D(G(z)): 0.4149 / 0.4611\n", + "[89/100][320/391] Loss_D: 3.1006 Loss_G: 2.7455 D(x): 0.6283 D(G(z)): 0.4451 / 0.4321\n", + "[89/100][321/391] Loss_D: 3.2680 Loss_G: 2.3342 D(x): 0.7057 D(G(z)): 0.5341 / 0.4698\n", + "[89/100][322/391] Loss_D: 3.2614 Loss_G: 2.6034 D(x): 0.6656 D(G(z)): 0.5374 / 0.4299\n", + "[89/100][323/391] Loss_D: 3.0931 Loss_G: 2.2460 D(x): 0.6367 D(G(z)): 0.4436 / 0.5094\n", + "[89/100][324/391] Loss_D: 2.7733 Loss_G: 1.9759 D(x): 0.6117 D(G(z)): 0.3960 / 0.5475\n", + "[89/100][325/391] Loss_D: 3.1651 Loss_G: 2.9635 D(x): 0.6751 D(G(z)): 0.4836 / 0.4079\n", + "[89/100][326/391] Loss_D: 3.0863 Loss_G: 2.9709 D(x): 0.6928 D(G(z)): 0.5013 / 0.3842\n", + "[89/100][327/391] Loss_D: 2.9297 Loss_G: 2.2708 D(x): 0.6974 D(G(z)): 0.4662 / 0.5006\n", + "[89/100][328/391] Loss_D: 2.6815 Loss_G: 3.1796 D(x): 0.6872 D(G(z)): 0.4544 / 0.3822\n", + "[89/100][329/391] Loss_D: 2.6854 Loss_G: 2.7377 D(x): 0.6489 D(G(z)): 0.2759 / 0.4475\n", + "[89/100][330/391] Loss_D: 4.0084 Loss_G: 2.6419 D(x): 0.5970 D(G(z)): 0.5983 / 0.4408\n", + "[89/100][331/391] Loss_D: 3.4275 Loss_G: 3.3199 D(x): 0.6205 D(G(z)): 0.4744 / 0.3659\n", + "[89/100][332/391] Loss_D: 2.8854 Loss_G: 3.9789 D(x): 0.7023 D(G(z)): 0.4939 / 0.3025\n", + "[89/100][333/391] Loss_D: 2.8593 Loss_G: 3.5126 D(x): 0.6399 D(G(z)): 0.3310 / 0.3377\n", + "[89/100][334/391] Loss_D: 2.2983 Loss_G: 2.6310 D(x): 0.6928 D(G(z)): 0.3506 / 0.4312\n", + "[89/100][335/391] Loss_D: 3.4083 Loss_G: 3.5063 D(x): 0.5910 D(G(z)): 0.5114 / 0.3336\n", + "[89/100][336/391] Loss_D: 2.9348 Loss_G: 3.3900 D(x): 0.6606 D(G(z)): 0.4033 / 0.3559\n", + "[89/100][337/391] Loss_D: 2.9777 Loss_G: 2.3522 D(x): 0.5949 D(G(z)): 0.3577 / 0.5008\n", + "[89/100][338/391] Loss_D: 2.8704 Loss_G: 2.6308 D(x): 0.6503 D(G(z)): 0.3993 / 0.4437\n", + "[89/100][339/391] Loss_D: 3.0189 Loss_G: 3.2347 D(x): 0.6923 D(G(z)): 0.4810 / 0.3833\n", + "[89/100][340/391] Loss_D: 2.7980 Loss_G: 2.2770 D(x): 0.6348 D(G(z)): 0.3775 / 0.4876\n", + "[89/100][341/391] Loss_D: 2.8371 Loss_G: 2.9289 D(x): 0.6543 D(G(z)): 0.4319 / 0.4129\n", + "[89/100][342/391] Loss_D: 3.3391 Loss_G: 2.2777 D(x): 0.6630 D(G(z)): 0.5380 / 0.4948\n", + "[89/100][343/391] Loss_D: 2.9845 Loss_G: 2.8319 D(x): 0.6529 D(G(z)): 0.4451 / 0.4242\n", + "[89/100][344/391] Loss_D: 3.2549 Loss_G: 2.4267 D(x): 0.6319 D(G(z)): 0.5256 / 0.4797\n", + "[89/100][345/391] Loss_D: 3.1647 Loss_G: 2.6638 D(x): 0.6318 D(G(z)): 0.4572 / 0.4378\n", + "[89/100][346/391] Loss_D: 3.3013 Loss_G: 2.1547 D(x): 0.6488 D(G(z)): 0.5055 / 0.5089\n", + "[89/100][347/391] Loss_D: 3.4579 Loss_G: 2.7933 D(x): 0.6036 D(G(z)): 0.4943 / 0.4080\n", + "[89/100][348/391] Loss_D: 2.8876 Loss_G: 3.0786 D(x): 0.6076 D(G(z)): 0.3863 / 0.3845\n", + "[89/100][349/391] Loss_D: 3.3411 Loss_G: 3.5022 D(x): 0.5779 D(G(z)): 0.4660 / 0.3578\n", + "[89/100][350/391] Loss_D: 2.7706 Loss_G: 2.8250 D(x): 0.6704 D(G(z)): 0.4500 / 0.4348\n", + "[89/100][351/391] Loss_D: 3.0318 Loss_G: 2.6083 D(x): 0.6064 D(G(z)): 0.4074 / 0.4544\n", + "[89/100][352/391] Loss_D: 3.1754 Loss_G: 1.9466 D(x): 0.6593 D(G(z)): 0.5385 / 0.5651\n", + "[89/100][353/391] Loss_D: 3.1466 Loss_G: 3.1580 D(x): 0.6149 D(G(z)): 0.4192 / 0.3755\n", + "[89/100][354/391] Loss_D: 3.2173 Loss_G: 2.8372 D(x): 0.5925 D(G(z)): 0.4757 / 0.4170\n", + "[89/100][355/391] Loss_D: 2.5438 Loss_G: 3.2130 D(x): 0.7555 D(G(z)): 0.4551 / 0.3732\n", + "[89/100][356/391] Loss_D: 3.2205 Loss_G: 3.1699 D(x): 0.6780 D(G(z)): 0.5330 / 0.3690\n", + "[89/100][357/391] Loss_D: 2.8866 Loss_G: 3.6247 D(x): 0.6703 D(G(z)): 0.4774 / 0.3194\n", + "[89/100][358/391] Loss_D: 2.9365 Loss_G: 3.6811 D(x): 0.5918 D(G(z)): 0.3854 / 0.3234\n", + "[89/100][359/391] Loss_D: 3.1412 Loss_G: 2.5227 D(x): 0.5545 D(G(z)): 0.3511 / 0.4663\n", + "[89/100][360/391] Loss_D: 3.1970 Loss_G: 2.8614 D(x): 0.5859 D(G(z)): 0.4280 / 0.4186\n", + "[89/100][361/391] Loss_D: 3.5993 Loss_G: 1.6521 D(x): 0.5872 D(G(z)): 0.4887 / 0.6134\n", + "[89/100][362/391] Loss_D: 2.8407 Loss_G: 1.4899 D(x): 0.7324 D(G(z)): 0.4605 / 0.6424\n", + "[89/100][363/391] Loss_D: 2.6857 Loss_G: 3.2011 D(x): 0.7639 D(G(z)): 0.4682 / 0.3668\n", + "[89/100][364/391] Loss_D: 3.4495 Loss_G: 2.5646 D(x): 0.5707 D(G(z)): 0.4203 / 0.4435\n", + "[89/100][365/391] Loss_D: 3.0484 Loss_G: 2.6743 D(x): 0.6504 D(G(z)): 0.4682 / 0.4332\n", + "[89/100][366/391] Loss_D: 2.6295 Loss_G: 2.8282 D(x): 0.7015 D(G(z)): 0.4200 / 0.4254\n", + "[89/100][367/391] Loss_D: 2.6587 Loss_G: 3.6941 D(x): 0.7465 D(G(z)): 0.4172 / 0.3117\n", + "[89/100][368/391] Loss_D: 2.8675 Loss_G: 3.3972 D(x): 0.6176 D(G(z)): 0.3731 / 0.3437\n", + "[89/100][369/391] Loss_D: 2.7679 Loss_G: 2.8011 D(x): 0.6470 D(G(z)): 0.4374 / 0.4142\n", + "[89/100][370/391] Loss_D: 2.7893 Loss_G: 2.9860 D(x): 0.5986 D(G(z)): 0.3062 / 0.4069\n", + "[89/100][371/391] Loss_D: 2.9657 Loss_G: 3.2583 D(x): 0.6342 D(G(z)): 0.4223 / 0.3706\n", + "[89/100][372/391] Loss_D: 2.5902 Loss_G: 2.0419 D(x): 0.6559 D(G(z)): 0.3913 / 0.5380\n", + "[89/100][373/391] Loss_D: 3.2885 Loss_G: 2.6412 D(x): 0.7317 D(G(z)): 0.5389 / 0.4403\n", + "[89/100][374/391] Loss_D: 2.7284 Loss_G: 2.7394 D(x): 0.6965 D(G(z)): 0.4446 / 0.4377\n", + "[89/100][375/391] Loss_D: 3.2162 Loss_G: 2.6693 D(x): 0.7072 D(G(z)): 0.5415 / 0.4318\n", + "[89/100][376/391] Loss_D: 2.7901 Loss_G: 3.6947 D(x): 0.7095 D(G(z)): 0.4117 / 0.3269\n", + "[89/100][377/391] Loss_D: 3.4126 Loss_G: 2.8036 D(x): 0.5777 D(G(z)): 0.4617 / 0.4156\n", + "[89/100][378/391] Loss_D: 2.9223 Loss_G: 1.8509 D(x): 0.5787 D(G(z)): 0.3645 / 0.5771\n", + "[89/100][379/391] Loss_D: 2.9334 Loss_G: 2.6313 D(x): 0.5930 D(G(z)): 0.3890 / 0.4484\n", + "[89/100][380/391] Loss_D: 2.8851 Loss_G: 2.7027 D(x): 0.7400 D(G(z)): 0.4991 / 0.4372\n", + "[89/100][381/391] Loss_D: 2.9780 Loss_G: 2.7080 D(x): 0.6458 D(G(z)): 0.4463 / 0.4149\n", + "[89/100][382/391] Loss_D: 2.8337 Loss_G: 2.3396 D(x): 0.6027 D(G(z)): 0.3813 / 0.4946\n", + "[89/100][383/391] Loss_D: 2.8143 Loss_G: 2.2519 D(x): 0.6881 D(G(z)): 0.4156 / 0.5018\n", + "[89/100][384/391] Loss_D: 3.1125 Loss_G: 2.6467 D(x): 0.6489 D(G(z)): 0.5234 / 0.4217\n", + "[89/100][385/391] Loss_D: 2.8389 Loss_G: 2.7213 D(x): 0.7341 D(G(z)): 0.4998 / 0.4436\n", + "[89/100][386/391] Loss_D: 2.4539 Loss_G: 2.8500 D(x): 0.7663 D(G(z)): 0.3582 / 0.4188\n", + "[89/100][387/391] Loss_D: 3.1017 Loss_G: 2.9701 D(x): 0.7112 D(G(z)): 0.5228 / 0.3911\n", + "[89/100][388/391] Loss_D: 2.9119 Loss_G: 3.2092 D(x): 0.6089 D(G(z)): 0.3876 / 0.3785\n", + "[89/100][389/391] Loss_D: 2.7750 Loss_G: 2.7581 D(x): 0.6567 D(G(z)): 0.3641 / 0.4220\n", + "[89/100][390/391] Loss_D: 2.8751 Loss_G: 2.6358 D(x): 0.6106 D(G(z)): 0.4131 / 0.4547\n", + "[89/100][391/391] Loss_D: 3.6776 Loss_G: 2.6207 D(x): 0.6770 D(G(z)): 0.3518 / 0.4527\n", + "[90/100][1/391] Loss_D: 3.5756 Loss_G: 3.0115 D(x): 0.6701 D(G(z)): 0.4733 / 0.3923\n", + "[90/100][2/391] Loss_D: 2.6073 Loss_G: 2.6259 D(x): 0.6954 D(G(z)): 0.3996 / 0.4449\n", + "[90/100][3/391] Loss_D: 2.7042 Loss_G: 2.4369 D(x): 0.6793 D(G(z)): 0.3991 / 0.4619\n", + "[90/100][4/391] Loss_D: 2.8330 Loss_G: 3.5127 D(x): 0.6862 D(G(z)): 0.4784 / 0.3428\n", + "[90/100][5/391] Loss_D: 2.8726 Loss_G: 2.2433 D(x): 0.6368 D(G(z)): 0.4158 / 0.4759\n", + "[90/100][6/391] Loss_D: 2.8982 Loss_G: 3.1601 D(x): 0.6173 D(G(z)): 0.3789 / 0.3612\n", + "[90/100][7/391] Loss_D: 3.4197 Loss_G: 2.7024 D(x): 0.6105 D(G(z)): 0.4951 / 0.4332\n", + "[90/100][8/391] Loss_D: 2.9492 Loss_G: 3.4112 D(x): 0.6088 D(G(z)): 0.4536 / 0.3368\n", + "[90/100][9/391] Loss_D: 2.6817 Loss_G: 3.0581 D(x): 0.7295 D(G(z)): 0.4540 / 0.3907\n", + "[90/100][10/391] Loss_D: 3.0087 Loss_G: 2.2595 D(x): 0.6620 D(G(z)): 0.5203 / 0.4990\n", + "[90/100][11/391] Loss_D: 3.7330 Loss_G: 2.7405 D(x): 0.5204 D(G(z)): 0.4764 / 0.4258\n", + "[90/100][12/391] Loss_D: 2.6187 Loss_G: 2.4771 D(x): 0.6862 D(G(z)): 0.3860 / 0.4670\n", + "[90/100][13/391] Loss_D: 2.7078 Loss_G: 2.7261 D(x): 0.6874 D(G(z)): 0.4308 / 0.4195\n", + "[90/100][14/391] Loss_D: 3.4423 Loss_G: 3.1130 D(x): 0.6402 D(G(z)): 0.5388 / 0.3795\n", + "[90/100][15/391] Loss_D: 2.8249 Loss_G: 2.2612 D(x): 0.6203 D(G(z)): 0.4268 / 0.4999\n", + "[90/100][16/391] Loss_D: 2.8210 Loss_G: 3.1306 D(x): 0.7870 D(G(z)): 0.4417 / 0.3725\n", + "[90/100][17/391] Loss_D: 3.0239 Loss_G: 2.6243 D(x): 0.6587 D(G(z)): 0.4735 / 0.4438\n", + "[90/100][18/391] Loss_D: 3.2426 Loss_G: 3.2258 D(x): 0.5529 D(G(z)): 0.3961 / 0.3759\n", + "[90/100][19/391] Loss_D: 2.7313 Loss_G: 2.9246 D(x): 0.6354 D(G(z)): 0.3561 / 0.4053\n", + "[90/100][20/391] Loss_D: 3.3093 Loss_G: 2.9735 D(x): 0.6391 D(G(z)): 0.5043 / 0.4057\n", + "[90/100][21/391] Loss_D: 3.3225 Loss_G: 2.8502 D(x): 0.6365 D(G(z)): 0.5689 / 0.4135\n", + "[90/100][22/391] Loss_D: 2.6146 Loss_G: 2.3027 D(x): 0.6870 D(G(z)): 0.3935 / 0.4903\n", + "[90/100][23/391] Loss_D: 2.8280 Loss_G: 3.0150 D(x): 0.6067 D(G(z)): 0.3178 / 0.3906\n", + "[90/100][24/391] Loss_D: 3.2009 Loss_G: 3.1044 D(x): 0.5442 D(G(z)): 0.3525 / 0.3752\n", + "[90/100][25/391] Loss_D: 2.8476 Loss_G: 2.7122 D(x): 0.7319 D(G(z)): 0.4886 / 0.4492\n", + "[90/100][26/391] Loss_D: 3.2908 Loss_G: 2.2851 D(x): 0.5675 D(G(z)): 0.4491 / 0.4909\n", + "[90/100][27/391] Loss_D: 2.8632 Loss_G: 2.6717 D(x): 0.6977 D(G(z)): 0.3858 / 0.4417\n", + "[90/100][28/391] Loss_D: 3.4399 Loss_G: 2.6851 D(x): 0.5739 D(G(z)): 0.5149 / 0.4418\n", + "[90/100][29/391] Loss_D: 2.9746 Loss_G: 2.5585 D(x): 0.7249 D(G(z)): 0.5268 / 0.4608\n", + "[90/100][30/391] Loss_D: 2.9799 Loss_G: 2.8790 D(x): 0.6583 D(G(z)): 0.4778 / 0.4178\n", + "[90/100][31/391] Loss_D: 3.8541 Loss_G: 2.8970 D(x): 0.7681 D(G(z)): 0.4839 / 0.4252\n", + "[90/100][32/391] Loss_D: 2.7161 Loss_G: 2.5405 D(x): 0.6404 D(G(z)): 0.3381 / 0.4542\n", + "[90/100][33/391] Loss_D: 3.2703 Loss_G: 3.0193 D(x): 0.5955 D(G(z)): 0.4596 / 0.3950\n", + "[90/100][34/391] Loss_D: 2.5564 Loss_G: 2.3839 D(x): 0.6931 D(G(z)): 0.4336 / 0.4738\n", + "[90/100][35/391] Loss_D: 3.2560 Loss_G: 2.7727 D(x): 0.5801 D(G(z)): 0.4787 / 0.4146\n", + "[90/100][36/391] Loss_D: 3.0628 Loss_G: 2.5786 D(x): 0.6429 D(G(z)): 0.4285 / 0.4276\n", + "[90/100][37/391] Loss_D: 2.7335 Loss_G: 2.6330 D(x): 0.7086 D(G(z)): 0.4401 / 0.4356\n", + "[90/100][38/391] Loss_D: 2.1942 Loss_G: 2.6567 D(x): 0.7549 D(G(z)): 0.3867 / 0.4569\n", + "[90/100][39/391] Loss_D: 3.0966 Loss_G: 3.3218 D(x): 0.6233 D(G(z)): 0.4035 / 0.3643\n", + "[90/100][40/391] Loss_D: 2.8146 Loss_G: 1.8297 D(x): 0.7226 D(G(z)): 0.4497 / 0.5771\n", + "[90/100][41/391] Loss_D: 3.4034 Loss_G: 2.1728 D(x): 0.6349 D(G(z)): 0.5163 / 0.5009\n", + "[90/100][42/391] Loss_D: 3.2471 Loss_G: 2.7675 D(x): 0.5948 D(G(z)): 0.4558 / 0.4218\n", + "[90/100][43/391] Loss_D: 2.8560 Loss_G: 2.7914 D(x): 0.7119 D(G(z)): 0.4588 / 0.4341\n", + "[90/100][44/391] Loss_D: 2.9954 Loss_G: 2.9009 D(x): 0.5782 D(G(z)): 0.4116 / 0.4131\n", + "[90/100][45/391] Loss_D: 2.6603 Loss_G: 2.3018 D(x): 0.7009 D(G(z)): 0.4064 / 0.4903\n", + "[90/100][46/391] Loss_D: 2.7200 Loss_G: 2.0138 D(x): 0.6937 D(G(z)): 0.4220 / 0.5312\n", + "[90/100][47/391] Loss_D: 2.9418 Loss_G: 2.7195 D(x): 0.6520 D(G(z)): 0.4662 / 0.4313\n", + "[90/100][48/391] Loss_D: 3.0792 Loss_G: 3.2805 D(x): 0.5736 D(G(z)): 0.4000 / 0.3641\n", + "[90/100][49/391] Loss_D: 3.1048 Loss_G: 2.6273 D(x): 0.5750 D(G(z)): 0.4061 / 0.4302\n", + "[90/100][50/391] Loss_D: 3.0722 Loss_G: 2.5519 D(x): 0.7167 D(G(z)): 0.5520 / 0.4696\n", + "[90/100][51/391] Loss_D: 3.6808 Loss_G: 2.7608 D(x): 0.6174 D(G(z)): 0.5512 / 0.4362\n", + "[90/100][52/391] Loss_D: 2.5912 Loss_G: 2.9262 D(x): 0.6610 D(G(z)): 0.3399 / 0.4143\n", + "[90/100][53/391] Loss_D: 3.0507 Loss_G: 2.8641 D(x): 0.5868 D(G(z)): 0.4081 / 0.4141\n", + "[90/100][54/391] Loss_D: 3.3030 Loss_G: 2.0353 D(x): 0.5553 D(G(z)): 0.4284 / 0.5177\n", + "[90/100][55/391] Loss_D: 2.7211 Loss_G: 2.1213 D(x): 0.7126 D(G(z)): 0.4681 / 0.5158\n", + "[90/100][56/391] Loss_D: 2.9125 Loss_G: 2.1980 D(x): 0.6595 D(G(z)): 0.3911 / 0.4997\n", + "[90/100][57/391] Loss_D: 2.7203 Loss_G: 3.3155 D(x): 0.7671 D(G(z)): 0.4701 / 0.3544\n", + "[90/100][58/391] Loss_D: 2.9358 Loss_G: 3.0886 D(x): 0.6009 D(G(z)): 0.4168 / 0.3846\n", + "[90/100][59/391] Loss_D: 2.6466 Loss_G: 2.4701 D(x): 0.7056 D(G(z)): 0.4309 / 0.4601\n", + "[90/100][60/391] Loss_D: 2.9824 Loss_G: 2.7440 D(x): 0.6436 D(G(z)): 0.4170 / 0.4361\n", + "[90/100][61/391] Loss_D: 3.6942 Loss_G: 2.5810 D(x): 0.6800 D(G(z)): 0.4923 / 0.4611\n", + "[90/100][62/391] Loss_D: 3.0542 Loss_G: 2.7184 D(x): 0.6536 D(G(z)): 0.4744 / 0.4299\n", + "[90/100][63/391] Loss_D: 3.1957 Loss_G: 2.2655 D(x): 0.6442 D(G(z)): 0.4987 / 0.5046\n", + "[90/100][64/391] Loss_D: 2.8150 Loss_G: 3.5811 D(x): 0.6326 D(G(z)): 0.4316 / 0.3238\n", + "[90/100][65/391] Loss_D: 3.2505 Loss_G: 2.4698 D(x): 0.5964 D(G(z)): 0.4417 / 0.4678\n", + "[90/100][66/391] Loss_D: 2.9940 Loss_G: 2.7306 D(x): 0.6026 D(G(z)): 0.4111 / 0.4335\n", + "[90/100][67/391] Loss_D: 2.9380 Loss_G: 2.8102 D(x): 0.6387 D(G(z)): 0.3287 / 0.4166\n", + "[90/100][68/391] Loss_D: 2.8960 Loss_G: 2.6463 D(x): 0.6228 D(G(z)): 0.4117 / 0.4588\n", + "[90/100][69/391] Loss_D: 2.9718 Loss_G: 1.9462 D(x): 0.7472 D(G(z)): 0.5259 / 0.5393\n", + "[90/100][70/391] Loss_D: 2.9336 Loss_G: 2.4247 D(x): 0.6316 D(G(z)): 0.3856 / 0.4858\n", + "[90/100][71/391] Loss_D: 3.0987 Loss_G: 2.7405 D(x): 0.6868 D(G(z)): 0.4716 / 0.4350\n", + "[90/100][72/391] Loss_D: 2.3322 Loss_G: 2.6799 D(x): 0.7005 D(G(z)): 0.3400 / 0.4425\n", + "[90/100][73/391] Loss_D: 3.4055 Loss_G: 2.3849 D(x): 0.6544 D(G(z)): 0.5755 / 0.5001\n", + "[90/100][74/391] Loss_D: 2.8216 Loss_G: 2.9911 D(x): 0.6085 D(G(z)): 0.3658 / 0.3964\n", + "[90/100][75/391] Loss_D: 2.9381 Loss_G: 3.4335 D(x): 0.6494 D(G(z)): 0.4611 / 0.3405\n", + "[90/100][76/391] Loss_D: 2.9566 Loss_G: 2.4173 D(x): 0.5936 D(G(z)): 0.3843 / 0.4758\n", + "[90/100][77/391] Loss_D: 3.3380 Loss_G: 2.6304 D(x): 0.5805 D(G(z)): 0.4491 / 0.4386\n", + "[90/100][78/391] Loss_D: 3.5572 Loss_G: 2.6266 D(x): 0.6245 D(G(z)): 0.5878 / 0.4483\n", + "[90/100][79/391] Loss_D: 2.8969 Loss_G: 2.1995 D(x): 0.6777 D(G(z)): 0.4669 / 0.5238\n", + "[90/100][80/391] Loss_D: 3.0220 Loss_G: 3.3082 D(x): 0.6914 D(G(z)): 0.4654 / 0.3717\n", + "[90/100][81/391] Loss_D: 3.2092 Loss_G: 3.3400 D(x): 0.6538 D(G(z)): 0.5328 / 0.3657\n", + "[90/100][82/391] Loss_D: 2.9779 Loss_G: 2.7690 D(x): 0.7076 D(G(z)): 0.4870 / 0.4339\n", + "[90/100][83/391] Loss_D: 3.2618 Loss_G: 3.4228 D(x): 0.5871 D(G(z)): 0.4612 / 0.3494\n", + "[90/100][84/391] Loss_D: 2.8011 Loss_G: 2.7360 D(x): 0.6480 D(G(z)): 0.4289 / 0.4162\n", + "[90/100][85/391] Loss_D: 2.4667 Loss_G: 3.3909 D(x): 0.7448 D(G(z)): 0.3916 / 0.3538\n", + "[90/100][86/391] Loss_D: 3.4493 Loss_G: 3.2519 D(x): 0.6037 D(G(z)): 0.5015 / 0.3727\n", + "[90/100][87/391] Loss_D: 3.0739 Loss_G: 2.9534 D(x): 0.6153 D(G(z)): 0.3280 / 0.3938\n", + "[90/100][88/391] Loss_D: 3.8822 Loss_G: 2.4460 D(x): 0.5019 D(G(z)): 0.4715 / 0.4704\n", + "[90/100][89/391] Loss_D: 3.0444 Loss_G: 2.6814 D(x): 0.6419 D(G(z)): 0.4473 / 0.4502\n", + "[90/100][90/391] Loss_D: 2.6950 Loss_G: 2.8660 D(x): 0.7281 D(G(z)): 0.4446 / 0.4300\n", + "[90/100][91/391] Loss_D: 3.5458 Loss_G: 2.9236 D(x): 0.7377 D(G(z)): 0.4370 / 0.4278\n", + "[90/100][92/391] Loss_D: 2.5448 Loss_G: 2.8150 D(x): 0.7165 D(G(z)): 0.4330 / 0.4111\n", + "[90/100][93/391] Loss_D: 3.5639 Loss_G: 2.9507 D(x): 0.5959 D(G(z)): 0.5272 / 0.4018\n", + "[90/100][94/391] Loss_D: 2.8269 Loss_G: 3.0502 D(x): 0.5803 D(G(z)): 0.3568 / 0.3892\n", + "[90/100][95/391] Loss_D: 3.3423 Loss_G: 3.0116 D(x): 0.5556 D(G(z)): 0.4092 / 0.3895\n", + "[90/100][96/391] Loss_D: 2.7438 Loss_G: 2.3247 D(x): 0.7145 D(G(z)): 0.4478 / 0.4916\n", + "[90/100][97/391] Loss_D: 2.6471 Loss_G: 2.2812 D(x): 0.7343 D(G(z)): 0.3482 / 0.4843\n", + "[90/100][98/391] Loss_D: 3.0529 Loss_G: 3.3950 D(x): 0.6585 D(G(z)): 0.4766 / 0.3545\n", + "[90/100][99/391] Loss_D: 3.2719 Loss_G: 2.7507 D(x): 0.6286 D(G(z)): 0.4765 / 0.4312\n", + "[90/100][100/391] Loss_D: 3.1922 Loss_G: 2.4264 D(x): 0.5578 D(G(z)): 0.4144 / 0.4546\n", + "[90/100][101/391] Loss_D: 2.5263 Loss_G: 2.0182 D(x): 0.7067 D(G(z)): 0.3954 / 0.5269\n", + "[90/100][102/391] Loss_D: 2.9309 Loss_G: 3.0700 D(x): 0.6492 D(G(z)): 0.3808 / 0.3905\n", + "[90/100][103/391] Loss_D: 3.4428 Loss_G: 2.4410 D(x): 0.5631 D(G(z)): 0.5043 / 0.4710\n", + "[90/100][104/391] Loss_D: 3.1663 Loss_G: 2.9171 D(x): 0.7333 D(G(z)): 0.5405 / 0.4089\n", + "[90/100][105/391] Loss_D: 3.8794 Loss_G: 2.8231 D(x): 0.6424 D(G(z)): 0.5996 / 0.4323\n", + "[90/100][106/391] Loss_D: 2.5655 Loss_G: 3.0822 D(x): 0.7296 D(G(z)): 0.4083 / 0.3850\n", + "[90/100][107/391] Loss_D: 2.9750 Loss_G: 2.8412 D(x): 0.5844 D(G(z)): 0.3547 / 0.4208\n", + "[90/100][108/391] Loss_D: 3.4767 Loss_G: 2.9278 D(x): 0.6346 D(G(z)): 0.5474 / 0.3906\n", + "[90/100][109/391] Loss_D: 2.8173 Loss_G: 2.8697 D(x): 0.6619 D(G(z)): 0.4646 / 0.4218\n", + "[90/100][110/391] Loss_D: 2.9768 Loss_G: 4.0037 D(x): 0.7077 D(G(z)): 0.4817 / 0.3008\n", + "[90/100][111/391] Loss_D: 2.7155 Loss_G: 3.7661 D(x): 0.6759 D(G(z)): 0.4103 / 0.3227\n", + "[90/100][112/391] Loss_D: 3.2940 Loss_G: 3.0964 D(x): 0.5675 D(G(z)): 0.4373 / 0.3912\n", + "[90/100][113/391] Loss_D: 3.3363 Loss_G: 2.7609 D(x): 0.6149 D(G(z)): 0.5131 / 0.4179\n", + "[90/100][114/391] Loss_D: 3.4841 Loss_G: 3.4848 D(x): 0.5513 D(G(z)): 0.4925 / 0.3285\n", + "[90/100][115/391] Loss_D: 3.4322 Loss_G: 2.9282 D(x): 0.5574 D(G(z)): 0.4308 / 0.4048\n", + "[90/100][116/391] Loss_D: 2.8797 Loss_G: 2.6661 D(x): 0.6387 D(G(z)): 0.4087 / 0.4397\n", + "[90/100][117/391] Loss_D: 2.7960 Loss_G: 2.4789 D(x): 0.6651 D(G(z)): 0.3673 / 0.4495\n", + "[90/100][118/391] Loss_D: 2.4142 Loss_G: 3.3499 D(x): 0.7554 D(G(z)): 0.4661 / 0.3558\n", + "[90/100][119/391] Loss_D: 3.0462 Loss_G: 2.7961 D(x): 0.6284 D(G(z)): 0.4475 / 0.4430\n", + "[90/100][120/391] Loss_D: 2.7366 Loss_G: 2.2465 D(x): 0.6963 D(G(z)): 0.4650 / 0.5037\n", + "[90/100][121/391] Loss_D: 3.5179 Loss_G: 3.1537 D(x): 0.6633 D(G(z)): 0.4439 / 0.3859\n", + "[90/100][122/391] Loss_D: 2.4276 Loss_G: 2.4845 D(x): 0.7324 D(G(z)): 0.3718 / 0.4676\n", + "[90/100][123/391] Loss_D: 3.1908 Loss_G: 2.6177 D(x): 0.5801 D(G(z)): 0.4304 / 0.4551\n", + "[90/100][124/391] Loss_D: 3.2658 Loss_G: 2.9905 D(x): 0.6269 D(G(z)): 0.4952 / 0.4039\n", + "[90/100][125/391] Loss_D: 2.5445 Loss_G: 3.3058 D(x): 0.7537 D(G(z)): 0.3977 / 0.3548\n", + "[90/100][126/391] Loss_D: 2.6371 Loss_G: 2.9994 D(x): 0.6304 D(G(z)): 0.3437 / 0.3850\n", + "[90/100][127/391] Loss_D: 2.9260 Loss_G: 2.2432 D(x): 0.6453 D(G(z)): 0.4220 / 0.5072\n", + "[90/100][128/391] Loss_D: 3.2652 Loss_G: 2.5373 D(x): 0.5895 D(G(z)): 0.4636 / 0.4594\n", + "[90/100][129/391] Loss_D: 3.0569 Loss_G: 2.8637 D(x): 0.6024 D(G(z)): 0.4120 / 0.4201\n", + "[90/100][130/391] Loss_D: 3.1767 Loss_G: 2.5133 D(x): 0.6407 D(G(z)): 0.4575 / 0.4617\n", + "[90/100][131/391] Loss_D: 2.7548 Loss_G: 2.7138 D(x): 0.7267 D(G(z)): 0.4376 / 0.4393\n", + "[90/100][132/391] Loss_D: 3.1783 Loss_G: 2.2434 D(x): 0.6473 D(G(z)): 0.4786 / 0.4941\n", + "[90/100][133/391] Loss_D: 3.1976 Loss_G: 3.1056 D(x): 0.6863 D(G(z)): 0.5691 / 0.3884\n", + "[90/100][134/391] Loss_D: 2.4733 Loss_G: 3.1081 D(x): 0.6433 D(G(z)): 0.3620 / 0.3890\n", + "[90/100][135/391] Loss_D: 3.0873 Loss_G: 2.2357 D(x): 0.6616 D(G(z)): 0.4882 / 0.5041\n", + "[90/100][136/391] Loss_D: 3.3958 Loss_G: 1.9503 D(x): 0.5535 D(G(z)): 0.4221 / 0.5481\n", + "[90/100][137/391] Loss_D: 3.1934 Loss_G: 3.1461 D(x): 0.6462 D(G(z)): 0.4964 / 0.3812\n", + "[90/100][138/391] Loss_D: 3.1694 Loss_G: 2.7402 D(x): 0.5521 D(G(z)): 0.3734 / 0.4170\n", + "[90/100][139/391] Loss_D: 3.2806 Loss_G: 2.9774 D(x): 0.6175 D(G(z)): 0.4860 / 0.4049\n", + "[90/100][140/391] Loss_D: 2.8108 Loss_G: 2.2302 D(x): 0.6562 D(G(z)): 0.4360 / 0.4945\n", + "[90/100][141/391] Loss_D: 2.8534 Loss_G: 3.2655 D(x): 0.6645 D(G(z)): 0.3973 / 0.3589\n", + "[90/100][142/391] Loss_D: 3.0177 Loss_G: 2.0780 D(x): 0.6710 D(G(z)): 0.4881 / 0.5233\n", + "[90/100][143/391] Loss_D: 3.1935 Loss_G: 3.0799 D(x): 0.6251 D(G(z)): 0.4596 / 0.3857\n", + "[90/100][144/391] Loss_D: 2.6360 Loss_G: 3.0481 D(x): 0.6842 D(G(z)): 0.4417 / 0.3878\n", + "[90/100][145/391] Loss_D: 3.4228 Loss_G: 2.8061 D(x): 0.6110 D(G(z)): 0.4669 / 0.4187\n", + "[90/100][146/391] Loss_D: 2.7728 Loss_G: 2.9510 D(x): 0.6875 D(G(z)): 0.3840 / 0.3975\n", + "[90/100][147/391] Loss_D: 2.7580 Loss_G: 2.3514 D(x): 0.6795 D(G(z)): 0.3839 / 0.4532\n", + "[90/100][148/391] Loss_D: 1.9308 Loss_G: 2.6082 D(x): 0.7669 D(G(z)): 0.3396 / 0.4336\n", + "[90/100][149/391] Loss_D: 3.2316 Loss_G: 2.9242 D(x): 0.6780 D(G(z)): 0.5341 / 0.4295\n", + "[90/100][150/391] Loss_D: 3.0419 Loss_G: 3.1377 D(x): 0.6784 D(G(z)): 0.4877 / 0.4011\n", + "[90/100][151/391] Loss_D: 3.6672 Loss_G: 2.2989 D(x): 0.6892 D(G(z)): 0.4625 / 0.4939\n", + "[90/100][152/391] Loss_D: 3.4782 Loss_G: 1.8914 D(x): 0.6469 D(G(z)): 0.5641 / 0.5551\n", + "[90/100][153/391] Loss_D: 3.1832 Loss_G: 2.3581 D(x): 0.6184 D(G(z)): 0.4824 / 0.4839\n", + "[90/100][154/391] Loss_D: 2.4024 Loss_G: 2.9730 D(x): 0.6863 D(G(z)): 0.3745 / 0.4130\n", + "[90/100][155/391] Loss_D: 2.9598 Loss_G: 3.2315 D(x): 0.6404 D(G(z)): 0.3497 / 0.3633\n", + "[90/100][156/391] Loss_D: 3.3173 Loss_G: 3.2539 D(x): 0.5526 D(G(z)): 0.4044 / 0.3809\n", + "[90/100][157/391] Loss_D: 2.9973 Loss_G: 2.5043 D(x): 0.6986 D(G(z)): 0.4548 / 0.4565\n", + "[90/100][158/391] Loss_D: 3.1592 Loss_G: 2.4668 D(x): 0.6118 D(G(z)): 0.4792 / 0.4683\n", + "[90/100][159/391] Loss_D: 2.7880 Loss_G: 2.1379 D(x): 0.6752 D(G(z)): 0.3924 / 0.5080\n", + "[90/100][160/391] Loss_D: 2.8688 Loss_G: 3.1214 D(x): 0.7257 D(G(z)): 0.5063 / 0.3990\n", + "[90/100][161/391] Loss_D: 3.1479 Loss_G: 2.1736 D(x): 0.7066 D(G(z)): 0.4900 / 0.5157\n", + "[90/100][162/391] Loss_D: 2.9899 Loss_G: 2.3167 D(x): 0.6647 D(G(z)): 0.4618 / 0.4798\n", + "[90/100][163/391] Loss_D: 3.1590 Loss_G: 2.7954 D(x): 0.6098 D(G(z)): 0.4614 / 0.3991\n", + "[90/100][164/391] Loss_D: 2.6494 Loss_G: 2.0983 D(x): 0.6604 D(G(z)): 0.3917 / 0.5125\n", + "[90/100][165/391] Loss_D: 3.1303 Loss_G: 2.9287 D(x): 0.6574 D(G(z)): 0.4916 / 0.4004\n", + "[90/100][166/391] Loss_D: 3.0168 Loss_G: 2.7078 D(x): 0.6696 D(G(z)): 0.4869 / 0.4384\n", + "[90/100][167/391] Loss_D: 3.0428 Loss_G: 2.5776 D(x): 0.6218 D(G(z)): 0.4099 / 0.4484\n", + "[90/100][168/391] Loss_D: 3.1403 Loss_G: 3.0613 D(x): 0.5993 D(G(z)): 0.4265 / 0.3972\n", + "[90/100][169/391] Loss_D: 2.5396 Loss_G: 2.1279 D(x): 0.6355 D(G(z)): 0.3519 / 0.5256\n", + "[90/100][170/391] Loss_D: 2.9332 Loss_G: 1.9582 D(x): 0.6396 D(G(z)): 0.4165 / 0.5436\n", + "[90/100][171/391] Loss_D: 2.5389 Loss_G: 1.9773 D(x): 0.7687 D(G(z)): 0.3864 / 0.5457\n", + "[90/100][172/391] Loss_D: 3.1926 Loss_G: 2.5896 D(x): 0.6819 D(G(z)): 0.5265 / 0.4452\n", + "[90/100][173/391] Loss_D: 2.9400 Loss_G: 3.0018 D(x): 0.7028 D(G(z)): 0.4810 / 0.3988\n", + "[90/100][174/391] Loss_D: 3.2433 Loss_G: 3.0423 D(x): 0.6459 D(G(z)): 0.5387 / 0.4006\n", + "[90/100][175/391] Loss_D: 2.9256 Loss_G: 2.3004 D(x): 0.5910 D(G(z)): 0.3484 / 0.5094\n", + "[90/100][176/391] Loss_D: 2.9226 Loss_G: 3.1656 D(x): 0.6419 D(G(z)): 0.3817 / 0.3833\n", + "[90/100][177/391] Loss_D: 2.7909 Loss_G: 2.4308 D(x): 0.6837 D(G(z)): 0.3588 / 0.4765\n", + "[90/100][178/391] Loss_D: 2.4494 Loss_G: 2.8770 D(x): 0.7059 D(G(z)): 0.3549 / 0.4013\n", + "[90/100][179/391] Loss_D: 3.1074 Loss_G: 2.6057 D(x): 0.6491 D(G(z)): 0.4517 / 0.4542\n", + "[90/100][180/391] Loss_D: 2.9381 Loss_G: 2.3329 D(x): 0.7215 D(G(z)): 0.4935 / 0.4745\n", + "[90/100][181/391] Loss_D: 3.5602 Loss_G: 2.9367 D(x): 0.6245 D(G(z)): 0.4799 / 0.4102\n", + "[90/100][182/391] Loss_D: 2.5501 Loss_G: 3.1670 D(x): 0.6533 D(G(z)): 0.3577 / 0.3859\n", + "[90/100][183/391] Loss_D: 2.4189 Loss_G: 2.2765 D(x): 0.6458 D(G(z)): 0.3204 / 0.4865\n", + "[90/100][184/391] Loss_D: 3.0637 Loss_G: 2.2141 D(x): 0.7321 D(G(z)): 0.5286 / 0.5001\n", + "[90/100][185/391] Loss_D: 2.7533 Loss_G: 2.7885 D(x): 0.6421 D(G(z)): 0.3737 / 0.4235\n", + "[90/100][186/391] Loss_D: 2.6193 Loss_G: 2.0715 D(x): 0.6671 D(G(z)): 0.3732 / 0.5339\n", + "[90/100][187/391] Loss_D: 2.6199 Loss_G: 2.7128 D(x): 0.6901 D(G(z)): 0.3876 / 0.4336\n", + "[90/100][188/391] Loss_D: 2.5703 Loss_G: 2.8697 D(x): 0.6883 D(G(z)): 0.4469 / 0.4210\n", + "[90/100][189/391] Loss_D: 2.7750 Loss_G: 2.0285 D(x): 0.6462 D(G(z)): 0.3866 / 0.5429\n", + "[90/100][190/391] Loss_D: 2.7078 Loss_G: 2.0582 D(x): 0.6584 D(G(z)): 0.3945 / 0.5517\n", + "[90/100][191/391] Loss_D: 2.8701 Loss_G: 2.4300 D(x): 0.7687 D(G(z)): 0.4712 / 0.4996\n", + "[90/100][192/391] Loss_D: 2.8811 Loss_G: 2.4690 D(x): 0.6110 D(G(z)): 0.4287 / 0.4715\n", + "[90/100][193/391] Loss_D: 2.9294 Loss_G: 3.1203 D(x): 0.6923 D(G(z)): 0.5078 / 0.3959\n", + "[90/100][194/391] Loss_D: 2.6527 Loss_G: 2.5692 D(x): 0.6408 D(G(z)): 0.3446 / 0.4441\n", + "[90/100][195/391] Loss_D: 2.8217 Loss_G: 3.0667 D(x): 0.6573 D(G(z)): 0.3698 / 0.3806\n", + "[90/100][196/391] Loss_D: 2.3757 Loss_G: 2.9096 D(x): 0.7637 D(G(z)): 0.4169 / 0.4021\n", + "[90/100][197/391] Loss_D: 2.9491 Loss_G: 2.1342 D(x): 0.7165 D(G(z)): 0.4590 / 0.5118\n", + "[90/100][198/391] Loss_D: 2.7684 Loss_G: 3.6352 D(x): 0.6318 D(G(z)): 0.2874 / 0.3304\n", + "[90/100][199/391] Loss_D: 3.3961 Loss_G: 2.9683 D(x): 0.5716 D(G(z)): 0.4536 / 0.4145\n", + "[90/100][200/391] Loss_D: 3.4354 Loss_G: 2.7127 D(x): 0.6651 D(G(z)): 0.5765 / 0.4400\n", + "[90/100][201/391] Loss_D: 3.4589 Loss_G: 2.6943 D(x): 0.5944 D(G(z)): 0.4916 / 0.4386\n", + "[90/100][202/391] Loss_D: 2.6784 Loss_G: 2.4092 D(x): 0.7344 D(G(z)): 0.4666 / 0.4859\n", + "[90/100][203/391] Loss_D: 3.0718 Loss_G: 2.9087 D(x): 0.6459 D(G(z)): 0.4950 / 0.4006\n", + "[90/100][204/391] Loss_D: 2.1782 Loss_G: 3.5607 D(x): 0.7807 D(G(z)): 0.4050 / 0.3304\n", + "[90/100][205/391] Loss_D: 2.8227 Loss_G: 2.7285 D(x): 0.6617 D(G(z)): 0.4549 / 0.4411\n", + "[90/100][206/391] Loss_D: 2.7890 Loss_G: 2.3132 D(x): 0.6531 D(G(z)): 0.4322 / 0.4937\n", + "[90/100][207/391] Loss_D: 3.5127 Loss_G: 2.6306 D(x): 0.5492 D(G(z)): 0.4806 / 0.4342\n", + "[90/100][208/391] Loss_D: 3.0024 Loss_G: 2.4976 D(x): 0.5856 D(G(z)): 0.3900 / 0.4593\n", + "[90/100][209/391] Loss_D: 2.7057 Loss_G: 2.6839 D(x): 0.6211 D(G(z)): 0.3131 / 0.4394\n", + "[90/100][210/391] Loss_D: 3.1484 Loss_G: 2.5338 D(x): 0.6456 D(G(z)): 0.4856 / 0.4769\n", + "[90/100][211/391] Loss_D: 3.5746 Loss_G: 2.2706 D(x): 0.6886 D(G(z)): 0.4638 / 0.5109\n", + "[90/100][212/391] Loss_D: 2.6710 Loss_G: 1.9126 D(x): 0.6959 D(G(z)): 0.3584 / 0.5507\n", + "[90/100][213/391] Loss_D: 3.4428 Loss_G: 2.6802 D(x): 0.6722 D(G(z)): 0.5636 / 0.4378\n", + "[90/100][214/391] Loss_D: 2.5881 Loss_G: 3.5118 D(x): 0.7444 D(G(z)): 0.4726 / 0.3335\n", + "[90/100][215/391] Loss_D: 3.3517 Loss_G: 2.4488 D(x): 0.6147 D(G(z)): 0.4892 / 0.4761\n", + "[90/100][216/391] Loss_D: 2.9481 Loss_G: 2.5240 D(x): 0.5553 D(G(z)): 0.3342 / 0.4580\n", + "[90/100][217/391] Loss_D: 2.9955 Loss_G: 2.5183 D(x): 0.6252 D(G(z)): 0.4330 / 0.4552\n", + "[90/100][218/391] Loss_D: 2.4096 Loss_G: 2.3989 D(x): 0.6815 D(G(z)): 0.3197 / 0.4684\n", + "[90/100][219/391] Loss_D: 2.5755 Loss_G: 2.3404 D(x): 0.7304 D(G(z)): 0.4298 / 0.4849\n", + "[90/100][220/391] Loss_D: 3.0267 Loss_G: 2.4898 D(x): 0.5847 D(G(z)): 0.3637 / 0.4678\n", + "[90/100][221/391] Loss_D: 2.6433 Loss_G: 2.1656 D(x): 0.6794 D(G(z)): 0.4089 / 0.5065\n", + "[90/100][222/391] Loss_D: 2.8391 Loss_G: 2.8272 D(x): 0.7315 D(G(z)): 0.4923 / 0.4183\n", + "[90/100][223/391] Loss_D: 3.1355 Loss_G: 1.7700 D(x): 0.5878 D(G(z)): 0.4006 / 0.5648\n", + "[90/100][224/391] Loss_D: 3.0353 Loss_G: 2.0671 D(x): 0.7273 D(G(z)): 0.5450 / 0.5504\n", + "[90/100][225/391] Loss_D: 2.8485 Loss_G: 2.3699 D(x): 0.6853 D(G(z)): 0.4392 / 0.4698\n", + "[90/100][226/391] Loss_D: 2.5753 Loss_G: 2.8505 D(x): 0.7384 D(G(z)): 0.4265 / 0.4287\n", + "[90/100][227/391] Loss_D: 2.8479 Loss_G: 2.4057 D(x): 0.6878 D(G(z)): 0.4555 / 0.4627\n", + "[90/100][228/391] Loss_D: 3.1138 Loss_G: 2.5745 D(x): 0.6722 D(G(z)): 0.5268 / 0.4576\n", + "[90/100][229/391] Loss_D: 2.7634 Loss_G: 2.4925 D(x): 0.6315 D(G(z)): 0.3348 / 0.4698\n", + "[90/100][230/391] Loss_D: 2.5002 Loss_G: 2.5303 D(x): 0.6854 D(G(z)): 0.4041 / 0.4658\n", + "[90/100][231/391] Loss_D: 3.2370 Loss_G: 3.1093 D(x): 0.5876 D(G(z)): 0.4438 / 0.3881\n", + "[90/100][232/391] Loss_D: 2.5884 Loss_G: 2.8076 D(x): 0.7090 D(G(z)): 0.3979 / 0.4224\n", + "[90/100][233/391] Loss_D: 2.6233 Loss_G: 2.6857 D(x): 0.6933 D(G(z)): 0.3989 / 0.4298\n", + "[90/100][234/391] Loss_D: 3.0492 Loss_G: 2.5064 D(x): 0.6611 D(G(z)): 0.4927 / 0.4663\n", + "[90/100][235/391] Loss_D: 2.9299 Loss_G: 2.7958 D(x): 0.6126 D(G(z)): 0.4454 / 0.4189\n", + "[90/100][236/391] Loss_D: 2.8403 Loss_G: 2.9949 D(x): 0.6799 D(G(z)): 0.4306 / 0.3981\n", + "[90/100][237/391] Loss_D: 3.3195 Loss_G: 2.6998 D(x): 0.6965 D(G(z)): 0.5624 / 0.4399\n", + "[90/100][238/391] Loss_D: 3.1423 Loss_G: 2.8428 D(x): 0.5540 D(G(z)): 0.3616 / 0.4186\n", + "[90/100][239/391] Loss_D: 2.7217 Loss_G: 2.4450 D(x): 0.6493 D(G(z)): 0.3748 / 0.4763\n", + "[90/100][240/391] Loss_D: 2.8638 Loss_G: 2.3335 D(x): 0.7044 D(G(z)): 0.4645 / 0.4973\n", + "[90/100][241/391] Loss_D: 3.8954 Loss_G: 2.2817 D(x): 0.7486 D(G(z)): 0.4851 / 0.4913\n", + "[90/100][242/391] Loss_D: 2.6360 Loss_G: 2.7581 D(x): 0.6423 D(G(z)): 0.3464 / 0.4320\n", + "[90/100][243/391] Loss_D: 2.6887 Loss_G: 2.8962 D(x): 0.6968 D(G(z)): 0.4004 / 0.4119\n", + "[90/100][244/391] Loss_D: 2.4492 Loss_G: 2.9214 D(x): 0.7366 D(G(z)): 0.4344 / 0.3901\n", + "[90/100][245/391] Loss_D: 2.9037 Loss_G: 3.3032 D(x): 0.6721 D(G(z)): 0.4914 / 0.3546\n", + "[90/100][246/391] Loss_D: 3.0324 Loss_G: 2.7066 D(x): 0.6185 D(G(z)): 0.4090 / 0.4255\n", + "[90/100][247/391] Loss_D: 3.0569 Loss_G: 3.3029 D(x): 0.6172 D(G(z)): 0.3912 / 0.3739\n", + "[90/100][248/391] Loss_D: 2.9742 Loss_G: 2.4934 D(x): 0.6729 D(G(z)): 0.4876 / 0.4535\n", + "[90/100][249/391] Loss_D: 3.4288 Loss_G: 2.7520 D(x): 0.6789 D(G(z)): 0.5882 / 0.4260\n", + "[90/100][250/391] Loss_D: 2.9526 Loss_G: 2.9073 D(x): 0.6072 D(G(z)): 0.3417 / 0.4200\n", + "[90/100][251/391] Loss_D: 3.0198 Loss_G: 3.2105 D(x): 0.6148 D(G(z)): 0.3915 / 0.3821\n", + "[90/100][252/391] Loss_D: 2.7968 Loss_G: 2.7247 D(x): 0.6919 D(G(z)): 0.4191 / 0.4239\n", + "[90/100][253/391] Loss_D: 2.7439 Loss_G: 2.3677 D(x): 0.7182 D(G(z)): 0.4452 / 0.5001\n", + "[90/100][254/391] Loss_D: 2.5800 Loss_G: 2.9407 D(x): 0.6673 D(G(z)): 0.4046 / 0.4035\n", + "[90/100][255/391] Loss_D: 3.3407 Loss_G: 2.9347 D(x): 0.5439 D(G(z)): 0.4057 / 0.4051\n", + "[90/100][256/391] Loss_D: 3.4876 Loss_G: 2.6562 D(x): 0.5958 D(G(z)): 0.5278 / 0.4234\n", + "[90/100][257/391] Loss_D: 2.8417 Loss_G: 2.8392 D(x): 0.6378 D(G(z)): 0.3578 / 0.4027\n", + "[90/100][258/391] Loss_D: 3.0091 Loss_G: 2.5690 D(x): 0.6900 D(G(z)): 0.5077 / 0.4315\n", + "[90/100][259/391] Loss_D: 3.0600 Loss_G: 2.4470 D(x): 0.6638 D(G(z)): 0.4704 / 0.4754\n", + "[90/100][260/391] Loss_D: 3.0375 Loss_G: 2.7116 D(x): 0.6460 D(G(z)): 0.4745 / 0.4489\n", + "[90/100][261/391] Loss_D: 3.0683 Loss_G: 3.9447 D(x): 0.7117 D(G(z)): 0.4850 / 0.3022\n", + "[90/100][262/391] Loss_D: 2.6317 Loss_G: 2.6385 D(x): 0.6479 D(G(z)): 0.3123 / 0.4483\n", + "[90/100][263/391] Loss_D: 3.2632 Loss_G: 2.9836 D(x): 0.6336 D(G(z)): 0.4373 / 0.3937\n", + "[90/100][264/391] Loss_D: 2.8484 Loss_G: 2.7608 D(x): 0.5852 D(G(z)): 0.3668 / 0.4525\n", + "[90/100][265/391] Loss_D: 3.6416 Loss_G: 2.4171 D(x): 0.6674 D(G(z)): 0.5943 / 0.4870\n", + "[90/100][266/391] Loss_D: 2.4890 Loss_G: 2.4973 D(x): 0.7967 D(G(z)): 0.3727 / 0.4488\n", + "[90/100][267/391] Loss_D: 2.9090 Loss_G: 2.5097 D(x): 0.6298 D(G(z)): 0.4256 / 0.4604\n", + "[90/100][268/391] Loss_D: 2.6058 Loss_G: 3.5088 D(x): 0.6195 D(G(z)): 0.3291 / 0.3467\n", + "[90/100][269/391] Loss_D: 2.8306 Loss_G: 2.9889 D(x): 0.7006 D(G(z)): 0.4301 / 0.3977\n", + "[90/100][270/391] Loss_D: 2.4679 Loss_G: 2.8809 D(x): 0.6967 D(G(z)): 0.3600 / 0.4205\n", + "[90/100][271/391] Loss_D: 3.6612 Loss_G: 1.8978 D(x): 0.6962 D(G(z)): 0.4804 / 0.5718\n", + "[90/100][272/391] Loss_D: 2.4849 Loss_G: 2.6948 D(x): 0.7432 D(G(z)): 0.4019 / 0.4510\n", + "[90/100][273/391] Loss_D: 3.2211 Loss_G: 2.8161 D(x): 0.6218 D(G(z)): 0.4573 / 0.4170\n", + "[90/100][274/391] Loss_D: 3.3338 Loss_G: 2.5638 D(x): 0.6572 D(G(z)): 0.5257 / 0.4457\n", + "[90/100][275/391] Loss_D: 2.6305 Loss_G: 3.3533 D(x): 0.7062 D(G(z)): 0.3870 / 0.3554\n", + "[90/100][276/391] Loss_D: 3.0988 Loss_G: 3.9401 D(x): 0.5578 D(G(z)): 0.3435 / 0.2891\n", + "[90/100][277/391] Loss_D: 3.4115 Loss_G: 2.8648 D(x): 0.6692 D(G(z)): 0.5626 / 0.4097\n", + "[90/100][278/391] Loss_D: 2.5494 Loss_G: 2.5699 D(x): 0.6808 D(G(z)): 0.4055 / 0.4578\n", + "[90/100][279/391] Loss_D: 2.5792 Loss_G: 2.6235 D(x): 0.6669 D(G(z)): 0.3710 / 0.4529\n", + "[90/100][280/391] Loss_D: 2.8637 Loss_G: 2.6707 D(x): 0.6561 D(G(z)): 0.4483 / 0.4401\n", + "[90/100][281/391] Loss_D: 3.1840 Loss_G: 2.6474 D(x): 0.6732 D(G(z)): 0.4949 / 0.4411\n", + "[90/100][282/391] Loss_D: 2.7262 Loss_G: 2.8226 D(x): 0.6663 D(G(z)): 0.3712 / 0.4206\n", + "[90/100][283/391] Loss_D: 3.4560 Loss_G: 3.0517 D(x): 0.6229 D(G(z)): 0.5076 / 0.3948\n", + "[90/100][284/391] Loss_D: 2.5442 Loss_G: 2.0654 D(x): 0.6909 D(G(z)): 0.4238 / 0.5426\n", + "[90/100][285/391] Loss_D: 2.6681 Loss_G: 2.1832 D(x): 0.6858 D(G(z)): 0.3989 / 0.5064\n", + "[90/100][286/391] Loss_D: 3.3351 Loss_G: 3.6046 D(x): 0.5487 D(G(z)): 0.4310 / 0.3264\n", + "[90/100][287/391] Loss_D: 2.7501 Loss_G: 2.7955 D(x): 0.7352 D(G(z)): 0.4187 / 0.4204\n", + "[90/100][288/391] Loss_D: 3.2423 Loss_G: 2.8800 D(x): 0.5648 D(G(z)): 0.4711 / 0.4138\n", + "[90/100][289/391] Loss_D: 3.2985 Loss_G: 2.3192 D(x): 0.5808 D(G(z)): 0.3793 / 0.4933\n", + "[90/100][290/391] Loss_D: 2.7920 Loss_G: 2.4281 D(x): 0.6840 D(G(z)): 0.4363 / 0.4621\n", + "[90/100][291/391] Loss_D: 2.9215 Loss_G: 2.3823 D(x): 0.6858 D(G(z)): 0.4444 / 0.4690\n", + "[90/100][292/391] Loss_D: 2.8497 Loss_G: 2.4268 D(x): 0.7330 D(G(z)): 0.4667 / 0.4684\n", + "[90/100][293/391] Loss_D: 3.1864 Loss_G: 2.0985 D(x): 0.5767 D(G(z)): 0.4311 / 0.5258\n", + "[90/100][294/391] Loss_D: 2.7282 Loss_G: 2.2439 D(x): 0.7156 D(G(z)): 0.4801 / 0.4790\n", + "[90/100][295/391] Loss_D: 2.9574 Loss_G: 2.5321 D(x): 0.5939 D(G(z)): 0.3513 / 0.4663\n", + "[90/100][296/391] Loss_D: 2.7222 Loss_G: 2.2818 D(x): 0.6921 D(G(z)): 0.4260 / 0.4979\n", + "[90/100][297/391] Loss_D: 3.0515 Loss_G: 3.0198 D(x): 0.6842 D(G(z)): 0.4737 / 0.3912\n", + "[90/100][298/391] Loss_D: 2.5233 Loss_G: 3.9450 D(x): 0.7004 D(G(z)): 0.3894 / 0.2997\n", + "[90/100][299/391] Loss_D: 2.6431 Loss_G: 2.8776 D(x): 0.6774 D(G(z)): 0.3749 / 0.4181\n", + "[90/100][300/391] Loss_D: 4.3989 Loss_G: 2.5889 D(x): 0.5993 D(G(z)): 0.6677 / 0.4580\n", + "[90/100][301/391] Loss_D: 3.6254 Loss_G: 3.4297 D(x): 0.6518 D(G(z)): 0.4732 / 0.3596\n", + "[90/100][302/391] Loss_D: 2.5285 Loss_G: 3.0371 D(x): 0.6624 D(G(z)): 0.3370 / 0.3947\n", + "[90/100][303/391] Loss_D: 2.8865 Loss_G: 2.4581 D(x): 0.5722 D(G(z)): 0.3483 / 0.4601\n", + "[90/100][304/391] Loss_D: 2.7477 Loss_G: 2.7149 D(x): 0.6047 D(G(z)): 0.3967 / 0.4143\n", + "[90/100][305/391] Loss_D: 2.9765 Loss_G: 2.7918 D(x): 0.5826 D(G(z)): 0.3867 / 0.4092\n", + "[90/100][306/391] Loss_D: 3.3768 Loss_G: 2.4188 D(x): 0.6823 D(G(z)): 0.5164 / 0.4747\n", + "[90/100][307/391] Loss_D: 3.1111 Loss_G: 2.6557 D(x): 0.6956 D(G(z)): 0.5349 / 0.4178\n", + "[90/100][308/391] Loss_D: 3.5745 Loss_G: 2.5010 D(x): 0.5633 D(G(z)): 0.5247 / 0.4624\n", + "[90/100][309/391] Loss_D: 2.7534 Loss_G: 2.9084 D(x): 0.7150 D(G(z)): 0.4887 / 0.4271\n", + "[90/100][310/391] Loss_D: 2.9992 Loss_G: 2.5280 D(x): 0.7010 D(G(z)): 0.4510 / 0.4803\n", + "[90/100][311/391] Loss_D: 3.0002 Loss_G: 1.9670 D(x): 0.6222 D(G(z)): 0.3860 / 0.5523\n", + "[90/100][312/391] Loss_D: 2.9659 Loss_G: 2.9554 D(x): 0.6079 D(G(z)): 0.3814 / 0.4111\n", + "[90/100][313/391] Loss_D: 3.0764 Loss_G: 2.3218 D(x): 0.6666 D(G(z)): 0.4845 / 0.4887\n", + "[90/100][314/391] Loss_D: 2.7354 Loss_G: 2.7731 D(x): 0.5767 D(G(z)): 0.3419 / 0.4149\n", + "[90/100][315/391] Loss_D: 2.7929 Loss_G: 2.0290 D(x): 0.6552 D(G(z)): 0.4010 / 0.5245\n", + "[90/100][316/391] Loss_D: 2.8379 Loss_G: 2.2278 D(x): 0.6414 D(G(z)): 0.3881 / 0.4938\n", + "[90/100][317/391] Loss_D: 2.9269 Loss_G: 2.6998 D(x): 0.6773 D(G(z)): 0.4525 / 0.4255\n", + "[90/100][318/391] Loss_D: 2.4724 Loss_G: 3.0229 D(x): 0.7090 D(G(z)): 0.3932 / 0.3982\n", + "[90/100][319/391] Loss_D: 3.2308 Loss_G: 3.3664 D(x): 0.7076 D(G(z)): 0.5591 / 0.3567\n", + "[90/100][320/391] Loss_D: 3.3383 Loss_G: 3.2808 D(x): 0.5984 D(G(z)): 0.4683 / 0.3747\n", + "[90/100][321/391] Loss_D: 2.9460 Loss_G: 2.6338 D(x): 0.6548 D(G(z)): 0.4303 / 0.4476\n", + "[90/100][322/391] Loss_D: 2.5368 Loss_G: 2.8568 D(x): 0.7027 D(G(z)): 0.4012 / 0.4226\n", + "[90/100][323/391] Loss_D: 2.8246 Loss_G: 3.2109 D(x): 0.7103 D(G(z)): 0.4297 / 0.3798\n", + "[90/100][324/391] Loss_D: 2.7729 Loss_G: 2.4420 D(x): 0.7537 D(G(z)): 0.5115 / 0.4739\n", + "[90/100][325/391] Loss_D: 3.4117 Loss_G: 3.3154 D(x): 0.5838 D(G(z)): 0.4412 / 0.3692\n", + "[90/100][326/391] Loss_D: 2.7902 Loss_G: 2.8819 D(x): 0.6646 D(G(z)): 0.3910 / 0.4149\n", + "[90/100][327/391] Loss_D: 3.4712 Loss_G: 2.8605 D(x): 0.5938 D(G(z)): 0.5222 / 0.4167\n", + "[90/100][328/391] Loss_D: 2.8549 Loss_G: 2.6038 D(x): 0.6612 D(G(z)): 0.4630 / 0.4517\n", + "[90/100][329/391] Loss_D: 3.5606 Loss_G: 2.9832 D(x): 0.6009 D(G(z)): 0.5438 / 0.4175\n", + "[90/100][330/391] Loss_D: 2.7720 Loss_G: 2.7094 D(x): 0.6791 D(G(z)): 0.4233 / 0.4464\n", + "[90/100][331/391] Loss_D: 3.4625 Loss_G: 2.3978 D(x): 0.6452 D(G(z)): 0.4093 / 0.4577\n", + "[90/100][332/391] Loss_D: 2.2361 Loss_G: 2.9785 D(x): 0.7280 D(G(z)): 0.3295 / 0.3993\n", + "[90/100][333/391] Loss_D: 3.2124 Loss_G: 2.5871 D(x): 0.6206 D(G(z)): 0.4091 / 0.4563\n", + "[90/100][334/391] Loss_D: 2.3019 Loss_G: 2.1724 D(x): 0.6880 D(G(z)): 0.3817 / 0.5082\n", + "[90/100][335/391] Loss_D: 3.3114 Loss_G: 2.1625 D(x): 0.5789 D(G(z)): 0.4658 / 0.4979\n", + "[90/100][336/391] Loss_D: 2.9929 Loss_G: 2.3027 D(x): 0.6203 D(G(z)): 0.3764 / 0.4975\n", + "[90/100][337/391] Loss_D: 3.1442 Loss_G: 2.8445 D(x): 0.6693 D(G(z)): 0.5051 / 0.4027\n", + "[90/100][338/391] Loss_D: 2.8337 Loss_G: 1.6519 D(x): 0.7095 D(G(z)): 0.4787 / 0.5908\n", + "[90/100][339/391] Loss_D: 3.6235 Loss_G: 2.2102 D(x): 0.5975 D(G(z)): 0.5623 / 0.5269\n", + "[90/100][340/391] Loss_D: 3.3202 Loss_G: 2.4979 D(x): 0.5887 D(G(z)): 0.4668 / 0.4695\n", + "[90/100][341/391] Loss_D: 3.1180 Loss_G: 2.5391 D(x): 0.6396 D(G(z)): 0.5049 / 0.4495\n", + "[90/100][342/391] Loss_D: 2.8921 Loss_G: 3.0359 D(x): 0.6576 D(G(z)): 0.4239 / 0.3825\n", + "[90/100][343/391] Loss_D: 3.0710 Loss_G: 3.3645 D(x): 0.7314 D(G(z)): 0.5184 / 0.3464\n", + "[90/100][344/391] Loss_D: 2.7106 Loss_G: 3.3951 D(x): 0.6945 D(G(z)): 0.4412 / 0.3573\n", + "[90/100][345/391] Loss_D: 2.6331 Loss_G: 2.8999 D(x): 0.6607 D(G(z)): 0.3576 / 0.4174\n", + "[90/100][346/391] Loss_D: 3.6355 Loss_G: 2.6456 D(x): 0.5857 D(G(z)): 0.5249 / 0.4409\n", + "[90/100][347/391] Loss_D: 3.5414 Loss_G: 3.3524 D(x): 0.5297 D(G(z)): 0.4677 / 0.3566\n", + "[90/100][348/391] Loss_D: 3.0243 Loss_G: 2.8142 D(x): 0.5723 D(G(z)): 0.3552 / 0.4148\n", + "[90/100][349/391] Loss_D: 2.4325 Loss_G: 2.2616 D(x): 0.7607 D(G(z)): 0.4112 / 0.5043\n", + "[90/100][350/391] Loss_D: 2.6951 Loss_G: 2.1220 D(x): 0.6794 D(G(z)): 0.4311 / 0.5221\n", + "[90/100][351/391] Loss_D: 3.3397 Loss_G: 3.2867 D(x): 0.6267 D(G(z)): 0.4910 / 0.3606\n", + "[90/100][352/391] Loss_D: 2.2835 Loss_G: 2.6048 D(x): 0.7207 D(G(z)): 0.3713 / 0.4430\n", + "[90/100][353/391] Loss_D: 3.2335 Loss_G: 3.9174 D(x): 0.5670 D(G(z)): 0.4307 / 0.2903\n", + "[90/100][354/391] Loss_D: 3.1344 Loss_G: 2.8021 D(x): 0.6547 D(G(z)): 0.5115 / 0.4267\n", + "[90/100][355/391] Loss_D: 2.7104 Loss_G: 2.6365 D(x): 0.6276 D(G(z)): 0.3739 / 0.4285\n", + "[90/100][356/391] Loss_D: 2.7201 Loss_G: 2.8094 D(x): 0.6456 D(G(z)): 0.3813 / 0.4022\n", + "[90/100][357/391] Loss_D: 2.9741 Loss_G: 2.2021 D(x): 0.6989 D(G(z)): 0.5133 / 0.5066\n", + "[90/100][358/391] Loss_D: 2.4214 Loss_G: 2.5639 D(x): 0.6788 D(G(z)): 0.3720 / 0.4573\n", + "[90/100][359/391] Loss_D: 2.8338 Loss_G: 2.1624 D(x): 0.5982 D(G(z)): 0.3607 / 0.5251\n", + "[90/100][360/391] Loss_D: 2.5022 Loss_G: 2.2053 D(x): 0.6972 D(G(z)): 0.3610 / 0.5013\n", + "[90/100][361/391] Loss_D: 3.4774 Loss_G: 2.5481 D(x): 0.5955 D(G(z)): 0.4272 / 0.4546\n", + "[90/100][362/391] Loss_D: 2.9137 Loss_G: 2.7533 D(x): 0.6994 D(G(z)): 0.4621 / 0.4364\n", + "[90/100][363/391] Loss_D: 3.0588 Loss_G: 2.7169 D(x): 0.6586 D(G(z)): 0.4723 / 0.4315\n", + "[90/100][364/391] Loss_D: 2.7650 Loss_G: 3.2755 D(x): 0.7024 D(G(z)): 0.4392 / 0.3617\n", + "[90/100][365/391] Loss_D: 3.0337 Loss_G: 3.3670 D(x): 0.5747 D(G(z)): 0.3368 / 0.3520\n", + "[90/100][366/391] Loss_D: 3.0362 Loss_G: 2.7823 D(x): 0.6891 D(G(z)): 0.5355 / 0.4295\n", + "[90/100][367/391] Loss_D: 3.1487 Loss_G: 2.5502 D(x): 0.6782 D(G(z)): 0.4867 / 0.4482\n", + "[90/100][368/391] Loss_D: 3.0065 Loss_G: 2.8455 D(x): 0.6529 D(G(z)): 0.4838 / 0.4229\n", + "[90/100][369/391] Loss_D: 2.6201 Loss_G: 2.4648 D(x): 0.6479 D(G(z)): 0.3515 / 0.4905\n", + "[90/100][370/391] Loss_D: 3.0361 Loss_G: 2.3333 D(x): 0.6508 D(G(z)): 0.4964 / 0.4780\n", + "[90/100][371/391] Loss_D: 3.3802 Loss_G: 3.8949 D(x): 0.6652 D(G(z)): 0.5369 / 0.3086\n", + "[90/100][372/391] Loss_D: 3.2670 Loss_G: 2.1971 D(x): 0.6334 D(G(z)): 0.5305 / 0.5248\n", + "[90/100][373/391] Loss_D: 3.0820 Loss_G: 3.0510 D(x): 0.6972 D(G(z)): 0.5119 / 0.3979\n", + "[90/100][374/391] Loss_D: 2.6511 Loss_G: 2.4370 D(x): 0.6560 D(G(z)): 0.3566 / 0.4647\n", + "[90/100][375/391] Loss_D: 2.9288 Loss_G: 2.4408 D(x): 0.6568 D(G(z)): 0.4363 / 0.4565\n", + "[90/100][376/391] Loss_D: 3.2342 Loss_G: 2.4215 D(x): 0.6075 D(G(z)): 0.4482 / 0.4789\n", + "[90/100][377/391] Loss_D: 3.1815 Loss_G: 3.2851 D(x): 0.5860 D(G(z)): 0.3865 / 0.3607\n", + "[90/100][378/391] Loss_D: 2.9271 Loss_G: 1.7563 D(x): 0.6331 D(G(z)): 0.4529 / 0.5698\n", + "[90/100][379/391] Loss_D: 2.4339 Loss_G: 3.0468 D(x): 0.7166 D(G(z)): 0.3908 / 0.3884\n", + "[90/100][380/391] Loss_D: 2.4904 Loss_G: 2.4211 D(x): 0.6983 D(G(z)): 0.3855 / 0.4838\n", + "[90/100][381/391] Loss_D: 3.0288 Loss_G: 1.9002 D(x): 0.5834 D(G(z)): 0.3791 / 0.5462\n", + "[90/100][382/391] Loss_D: 2.9926 Loss_G: 2.4904 D(x): 0.5884 D(G(z)): 0.4111 / 0.4709\n", + "[90/100][383/391] Loss_D: 2.7515 Loss_G: 2.3693 D(x): 0.6580 D(G(z)): 0.4042 / 0.4789\n", + "[90/100][384/391] Loss_D: 2.5553 Loss_G: 2.7479 D(x): 0.6835 D(G(z)): 0.4443 / 0.4197\n", + "[90/100][385/391] Loss_D: 2.5015 Loss_G: 2.1462 D(x): 0.7389 D(G(z)): 0.4114 / 0.5123\n", + "[90/100][386/391] Loss_D: 2.8419 Loss_G: 3.2069 D(x): 0.6656 D(G(z)): 0.4113 / 0.3672\n", + "[90/100][387/391] Loss_D: 2.6967 Loss_G: 2.8697 D(x): 0.7532 D(G(z)): 0.4394 / 0.4127\n", + "[90/100][388/391] Loss_D: 2.9956 Loss_G: 3.2018 D(x): 0.6277 D(G(z)): 0.5044 / 0.3939\n", + "[90/100][389/391] Loss_D: 2.7148 Loss_G: 2.4971 D(x): 0.6732 D(G(z)): 0.3972 / 0.4712\n", + "[90/100][390/391] Loss_D: 2.4985 Loss_G: 2.0273 D(x): 0.7141 D(G(z)): 0.4217 / 0.5408\n", + "[90/100][391/391] Loss_D: 3.8347 Loss_G: 2.2114 D(x): 0.6409 D(G(z)): 0.5341 / 0.4991\n", + "[91/100][1/391] Loss_D: 3.6940 Loss_G: 2.8243 D(x): 0.6774 D(G(z)): 0.5420 / 0.4345\n", + "[91/100][2/391] Loss_D: 2.6889 Loss_G: 3.7649 D(x): 0.6547 D(G(z)): 0.3825 / 0.2999\n", + "[91/100][3/391] Loss_D: 3.0617 Loss_G: 3.2915 D(x): 0.6104 D(G(z)): 0.4373 / 0.3684\n", + "[91/100][4/391] Loss_D: 2.5592 Loss_G: 2.6386 D(x): 0.6399 D(G(z)): 0.3503 / 0.4295\n", + "[91/100][5/391] Loss_D: 3.2485 Loss_G: 2.7360 D(x): 0.5982 D(G(z)): 0.4837 / 0.4301\n", + "[91/100][6/391] Loss_D: 3.1161 Loss_G: 4.0537 D(x): 0.6636 D(G(z)): 0.4730 / 0.2767\n", + "[91/100][7/391] Loss_D: 3.1854 Loss_G: 2.6816 D(x): 0.6942 D(G(z)): 0.4764 / 0.4339\n", + "[91/100][8/391] Loss_D: 3.3424 Loss_G: 2.9774 D(x): 0.6273 D(G(z)): 0.5772 / 0.4056\n", + "[91/100][9/391] Loss_D: 2.7434 Loss_G: 3.0323 D(x): 0.6341 D(G(z)): 0.3645 / 0.3997\n", + "[91/100][10/391] Loss_D: 2.9414 Loss_G: 2.5554 D(x): 0.6320 D(G(z)): 0.4383 / 0.4652\n", + "[91/100][11/391] Loss_D: 2.7466 Loss_G: 2.9838 D(x): 0.6429 D(G(z)): 0.3726 / 0.4086\n", + "[91/100][12/391] Loss_D: 2.5140 Loss_G: 2.9773 D(x): 0.7227 D(G(z)): 0.3710 / 0.3991\n", + "[91/100][13/391] Loss_D: 3.1430 Loss_G: 3.2989 D(x): 0.6520 D(G(z)): 0.5196 / 0.3652\n", + "[91/100][14/391] Loss_D: 2.8104 Loss_G: 3.3645 D(x): 0.5952 D(G(z)): 0.3812 / 0.3417\n", + "[91/100][15/391] Loss_D: 2.9505 Loss_G: 2.9309 D(x): 0.6858 D(G(z)): 0.5083 / 0.3991\n", + "[91/100][16/391] Loss_D: 2.9474 Loss_G: 2.4304 D(x): 0.5586 D(G(z)): 0.2791 / 0.4729\n", + "[91/100][17/391] Loss_D: 3.1021 Loss_G: 2.8561 D(x): 0.6429 D(G(z)): 0.4975 / 0.4106\n", + "[91/100][18/391] Loss_D: 2.8816 Loss_G: 2.8873 D(x): 0.7026 D(G(z)): 0.5056 / 0.4040\n", + "[91/100][19/391] Loss_D: 2.6538 Loss_G: 2.3391 D(x): 0.6958 D(G(z)): 0.4421 / 0.4961\n", + "[91/100][20/391] Loss_D: 3.1308 Loss_G: 1.7770 D(x): 0.6285 D(G(z)): 0.4764 / 0.5814\n", + "[91/100][21/391] Loss_D: 2.6299 Loss_G: 4.1617 D(x): 0.7181 D(G(z)): 0.4825 / 0.2773\n", + "[91/100][22/391] Loss_D: 2.9232 Loss_G: 2.8341 D(x): 0.6262 D(G(z)): 0.4374 / 0.4199\n", + "[91/100][23/391] Loss_D: 4.1279 Loss_G: 2.3064 D(x): 0.4219 D(G(z)): 0.4483 / 0.4879\n", + "[91/100][24/391] Loss_D: 3.1774 Loss_G: 2.6034 D(x): 0.6201 D(G(z)): 0.4488 / 0.4456\n", + "[91/100][25/391] Loss_D: 3.5021 Loss_G: 3.0596 D(x): 0.7574 D(G(z)): 0.6187 / 0.3913\n", + "[91/100][26/391] Loss_D: 3.7391 Loss_G: 3.6795 D(x): 0.5377 D(G(z)): 0.5020 / 0.3138\n", + "[91/100][27/391] Loss_D: 3.0647 Loss_G: 2.6677 D(x): 0.6151 D(G(z)): 0.3727 / 0.4257\n", + "[91/100][28/391] Loss_D: 2.8024 Loss_G: 1.9909 D(x): 0.6295 D(G(z)): 0.4152 / 0.5401\n", + "[91/100][29/391] Loss_D: 2.9116 Loss_G: 2.4462 D(x): 0.6381 D(G(z)): 0.3937 / 0.4608\n", + "[91/100][30/391] Loss_D: 3.1819 Loss_G: 2.8323 D(x): 0.6483 D(G(z)): 0.5288 / 0.4097\n", + "[91/100][31/391] Loss_D: 3.5289 Loss_G: 2.4930 D(x): 0.6881 D(G(z)): 0.4301 / 0.4607\n", + "[91/100][32/391] Loss_D: 3.1333 Loss_G: 2.8323 D(x): 0.6097 D(G(z)): 0.4302 / 0.4266\n", + "[91/100][33/391] Loss_D: 2.9622 Loss_G: 1.9511 D(x): 0.6270 D(G(z)): 0.4171 / 0.5583\n", + "[91/100][34/391] Loss_D: 2.7235 Loss_G: 3.1442 D(x): 0.6775 D(G(z)): 0.4545 / 0.3788\n", + "[91/100][35/391] Loss_D: 2.7501 Loss_G: 2.6188 D(x): 0.6538 D(G(z)): 0.4070 / 0.4293\n", + "[91/100][36/391] Loss_D: 3.4447 Loss_G: 2.5213 D(x): 0.5901 D(G(z)): 0.4863 / 0.4584\n", + "[91/100][37/391] Loss_D: 2.8622 Loss_G: 2.8486 D(x): 0.6556 D(G(z)): 0.3990 / 0.4257\n", + "[91/100][38/391] Loss_D: 2.3403 Loss_G: 2.6249 D(x): 0.7582 D(G(z)): 0.4297 / 0.4582\n", + "[91/100][39/391] Loss_D: 3.0293 Loss_G: 2.6751 D(x): 0.6247 D(G(z)): 0.3885 / 0.4496\n", + "[91/100][40/391] Loss_D: 3.1470 Loss_G: 2.2939 D(x): 0.7115 D(G(z)): 0.5089 / 0.4896\n", + "[91/100][41/391] Loss_D: 3.0733 Loss_G: 3.1601 D(x): 0.6397 D(G(z)): 0.4264 / 0.3812\n", + "[91/100][42/391] Loss_D: 2.6607 Loss_G: 2.1071 D(x): 0.7377 D(G(z)): 0.4343 / 0.5083\n", + "[91/100][43/391] Loss_D: 3.2092 Loss_G: 2.5830 D(x): 0.5985 D(G(z)): 0.4337 / 0.4504\n", + "[91/100][44/391] Loss_D: 2.6551 Loss_G: 2.4223 D(x): 0.6954 D(G(z)): 0.4407 / 0.4770\n", + "[91/100][45/391] Loss_D: 3.2236 Loss_G: 3.2427 D(x): 0.6453 D(G(z)): 0.4638 / 0.3617\n", + "[91/100][46/391] Loss_D: 2.9536 Loss_G: 3.5989 D(x): 0.7025 D(G(z)): 0.4792 / 0.3314\n", + "[91/100][47/391] Loss_D: 2.9616 Loss_G: 3.5478 D(x): 0.5926 D(G(z)): 0.4047 / 0.3213\n", + "[91/100][48/391] Loss_D: 2.5223 Loss_G: 2.9300 D(x): 0.6567 D(G(z)): 0.3141 / 0.4216\n", + "[91/100][49/391] Loss_D: 2.8973 Loss_G: 2.8467 D(x): 0.6155 D(G(z)): 0.4193 / 0.4169\n", + "[91/100][50/391] Loss_D: 3.0340 Loss_G: 2.3866 D(x): 0.6463 D(G(z)): 0.4928 / 0.4724\n", + "[91/100][51/391] Loss_D: 3.3031 Loss_G: 2.8133 D(x): 0.6040 D(G(z)): 0.4793 / 0.4173\n", + "[91/100][52/391] Loss_D: 2.7960 Loss_G: 2.7739 D(x): 0.6095 D(G(z)): 0.3755 / 0.4404\n", + "[91/100][53/391] Loss_D: 2.8625 Loss_G: 2.2727 D(x): 0.6209 D(G(z)): 0.3853 / 0.5018\n", + "[91/100][54/391] Loss_D: 3.9236 Loss_G: 2.2791 D(x): 0.5560 D(G(z)): 0.5391 / 0.4835\n", + "[91/100][55/391] Loss_D: 3.2688 Loss_G: 2.5596 D(x): 0.6262 D(G(z)): 0.5121 / 0.4522\n", + "[91/100][56/391] Loss_D: 3.4363 Loss_G: 2.5227 D(x): 0.5801 D(G(z)): 0.4694 / 0.4449\n", + "[91/100][57/391] Loss_D: 2.8937 Loss_G: 3.2527 D(x): 0.7354 D(G(z)): 0.4842 / 0.3444\n", + "[91/100][58/391] Loss_D: 2.4175 Loss_G: 3.1411 D(x): 0.7035 D(G(z)): 0.3581 / 0.3723\n", + "[91/100][59/391] Loss_D: 2.8805 Loss_G: 3.0802 D(x): 0.7494 D(G(z)): 0.5137 / 0.3826\n", + "[91/100][60/391] Loss_D: 3.0788 Loss_G: 3.3928 D(x): 0.6220 D(G(z)): 0.4613 / 0.3559\n", + "[91/100][61/391] Loss_D: 3.5746 Loss_G: 2.3180 D(x): 0.6088 D(G(z)): 0.3767 / 0.4995\n", + "[91/100][62/391] Loss_D: 3.2837 Loss_G: 3.1312 D(x): 0.6209 D(G(z)): 0.4833 / 0.3884\n", + "[91/100][63/391] Loss_D: 3.2222 Loss_G: 2.9853 D(x): 0.5400 D(G(z)): 0.3785 / 0.3968\n", + "[91/100][64/391] Loss_D: 3.2646 Loss_G: 2.4992 D(x): 0.5920 D(G(z)): 0.4762 / 0.4612\n", + "[91/100][65/391] Loss_D: 2.8780 Loss_G: 2.6837 D(x): 0.7016 D(G(z)): 0.4816 / 0.4224\n", + "[91/100][66/391] Loss_D: 2.8865 Loss_G: 2.9227 D(x): 0.6612 D(G(z)): 0.4264 / 0.4143\n", + "[91/100][67/391] Loss_D: 3.2939 Loss_G: 2.9510 D(x): 0.6265 D(G(z)): 0.4716 / 0.4024\n", + "[91/100][68/391] Loss_D: 2.9539 Loss_G: 2.1204 D(x): 0.6507 D(G(z)): 0.4972 / 0.5056\n", + "[91/100][69/391] Loss_D: 3.1007 Loss_G: 2.5011 D(x): 0.5948 D(G(z)): 0.3634 / 0.4625\n", + "[91/100][70/391] Loss_D: 3.1448 Loss_G: 3.3029 D(x): 0.6319 D(G(z)): 0.4785 / 0.3757\n", + "[91/100][71/391] Loss_D: 3.1593 Loss_G: 2.4471 D(x): 0.6798 D(G(z)): 0.4792 / 0.4677\n", + "[91/100][72/391] Loss_D: 2.4906 Loss_G: 2.1387 D(x): 0.7115 D(G(z)): 0.4068 / 0.5228\n", + "[91/100][73/391] Loss_D: 3.2631 Loss_G: 2.6519 D(x): 0.6131 D(G(z)): 0.4971 / 0.4389\n", + "[91/100][74/391] Loss_D: 2.1683 Loss_G: 2.7926 D(x): 0.7820 D(G(z)): 0.4047 / 0.4187\n", + "[91/100][75/391] Loss_D: 3.5432 Loss_G: 2.8385 D(x): 0.6288 D(G(z)): 0.5567 / 0.4158\n", + "[91/100][76/391] Loss_D: 2.5999 Loss_G: 3.6891 D(x): 0.6685 D(G(z)): 0.3684 / 0.3139\n", + "[91/100][77/391] Loss_D: 3.7416 Loss_G: 3.1184 D(x): 0.5486 D(G(z)): 0.5129 / 0.3836\n", + "[91/100][78/391] Loss_D: 3.2827 Loss_G: 3.0356 D(x): 0.5171 D(G(z)): 0.3123 / 0.3845\n", + "[91/100][79/391] Loss_D: 2.9244 Loss_G: 2.3373 D(x): 0.6443 D(G(z)): 0.4570 / 0.4902\n", + "[91/100][80/391] Loss_D: 3.1251 Loss_G: 3.0810 D(x): 0.6670 D(G(z)): 0.4593 / 0.3868\n", + "[91/100][81/391] Loss_D: 3.4783 Loss_G: 3.0660 D(x): 0.6499 D(G(z)): 0.5800 / 0.3893\n", + "[91/100][82/391] Loss_D: 3.2016 Loss_G: 3.3932 D(x): 0.6945 D(G(z)): 0.5089 / 0.3453\n", + "[91/100][83/391] Loss_D: 2.7149 Loss_G: 3.5010 D(x): 0.7134 D(G(z)): 0.4433 / 0.3434\n", + "[91/100][84/391] Loss_D: 3.2037 Loss_G: 3.3199 D(x): 0.5904 D(G(z)): 0.4696 / 0.3709\n", + "[91/100][85/391] Loss_D: 3.8415 Loss_G: 3.6756 D(x): 0.5769 D(G(z)): 0.5698 / 0.3344\n", + "[91/100][86/391] Loss_D: 3.6617 Loss_G: 2.6366 D(x): 0.5123 D(G(z)): 0.4518 / 0.4382\n", + "[91/100][87/391] Loss_D: 3.2586 Loss_G: 3.1238 D(x): 0.6464 D(G(z)): 0.4691 / 0.3913\n", + "[91/100][88/391] Loss_D: 2.8453 Loss_G: 2.5527 D(x): 0.6519 D(G(z)): 0.4653 / 0.4554\n", + "[91/100][89/391] Loss_D: 2.9261 Loss_G: 3.1193 D(x): 0.6744 D(G(z)): 0.4707 / 0.3904\n", + "[91/100][90/391] Loss_D: 2.9178 Loss_G: 2.6657 D(x): 0.6281 D(G(z)): 0.4202 / 0.4639\n", + "[91/100][91/391] Loss_D: 3.6179 Loss_G: 2.1981 D(x): 0.6242 D(G(z)): 0.3158 / 0.4916\n", + "[91/100][92/391] Loss_D: 2.7473 Loss_G: 2.9821 D(x): 0.6389 D(G(z)): 0.3939 / 0.3909\n", + "[91/100][93/391] Loss_D: 2.7378 Loss_G: 2.5956 D(x): 0.7172 D(G(z)): 0.4487 / 0.4607\n", + "[91/100][94/391] Loss_D: 3.2717 Loss_G: 3.5932 D(x): 0.6673 D(G(z)): 0.5523 / 0.3381\n", + "[91/100][95/391] Loss_D: 2.7888 Loss_G: 2.5215 D(x): 0.6679 D(G(z)): 0.3799 / 0.4585\n", + "[91/100][96/391] Loss_D: 2.9164 Loss_G: 2.8413 D(x): 0.6008 D(G(z)): 0.3818 / 0.4117\n", + "[91/100][97/391] Loss_D: 3.3056 Loss_G: 3.8964 D(x): 0.6686 D(G(z)): 0.5270 / 0.3060\n", + "[91/100][98/391] Loss_D: 2.5936 Loss_G: 2.7536 D(x): 0.7025 D(G(z)): 0.4121 / 0.4106\n", + "[91/100][99/391] Loss_D: 3.0680 Loss_G: 2.5856 D(x): 0.6469 D(G(z)): 0.4440 / 0.4533\n", + "[91/100][100/391] Loss_D: 2.9293 Loss_G: 2.7301 D(x): 0.6697 D(G(z)): 0.4871 / 0.4406\n", + "[91/100][101/391] Loss_D: 2.7541 Loss_G: 3.3119 D(x): 0.6802 D(G(z)): 0.4240 / 0.3660\n", + "[91/100][102/391] Loss_D: 2.6791 Loss_G: 2.2216 D(x): 0.6464 D(G(z)): 0.3209 / 0.5090\n", + "[91/100][103/391] Loss_D: 2.7692 Loss_G: 2.9662 D(x): 0.7191 D(G(z)): 0.4435 / 0.3976\n", + "[91/100][104/391] Loss_D: 3.0345 Loss_G: 3.4657 D(x): 0.6390 D(G(z)): 0.4791 / 0.3406\n", + "[91/100][105/391] Loss_D: 3.3359 Loss_G: 2.8421 D(x): 0.5836 D(G(z)): 0.4531 / 0.4099\n", + "[91/100][106/391] Loss_D: 3.1841 Loss_G: 2.7472 D(x): 0.6057 D(G(z)): 0.4834 / 0.4166\n", + "[91/100][107/391] Loss_D: 2.9141 Loss_G: 3.0144 D(x): 0.6747 D(G(z)): 0.4119 / 0.3810\n", + "[91/100][108/391] Loss_D: 2.5996 Loss_G: 3.3192 D(x): 0.6842 D(G(z)): 0.3571 / 0.3602\n", + "[91/100][109/391] Loss_D: 3.2235 Loss_G: 3.1239 D(x): 0.6450 D(G(z)): 0.5259 / 0.3846\n", + "[91/100][110/391] Loss_D: 2.8342 Loss_G: 1.8096 D(x): 0.6645 D(G(z)): 0.4052 / 0.5810\n", + "[91/100][111/391] Loss_D: 2.3316 Loss_G: 2.2853 D(x): 0.7438 D(G(z)): 0.3712 / 0.5027\n", + "[91/100][112/391] Loss_D: 2.8594 Loss_G: 2.4354 D(x): 0.6360 D(G(z)): 0.3973 / 0.4643\n", + "[91/100][113/391] Loss_D: 2.7292 Loss_G: 2.1814 D(x): 0.6987 D(G(z)): 0.4315 / 0.5211\n", + "[91/100][114/391] Loss_D: 3.2784 Loss_G: 2.7568 D(x): 0.6391 D(G(z)): 0.5205 / 0.4259\n", + "[91/100][115/391] Loss_D: 3.2873 Loss_G: 2.4506 D(x): 0.6373 D(G(z)): 0.4815 / 0.4628\n", + "[91/100][116/391] Loss_D: 3.0498 Loss_G: 2.6177 D(x): 0.5787 D(G(z)): 0.3899 / 0.4507\n", + "[91/100][117/391] Loss_D: 3.9305 Loss_G: 3.1581 D(x): 0.5685 D(G(z)): 0.5605 / 0.3740\n", + "[91/100][118/391] Loss_D: 2.6859 Loss_G: 3.2839 D(x): 0.6731 D(G(z)): 0.4350 / 0.3700\n", + "[91/100][119/391] Loss_D: 2.8502 Loss_G: 2.3256 D(x): 0.6576 D(G(z)): 0.4163 / 0.4772\n", + "[91/100][120/391] Loss_D: 2.7651 Loss_G: 3.1928 D(x): 0.7246 D(G(z)): 0.4727 / 0.4003\n", + "[91/100][121/391] Loss_D: 3.7518 Loss_G: 1.9394 D(x): 0.7927 D(G(z)): 0.3691 / 0.5407\n", + "[91/100][122/391] Loss_D: 2.9495 Loss_G: 2.9255 D(x): 0.6445 D(G(z)): 0.4166 / 0.4135\n", + "[91/100][123/391] Loss_D: 2.7870 Loss_G: 3.1196 D(x): 0.6379 D(G(z)): 0.3254 / 0.3882\n", + "[91/100][124/391] Loss_D: 3.1334 Loss_G: 2.7380 D(x): 0.6324 D(G(z)): 0.4661 / 0.4317\n", + "[91/100][125/391] Loss_D: 3.1865 Loss_G: 2.2916 D(x): 0.6799 D(G(z)): 0.4654 / 0.4838\n", + "[91/100][126/391] Loss_D: 2.8195 Loss_G: 2.5501 D(x): 0.6164 D(G(z)): 0.3719 / 0.4438\n", + "[91/100][127/391] Loss_D: 2.9947 Loss_G: 2.5582 D(x): 0.6293 D(G(z)): 0.3863 / 0.4399\n", + "[91/100][128/391] Loss_D: 2.3163 Loss_G: 3.1494 D(x): 0.7487 D(G(z)): 0.4235 / 0.3798\n", + "[91/100][129/391] Loss_D: 3.0369 Loss_G: 2.3065 D(x): 0.5931 D(G(z)): 0.4211 / 0.4802\n", + "[91/100][130/391] Loss_D: 3.0170 Loss_G: 2.9277 D(x): 0.6793 D(G(z)): 0.4412 / 0.4066\n", + "[91/100][131/391] Loss_D: 3.0422 Loss_G: 2.9292 D(x): 0.6623 D(G(z)): 0.4690 / 0.4128\n", + "[91/100][132/391] Loss_D: 2.7921 Loss_G: 2.8829 D(x): 0.7239 D(G(z)): 0.4681 / 0.4174\n", + "[91/100][133/391] Loss_D: 2.3218 Loss_G: 3.3661 D(x): 0.7666 D(G(z)): 0.4338 / 0.3552\n", + "[91/100][134/391] Loss_D: 2.8553 Loss_G: 2.8881 D(x): 0.6249 D(G(z)): 0.4264 / 0.4144\n", + "[91/100][135/391] Loss_D: 3.0965 Loss_G: 2.6366 D(x): 0.6345 D(G(z)): 0.4800 / 0.4581\n", + "[91/100][136/391] Loss_D: 2.7721 Loss_G: 3.8402 D(x): 0.6090 D(G(z)): 0.3357 / 0.3137\n", + "[91/100][137/391] Loss_D: 3.0415 Loss_G: 2.8926 D(x): 0.5753 D(G(z)): 0.3877 / 0.4030\n", + "[91/100][138/391] Loss_D: 3.0892 Loss_G: 3.0972 D(x): 0.6709 D(G(z)): 0.5596 / 0.3846\n", + "[91/100][139/391] Loss_D: 2.7852 Loss_G: 3.2730 D(x): 0.6877 D(G(z)): 0.4123 / 0.3674\n", + "[91/100][140/391] Loss_D: 2.8661 Loss_G: 2.8303 D(x): 0.6557 D(G(z)): 0.4473 / 0.4332\n", + "[91/100][141/391] Loss_D: 3.3055 Loss_G: 2.7118 D(x): 0.6037 D(G(z)): 0.4503 / 0.4334\n", + "[91/100][142/391] Loss_D: 2.4650 Loss_G: 2.7758 D(x): 0.6661 D(G(z)): 0.3608 / 0.4408\n", + "[91/100][143/391] Loss_D: 2.7611 Loss_G: 2.6941 D(x): 0.7006 D(G(z)): 0.4148 / 0.4299\n", + "[91/100][144/391] Loss_D: 2.8741 Loss_G: 3.0089 D(x): 0.6835 D(G(z)): 0.4869 / 0.3942\n", + "[91/100][145/391] Loss_D: 3.4320 Loss_G: 2.9143 D(x): 0.6716 D(G(z)): 0.5323 / 0.4138\n", + "[91/100][146/391] Loss_D: 3.2679 Loss_G: 3.8064 D(x): 0.5748 D(G(z)): 0.4381 / 0.3031\n", + "[91/100][147/391] Loss_D: 2.9380 Loss_G: 2.7409 D(x): 0.6561 D(G(z)): 0.4067 / 0.4235\n", + "[91/100][148/391] Loss_D: 2.9849 Loss_G: 2.7181 D(x): 0.6033 D(G(z)): 0.4289 / 0.4338\n", + "[91/100][149/391] Loss_D: 2.8429 Loss_G: 2.7703 D(x): 0.6821 D(G(z)): 0.4531 / 0.4271\n", + "[91/100][150/391] Loss_D: 2.8524 Loss_G: 2.1654 D(x): 0.7058 D(G(z)): 0.4570 / 0.5073\n", + "[91/100][151/391] Loss_D: 3.8732 Loss_G: 2.1925 D(x): 0.6386 D(G(z)): 0.5436 / 0.5061\n", + "[91/100][152/391] Loss_D: 2.5037 Loss_G: 2.7747 D(x): 0.6340 D(G(z)): 0.3711 / 0.4327\n", + "[91/100][153/391] Loss_D: 3.1275 Loss_G: 2.6890 D(x): 0.6230 D(G(z)): 0.4744 / 0.4327\n", + "[91/100][154/391] Loss_D: 3.0834 Loss_G: 3.6440 D(x): 0.6623 D(G(z)): 0.5176 / 0.3293\n", + "[91/100][155/391] Loss_D: 3.2098 Loss_G: 3.6890 D(x): 0.6110 D(G(z)): 0.4193 / 0.3189\n", + "[91/100][156/391] Loss_D: 2.8498 Loss_G: 1.7794 D(x): 0.6306 D(G(z)): 0.3837 / 0.5787\n", + "[91/100][157/391] Loss_D: 2.6134 Loss_G: 2.7260 D(x): 0.7305 D(G(z)): 0.3418 / 0.4282\n", + "[91/100][158/391] Loss_D: 2.5549 Loss_G: 2.1052 D(x): 0.7120 D(G(z)): 0.4384 / 0.5433\n", + "[91/100][159/391] Loss_D: 2.9473 Loss_G: 2.7957 D(x): 0.6641 D(G(z)): 0.4601 / 0.4289\n", + "[91/100][160/391] Loss_D: 3.4334 Loss_G: 2.9407 D(x): 0.5443 D(G(z)): 0.4386 / 0.4078\n", + "[91/100][161/391] Loss_D: 3.0042 Loss_G: 2.2921 D(x): 0.6336 D(G(z)): 0.4526 / 0.4810\n", + "[91/100][162/391] Loss_D: 2.7896 Loss_G: 2.5464 D(x): 0.7140 D(G(z)): 0.4660 / 0.4583\n", + "[91/100][163/391] Loss_D: 2.9613 Loss_G: 3.5929 D(x): 0.6761 D(G(z)): 0.4778 / 0.3437\n", + "[91/100][164/391] Loss_D: 2.5102 Loss_G: 2.8287 D(x): 0.7214 D(G(z)): 0.4398 / 0.4237\n", + "[91/100][165/391] Loss_D: 2.7985 Loss_G: 2.7726 D(x): 0.6674 D(G(z)): 0.4213 / 0.4078\n", + "[91/100][166/391] Loss_D: 2.7423 Loss_G: 2.6178 D(x): 0.5854 D(G(z)): 0.3254 / 0.4416\n", + "[91/100][167/391] Loss_D: 2.8204 Loss_G: 2.4358 D(x): 0.7352 D(G(z)): 0.4348 / 0.4543\n", + "[91/100][168/391] Loss_D: 3.1374 Loss_G: 3.6838 D(x): 0.6555 D(G(z)): 0.5071 / 0.3337\n", + "[91/100][169/391] Loss_D: 3.0049 Loss_G: 3.1663 D(x): 0.6101 D(G(z)): 0.4257 / 0.3737\n", + "[91/100][170/391] Loss_D: 2.7372 Loss_G: 3.2378 D(x): 0.6663 D(G(z)): 0.3950 / 0.3634\n", + "[91/100][171/391] Loss_D: 2.5314 Loss_G: 2.6002 D(x): 0.6995 D(G(z)): 0.3801 / 0.4312\n", + "[91/100][172/391] Loss_D: 3.8895 Loss_G: 2.0570 D(x): 0.5402 D(G(z)): 0.5304 / 0.5322\n", + "[91/100][173/391] Loss_D: 2.6308 Loss_G: 2.3546 D(x): 0.6949 D(G(z)): 0.4152 / 0.4788\n", + "[91/100][174/391] Loss_D: 2.8130 Loss_G: 2.3619 D(x): 0.6912 D(G(z)): 0.4988 / 0.4802\n", + "[91/100][175/391] Loss_D: 2.9193 Loss_G: 2.7552 D(x): 0.6733 D(G(z)): 0.4407 / 0.4356\n", + "[91/100][176/391] Loss_D: 2.7883 Loss_G: 3.4402 D(x): 0.7150 D(G(z)): 0.4304 / 0.3586\n", + "[91/100][177/391] Loss_D: 2.8719 Loss_G: 2.5356 D(x): 0.6673 D(G(z)): 0.4178 / 0.4331\n", + "[91/100][178/391] Loss_D: 2.8461 Loss_G: 2.5143 D(x): 0.6246 D(G(z)): 0.3950 / 0.4800\n", + "[91/100][179/391] Loss_D: 3.1271 Loss_G: 2.6064 D(x): 0.5786 D(G(z)): 0.3802 / 0.4542\n", + "[91/100][180/391] Loss_D: 2.3166 Loss_G: 2.6855 D(x): 0.7351 D(G(z)): 0.3462 / 0.4386\n", + "[91/100][181/391] Loss_D: 3.7794 Loss_G: 2.8870 D(x): 0.7564 D(G(z)): 0.5316 / 0.4024\n", + "[91/100][182/391] Loss_D: 2.7599 Loss_G: 3.4793 D(x): 0.6470 D(G(z)): 0.4135 / 0.3665\n", + "[91/100][183/391] Loss_D: 2.9042 Loss_G: 2.9774 D(x): 0.5862 D(G(z)): 0.4079 / 0.4033\n", + "[91/100][184/391] Loss_D: 2.8775 Loss_G: 3.2552 D(x): 0.6286 D(G(z)): 0.3975 / 0.3635\n", + "[91/100][185/391] Loss_D: 3.2581 Loss_G: 2.6449 D(x): 0.6340 D(G(z)): 0.5100 / 0.4478\n", + "[91/100][186/391] Loss_D: 3.2327 Loss_G: 3.1438 D(x): 0.5626 D(G(z)): 0.4642 / 0.3790\n", + "[91/100][187/391] Loss_D: 2.7633 Loss_G: 2.3499 D(x): 0.6574 D(G(z)): 0.3980 / 0.4934\n", + "[91/100][188/391] Loss_D: 2.5468 Loss_G: 2.6376 D(x): 0.7245 D(G(z)): 0.4639 / 0.4376\n", + "[91/100][189/391] Loss_D: 2.8479 Loss_G: 2.4461 D(x): 0.6849 D(G(z)): 0.4459 / 0.4696\n", + "[91/100][190/391] Loss_D: 3.1416 Loss_G: 2.4670 D(x): 0.6071 D(G(z)): 0.4675 / 0.4807\n", + "[91/100][191/391] Loss_D: 3.7623 Loss_G: 2.6513 D(x): 0.6170 D(G(z)): 0.5411 / 0.4509\n", + "[91/100][192/391] Loss_D: 2.6647 Loss_G: 2.7457 D(x): 0.7268 D(G(z)): 0.4555 / 0.4241\n", + "[91/100][193/391] Loss_D: 3.0807 Loss_G: 2.6528 D(x): 0.6425 D(G(z)): 0.4999 / 0.4416\n", + "[91/100][194/391] Loss_D: 2.9064 Loss_G: 2.3029 D(x): 0.6757 D(G(z)): 0.4349 / 0.4819\n", + "[91/100][195/391] Loss_D: 3.4119 Loss_G: 3.5611 D(x): 0.5094 D(G(z)): 0.3734 / 0.3348\n", + "[91/100][196/391] Loss_D: 2.2126 Loss_G: 2.8103 D(x): 0.7879 D(G(z)): 0.3797 / 0.4292\n", + "[91/100][197/391] Loss_D: 2.7710 Loss_G: 2.4538 D(x): 0.6933 D(G(z)): 0.3930 / 0.4686\n", + "[91/100][198/391] Loss_D: 2.7286 Loss_G: 1.8595 D(x): 0.6814 D(G(z)): 0.4363 / 0.5525\n", + "[91/100][199/391] Loss_D: 2.9032 Loss_G: 2.5702 D(x): 0.6297 D(G(z)): 0.3887 / 0.4575\n", + "[91/100][200/391] Loss_D: 2.9046 Loss_G: 2.0111 D(x): 0.6356 D(G(z)): 0.4318 / 0.5529\n", + "[91/100][201/391] Loss_D: 2.4825 Loss_G: 2.6658 D(x): 0.7550 D(G(z)): 0.3652 / 0.4394\n", + "[91/100][202/391] Loss_D: 2.8417 Loss_G: 2.4418 D(x): 0.7313 D(G(z)): 0.4991 / 0.4727\n", + "[91/100][203/391] Loss_D: 3.0277 Loss_G: 2.3667 D(x): 0.6904 D(G(z)): 0.4953 / 0.4749\n", + "[91/100][204/391] Loss_D: 2.5127 Loss_G: 3.1468 D(x): 0.6167 D(G(z)): 0.3391 / 0.3694\n", + "[91/100][205/391] Loss_D: 2.7895 Loss_G: 3.3153 D(x): 0.5947 D(G(z)): 0.3675 / 0.3597\n", + "[91/100][206/391] Loss_D: 2.3903 Loss_G: 2.5451 D(x): 0.6774 D(G(z)): 0.3114 / 0.4556\n", + "[91/100][207/391] Loss_D: 2.9973 Loss_G: 2.8287 D(x): 0.6944 D(G(z)): 0.4753 / 0.4293\n", + "[91/100][208/391] Loss_D: 2.3200 Loss_G: 2.9250 D(x): 0.7164 D(G(z)): 0.3607 / 0.4008\n", + "[91/100][209/391] Loss_D: 3.2876 Loss_G: 3.4384 D(x): 0.6543 D(G(z)): 0.5288 / 0.3612\n", + "[91/100][210/391] Loss_D: 2.8506 Loss_G: 2.1859 D(x): 0.6835 D(G(z)): 0.4416 / 0.5201\n", + "[91/100][211/391] Loss_D: 3.6369 Loss_G: 2.8548 D(x): 0.7432 D(G(z)): 0.4838 / 0.4088\n", + "[91/100][212/391] Loss_D: 2.5497 Loss_G: 2.9437 D(x): 0.6848 D(G(z)): 0.3306 / 0.4083\n", + "[91/100][213/391] Loss_D: 2.6922 Loss_G: 2.6988 D(x): 0.7358 D(G(z)): 0.4289 / 0.4359\n", + "[91/100][214/391] Loss_D: 2.6327 Loss_G: 2.5677 D(x): 0.6785 D(G(z)): 0.4362 / 0.4451\n", + "[91/100][215/391] Loss_D: 2.4686 Loss_G: 3.1170 D(x): 0.6668 D(G(z)): 0.2691 / 0.3819\n", + "[91/100][216/391] Loss_D: 2.8076 Loss_G: 3.3910 D(x): 0.6517 D(G(z)): 0.4185 / 0.3516\n", + "[91/100][217/391] Loss_D: 2.7449 Loss_G: 3.0983 D(x): 0.6755 D(G(z)): 0.4199 / 0.3946\n", + "[91/100][218/391] Loss_D: 2.8024 Loss_G: 3.2653 D(x): 0.6532 D(G(z)): 0.4304 / 0.3734\n", + "[91/100][219/391] Loss_D: 2.6192 Loss_G: 2.1194 D(x): 0.6471 D(G(z)): 0.3631 / 0.5264\n", + "[91/100][220/391] Loss_D: 3.0572 Loss_G: 2.2366 D(x): 0.6622 D(G(z)): 0.4773 / 0.5063\n", + "[91/100][221/391] Loss_D: 3.1621 Loss_G: 3.3711 D(x): 0.6473 D(G(z)): 0.4997 / 0.3617\n", + "[91/100][222/391] Loss_D: 2.8150 Loss_G: 3.0238 D(x): 0.7128 D(G(z)): 0.4835 / 0.3925\n", + "[91/100][223/391] Loss_D: 3.0422 Loss_G: 2.9498 D(x): 0.7058 D(G(z)): 0.5197 / 0.4111\n", + "[91/100][224/391] Loss_D: 3.1411 Loss_G: 3.0177 D(x): 0.6290 D(G(z)): 0.4466 / 0.3895\n", + "[91/100][225/391] Loss_D: 2.7400 Loss_G: 3.5057 D(x): 0.6485 D(G(z)): 0.4064 / 0.3445\n", + "[91/100][226/391] Loss_D: 2.7883 Loss_G: 1.7966 D(x): 0.6670 D(G(z)): 0.4058 / 0.5493\n", + "[91/100][227/391] Loss_D: 2.8506 Loss_G: 3.7484 D(x): 0.6330 D(G(z)): 0.4376 / 0.3202\n", + "[91/100][228/391] Loss_D: 3.4067 Loss_G: 3.1924 D(x): 0.6317 D(G(z)): 0.5448 / 0.3767\n", + "[91/100][229/391] Loss_D: 2.7885 Loss_G: 2.7712 D(x): 0.6310 D(G(z)): 0.3426 / 0.4309\n", + "[91/100][230/391] Loss_D: 3.0296 Loss_G: 2.9080 D(x): 0.6249 D(G(z)): 0.4611 / 0.4194\n", + "[91/100][231/391] Loss_D: 3.2815 Loss_G: 2.6519 D(x): 0.6569 D(G(z)): 0.5246 / 0.4545\n", + "[91/100][232/391] Loss_D: 2.7022 Loss_G: 2.8387 D(x): 0.6404 D(G(z)): 0.3433 / 0.4177\n", + "[91/100][233/391] Loss_D: 2.9794 Loss_G: 2.3303 D(x): 0.7303 D(G(z)): 0.5067 / 0.4801\n", + "[91/100][234/391] Loss_D: 2.8354 Loss_G: 3.1988 D(x): 0.6773 D(G(z)): 0.4484 / 0.3949\n", + "[91/100][235/391] Loss_D: 2.8597 Loss_G: 2.9412 D(x): 0.5997 D(G(z)): 0.4030 / 0.3922\n", + "[91/100][236/391] Loss_D: 2.8533 Loss_G: 3.0533 D(x): 0.6654 D(G(z)): 0.4241 / 0.3732\n", + "[91/100][237/391] Loss_D: 3.3624 Loss_G: 2.7270 D(x): 0.5986 D(G(z)): 0.4991 / 0.4150\n", + "[91/100][238/391] Loss_D: 2.5667 Loss_G: 3.8944 D(x): 0.6896 D(G(z)): 0.4408 / 0.3057\n", + "[91/100][239/391] Loss_D: 3.5004 Loss_G: 2.4144 D(x): 0.5796 D(G(z)): 0.4953 / 0.4623\n", + "[91/100][240/391] Loss_D: 3.1890 Loss_G: 3.2735 D(x): 0.6548 D(G(z)): 0.4814 / 0.3719\n", + "[91/100][241/391] Loss_D: 3.9437 Loss_G: 3.1010 D(x): 0.5618 D(G(z)): 0.3353 / 0.3915\n", + "[91/100][242/391] Loss_D: 2.5235 Loss_G: 3.2793 D(x): 0.7100 D(G(z)): 0.4018 / 0.3799\n", + "[91/100][243/391] Loss_D: 2.4607 Loss_G: 3.3959 D(x): 0.7617 D(G(z)): 0.4354 / 0.3518\n", + "[91/100][244/391] Loss_D: 2.5257 Loss_G: 2.2151 D(x): 0.6445 D(G(z)): 0.3441 / 0.5004\n", + "[91/100][245/391] Loss_D: 2.8418 Loss_G: 2.2317 D(x): 0.6394 D(G(z)): 0.4054 / 0.4941\n", + "[91/100][246/391] Loss_D: 2.7419 Loss_G: 2.2075 D(x): 0.6939 D(G(z)): 0.3791 / 0.5214\n", + "[91/100][247/391] Loss_D: 3.0395 Loss_G: 2.3852 D(x): 0.6589 D(G(z)): 0.4664 / 0.4799\n", + "[91/100][248/391] Loss_D: 3.1298 Loss_G: 3.3730 D(x): 0.5971 D(G(z)): 0.4442 / 0.3547\n", + "[91/100][249/391] Loss_D: 2.6604 Loss_G: 1.9771 D(x): 0.6213 D(G(z)): 0.3840 / 0.5590\n", + "[91/100][250/391] Loss_D: 2.8812 Loss_G: 2.4960 D(x): 0.7609 D(G(z)): 0.4904 / 0.4602\n", + "[91/100][251/391] Loss_D: 3.0921 Loss_G: 2.1537 D(x): 0.6417 D(G(z)): 0.4839 / 0.5087\n", + "[91/100][252/391] Loss_D: 2.8011 Loss_G: 2.4941 D(x): 0.6448 D(G(z)): 0.3917 / 0.4689\n", + "[91/100][253/391] Loss_D: 3.1299 Loss_G: 2.2137 D(x): 0.5953 D(G(z)): 0.3756 / 0.4933\n", + "[91/100][254/391] Loss_D: 2.6236 Loss_G: 1.9387 D(x): 0.6663 D(G(z)): 0.4093 / 0.5400\n", + "[91/100][255/391] Loss_D: 3.1645 Loss_G: 2.1568 D(x): 0.6884 D(G(z)): 0.5043 / 0.5253\n", + "[91/100][256/391] Loss_D: 2.8151 Loss_G: 2.5169 D(x): 0.6016 D(G(z)): 0.3723 / 0.4527\n", + "[91/100][257/391] Loss_D: 3.6886 Loss_G: 2.7497 D(x): 0.6178 D(G(z)): 0.5248 / 0.4175\n", + "[91/100][258/391] Loss_D: 2.6628 Loss_G: 2.8641 D(x): 0.7074 D(G(z)): 0.4162 / 0.4266\n", + "[91/100][259/391] Loss_D: 2.6905 Loss_G: 2.7553 D(x): 0.7266 D(G(z)): 0.4540 / 0.4220\n", + "[91/100][260/391] Loss_D: 2.6343 Loss_G: 2.5699 D(x): 0.7912 D(G(z)): 0.4451 / 0.4515\n", + "[91/100][261/391] Loss_D: 2.6469 Loss_G: 3.1155 D(x): 0.7420 D(G(z)): 0.4082 / 0.4084\n", + "[91/100][262/391] Loss_D: 2.7418 Loss_G: 3.4959 D(x): 0.7009 D(G(z)): 0.3949 / 0.3394\n", + "[91/100][263/391] Loss_D: 3.5297 Loss_G: 2.7799 D(x): 0.5576 D(G(z)): 0.4458 / 0.4371\n", + "[91/100][264/391] Loss_D: 2.2395 Loss_G: 2.6271 D(x): 0.7282 D(G(z)): 0.3834 / 0.4352\n", + "[91/100][265/391] Loss_D: 2.3694 Loss_G: 2.9224 D(x): 0.7757 D(G(z)): 0.3719 / 0.4009\n", + "[91/100][266/391] Loss_D: 3.2218 Loss_G: 2.6584 D(x): 0.7391 D(G(z)): 0.5138 / 0.4540\n", + "[91/100][267/391] Loss_D: 2.6232 Loss_G: 3.1810 D(x): 0.6338 D(G(z)): 0.3180 / 0.3755\n", + "[91/100][268/391] Loss_D: 3.1817 Loss_G: 2.6084 D(x): 0.5756 D(G(z)): 0.4521 / 0.4570\n", + "[91/100][269/391] Loss_D: 2.9715 Loss_G: 2.8551 D(x): 0.6477 D(G(z)): 0.4552 / 0.4126\n", + "[91/100][270/391] Loss_D: 2.8331 Loss_G: 2.7334 D(x): 0.6246 D(G(z)): 0.3439 / 0.4231\n", + "[91/100][271/391] Loss_D: 3.7154 Loss_G: 3.0171 D(x): 0.7207 D(G(z)): 0.3535 / 0.4034\n", + "[91/100][272/391] Loss_D: 3.2296 Loss_G: 2.3641 D(x): 0.7104 D(G(z)): 0.5440 / 0.4877\n", + "[91/100][273/391] Loss_D: 3.0854 Loss_G: 2.3771 D(x): 0.6892 D(G(z)): 0.5204 / 0.4844\n", + "[91/100][274/391] Loss_D: 2.8336 Loss_G: 2.9015 D(x): 0.6330 D(G(z)): 0.4015 / 0.4115\n", + "[91/100][275/391] Loss_D: 2.6229 Loss_G: 2.7966 D(x): 0.6819 D(G(z)): 0.3738 / 0.4164\n", + "[91/100][276/391] Loss_D: 3.3647 Loss_G: 3.8032 D(x): 0.6706 D(G(z)): 0.5541 / 0.3150\n", + "[91/100][277/391] Loss_D: 2.8729 Loss_G: 2.8889 D(x): 0.6982 D(G(z)): 0.4572 / 0.3999\n", + "[91/100][278/391] Loss_D: 2.5937 Loss_G: 3.5147 D(x): 0.6804 D(G(z)): 0.4288 / 0.3428\n", + "[91/100][279/391] Loss_D: 2.6972 Loss_G: 3.0353 D(x): 0.6457 D(G(z)): 0.3474 / 0.3997\n", + "[91/100][280/391] Loss_D: 2.6473 Loss_G: 3.0422 D(x): 0.6646 D(G(z)): 0.3839 / 0.3925\n", + "[91/100][281/391] Loss_D: 3.6302 Loss_G: 3.2383 D(x): 0.6403 D(G(z)): 0.5669 / 0.3704\n", + "[91/100][282/391] Loss_D: 2.7014 Loss_G: 2.6148 D(x): 0.7516 D(G(z)): 0.4513 / 0.4503\n", + "[91/100][283/391] Loss_D: 2.7881 Loss_G: 2.7750 D(x): 0.6722 D(G(z)): 0.4062 / 0.4238\n", + "[91/100][284/391] Loss_D: 2.8793 Loss_G: 3.3523 D(x): 0.6637 D(G(z)): 0.4449 / 0.3521\n", + "[91/100][285/391] Loss_D: 3.1016 Loss_G: 3.3780 D(x): 0.5962 D(G(z)): 0.4255 / 0.3470\n", + "[91/100][286/391] Loss_D: 3.4515 Loss_G: 3.0353 D(x): 0.5408 D(G(z)): 0.4467 / 0.3853\n", + "[91/100][287/391] Loss_D: 2.6869 Loss_G: 2.9944 D(x): 0.7185 D(G(z)): 0.3321 / 0.4004\n", + "[91/100][288/391] Loss_D: 2.3603 Loss_G: 1.9065 D(x): 0.6547 D(G(z)): 0.3093 / 0.5645\n", + "[91/100][289/391] Loss_D: 3.3429 Loss_G: 2.1576 D(x): 0.6630 D(G(z)): 0.5031 / 0.4939\n", + "[91/100][290/391] Loss_D: 2.8695 Loss_G: 2.4137 D(x): 0.6523 D(G(z)): 0.4520 / 0.4860\n", + "[91/100][291/391] Loss_D: 3.5662 Loss_G: 2.9180 D(x): 0.6061 D(G(z)): 0.5358 / 0.4050\n", + "[91/100][292/391] Loss_D: 3.6331 Loss_G: 3.3332 D(x): 0.6319 D(G(z)): 0.5466 / 0.3698\n", + "[91/100][293/391] Loss_D: 3.3823 Loss_G: 3.3227 D(x): 0.6316 D(G(z)): 0.5316 / 0.3699\n", + "[91/100][294/391] Loss_D: 3.3563 Loss_G: 3.5619 D(x): 0.5005 D(G(z)): 0.3653 / 0.3451\n", + "[91/100][295/391] Loss_D: 2.8823 Loss_G: 2.6142 D(x): 0.6428 D(G(z)): 0.3994 / 0.4429\n", + "[91/100][296/391] Loss_D: 2.7471 Loss_G: 2.6771 D(x): 0.6615 D(G(z)): 0.3797 / 0.4148\n", + "[91/100][297/391] Loss_D: 2.9429 Loss_G: 2.9761 D(x): 0.6620 D(G(z)): 0.4320 / 0.4083\n", + "[91/100][298/391] Loss_D: 3.1698 Loss_G: 2.4378 D(x): 0.6514 D(G(z)): 0.5140 / 0.4562\n", + "[91/100][299/391] Loss_D: 2.5778 Loss_G: 3.4584 D(x): 0.7724 D(G(z)): 0.4493 / 0.3459\n", + "[91/100][300/391] Loss_D: 2.8219 Loss_G: 3.4439 D(x): 0.6197 D(G(z)): 0.3494 / 0.3698\n", + "[91/100][301/391] Loss_D: 3.5692 Loss_G: 3.2687 D(x): 0.6662 D(G(z)): 0.4628 / 0.3701\n", + "[91/100][302/391] Loss_D: 2.8124 Loss_G: 2.7455 D(x): 0.5960 D(G(z)): 0.3463 / 0.4221\n", + "[91/100][303/391] Loss_D: 2.6469 Loss_G: 2.3438 D(x): 0.6972 D(G(z)): 0.4350 / 0.4890\n", + "[91/100][304/391] Loss_D: 2.4349 Loss_G: 2.1787 D(x): 0.7313 D(G(z)): 0.4678 / 0.5190\n", + "[91/100][305/391] Loss_D: 2.5522 Loss_G: 2.3474 D(x): 0.6745 D(G(z)): 0.3560 / 0.4816\n", + "[91/100][306/391] Loss_D: 2.7419 Loss_G: 3.0862 D(x): 0.6251 D(G(z)): 0.3165 / 0.3786\n", + "[91/100][307/391] Loss_D: 2.7596 Loss_G: 3.0131 D(x): 0.6410 D(G(z)): 0.4059 / 0.3956\n", + "[91/100][308/391] Loss_D: 2.9043 Loss_G: 3.3339 D(x): 0.6396 D(G(z)): 0.4342 / 0.3656\n", + "[91/100][309/391] Loss_D: 2.8260 Loss_G: 1.9493 D(x): 0.7263 D(G(z)): 0.5085 / 0.5519\n", + "[91/100][310/391] Loss_D: 2.7848 Loss_G: 3.1114 D(x): 0.7252 D(G(z)): 0.4387 / 0.3884\n", + "[91/100][311/391] Loss_D: 2.8941 Loss_G: 2.5524 D(x): 0.6999 D(G(z)): 0.4414 / 0.4561\n", + "[91/100][312/391] Loss_D: 2.5958 Loss_G: 3.4337 D(x): 0.6551 D(G(z)): 0.3844 / 0.3539\n", + "[91/100][313/391] Loss_D: 3.1693 Loss_G: 3.5993 D(x): 0.6295 D(G(z)): 0.4862 / 0.3426\n", + "[91/100][314/391] Loss_D: 2.5550 Loss_G: 3.8792 D(x): 0.6749 D(G(z)): 0.4201 / 0.3069\n", + "[91/100][315/391] Loss_D: 3.0947 Loss_G: 2.7249 D(x): 0.6331 D(G(z)): 0.4351 / 0.4243\n", + "[91/100][316/391] Loss_D: 3.2053 Loss_G: 2.8865 D(x): 0.5535 D(G(z)): 0.3790 / 0.4138\n", + "[91/100][317/391] Loss_D: 2.6567 Loss_G: 2.4108 D(x): 0.6918 D(G(z)): 0.3185 / 0.4663\n", + "[91/100][318/391] Loss_D: 2.7818 Loss_G: 2.6626 D(x): 0.6457 D(G(z)): 0.4217 / 0.4404\n", + "[91/100][319/391] Loss_D: 2.5073 Loss_G: 2.1193 D(x): 0.7122 D(G(z)): 0.3646 / 0.5170\n", + "[91/100][320/391] Loss_D: 2.8654 Loss_G: 2.0501 D(x): 0.7213 D(G(z)): 0.4796 / 0.5196\n", + "[91/100][321/391] Loss_D: 2.9854 Loss_G: 1.7441 D(x): 0.6540 D(G(z)): 0.4187 / 0.5749\n", + "[91/100][322/391] Loss_D: 3.2673 Loss_G: 2.0244 D(x): 0.6159 D(G(z)): 0.4939 / 0.5388\n", + "[91/100][323/391] Loss_D: 3.2281 Loss_G: 2.5307 D(x): 0.6899 D(G(z)): 0.5082 / 0.4514\n", + "[91/100][324/391] Loss_D: 2.8631 Loss_G: 2.8923 D(x): 0.7082 D(G(z)): 0.5196 / 0.4109\n", + "[91/100][325/391] Loss_D: 4.2961 Loss_G: 3.9806 D(x): 0.5388 D(G(z)): 0.5910 / 0.3054\n", + "[91/100][326/391] Loss_D: 2.9768 Loss_G: 2.4609 D(x): 0.5908 D(G(z)): 0.3770 / 0.4584\n", + "[91/100][327/391] Loss_D: 2.4846 Loss_G: 2.3460 D(x): 0.6752 D(G(z)): 0.3221 / 0.4664\n", + "[91/100][328/391] Loss_D: 3.0863 Loss_G: 2.3263 D(x): 0.6279 D(G(z)): 0.5021 / 0.4791\n", + "[91/100][329/391] Loss_D: 2.7066 Loss_G: 2.3942 D(x): 0.7532 D(G(z)): 0.4683 / 0.4799\n", + "[91/100][330/391] Loss_D: 2.6418 Loss_G: 2.4168 D(x): 0.6756 D(G(z)): 0.3621 / 0.4894\n", + "[91/100][331/391] Loss_D: 3.4854 Loss_G: 3.2628 D(x): 0.7058 D(G(z)): 0.3744 / 0.3632\n", + "[91/100][332/391] Loss_D: 2.6085 Loss_G: 2.3263 D(x): 0.6805 D(G(z)): 0.4279 / 0.4970\n", + "[91/100][333/391] Loss_D: 3.0197 Loss_G: 2.3682 D(x): 0.6281 D(G(z)): 0.3607 / 0.4866\n", + "[91/100][334/391] Loss_D: 2.4971 Loss_G: 2.8811 D(x): 0.7204 D(G(z)): 0.4301 / 0.4252\n", + "[91/100][335/391] Loss_D: 2.5894 Loss_G: 3.2826 D(x): 0.7081 D(G(z)): 0.3864 / 0.3623\n", + "[91/100][336/391] Loss_D: 3.4012 Loss_G: 2.8176 D(x): 0.6762 D(G(z)): 0.5258 / 0.4232\n", + "[91/100][337/391] Loss_D: 3.3947 Loss_G: 3.1038 D(x): 0.6186 D(G(z)): 0.5121 / 0.3830\n", + "[91/100][338/391] Loss_D: 2.8396 Loss_G: 3.3543 D(x): 0.6834 D(G(z)): 0.4827 / 0.3599\n", + "[91/100][339/391] Loss_D: 3.3066 Loss_G: 2.3539 D(x): 0.5675 D(G(z)): 0.4082 / 0.4819\n", + "[91/100][340/391] Loss_D: 2.3933 Loss_G: 2.6680 D(x): 0.7254 D(G(z)): 0.3477 / 0.4519\n", + "[91/100][341/391] Loss_D: 3.1345 Loss_G: 2.8899 D(x): 0.6967 D(G(z)): 0.5066 / 0.4106\n", + "[91/100][342/391] Loss_D: 2.8672 Loss_G: 2.7185 D(x): 0.6428 D(G(z)): 0.4268 / 0.4547\n", + "[91/100][343/391] Loss_D: 3.0459 Loss_G: 2.2702 D(x): 0.6752 D(G(z)): 0.4554 / 0.5042\n", + "[91/100][344/391] Loss_D: 2.9732 Loss_G: 2.4644 D(x): 0.5747 D(G(z)): 0.3810 / 0.4612\n", + "[91/100][345/391] Loss_D: 2.7427 Loss_G: 2.2346 D(x): 0.6849 D(G(z)): 0.4308 / 0.4939\n", + "[91/100][346/391] Loss_D: 2.9519 Loss_G: 3.1253 D(x): 0.6136 D(G(z)): 0.4272 / 0.3713\n", + "[91/100][347/391] Loss_D: 2.6716 Loss_G: 2.8793 D(x): 0.7602 D(G(z)): 0.4323 / 0.4010\n", + "[91/100][348/391] Loss_D: 3.0192 Loss_G: 2.2254 D(x): 0.6001 D(G(z)): 0.3500 / 0.4936\n", + "[91/100][349/391] Loss_D: 2.5629 Loss_G: 4.1360 D(x): 0.7626 D(G(z)): 0.4325 / 0.2818\n", + "[91/100][350/391] Loss_D: 2.5307 Loss_G: 2.6357 D(x): 0.6833 D(G(z)): 0.3521 / 0.4515\n", + "[91/100][351/391] Loss_D: 3.2540 Loss_G: 2.5055 D(x): 0.6786 D(G(z)): 0.5294 / 0.4809\n", + "[91/100][352/391] Loss_D: 2.7990 Loss_G: 2.3911 D(x): 0.6830 D(G(z)): 0.4668 / 0.4868\n", + "[91/100][353/391] Loss_D: 2.7638 Loss_G: 3.4865 D(x): 0.6553 D(G(z)): 0.3732 / 0.3412\n", + "[91/100][354/391] Loss_D: 2.6461 Loss_G: 2.9846 D(x): 0.6536 D(G(z)): 0.4013 / 0.4119\n", + "[91/100][355/391] Loss_D: 3.0275 Loss_G: 2.6024 D(x): 0.7090 D(G(z)): 0.5292 / 0.4486\n", + "[91/100][356/391] Loss_D: 2.8549 Loss_G: 3.0079 D(x): 0.6015 D(G(z)): 0.3802 / 0.4030\n", + "[91/100][357/391] Loss_D: 2.8461 Loss_G: 2.8076 D(x): 0.6001 D(G(z)): 0.3670 / 0.4053\n", + "[91/100][358/391] Loss_D: 3.1657 Loss_G: 2.0954 D(x): 0.5900 D(G(z)): 0.4495 / 0.5109\n", + "[91/100][359/391] Loss_D: 2.9319 Loss_G: 3.1398 D(x): 0.7194 D(G(z)): 0.5273 / 0.3793\n", + "[91/100][360/391] Loss_D: 2.8968 Loss_G: 2.6839 D(x): 0.6001 D(G(z)): 0.3698 / 0.4483\n", + "[91/100][361/391] Loss_D: 3.4995 Loss_G: 2.5198 D(x): 0.5684 D(G(z)): 0.3713 / 0.4703\n", + "[91/100][362/391] Loss_D: 2.5957 Loss_G: 2.4292 D(x): 0.8225 D(G(z)): 0.4601 / 0.4818\n", + "[91/100][363/391] Loss_D: 3.1565 Loss_G: 2.8592 D(x): 0.7443 D(G(z)): 0.5298 / 0.4255\n", + "[91/100][364/391] Loss_D: 2.8976 Loss_G: 2.5192 D(x): 0.6723 D(G(z)): 0.4006 / 0.4477\n", + "[91/100][365/391] Loss_D: 3.0035 Loss_G: 2.6664 D(x): 0.5548 D(G(z)): 0.3581 / 0.4393\n", + "[91/100][366/391] Loss_D: 2.6640 Loss_G: 2.3841 D(x): 0.6519 D(G(z)): 0.3941 / 0.4640\n", + "[91/100][367/391] Loss_D: 3.3177 Loss_G: 2.4767 D(x): 0.6740 D(G(z)): 0.5595 / 0.4674\n", + "[91/100][368/391] Loss_D: 2.9897 Loss_G: 3.2292 D(x): 0.6418 D(G(z)): 0.4526 / 0.3651\n", + "[91/100][369/391] Loss_D: 2.5980 Loss_G: 3.1870 D(x): 0.7221 D(G(z)): 0.4633 / 0.3765\n", + "[91/100][370/391] Loss_D: 3.0503 Loss_G: 2.3305 D(x): 0.5457 D(G(z)): 0.3456 / 0.4909\n", + "[91/100][371/391] Loss_D: 2.5589 Loss_G: 3.0005 D(x): 0.7084 D(G(z)): 0.3905 / 0.4115\n", + "[91/100][372/391] Loss_D: 2.5374 Loss_G: 3.4157 D(x): 0.7179 D(G(z)): 0.4400 / 0.3440\n", + "[91/100][373/391] Loss_D: 2.3601 Loss_G: 3.1188 D(x): 0.7287 D(G(z)): 0.3855 / 0.3831\n", + "[91/100][374/391] Loss_D: 2.6993 Loss_G: 3.0264 D(x): 0.6697 D(G(z)): 0.4310 / 0.4007\n", + "[91/100][375/391] Loss_D: 3.3899 Loss_G: 2.6042 D(x): 0.5998 D(G(z)): 0.4489 / 0.4266\n", + "[91/100][376/391] Loss_D: 3.8809 Loss_G: 2.9000 D(x): 0.5195 D(G(z)): 0.4955 / 0.4070\n", + "[91/100][377/391] Loss_D: 2.8993 Loss_G: 2.5158 D(x): 0.6707 D(G(z)): 0.4396 / 0.4633\n", + "[91/100][378/391] Loss_D: 2.5371 Loss_G: 2.6011 D(x): 0.6697 D(G(z)): 0.3952 / 0.4526\n", + "[91/100][379/391] Loss_D: 2.8613 Loss_G: 2.0436 D(x): 0.6426 D(G(z)): 0.4365 / 0.5469\n", + "[91/100][380/391] Loss_D: 2.9739 Loss_G: 2.8179 D(x): 0.7295 D(G(z)): 0.4936 / 0.4231\n", + "[91/100][381/391] Loss_D: 3.5120 Loss_G: 2.7160 D(x): 0.6763 D(G(z)): 0.5852 / 0.4287\n", + "[91/100][382/391] Loss_D: 2.9179 Loss_G: 3.3973 D(x): 0.5419 D(G(z)): 0.2882 / 0.3429\n", + "[91/100][383/391] Loss_D: 2.5198 Loss_G: 2.5278 D(x): 0.7128 D(G(z)): 0.3710 / 0.4708\n", + "[91/100][384/391] Loss_D: 2.6308 Loss_G: 2.6744 D(x): 0.6734 D(G(z)): 0.4462 / 0.4420\n", + "[91/100][385/391] Loss_D: 3.0221 Loss_G: 3.1642 D(x): 0.6511 D(G(z)): 0.4726 / 0.3714\n", + "[91/100][386/391] Loss_D: 3.2456 Loss_G: 2.5540 D(x): 0.6422 D(G(z)): 0.4619 / 0.4480\n", + "[91/100][387/391] Loss_D: 2.6662 Loss_G: 2.7044 D(x): 0.7187 D(G(z)): 0.3921 / 0.4329\n", + "[91/100][388/391] Loss_D: 3.1288 Loss_G: 3.0734 D(x): 0.6155 D(G(z)): 0.4670 / 0.3870\n", + "[91/100][389/391] Loss_D: 3.0605 Loss_G: 2.8750 D(x): 0.6113 D(G(z)): 0.4052 / 0.4162\n", + "[91/100][390/391] Loss_D: 2.6725 Loss_G: 2.6985 D(x): 0.6084 D(G(z)): 0.3210 / 0.4299\n", + "[91/100][391/391] Loss_D: 3.6451 Loss_G: 2.5174 D(x): 0.6795 D(G(z)): 0.4214 / 0.4593\n", + "[92/100][1/391] Loss_D: 3.6267 Loss_G: 2.3532 D(x): 0.6009 D(G(z)): 0.4923 / 0.4925\n", + "[92/100][2/391] Loss_D: 2.7450 Loss_G: 2.2572 D(x): 0.6174 D(G(z)): 0.3794 / 0.4904\n", + "[92/100][3/391] Loss_D: 2.8849 Loss_G: 1.2810 D(x): 0.6813 D(G(z)): 0.4609 / 0.6690\n", + "[92/100][4/391] Loss_D: 2.0356 Loss_G: 3.0320 D(x): 0.7679 D(G(z)): 0.3548 / 0.3983\n", + "[92/100][5/391] Loss_D: 2.8226 Loss_G: 3.0060 D(x): 0.6241 D(G(z)): 0.3486 / 0.3970\n", + "[92/100][6/391] Loss_D: 3.1983 Loss_G: 2.7316 D(x): 0.7549 D(G(z)): 0.5105 / 0.3982\n", + "[92/100][7/391] Loss_D: 2.6721 Loss_G: 2.3334 D(x): 0.7064 D(G(z)): 0.3562 / 0.4825\n", + "[92/100][8/391] Loss_D: 2.2800 Loss_G: 1.9171 D(x): 0.7087 D(G(z)): 0.3581 / 0.5255\n", + "[92/100][9/391] Loss_D: 3.3081 Loss_G: 2.4865 D(x): 0.5967 D(G(z)): 0.4992 / 0.4696\n", + "[92/100][10/391] Loss_D: 2.4627 Loss_G: 2.9325 D(x): 0.7566 D(G(z)): 0.4469 / 0.4296\n", + "[92/100][11/391] Loss_D: 2.8751 Loss_G: 2.4292 D(x): 0.6750 D(G(z)): 0.4157 / 0.4739\n", + "[92/100][12/391] Loss_D: 2.9415 Loss_G: 2.7019 D(x): 0.6822 D(G(z)): 0.4660 / 0.4317\n", + "[92/100][13/391] Loss_D: 2.6343 Loss_G: 2.8382 D(x): 0.6433 D(G(z)): 0.3277 / 0.4279\n", + "[92/100][14/391] Loss_D: 2.2812 Loss_G: 2.6108 D(x): 0.7373 D(G(z)): 0.3628 / 0.4456\n", + "[92/100][15/391] Loss_D: 2.4918 Loss_G: 2.0632 D(x): 0.6868 D(G(z)): 0.3813 / 0.5127\n", + "[92/100][16/391] Loss_D: 3.4505 Loss_G: 2.4814 D(x): 0.6704 D(G(z)): 0.5341 / 0.4748\n", + "[92/100][17/391] Loss_D: 3.8424 Loss_G: 4.0921 D(x): 0.5543 D(G(z)): 0.5711 / 0.2803\n", + "[92/100][18/391] Loss_D: 2.8776 Loss_G: 1.8445 D(x): 0.6068 D(G(z)): 0.4003 / 0.5626\n", + "[92/100][19/391] Loss_D: 3.1952 Loss_G: 2.5469 D(x): 0.6481 D(G(z)): 0.4974 / 0.4663\n", + "[92/100][20/391] Loss_D: 2.9107 Loss_G: 2.6764 D(x): 0.7107 D(G(z)): 0.5026 / 0.4432\n", + "[92/100][21/391] Loss_D: 2.5482 Loss_G: 2.4308 D(x): 0.7231 D(G(z)): 0.4571 / 0.4867\n", + "[92/100][22/391] Loss_D: 2.8290 Loss_G: 3.4017 D(x): 0.6951 D(G(z)): 0.4410 / 0.3544\n", + "[92/100][23/391] Loss_D: 2.9954 Loss_G: 3.7303 D(x): 0.6104 D(G(z)): 0.4261 / 0.3140\n", + "[92/100][24/391] Loss_D: 3.0037 Loss_G: 3.4753 D(x): 0.6050 D(G(z)): 0.3566 / 0.3348\n", + "[92/100][25/391] Loss_D: 2.7835 Loss_G: 3.0943 D(x): 0.7067 D(G(z)): 0.4243 / 0.3981\n", + "[92/100][26/391] Loss_D: 3.3359 Loss_G: 2.7448 D(x): 0.5486 D(G(z)): 0.4377 / 0.4313\n", + "[92/100][27/391] Loss_D: 3.1947 Loss_G: 3.4609 D(x): 0.6434 D(G(z)): 0.4503 / 0.3368\n", + "[92/100][28/391] Loss_D: 2.9868 Loss_G: 3.0473 D(x): 0.6080 D(G(z)): 0.4316 / 0.3892\n", + "[92/100][29/391] Loss_D: 2.6778 Loss_G: 2.8396 D(x): 0.7176 D(G(z)): 0.4557 / 0.4145\n", + "[92/100][30/391] Loss_D: 2.6794 Loss_G: 2.8041 D(x): 0.7023 D(G(z)): 0.4637 / 0.4386\n", + "[92/100][31/391] Loss_D: 3.7106 Loss_G: 3.0550 D(x): 0.7342 D(G(z)): 0.4226 / 0.3945\n", + "[92/100][32/391] Loss_D: 3.1531 Loss_G: 2.5268 D(x): 0.6774 D(G(z)): 0.4775 / 0.4456\n", + "[92/100][33/391] Loss_D: 3.3862 Loss_G: 3.6092 D(x): 0.6952 D(G(z)): 0.5601 / 0.3310\n", + "[92/100][34/391] Loss_D: 2.3386 Loss_G: 2.6739 D(x): 0.6720 D(G(z)): 0.2927 / 0.4460\n", + "[92/100][35/391] Loss_D: 2.6249 Loss_G: 3.0364 D(x): 0.7146 D(G(z)): 0.4338 / 0.3992\n", + "[92/100][36/391] Loss_D: 3.0952 Loss_G: 3.7149 D(x): 0.6165 D(G(z)): 0.4095 / 0.3221\n", + "[92/100][37/391] Loss_D: 2.8650 Loss_G: 3.7530 D(x): 0.6248 D(G(z)): 0.4103 / 0.3205\n", + "[92/100][38/391] Loss_D: 3.0999 Loss_G: 2.8089 D(x): 0.6344 D(G(z)): 0.4853 / 0.4112\n", + "[92/100][39/391] Loss_D: 2.7328 Loss_G: 1.9804 D(x): 0.7332 D(G(z)): 0.4678 / 0.5277\n", + "[92/100][40/391] Loss_D: 3.1198 Loss_G: 3.1922 D(x): 0.6219 D(G(z)): 0.4199 / 0.3731\n", + "[92/100][41/391] Loss_D: 3.5995 Loss_G: 2.7200 D(x): 0.4711 D(G(z)): 0.3337 / 0.4330\n", + "[92/100][42/391] Loss_D: 3.0801 Loss_G: 2.3443 D(x): 0.6358 D(G(z)): 0.4715 / 0.4801\n", + "[92/100][43/391] Loss_D: 3.2417 Loss_G: 2.9576 D(x): 0.6730 D(G(z)): 0.5355 / 0.4009\n", + "[92/100][44/391] Loss_D: 2.4721 Loss_G: 3.0971 D(x): 0.7078 D(G(z)): 0.4087 / 0.3861\n", + "[92/100][45/391] Loss_D: 3.2449 Loss_G: 3.4423 D(x): 0.6215 D(G(z)): 0.4742 / 0.3420\n", + "[92/100][46/391] Loss_D: 3.2298 Loss_G: 2.3095 D(x): 0.6855 D(G(z)): 0.5311 / 0.4838\n", + "[92/100][47/391] Loss_D: 3.4975 Loss_G: 3.1619 D(x): 0.5700 D(G(z)): 0.5094 / 0.3592\n", + "[92/100][48/391] Loss_D: 2.9605 Loss_G: 2.6062 D(x): 0.5789 D(G(z)): 0.3325 / 0.4554\n", + "[92/100][49/391] Loss_D: 3.0180 Loss_G: 3.6587 D(x): 0.6435 D(G(z)): 0.4607 / 0.3342\n", + "[92/100][50/391] Loss_D: 3.1099 Loss_G: 1.9417 D(x): 0.6044 D(G(z)): 0.4604 / 0.5647\n", + "[92/100][51/391] Loss_D: 2.4167 Loss_G: 2.0049 D(x): 0.7465 D(G(z)): 0.3965 / 0.5354\n", + "[92/100][52/391] Loss_D: 2.6198 Loss_G: 2.9386 D(x): 0.6580 D(G(z)): 0.3537 / 0.3880\n", + "[92/100][53/391] Loss_D: 2.6237 Loss_G: 2.4054 D(x): 0.7471 D(G(z)): 0.4379 / 0.4897\n", + "[92/100][54/391] Loss_D: 2.6097 Loss_G: 2.7251 D(x): 0.7335 D(G(z)): 0.4464 / 0.4114\n", + "[92/100][55/391] Loss_D: 2.5465 Loss_G: 2.6236 D(x): 0.6877 D(G(z)): 0.3758 / 0.4464\n", + "[92/100][56/391] Loss_D: 2.6993 Loss_G: 2.8584 D(x): 0.6776 D(G(z)): 0.3747 / 0.4104\n", + "[92/100][57/391] Loss_D: 2.8284 Loss_G: 3.2062 D(x): 0.6481 D(G(z)): 0.4135 / 0.3718\n", + "[92/100][58/391] Loss_D: 2.4508 Loss_G: 3.1487 D(x): 0.6986 D(G(z)): 0.3750 / 0.3865\n", + "[92/100][59/391] Loss_D: 2.7352 Loss_G: 2.8204 D(x): 0.6361 D(G(z)): 0.3649 / 0.4245\n", + "[92/100][60/391] Loss_D: 2.8832 Loss_G: 2.6126 D(x): 0.6725 D(G(z)): 0.4625 / 0.4419\n", + "[92/100][61/391] Loss_D: 3.6408 Loss_G: 2.6289 D(x): 0.5843 D(G(z)): 0.3933 / 0.4550\n", + "[92/100][62/391] Loss_D: 3.5033 Loss_G: 1.9257 D(x): 0.5576 D(G(z)): 0.4739 / 0.5372\n", + "[92/100][63/391] Loss_D: 3.1750 Loss_G: 2.1006 D(x): 0.6381 D(G(z)): 0.4713 / 0.5111\n", + "[92/100][64/391] Loss_D: 2.8493 Loss_G: 2.3446 D(x): 0.6879 D(G(z)): 0.5063 / 0.4958\n", + "[92/100][65/391] Loss_D: 2.7528 Loss_G: 3.1491 D(x): 0.6762 D(G(z)): 0.4012 / 0.3853\n", + "[92/100][66/391] Loss_D: 2.3315 Loss_G: 2.8623 D(x): 0.7363 D(G(z)): 0.3560 / 0.4110\n", + "[92/100][67/391] Loss_D: 3.1078 Loss_G: 2.7589 D(x): 0.7232 D(G(z)): 0.4860 / 0.4095\n", + "[92/100][68/391] Loss_D: 2.7505 Loss_G: 2.1711 D(x): 0.6608 D(G(z)): 0.4521 / 0.5151\n", + "[92/100][69/391] Loss_D: 3.3032 Loss_G: 1.6914 D(x): 0.5319 D(G(z)): 0.4045 / 0.5869\n", + "[92/100][70/391] Loss_D: 2.6696 Loss_G: 2.4013 D(x): 0.7138 D(G(z)): 0.3907 / 0.4829\n", + "[92/100][71/391] Loss_D: 2.9674 Loss_G: 2.7951 D(x): 0.7634 D(G(z)): 0.4925 / 0.4235\n", + "[92/100][72/391] Loss_D: 3.3544 Loss_G: 2.6867 D(x): 0.6306 D(G(z)): 0.5440 / 0.4267\n", + "[92/100][73/391] Loss_D: 2.7751 Loss_G: 3.0262 D(x): 0.7204 D(G(z)): 0.4634 / 0.3942\n", + "[92/100][74/391] Loss_D: 2.9939 Loss_G: 2.6403 D(x): 0.5573 D(G(z)): 0.3387 / 0.4362\n", + "[92/100][75/391] Loss_D: 3.0306 Loss_G: 3.0683 D(x): 0.5823 D(G(z)): 0.3915 / 0.3890\n", + "[92/100][76/391] Loss_D: 3.3229 Loss_G: 2.6493 D(x): 0.5972 D(G(z)): 0.4938 / 0.4356\n", + "[92/100][77/391] Loss_D: 2.6863 Loss_G: 2.5462 D(x): 0.7192 D(G(z)): 0.4004 / 0.4458\n", + "[92/100][78/391] Loss_D: 2.8835 Loss_G: 2.4166 D(x): 0.6478 D(G(z)): 0.4467 / 0.4649\n", + "[92/100][79/391] Loss_D: 2.9165 Loss_G: 2.3034 D(x): 0.7134 D(G(z)): 0.5401 / 0.4996\n", + "[92/100][80/391] Loss_D: 2.5184 Loss_G: 2.9592 D(x): 0.7424 D(G(z)): 0.3334 / 0.4168\n", + "[92/100][81/391] Loss_D: 3.3161 Loss_G: 3.0508 D(x): 0.6384 D(G(z)): 0.5275 / 0.4028\n", + "[92/100][82/391] Loss_D: 2.8357 Loss_G: 2.3486 D(x): 0.7308 D(G(z)): 0.4471 / 0.4855\n", + "[92/100][83/391] Loss_D: 2.8031 Loss_G: 3.8475 D(x): 0.6837 D(G(z)): 0.4097 / 0.3109\n", + "[92/100][84/391] Loss_D: 2.4894 Loss_G: 3.5084 D(x): 0.6454 D(G(z)): 0.3412 / 0.3375\n", + "[92/100][85/391] Loss_D: 2.8441 Loss_G: 3.5589 D(x): 0.6320 D(G(z)): 0.3914 / 0.3312\n", + "[92/100][86/391] Loss_D: 2.4100 Loss_G: 2.7859 D(x): 0.7240 D(G(z)): 0.3015 / 0.4306\n", + "[92/100][87/391] Loss_D: 2.8854 Loss_G: 2.4162 D(x): 0.6191 D(G(z)): 0.3005 / 0.4618\n", + "[92/100][88/391] Loss_D: 2.8419 Loss_G: 2.4492 D(x): 0.6339 D(G(z)): 0.4020 / 0.4726\n", + "[92/100][89/391] Loss_D: 2.6460 Loss_G: 3.1456 D(x): 0.6994 D(G(z)): 0.4207 / 0.3938\n", + "[92/100][90/391] Loss_D: 2.9233 Loss_G: 2.7171 D(x): 0.6922 D(G(z)): 0.5013 / 0.4458\n", + "[92/100][91/391] Loss_D: 3.6112 Loss_G: 2.4279 D(x): 0.7322 D(G(z)): 0.4064 / 0.4680\n", + "[92/100][92/391] Loss_D: 2.9515 Loss_G: 2.8285 D(x): 0.6154 D(G(z)): 0.4380 / 0.4303\n", + "[92/100][93/391] Loss_D: 3.8142 Loss_G: 3.1823 D(x): 0.5203 D(G(z)): 0.4561 / 0.3600\n", + "[92/100][94/391] Loss_D: 3.0450 Loss_G: 2.5339 D(x): 0.6995 D(G(z)): 0.5135 / 0.4521\n", + "[92/100][95/391] Loss_D: 3.0560 Loss_G: 2.8964 D(x): 0.7168 D(G(z)): 0.4992 / 0.4251\n", + "[92/100][96/391] Loss_D: 2.8809 Loss_G: 2.4987 D(x): 0.7697 D(G(z)): 0.5159 / 0.4674\n", + "[92/100][97/391] Loss_D: 2.9943 Loss_G: 2.9025 D(x): 0.7200 D(G(z)): 0.4852 / 0.4003\n", + "[92/100][98/391] Loss_D: 3.1228 Loss_G: 3.6296 D(x): 0.6294 D(G(z)): 0.4964 / 0.3342\n", + "[92/100][99/391] Loss_D: 4.0728 Loss_G: 3.1069 D(x): 0.4188 D(G(z)): 0.3980 / 0.3910\n", + "[92/100][100/391] Loss_D: 2.7342 Loss_G: 3.7824 D(x): 0.6426 D(G(z)): 0.3864 / 0.3257\n", + "[92/100][101/391] Loss_D: 2.9007 Loss_G: 2.9416 D(x): 0.6807 D(G(z)): 0.4562 / 0.4203\n", + "[92/100][102/391] Loss_D: 3.3650 Loss_G: 1.9830 D(x): 0.7157 D(G(z)): 0.5663 / 0.5448\n", + "[92/100][103/391] Loss_D: 2.7574 Loss_G: 2.5826 D(x): 0.6520 D(G(z)): 0.3709 / 0.4657\n", + "[92/100][104/391] Loss_D: 2.3736 Loss_G: 3.7445 D(x): 0.6965 D(G(z)): 0.3266 / 0.3204\n", + "[92/100][105/391] Loss_D: 2.7158 Loss_G: 2.9482 D(x): 0.6633 D(G(z)): 0.3835 / 0.3961\n", + "[92/100][106/391] Loss_D: 3.2665 Loss_G: 2.3887 D(x): 0.6822 D(G(z)): 0.5441 / 0.4919\n", + "[92/100][107/391] Loss_D: 2.5787 Loss_G: 2.5446 D(x): 0.7114 D(G(z)): 0.3469 / 0.4571\n", + "[92/100][108/391] Loss_D: 2.8326 Loss_G: 2.8208 D(x): 0.6720 D(G(z)): 0.4325 / 0.4131\n", + "[92/100][109/391] Loss_D: 2.7144 Loss_G: 2.3435 D(x): 0.6402 D(G(z)): 0.4052 / 0.4855\n", + "[92/100][110/391] Loss_D: 2.2958 Loss_G: 2.5858 D(x): 0.7594 D(G(z)): 0.3595 / 0.4509\n", + "[92/100][111/391] Loss_D: 2.8403 Loss_G: 2.3308 D(x): 0.7652 D(G(z)): 0.4880 / 0.4847\n", + "[92/100][112/391] Loss_D: 3.0922 Loss_G: 2.7355 D(x): 0.6369 D(G(z)): 0.4747 / 0.4313\n", + "[92/100][113/391] Loss_D: 2.6233 Loss_G: 2.9854 D(x): 0.6458 D(G(z)): 0.3529 / 0.4031\n", + "[92/100][114/391] Loss_D: 2.3340 Loss_G: 3.1479 D(x): 0.7356 D(G(z)): 0.4312 / 0.3997\n", + "[92/100][115/391] Loss_D: 3.0157 Loss_G: 3.3599 D(x): 0.6058 D(G(z)): 0.3831 / 0.3658\n", + "[92/100][116/391] Loss_D: 3.1611 Loss_G: 3.1211 D(x): 0.5385 D(G(z)): 0.3556 / 0.3830\n", + "[92/100][117/391] Loss_D: 3.3172 Loss_G: 2.8834 D(x): 0.7202 D(G(z)): 0.5411 / 0.4186\n", + "[92/100][118/391] Loss_D: 3.1163 Loss_G: 2.7516 D(x): 0.5922 D(G(z)): 0.4071 / 0.4227\n", + "[92/100][119/391] Loss_D: 2.9879 Loss_G: 3.3467 D(x): 0.6240 D(G(z)): 0.4183 / 0.3577\n", + "[92/100][120/391] Loss_D: 2.9665 Loss_G: 2.5066 D(x): 0.6917 D(G(z)): 0.4843 / 0.4539\n", + "[92/100][121/391] Loss_D: 3.5027 Loss_G: 2.5597 D(x): 0.6942 D(G(z)): 0.3768 / 0.4666\n", + "[92/100][122/391] Loss_D: 3.2910 Loss_G: 3.0197 D(x): 0.7239 D(G(z)): 0.5617 / 0.3819\n", + "[92/100][123/391] Loss_D: 2.5145 Loss_G: 3.3148 D(x): 0.7119 D(G(z)): 0.3399 / 0.3659\n", + "[92/100][124/391] Loss_D: 3.4629 Loss_G: 2.4977 D(x): 0.5655 D(G(z)): 0.4662 / 0.4532\n", + "[92/100][125/391] Loss_D: 3.7968 Loss_G: 2.1239 D(x): 0.5515 D(G(z)): 0.4893 / 0.5071\n", + "[92/100][126/391] Loss_D: 2.6313 Loss_G: 3.1508 D(x): 0.6780 D(G(z)): 0.3843 / 0.3817\n", + "[92/100][127/391] Loss_D: 2.8811 Loss_G: 3.1696 D(x): 0.7047 D(G(z)): 0.4324 / 0.3731\n", + "[92/100][128/391] Loss_D: 2.3138 Loss_G: 2.5056 D(x): 0.7422 D(G(z)): 0.4193 / 0.4586\n", + "[92/100][129/391] Loss_D: 2.5646 Loss_G: 2.5198 D(x): 0.7516 D(G(z)): 0.4560 / 0.4504\n", + "[92/100][130/391] Loss_D: 2.7130 Loss_G: 2.3152 D(x): 0.7270 D(G(z)): 0.4220 / 0.4983\n", + "[92/100][131/391] Loss_D: 3.6618 Loss_G: 2.6964 D(x): 0.5656 D(G(z)): 0.5387 / 0.4425\n", + "[92/100][132/391] Loss_D: 2.8600 Loss_G: 3.3995 D(x): 0.6799 D(G(z)): 0.4376 / 0.3622\n", + "[92/100][133/391] Loss_D: 2.5283 Loss_G: 2.5244 D(x): 0.6607 D(G(z)): 0.3719 / 0.4573\n", + "[92/100][134/391] Loss_D: 3.0526 Loss_G: 3.8152 D(x): 0.6268 D(G(z)): 0.4743 / 0.3111\n", + "[92/100][135/391] Loss_D: 2.6052 Loss_G: 2.1960 D(x): 0.6662 D(G(z)): 0.3981 / 0.5055\n", + "[92/100][136/391] Loss_D: 2.8001 Loss_G: 4.2631 D(x): 0.6381 D(G(z)): 0.3791 / 0.2568\n", + "[92/100][137/391] Loss_D: 3.0383 Loss_G: 3.1151 D(x): 0.6056 D(G(z)): 0.4034 / 0.3831\n", + "[92/100][138/391] Loss_D: 2.9028 Loss_G: 3.1968 D(x): 0.6452 D(G(z)): 0.4801 / 0.3649\n", + "[92/100][139/391] Loss_D: 3.2070 Loss_G: 1.9363 D(x): 0.5891 D(G(z)): 0.3762 / 0.5575\n", + "[92/100][140/391] Loss_D: 2.8277 Loss_G: 2.3249 D(x): 0.6341 D(G(z)): 0.3985 / 0.5026\n", + "[92/100][141/391] Loss_D: 2.9040 Loss_G: 3.3130 D(x): 0.7355 D(G(z)): 0.4758 / 0.3539\n", + "[92/100][142/391] Loss_D: 2.8554 Loss_G: 2.4417 D(x): 0.6280 D(G(z)): 0.4225 / 0.4796\n", + "[92/100][143/391] Loss_D: 2.9743 Loss_G: 2.5440 D(x): 0.6655 D(G(z)): 0.4312 / 0.4516\n", + "[92/100][144/391] Loss_D: 2.8818 Loss_G: 2.3447 D(x): 0.6227 D(G(z)): 0.4250 / 0.4947\n", + "[92/100][145/391] Loss_D: 2.7018 Loss_G: 2.5234 D(x): 0.6621 D(G(z)): 0.3409 / 0.4702\n", + "[92/100][146/391] Loss_D: 2.8449 Loss_G: 2.2770 D(x): 0.7248 D(G(z)): 0.4625 / 0.4873\n", + "[92/100][147/391] Loss_D: 2.8351 Loss_G: 2.3435 D(x): 0.6823 D(G(z)): 0.4158 / 0.4819\n", + "[92/100][148/391] Loss_D: 2.7454 Loss_G: 2.9940 D(x): 0.6642 D(G(z)): 0.4696 / 0.3943\n", + "[92/100][149/391] Loss_D: 2.4448 Loss_G: 3.9694 D(x): 0.7513 D(G(z)): 0.3997 / 0.2832\n", + "[92/100][150/391] Loss_D: 2.9734 Loss_G: 2.7787 D(x): 0.6771 D(G(z)): 0.4668 / 0.4162\n", + "[92/100][151/391] Loss_D: 3.6294 Loss_G: 2.6181 D(x): 0.6116 D(G(z)): 0.4821 / 0.4533\n", + "[92/100][152/391] Loss_D: 2.9378 Loss_G: 2.3340 D(x): 0.6481 D(G(z)): 0.4769 / 0.4827\n", + "[92/100][153/391] Loss_D: 2.8141 Loss_G: 3.2083 D(x): 0.6866 D(G(z)): 0.4104 / 0.3766\n", + "[92/100][154/391] Loss_D: 2.5933 Loss_G: 2.3004 D(x): 0.7120 D(G(z)): 0.4247 / 0.4954\n", + "[92/100][155/391] Loss_D: 3.0855 Loss_G: 2.8487 D(x): 0.6744 D(G(z)): 0.4760 / 0.4022\n", + "[92/100][156/391] Loss_D: 3.1129 Loss_G: 3.8174 D(x): 0.5598 D(G(z)): 0.4177 / 0.3087\n", + "[92/100][157/391] Loss_D: 3.1183 Loss_G: 3.4938 D(x): 0.6146 D(G(z)): 0.4486 / 0.3354\n", + "[92/100][158/391] Loss_D: 2.9266 Loss_G: 2.8497 D(x): 0.6104 D(G(z)): 0.3939 / 0.4293\n", + "[92/100][159/391] Loss_D: 2.7927 Loss_G: 2.8771 D(x): 0.6712 D(G(z)): 0.4019 / 0.4139\n", + "[92/100][160/391] Loss_D: 2.6344 Loss_G: 2.1739 D(x): 0.6421 D(G(z)): 0.3092 / 0.5080\n", + "[92/100][161/391] Loss_D: 2.7352 Loss_G: 1.8870 D(x): 0.6763 D(G(z)): 0.4047 / 0.5639\n", + "[92/100][162/391] Loss_D: 2.8841 Loss_G: 2.0607 D(x): 0.6974 D(G(z)): 0.4704 / 0.5385\n", + "[92/100][163/391] Loss_D: 3.2116 Loss_G: 2.2922 D(x): 0.6442 D(G(z)): 0.5051 / 0.4990\n", + "[92/100][164/391] Loss_D: 2.4353 Loss_G: 3.6368 D(x): 0.7414 D(G(z)): 0.4292 / 0.3427\n", + "[92/100][165/391] Loss_D: 2.4085 Loss_G: 2.1632 D(x): 0.7412 D(G(z)): 0.4136 / 0.5294\n", + "[92/100][166/391] Loss_D: 2.3346 Loss_G: 2.2919 D(x): 0.7925 D(G(z)): 0.3918 / 0.4998\n", + "[92/100][167/391] Loss_D: 3.2258 Loss_G: 3.0832 D(x): 0.5763 D(G(z)): 0.4125 / 0.3888\n", + "[92/100][168/391] Loss_D: 2.4394 Loss_G: 2.8877 D(x): 0.7705 D(G(z)): 0.4141 / 0.4158\n", + "[92/100][169/391] Loss_D: 2.6435 Loss_G: 2.5691 D(x): 0.6477 D(G(z)): 0.3464 / 0.4574\n", + "[92/100][170/391] Loss_D: 2.7081 Loss_G: 2.2534 D(x): 0.6596 D(G(z)): 0.3920 / 0.5125\n", + "[92/100][171/391] Loss_D: 2.8070 Loss_G: 2.9931 D(x): 0.7306 D(G(z)): 0.4731 / 0.4016\n", + "[92/100][172/391] Loss_D: 2.6868 Loss_G: 2.6237 D(x): 0.6774 D(G(z)): 0.4077 / 0.4448\n", + "[92/100][173/391] Loss_D: 2.8282 Loss_G: 2.5725 D(x): 0.7060 D(G(z)): 0.4371 / 0.4537\n", + "[92/100][174/391] Loss_D: 3.4230 Loss_G: 3.3610 D(x): 0.5695 D(G(z)): 0.4950 / 0.3571\n", + "[92/100][175/391] Loss_D: 3.6310 Loss_G: 2.6081 D(x): 0.5074 D(G(z)): 0.4304 / 0.4396\n", + "[92/100][176/391] Loss_D: 2.7488 Loss_G: 3.4643 D(x): 0.7429 D(G(z)): 0.4459 / 0.3377\n", + "[92/100][177/391] Loss_D: 2.5814 Loss_G: 3.1174 D(x): 0.7442 D(G(z)): 0.3777 / 0.3885\n", + "[92/100][178/391] Loss_D: 2.9502 Loss_G: 2.2906 D(x): 0.6042 D(G(z)): 0.3811 / 0.4794\n", + "[92/100][179/391] Loss_D: 3.8164 Loss_G: 2.9711 D(x): 0.5522 D(G(z)): 0.5093 / 0.3985\n", + "[92/100][180/391] Loss_D: 2.9717 Loss_G: 2.2589 D(x): 0.6434 D(G(z)): 0.4311 / 0.4967\n", + "[92/100][181/391] Loss_D: 3.4851 Loss_G: 2.2677 D(x): 0.6387 D(G(z)): 0.4236 / 0.4991\n", + "[92/100][182/391] Loss_D: 3.3977 Loss_G: 2.0745 D(x): 0.6438 D(G(z)): 0.5543 / 0.5389\n", + "[92/100][183/391] Loss_D: 2.5721 Loss_G: 2.4543 D(x): 0.6769 D(G(z)): 0.4254 / 0.4483\n", + "[92/100][184/391] Loss_D: 2.7212 Loss_G: 3.3182 D(x): 0.7458 D(G(z)): 0.4696 / 0.3451\n", + "[92/100][185/391] Loss_D: 3.0739 Loss_G: 3.2374 D(x): 0.6978 D(G(z)): 0.4984 / 0.3534\n", + "[92/100][186/391] Loss_D: 2.9310 Loss_G: 3.6586 D(x): 0.5962 D(G(z)): 0.3883 / 0.3268\n", + "[92/100][187/391] Loss_D: 2.7107 Loss_G: 2.9618 D(x): 0.7539 D(G(z)): 0.4599 / 0.3928\n", + "[92/100][188/391] Loss_D: 2.7585 Loss_G: 3.4430 D(x): 0.6375 D(G(z)): 0.3424 / 0.3423\n", + "[92/100][189/391] Loss_D: 2.9367 Loss_G: 2.6974 D(x): 0.6241 D(G(z)): 0.4089 / 0.4389\n", + "[92/100][190/391] Loss_D: 2.9708 Loss_G: 2.8033 D(x): 0.5945 D(G(z)): 0.3930 / 0.4384\n", + "[92/100][191/391] Loss_D: 2.4388 Loss_G: 3.1682 D(x): 0.7765 D(G(z)): 0.3879 / 0.3811\n", + "[92/100][192/391] Loss_D: 2.7407 Loss_G: 2.1749 D(x): 0.7375 D(G(z)): 0.4835 / 0.5239\n", + "[92/100][193/391] Loss_D: 3.0147 Loss_G: 3.2119 D(x): 0.7180 D(G(z)): 0.5374 / 0.3762\n", + "[92/100][194/391] Loss_D: 3.6156 Loss_G: 2.8643 D(x): 0.4973 D(G(z)): 0.4059 / 0.4316\n", + "[92/100][195/391] Loss_D: 3.1666 Loss_G: 2.5184 D(x): 0.5281 D(G(z)): 0.3670 / 0.4641\n", + "[92/100][196/391] Loss_D: 2.8149 Loss_G: 2.8653 D(x): 0.5772 D(G(z)): 0.4003 / 0.4186\n", + "[92/100][197/391] Loss_D: 2.6080 Loss_G: 2.2131 D(x): 0.7110 D(G(z)): 0.3426 / 0.5196\n", + "[92/100][198/391] Loss_D: 2.1307 Loss_G: 2.2829 D(x): 0.8020 D(G(z)): 0.3877 / 0.4993\n", + "[92/100][199/391] Loss_D: 2.7597 Loss_G: 2.5377 D(x): 0.7018 D(G(z)): 0.4708 / 0.4655\n", + "[92/100][200/391] Loss_D: 3.0672 Loss_G: 2.4492 D(x): 0.7216 D(G(z)): 0.5092 / 0.4726\n", + "[92/100][201/391] Loss_D: 2.4806 Loss_G: 2.4994 D(x): 0.7955 D(G(z)): 0.3812 / 0.4684\n", + "[92/100][202/391] Loss_D: 3.0725 Loss_G: 2.0583 D(x): 0.6115 D(G(z)): 0.4538 / 0.5294\n", + "[92/100][203/391] Loss_D: 2.6165 Loss_G: 3.9327 D(x): 0.7491 D(G(z)): 0.4347 / 0.2973\n", + "[92/100][204/391] Loss_D: 2.2516 Loss_G: 2.8296 D(x): 0.7195 D(G(z)): 0.3859 / 0.4406\n", + "[92/100][205/391] Loss_D: 3.0879 Loss_G: 3.0728 D(x): 0.5676 D(G(z)): 0.4132 / 0.3957\n", + "[92/100][206/391] Loss_D: 3.1717 Loss_G: 2.7392 D(x): 0.5872 D(G(z)): 0.4392 / 0.4249\n", + "[92/100][207/391] Loss_D: 3.0141 Loss_G: 3.0559 D(x): 0.6131 D(G(z)): 0.3922 / 0.3863\n", + "[92/100][208/391] Loss_D: 2.6050 Loss_G: 1.9673 D(x): 0.6573 D(G(z)): 0.3522 / 0.5294\n", + "[92/100][209/391] Loss_D: 2.9480 Loss_G: 2.4241 D(x): 0.6333 D(G(z)): 0.4402 / 0.4787\n", + "[92/100][210/391] Loss_D: 3.0734 Loss_G: 2.4125 D(x): 0.7568 D(G(z)): 0.5436 / 0.4791\n", + "[92/100][211/391] Loss_D: 3.7038 Loss_G: 3.0040 D(x): 0.7073 D(G(z)): 0.5018 / 0.4156\n", + "[92/100][212/391] Loss_D: 3.9284 Loss_G: 3.1312 D(x): 0.6788 D(G(z)): 0.6328 / 0.3787\n", + "[92/100][213/391] Loss_D: 2.5939 Loss_G: 2.8767 D(x): 0.7242 D(G(z)): 0.4031 / 0.4206\n", + "[92/100][214/391] Loss_D: 2.6641 Loss_G: 2.0894 D(x): 0.5624 D(G(z)): 0.2890 / 0.5366\n", + "[92/100][215/391] Loss_D: 3.1471 Loss_G: 2.8900 D(x): 0.5685 D(G(z)): 0.3970 / 0.4056\n", + "[92/100][216/391] Loss_D: 2.5985 Loss_G: 2.7854 D(x): 0.6913 D(G(z)): 0.3552 / 0.4194\n", + "[92/100][217/391] Loss_D: 2.8411 Loss_G: 2.7245 D(x): 0.6854 D(G(z)): 0.4387 / 0.4270\n", + "[92/100][218/391] Loss_D: 2.3930 Loss_G: 2.5750 D(x): 0.6976 D(G(z)): 0.3246 / 0.4452\n", + "[92/100][219/391] Loss_D: 3.0546 Loss_G: 2.7335 D(x): 0.6675 D(G(z)): 0.4968 / 0.4303\n", + "[92/100][220/391] Loss_D: 2.6208 Loss_G: 2.8230 D(x): 0.6264 D(G(z)): 0.2882 / 0.4350\n", + "[92/100][221/391] Loss_D: 3.5044 Loss_G: 3.6581 D(x): 0.6454 D(G(z)): 0.5700 / 0.3235\n", + "[92/100][222/391] Loss_D: 3.1523 Loss_G: 2.6190 D(x): 0.6195 D(G(z)): 0.4752 / 0.4566\n", + "[92/100][223/391] Loss_D: 3.3139 Loss_G: 3.2228 D(x): 0.6132 D(G(z)): 0.4793 / 0.3696\n", + "[92/100][224/391] Loss_D: 3.0775 Loss_G: 2.4362 D(x): 0.6231 D(G(z)): 0.4955 / 0.4788\n", + "[92/100][225/391] Loss_D: 3.2762 Loss_G: 2.7878 D(x): 0.6005 D(G(z)): 0.4584 / 0.4352\n", + "[92/100][226/391] Loss_D: 2.7041 Loss_G: 2.7805 D(x): 0.6476 D(G(z)): 0.4076 / 0.4235\n", + "[92/100][227/391] Loss_D: 2.6136 Loss_G: 3.1265 D(x): 0.7005 D(G(z)): 0.3834 / 0.3798\n", + "[92/100][228/391] Loss_D: 2.5702 Loss_G: 2.6957 D(x): 0.6806 D(G(z)): 0.3686 / 0.4457\n", + "[92/100][229/391] Loss_D: 2.8911 Loss_G: 2.5813 D(x): 0.6124 D(G(z)): 0.3883 / 0.4458\n", + "[92/100][230/391] Loss_D: 3.2063 Loss_G: 2.1197 D(x): 0.7154 D(G(z)): 0.6072 / 0.5195\n", + "[92/100][231/391] Loss_D: 2.8071 Loss_G: 2.4559 D(x): 0.6806 D(G(z)): 0.4325 / 0.4710\n", + "[92/100][232/391] Loss_D: 3.0468 Loss_G: 3.2136 D(x): 0.7073 D(G(z)): 0.5160 / 0.3860\n", + "[92/100][233/391] Loss_D: 3.2233 Loss_G: 2.1138 D(x): 0.6605 D(G(z)): 0.5319 / 0.5221\n", + "[92/100][234/391] Loss_D: 2.7098 Loss_G: 3.3023 D(x): 0.6026 D(G(z)): 0.3638 / 0.3615\n", + "[92/100][235/391] Loss_D: 2.8221 Loss_G: 2.6815 D(x): 0.5777 D(G(z)): 0.3411 / 0.4388\n", + "[92/100][236/391] Loss_D: 2.4516 Loss_G: 3.1562 D(x): 0.7393 D(G(z)): 0.3703 / 0.3676\n", + "[92/100][237/391] Loss_D: 2.5795 Loss_G: 2.8379 D(x): 0.7061 D(G(z)): 0.4001 / 0.4254\n", + "[92/100][238/391] Loss_D: 2.9963 Loss_G: 2.6880 D(x): 0.6001 D(G(z)): 0.3984 / 0.4415\n", + "[92/100][239/391] Loss_D: 2.8310 Loss_G: 2.5928 D(x): 0.7128 D(G(z)): 0.4862 / 0.4580\n", + "[92/100][240/391] Loss_D: 3.1080 Loss_G: 3.8021 D(x): 0.6559 D(G(z)): 0.4779 / 0.3138\n", + "[92/100][241/391] Loss_D: 3.8456 Loss_G: 3.1903 D(x): 0.6418 D(G(z)): 0.3411 / 0.3831\n", + "[92/100][242/391] Loss_D: 3.1194 Loss_G: 3.2832 D(x): 0.6455 D(G(z)): 0.5077 / 0.3641\n", + "[92/100][243/391] Loss_D: 2.8952 Loss_G: 2.8600 D(x): 0.5812 D(G(z)): 0.3281 / 0.4060\n", + "[92/100][244/391] Loss_D: 2.9696 Loss_G: 2.1221 D(x): 0.6059 D(G(z)): 0.3924 / 0.5190\n", + "[92/100][245/391] Loss_D: 2.6626 Loss_G: 2.6083 D(x): 0.6207 D(G(z)): 0.3305 / 0.4377\n", + "[92/100][246/391] Loss_D: 3.0313 Loss_G: 1.9603 D(x): 0.7746 D(G(z)): 0.5100 / 0.5355\n", + "[92/100][247/391] Loss_D: 3.2312 Loss_G: 2.1641 D(x): 0.6453 D(G(z)): 0.4964 / 0.4927\n", + "[92/100][248/391] Loss_D: 2.4088 Loss_G: 2.6739 D(x): 0.7384 D(G(z)): 0.4011 / 0.4368\n", + "[92/100][249/391] Loss_D: 2.6806 Loss_G: 3.5228 D(x): 0.6898 D(G(z)): 0.4320 / 0.3511\n", + "[92/100][250/391] Loss_D: 3.1670 Loss_G: 1.7577 D(x): 0.6245 D(G(z)): 0.4470 / 0.5946\n", + "[92/100][251/391] Loss_D: 2.2301 Loss_G: 3.0468 D(x): 0.7807 D(G(z)): 0.3229 / 0.3899\n", + "[92/100][252/391] Loss_D: 3.3774 Loss_G: 2.9304 D(x): 0.6598 D(G(z)): 0.5249 / 0.4160\n", + "[92/100][253/391] Loss_D: 3.0612 Loss_G: 2.8751 D(x): 0.6144 D(G(z)): 0.4378 / 0.4279\n", + "[92/100][254/391] Loss_D: 2.5177 Loss_G: 2.2553 D(x): 0.6785 D(G(z)): 0.4029 / 0.4930\n", + "[92/100][255/391] Loss_D: 2.4001 Loss_G: 2.8704 D(x): 0.7130 D(G(z)): 0.3498 / 0.4081\n", + "[92/100][256/391] Loss_D: 2.7036 Loss_G: 2.6368 D(x): 0.6757 D(G(z)): 0.4039 / 0.4401\n", + "[92/100][257/391] Loss_D: 3.5628 Loss_G: 2.4435 D(x): 0.5558 D(G(z)): 0.4703 / 0.4713\n", + "[92/100][258/391] Loss_D: 3.7393 Loss_G: 2.8014 D(x): 0.5862 D(G(z)): 0.5563 / 0.4177\n", + "[92/100][259/391] Loss_D: 2.8512 Loss_G: 2.8173 D(x): 0.7100 D(G(z)): 0.4766 / 0.4142\n", + "[92/100][260/391] Loss_D: 3.9772 Loss_G: 2.8200 D(x): 0.5826 D(G(z)): 0.5985 / 0.4155\n", + "[92/100][261/391] Loss_D: 3.2612 Loss_G: 2.6595 D(x): 0.6400 D(G(z)): 0.4760 / 0.4640\n", + "[92/100][262/391] Loss_D: 3.0797 Loss_G: 2.7780 D(x): 0.6855 D(G(z)): 0.4709 / 0.4323\n", + "[92/100][263/391] Loss_D: 2.8179 Loss_G: 2.6398 D(x): 0.6896 D(G(z)): 0.3869 / 0.4412\n", + "[92/100][264/391] Loss_D: 2.7633 Loss_G: 3.7927 D(x): 0.5748 D(G(z)): 0.3279 / 0.3157\n", + "[92/100][265/391] Loss_D: 3.1778 Loss_G: 3.2423 D(x): 0.6316 D(G(z)): 0.4550 / 0.3676\n", + "[92/100][266/391] Loss_D: 2.7288 Loss_G: 2.4428 D(x): 0.6900 D(G(z)): 0.3680 / 0.4658\n", + "[92/100][267/391] Loss_D: 2.8067 Loss_G: 2.2433 D(x): 0.7144 D(G(z)): 0.4599 / 0.4984\n", + "[92/100][268/391] Loss_D: 2.2654 Loss_G: 3.5618 D(x): 0.7358 D(G(z)): 0.4111 / 0.3440\n", + "[92/100][269/391] Loss_D: 3.0277 Loss_G: 3.1047 D(x): 0.7739 D(G(z)): 0.5389 / 0.3879\n", + "[92/100][270/391] Loss_D: 2.7269 Loss_G: 2.8440 D(x): 0.7177 D(G(z)): 0.4337 / 0.4262\n", + "[92/100][271/391] Loss_D: 3.6363 Loss_G: 3.9933 D(x): 0.6766 D(G(z)): 0.4373 / 0.3056\n", + "[92/100][272/391] Loss_D: 2.8385 Loss_G: 3.6618 D(x): 0.6243 D(G(z)): 0.3861 / 0.3268\n", + "[92/100][273/391] Loss_D: 2.8220 Loss_G: 3.1899 D(x): 0.6199 D(G(z)): 0.3834 / 0.3733\n", + "[92/100][274/391] Loss_D: 2.7392 Loss_G: 2.9986 D(x): 0.6035 D(G(z)): 0.3190 / 0.3935\n", + "[92/100][275/391] Loss_D: 2.8408 Loss_G: 3.2532 D(x): 0.6470 D(G(z)): 0.3885 / 0.3825\n", + "[92/100][276/391] Loss_D: 2.6541 Loss_G: 3.1687 D(x): 0.7144 D(G(z)): 0.3835 / 0.3838\n", + "[92/100][277/391] Loss_D: 2.5443 Loss_G: 2.7258 D(x): 0.7259 D(G(z)): 0.3669 / 0.4267\n", + "[92/100][278/391] Loss_D: 2.5309 Loss_G: 2.9609 D(x): 0.6714 D(G(z)): 0.4102 / 0.4166\n", + "[92/100][279/391] Loss_D: 2.6025 Loss_G: 2.1355 D(x): 0.6874 D(G(z)): 0.3644 / 0.5076\n", + "[92/100][280/391] Loss_D: 2.7391 Loss_G: 2.4317 D(x): 0.7092 D(G(z)): 0.4577 / 0.4823\n", + "[92/100][281/391] Loss_D: 3.4723 Loss_G: 2.2010 D(x): 0.5635 D(G(z)): 0.4771 / 0.5125\n", + "[92/100][282/391] Loss_D: 2.6924 Loss_G: 2.7122 D(x): 0.6711 D(G(z)): 0.3821 / 0.4357\n", + "[92/100][283/391] Loss_D: 2.3853 Loss_G: 2.3291 D(x): 0.7581 D(G(z)): 0.3911 / 0.4887\n", + "[92/100][284/391] Loss_D: 2.5810 Loss_G: 2.5826 D(x): 0.6881 D(G(z)): 0.4302 / 0.4510\n", + "[92/100][285/391] Loss_D: 3.5605 Loss_G: 3.1787 D(x): 0.6745 D(G(z)): 0.6032 / 0.3716\n", + "[92/100][286/391] Loss_D: 3.3146 Loss_G: 2.7865 D(x): 0.6319 D(G(z)): 0.5299 / 0.4356\n", + "[92/100][287/391] Loss_D: 3.0214 Loss_G: 3.8296 D(x): 0.6620 D(G(z)): 0.4605 / 0.2949\n", + "[92/100][288/391] Loss_D: 2.7762 Loss_G: 2.9538 D(x): 0.6232 D(G(z)): 0.4128 / 0.4041\n", + "[92/100][289/391] Loss_D: 2.3927 Loss_G: 2.3361 D(x): 0.7224 D(G(z)): 0.3472 / 0.5021\n", + "[92/100][290/391] Loss_D: 3.0587 Loss_G: 2.6037 D(x): 0.5811 D(G(z)): 0.3294 / 0.4544\n", + "[92/100][291/391] Loss_D: 3.3156 Loss_G: 2.6866 D(x): 0.5865 D(G(z)): 0.4487 / 0.4310\n", + "[92/100][292/391] Loss_D: 3.3551 Loss_G: 2.6780 D(x): 0.6281 D(G(z)): 0.4845 / 0.4457\n", + "[92/100][293/391] Loss_D: 2.5747 Loss_G: 3.3100 D(x): 0.7543 D(G(z)): 0.4373 / 0.3587\n", + "[92/100][294/391] Loss_D: 2.4208 Loss_G: 2.1847 D(x): 0.7285 D(G(z)): 0.4128 / 0.4898\n", + "[92/100][295/391] Loss_D: 3.5569 Loss_G: 2.5811 D(x): 0.5843 D(G(z)): 0.5198 / 0.4431\n", + "[92/100][296/391] Loss_D: 2.9942 Loss_G: 2.9492 D(x): 0.6368 D(G(z)): 0.4357 / 0.3878\n", + "[92/100][297/391] Loss_D: 3.0854 Loss_G: 3.0859 D(x): 0.6365 D(G(z)): 0.4315 / 0.3822\n", + "[92/100][298/391] Loss_D: 2.2843 Loss_G: 2.4120 D(x): 0.7342 D(G(z)): 0.3415 / 0.4638\n", + "[92/100][299/391] Loss_D: 3.2426 Loss_G: 2.6125 D(x): 0.6142 D(G(z)): 0.4794 / 0.4488\n", + "[92/100][300/391] Loss_D: 3.1327 Loss_G: 2.4827 D(x): 0.5646 D(G(z)): 0.3751 / 0.4728\n", + "[92/100][301/391] Loss_D: 3.6833 Loss_G: 2.4902 D(x): 0.7532 D(G(z)): 0.4131 / 0.4547\n", + "[92/100][302/391] Loss_D: 2.4961 Loss_G: 1.9194 D(x): 0.7200 D(G(z)): 0.4147 / 0.5650\n", + "[92/100][303/391] Loss_D: 2.5572 Loss_G: 3.7605 D(x): 0.7067 D(G(z)): 0.3971 / 0.3253\n", + "[92/100][304/391] Loss_D: 2.2052 Loss_G: 2.8801 D(x): 0.7108 D(G(z)): 0.3616 / 0.4089\n", + "[92/100][305/391] Loss_D: 2.9201 Loss_G: 3.0155 D(x): 0.6149 D(G(z)): 0.3962 / 0.3861\n", + "[92/100][306/391] Loss_D: 3.1605 Loss_G: 2.6754 D(x): 0.5901 D(G(z)): 0.4345 / 0.4403\n", + "[92/100][307/391] Loss_D: 2.8634 Loss_G: 2.2460 D(x): 0.6930 D(G(z)): 0.4615 / 0.4860\n", + "[92/100][308/391] Loss_D: 2.6484 Loss_G: 3.9562 D(x): 0.6625 D(G(z)): 0.4045 / 0.3030\n", + "[92/100][309/391] Loss_D: 2.9253 Loss_G: 2.7004 D(x): 0.6973 D(G(z)): 0.4898 / 0.4458\n", + "[92/100][310/391] Loss_D: 3.1251 Loss_G: 2.4097 D(x): 0.6276 D(G(z)): 0.4264 / 0.4793\n", + "[92/100][311/391] Loss_D: 3.7304 Loss_G: 2.3203 D(x): 0.7007 D(G(z)): 0.5972 / 0.4918\n", + "[92/100][312/391] Loss_D: 3.0908 Loss_G: 3.1637 D(x): 0.6852 D(G(z)): 0.4954 / 0.3800\n", + "[92/100][313/391] Loss_D: 3.2617 Loss_G: 3.6545 D(x): 0.5584 D(G(z)): 0.4154 / 0.3297\n", + "[92/100][314/391] Loss_D: 3.4669 Loss_G: 3.2776 D(x): 0.5989 D(G(z)): 0.5386 / 0.3609\n", + "[92/100][315/391] Loss_D: 2.9631 Loss_G: 3.3274 D(x): 0.6943 D(G(z)): 0.4912 / 0.3594\n", + "[92/100][316/391] Loss_D: 2.6371 Loss_G: 2.4518 D(x): 0.6286 D(G(z)): 0.2883 / 0.4621\n", + "[92/100][317/391] Loss_D: 2.7594 Loss_G: 2.8124 D(x): 0.7000 D(G(z)): 0.3967 / 0.4204\n", + "[92/100][318/391] Loss_D: 2.8809 Loss_G: 2.8114 D(x): 0.6138 D(G(z)): 0.4368 / 0.4192\n", + "[92/100][319/391] Loss_D: 2.7207 Loss_G: 2.8576 D(x): 0.7304 D(G(z)): 0.4649 / 0.4154\n", + "[92/100][320/391] Loss_D: 3.2123 Loss_G: 2.6877 D(x): 0.5413 D(G(z)): 0.3019 / 0.4225\n", + "[92/100][321/391] Loss_D: 2.6808 Loss_G: 2.7469 D(x): 0.7459 D(G(z)): 0.4203 / 0.4262\n", + "[92/100][322/391] Loss_D: 2.6421 Loss_G: 3.0552 D(x): 0.6519 D(G(z)): 0.3760 / 0.3910\n", + "[92/100][323/391] Loss_D: 3.0444 Loss_G: 2.6252 D(x): 0.7164 D(G(z)): 0.5077 / 0.4526\n", + "[92/100][324/391] Loss_D: 2.8859 Loss_G: 2.4107 D(x): 0.6730 D(G(z)): 0.4872 / 0.4833\n", + "[92/100][325/391] Loss_D: 2.7122 Loss_G: 3.0441 D(x): 0.6897 D(G(z)): 0.3723 / 0.3981\n", + "[92/100][326/391] Loss_D: 2.7978 Loss_G: 3.6509 D(x): 0.6314 D(G(z)): 0.3792 / 0.3236\n", + "[92/100][327/391] Loss_D: 3.1913 Loss_G: 2.7681 D(x): 0.5882 D(G(z)): 0.4400 / 0.4146\n", + "[92/100][328/391] Loss_D: 2.5441 Loss_G: 2.4718 D(x): 0.7027 D(G(z)): 0.4122 / 0.4524\n", + "[92/100][329/391] Loss_D: 2.3959 Loss_G: 2.3933 D(x): 0.8014 D(G(z)): 0.4100 / 0.4870\n", + "[92/100][330/391] Loss_D: 2.7597 Loss_G: 2.0701 D(x): 0.6526 D(G(z)): 0.3751 / 0.5377\n", + "[92/100][331/391] Loss_D: 3.5992 Loss_G: 2.5420 D(x): 0.7557 D(G(z)): 0.3437 / 0.4636\n", + "[92/100][332/391] Loss_D: 2.4856 Loss_G: 2.2693 D(x): 0.6942 D(G(z)): 0.3944 / 0.4914\n", + "[92/100][333/391] Loss_D: 3.5841 Loss_G: 3.1813 D(x): 0.5744 D(G(z)): 0.4587 / 0.3607\n", + "[92/100][334/391] Loss_D: 2.9178 Loss_G: 2.9030 D(x): 0.6546 D(G(z)): 0.4735 / 0.4087\n", + "[92/100][335/391] Loss_D: 2.4705 Loss_G: 2.5878 D(x): 0.7066 D(G(z)): 0.3488 / 0.4538\n", + "[92/100][336/391] Loss_D: 2.9869 Loss_G: 3.6821 D(x): 0.7056 D(G(z)): 0.4618 / 0.3128\n", + "[92/100][337/391] Loss_D: 3.0174 Loss_G: 1.6217 D(x): 0.6218 D(G(z)): 0.4368 / 0.5707\n", + "[92/100][338/391] Loss_D: 3.1823 Loss_G: 2.2323 D(x): 0.5862 D(G(z)): 0.4408 / 0.5000\n", + "[92/100][339/391] Loss_D: 2.7130 Loss_G: 2.2835 D(x): 0.7331 D(G(z)): 0.4327 / 0.4931\n", + "[92/100][340/391] Loss_D: 2.7687 Loss_G: 2.1558 D(x): 0.6824 D(G(z)): 0.4151 / 0.5151\n", + "[92/100][341/391] Loss_D: 2.5581 Loss_G: 1.8404 D(x): 0.7358 D(G(z)): 0.4127 / 0.5590\n", + "[92/100][342/391] Loss_D: 2.7354 Loss_G: 2.8319 D(x): 0.7550 D(G(z)): 0.4715 / 0.4160\n", + "[92/100][343/391] Loss_D: 3.6945 Loss_G: 2.9490 D(x): 0.5760 D(G(z)): 0.5284 / 0.3988\n", + "[92/100][344/391] Loss_D: 2.5311 Loss_G: 3.4890 D(x): 0.6439 D(G(z)): 0.3497 / 0.3479\n", + "[92/100][345/391] Loss_D: 3.0505 Loss_G: 2.8324 D(x): 0.6374 D(G(z)): 0.4365 / 0.4063\n", + "[92/100][346/391] Loss_D: 2.8053 Loss_G: 2.4438 D(x): 0.6884 D(G(z)): 0.4362 / 0.4749\n", + "[92/100][347/391] Loss_D: 2.9210 Loss_G: 2.1817 D(x): 0.6793 D(G(z)): 0.4157 / 0.5035\n", + "[92/100][348/391] Loss_D: 3.6279 Loss_G: 2.9926 D(x): 0.5146 D(G(z)): 0.3890 / 0.3994\n", + "[92/100][349/391] Loss_D: 3.2608 Loss_G: 2.8130 D(x): 0.6158 D(G(z)): 0.4977 / 0.4199\n", + "[92/100][350/391] Loss_D: 2.9448 Loss_G: 2.3259 D(x): 0.7235 D(G(z)): 0.5070 / 0.4887\n", + "[92/100][351/391] Loss_D: 3.2700 Loss_G: 3.0402 D(x): 0.6838 D(G(z)): 0.5202 / 0.3979\n", + "[92/100][352/391] Loss_D: 2.5327 Loss_G: 2.4078 D(x): 0.7280 D(G(z)): 0.4383 / 0.4992\n", + "[92/100][353/391] Loss_D: 3.2423 Loss_G: 1.8370 D(x): 0.5556 D(G(z)): 0.4283 / 0.5712\n", + "[92/100][354/391] Loss_D: 2.5759 Loss_G: 2.7037 D(x): 0.6363 D(G(z)): 0.3370 / 0.4360\n", + "[92/100][355/391] Loss_D: 2.7958 Loss_G: 3.1373 D(x): 0.7053 D(G(z)): 0.4821 / 0.3799\n", + "[92/100][356/391] Loss_D: 2.1444 Loss_G: 2.9407 D(x): 0.7762 D(G(z)): 0.3119 / 0.4085\n", + "[92/100][357/391] Loss_D: 3.3831 Loss_G: 2.7071 D(x): 0.6432 D(G(z)): 0.5416 / 0.4130\n", + "[92/100][358/391] Loss_D: 2.4765 Loss_G: 2.5477 D(x): 0.6965 D(G(z)): 0.4006 / 0.4631\n", + "[92/100][359/391] Loss_D: 3.2488 Loss_G: 2.5623 D(x): 0.5750 D(G(z)): 0.4292 / 0.4722\n", + "[92/100][360/391] Loss_D: 2.8313 Loss_G: 2.4118 D(x): 0.6626 D(G(z)): 0.3995 / 0.4609\n", + "[92/100][361/391] Loss_D: 3.5324 Loss_G: 2.6729 D(x): 0.6007 D(G(z)): 0.3533 / 0.4420\n", + "[92/100][362/391] Loss_D: 3.1588 Loss_G: 2.1811 D(x): 0.6705 D(G(z)): 0.4819 / 0.5075\n", + "[92/100][363/391] Loss_D: 2.9833 Loss_G: 3.6088 D(x): 0.6404 D(G(z)): 0.4484 / 0.3353\n", + "[92/100][364/391] Loss_D: 3.6417 Loss_G: 2.7418 D(x): 0.7063 D(G(z)): 0.5957 / 0.4232\n", + "[92/100][365/391] Loss_D: 2.5910 Loss_G: 3.7382 D(x): 0.7625 D(G(z)): 0.4300 / 0.3101\n", + "[92/100][366/391] Loss_D: 2.7964 Loss_G: 3.2908 D(x): 0.6643 D(G(z)): 0.4218 / 0.3496\n", + "[92/100][367/391] Loss_D: 2.8167 Loss_G: 2.8717 D(x): 0.6208 D(G(z)): 0.3703 / 0.4225\n", + "[92/100][368/391] Loss_D: 2.7684 Loss_G: 3.6640 D(x): 0.6621 D(G(z)): 0.3841 / 0.3324\n", + "[92/100][369/391] Loss_D: 2.8763 Loss_G: 2.7972 D(x): 0.6172 D(G(z)): 0.3950 / 0.4336\n", + "[92/100][370/391] Loss_D: 2.3069 Loss_G: 2.6427 D(x): 0.6845 D(G(z)): 0.2794 / 0.4487\n", + "[92/100][371/391] Loss_D: 2.5955 Loss_G: 2.7319 D(x): 0.7724 D(G(z)): 0.4315 / 0.4295\n", + "[92/100][372/391] Loss_D: 2.8736 Loss_G: 3.1314 D(x): 0.6277 D(G(z)): 0.4264 / 0.3718\n", + "[92/100][373/391] Loss_D: 2.8625 Loss_G: 2.0493 D(x): 0.7007 D(G(z)): 0.4463 / 0.5405\n", + "[92/100][374/391] Loss_D: 2.8681 Loss_G: 2.7609 D(x): 0.7231 D(G(z)): 0.5138 / 0.4349\n", + "[92/100][375/391] Loss_D: 2.9951 Loss_G: 2.7318 D(x): 0.6482 D(G(z)): 0.4356 / 0.4297\n", + "[92/100][376/391] Loss_D: 2.8663 Loss_G: 3.2706 D(x): 0.6548 D(G(z)): 0.3732 / 0.3644\n", + "[92/100][377/391] Loss_D: 2.9895 Loss_G: 2.7980 D(x): 0.6220 D(G(z)): 0.3907 / 0.4097\n", + "[92/100][378/391] Loss_D: 2.3456 Loss_G: 2.4323 D(x): 0.7092 D(G(z)): 0.3971 / 0.4793\n", + "[92/100][379/391] Loss_D: 2.9807 Loss_G: 3.0813 D(x): 0.6765 D(G(z)): 0.5087 / 0.3959\n", + "[92/100][380/391] Loss_D: 2.6145 Loss_G: 2.9679 D(x): 0.6859 D(G(z)): 0.3605 / 0.4085\n", + "[92/100][381/391] Loss_D: 2.8307 Loss_G: 1.8864 D(x): 0.6772 D(G(z)): 0.4430 / 0.5757\n", + "[92/100][382/391] Loss_D: 3.0737 Loss_G: 2.1339 D(x): 0.6219 D(G(z)): 0.4263 / 0.5234\n", + "[92/100][383/391] Loss_D: 2.8537 Loss_G: 3.0772 D(x): 0.6279 D(G(z)): 0.3875 / 0.3813\n", + "[92/100][384/391] Loss_D: 2.6114 Loss_G: 2.8813 D(x): 0.7148 D(G(z)): 0.4752 / 0.4133\n", + "[92/100][385/391] Loss_D: 2.7375 Loss_G: 2.0734 D(x): 0.6675 D(G(z)): 0.4137 / 0.5134\n", + "[92/100][386/391] Loss_D: 3.5370 Loss_G: 2.7330 D(x): 0.6073 D(G(z)): 0.5235 / 0.4262\n", + "[92/100][387/391] Loss_D: 3.1294 Loss_G: 3.2988 D(x): 0.5885 D(G(z)): 0.4143 / 0.3540\n", + "[92/100][388/391] Loss_D: 2.3215 Loss_G: 2.9204 D(x): 0.7548 D(G(z)): 0.3959 / 0.4069\n", + "[92/100][389/391] Loss_D: 3.0585 Loss_G: 3.1019 D(x): 0.5699 D(G(z)): 0.3468 / 0.3855\n", + "[92/100][390/391] Loss_D: 2.4110 Loss_G: 1.8138 D(x): 0.6852 D(G(z)): 0.2995 / 0.5725\n", + "[92/100][391/391] Loss_D: 3.7672 Loss_G: 2.9298 D(x): 0.7336 D(G(z)): 0.3984 / 0.4176\n", + "[93/100][1/391] Loss_D: 3.7989 Loss_G: 3.6143 D(x): 0.7510 D(G(z)): 0.5175 / 0.3390\n", + "[93/100][2/391] Loss_D: 2.4459 Loss_G: 2.5135 D(x): 0.7340 D(G(z)): 0.3839 / 0.4555\n", + "[93/100][3/391] Loss_D: 2.4814 Loss_G: 3.1298 D(x): 0.7461 D(G(z)): 0.4189 / 0.4017\n", + "[93/100][4/391] Loss_D: 2.5050 Loss_G: 2.5211 D(x): 0.7047 D(G(z)): 0.4170 / 0.4642\n", + "[93/100][5/391] Loss_D: 3.0631 Loss_G: 2.5481 D(x): 0.6212 D(G(z)): 0.4395 / 0.4407\n", + "[93/100][6/391] Loss_D: 2.7081 Loss_G: 2.4634 D(x): 0.7207 D(G(z)): 0.4304 / 0.4634\n", + "[93/100][7/391] Loss_D: 2.9319 Loss_G: 2.6356 D(x): 0.6183 D(G(z)): 0.3289 / 0.4394\n", + "[93/100][8/391] Loss_D: 2.5785 Loss_G: 3.0737 D(x): 0.6290 D(G(z)): 0.3554 / 0.3994\n", + "[93/100][9/391] Loss_D: 2.9549 Loss_G: 2.5358 D(x): 0.6310 D(G(z)): 0.4740 / 0.4622\n", + "[93/100][10/391] Loss_D: 2.4333 Loss_G: 2.7271 D(x): 0.6928 D(G(z)): 0.3454 / 0.4320\n", + "[93/100][11/391] Loss_D: 3.1440 Loss_G: 2.1141 D(x): 0.6505 D(G(z)): 0.4982 / 0.5359\n", + "[93/100][12/391] Loss_D: 3.1296 Loss_G: 3.2277 D(x): 0.6289 D(G(z)): 0.4694 / 0.3760\n", + "[93/100][13/391] Loss_D: 3.4276 Loss_G: 2.4317 D(x): 0.6957 D(G(z)): 0.5682 / 0.4671\n", + "[93/100][14/391] Loss_D: 3.1982 Loss_G: 2.6557 D(x): 0.6247 D(G(z)): 0.4801 / 0.4304\n", + "[93/100][15/391] Loss_D: 3.0722 Loss_G: 2.5818 D(x): 0.6396 D(G(z)): 0.4894 / 0.4315\n", + "[93/100][16/391] Loss_D: 3.1691 Loss_G: 2.3455 D(x): 0.6519 D(G(z)): 0.4509 / 0.4805\n", + "[93/100][17/391] Loss_D: 2.8502 Loss_G: 3.1753 D(x): 0.6202 D(G(z)): 0.3911 / 0.3726\n", + "[93/100][18/391] Loss_D: 2.6647 Loss_G: 3.0188 D(x): 0.6496 D(G(z)): 0.3726 / 0.3928\n", + "[93/100][19/391] Loss_D: 2.4057 Loss_G: 2.1631 D(x): 0.7413 D(G(z)): 0.4035 / 0.5050\n", + "[93/100][20/391] Loss_D: 3.3207 Loss_G: 2.9132 D(x): 0.5935 D(G(z)): 0.4880 / 0.4333\n", + "[93/100][21/391] Loss_D: 2.9180 Loss_G: 3.4704 D(x): 0.6862 D(G(z)): 0.5038 / 0.3411\n", + "[93/100][22/391] Loss_D: 2.7087 Loss_G: 2.4067 D(x): 0.6961 D(G(z)): 0.4359 / 0.4799\n", + "[93/100][23/391] Loss_D: 2.7022 Loss_G: 2.6830 D(x): 0.6138 D(G(z)): 0.3233 / 0.4469\n", + "[93/100][24/391] Loss_D: 3.4556 Loss_G: 2.4188 D(x): 0.6302 D(G(z)): 0.5043 / 0.4728\n", + "[93/100][25/391] Loss_D: 3.0072 Loss_G: 3.0001 D(x): 0.6113 D(G(z)): 0.4127 / 0.3957\n", + "[93/100][26/391] Loss_D: 3.2850 Loss_G: 3.1776 D(x): 0.6229 D(G(z)): 0.4825 / 0.3779\n", + "[93/100][27/391] Loss_D: 2.9329 Loss_G: 3.4040 D(x): 0.6605 D(G(z)): 0.3404 / 0.3482\n", + "[93/100][28/391] Loss_D: 3.7929 Loss_G: 2.5419 D(x): 0.4598 D(G(z)): 0.4065 / 0.4492\n", + "[93/100][29/391] Loss_D: 2.6562 Loss_G: 2.2221 D(x): 0.7579 D(G(z)): 0.4835 / 0.5073\n", + "[93/100][30/391] Loss_D: 2.8121 Loss_G: 2.1797 D(x): 0.6293 D(G(z)): 0.4330 / 0.5350\n", + "[93/100][31/391] Loss_D: 3.7812 Loss_G: 2.7655 D(x): 0.7056 D(G(z)): 0.5699 / 0.4288\n", + "[93/100][32/391] Loss_D: 3.3693 Loss_G: 3.1395 D(x): 0.6028 D(G(z)): 0.4991 / 0.3759\n", + "[93/100][33/391] Loss_D: 2.3711 Loss_G: 2.3248 D(x): 0.7739 D(G(z)): 0.3582 / 0.4831\n", + "[93/100][34/391] Loss_D: 2.6581 Loss_G: 3.3532 D(x): 0.7208 D(G(z)): 0.4583 / 0.3490\n", + "[93/100][35/391] Loss_D: 3.0465 Loss_G: 2.9101 D(x): 0.6388 D(G(z)): 0.4629 / 0.4097\n", + "[93/100][36/391] Loss_D: 3.1469 Loss_G: 3.3298 D(x): 0.5861 D(G(z)): 0.4061 / 0.3664\n", + "[93/100][37/391] Loss_D: 3.1397 Loss_G: 2.5477 D(x): 0.5467 D(G(z)): 0.4054 / 0.4564\n", + "[93/100][38/391] Loss_D: 2.6056 Loss_G: 2.2028 D(x): 0.6894 D(G(z)): 0.3805 / 0.5075\n", + "[93/100][39/391] Loss_D: 2.5920 Loss_G: 2.2930 D(x): 0.7180 D(G(z)): 0.3982 / 0.4971\n", + "[93/100][40/391] Loss_D: 3.1149 Loss_G: 2.5823 D(x): 0.6868 D(G(z)): 0.4807 / 0.4527\n", + "[93/100][41/391] Loss_D: 3.4151 Loss_G: 2.2746 D(x): 0.7040 D(G(z)): 0.5494 / 0.4960\n", + "[93/100][42/391] Loss_D: 2.7713 Loss_G: 2.4443 D(x): 0.7006 D(G(z)): 0.4353 / 0.4766\n", + "[93/100][43/391] Loss_D: 2.7467 Loss_G: 2.6619 D(x): 0.7209 D(G(z)): 0.4043 / 0.4298\n", + "[93/100][44/391] Loss_D: 2.9984 Loss_G: 2.9091 D(x): 0.6238 D(G(z)): 0.4530 / 0.4148\n", + "[93/100][45/391] Loss_D: 3.3096 Loss_G: 2.9020 D(x): 0.5314 D(G(z)): 0.4228 / 0.4155\n", + "[93/100][46/391] Loss_D: 2.8522 Loss_G: 2.3946 D(x): 0.6833 D(G(z)): 0.4232 / 0.4772\n", + "[93/100][47/391] Loss_D: 2.8365 Loss_G: 2.6085 D(x): 0.6748 D(G(z)): 0.4244 / 0.4427\n", + "[93/100][48/391] Loss_D: 2.4973 Loss_G: 2.6501 D(x): 0.7365 D(G(z)): 0.4491 / 0.4472\n", + "[93/100][49/391] Loss_D: 3.0788 Loss_G: 3.2448 D(x): 0.6272 D(G(z)): 0.4679 / 0.3491\n", + "[93/100][50/391] Loss_D: 2.4118 Loss_G: 3.5591 D(x): 0.6888 D(G(z)): 0.3409 / 0.3348\n", + "[93/100][51/391] Loss_D: 2.6556 Loss_G: 3.4754 D(x): 0.6294 D(G(z)): 0.3196 / 0.3450\n", + "[93/100][52/391] Loss_D: 3.1131 Loss_G: 3.5449 D(x): 0.6250 D(G(z)): 0.4507 / 0.3366\n", + "[93/100][53/391] Loss_D: 2.4942 Loss_G: 2.3828 D(x): 0.7890 D(G(z)): 0.4599 / 0.4740\n", + "[93/100][54/391] Loss_D: 2.6449 Loss_G: 3.2934 D(x): 0.7314 D(G(z)): 0.4399 / 0.3582\n", + "[93/100][55/391] Loss_D: 2.6877 Loss_G: 3.4101 D(x): 0.6643 D(G(z)): 0.3735 / 0.3483\n", + "[93/100][56/391] Loss_D: 3.0009 Loss_G: 3.0071 D(x): 0.6489 D(G(z)): 0.4072 / 0.4014\n", + "[93/100][57/391] Loss_D: 2.9369 Loss_G: 3.6142 D(x): 0.6145 D(G(z)): 0.3595 / 0.3296\n", + "[93/100][58/391] Loss_D: 2.7526 Loss_G: 2.5330 D(x): 0.6282 D(G(z)): 0.3630 / 0.4537\n", + "[93/100][59/391] Loss_D: 2.8121 Loss_G: 2.9171 D(x): 0.7370 D(G(z)): 0.4925 / 0.4119\n", + "[93/100][60/391] Loss_D: 3.0618 Loss_G: 2.8412 D(x): 0.7485 D(G(z)): 0.5238 / 0.4227\n", + "[93/100][61/391] Loss_D: 3.5918 Loss_G: 2.8083 D(x): 0.6281 D(G(z)): 0.3478 / 0.4261\n", + "[93/100][62/391] Loss_D: 3.4324 Loss_G: 2.9695 D(x): 0.6467 D(G(z)): 0.5646 / 0.3936\n", + "[93/100][63/391] Loss_D: 3.0359 Loss_G: 4.1886 D(x): 0.6078 D(G(z)): 0.4313 / 0.2807\n", + "[93/100][64/391] Loss_D: 2.4538 Loss_G: 2.4758 D(x): 0.7166 D(G(z)): 0.4163 / 0.4687\n", + "[93/100][65/391] Loss_D: 3.2440 Loss_G: 3.0249 D(x): 0.5040 D(G(z)): 0.2961 / 0.4014\n", + "[93/100][66/391] Loss_D: 2.8286 Loss_G: 3.3818 D(x): 0.6109 D(G(z)): 0.3746 / 0.3518\n", + "[93/100][67/391] Loss_D: 2.8852 Loss_G: 3.4327 D(x): 0.7793 D(G(z)): 0.4403 / 0.3579\n", + "[93/100][68/391] Loss_D: 2.7540 Loss_G: 2.2292 D(x): 0.6675 D(G(z)): 0.4480 / 0.4963\n", + "[93/100][69/391] Loss_D: 3.6547 Loss_G: 1.5934 D(x): 0.4985 D(G(z)): 0.3824 / 0.6242\n", + "[93/100][70/391] Loss_D: 2.8210 Loss_G: 2.3182 D(x): 0.7085 D(G(z)): 0.4540 / 0.5039\n", + "[93/100][71/391] Loss_D: 2.5643 Loss_G: 2.7485 D(x): 0.7752 D(G(z)): 0.4294 / 0.4274\n", + "[93/100][72/391] Loss_D: 3.0013 Loss_G: 2.5826 D(x): 0.6540 D(G(z)): 0.4948 / 0.4531\n", + "[93/100][73/391] Loss_D: 2.5595 Loss_G: 2.9377 D(x): 0.7064 D(G(z)): 0.4271 / 0.4042\n", + "[93/100][74/391] Loss_D: 2.7470 Loss_G: 2.6216 D(x): 0.7193 D(G(z)): 0.4790 / 0.4567\n", + "[93/100][75/391] Loss_D: 2.1731 Loss_G: 3.1321 D(x): 0.7720 D(G(z)): 0.3095 / 0.3891\n", + "[93/100][76/391] Loss_D: 2.7929 Loss_G: 3.0820 D(x): 0.6419 D(G(z)): 0.3881 / 0.3903\n", + "[93/100][77/391] Loss_D: 2.6509 Loss_G: 2.3179 D(x): 0.7387 D(G(z)): 0.3791 / 0.4783\n", + "[93/100][78/391] Loss_D: 3.2641 Loss_G: 3.4744 D(x): 0.5209 D(G(z)): 0.3471 / 0.3327\n", + "[93/100][79/391] Loss_D: 3.0995 Loss_G: 2.2940 D(x): 0.6492 D(G(z)): 0.5132 / 0.4868\n", + "[93/100][80/391] Loss_D: 3.2816 Loss_G: 2.3338 D(x): 0.6261 D(G(z)): 0.4563 / 0.5035\n", + "[93/100][81/391] Loss_D: 2.7827 Loss_G: 2.7375 D(x): 0.7069 D(G(z)): 0.4947 / 0.4430\n", + "[93/100][82/391] Loss_D: 3.4743 Loss_G: 2.2260 D(x): 0.6840 D(G(z)): 0.5459 / 0.4843\n", + "[93/100][83/391] Loss_D: 2.8850 Loss_G: 2.4978 D(x): 0.6564 D(G(z)): 0.4054 / 0.4626\n", + "[93/100][84/391] Loss_D: 2.0653 Loss_G: 2.5977 D(x): 0.7188 D(G(z)): 0.3060 / 0.4633\n", + "[93/100][85/391] Loss_D: 2.8717 Loss_G: 3.0012 D(x): 0.5863 D(G(z)): 0.3492 / 0.3834\n", + "[93/100][86/391] Loss_D: 3.4692 Loss_G: 3.7299 D(x): 0.6600 D(G(z)): 0.5586 / 0.3301\n", + "[93/100][87/391] Loss_D: 3.3569 Loss_G: 2.5627 D(x): 0.6387 D(G(z)): 0.4849 / 0.4534\n", + "[93/100][88/391] Loss_D: 3.1080 Loss_G: 2.2747 D(x): 0.5997 D(G(z)): 0.4757 / 0.4970\n", + "[93/100][89/391] Loss_D: 2.7027 Loss_G: 1.9822 D(x): 0.6699 D(G(z)): 0.3710 / 0.5510\n", + "[93/100][90/391] Loss_D: 2.8569 Loss_G: 2.8129 D(x): 0.7018 D(G(z)): 0.4671 / 0.4254\n", + "[93/100][91/391] Loss_D: 3.5551 Loss_G: 3.1646 D(x): 0.6794 D(G(z)): 0.4901 / 0.3856\n", + "[93/100][92/391] Loss_D: 2.7436 Loss_G: 3.5166 D(x): 0.6966 D(G(z)): 0.4740 / 0.3476\n", + "[93/100][93/391] Loss_D: 2.6256 Loss_G: 1.9616 D(x): 0.6838 D(G(z)): 0.3739 / 0.5526\n", + "[93/100][94/391] Loss_D: 2.9391 Loss_G: 2.9539 D(x): 0.5487 D(G(z)): 0.3538 / 0.4109\n", + "[93/100][95/391] Loss_D: 3.1760 Loss_G: 2.4771 D(x): 0.6014 D(G(z)): 0.4542 / 0.4518\n", + "[93/100][96/391] Loss_D: 3.7496 Loss_G: 2.3344 D(x): 0.5656 D(G(z)): 0.5405 / 0.4750\n", + "[93/100][97/391] Loss_D: 2.8374 Loss_G: 3.1733 D(x): 0.6927 D(G(z)): 0.4114 / 0.3765\n", + "[93/100][98/391] Loss_D: 2.8253 Loss_G: 2.6603 D(x): 0.6721 D(G(z)): 0.4421 / 0.4326\n", + "[93/100][99/391] Loss_D: 3.5318 Loss_G: 3.5005 D(x): 0.6014 D(G(z)): 0.5159 / 0.3692\n", + "[93/100][100/391] Loss_D: 3.3085 Loss_G: 1.6285 D(x): 0.6327 D(G(z)): 0.5090 / 0.6174\n", + "[93/100][101/391] Loss_D: 2.6889 Loss_G: 3.2711 D(x): 0.7368 D(G(z)): 0.4681 / 0.3654\n", + "[93/100][102/391] Loss_D: 2.3405 Loss_G: 2.1962 D(x): 0.7416 D(G(z)): 0.2995 / 0.4992\n", + "[93/100][103/391] Loss_D: 2.7728 Loss_G: 3.2348 D(x): 0.6529 D(G(z)): 0.3868 / 0.3757\n", + "[93/100][104/391] Loss_D: 2.8764 Loss_G: 3.1092 D(x): 0.6689 D(G(z)): 0.4402 / 0.3868\n", + "[93/100][105/391] Loss_D: 2.6150 Loss_G: 3.4039 D(x): 0.7334 D(G(z)): 0.3840 / 0.3415\n", + "[93/100][106/391] Loss_D: 2.4786 Loss_G: 3.1213 D(x): 0.6708 D(G(z)): 0.3545 / 0.3915\n", + "[93/100][107/391] Loss_D: 2.9587 Loss_G: 2.8986 D(x): 0.6437 D(G(z)): 0.4060 / 0.4118\n", + "[93/100][108/391] Loss_D: 2.7323 Loss_G: 3.6217 D(x): 0.7344 D(G(z)): 0.4797 / 0.3288\n", + "[93/100][109/391] Loss_D: 2.3820 Loss_G: 3.2792 D(x): 0.7094 D(G(z)): 0.3873 / 0.3594\n", + "[93/100][110/391] Loss_D: 2.8557 Loss_G: 2.8817 D(x): 0.6835 D(G(z)): 0.4143 / 0.4209\n", + "[93/100][111/391] Loss_D: 3.0375 Loss_G: 4.4939 D(x): 0.6347 D(G(z)): 0.3881 / 0.2549\n", + "[93/100][112/391] Loss_D: 3.5099 Loss_G: 3.4160 D(x): 0.6435 D(G(z)): 0.5544 / 0.3438\n", + "[93/100][113/391] Loss_D: 2.9756 Loss_G: 2.4168 D(x): 0.6767 D(G(z)): 0.4550 / 0.4723\n", + "[93/100][114/391] Loss_D: 2.7467 Loss_G: 2.3511 D(x): 0.6480 D(G(z)): 0.4004 / 0.4856\n", + "[93/100][115/391] Loss_D: 3.3914 Loss_G: 3.3791 D(x): 0.6325 D(G(z)): 0.4910 / 0.3547\n", + "[93/100][116/391] Loss_D: 2.9220 Loss_G: 2.8378 D(x): 0.6561 D(G(z)): 0.4353 / 0.4045\n", + "[93/100][117/391] Loss_D: 2.8169 Loss_G: 2.5004 D(x): 0.6290 D(G(z)): 0.3333 / 0.4556\n", + "[93/100][118/391] Loss_D: 2.3896 Loss_G: 2.2069 D(x): 0.7782 D(G(z)): 0.4490 / 0.5082\n", + "[93/100][119/391] Loss_D: 2.6298 Loss_G: 3.0784 D(x): 0.7739 D(G(z)): 0.4568 / 0.4005\n", + "[93/100][120/391] Loss_D: 3.1280 Loss_G: 3.2866 D(x): 0.6285 D(G(z)): 0.4853 / 0.3611\n", + "[93/100][121/391] Loss_D: 3.7041 Loss_G: 3.5126 D(x): 0.6660 D(G(z)): 0.5377 / 0.3556\n", + "[93/100][122/391] Loss_D: 2.7303 Loss_G: 2.6296 D(x): 0.6910 D(G(z)): 0.4096 / 0.4344\n", + "[93/100][123/391] Loss_D: 3.1073 Loss_G: 3.5441 D(x): 0.5822 D(G(z)): 0.3904 / 0.3459\n", + "[93/100][124/391] Loss_D: 2.7548 Loss_G: 2.7573 D(x): 0.6258 D(G(z)): 0.3906 / 0.4416\n", + "[93/100][125/391] Loss_D: 2.6969 Loss_G: 4.4649 D(x): 0.6430 D(G(z)): 0.3172 / 0.2470\n", + "[93/100][126/391] Loss_D: 2.7482 Loss_G: 3.2355 D(x): 0.6710 D(G(z)): 0.4225 / 0.3592\n", + "[93/100][127/391] Loss_D: 2.9009 Loss_G: 2.7654 D(x): 0.6196 D(G(z)): 0.3833 / 0.4119\n", + "[93/100][128/391] Loss_D: 2.4717 Loss_G: 2.6566 D(x): 0.7397 D(G(z)): 0.4445 / 0.4297\n", + "[93/100][129/391] Loss_D: 3.4954 Loss_G: 4.0181 D(x): 0.6122 D(G(z)): 0.5441 / 0.2947\n", + "[93/100][130/391] Loss_D: 2.7636 Loss_G: 2.5914 D(x): 0.6448 D(G(z)): 0.3382 / 0.4503\n", + "[93/100][131/391] Loss_D: 2.7041 Loss_G: 3.2878 D(x): 0.7858 D(G(z)): 0.4641 / 0.3608\n", + "[93/100][132/391] Loss_D: 3.3022 Loss_G: 3.7201 D(x): 0.6707 D(G(z)): 0.5076 / 0.3123\n", + "[93/100][133/391] Loss_D: 2.7407 Loss_G: 3.0842 D(x): 0.6699 D(G(z)): 0.4404 / 0.3971\n", + "[93/100][134/391] Loss_D: 2.6558 Loss_G: 3.5967 D(x): 0.6471 D(G(z)): 0.3938 / 0.3289\n", + "[93/100][135/391] Loss_D: 3.3336 Loss_G: 2.3523 D(x): 0.6004 D(G(z)): 0.4972 / 0.4853\n", + "[93/100][136/391] Loss_D: 3.0224 Loss_G: 3.8389 D(x): 0.5901 D(G(z)): 0.3961 / 0.3040\n", + "[93/100][137/391] Loss_D: 3.3465 Loss_G: 3.4803 D(x): 0.5823 D(G(z)): 0.4730 / 0.3388\n", + "[93/100][138/391] Loss_D: 2.7099 Loss_G: 2.3966 D(x): 0.6460 D(G(z)): 0.4381 / 0.4854\n", + "[93/100][139/391] Loss_D: 3.1238 Loss_G: 2.8926 D(x): 0.6007 D(G(z)): 0.4024 / 0.4136\n", + "[93/100][140/391] Loss_D: 2.4156 Loss_G: 3.2271 D(x): 0.7660 D(G(z)): 0.4053 / 0.3645\n", + "[93/100][141/391] Loss_D: 2.8819 Loss_G: 2.5840 D(x): 0.6238 D(G(z)): 0.3690 / 0.4562\n", + "[93/100][142/391] Loss_D: 3.1504 Loss_G: 3.4068 D(x): 0.7140 D(G(z)): 0.5609 / 0.3594\n", + "[93/100][143/391] Loss_D: 2.9992 Loss_G: 2.9532 D(x): 0.6918 D(G(z)): 0.4764 / 0.4109\n", + "[93/100][144/391] Loss_D: 2.6324 Loss_G: 2.7179 D(x): 0.6425 D(G(z)): 0.3851 / 0.4419\n", + "[93/100][145/391] Loss_D: 2.8199 Loss_G: 2.9244 D(x): 0.6532 D(G(z)): 0.3794 / 0.3985\n", + "[93/100][146/391] Loss_D: 2.6473 Loss_G: 3.1085 D(x): 0.7346 D(G(z)): 0.4153 / 0.3939\n", + "[93/100][147/391] Loss_D: 2.7381 Loss_G: 2.1457 D(x): 0.7198 D(G(z)): 0.4102 / 0.5131\n", + "[93/100][148/391] Loss_D: 2.4216 Loss_G: 2.4860 D(x): 0.6766 D(G(z)): 0.3835 / 0.4585\n", + "[93/100][149/391] Loss_D: 2.8487 Loss_G: 2.5988 D(x): 0.6977 D(G(z)): 0.4397 / 0.4466\n", + "[93/100][150/391] Loss_D: 2.8575 Loss_G: 2.4514 D(x): 0.6790 D(G(z)): 0.4538 / 0.4970\n", + "[93/100][151/391] Loss_D: 3.5481 Loss_G: 3.4751 D(x): 0.7105 D(G(z)): 0.4016 / 0.3398\n", + "[93/100][152/391] Loss_D: 2.4424 Loss_G: 3.4484 D(x): 0.6273 D(G(z)): 0.2981 / 0.3581\n", + "[93/100][153/391] Loss_D: 3.0040 Loss_G: 3.8143 D(x): 0.6677 D(G(z)): 0.4532 / 0.3198\n", + "[93/100][154/391] Loss_D: 3.0192 Loss_G: 2.2458 D(x): 0.6845 D(G(z)): 0.5094 / 0.5076\n", + "[93/100][155/391] Loss_D: 3.0435 Loss_G: 3.1489 D(x): 0.6288 D(G(z)): 0.4050 / 0.3835\n", + "[93/100][156/391] Loss_D: 3.1230 Loss_G: 1.6615 D(x): 0.5746 D(G(z)): 0.3954 / 0.6021\n", + "[93/100][157/391] Loss_D: 2.8363 Loss_G: 2.2579 D(x): 0.7221 D(G(z)): 0.4411 / 0.4968\n", + "[93/100][158/391] Loss_D: 2.6757 Loss_G: 3.4467 D(x): 0.7570 D(G(z)): 0.5232 / 0.3580\n", + "[93/100][159/391] Loss_D: 2.6537 Loss_G: 2.5071 D(x): 0.7520 D(G(z)): 0.4187 / 0.4515\n", + "[93/100][160/391] Loss_D: 2.8123 Loss_G: 4.7159 D(x): 0.6954 D(G(z)): 0.4453 / 0.2497\n", + "[93/100][161/391] Loss_D: 2.5887 Loss_G: 2.7896 D(x): 0.7262 D(G(z)): 0.3790 / 0.4352\n", + "[93/100][162/391] Loss_D: 2.4364 Loss_G: 3.3848 D(x): 0.6899 D(G(z)): 0.3542 / 0.3418\n", + "[93/100][163/391] Loss_D: 2.5283 Loss_G: 2.6295 D(x): 0.6725 D(G(z)): 0.3374 / 0.4444\n", + "[93/100][164/391] Loss_D: 2.6063 Loss_G: 2.9442 D(x): 0.6360 D(G(z)): 0.3402 / 0.4045\n", + "[93/100][165/391] Loss_D: 3.1213 Loss_G: 2.8765 D(x): 0.5649 D(G(z)): 0.4122 / 0.4042\n", + "[93/100][166/391] Loss_D: 2.8929 Loss_G: 2.6456 D(x): 0.6402 D(G(z)): 0.4228 / 0.4343\n", + "[93/100][167/391] Loss_D: 3.5107 Loss_G: 2.0603 D(x): 0.5563 D(G(z)): 0.4793 / 0.5236\n", + "[93/100][168/391] Loss_D: 2.6206 Loss_G: 2.1634 D(x): 0.6900 D(G(z)): 0.3860 / 0.5156\n", + "[93/100][169/391] Loss_D: 2.4553 Loss_G: 2.2448 D(x): 0.7304 D(G(z)): 0.4255 / 0.5093\n", + "[93/100][170/391] Loss_D: 3.0265 Loss_G: 1.9773 D(x): 0.6768 D(G(z)): 0.4677 / 0.5480\n", + "[93/100][171/391] Loss_D: 2.8095 Loss_G: 3.0226 D(x): 0.7086 D(G(z)): 0.4511 / 0.4117\n", + "[93/100][172/391] Loss_D: 3.0389 Loss_G: 2.7396 D(x): 0.5847 D(G(z)): 0.3950 / 0.4383\n", + "[93/100][173/391] Loss_D: 2.9207 Loss_G: 3.2562 D(x): 0.7292 D(G(z)): 0.5014 / 0.3554\n", + "[93/100][174/391] Loss_D: 3.1058 Loss_G: 3.4226 D(x): 0.6646 D(G(z)): 0.5082 / 0.3637\n", + "[93/100][175/391] Loss_D: 3.1800 Loss_G: 2.4000 D(x): 0.6159 D(G(z)): 0.4492 / 0.4954\n", + "[93/100][176/391] Loss_D: 2.6286 Loss_G: 3.6171 D(x): 0.7254 D(G(z)): 0.3876 / 0.3201\n", + "[93/100][177/391] Loss_D: 2.8616 Loss_G: 3.3977 D(x): 0.7083 D(G(z)): 0.4405 / 0.3465\n", + "[93/100][178/391] Loss_D: 2.7035 Loss_G: 2.2379 D(x): 0.6241 D(G(z)): 0.3221 / 0.5161\n", + "[93/100][179/391] Loss_D: 2.6416 Loss_G: 3.7217 D(x): 0.6371 D(G(z)): 0.3072 / 0.3235\n", + "[93/100][180/391] Loss_D: 2.6275 Loss_G: 2.9872 D(x): 0.7096 D(G(z)): 0.4224 / 0.4121\n", + "[93/100][181/391] Loss_D: 3.6988 Loss_G: 2.7325 D(x): 0.6426 D(G(z)): 0.5450 / 0.4493\n", + "[93/100][182/391] Loss_D: 2.4484 Loss_G: 2.3959 D(x): 0.7308 D(G(z)): 0.4101 / 0.4942\n", + "[93/100][183/391] Loss_D: 2.8723 Loss_G: 2.8051 D(x): 0.6553 D(G(z)): 0.4722 / 0.4249\n", + "[93/100][184/391] Loss_D: 3.4478 Loss_G: 3.4845 D(x): 0.5235 D(G(z)): 0.3749 / 0.3427\n", + "[93/100][185/391] Loss_D: 2.4746 Loss_G: 2.2937 D(x): 0.7025 D(G(z)): 0.3545 / 0.4780\n", + "[93/100][186/391] Loss_D: 3.0099 Loss_G: 2.4204 D(x): 0.6584 D(G(z)): 0.4645 / 0.4770\n", + "[93/100][187/391] Loss_D: 2.8519 Loss_G: 3.2775 D(x): 0.6715 D(G(z)): 0.4483 / 0.3693\n", + "[93/100][188/391] Loss_D: 2.7961 Loss_G: 1.8546 D(x): 0.6446 D(G(z)): 0.4194 / 0.5564\n", + "[93/100][189/391] Loss_D: 2.5576 Loss_G: 2.5042 D(x): 0.7464 D(G(z)): 0.4353 / 0.4590\n", + "[93/100][190/391] Loss_D: 2.6235 Loss_G: 3.0391 D(x): 0.7187 D(G(z)): 0.4350 / 0.4079\n", + "[93/100][191/391] Loss_D: 3.2699 Loss_G: 2.6663 D(x): 0.5971 D(G(z)): 0.4447 / 0.4550\n", + "[93/100][192/391] Loss_D: 3.4218 Loss_G: 2.4859 D(x): 0.5125 D(G(z)): 0.4124 / 0.4651\n", + "[93/100][193/391] Loss_D: 2.7050 Loss_G: 3.4827 D(x): 0.7816 D(G(z)): 0.5036 / 0.3428\n", + "[93/100][194/391] Loss_D: 2.8552 Loss_G: 2.4943 D(x): 0.6276 D(G(z)): 0.3616 / 0.4525\n", + "[93/100][195/391] Loss_D: 3.0184 Loss_G: 2.1794 D(x): 0.6242 D(G(z)): 0.4211 / 0.4971\n", + "[93/100][196/391] Loss_D: 2.9795 Loss_G: 2.6840 D(x): 0.7047 D(G(z)): 0.5332 / 0.4263\n", + "[93/100][197/391] Loss_D: 2.8388 Loss_G: 3.1477 D(x): 0.6934 D(G(z)): 0.3975 / 0.3876\n", + "[93/100][198/391] Loss_D: 2.9002 Loss_G: 3.8240 D(x): 0.6588 D(G(z)): 0.4163 / 0.3021\n", + "[93/100][199/391] Loss_D: 2.7547 Loss_G: 2.5526 D(x): 0.6635 D(G(z)): 0.3962 / 0.4631\n", + "[93/100][200/391] Loss_D: 2.9049 Loss_G: 2.0837 D(x): 0.6836 D(G(z)): 0.4870 / 0.5181\n", + "[93/100][201/391] Loss_D: 3.1929 Loss_G: 2.5677 D(x): 0.6737 D(G(z)): 0.4976 / 0.4590\n", + "[93/100][202/391] Loss_D: 3.3672 Loss_G: 3.3661 D(x): 0.6443 D(G(z)): 0.5368 / 0.3592\n", + "[93/100][203/391] Loss_D: 3.0358 Loss_G: 2.9802 D(x): 0.6163 D(G(z)): 0.4226 / 0.4153\n", + "[93/100][204/391] Loss_D: 2.7323 Loss_G: 3.2090 D(x): 0.6317 D(G(z)): 0.4175 / 0.3876\n", + "[93/100][205/391] Loss_D: 2.5614 Loss_G: 2.4075 D(x): 0.6597 D(G(z)): 0.3420 / 0.4881\n", + "[93/100][206/391] Loss_D: 3.4675 Loss_G: 2.8298 D(x): 0.5661 D(G(z)): 0.5271 / 0.4153\n", + "[93/100][207/391] Loss_D: 2.5923 Loss_G: 2.4688 D(x): 0.6849 D(G(z)): 0.3468 / 0.4620\n", + "[93/100][208/391] Loss_D: 2.7423 Loss_G: 2.7452 D(x): 0.5963 D(G(z)): 0.3015 / 0.4349\n", + "[93/100][209/391] Loss_D: 3.2079 Loss_G: 2.8136 D(x): 0.5714 D(G(z)): 0.4033 / 0.4200\n", + "[93/100][210/391] Loss_D: 2.6995 Loss_G: 2.8079 D(x): 0.7385 D(G(z)): 0.4016 / 0.4215\n", + "[93/100][211/391] Loss_D: 3.8342 Loss_G: 2.8951 D(x): 0.6880 D(G(z)): 0.5169 / 0.4085\n", + "[93/100][212/391] Loss_D: 2.9505 Loss_G: 2.4481 D(x): 0.6723 D(G(z)): 0.4473 / 0.4634\n", + "[93/100][213/391] Loss_D: 3.0646 Loss_G: 2.5073 D(x): 0.6175 D(G(z)): 0.4350 / 0.4541\n", + "[93/100][214/391] Loss_D: 3.2405 Loss_G: 2.6488 D(x): 0.6365 D(G(z)): 0.5188 / 0.4494\n", + "[93/100][215/391] Loss_D: 2.9002 Loss_G: 3.3630 D(x): 0.6630 D(G(z)): 0.4118 / 0.3534\n", + "[93/100][216/391] Loss_D: 3.5000 Loss_G: 2.5445 D(x): 0.6991 D(G(z)): 0.5800 / 0.4514\n", + "[93/100][217/391] Loss_D: 2.9222 Loss_G: 2.2066 D(x): 0.6192 D(G(z)): 0.4056 / 0.5106\n", + "[93/100][218/391] Loss_D: 3.1693 Loss_G: 2.7601 D(x): 0.6702 D(G(z)): 0.5550 / 0.4340\n", + "[93/100][219/391] Loss_D: 3.0712 Loss_G: 2.0880 D(x): 0.6401 D(G(z)): 0.4770 / 0.5237\n", + "[93/100][220/391] Loss_D: 3.2580 Loss_G: 2.7671 D(x): 0.5683 D(G(z)): 0.4394 / 0.4273\n", + "[93/100][221/391] Loss_D: 2.6827 Loss_G: 3.2662 D(x): 0.6507 D(G(z)): 0.3657 / 0.3767\n", + "[93/100][222/391] Loss_D: 2.5595 Loss_G: 2.5961 D(x): 0.7010 D(G(z)): 0.4195 / 0.4528\n", + "[93/100][223/391] Loss_D: 2.5867 Loss_G: 2.4714 D(x): 0.6855 D(G(z)): 0.3422 / 0.4574\n", + "[93/100][224/391] Loss_D: 3.0792 Loss_G: 2.6696 D(x): 0.6935 D(G(z)): 0.5370 / 0.4452\n", + "[93/100][225/391] Loss_D: 3.5442 Loss_G: 2.4575 D(x): 0.6010 D(G(z)): 0.5341 / 0.4807\n", + "[93/100][226/391] Loss_D: 2.5616 Loss_G: 2.7361 D(x): 0.6277 D(G(z)): 0.3323 / 0.4487\n", + "[93/100][227/391] Loss_D: 3.1250 Loss_G: 2.9539 D(x): 0.6290 D(G(z)): 0.4854 / 0.4054\n", + "[93/100][228/391] Loss_D: 2.8034 Loss_G: 2.3086 D(x): 0.6970 D(G(z)): 0.4752 / 0.4999\n", + "[93/100][229/391] Loss_D: 2.9446 Loss_G: 2.6354 D(x): 0.6418 D(G(z)): 0.4470 / 0.4550\n", + "[93/100][230/391] Loss_D: 2.7633 Loss_G: 2.0264 D(x): 0.6519 D(G(z)): 0.4289 / 0.5421\n", + "[93/100][231/391] Loss_D: 2.8025 Loss_G: 2.8154 D(x): 0.6700 D(G(z)): 0.4144 / 0.4293\n", + "[93/100][232/391] Loss_D: 2.7802 Loss_G: 2.9025 D(x): 0.7154 D(G(z)): 0.4438 / 0.4015\n", + "[93/100][233/391] Loss_D: 2.8682 Loss_G: 2.5492 D(x): 0.6143 D(G(z)): 0.3983 / 0.4587\n", + "[93/100][234/391] Loss_D: 3.0086 Loss_G: 2.7732 D(x): 0.6447 D(G(z)): 0.4665 / 0.4319\n", + "[93/100][235/391] Loss_D: 2.5322 Loss_G: 2.3739 D(x): 0.7100 D(G(z)): 0.4067 / 0.4697\n", + "[93/100][236/391] Loss_D: 3.1703 Loss_G: 2.9662 D(x): 0.6079 D(G(z)): 0.4398 / 0.3933\n", + "[93/100][237/391] Loss_D: 2.8921 Loss_G: 3.1260 D(x): 0.6459 D(G(z)): 0.4360 / 0.3758\n", + "[93/100][238/391] Loss_D: 3.4003 Loss_G: 2.6142 D(x): 0.6475 D(G(z)): 0.5430 / 0.4353\n", + "[93/100][239/391] Loss_D: 2.7595 Loss_G: 2.9333 D(x): 0.6887 D(G(z)): 0.4603 / 0.4081\n", + "[93/100][240/391] Loss_D: 2.6233 Loss_G: 2.4562 D(x): 0.6505 D(G(z)): 0.3718 / 0.4815\n", + "[93/100][241/391] Loss_D: 3.7816 Loss_G: 1.9799 D(x): 0.5838 D(G(z)): 0.4538 / 0.5489\n", + "[93/100][242/391] Loss_D: 3.0656 Loss_G: 2.9520 D(x): 0.6435 D(G(z)): 0.4887 / 0.4157\n", + "[93/100][243/391] Loss_D: 3.0135 Loss_G: 2.9222 D(x): 0.7159 D(G(z)): 0.5166 / 0.3976\n", + "[93/100][244/391] Loss_D: 2.1378 Loss_G: 1.9803 D(x): 0.6883 D(G(z)): 0.2942 / 0.5428\n", + "[93/100][245/391] Loss_D: 2.5213 Loss_G: 2.2923 D(x): 0.7133 D(G(z)): 0.4060 / 0.4982\n", + "[93/100][246/391] Loss_D: 2.9117 Loss_G: 2.8814 D(x): 0.6156 D(G(z)): 0.3617 / 0.3885\n", + "[93/100][247/391] Loss_D: 3.4058 Loss_G: 3.0182 D(x): 0.6015 D(G(z)): 0.5195 / 0.4017\n", + "[93/100][248/391] Loss_D: 3.2253 Loss_G: 3.5776 D(x): 0.5851 D(G(z)): 0.4801 / 0.3325\n", + "[93/100][249/391] Loss_D: 2.7263 Loss_G: 2.3545 D(x): 0.6909 D(G(z)): 0.4469 / 0.5142\n", + "[93/100][250/391] Loss_D: 2.8851 Loss_G: 2.5810 D(x): 0.7031 D(G(z)): 0.4297 / 0.4600\n", + "[93/100][251/391] Loss_D: 3.3003 Loss_G: 2.8733 D(x): 0.5980 D(G(z)): 0.4540 / 0.4178\n", + "[93/100][252/391] Loss_D: 2.4954 Loss_G: 3.1962 D(x): 0.7201 D(G(z)): 0.3590 / 0.3792\n", + "[93/100][253/391] Loss_D: 2.6784 Loss_G: 2.8538 D(x): 0.6641 D(G(z)): 0.3472 / 0.4059\n", + "[93/100][254/391] Loss_D: 2.7638 Loss_G: 2.4873 D(x): 0.7416 D(G(z)): 0.5200 / 0.4697\n", + "[93/100][255/391] Loss_D: 2.7289 Loss_G: 3.2632 D(x): 0.6582 D(G(z)): 0.3717 / 0.3589\n", + "[93/100][256/391] Loss_D: 3.4367 Loss_G: 2.7540 D(x): 0.4963 D(G(z)): 0.3898 / 0.4225\n", + "[93/100][257/391] Loss_D: 3.3426 Loss_G: 3.1825 D(x): 0.6340 D(G(z)): 0.4853 / 0.3785\n", + "[93/100][258/391] Loss_D: 2.6585 Loss_G: 2.4915 D(x): 0.6778 D(G(z)): 0.3943 / 0.4857\n", + "[93/100][259/391] Loss_D: 2.7683 Loss_G: 2.6363 D(x): 0.6673 D(G(z)): 0.4227 / 0.4450\n", + "[93/100][260/391] Loss_D: 2.8259 Loss_G: 2.4414 D(x): 0.6713 D(G(z)): 0.4308 / 0.4723\n", + "[93/100][261/391] Loss_D: 3.1714 Loss_G: 2.6200 D(x): 0.6559 D(G(z)): 0.4779 / 0.4714\n", + "[93/100][262/391] Loss_D: 2.6506 Loss_G: 2.5277 D(x): 0.7335 D(G(z)): 0.4197 / 0.4565\n", + "[93/100][263/391] Loss_D: 2.8223 Loss_G: 3.4303 D(x): 0.7622 D(G(z)): 0.4567 / 0.3432\n", + "[93/100][264/391] Loss_D: 2.7436 Loss_G: 2.4278 D(x): 0.6726 D(G(z)): 0.4538 / 0.4869\n", + "[93/100][265/391] Loss_D: 2.8485 Loss_G: 2.9350 D(x): 0.6965 D(G(z)): 0.4625 / 0.3987\n", + "[93/100][266/391] Loss_D: 3.0210 Loss_G: 3.4776 D(x): 0.6129 D(G(z)): 0.3640 / 0.3434\n", + "[93/100][267/391] Loss_D: 2.7700 Loss_G: 2.1776 D(x): 0.5854 D(G(z)): 0.3371 / 0.5154\n", + "[93/100][268/391] Loss_D: 2.3748 Loss_G: 2.8222 D(x): 0.6885 D(G(z)): 0.3518 / 0.4251\n", + "[93/100][269/391] Loss_D: 3.2394 Loss_G: 2.5448 D(x): 0.6868 D(G(z)): 0.5408 / 0.4563\n", + "[93/100][270/391] Loss_D: 2.8998 Loss_G: 3.4961 D(x): 0.6171 D(G(z)): 0.3949 / 0.3454\n", + "[93/100][271/391] Loss_D: 3.5560 Loss_G: 2.3099 D(x): 0.6785 D(G(z)): 0.4110 / 0.4926\n", + "[93/100][272/391] Loss_D: 2.4713 Loss_G: 2.5806 D(x): 0.6959 D(G(z)): 0.3332 / 0.4569\n", + "[93/100][273/391] Loss_D: 2.8939 Loss_G: 2.6409 D(x): 0.7155 D(G(z)): 0.4930 / 0.4448\n", + "[93/100][274/391] Loss_D: 2.7785 Loss_G: 2.8625 D(x): 0.6015 D(G(z)): 0.3625 / 0.4232\n", + "[93/100][275/391] Loss_D: 2.7799 Loss_G: 1.8082 D(x): 0.6153 D(G(z)): 0.3468 / 0.5663\n", + "[93/100][276/391] Loss_D: 2.8346 Loss_G: 3.4921 D(x): 0.7495 D(G(z)): 0.4647 / 0.3485\n", + "[93/100][277/391] Loss_D: 2.9556 Loss_G: 2.9411 D(x): 0.6506 D(G(z)): 0.4590 / 0.4122\n", + "[93/100][278/391] Loss_D: 2.3050 Loss_G: 1.9580 D(x): 0.7372 D(G(z)): 0.3994 / 0.5430\n", + "[93/100][279/391] Loss_D: 2.5963 Loss_G: 2.7760 D(x): 0.6910 D(G(z)): 0.3592 / 0.4261\n", + "[93/100][280/391] Loss_D: 2.8753 Loss_G: 2.1736 D(x): 0.6335 D(G(z)): 0.3950 / 0.5114\n", + "[93/100][281/391] Loss_D: 3.3381 Loss_G: 3.2619 D(x): 0.7688 D(G(z)): 0.5850 / 0.3685\n", + "[93/100][282/391] Loss_D: 2.3081 Loss_G: 2.6966 D(x): 0.7624 D(G(z)): 0.3546 / 0.4511\n", + "[93/100][283/391] Loss_D: 3.7985 Loss_G: 2.4737 D(x): 0.5963 D(G(z)): 0.5649 / 0.4634\n", + "[93/100][284/391] Loss_D: 2.3437 Loss_G: 2.6815 D(x): 0.6995 D(G(z)): 0.3626 / 0.4404\n", + "[93/100][285/391] Loss_D: 3.2749 Loss_G: 2.9277 D(x): 0.5769 D(G(z)): 0.4334 / 0.4008\n", + "[93/100][286/391] Loss_D: 2.6649 Loss_G: 3.1338 D(x): 0.6177 D(G(z)): 0.2761 / 0.3859\n", + "[93/100][287/391] Loss_D: 3.1910 Loss_G: 2.3514 D(x): 0.6302 D(G(z)): 0.4643 / 0.4875\n", + "[93/100][288/391] Loss_D: 2.6664 Loss_G: 3.7565 D(x): 0.6735 D(G(z)): 0.4335 / 0.3330\n", + "[93/100][289/391] Loss_D: 2.8457 Loss_G: 3.0280 D(x): 0.6409 D(G(z)): 0.3886 / 0.3951\n", + "[93/100][290/391] Loss_D: 2.5769 Loss_G: 2.5079 D(x): 0.7694 D(G(z)): 0.4439 / 0.4718\n", + "[93/100][291/391] Loss_D: 2.5825 Loss_G: 3.0809 D(x): 0.7099 D(G(z)): 0.3878 / 0.3959\n", + "[93/100][292/391] Loss_D: 2.6412 Loss_G: 2.8644 D(x): 0.7031 D(G(z)): 0.4053 / 0.4136\n", + "[93/100][293/391] Loss_D: 3.4720 Loss_G: 2.8250 D(x): 0.6302 D(G(z)): 0.5507 / 0.4308\n", + "[93/100][294/391] Loss_D: 2.2855 Loss_G: 2.7240 D(x): 0.7373 D(G(z)): 0.3862 / 0.4218\n", + "[93/100][295/391] Loss_D: 3.1576 Loss_G: 2.4880 D(x): 0.6904 D(G(z)): 0.5177 / 0.4558\n", + "[93/100][296/391] Loss_D: 3.3881 Loss_G: 3.6427 D(x): 0.5140 D(G(z)): 0.3654 / 0.3215\n", + "[93/100][297/391] Loss_D: 2.6195 Loss_G: 3.5940 D(x): 0.7064 D(G(z)): 0.3159 / 0.3364\n", + "[93/100][298/391] Loss_D: 2.1414 Loss_G: 2.2061 D(x): 0.8004 D(G(z)): 0.3786 / 0.4946\n", + "[93/100][299/391] Loss_D: 2.9239 Loss_G: 2.8578 D(x): 0.6928 D(G(z)): 0.4820 / 0.4115\n", + "[93/100][300/391] Loss_D: 2.7355 Loss_G: 2.4658 D(x): 0.6792 D(G(z)): 0.4095 / 0.4645\n", + "[93/100][301/391] Loss_D: 3.5592 Loss_G: 3.0185 D(x): 0.6733 D(G(z)): 0.4432 / 0.4000\n", + "[93/100][302/391] Loss_D: 2.5576 Loss_G: 3.0212 D(x): 0.7185 D(G(z)): 0.4216 / 0.3937\n", + "[93/100][303/391] Loss_D: 2.5198 Loss_G: 2.9257 D(x): 0.6780 D(G(z)): 0.3382 / 0.3999\n", + "[93/100][304/391] Loss_D: 2.3210 Loss_G: 2.6359 D(x): 0.6713 D(G(z)): 0.3328 / 0.4521\n", + "[93/100][305/391] Loss_D: 3.2159 Loss_G: 2.7686 D(x): 0.5145 D(G(z)): 0.2960 / 0.4339\n", + "[93/100][306/391] Loss_D: 3.7354 Loss_G: 2.3846 D(x): 0.5503 D(G(z)): 0.4976 / 0.4866\n", + "[93/100][307/391] Loss_D: 2.9701 Loss_G: 2.2758 D(x): 0.6356 D(G(z)): 0.4327 / 0.4901\n", + "[93/100][308/391] Loss_D: 2.4349 Loss_G: 2.2263 D(x): 0.7591 D(G(z)): 0.4447 / 0.5256\n", + "[93/100][309/391] Loss_D: 2.9974 Loss_G: 3.4878 D(x): 0.6939 D(G(z)): 0.5314 / 0.3493\n", + "[93/100][310/391] Loss_D: 3.1760 Loss_G: 2.0174 D(x): 0.7198 D(G(z)): 0.5224 / 0.5483\n", + "[93/100][311/391] Loss_D: 3.2081 Loss_G: 3.1423 D(x): 0.6519 D(G(z)): 0.4669 / 0.3872\n", + "[93/100][312/391] Loss_D: 3.0244 Loss_G: 2.8298 D(x): 0.6226 D(G(z)): 0.4223 / 0.4094\n", + "[93/100][313/391] Loss_D: 2.5157 Loss_G: 2.3326 D(x): 0.6409 D(G(z)): 0.3067 / 0.4915\n", + "[93/100][314/391] Loss_D: 2.7009 Loss_G: 3.1496 D(x): 0.6688 D(G(z)): 0.4758 / 0.3906\n", + "[93/100][315/391] Loss_D: 3.1559 Loss_G: 3.1032 D(x): 0.6286 D(G(z)): 0.4736 / 0.3912\n", + "[93/100][316/391] Loss_D: 2.6462 Loss_G: 3.2237 D(x): 0.7003 D(G(z)): 0.3786 / 0.3787\n", + "[93/100][317/391] Loss_D: 2.5025 Loss_G: 2.9161 D(x): 0.7064 D(G(z)): 0.3368 / 0.4111\n", + "[93/100][318/391] Loss_D: 3.1022 Loss_G: 3.4400 D(x): 0.5863 D(G(z)): 0.4602 / 0.3544\n", + "[93/100][319/391] Loss_D: 2.9253 Loss_G: 2.6745 D(x): 0.6158 D(G(z)): 0.4065 / 0.4416\n", + "[93/100][320/391] Loss_D: 2.6066 Loss_G: 2.5985 D(x): 0.7352 D(G(z)): 0.3986 / 0.4650\n", + "[93/100][321/391] Loss_D: 2.7841 Loss_G: 3.2340 D(x): 0.7631 D(G(z)): 0.4630 / 0.3687\n", + "[93/100][322/391] Loss_D: 2.6519 Loss_G: 2.9651 D(x): 0.6905 D(G(z)): 0.4225 / 0.4042\n", + "[93/100][323/391] Loss_D: 3.0037 Loss_G: 2.0433 D(x): 0.6644 D(G(z)): 0.4559 / 0.5293\n", + "[93/100][324/391] Loss_D: 2.9088 Loss_G: 2.5079 D(x): 0.6648 D(G(z)): 0.4752 / 0.4653\n", + "[93/100][325/391] Loss_D: 2.7091 Loss_G: 3.2577 D(x): 0.7117 D(G(z)): 0.3769 / 0.3713\n", + "[93/100][326/391] Loss_D: 2.5755 Loss_G: 2.9046 D(x): 0.7127 D(G(z)): 0.4008 / 0.4075\n", + "[93/100][327/391] Loss_D: 2.6619 Loss_G: 2.3182 D(x): 0.6542 D(G(z)): 0.3526 / 0.4885\n", + "[93/100][328/391] Loss_D: 2.4861 Loss_G: 3.8629 D(x): 0.7043 D(G(z)): 0.4133 / 0.3110\n", + "[93/100][329/391] Loss_D: 2.7033 Loss_G: 2.1559 D(x): 0.7045 D(G(z)): 0.4048 / 0.5131\n", + "[93/100][330/391] Loss_D: 2.8586 Loss_G: 2.0252 D(x): 0.5745 D(G(z)): 0.2706 / 0.5462\n", + "[93/100][331/391] Loss_D: 3.6837 Loss_G: 2.7682 D(x): 0.6710 D(G(z)): 0.5398 / 0.4285\n", + "[93/100][332/391] Loss_D: 2.8907 Loss_G: 2.6981 D(x): 0.5969 D(G(z)): 0.3796 / 0.4337\n", + "[93/100][333/391] Loss_D: 3.4994 Loss_G: 2.2124 D(x): 0.6916 D(G(z)): 0.5565 / 0.5039\n", + "[93/100][334/391] Loss_D: 3.1237 Loss_G: 3.8157 D(x): 0.6948 D(G(z)): 0.5386 / 0.3079\n", + "[93/100][335/391] Loss_D: 3.3761 Loss_G: 3.7146 D(x): 0.5877 D(G(z)): 0.4850 / 0.3145\n", + "[93/100][336/391] Loss_D: 2.7302 Loss_G: 3.0862 D(x): 0.6937 D(G(z)): 0.3845 / 0.3944\n", + "[93/100][337/391] Loss_D: 3.3588 Loss_G: 3.2016 D(x): 0.5975 D(G(z)): 0.4836 / 0.3680\n", + "[93/100][338/391] Loss_D: 2.9850 Loss_G: 2.9314 D(x): 0.6446 D(G(z)): 0.4729 / 0.4270\n", + "[93/100][339/391] Loss_D: 2.4952 Loss_G: 2.8824 D(x): 0.6981 D(G(z)): 0.3739 / 0.4025\n", + "[93/100][340/391] Loss_D: 3.6387 Loss_G: 3.2719 D(x): 0.5814 D(G(z)): 0.4976 / 0.3673\n", + "[93/100][341/391] Loss_D: 3.2323 Loss_G: 1.9605 D(x): 0.6569 D(G(z)): 0.4999 / 0.5360\n", + "[93/100][342/391] Loss_D: 2.4346 Loss_G: 2.4858 D(x): 0.6693 D(G(z)): 0.3117 / 0.4712\n", + "[93/100][343/391] Loss_D: 3.0511 Loss_G: 2.1750 D(x): 0.6001 D(G(z)): 0.3513 / 0.5209\n", + "[93/100][344/391] Loss_D: 2.5317 Loss_G: 2.3023 D(x): 0.7602 D(G(z)): 0.4726 / 0.4978\n", + "[93/100][345/391] Loss_D: 3.3968 Loss_G: 2.1002 D(x): 0.6623 D(G(z)): 0.5419 / 0.5199\n", + "[93/100][346/391] Loss_D: 2.8657 Loss_G: 2.3461 D(x): 0.6490 D(G(z)): 0.3998 / 0.4881\n", + "[93/100][347/391] Loss_D: 3.3058 Loss_G: 2.7769 D(x): 0.5513 D(G(z)): 0.3941 / 0.4158\n", + "[93/100][348/391] Loss_D: 2.5687 Loss_G: 2.9528 D(x): 0.7942 D(G(z)): 0.5319 / 0.4028\n", + "[93/100][349/391] Loss_D: 2.7277 Loss_G: 2.7137 D(x): 0.6693 D(G(z)): 0.4013 / 0.4300\n", + "[93/100][350/391] Loss_D: 3.5135 Loss_G: 3.5403 D(x): 0.6799 D(G(z)): 0.5957 / 0.3467\n", + "[93/100][351/391] Loss_D: 2.8857 Loss_G: 3.7616 D(x): 0.5991 D(G(z)): 0.3103 / 0.3300\n", + "[93/100][352/391] Loss_D: 2.1119 Loss_G: 3.3352 D(x): 0.7784 D(G(z)): 0.3562 / 0.3669\n", + "[93/100][353/391] Loss_D: 3.0594 Loss_G: 2.9782 D(x): 0.6479 D(G(z)): 0.4798 / 0.4033\n", + "[93/100][354/391] Loss_D: 2.7799 Loss_G: 3.2475 D(x): 0.6656 D(G(z)): 0.4576 / 0.3852\n", + "[93/100][355/391] Loss_D: 3.0629 Loss_G: 2.5260 D(x): 0.6732 D(G(z)): 0.5076 / 0.4825\n", + "[93/100][356/391] Loss_D: 2.9085 Loss_G: 2.8086 D(x): 0.6205 D(G(z)): 0.4125 / 0.4302\n", + "[93/100][357/391] Loss_D: 2.6992 Loss_G: 2.5714 D(x): 0.6698 D(G(z)): 0.3704 / 0.4543\n", + "[93/100][358/391] Loss_D: 2.7047 Loss_G: 3.3597 D(x): 0.6268 D(G(z)): 0.3634 / 0.3514\n", + "[93/100][359/391] Loss_D: 2.7578 Loss_G: 3.3063 D(x): 0.6444 D(G(z)): 0.3374 / 0.3684\n", + "[93/100][360/391] Loss_D: 2.8920 Loss_G: 3.1905 D(x): 0.6847 D(G(z)): 0.4643 / 0.3923\n", + "[93/100][361/391] Loss_D: 3.5719 Loss_G: 2.9036 D(x): 0.6704 D(G(z)): 0.5364 / 0.4049\n", + "[93/100][362/391] Loss_D: 2.7593 Loss_G: 2.5438 D(x): 0.6412 D(G(z)): 0.3612 / 0.4642\n", + "[93/100][363/391] Loss_D: 2.6336 Loss_G: 3.3943 D(x): 0.7063 D(G(z)): 0.4316 / 0.3541\n", + "[93/100][364/391] Loss_D: 2.7266 Loss_G: 3.4423 D(x): 0.6367 D(G(z)): 0.3243 / 0.3584\n", + "[93/100][365/391] Loss_D: 2.6590 Loss_G: 2.6864 D(x): 0.6608 D(G(z)): 0.3555 / 0.4284\n", + "[93/100][366/391] Loss_D: 2.7032 Loss_G: 2.9581 D(x): 0.7261 D(G(z)): 0.4506 / 0.4042\n", + "[93/100][367/391] Loss_D: 2.8171 Loss_G: 2.4284 D(x): 0.6292 D(G(z)): 0.3338 / 0.4711\n", + "[93/100][368/391] Loss_D: 3.3929 Loss_G: 2.6647 D(x): 0.5365 D(G(z)): 0.3816 / 0.4470\n", + "[93/100][369/391] Loss_D: 2.8514 Loss_G: 2.5140 D(x): 0.7386 D(G(z)): 0.5252 / 0.4654\n", + "[93/100][370/391] Loss_D: 3.3277 Loss_G: 3.2130 D(x): 0.7148 D(G(z)): 0.5965 / 0.3770\n", + "[93/100][371/391] Loss_D: 3.2180 Loss_G: 3.1626 D(x): 0.6978 D(G(z)): 0.5273 / 0.3787\n", + "[93/100][372/391] Loss_D: 2.5222 Loss_G: 2.5747 D(x): 0.6263 D(G(z)): 0.3140 / 0.4616\n", + "[93/100][373/391] Loss_D: 2.4942 Loss_G: 2.8460 D(x): 0.7355 D(G(z)): 0.3523 / 0.4297\n", + "[93/100][374/391] Loss_D: 4.0983 Loss_G: 2.5914 D(x): 0.4823 D(G(z)): 0.4807 / 0.4642\n", + "[93/100][375/391] Loss_D: 3.2034 Loss_G: 3.6968 D(x): 0.7441 D(G(z)): 0.5555 / 0.3205\n", + "[93/100][376/391] Loss_D: 2.6311 Loss_G: 2.2249 D(x): 0.7009 D(G(z)): 0.3402 / 0.5120\n", + "[93/100][377/391] Loss_D: 3.3307 Loss_G: 2.2584 D(x): 0.5780 D(G(z)): 0.4389 / 0.5000\n", + "[93/100][378/391] Loss_D: 3.2571 Loss_G: 2.6161 D(x): 0.5506 D(G(z)): 0.4544 / 0.4539\n", + "[93/100][379/391] Loss_D: 2.8926 Loss_G: 2.4662 D(x): 0.6235 D(G(z)): 0.4054 / 0.4634\n", + "[93/100][380/391] Loss_D: 2.8745 Loss_G: 2.2213 D(x): 0.6450 D(G(z)): 0.4281 / 0.5139\n", + "[93/100][381/391] Loss_D: 2.9958 Loss_G: 3.1780 D(x): 0.6773 D(G(z)): 0.4842 / 0.3895\n", + "[93/100][382/391] Loss_D: 2.9941 Loss_G: 2.5939 D(x): 0.7075 D(G(z)): 0.4953 / 0.4292\n", + "[93/100][383/391] Loss_D: 3.2978 Loss_G: 3.5685 D(x): 0.7338 D(G(z)): 0.5745 / 0.3378\n", + "[93/100][384/391] Loss_D: 2.3247 Loss_G: 2.6776 D(x): 0.6616 D(G(z)): 0.3518 / 0.4420\n", + "[93/100][385/391] Loss_D: 2.5708 Loss_G: 2.9896 D(x): 0.7490 D(G(z)): 0.4255 / 0.3868\n", + "[93/100][386/391] Loss_D: 3.1945 Loss_G: 2.8822 D(x): 0.5656 D(G(z)): 0.3808 / 0.4040\n", + "[93/100][387/391] Loss_D: 3.1697 Loss_G: 2.9116 D(x): 0.6375 D(G(z)): 0.4866 / 0.4123\n", + "[93/100][388/391] Loss_D: 2.5764 Loss_G: 2.2085 D(x): 0.6496 D(G(z)): 0.3743 / 0.5002\n", + "[93/100][389/391] Loss_D: 2.7954 Loss_G: 2.3139 D(x): 0.6752 D(G(z)): 0.3590 / 0.4932\n", + "[93/100][390/391] Loss_D: 2.4327 Loss_G: 2.8607 D(x): 0.7833 D(G(z)): 0.4423 / 0.4306\n", + "[93/100][391/391] Loss_D: 3.7485 Loss_G: 4.1181 D(x): 0.6308 D(G(z)): 0.4227 / 0.2814\n", + "[94/100][1/391] Loss_D: 3.6220 Loss_G: 2.8179 D(x): 0.7093 D(G(z)): 0.3798 / 0.4233\n", + "[94/100][2/391] Loss_D: 2.9190 Loss_G: 3.1071 D(x): 0.6538 D(G(z)): 0.4564 / 0.3981\n", + "[94/100][3/391] Loss_D: 3.2908 Loss_G: 2.9655 D(x): 0.6006 D(G(z)): 0.4626 / 0.4077\n", + "[94/100][4/391] Loss_D: 2.7040 Loss_G: 2.9345 D(x): 0.6846 D(G(z)): 0.4280 / 0.3953\n", + "[94/100][5/391] Loss_D: 2.8586 Loss_G: 3.0043 D(x): 0.6204 D(G(z)): 0.3433 / 0.3959\n", + "[94/100][6/391] Loss_D: 3.0363 Loss_G: 3.6519 D(x): 0.6010 D(G(z)): 0.3797 / 0.3372\n", + "[94/100][7/391] Loss_D: 3.0945 Loss_G: 3.2814 D(x): 0.6899 D(G(z)): 0.4640 / 0.3725\n", + "[94/100][8/391] Loss_D: 2.3110 Loss_G: 3.4023 D(x): 0.6912 D(G(z)): 0.3180 / 0.3656\n", + "[94/100][9/391] Loss_D: 2.7627 Loss_G: 2.6404 D(x): 0.6646 D(G(z)): 0.4164 / 0.4444\n", + "[94/100][10/391] Loss_D: 2.6539 Loss_G: 1.9922 D(x): 0.7179 D(G(z)): 0.4452 / 0.5508\n", + "[94/100][11/391] Loss_D: 3.1170 Loss_G: 2.8082 D(x): 0.7100 D(G(z)): 0.5146 / 0.4154\n", + "[94/100][12/391] Loss_D: 2.9845 Loss_G: 3.2952 D(x): 0.7055 D(G(z)): 0.5025 / 0.3629\n", + "[94/100][13/391] Loss_D: 2.5984 Loss_G: 2.7907 D(x): 0.6760 D(G(z)): 0.3721 / 0.4189\n", + "[94/100][14/391] Loss_D: 2.4388 Loss_G: 2.6341 D(x): 0.6700 D(G(z)): 0.3592 / 0.4426\n", + "[94/100][15/391] Loss_D: 2.6995 Loss_G: 2.4941 D(x): 0.6649 D(G(z)): 0.4227 / 0.4559\n", + "[94/100][16/391] Loss_D: 3.2743 Loss_G: 2.7590 D(x): 0.6737 D(G(z)): 0.4880 / 0.4319\n", + "[94/100][17/391] Loss_D: 2.8196 Loss_G: 2.9088 D(x): 0.6105 D(G(z)): 0.3464 / 0.4132\n", + "[94/100][18/391] Loss_D: 2.9048 Loss_G: 2.7661 D(x): 0.6393 D(G(z)): 0.4093 / 0.4211\n", + "[94/100][19/391] Loss_D: 2.9794 Loss_G: 3.0601 D(x): 0.6261 D(G(z)): 0.4007 / 0.4087\n", + "[94/100][20/391] Loss_D: 2.5676 Loss_G: 2.7404 D(x): 0.6646 D(G(z)): 0.3540 / 0.4366\n", + "[94/100][21/391] Loss_D: 2.3370 Loss_G: 2.7407 D(x): 0.7308 D(G(z)): 0.3737 / 0.4356\n", + "[94/100][22/391] Loss_D: 2.7446 Loss_G: 3.0500 D(x): 0.7074 D(G(z)): 0.4179 / 0.3870\n", + "[94/100][23/391] Loss_D: 2.6985 Loss_G: 2.3191 D(x): 0.7026 D(G(z)): 0.4625 / 0.4897\n", + "[94/100][24/391] Loss_D: 3.9271 Loss_G: 1.6852 D(x): 0.5627 D(G(z)): 0.5396 / 0.5799\n", + "[94/100][25/391] Loss_D: 2.5710 Loss_G: 2.6207 D(x): 0.7696 D(G(z)): 0.4122 / 0.4256\n", + "[94/100][26/391] Loss_D: 2.8686 Loss_G: 2.6085 D(x): 0.6957 D(G(z)): 0.4585 / 0.4350\n", + "[94/100][27/391] Loss_D: 3.5968 Loss_G: 2.8189 D(x): 0.5843 D(G(z)): 0.4874 / 0.4080\n", + "[94/100][28/391] Loss_D: 2.8609 Loss_G: 2.6910 D(x): 0.6203 D(G(z)): 0.4332 / 0.4326\n", + "[94/100][29/391] Loss_D: 2.9987 Loss_G: 3.2787 D(x): 0.6972 D(G(z)): 0.4921 / 0.3683\n", + "[94/100][30/391] Loss_D: 2.9097 Loss_G: 3.3314 D(x): 0.7062 D(G(z)): 0.4912 / 0.3590\n", + "[94/100][31/391] Loss_D: 3.7532 Loss_G: 2.9376 D(x): 0.6214 D(G(z)): 0.3200 / 0.4088\n", + "[94/100][32/391] Loss_D: 3.0076 Loss_G: 2.9973 D(x): 0.6026 D(G(z)): 0.4127 / 0.3952\n", + "[94/100][33/391] Loss_D: 3.1708 Loss_G: 3.7237 D(x): 0.5950 D(G(z)): 0.3989 / 0.3160\n", + "[94/100][34/391] Loss_D: 2.6496 Loss_G: 2.9283 D(x): 0.6759 D(G(z)): 0.4187 / 0.3999\n", + "[94/100][35/391] Loss_D: 3.1501 Loss_G: 2.5222 D(x): 0.7379 D(G(z)): 0.5583 / 0.4489\n", + "[94/100][36/391] Loss_D: 3.4606 Loss_G: 2.3980 D(x): 0.5525 D(G(z)): 0.4400 / 0.4766\n", + "[94/100][37/391] Loss_D: 2.8310 Loss_G: 3.4991 D(x): 0.6629 D(G(z)): 0.4444 / 0.3372\n", + "[94/100][38/391] Loss_D: 2.8925 Loss_G: 2.6472 D(x): 0.6015 D(G(z)): 0.3673 / 0.4472\n", + "[94/100][39/391] Loss_D: 2.9728 Loss_G: 3.0067 D(x): 0.7292 D(G(z)): 0.5072 / 0.3829\n", + "[94/100][40/391] Loss_D: 2.6361 Loss_G: 2.7920 D(x): 0.7315 D(G(z)): 0.3817 / 0.4293\n", + "[94/100][41/391] Loss_D: 3.3372 Loss_G: 2.3355 D(x): 0.6592 D(G(z)): 0.5095 / 0.4815\n", + "[94/100][42/391] Loss_D: 3.2464 Loss_G: 2.2729 D(x): 0.5178 D(G(z)): 0.3755 / 0.5013\n", + "[94/100][43/391] Loss_D: 2.9833 Loss_G: 2.9828 D(x): 0.6623 D(G(z)): 0.4220 / 0.4177\n", + "[94/100][44/391] Loss_D: 2.6834 Loss_G: 2.7066 D(x): 0.6659 D(G(z)): 0.4048 / 0.4390\n", + "[94/100][45/391] Loss_D: 2.7241 Loss_G: 1.7954 D(x): 0.7364 D(G(z)): 0.4237 / 0.5754\n", + "[94/100][46/391] Loss_D: 2.6887 Loss_G: 2.8009 D(x): 0.6565 D(G(z)): 0.3434 / 0.4138\n", + "[94/100][47/391] Loss_D: 2.8689 Loss_G: 2.2494 D(x): 0.6413 D(G(z)): 0.4026 / 0.5100\n", + "[94/100][48/391] Loss_D: 3.1477 Loss_G: 2.0480 D(x): 0.6332 D(G(z)): 0.4838 / 0.5488\n", + "[94/100][49/391] Loss_D: 2.4464 Loss_G: 2.4381 D(x): 0.7240 D(G(z)): 0.4315 / 0.4631\n", + "[94/100][50/391] Loss_D: 2.7574 Loss_G: 2.7998 D(x): 0.6449 D(G(z)): 0.4129 / 0.4206\n", + "[94/100][51/391] Loss_D: 2.9080 Loss_G: 2.2340 D(x): 0.6309 D(G(z)): 0.3975 / 0.4968\n", + "[94/100][52/391] Loss_D: 2.7493 Loss_G: 2.4165 D(x): 0.7136 D(G(z)): 0.4293 / 0.4872\n", + "[94/100][53/391] Loss_D: 2.8252 Loss_G: 2.4477 D(x): 0.7308 D(G(z)): 0.4689 / 0.4789\n", + "[94/100][54/391] Loss_D: 2.9029 Loss_G: 2.4376 D(x): 0.6254 D(G(z)): 0.4023 / 0.4744\n", + "[94/100][55/391] Loss_D: 2.6728 Loss_G: 2.8296 D(x): 0.6850 D(G(z)): 0.4038 / 0.4095\n", + "[94/100][56/391] Loss_D: 3.1401 Loss_G: 3.2091 D(x): 0.7325 D(G(z)): 0.5175 / 0.3702\n", + "[94/100][57/391] Loss_D: 2.6131 Loss_G: 2.7591 D(x): 0.7538 D(G(z)): 0.3952 / 0.4279\n", + "[94/100][58/391] Loss_D: 3.0306 Loss_G: 3.7329 D(x): 0.5958 D(G(z)): 0.4153 / 0.3079\n", + "[94/100][59/391] Loss_D: 3.0472 Loss_G: 2.3418 D(x): 0.6369 D(G(z)): 0.4711 / 0.4928\n", + "[94/100][60/391] Loss_D: 2.9912 Loss_G: 3.5195 D(x): 0.6419 D(G(z)): 0.4466 / 0.3563\n", + "[94/100][61/391] Loss_D: 3.5756 Loss_G: 3.0314 D(x): 0.7220 D(G(z)): 0.3840 / 0.4082\n", + "[94/100][62/391] Loss_D: 2.4160 Loss_G: 3.6689 D(x): 0.7656 D(G(z)): 0.4243 / 0.3247\n", + "[94/100][63/391] Loss_D: 3.2324 Loss_G: 3.0167 D(x): 0.5939 D(G(z)): 0.4491 / 0.3898\n", + "[94/100][64/391] Loss_D: 2.6244 Loss_G: 3.6085 D(x): 0.7079 D(G(z)): 0.4583 / 0.3405\n", + "[94/100][65/391] Loss_D: 2.6090 Loss_G: 3.4482 D(x): 0.6646 D(G(z)): 0.3911 / 0.3524\n", + "[94/100][66/391] Loss_D: 2.8129 Loss_G: 3.2742 D(x): 0.5622 D(G(z)): 0.2867 / 0.3769\n", + "[94/100][67/391] Loss_D: 3.5271 Loss_G: 2.1915 D(x): 0.5113 D(G(z)): 0.3869 / 0.4888\n", + "[94/100][68/391] Loss_D: 2.5635 Loss_G: 2.1326 D(x): 0.6679 D(G(z)): 0.3767 / 0.5063\n", + "[94/100][69/391] Loss_D: 2.9456 Loss_G: 1.8388 D(x): 0.6760 D(G(z)): 0.4655 / 0.5610\n", + "[94/100][70/391] Loss_D: 2.5555 Loss_G: 2.8610 D(x): 0.7150 D(G(z)): 0.4105 / 0.4098\n", + "[94/100][71/391] Loss_D: 3.8176 Loss_G: 2.3844 D(x): 0.7322 D(G(z)): 0.6463 / 0.5039\n", + "[94/100][72/391] Loss_D: 2.7917 Loss_G: 2.5845 D(x): 0.6625 D(G(z)): 0.4410 / 0.4524\n", + "[94/100][73/391] Loss_D: 2.7018 Loss_G: 3.1010 D(x): 0.6738 D(G(z)): 0.4086 / 0.3861\n", + "[94/100][74/391] Loss_D: 3.0651 Loss_G: 2.3803 D(x): 0.6858 D(G(z)): 0.5042 / 0.4981\n", + "[94/100][75/391] Loss_D: 2.7011 Loss_G: 3.7981 D(x): 0.6789 D(G(z)): 0.4283 / 0.3121\n", + "[94/100][76/391] Loss_D: 3.1539 Loss_G: 3.5886 D(x): 0.5835 D(G(z)): 0.4526 / 0.3395\n", + "[94/100][77/391] Loss_D: 3.6412 Loss_G: 2.6081 D(x): 0.5997 D(G(z)): 0.5343 / 0.4565\n", + "[94/100][78/391] Loss_D: 3.3215 Loss_G: 2.9446 D(x): 0.5325 D(G(z)): 0.4012 / 0.4184\n", + "[94/100][79/391] Loss_D: 2.2060 Loss_G: 2.6984 D(x): 0.7317 D(G(z)): 0.3093 / 0.4419\n", + "[94/100][80/391] Loss_D: 3.1120 Loss_G: 3.0521 D(x): 0.6701 D(G(z)): 0.4851 / 0.4053\n", + "[94/100][81/391] Loss_D: 2.8376 Loss_G: 2.4960 D(x): 0.6927 D(G(z)): 0.4603 / 0.4834\n", + "[94/100][82/391] Loss_D: 3.0194 Loss_G: 1.9693 D(x): 0.6309 D(G(z)): 0.4161 / 0.5448\n", + "[94/100][83/391] Loss_D: 2.6425 Loss_G: 2.7273 D(x): 0.7221 D(G(z)): 0.4168 / 0.4433\n", + "[94/100][84/391] Loss_D: 2.3261 Loss_G: 2.8283 D(x): 0.7420 D(G(z)): 0.4088 / 0.4273\n", + "[94/100][85/391] Loss_D: 2.4533 Loss_G: 4.2635 D(x): 0.6934 D(G(z)): 0.3148 / 0.2758\n", + "[94/100][86/391] Loss_D: 3.2015 Loss_G: 2.7660 D(x): 0.6328 D(G(z)): 0.4709 / 0.4017\n", + "[94/100][87/391] Loss_D: 3.2383 Loss_G: 2.6016 D(x): 0.6426 D(G(z)): 0.4679 / 0.4480\n", + "[94/100][88/391] Loss_D: 2.8730 Loss_G: 3.0196 D(x): 0.5917 D(G(z)): 0.3829 / 0.4001\n", + "[94/100][89/391] Loss_D: 2.6479 Loss_G: 3.0473 D(x): 0.7537 D(G(z)): 0.4605 / 0.3796\n", + "[94/100][90/391] Loss_D: 2.8209 Loss_G: 2.8820 D(x): 0.6738 D(G(z)): 0.4450 / 0.4193\n", + "[94/100][91/391] Loss_D: 3.6104 Loss_G: 3.1727 D(x): 0.7445 D(G(z)): 0.4491 / 0.3752\n", + "[94/100][92/391] Loss_D: 2.5839 Loss_G: 3.5203 D(x): 0.6403 D(G(z)): 0.3444 / 0.3300\n", + "[94/100][93/391] Loss_D: 3.4313 Loss_G: 2.4095 D(x): 0.5477 D(G(z)): 0.4273 / 0.4715\n", + "[94/100][94/391] Loss_D: 2.5616 Loss_G: 2.3773 D(x): 0.6677 D(G(z)): 0.4155 / 0.4866\n", + "[94/100][95/391] Loss_D: 3.1716 Loss_G: 2.5345 D(x): 0.6404 D(G(z)): 0.4697 / 0.4511\n", + "[94/100][96/391] Loss_D: 3.4975 Loss_G: 3.7430 D(x): 0.6013 D(G(z)): 0.5225 / 0.3065\n", + "[94/100][97/391] Loss_D: 3.2393 Loss_G: 2.7928 D(x): 0.5926 D(G(z)): 0.4172 / 0.4166\n", + "[94/100][98/391] Loss_D: 2.7426 Loss_G: 3.1953 D(x): 0.7124 D(G(z)): 0.4595 / 0.4007\n", + "[94/100][99/391] Loss_D: 3.4501 Loss_G: 2.7600 D(x): 0.6817 D(G(z)): 0.5665 / 0.4289\n", + "[94/100][100/391] Loss_D: 2.9312 Loss_G: 2.3228 D(x): 0.6487 D(G(z)): 0.4465 / 0.5021\n", + "[94/100][101/391] Loss_D: 3.4066 Loss_G: 2.6071 D(x): 0.5548 D(G(z)): 0.4462 / 0.4536\n", + "[94/100][102/391] Loss_D: 2.8060 Loss_G: 3.2236 D(x): 0.7721 D(G(z)): 0.4539 / 0.3706\n", + "[94/100][103/391] Loss_D: 2.7134 Loss_G: 2.9489 D(x): 0.7159 D(G(z)): 0.4190 / 0.4087\n", + "[94/100][104/391] Loss_D: 2.3871 Loss_G: 3.2302 D(x): 0.7891 D(G(z)): 0.4451 / 0.3606\n", + "[94/100][105/391] Loss_D: 2.3937 Loss_G: 2.8839 D(x): 0.7289 D(G(z)): 0.3119 / 0.4119\n", + "[94/100][106/391] Loss_D: 2.8206 Loss_G: 2.7100 D(x): 0.5909 D(G(z)): 0.3426 / 0.4380\n", + "[94/100][107/391] Loss_D: 2.9266 Loss_G: 2.9689 D(x): 0.6562 D(G(z)): 0.4020 / 0.4037\n", + "[94/100][108/391] Loss_D: 2.6416 Loss_G: 2.0331 D(x): 0.6499 D(G(z)): 0.2579 / 0.5421\n", + "[94/100][109/391] Loss_D: 2.7765 Loss_G: 2.6755 D(x): 0.6733 D(G(z)): 0.4555 / 0.4504\n", + "[94/100][110/391] Loss_D: 3.0245 Loss_G: 2.5057 D(x): 0.6641 D(G(z)): 0.4534 / 0.4755\n", + "[94/100][111/391] Loss_D: 2.7207 Loss_G: 3.0538 D(x): 0.7152 D(G(z)): 0.4204 / 0.3966\n", + "[94/100][112/391] Loss_D: 3.5615 Loss_G: 3.3838 D(x): 0.6393 D(G(z)): 0.5674 / 0.3494\n", + "[94/100][113/391] Loss_D: 2.5172 Loss_G: 3.1589 D(x): 0.7430 D(G(z)): 0.4079 / 0.3766\n", + "[94/100][114/391] Loss_D: 2.3620 Loss_G: 2.8602 D(x): 0.7000 D(G(z)): 0.3882 / 0.4178\n", + "[94/100][115/391] Loss_D: 3.8516 Loss_G: 2.9398 D(x): 0.5541 D(G(z)): 0.5449 / 0.3954\n", + "[94/100][116/391] Loss_D: 2.6225 Loss_G: 3.4302 D(x): 0.6554 D(G(z)): 0.3365 / 0.3526\n", + "[94/100][117/391] Loss_D: 2.9585 Loss_G: 3.3509 D(x): 0.5923 D(G(z)): 0.3480 / 0.3598\n", + "[94/100][118/391] Loss_D: 2.6110 Loss_G: 2.1644 D(x): 0.6552 D(G(z)): 0.3542 / 0.5070\n", + "[94/100][119/391] Loss_D: 2.6155 Loss_G: 3.0602 D(x): 0.7046 D(G(z)): 0.4093 / 0.3914\n", + "[94/100][120/391] Loss_D: 3.2663 Loss_G: 2.6071 D(x): 0.6421 D(G(z)): 0.5126 / 0.4606\n", + "[94/100][121/391] Loss_D: 3.6212 Loss_G: 2.7085 D(x): 0.6672 D(G(z)): 0.4178 / 0.4374\n", + "[94/100][122/391] Loss_D: 2.8435 Loss_G: 3.2602 D(x): 0.7724 D(G(z)): 0.5122 / 0.3574\n", + "[94/100][123/391] Loss_D: 2.6058 Loss_G: 3.2226 D(x): 0.6824 D(G(z)): 0.3855 / 0.3710\n", + "[94/100][124/391] Loss_D: 3.1328 Loss_G: 3.1588 D(x): 0.6203 D(G(z)): 0.4577 / 0.3866\n", + "[94/100][125/391] Loss_D: 3.4817 Loss_G: 2.6525 D(x): 0.5825 D(G(z)): 0.4247 / 0.4394\n", + "[94/100][126/391] Loss_D: 2.5897 Loss_G: 3.0571 D(x): 0.6708 D(G(z)): 0.3931 / 0.3945\n", + "[94/100][127/391] Loss_D: 2.7442 Loss_G: 2.6123 D(x): 0.6787 D(G(z)): 0.3452 / 0.4324\n", + "[94/100][128/391] Loss_D: 3.0503 Loss_G: 3.0621 D(x): 0.6249 D(G(z)): 0.5007 / 0.3950\n", + "[94/100][129/391] Loss_D: 2.8915 Loss_G: 3.0112 D(x): 0.7137 D(G(z)): 0.5036 / 0.3989\n", + "[94/100][130/391] Loss_D: 2.9499 Loss_G: 1.9811 D(x): 0.7219 D(G(z)): 0.4714 / 0.5528\n", + "[94/100][131/391] Loss_D: 2.9607 Loss_G: 3.3205 D(x): 0.7466 D(G(z)): 0.5207 / 0.3656\n", + "[94/100][132/391] Loss_D: 2.3739 Loss_G: 3.0987 D(x): 0.7515 D(G(z)): 0.3637 / 0.3834\n", + "[94/100][133/391] Loss_D: 2.7053 Loss_G: 3.7733 D(x): 0.6501 D(G(z)): 0.3962 / 0.3216\n", + "[94/100][134/391] Loss_D: 2.5753 Loss_G: 3.4409 D(x): 0.6221 D(G(z)): 0.3551 / 0.3489\n", + "[94/100][135/391] Loss_D: 3.3711 Loss_G: 2.5887 D(x): 0.4798 D(G(z)): 0.3226 / 0.4407\n", + "[94/100][136/391] Loss_D: 2.5530 Loss_G: 2.9244 D(x): 0.7207 D(G(z)): 0.3725 / 0.4046\n", + "[94/100][137/391] Loss_D: 3.0342 Loss_G: 2.3236 D(x): 0.6159 D(G(z)): 0.4078 / 0.4692\n", + "[94/100][138/391] Loss_D: 2.7512 Loss_G: 2.5900 D(x): 0.6417 D(G(z)): 0.3994 / 0.4577\n", + "[94/100][139/391] Loss_D: 3.7042 Loss_G: 2.1023 D(x): 0.5837 D(G(z)): 0.5245 / 0.5310\n", + "[94/100][140/391] Loss_D: 2.4809 Loss_G: 2.4601 D(x): 0.7373 D(G(z)): 0.3788 / 0.4788\n", + "[94/100][141/391] Loss_D: 3.0812 Loss_G: 2.9740 D(x): 0.7423 D(G(z)): 0.5249 / 0.4059\n", + "[94/100][142/391] Loss_D: 2.9335 Loss_G: 2.1398 D(x): 0.6759 D(G(z)): 0.4846 / 0.5194\n", + "[94/100][143/391] Loss_D: 2.6141 Loss_G: 2.6196 D(x): 0.7576 D(G(z)): 0.4498 / 0.4495\n", + "[94/100][144/391] Loss_D: 2.4636 Loss_G: 3.3040 D(x): 0.6936 D(G(z)): 0.4004 / 0.3652\n", + "[94/100][145/391] Loss_D: 3.3778 Loss_G: 3.6485 D(x): 0.6112 D(G(z)): 0.4867 / 0.3175\n", + "[94/100][146/391] Loss_D: 3.1169 Loss_G: 2.5459 D(x): 0.5574 D(G(z)): 0.3547 / 0.4671\n", + "[94/100][147/391] Loss_D: 2.8718 Loss_G: 2.1662 D(x): 0.7027 D(G(z)): 0.4404 / 0.5069\n", + "[94/100][148/391] Loss_D: 2.2605 Loss_G: 3.4163 D(x): 0.8008 D(G(z)): 0.4688 / 0.3508\n", + "[94/100][149/391] Loss_D: 2.5143 Loss_G: 2.1253 D(x): 0.7423 D(G(z)): 0.4137 / 0.5374\n", + "[94/100][150/391] Loss_D: 2.7695 Loss_G: 3.1864 D(x): 0.6506 D(G(z)): 0.3675 / 0.3946\n", + "[94/100][151/391] Loss_D: 3.5084 Loss_G: 2.9147 D(x): 0.6857 D(G(z)): 0.3513 / 0.4028\n", + "[94/100][152/391] Loss_D: 2.5573 Loss_G: 3.3363 D(x): 0.6604 D(G(z)): 0.3729 / 0.3621\n", + "[94/100][153/391] Loss_D: 2.4976 Loss_G: 2.9073 D(x): 0.7060 D(G(z)): 0.3439 / 0.4071\n", + "[94/100][154/391] Loss_D: 2.4884 Loss_G: 2.8165 D(x): 0.7475 D(G(z)): 0.4533 / 0.4159\n", + "[94/100][155/391] Loss_D: 3.0534 Loss_G: 3.3297 D(x): 0.6649 D(G(z)): 0.4258 / 0.3476\n", + "[94/100][156/391] Loss_D: 3.0132 Loss_G: 2.6870 D(x): 0.6492 D(G(z)): 0.4474 / 0.4448\n", + "[94/100][157/391] Loss_D: 2.9578 Loss_G: 3.7600 D(x): 0.6923 D(G(z)): 0.4675 / 0.3286\n", + "[94/100][158/391] Loss_D: 3.7139 Loss_G: 2.7014 D(x): 0.4654 D(G(z)): 0.3479 / 0.4439\n", + "[94/100][159/391] Loss_D: 3.1909 Loss_G: 2.5028 D(x): 0.6094 D(G(z)): 0.4274 / 0.4687\n", + "[94/100][160/391] Loss_D: 3.8127 Loss_G: 2.0550 D(x): 0.6062 D(G(z)): 0.5785 / 0.5319\n", + "[94/100][161/391] Loss_D: 3.1324 Loss_G: 2.1650 D(x): 0.7005 D(G(z)): 0.5032 / 0.5179\n", + "[94/100][162/391] Loss_D: 2.5249 Loss_G: 2.8583 D(x): 0.6967 D(G(z)): 0.3932 / 0.4156\n", + "[94/100][163/391] Loss_D: 2.5188 Loss_G: 2.8026 D(x): 0.7119 D(G(z)): 0.3695 / 0.4122\n", + "[94/100][164/391] Loss_D: 1.9719 Loss_G: 2.2609 D(x): 0.7865 D(G(z)): 0.3221 / 0.5011\n", + "[94/100][165/391] Loss_D: 2.4971 Loss_G: 2.7383 D(x): 0.7866 D(G(z)): 0.4310 / 0.4251\n", + "[94/100][166/391] Loss_D: 2.5353 Loss_G: 2.5523 D(x): 0.7446 D(G(z)): 0.4197 / 0.4425\n", + "[94/100][167/391] Loss_D: 2.8829 Loss_G: 2.4970 D(x): 0.6008 D(G(z)): 0.3144 / 0.4586\n", + "[94/100][168/391] Loss_D: 2.7658 Loss_G: 2.6158 D(x): 0.6352 D(G(z)): 0.3139 / 0.4483\n", + "[94/100][169/391] Loss_D: 2.6604 Loss_G: 2.8775 D(x): 0.7212 D(G(z)): 0.4467 / 0.4064\n", + "[94/100][170/391] Loss_D: 2.8750 Loss_G: 2.8050 D(x): 0.6770 D(G(z)): 0.4444 / 0.4346\n", + "[94/100][171/391] Loss_D: 3.3964 Loss_G: 2.9888 D(x): 0.5978 D(G(z)): 0.4550 / 0.4013\n", + "[94/100][172/391] Loss_D: 3.5273 Loss_G: 2.6711 D(x): 0.5642 D(G(z)): 0.4934 / 0.4402\n", + "[94/100][173/391] Loss_D: 2.4442 Loss_G: 3.3793 D(x): 0.7089 D(G(z)): 0.3601 / 0.3563\n", + "[94/100][174/391] Loss_D: 2.0460 Loss_G: 2.8820 D(x): 0.7452 D(G(z)): 0.3268 / 0.4237\n", + "[94/100][175/391] Loss_D: 2.9180 Loss_G: 2.8568 D(x): 0.7145 D(G(z)): 0.4896 / 0.4152\n", + "[94/100][176/391] Loss_D: 2.8531 Loss_G: 2.5417 D(x): 0.6761 D(G(z)): 0.4107 / 0.4665\n", + "[94/100][177/391] Loss_D: 2.7759 Loss_G: 2.7357 D(x): 0.7344 D(G(z)): 0.4362 / 0.4269\n", + "[94/100][178/391] Loss_D: 2.1466 Loss_G: 2.9881 D(x): 0.7347 D(G(z)): 0.3522 / 0.4083\n", + "[94/100][179/391] Loss_D: 3.1305 Loss_G: 1.8723 D(x): 0.5654 D(G(z)): 0.3402 / 0.5609\n", + "[94/100][180/391] Loss_D: 2.9324 Loss_G: 2.5250 D(x): 0.6788 D(G(z)): 0.4827 / 0.4582\n", + "[94/100][181/391] Loss_D: 3.5397 Loss_G: 2.6166 D(x): 0.6334 D(G(z)): 0.4073 / 0.4473\n", + "[94/100][182/391] Loss_D: 3.6431 Loss_G: 2.5219 D(x): 0.5124 D(G(z)): 0.4719 / 0.4628\n", + "[94/100][183/391] Loss_D: 2.8173 Loss_G: 2.3976 D(x): 0.6833 D(G(z)): 0.4625 / 0.4810\n", + "[94/100][184/391] Loss_D: 2.9451 Loss_G: 2.7135 D(x): 0.7395 D(G(z)): 0.5228 / 0.4436\n", + "[94/100][185/391] Loss_D: 2.8822 Loss_G: 3.4005 D(x): 0.6811 D(G(z)): 0.4211 / 0.3457\n", + "[94/100][186/391] Loss_D: 2.7806 Loss_G: 3.6583 D(x): 0.6471 D(G(z)): 0.4093 / 0.3342\n", + "[94/100][187/391] Loss_D: 2.8691 Loss_G: 2.8780 D(x): 0.6683 D(G(z)): 0.4730 / 0.4176\n", + "[94/100][188/391] Loss_D: 2.5635 Loss_G: 2.5238 D(x): 0.6745 D(G(z)): 0.4050 / 0.4538\n", + "[94/100][189/391] Loss_D: 2.9398 Loss_G: 3.0881 D(x): 0.7145 D(G(z)): 0.4642 / 0.3828\n", + "[94/100][190/391] Loss_D: 2.6312 Loss_G: 2.9771 D(x): 0.7246 D(G(z)): 0.4328 / 0.4037\n", + "[94/100][191/391] Loss_D: 3.1666 Loss_G: 2.7253 D(x): 0.6049 D(G(z)): 0.4167 / 0.4303\n", + "[94/100][192/391] Loss_D: 2.9057 Loss_G: 3.3863 D(x): 0.7292 D(G(z)): 0.5003 / 0.3571\n", + "[94/100][193/391] Loss_D: 2.6008 Loss_G: 4.0107 D(x): 0.6360 D(G(z)): 0.3481 / 0.2982\n", + "[94/100][194/391] Loss_D: 2.8467 Loss_G: 3.5247 D(x): 0.6357 D(G(z)): 0.4167 / 0.3323\n", + "[94/100][195/391] Loss_D: 2.6482 Loss_G: 2.5790 D(x): 0.6745 D(G(z)): 0.3677 / 0.4490\n", + "[94/100][196/391] Loss_D: 2.8590 Loss_G: 2.2764 D(x): 0.6548 D(G(z)): 0.4609 / 0.5026\n", + "[94/100][197/391] Loss_D: 2.7250 Loss_G: 2.9435 D(x): 0.6920 D(G(z)): 0.3372 / 0.3984\n", + "[94/100][198/391] Loss_D: 2.6201 Loss_G: 1.9088 D(x): 0.6870 D(G(z)): 0.3957 / 0.5520\n", + "[94/100][199/391] Loss_D: 2.8472 Loss_G: 2.0641 D(x): 0.6935 D(G(z)): 0.4671 / 0.5466\n", + "[94/100][200/391] Loss_D: 2.7035 Loss_G: 2.6917 D(x): 0.6243 D(G(z)): 0.3537 / 0.4392\n", + "[94/100][201/391] Loss_D: 2.5554 Loss_G: 2.6266 D(x): 0.7698 D(G(z)): 0.4281 / 0.4498\n", + "[94/100][202/391] Loss_D: 3.3516 Loss_G: 2.2083 D(x): 0.6459 D(G(z)): 0.5279 / 0.5160\n", + "[94/100][203/391] Loss_D: 2.6272 Loss_G: 2.8179 D(x): 0.6999 D(G(z)): 0.3740 / 0.4359\n", + "[94/100][204/391] Loss_D: 2.8342 Loss_G: 2.7728 D(x): 0.6280 D(G(z)): 0.4373 / 0.4336\n", + "[94/100][205/391] Loss_D: 2.6991 Loss_G: 2.2769 D(x): 0.6156 D(G(z)): 0.3610 / 0.4915\n", + "[94/100][206/391] Loss_D: 2.7704 Loss_G: 2.1734 D(x): 0.6746 D(G(z)): 0.4340 / 0.5126\n", + "[94/100][207/391] Loss_D: 2.9134 Loss_G: 3.1280 D(x): 0.5962 D(G(z)): 0.3937 / 0.3755\n", + "[94/100][208/391] Loss_D: 2.5703 Loss_G: 2.5324 D(x): 0.7102 D(G(z)): 0.4631 / 0.4527\n", + "[94/100][209/391] Loss_D: 2.7838 Loss_G: 2.7999 D(x): 0.6098 D(G(z)): 0.3503 / 0.4290\n", + "[94/100][210/391] Loss_D: 2.9211 Loss_G: 2.8467 D(x): 0.6332 D(G(z)): 0.3872 / 0.4377\n", + "[94/100][211/391] Loss_D: 3.6273 Loss_G: 2.3769 D(x): 0.6393 D(G(z)): 0.4700 / 0.4735\n", + "[94/100][212/391] Loss_D: 2.7655 Loss_G: 2.1441 D(x): 0.7343 D(G(z)): 0.4574 / 0.5333\n", + "[94/100][213/391] Loss_D: 3.0955 Loss_G: 2.2551 D(x): 0.7265 D(G(z)): 0.5183 / 0.5154\n", + "[94/100][214/391] Loss_D: 2.9064 Loss_G: 2.5792 D(x): 0.6600 D(G(z)): 0.4592 / 0.4509\n", + "[94/100][215/391] Loss_D: 3.0637 Loss_G: 2.1893 D(x): 0.5466 D(G(z)): 0.3489 / 0.4923\n", + "[94/100][216/391] Loss_D: 2.4957 Loss_G: 2.4298 D(x): 0.7037 D(G(z)): 0.3305 / 0.4738\n", + "[94/100][217/391] Loss_D: 3.3039 Loss_G: 2.2368 D(x): 0.6430 D(G(z)): 0.5279 / 0.4998\n", + "[94/100][218/391] Loss_D: 2.8684 Loss_G: 1.9777 D(x): 0.6511 D(G(z)): 0.4604 / 0.5489\n", + "[94/100][219/391] Loss_D: 3.3549 Loss_G: 2.8916 D(x): 0.6178 D(G(z)): 0.5217 / 0.4216\n", + "[94/100][220/391] Loss_D: 2.9491 Loss_G: 3.2550 D(x): 0.7486 D(G(z)): 0.5395 / 0.3863\n", + "[94/100][221/391] Loss_D: 2.7119 Loss_G: 3.7730 D(x): 0.6541 D(G(z)): 0.4322 / 0.3165\n", + "[94/100][222/391] Loss_D: 2.6406 Loss_G: 2.9982 D(x): 0.6177 D(G(z)): 0.3346 / 0.3996\n", + "[94/100][223/391] Loss_D: 2.5706 Loss_G: 3.0624 D(x): 0.6596 D(G(z)): 0.3216 / 0.3887\n", + "[94/100][224/391] Loss_D: 2.8426 Loss_G: 3.5748 D(x): 0.6617 D(G(z)): 0.4210 / 0.3240\n", + "[94/100][225/391] Loss_D: 2.6055 Loss_G: 2.6193 D(x): 0.6939 D(G(z)): 0.3730 / 0.4545\n", + "[94/100][226/391] Loss_D: 2.7018 Loss_G: 2.6638 D(x): 0.6920 D(G(z)): 0.4636 / 0.4441\n", + "[94/100][227/391] Loss_D: 2.5605 Loss_G: 3.2305 D(x): 0.6457 D(G(z)): 0.2691 / 0.3747\n", + "[94/100][228/391] Loss_D: 2.8721 Loss_G: 2.5936 D(x): 0.6894 D(G(z)): 0.4767 / 0.4564\n", + "[94/100][229/391] Loss_D: 3.8948 Loss_G: 3.1039 D(x): 0.5102 D(G(z)): 0.5051 / 0.3928\n", + "[94/100][230/391] Loss_D: 2.2765 Loss_G: 2.4258 D(x): 0.7168 D(G(z)): 0.3583 / 0.4775\n", + "[94/100][231/391] Loss_D: 2.9541 Loss_G: 1.5950 D(x): 0.7472 D(G(z)): 0.5056 / 0.6192\n", + "[94/100][232/391] Loss_D: 2.7080 Loss_G: 2.0607 D(x): 0.7732 D(G(z)): 0.4677 / 0.5230\n", + "[94/100][233/391] Loss_D: 2.5658 Loss_G: 2.6705 D(x): 0.6760 D(G(z)): 0.3900 / 0.4411\n", + "[94/100][234/391] Loss_D: 3.0518 Loss_G: 2.5637 D(x): 0.6233 D(G(z)): 0.4641 / 0.4494\n", + "[94/100][235/391] Loss_D: 2.9169 Loss_G: 2.7131 D(x): 0.6924 D(G(z)): 0.4751 / 0.4272\n", + "[94/100][236/391] Loss_D: 3.5040 Loss_G: 4.2606 D(x): 0.5860 D(G(z)): 0.4849 / 0.2660\n", + "[94/100][237/391] Loss_D: 3.0452 Loss_G: 2.4797 D(x): 0.6549 D(G(z)): 0.4961 / 0.4518\n", + "[94/100][238/391] Loss_D: 2.4058 Loss_G: 3.8025 D(x): 0.7719 D(G(z)): 0.4911 / 0.3141\n", + "[94/100][239/391] Loss_D: 2.8281 Loss_G: 2.9227 D(x): 0.5861 D(G(z)): 0.3105 / 0.4095\n", + "[94/100][240/391] Loss_D: 3.2215 Loss_G: 3.9685 D(x): 0.5745 D(G(z)): 0.4241 / 0.3089\n", + "[94/100][241/391] Loss_D: 3.8306 Loss_G: 2.4344 D(x): 0.6329 D(G(z)): 0.5184 / 0.4807\n", + "[94/100][242/391] Loss_D: 3.1013 Loss_G: 2.2159 D(x): 0.6741 D(G(z)): 0.5236 / 0.5000\n", + "[94/100][243/391] Loss_D: 2.7577 Loss_G: 1.8393 D(x): 0.6362 D(G(z)): 0.3982 / 0.5650\n", + "[94/100][244/391] Loss_D: 3.1038 Loss_G: 3.1013 D(x): 0.6350 D(G(z)): 0.4641 / 0.3967\n", + "[94/100][245/391] Loss_D: 3.2969 Loss_G: 2.9346 D(x): 0.5624 D(G(z)): 0.4739 / 0.4158\n", + "[94/100][246/391] Loss_D: 3.1025 Loss_G: 2.1373 D(x): 0.6709 D(G(z)): 0.4680 / 0.5123\n", + "[94/100][247/391] Loss_D: 2.8321 Loss_G: 2.7305 D(x): 0.6802 D(G(z)): 0.4200 / 0.4313\n", + "[94/100][248/391] Loss_D: 3.6557 Loss_G: 2.8870 D(x): 0.4681 D(G(z)): 0.3762 / 0.3981\n", + "[94/100][249/391] Loss_D: 2.6318 Loss_G: 2.7437 D(x): 0.6594 D(G(z)): 0.3948 / 0.4296\n", + "[94/100][250/391] Loss_D: 2.6006 Loss_G: 3.3298 D(x): 0.7404 D(G(z)): 0.3830 / 0.3584\n", + "[94/100][251/391] Loss_D: 3.0137 Loss_G: 2.7001 D(x): 0.6110 D(G(z)): 0.3755 / 0.4372\n", + "[94/100][252/391] Loss_D: 3.3602 Loss_G: 3.0141 D(x): 0.6556 D(G(z)): 0.5106 / 0.3967\n", + "[94/100][253/391] Loss_D: 2.6109 Loss_G: 3.0915 D(x): 0.7479 D(G(z)): 0.4516 / 0.3899\n", + "[94/100][254/391] Loss_D: 2.2521 Loss_G: 3.8057 D(x): 0.7604 D(G(z)): 0.4092 / 0.3109\n", + "[94/100][255/391] Loss_D: 3.3687 Loss_G: 2.3626 D(x): 0.6196 D(G(z)): 0.4934 / 0.4638\n", + "[94/100][256/391] Loss_D: 2.8153 Loss_G: 3.4781 D(x): 0.6273 D(G(z)): 0.4149 / 0.3412\n", + "[94/100][257/391] Loss_D: 2.9543 Loss_G: 2.6649 D(x): 0.6921 D(G(z)): 0.4272 / 0.4291\n", + "[94/100][258/391] Loss_D: 2.8315 Loss_G: 2.3687 D(x): 0.6320 D(G(z)): 0.3385 / 0.4828\n", + "[94/100][259/391] Loss_D: 3.1697 Loss_G: 2.5737 D(x): 0.5782 D(G(z)): 0.4192 / 0.4506\n", + "[94/100][260/391] Loss_D: 2.3588 Loss_G: 1.8161 D(x): 0.7491 D(G(z)): 0.3674 / 0.5694\n", + "[94/100][261/391] Loss_D: 3.1205 Loss_G: 2.1310 D(x): 0.7249 D(G(z)): 0.4952 / 0.5136\n", + "[94/100][262/391] Loss_D: 2.8757 Loss_G: 2.4193 D(x): 0.6092 D(G(z)): 0.3414 / 0.4561\n", + "[94/100][263/391] Loss_D: 3.2392 Loss_G: 2.5489 D(x): 0.6335 D(G(z)): 0.4803 / 0.4476\n", + "[94/100][264/391] Loss_D: 3.1776 Loss_G: 3.5578 D(x): 0.6615 D(G(z)): 0.5468 / 0.3533\n", + "[94/100][265/391] Loss_D: 3.0564 Loss_G: 3.0905 D(x): 0.6521 D(G(z)): 0.4663 / 0.3852\n", + "[94/100][266/391] Loss_D: 2.8940 Loss_G: 3.0190 D(x): 0.7348 D(G(z)): 0.4517 / 0.4158\n", + "[94/100][267/391] Loss_D: 2.9321 Loss_G: 2.5776 D(x): 0.6262 D(G(z)): 0.4023 / 0.4501\n", + "[94/100][268/391] Loss_D: 2.8176 Loss_G: 2.6257 D(x): 0.6094 D(G(z)): 0.4314 / 0.4511\n", + "[94/100][269/391] Loss_D: 2.8661 Loss_G: 2.6378 D(x): 0.6928 D(G(z)): 0.4343 / 0.4517\n", + "[94/100][270/391] Loss_D: 2.7209 Loss_G: 2.7752 D(x): 0.7005 D(G(z)): 0.3896 / 0.4297\n", + "[94/100][271/391] Loss_D: 3.8142 Loss_G: 2.9682 D(x): 0.6185 D(G(z)): 0.5256 / 0.4043\n", + "[94/100][272/391] Loss_D: 2.9586 Loss_G: 2.9346 D(x): 0.6302 D(G(z)): 0.4088 / 0.4150\n", + "[94/100][273/391] Loss_D: 2.8017 Loss_G: 2.8548 D(x): 0.7419 D(G(z)): 0.4899 / 0.4125\n", + "[94/100][274/391] Loss_D: 2.7707 Loss_G: 2.3025 D(x): 0.6525 D(G(z)): 0.4039 / 0.4894\n", + "[94/100][275/391] Loss_D: 2.5850 Loss_G: 2.9337 D(x): 0.6408 D(G(z)): 0.3050 / 0.4069\n", + "[94/100][276/391] Loss_D: 3.2731 Loss_G: 2.2447 D(x): 0.6079 D(G(z)): 0.4640 / 0.5056\n", + "[94/100][277/391] Loss_D: 2.4373 Loss_G: 3.4451 D(x): 0.7307 D(G(z)): 0.3387 / 0.3487\n", + "[94/100][278/391] Loss_D: 2.0684 Loss_G: 3.3882 D(x): 0.7648 D(G(z)): 0.3304 / 0.3552\n", + "[94/100][279/391] Loss_D: 2.7297 Loss_G: 3.0016 D(x): 0.8147 D(G(z)): 0.5421 / 0.4038\n", + "[94/100][280/391] Loss_D: 2.5211 Loss_G: 2.6375 D(x): 0.6702 D(G(z)): 0.3031 / 0.4512\n", + "[94/100][281/391] Loss_D: 3.4001 Loss_G: 2.0880 D(x): 0.6768 D(G(z)): 0.5485 / 0.5262\n", + "[94/100][282/391] Loss_D: 2.4476 Loss_G: 3.4108 D(x): 0.7139 D(G(z)): 0.3717 / 0.3726\n", + "[94/100][283/391] Loss_D: 3.1105 Loss_G: 3.4496 D(x): 0.5578 D(G(z)): 0.3651 / 0.3528\n", + "[94/100][284/391] Loss_D: 2.5473 Loss_G: 2.7574 D(x): 0.7510 D(G(z)): 0.4838 / 0.4281\n", + "[94/100][285/391] Loss_D: 2.5282 Loss_G: 3.4910 D(x): 0.7188 D(G(z)): 0.3597 / 0.3467\n", + "[94/100][286/391] Loss_D: 2.6902 Loss_G: 3.3758 D(x): 0.6907 D(G(z)): 0.4248 / 0.3556\n", + "[94/100][287/391] Loss_D: 2.7458 Loss_G: 2.7399 D(x): 0.6496 D(G(z)): 0.2779 / 0.4311\n", + "[94/100][288/391] Loss_D: 2.6650 Loss_G: 3.3689 D(x): 0.6945 D(G(z)): 0.4670 / 0.3731\n", + "[94/100][289/391] Loss_D: 2.8652 Loss_G: 2.5945 D(x): 0.6247 D(G(z)): 0.3197 / 0.4580\n", + "[94/100][290/391] Loss_D: 2.6539 Loss_G: 2.0537 D(x): 0.7194 D(G(z)): 0.4391 / 0.5442\n", + "[94/100][291/391] Loss_D: 3.0519 Loss_G: 2.2022 D(x): 0.6033 D(G(z)): 0.3771 / 0.5069\n", + "[94/100][292/391] Loss_D: 2.9025 Loss_G: 2.6184 D(x): 0.6712 D(G(z)): 0.4345 / 0.4581\n", + "[94/100][293/391] Loss_D: 2.9583 Loss_G: 3.7179 D(x): 0.7084 D(G(z)): 0.4746 / 0.3067\n", + "[94/100][294/391] Loss_D: 2.6365 Loss_G: 2.5843 D(x): 0.6666 D(G(z)): 0.4329 / 0.4565\n", + "[94/100][295/391] Loss_D: 2.5946 Loss_G: 2.4886 D(x): 0.7055 D(G(z)): 0.3892 / 0.4691\n", + "[94/100][296/391] Loss_D: 2.9852 Loss_G: 2.7177 D(x): 0.6074 D(G(z)): 0.3745 / 0.4329\n", + "[94/100][297/391] Loss_D: 2.8762 Loss_G: 2.6571 D(x): 0.6497 D(G(z)): 0.3879 / 0.4462\n", + "[94/100][298/391] Loss_D: 2.9914 Loss_G: 2.6261 D(x): 0.6937 D(G(z)): 0.5014 / 0.4370\n", + "[94/100][299/391] Loss_D: 2.9013 Loss_G: 2.5615 D(x): 0.7529 D(G(z)): 0.4990 / 0.4593\n", + "[94/100][300/391] Loss_D: 3.3826 Loss_G: 2.6501 D(x): 0.6666 D(G(z)): 0.5329 / 0.4392\n", + "[94/100][301/391] Loss_D: 3.6368 Loss_G: 2.5871 D(x): 0.6000 D(G(z)): 0.3819 / 0.4479\n", + "[94/100][302/391] Loss_D: 2.9292 Loss_G: 2.6668 D(x): 0.6050 D(G(z)): 0.4097 / 0.4572\n", + "[94/100][303/391] Loss_D: 2.7355 Loss_G: 3.2229 D(x): 0.7464 D(G(z)): 0.4720 / 0.3748\n", + "[94/100][304/391] Loss_D: 2.2404 Loss_G: 3.4610 D(x): 0.7184 D(G(z)): 0.3803 / 0.3475\n", + "[94/100][305/391] Loss_D: 3.7116 Loss_G: 2.9688 D(x): 0.6312 D(G(z)): 0.5986 / 0.3993\n", + "[94/100][306/391] Loss_D: 2.3939 Loss_G: 3.5879 D(x): 0.7105 D(G(z)): 0.3008 / 0.3354\n", + "[94/100][307/391] Loss_D: 2.9223 Loss_G: 3.2870 D(x): 0.7173 D(G(z)): 0.4749 / 0.3661\n", + "[94/100][308/391] Loss_D: 3.0333 Loss_G: 3.0635 D(x): 0.5664 D(G(z)): 0.3356 / 0.3972\n", + "[94/100][309/391] Loss_D: 2.9592 Loss_G: 3.5320 D(x): 0.5927 D(G(z)): 0.3909 / 0.3392\n", + "[94/100][310/391] Loss_D: 3.2610 Loss_G: 2.7162 D(x): 0.6103 D(G(z)): 0.4662 / 0.4367\n", + "[94/100][311/391] Loss_D: 2.9677 Loss_G: 2.3031 D(x): 0.6547 D(G(z)): 0.3920 / 0.5037\n", + "[94/100][312/391] Loss_D: 2.3751 Loss_G: 2.6191 D(x): 0.7505 D(G(z)): 0.3736 / 0.4723\n", + "[94/100][313/391] Loss_D: 2.8715 Loss_G: 2.1954 D(x): 0.6516 D(G(z)): 0.4444 / 0.5147\n", + "[94/100][314/391] Loss_D: 2.5465 Loss_G: 2.2467 D(x): 0.6520 D(G(z)): 0.4058 / 0.4959\n", + "[94/100][315/391] Loss_D: 2.5653 Loss_G: 2.5433 D(x): 0.7174 D(G(z)): 0.4010 / 0.4447\n", + "[94/100][316/391] Loss_D: 2.7971 Loss_G: 3.1305 D(x): 0.6778 D(G(z)): 0.4142 / 0.3810\n", + "[94/100][317/391] Loss_D: 2.7230 Loss_G: 2.1561 D(x): 0.7318 D(G(z)): 0.4284 / 0.5021\n", + "[94/100][318/391] Loss_D: 2.3415 Loss_G: 2.3230 D(x): 0.7112 D(G(z)): 0.3667 / 0.4912\n", + "[94/100][319/391] Loss_D: 2.8262 Loss_G: 2.8883 D(x): 0.6729 D(G(z)): 0.4553 / 0.4325\n", + "[94/100][320/391] Loss_D: 2.9922 Loss_G: 2.6789 D(x): 0.6602 D(G(z)): 0.4556 / 0.4531\n", + "[94/100][321/391] Loss_D: 3.3344 Loss_G: 3.5580 D(x): 0.5851 D(G(z)): 0.4426 / 0.3380\n", + "[94/100][322/391] Loss_D: 3.1174 Loss_G: 2.8029 D(x): 0.6788 D(G(z)): 0.5205 / 0.4083\n", + "[94/100][323/391] Loss_D: 2.3429 Loss_G: 1.9441 D(x): 0.7713 D(G(z)): 0.3380 / 0.5448\n", + "[94/100][324/391] Loss_D: 2.8424 Loss_G: 3.0453 D(x): 0.6508 D(G(z)): 0.4312 / 0.3866\n", + "[94/100][325/391] Loss_D: 3.1141 Loss_G: 2.8100 D(x): 0.6497 D(G(z)): 0.4256 / 0.4200\n", + "[94/100][326/391] Loss_D: 2.9635 Loss_G: 3.6069 D(x): 0.6371 D(G(z)): 0.4472 / 0.3224\n", + "[94/100][327/391] Loss_D: 2.8746 Loss_G: 2.3763 D(x): 0.6422 D(G(z)): 0.4307 / 0.4793\n", + "[94/100][328/391] Loss_D: 2.3445 Loss_G: 3.1030 D(x): 0.7075 D(G(z)): 0.3045 / 0.3847\n", + "[94/100][329/391] Loss_D: 2.9880 Loss_G: 1.8152 D(x): 0.6980 D(G(z)): 0.4760 / 0.5856\n", + "[94/100][330/391] Loss_D: 2.8875 Loss_G: 2.4468 D(x): 0.6428 D(G(z)): 0.4062 / 0.5050\n", + "[94/100][331/391] Loss_D: 3.5130 Loss_G: 2.4720 D(x): 0.6472 D(G(z)): 0.3779 / 0.4692\n", + "[94/100][332/391] Loss_D: 2.3470 Loss_G: 2.4145 D(x): 0.7108 D(G(z)): 0.3467 / 0.4736\n", + "[94/100][333/391] Loss_D: 3.0011 Loss_G: 2.6581 D(x): 0.7491 D(G(z)): 0.4936 / 0.4469\n", + "[94/100][334/391] Loss_D: 2.6137 Loss_G: 2.7017 D(x): 0.7416 D(G(z)): 0.4749 / 0.4250\n", + "[94/100][335/391] Loss_D: 2.7778 Loss_G: 4.3146 D(x): 0.7120 D(G(z)): 0.4591 / 0.2699\n", + "[94/100][336/391] Loss_D: 3.3628 Loss_G: 3.7359 D(x): 0.5976 D(G(z)): 0.4330 / 0.3243\n", + "[94/100][337/391] Loss_D: 2.7654 Loss_G: 3.2659 D(x): 0.6505 D(G(z)): 0.2741 / 0.3563\n", + "[94/100][338/391] Loss_D: 2.9153 Loss_G: 2.4756 D(x): 0.6226 D(G(z)): 0.3810 / 0.4577\n", + "[94/100][339/391] Loss_D: 3.1325 Loss_G: 2.6347 D(x): 0.6379 D(G(z)): 0.4827 / 0.4518\n", + "[94/100][340/391] Loss_D: 2.7242 Loss_G: 3.6195 D(x): 0.6771 D(G(z)): 0.4210 / 0.3391\n", + "[94/100][341/391] Loss_D: 2.6972 Loss_G: 3.1653 D(x): 0.7531 D(G(z)): 0.4521 / 0.3934\n", + "[94/100][342/391] Loss_D: 2.8314 Loss_G: 2.9312 D(x): 0.6446 D(G(z)): 0.3941 / 0.3992\n", + "[94/100][343/391] Loss_D: 2.7412 Loss_G: 2.8702 D(x): 0.6594 D(G(z)): 0.3893 / 0.4138\n", + "[94/100][344/391] Loss_D: 2.7420 Loss_G: 2.7557 D(x): 0.6516 D(G(z)): 0.4352 / 0.4204\n", + "[94/100][345/391] Loss_D: 2.7251 Loss_G: 2.6508 D(x): 0.7154 D(G(z)): 0.4382 / 0.4300\n", + "[94/100][346/391] Loss_D: 3.2497 Loss_G: 3.0687 D(x): 0.7440 D(G(z)): 0.5628 / 0.3942\n", + "[94/100][347/391] Loss_D: 3.2836 Loss_G: 3.2638 D(x): 0.6228 D(G(z)): 0.4751 / 0.3399\n", + "[94/100][348/391] Loss_D: 2.9531 Loss_G: 3.1775 D(x): 0.6221 D(G(z)): 0.4344 / 0.3873\n", + "[94/100][349/391] Loss_D: 3.0496 Loss_G: 4.0234 D(x): 0.5743 D(G(z)): 0.3458 / 0.2909\n", + "[94/100][350/391] Loss_D: 2.7407 Loss_G: 2.5942 D(x): 0.6159 D(G(z)): 0.3470 / 0.4677\n", + "[94/100][351/391] Loss_D: 2.7135 Loss_G: 2.9476 D(x): 0.7195 D(G(z)): 0.4304 / 0.4105\n", + "[94/100][352/391] Loss_D: 2.6339 Loss_G: 2.5201 D(x): 0.6636 D(G(z)): 0.3997 / 0.4485\n", + "[94/100][353/391] Loss_D: 2.6596 Loss_G: 3.5026 D(x): 0.7570 D(G(z)): 0.4417 / 0.3452\n", + "[94/100][354/391] Loss_D: 2.8646 Loss_G: 3.1572 D(x): 0.6516 D(G(z)): 0.4689 / 0.3941\n", + "[94/100][355/391] Loss_D: 2.6614 Loss_G: 2.6802 D(x): 0.6428 D(G(z)): 0.3821 / 0.4357\n", + "[94/100][356/391] Loss_D: 2.8896 Loss_G: 2.0655 D(x): 0.5676 D(G(z)): 0.3633 / 0.5338\n", + "[94/100][357/391] Loss_D: 2.9123 Loss_G: 2.7055 D(x): 0.6850 D(G(z)): 0.4589 / 0.4197\n", + "[94/100][358/391] Loss_D: 2.9369 Loss_G: 2.2514 D(x): 0.6382 D(G(z)): 0.4705 / 0.4991\n", + "[94/100][359/391] Loss_D: 2.4276 Loss_G: 2.9192 D(x): 0.7464 D(G(z)): 0.4138 / 0.4007\n", + "[94/100][360/391] Loss_D: 2.8604 Loss_G: 2.9440 D(x): 0.6396 D(G(z)): 0.4099 / 0.4162\n", + "[94/100][361/391] Loss_D: 3.4497 Loss_G: 2.9190 D(x): 0.6430 D(G(z)): 0.3728 / 0.4091\n", + "[94/100][362/391] Loss_D: 2.6182 Loss_G: 2.3421 D(x): 0.7289 D(G(z)): 0.4204 / 0.4825\n", + "[94/100][363/391] Loss_D: 2.7704 Loss_G: 1.9602 D(x): 0.6874 D(G(z)): 0.4405 / 0.5434\n", + "[94/100][364/391] Loss_D: 3.3195 Loss_G: 2.6664 D(x): 0.6494 D(G(z)): 0.5240 / 0.4395\n", + "[94/100][365/391] Loss_D: 2.7974 Loss_G: 4.7304 D(x): 0.6776 D(G(z)): 0.4129 / 0.2330\n", + "[94/100][366/391] Loss_D: 2.5724 Loss_G: 3.2900 D(x): 0.6085 D(G(z)): 0.3061 / 0.3717\n", + "[94/100][367/391] Loss_D: 2.5301 Loss_G: 2.7304 D(x): 0.7694 D(G(z)): 0.3800 / 0.4272\n", + "[94/100][368/391] Loss_D: 2.6893 Loss_G: 2.2295 D(x): 0.6745 D(G(z)): 0.3930 / 0.5001\n", + "[94/100][369/391] Loss_D: 2.2695 Loss_G: 2.6142 D(x): 0.7185 D(G(z)): 0.3365 / 0.4515\n", + "[94/100][370/391] Loss_D: 2.9638 Loss_G: 1.6773 D(x): 0.6883 D(G(z)): 0.4938 / 0.5774\n", + "[94/100][371/391] Loss_D: 3.2594 Loss_G: 2.7821 D(x): 0.6527 D(G(z)): 0.4902 / 0.4273\n", + "[94/100][372/391] Loss_D: 3.1860 Loss_G: 2.7133 D(x): 0.5632 D(G(z)): 0.4273 / 0.4369\n", + "[94/100][373/391] Loss_D: 3.2444 Loss_G: 2.6594 D(x): 0.6084 D(G(z)): 0.4558 / 0.4245\n", + "[94/100][374/391] Loss_D: 2.2109 Loss_G: 2.9306 D(x): 0.7677 D(G(z)): 0.3565 / 0.4106\n", + "[94/100][375/391] Loss_D: 2.7673 Loss_G: 3.8774 D(x): 0.6757 D(G(z)): 0.3600 / 0.3044\n", + "[94/100][376/391] Loss_D: 3.2510 Loss_G: 3.1025 D(x): 0.6727 D(G(z)): 0.5211 / 0.3906\n", + "[94/100][377/391] Loss_D: 3.2320 Loss_G: 2.3716 D(x): 0.6425 D(G(z)): 0.4829 / 0.4631\n", + "[94/100][378/391] Loss_D: 2.2555 Loss_G: 2.5378 D(x): 0.7164 D(G(z)): 0.3516 / 0.4715\n", + "[94/100][379/391] Loss_D: 3.1004 Loss_G: 2.9880 D(x): 0.5629 D(G(z)): 0.4221 / 0.4002\n", + "[94/100][380/391] Loss_D: 2.7290 Loss_G: 3.1910 D(x): 0.7036 D(G(z)): 0.4350 / 0.3837\n", + "[94/100][381/391] Loss_D: 2.4885 Loss_G: 3.0060 D(x): 0.8065 D(G(z)): 0.4553 / 0.4094\n", + "[94/100][382/391] Loss_D: 2.9369 Loss_G: 2.6590 D(x): 0.7173 D(G(z)): 0.5116 / 0.4700\n", + "[94/100][383/391] Loss_D: 2.6171 Loss_G: 3.0974 D(x): 0.6766 D(G(z)): 0.3612 / 0.3862\n", + "[94/100][384/391] Loss_D: 2.4092 Loss_G: 2.8238 D(x): 0.6609 D(G(z)): 0.3651 / 0.4278\n", + "[94/100][385/391] Loss_D: 2.5036 Loss_G: 2.8496 D(x): 0.6547 D(G(z)): 0.3322 / 0.4078\n", + "[94/100][386/391] Loss_D: 3.5799 Loss_G: 2.6734 D(x): 0.6677 D(G(z)): 0.5531 / 0.4394\n", + "[94/100][387/391] Loss_D: 3.1201 Loss_G: 2.7423 D(x): 0.6708 D(G(z)): 0.4964 / 0.4430\n", + "[94/100][388/391] Loss_D: 2.7917 Loss_G: 2.6678 D(x): 0.6250 D(G(z)): 0.3520 / 0.4628\n", + "[94/100][389/391] Loss_D: 2.9513 Loss_G: 2.7557 D(x): 0.6042 D(G(z)): 0.3810 / 0.4470\n", + "[94/100][390/391] Loss_D: 2.7524 Loss_G: 2.6905 D(x): 0.7033 D(G(z)): 0.4600 / 0.4520\n", + "[94/100][391/391] Loss_D: 3.6457 Loss_G: 2.9977 D(x): 0.6446 D(G(z)): 0.3955 / 0.3832\n", + "[95/100][1/391] Loss_D: 3.4635 Loss_G: 3.3887 D(x): 0.6727 D(G(z)): 0.4406 / 0.3565\n", + "[95/100][2/391] Loss_D: 2.7511 Loss_G: 2.5762 D(x): 0.6476 D(G(z)): 0.4106 / 0.4559\n", + "[95/100][3/391] Loss_D: 2.4480 Loss_G: 2.6591 D(x): 0.7200 D(G(z)): 0.3496 / 0.4435\n", + "[95/100][4/391] Loss_D: 2.5494 Loss_G: 3.0519 D(x): 0.6753 D(G(z)): 0.4121 / 0.4119\n", + "[95/100][5/391] Loss_D: 2.7651 Loss_G: 2.6680 D(x): 0.6599 D(G(z)): 0.4308 / 0.4402\n", + "[95/100][6/391] Loss_D: 3.3813 Loss_G: 3.2788 D(x): 0.5680 D(G(z)): 0.4485 / 0.3476\n", + "[95/100][7/391] Loss_D: 2.8880 Loss_G: 2.9078 D(x): 0.7089 D(G(z)): 0.4008 / 0.4081\n", + "[95/100][8/391] Loss_D: 2.0584 Loss_G: 2.4101 D(x): 0.7728 D(G(z)): 0.3797 / 0.4841\n", + "[95/100][9/391] Loss_D: 2.3753 Loss_G: 3.1920 D(x): 0.7681 D(G(z)): 0.4098 / 0.3792\n", + "[95/100][10/391] Loss_D: 3.4095 Loss_G: 2.4563 D(x): 0.6085 D(G(z)): 0.5240 / 0.4760\n", + "[95/100][11/391] Loss_D: 3.2866 Loss_G: 3.1951 D(x): 0.6344 D(G(z)): 0.5378 / 0.3852\n", + "[95/100][12/391] Loss_D: 3.2903 Loss_G: 3.9162 D(x): 0.6395 D(G(z)): 0.4957 / 0.3010\n", + "[95/100][13/391] Loss_D: 3.0165 Loss_G: 2.4338 D(x): 0.5772 D(G(z)): 0.3913 / 0.4740\n", + "[95/100][14/391] Loss_D: 2.7476 Loss_G: 2.4558 D(x): 0.5836 D(G(z)): 0.3266 / 0.4556\n", + "[95/100][15/391] Loss_D: 2.9335 Loss_G: 3.5037 D(x): 0.6797 D(G(z)): 0.5119 / 0.3368\n", + "[95/100][16/391] Loss_D: 3.1036 Loss_G: 3.1682 D(x): 0.5943 D(G(z)): 0.3613 / 0.3685\n", + "[95/100][17/391] Loss_D: 2.9571 Loss_G: 2.3826 D(x): 0.6561 D(G(z)): 0.4694 / 0.4701\n", + "[95/100][18/391] Loss_D: 2.0949 Loss_G: 2.9867 D(x): 0.7622 D(G(z)): 0.3321 / 0.3975\n", + "[95/100][19/391] Loss_D: 2.8480 Loss_G: 3.1352 D(x): 0.6953 D(G(z)): 0.5054 / 0.3872\n", + "[95/100][20/391] Loss_D: 2.7189 Loss_G: 3.6246 D(x): 0.6508 D(G(z)): 0.3640 / 0.3303\n", + "[95/100][21/391] Loss_D: 2.8869 Loss_G: 2.3030 D(x): 0.6373 D(G(z)): 0.4736 / 0.5026\n", + "[95/100][22/391] Loss_D: 2.7961 Loss_G: 3.1478 D(x): 0.6669 D(G(z)): 0.4201 / 0.3922\n", + "[95/100][23/391] Loss_D: 2.5650 Loss_G: 1.5607 D(x): 0.6801 D(G(z)): 0.4081 / 0.6168\n", + "[95/100][24/391] Loss_D: 2.5146 Loss_G: 2.1550 D(x): 0.7440 D(G(z)): 0.4203 / 0.5174\n", + "[95/100][25/391] Loss_D: 2.5399 Loss_G: 2.2327 D(x): 0.7242 D(G(z)): 0.3471 / 0.5122\n", + "[95/100][26/391] Loss_D: 2.5260 Loss_G: 3.3648 D(x): 0.7732 D(G(z)): 0.3836 / 0.3614\n", + "[95/100][27/391] Loss_D: 2.9646 Loss_G: 2.9253 D(x): 0.6593 D(G(z)): 0.4002 / 0.4175\n", + "[95/100][28/391] Loss_D: 2.9606 Loss_G: 2.6476 D(x): 0.6093 D(G(z)): 0.4135 / 0.4399\n", + "[95/100][29/391] Loss_D: 2.8873 Loss_G: 2.1840 D(x): 0.7512 D(G(z)): 0.5345 / 0.5173\n", + "[95/100][30/391] Loss_D: 2.5187 Loss_G: 3.1373 D(x): 0.6811 D(G(z)): 0.4032 / 0.3822\n", + "[95/100][31/391] Loss_D: 3.7426 Loss_G: 3.1547 D(x): 0.7304 D(G(z)): 0.3396 / 0.4077\n", + "[95/100][32/391] Loss_D: 3.4254 Loss_G: 2.7233 D(x): 0.6716 D(G(z)): 0.5563 / 0.4435\n", + "[95/100][33/391] Loss_D: 3.5238 Loss_G: 2.9126 D(x): 0.5370 D(G(z)): 0.4313 / 0.4452\n", + "[95/100][34/391] Loss_D: 3.0064 Loss_G: 2.1407 D(x): 0.6411 D(G(z)): 0.4756 / 0.5157\n", + "[95/100][35/391] Loss_D: 2.5319 Loss_G: 3.5955 D(x): 0.6336 D(G(z)): 0.2995 / 0.3262\n", + "[95/100][36/391] Loss_D: 2.5077 Loss_G: 3.0059 D(x): 0.7194 D(G(z)): 0.3397 / 0.4110\n", + "[95/100][37/391] Loss_D: 3.0283 Loss_G: 2.9668 D(x): 0.6365 D(G(z)): 0.4527 / 0.4072\n", + "[95/100][38/391] Loss_D: 2.5939 Loss_G: 3.3400 D(x): 0.7181 D(G(z)): 0.4550 / 0.3732\n", + "[95/100][39/391] Loss_D: 2.9140 Loss_G: 2.9809 D(x): 0.7208 D(G(z)): 0.4724 / 0.4057\n", + "[95/100][40/391] Loss_D: 2.8054 Loss_G: 2.5950 D(x): 0.7339 D(G(z)): 0.4300 / 0.4702\n", + "[95/100][41/391] Loss_D: 3.6178 Loss_G: 2.5380 D(x): 0.5493 D(G(z)): 0.4746 / 0.4649\n", + "[95/100][42/391] Loss_D: 2.3823 Loss_G: 2.4862 D(x): 0.7432 D(G(z)): 0.3560 / 0.4739\n", + "[95/100][43/391] Loss_D: 2.7613 Loss_G: 3.3265 D(x): 0.6776 D(G(z)): 0.3779 / 0.3532\n", + "[95/100][44/391] Loss_D: 2.9990 Loss_G: 1.9037 D(x): 0.6127 D(G(z)): 0.4608 / 0.5532\n", + "[95/100][45/391] Loss_D: 2.3853 Loss_G: 2.1509 D(x): 0.7155 D(G(z)): 0.3098 / 0.5033\n", + "[95/100][46/391] Loss_D: 2.5129 Loss_G: 2.5679 D(x): 0.6732 D(G(z)): 0.2940 / 0.4493\n", + "[95/100][47/391] Loss_D: 3.1931 Loss_G: 2.4881 D(x): 0.6493 D(G(z)): 0.5399 / 0.4668\n", + "[95/100][48/391] Loss_D: 3.2175 Loss_G: 2.5806 D(x): 0.6260 D(G(z)): 0.5290 / 0.4591\n", + "[95/100][49/391] Loss_D: 2.8521 Loss_G: 3.0137 D(x): 0.6824 D(G(z)): 0.4724 / 0.3916\n", + "[95/100][50/391] Loss_D: 3.2725 Loss_G: 3.4148 D(x): 0.6110 D(G(z)): 0.4913 / 0.3520\n", + "[95/100][51/391] Loss_D: 2.8104 Loss_G: 4.0553 D(x): 0.6501 D(G(z)): 0.3861 / 0.2941\n", + "[95/100][52/391] Loss_D: 2.3105 Loss_G: 2.2478 D(x): 0.6808 D(G(z)): 0.2981 / 0.5122\n", + "[95/100][53/391] Loss_D: 2.5585 Loss_G: 3.3400 D(x): 0.8083 D(G(z)): 0.4559 / 0.3551\n", + "[95/100][54/391] Loss_D: 2.6776 Loss_G: 3.4990 D(x): 0.6698 D(G(z)): 0.3811 / 0.3394\n", + "[95/100][55/391] Loss_D: 2.7158 Loss_G: 2.3943 D(x): 0.7219 D(G(z)): 0.4631 / 0.4674\n", + "[95/100][56/391] Loss_D: 2.4881 Loss_G: 2.6432 D(x): 0.6662 D(G(z)): 0.2491 / 0.4210\n", + "[95/100][57/391] Loss_D: 2.9309 Loss_G: 2.7092 D(x): 0.6625 D(G(z)): 0.4415 / 0.4504\n", + "[95/100][58/391] Loss_D: 3.0980 Loss_G: 3.3482 D(x): 0.6338 D(G(z)): 0.4610 / 0.3627\n", + "[95/100][59/391] Loss_D: 3.2117 Loss_G: 2.4051 D(x): 0.6451 D(G(z)): 0.4828 / 0.4835\n", + "[95/100][60/391] Loss_D: 3.0426 Loss_G: 2.8201 D(x): 0.6467 D(G(z)): 0.4486 / 0.4247\n", + "[95/100][61/391] Loss_D: 3.6422 Loss_G: 2.6762 D(x): 0.6668 D(G(z)): 0.3433 / 0.4438\n", + "[95/100][62/391] Loss_D: 2.7337 Loss_G: 3.5412 D(x): 0.6947 D(G(z)): 0.4559 / 0.3368\n", + "[95/100][63/391] Loss_D: 2.9930 Loss_G: 3.4292 D(x): 0.5980 D(G(z)): 0.3646 / 0.3602\n", + "[95/100][64/391] Loss_D: 2.6039 Loss_G: 3.5762 D(x): 0.7087 D(G(z)): 0.4723 / 0.3386\n", + "[95/100][65/391] Loss_D: 2.6019 Loss_G: 2.5268 D(x): 0.7204 D(G(z)): 0.4122 / 0.4619\n", + "[95/100][66/391] Loss_D: 2.4369 Loss_G: 1.9893 D(x): 0.7336 D(G(z)): 0.3889 / 0.5356\n", + "[95/100][67/391] Loss_D: 3.0571 Loss_G: 3.5544 D(x): 0.6296 D(G(z)): 0.3961 / 0.3324\n", + "[95/100][68/391] Loss_D: 2.2774 Loss_G: 2.9286 D(x): 0.7027 D(G(z)): 0.3383 / 0.4157\n", + "[95/100][69/391] Loss_D: 2.8633 Loss_G: 2.4414 D(x): 0.7047 D(G(z)): 0.4744 / 0.4809\n", + "[95/100][70/391] Loss_D: 2.6361 Loss_G: 2.0434 D(x): 0.7063 D(G(z)): 0.3750 / 0.5446\n", + "[95/100][71/391] Loss_D: 2.7805 Loss_G: 2.6172 D(x): 0.6963 D(G(z)): 0.4429 / 0.4575\n", + "[95/100][72/391] Loss_D: 3.0479 Loss_G: 3.0797 D(x): 0.6339 D(G(z)): 0.4737 / 0.3844\n", + "[95/100][73/391] Loss_D: 3.3297 Loss_G: 2.9186 D(x): 0.5435 D(G(z)): 0.4444 / 0.4084\n", + "[95/100][74/391] Loss_D: 3.1065 Loss_G: 2.4832 D(x): 0.6654 D(G(z)): 0.5071 / 0.4639\n", + "[95/100][75/391] Loss_D: 2.9246 Loss_G: 3.3147 D(x): 0.6079 D(G(z)): 0.4150 / 0.3591\n", + "[95/100][76/391] Loss_D: 2.4827 Loss_G: 2.5457 D(x): 0.7179 D(G(z)): 0.3581 / 0.4433\n", + "[95/100][77/391] Loss_D: 3.1074 Loss_G: 2.4551 D(x): 0.6266 D(G(z)): 0.4039 / 0.4553\n", + "[95/100][78/391] Loss_D: 3.1088 Loss_G: 3.8776 D(x): 0.6284 D(G(z)): 0.4873 / 0.2982\n", + "[95/100][79/391] Loss_D: 2.9722 Loss_G: 2.3188 D(x): 0.6554 D(G(z)): 0.5149 / 0.4886\n", + "[95/100][80/391] Loss_D: 2.8610 Loss_G: 2.0662 D(x): 0.7534 D(G(z)): 0.4638 / 0.5262\n", + "[95/100][81/391] Loss_D: 2.5247 Loss_G: 3.5662 D(x): 0.6889 D(G(z)): 0.3721 / 0.3364\n", + "[95/100][82/391] Loss_D: 3.1801 Loss_G: 3.7877 D(x): 0.7536 D(G(z)): 0.5456 / 0.3187\n", + "[95/100][83/391] Loss_D: 2.7619 Loss_G: 2.8053 D(x): 0.6564 D(G(z)): 0.3811 / 0.4150\n", + "[95/100][84/391] Loss_D: 2.6485 Loss_G: 3.4287 D(x): 0.6057 D(G(z)): 0.3466 / 0.3568\n", + "[95/100][85/391] Loss_D: 2.9220 Loss_G: 3.3436 D(x): 0.6132 D(G(z)): 0.3929 / 0.3619\n", + "[95/100][86/391] Loss_D: 2.8956 Loss_G: 2.3461 D(x): 0.6428 D(G(z)): 0.4064 / 0.4632\n", + "[95/100][87/391] Loss_D: 3.0120 Loss_G: 3.7128 D(x): 0.6836 D(G(z)): 0.4296 / 0.3143\n", + "[95/100][88/391] Loss_D: 2.8068 Loss_G: 2.2864 D(x): 0.5973 D(G(z)): 0.3427 / 0.4967\n", + "[95/100][89/391] Loss_D: 3.4709 Loss_G: 2.7644 D(x): 0.6306 D(G(z)): 0.5547 / 0.4383\n", + "[95/100][90/391] Loss_D: 2.5645 Loss_G: 2.5097 D(x): 0.7226 D(G(z)): 0.3974 / 0.4592\n", + "[95/100][91/391] Loss_D: 3.7215 Loss_G: 2.5673 D(x): 0.7066 D(G(z)): 0.5542 / 0.4602\n", + "[95/100][92/391] Loss_D: 3.1720 Loss_G: 1.8851 D(x): 0.4992 D(G(z)): 0.3150 / 0.5471\n", + "[95/100][93/391] Loss_D: 2.8541 Loss_G: 4.3071 D(x): 0.7079 D(G(z)): 0.4693 / 0.2583\n", + "[95/100][94/391] Loss_D: 2.6357 Loss_G: 2.3853 D(x): 0.6799 D(G(z)): 0.4139 / 0.4699\n", + "[95/100][95/391] Loss_D: 2.7220 Loss_G: 2.8941 D(x): 0.6960 D(G(z)): 0.4085 / 0.4084\n", + "[95/100][96/391] Loss_D: 2.6802 Loss_G: 3.4145 D(x): 0.7178 D(G(z)): 0.4335 / 0.3369\n", + "[95/100][97/391] Loss_D: 2.7561 Loss_G: 2.0615 D(x): 0.7423 D(G(z)): 0.4149 / 0.5247\n", + "[95/100][98/391] Loss_D: 3.2744 Loss_G: 2.6602 D(x): 0.5598 D(G(z)): 0.4071 / 0.4541\n", + "[95/100][99/391] Loss_D: 3.4573 Loss_G: 3.3870 D(x): 0.5713 D(G(z)): 0.4873 / 0.3660\n", + "[95/100][100/391] Loss_D: 3.1338 Loss_G: 2.2825 D(x): 0.6695 D(G(z)): 0.5184 / 0.5176\n", + "[95/100][101/391] Loss_D: 3.2012 Loss_G: 2.2191 D(x): 0.6558 D(G(z)): 0.5237 / 0.5062\n", + "[95/100][102/391] Loss_D: 3.3746 Loss_G: 3.0351 D(x): 0.6635 D(G(z)): 0.5265 / 0.3942\n", + "[95/100][103/391] Loss_D: 2.4832 Loss_G: 4.1126 D(x): 0.7507 D(G(z)): 0.4064 / 0.2890\n", + "[95/100][104/391] Loss_D: 2.5144 Loss_G: 3.9588 D(x): 0.7187 D(G(z)): 0.3970 / 0.3008\n", + "[95/100][105/391] Loss_D: 2.9915 Loss_G: 2.6170 D(x): 0.5779 D(G(z)): 0.2571 / 0.4461\n", + "[95/100][106/391] Loss_D: 2.9515 Loss_G: 2.2701 D(x): 0.6326 D(G(z)): 0.4702 / 0.5024\n", + "[95/100][107/391] Loss_D: 3.1664 Loss_G: 2.3908 D(x): 0.6420 D(G(z)): 0.4615 / 0.4694\n", + "[95/100][108/391] Loss_D: 3.1497 Loss_G: 3.0644 D(x): 0.6644 D(G(z)): 0.4993 / 0.3971\n", + "[95/100][109/391] Loss_D: 3.1007 Loss_G: 3.0887 D(x): 0.6006 D(G(z)): 0.4758 / 0.3850\n", + "[95/100][110/391] Loss_D: 3.5175 Loss_G: 3.0853 D(x): 0.6195 D(G(z)): 0.5530 / 0.4051\n", + "[95/100][111/391] Loss_D: 2.5121 Loss_G: 3.9931 D(x): 0.6822 D(G(z)): 0.3637 / 0.2996\n", + "[95/100][112/391] Loss_D: 2.6607 Loss_G: 1.9857 D(x): 0.6936 D(G(z)): 0.4252 / 0.5720\n", + "[95/100][113/391] Loss_D: 3.2004 Loss_G: 2.3780 D(x): 0.6247 D(G(z)): 0.4614 / 0.4768\n", + "[95/100][114/391] Loss_D: 2.2932 Loss_G: 3.5019 D(x): 0.7012 D(G(z)): 0.3624 / 0.3274\n", + "[95/100][115/391] Loss_D: 2.8958 Loss_G: 4.1124 D(x): 0.5883 D(G(z)): 0.3452 / 0.2773\n", + "[95/100][116/391] Loss_D: 2.7609 Loss_G: 2.2116 D(x): 0.6543 D(G(z)): 0.4090 / 0.4937\n", + "[95/100][117/391] Loss_D: 2.8060 Loss_G: 3.3180 D(x): 0.6776 D(G(z)): 0.3863 / 0.3692\n", + "[95/100][118/391] Loss_D: 2.1512 Loss_G: 2.9471 D(x): 0.7633 D(G(z)): 0.3110 / 0.4009\n", + "[95/100][119/391] Loss_D: 3.0376 Loss_G: 2.7586 D(x): 0.6803 D(G(z)): 0.4852 / 0.4325\n", + "[95/100][120/391] Loss_D: 2.6196 Loss_G: 2.7368 D(x): 0.7666 D(G(z)): 0.4839 / 0.4397\n", + "[95/100][121/391] Loss_D: 3.6203 Loss_G: 2.8187 D(x): 0.5733 D(G(z)): 0.3871 / 0.4266\n", + "[95/100][122/391] Loss_D: 2.9484 Loss_G: 3.2545 D(x): 0.7157 D(G(z)): 0.4752 / 0.3751\n", + "[95/100][123/391] Loss_D: 3.7168 Loss_G: 3.5330 D(x): 0.6084 D(G(z)): 0.5631 / 0.3475\n", + "[95/100][124/391] Loss_D: 3.8787 Loss_G: 2.8150 D(x): 0.5659 D(G(z)): 0.5551 / 0.4183\n", + "[95/100][125/391] Loss_D: 2.8852 Loss_G: 3.0271 D(x): 0.7017 D(G(z)): 0.4278 / 0.4011\n", + "[95/100][126/391] Loss_D: 3.0889 Loss_G: 2.9832 D(x): 0.5659 D(G(z)): 0.4023 / 0.3980\n", + "[95/100][127/391] Loss_D: 3.1110 Loss_G: 2.8480 D(x): 0.5742 D(G(z)): 0.3574 / 0.4138\n", + "[95/100][128/391] Loss_D: 2.8528 Loss_G: 3.4125 D(x): 0.7034 D(G(z)): 0.5095 / 0.3602\n", + "[95/100][129/391] Loss_D: 2.7053 Loss_G: 3.0208 D(x): 0.6634 D(G(z)): 0.4128 / 0.4049\n", + "[95/100][130/391] Loss_D: 2.7662 Loss_G: 2.9977 D(x): 0.7212 D(G(z)): 0.4542 / 0.4085\n", + "[95/100][131/391] Loss_D: 2.8958 Loss_G: 3.0350 D(x): 0.6250 D(G(z)): 0.4347 / 0.4013\n", + "[95/100][132/391] Loss_D: 2.6251 Loss_G: 3.1482 D(x): 0.7364 D(G(z)): 0.4199 / 0.3763\n", + "[95/100][133/391] Loss_D: 2.8832 Loss_G: 2.2485 D(x): 0.6150 D(G(z)): 0.4089 / 0.4980\n", + "[95/100][134/391] Loss_D: 2.9560 Loss_G: 2.2044 D(x): 0.6476 D(G(z)): 0.4691 / 0.5076\n", + "[95/100][135/391] Loss_D: 2.5572 Loss_G: 2.4510 D(x): 0.6838 D(G(z)): 0.3929 / 0.4812\n", + "[95/100][136/391] Loss_D: 2.8199 Loss_G: 3.3312 D(x): 0.7033 D(G(z)): 0.4390 / 0.3678\n", + "[95/100][137/391] Loss_D: 2.8889 Loss_G: 4.0723 D(x): 0.6845 D(G(z)): 0.4424 / 0.2738\n", + "[95/100][138/391] Loss_D: 2.2650 Loss_G: 2.9764 D(x): 0.7158 D(G(z)): 0.3395 / 0.4092\n", + "[95/100][139/391] Loss_D: 2.8624 Loss_G: 3.5854 D(x): 0.7044 D(G(z)): 0.4493 / 0.3463\n", + "[95/100][140/391] Loss_D: 3.2620 Loss_G: 2.9915 D(x): 0.5920 D(G(z)): 0.4602 / 0.4036\n", + "[95/100][141/391] Loss_D: 3.3437 Loss_G: 4.4769 D(x): 0.6913 D(G(z)): 0.5602 / 0.2549\n", + "[95/100][142/391] Loss_D: 2.8081 Loss_G: 2.9950 D(x): 0.6171 D(G(z)): 0.3840 / 0.3961\n", + "[95/100][143/391] Loss_D: 2.4753 Loss_G: 3.1252 D(x): 0.7196 D(G(z)): 0.3536 / 0.3759\n", + "[95/100][144/391] Loss_D: 2.8555 Loss_G: 3.5648 D(x): 0.6055 D(G(z)): 0.3747 / 0.3343\n", + "[95/100][145/391] Loss_D: 2.7251 Loss_G: 2.7327 D(x): 0.7954 D(G(z)): 0.4709 / 0.4422\n", + "[95/100][146/391] Loss_D: 2.6850 Loss_G: 2.9725 D(x): 0.6270 D(G(z)): 0.2926 / 0.3850\n", + "[95/100][147/391] Loss_D: 3.4113 Loss_G: 3.0424 D(x): 0.5593 D(G(z)): 0.4419 / 0.3946\n", + "[95/100][148/391] Loss_D: 2.8168 Loss_G: 2.7001 D(x): 0.6290 D(G(z)): 0.4457 / 0.4302\n", + "[95/100][149/391] Loss_D: 2.7447 Loss_G: 3.0547 D(x): 0.6878 D(G(z)): 0.4107 / 0.3870\n", + "[95/100][150/391] Loss_D: 2.8563 Loss_G: 3.3134 D(x): 0.6979 D(G(z)): 0.4462 / 0.3669\n", + "[95/100][151/391] Loss_D: 3.5924 Loss_G: 2.9150 D(x): 0.6238 D(G(z)): 0.4980 / 0.4103\n", + "[95/100][152/391] Loss_D: 2.9425 Loss_G: 2.8423 D(x): 0.7481 D(G(z)): 0.5336 / 0.4118\n", + "[95/100][153/391] Loss_D: 3.2661 Loss_G: 2.7461 D(x): 0.6114 D(G(z)): 0.4591 / 0.4240\n", + "[95/100][154/391] Loss_D: 2.3361 Loss_G: 3.1492 D(x): 0.7567 D(G(z)): 0.4266 / 0.3971\n", + "[95/100][155/391] Loss_D: 2.5937 Loss_G: 2.7501 D(x): 0.7087 D(G(z)): 0.3309 / 0.4349\n", + "[95/100][156/391] Loss_D: 2.4363 Loss_G: 2.9135 D(x): 0.6771 D(G(z)): 0.2862 / 0.4050\n", + "[95/100][157/391] Loss_D: 3.0085 Loss_G: 3.4648 D(x): 0.6443 D(G(z)): 0.4314 / 0.3398\n", + "[95/100][158/391] Loss_D: 2.3900 Loss_G: 2.4554 D(x): 0.6832 D(G(z)): 0.3234 / 0.4789\n", + "[95/100][159/391] Loss_D: 2.8215 Loss_G: 2.5118 D(x): 0.6800 D(G(z)): 0.4087 / 0.4764\n", + "[95/100][160/391] Loss_D: 2.6522 Loss_G: 2.7831 D(x): 0.7730 D(G(z)): 0.4823 / 0.4296\n", + "[95/100][161/391] Loss_D: 2.9872 Loss_G: 2.6125 D(x): 0.6059 D(G(z)): 0.3800 / 0.4558\n", + "[95/100][162/391] Loss_D: 2.7355 Loss_G: 2.7097 D(x): 0.7040 D(G(z)): 0.4561 / 0.4334\n", + "[95/100][163/391] Loss_D: 2.8182 Loss_G: 3.5457 D(x): 0.6406 D(G(z)): 0.4006 / 0.3283\n", + "[95/100][164/391] Loss_D: 2.5493 Loss_G: 3.1880 D(x): 0.7184 D(G(z)): 0.4403 / 0.3696\n", + "[95/100][165/391] Loss_D: 2.9515 Loss_G: 2.9367 D(x): 0.6032 D(G(z)): 0.4030 / 0.3989\n", + "[95/100][166/391] Loss_D: 2.8284 Loss_G: 2.8609 D(x): 0.6119 D(G(z)): 0.3865 / 0.4096\n", + "[95/100][167/391] Loss_D: 2.8148 Loss_G: 2.1974 D(x): 0.7777 D(G(z)): 0.4604 / 0.4987\n", + "[95/100][168/391] Loss_D: 2.1665 Loss_G: 2.3932 D(x): 0.7505 D(G(z)): 0.2742 / 0.4711\n", + "[95/100][169/391] Loss_D: 2.8595 Loss_G: 2.7686 D(x): 0.6542 D(G(z)): 0.4544 / 0.4393\n", + "[95/100][170/391] Loss_D: 3.0883 Loss_G: 2.6788 D(x): 0.6280 D(G(z)): 0.4266 / 0.4360\n", + "[95/100][171/391] Loss_D: 2.6202 Loss_G: 3.9743 D(x): 0.7332 D(G(z)): 0.4285 / 0.3048\n", + "[95/100][172/391] Loss_D: 2.9770 Loss_G: 2.4779 D(x): 0.6663 D(G(z)): 0.4606 / 0.4661\n", + "[95/100][173/391] Loss_D: 2.8085 Loss_G: 2.5508 D(x): 0.7051 D(G(z)): 0.4797 / 0.4572\n", + "[95/100][174/391] Loss_D: 2.5621 Loss_G: 2.3987 D(x): 0.7178 D(G(z)): 0.4457 / 0.4943\n", + "[95/100][175/391] Loss_D: 2.8890 Loss_G: 2.5496 D(x): 0.6454 D(G(z)): 0.4230 / 0.4554\n", + "[95/100][176/391] Loss_D: 3.1830 Loss_G: 3.5318 D(x): 0.5764 D(G(z)): 0.3831 / 0.3399\n", + "[95/100][177/391] Loss_D: 2.7950 Loss_G: 3.4330 D(x): 0.6096 D(G(z)): 0.3027 / 0.3430\n", + "[95/100][178/391] Loss_D: 2.5246 Loss_G: 2.3661 D(x): 0.7037 D(G(z)): 0.3816 / 0.4797\n", + "[95/100][179/391] Loss_D: 3.0516 Loss_G: 2.4909 D(x): 0.6833 D(G(z)): 0.4775 / 0.4703\n", + "[95/100][180/391] Loss_D: 3.2967 Loss_G: 2.8516 D(x): 0.5843 D(G(z)): 0.4572 / 0.4318\n", + "[95/100][181/391] Loss_D: 3.9953 Loss_G: 2.2078 D(x): 0.6918 D(G(z)): 0.6216 / 0.5052\n", + "[95/100][182/391] Loss_D: 2.8293 Loss_G: 2.8752 D(x): 0.7058 D(G(z)): 0.4910 / 0.4158\n", + "[95/100][183/391] Loss_D: 2.5378 Loss_G: 3.6737 D(x): 0.6654 D(G(z)): 0.3897 / 0.3376\n", + "[95/100][184/391] Loss_D: 2.5340 Loss_G: 2.8537 D(x): 0.6873 D(G(z)): 0.3686 / 0.4121\n", + "[95/100][185/391] Loss_D: 2.8071 Loss_G: 3.1616 D(x): 0.7225 D(G(z)): 0.4427 / 0.3859\n", + "[95/100][186/391] Loss_D: 2.5760 Loss_G: 2.2742 D(x): 0.6423 D(G(z)): 0.3536 / 0.5107\n", + "[95/100][187/391] Loss_D: 2.4349 Loss_G: 3.5092 D(x): 0.7201 D(G(z)): 0.3410 / 0.3347\n", + "[95/100][188/391] Loss_D: 2.6962 Loss_G: 2.8349 D(x): 0.6808 D(G(z)): 0.4690 / 0.4215\n", + "[95/100][189/391] Loss_D: 2.9150 Loss_G: 3.1183 D(x): 0.6699 D(G(z)): 0.4645 / 0.3922\n", + "[95/100][190/391] Loss_D: 2.7450 Loss_G: 3.0660 D(x): 0.7031 D(G(z)): 0.4272 / 0.3816\n", + "[95/100][191/391] Loss_D: 3.1206 Loss_G: 2.9545 D(x): 0.6697 D(G(z)): 0.4552 / 0.4037\n", + "[95/100][192/391] Loss_D: 2.4140 Loss_G: 2.6812 D(x): 0.6243 D(G(z)): 0.2675 / 0.4366\n", + "[95/100][193/391] Loss_D: 2.9034 Loss_G: 2.6777 D(x): 0.6100 D(G(z)): 0.4299 / 0.4497\n", + "[95/100][194/391] Loss_D: 3.2986 Loss_G: 2.6436 D(x): 0.5900 D(G(z)): 0.4467 / 0.4321\n", + "[95/100][195/391] Loss_D: 3.6379 Loss_G: 3.0433 D(x): 0.5857 D(G(z)): 0.5508 / 0.3996\n", + "[95/100][196/391] Loss_D: 2.4864 Loss_G: 2.6577 D(x): 0.7448 D(G(z)): 0.4512 / 0.4427\n", + "[95/100][197/391] Loss_D: 2.5165 Loss_G: 3.4529 D(x): 0.7756 D(G(z)): 0.3562 / 0.3370\n", + "[95/100][198/391] Loss_D: 2.7627 Loss_G: 3.3532 D(x): 0.6797 D(G(z)): 0.4294 / 0.3518\n", + "[95/100][199/391] Loss_D: 3.5096 Loss_G: 2.1079 D(x): 0.5034 D(G(z)): 0.3685 / 0.5253\n", + "[95/100][200/391] Loss_D: 2.9712 Loss_G: 2.4084 D(x): 0.6968 D(G(z)): 0.4927 / 0.4912\n", + "[95/100][201/391] Loss_D: 2.9476 Loss_G: 3.5264 D(x): 0.6817 D(G(z)): 0.4174 / 0.3339\n", + "[95/100][202/391] Loss_D: 3.6070 Loss_G: 3.0902 D(x): 0.6111 D(G(z)): 0.5708 / 0.3955\n", + "[95/100][203/391] Loss_D: 2.6175 Loss_G: 2.7464 D(x): 0.7118 D(G(z)): 0.3965 / 0.4312\n", + "[95/100][204/391] Loss_D: 2.2640 Loss_G: 3.1470 D(x): 0.7362 D(G(z)): 0.4137 / 0.3854\n", + "[95/100][205/391] Loss_D: 2.7462 Loss_G: 3.8530 D(x): 0.6679 D(G(z)): 0.4423 / 0.2952\n", + "[95/100][206/391] Loss_D: 2.7294 Loss_G: 3.3088 D(x): 0.6325 D(G(z)): 0.3480 / 0.3784\n", + "[95/100][207/391] Loss_D: 2.9478 Loss_G: 2.5925 D(x): 0.5889 D(G(z)): 0.3824 / 0.4560\n", + "[95/100][208/391] Loss_D: 2.5152 Loss_G: 3.1573 D(x): 0.7422 D(G(z)): 0.4648 / 0.3791\n", + "[95/100][209/391] Loss_D: 2.7780 Loss_G: 3.3949 D(x): 0.6741 D(G(z)): 0.4290 / 0.3652\n", + "[95/100][210/391] Loss_D: 2.8371 Loss_G: 3.7274 D(x): 0.6935 D(G(z)): 0.4457 / 0.3338\n", + "[95/100][211/391] Loss_D: 3.7008 Loss_G: 2.3595 D(x): 0.7036 D(G(z)): 0.5166 / 0.4984\n", + "[95/100][212/391] Loss_D: 3.7951 Loss_G: 2.3363 D(x): 0.4776 D(G(z)): 0.4041 / 0.4863\n", + "[95/100][213/391] Loss_D: 2.6331 Loss_G: 2.5548 D(x): 0.7935 D(G(z)): 0.4413 / 0.4602\n", + "[95/100][214/391] Loss_D: 2.7257 Loss_G: 3.5618 D(x): 0.6559 D(G(z)): 0.3965 / 0.3384\n", + "[95/100][215/391] Loss_D: 3.1928 Loss_G: 2.4195 D(x): 0.5662 D(G(z)): 0.3716 / 0.4844\n", + "[95/100][216/391] Loss_D: 2.8498 Loss_G: 2.7010 D(x): 0.6654 D(G(z)): 0.4272 / 0.4396\n", + "[95/100][217/391] Loss_D: 2.7099 Loss_G: 2.5410 D(x): 0.7504 D(G(z)): 0.4632 / 0.4665\n", + "[95/100][218/391] Loss_D: 3.2481 Loss_G: 2.4893 D(x): 0.5312 D(G(z)): 0.3016 / 0.4578\n", + "[95/100][219/391] Loss_D: 2.8562 Loss_G: 3.5496 D(x): 0.7142 D(G(z)): 0.5188 / 0.3399\n", + "[95/100][220/391] Loss_D: 2.5945 Loss_G: 2.2254 D(x): 0.8186 D(G(z)): 0.4901 / 0.5172\n", + "[95/100][221/391] Loss_D: 2.7390 Loss_G: 2.6480 D(x): 0.7148 D(G(z)): 0.4551 / 0.4552\n", + "[95/100][222/391] Loss_D: 3.4577 Loss_G: 2.8464 D(x): 0.6760 D(G(z)): 0.5776 / 0.4134\n", + "[95/100][223/391] Loss_D: 3.2789 Loss_G: 3.1934 D(x): 0.5500 D(G(z)): 0.3528 / 0.3809\n", + "[95/100][224/391] Loss_D: 2.2961 Loss_G: 3.2319 D(x): 0.7277 D(G(z)): 0.3607 / 0.3713\n", + "[95/100][225/391] Loss_D: 2.7334 Loss_G: 2.9467 D(x): 0.6048 D(G(z)): 0.3036 / 0.4209\n", + "[95/100][226/391] Loss_D: 3.0553 Loss_G: 2.1487 D(x): 0.6147 D(G(z)): 0.4822 / 0.5055\n", + "[95/100][227/391] Loss_D: 3.1342 Loss_G: 2.3061 D(x): 0.6046 D(G(z)): 0.4347 / 0.4910\n", + "[95/100][228/391] Loss_D: 3.1377 Loss_G: 3.0786 D(x): 0.5996 D(G(z)): 0.4024 / 0.3868\n", + "[95/100][229/391] Loss_D: 3.1812 Loss_G: 2.4594 D(x): 0.6527 D(G(z)): 0.5454 / 0.4760\n", + "[95/100][230/391] Loss_D: 2.8944 Loss_G: 2.9967 D(x): 0.6446 D(G(z)): 0.5043 / 0.4038\n", + "[95/100][231/391] Loss_D: 2.6854 Loss_G: 3.0089 D(x): 0.7301 D(G(z)): 0.4184 / 0.4068\n", + "[95/100][232/391] Loss_D: 1.9228 Loss_G: 2.1718 D(x): 0.7665 D(G(z)): 0.2586 / 0.5209\n", + "[95/100][233/391] Loss_D: 3.1255 Loss_G: 2.7244 D(x): 0.6422 D(G(z)): 0.4973 / 0.4252\n", + "[95/100][234/391] Loss_D: 2.7019 Loss_G: 2.0712 D(x): 0.6582 D(G(z)): 0.4191 / 0.5378\n", + "[95/100][235/391] Loss_D: 2.8872 Loss_G: 2.7001 D(x): 0.6788 D(G(z)): 0.4747 / 0.4175\n", + "[95/100][236/391] Loss_D: 2.7199 Loss_G: 2.6848 D(x): 0.6367 D(G(z)): 0.3375 / 0.4252\n", + "[95/100][237/391] Loss_D: 3.2749 Loss_G: 3.0387 D(x): 0.6821 D(G(z)): 0.5385 / 0.4079\n", + "[95/100][238/391] Loss_D: 3.0095 Loss_G: 2.7073 D(x): 0.6563 D(G(z)): 0.5062 / 0.4341\n", + "[95/100][239/391] Loss_D: 3.2048 Loss_G: 2.2905 D(x): 0.6218 D(G(z)): 0.4924 / 0.5109\n", + "[95/100][240/391] Loss_D: 2.9819 Loss_G: 2.2625 D(x): 0.6051 D(G(z)): 0.3985 / 0.5049\n", + "[95/100][241/391] Loss_D: 3.8791 Loss_G: 2.6341 D(x): 0.6457 D(G(z)): 0.4390 / 0.4529\n", + "[95/100][242/391] Loss_D: 2.9518 Loss_G: 3.2606 D(x): 0.6435 D(G(z)): 0.4486 / 0.3787\n", + "[95/100][243/391] Loss_D: 2.4597 Loss_G: 2.8758 D(x): 0.7672 D(G(z)): 0.4152 / 0.4118\n", + "[95/100][244/391] Loss_D: 2.0910 Loss_G: 3.8641 D(x): 0.7706 D(G(z)): 0.3673 / 0.3042\n", + "[95/100][245/391] Loss_D: 2.4642 Loss_G: 2.7033 D(x): 0.7598 D(G(z)): 0.4119 / 0.4386\n", + "[95/100][246/391] Loss_D: 3.0922 Loss_G: 3.5679 D(x): 0.5908 D(G(z)): 0.3819 / 0.3349\n", + "[95/100][247/391] Loss_D: 2.9818 Loss_G: 2.5436 D(x): 0.5870 D(G(z)): 0.3540 / 0.4512\n", + "[95/100][248/391] Loss_D: 2.5366 Loss_G: 2.7698 D(x): 0.7127 D(G(z)): 0.4427 / 0.4425\n", + "[95/100][249/391] Loss_D: 3.1081 Loss_G: 1.8070 D(x): 0.6040 D(G(z)): 0.4253 / 0.5814\n", + "[95/100][250/391] Loss_D: 3.4229 Loss_G: 3.0852 D(x): 0.5857 D(G(z)): 0.4459 / 0.3927\n", + "[95/100][251/391] Loss_D: 2.7542 Loss_G: 2.2299 D(x): 0.7156 D(G(z)): 0.4212 / 0.5136\n", + "[95/100][252/391] Loss_D: 2.7254 Loss_G: 2.6210 D(x): 0.7183 D(G(z)): 0.4345 / 0.4465\n", + "[95/100][253/391] Loss_D: 2.7592 Loss_G: 2.9535 D(x): 0.6957 D(G(z)): 0.4142 / 0.4021\n", + "[95/100][254/391] Loss_D: 2.1342 Loss_G: 2.8378 D(x): 0.7778 D(G(z)): 0.3968 / 0.4312\n", + "[95/100][255/391] Loss_D: 3.0278 Loss_G: 2.5805 D(x): 0.6489 D(G(z)): 0.4431 / 0.4439\n", + "[95/100][256/391] Loss_D: 2.4781 Loss_G: 4.8529 D(x): 0.6776 D(G(z)): 0.3602 / 0.2198\n", + "[95/100][257/391] Loss_D: 3.7053 Loss_G: 2.4901 D(x): 0.5252 D(G(z)): 0.4570 / 0.4734\n", + "[95/100][258/391] Loss_D: 2.8052 Loss_G: 2.1026 D(x): 0.6498 D(G(z)): 0.3908 / 0.5066\n", + "[95/100][259/391] Loss_D: 3.0837 Loss_G: 2.1785 D(x): 0.6126 D(G(z)): 0.4620 / 0.5081\n", + "[95/100][260/391] Loss_D: 3.1884 Loss_G: 2.6214 D(x): 0.6822 D(G(z)): 0.5273 / 0.4469\n", + "[95/100][261/391] Loss_D: 3.0829 Loss_G: 1.9856 D(x): 0.6377 D(G(z)): 0.4406 / 0.5565\n", + "[95/100][262/391] Loss_D: 3.5292 Loss_G: 2.5957 D(x): 0.6174 D(G(z)): 0.4980 / 0.4628\n", + "[95/100][263/391] Loss_D: 3.1739 Loss_G: 2.4441 D(x): 0.6239 D(G(z)): 0.4566 / 0.4637\n", + "[95/100][264/391] Loss_D: 2.6856 Loss_G: 2.9060 D(x): 0.6459 D(G(z)): 0.4106 / 0.4073\n", + "[95/100][265/391] Loss_D: 3.1118 Loss_G: 2.9570 D(x): 0.5896 D(G(z)): 0.4278 / 0.4166\n", + "[95/100][266/391] Loss_D: 3.2014 Loss_G: 2.8229 D(x): 0.6392 D(G(z)): 0.4272 / 0.4229\n", + "[95/100][267/391] Loss_D: 2.5579 Loss_G: 3.3133 D(x): 0.7183 D(G(z)): 0.3946 / 0.3678\n", + "[95/100][268/391] Loss_D: 2.3097 Loss_G: 1.9287 D(x): 0.6860 D(G(z)): 0.3444 / 0.5416\n", + "[95/100][269/391] Loss_D: 3.5082 Loss_G: 2.1521 D(x): 0.6016 D(G(z)): 0.4977 / 0.5253\n", + "[95/100][270/391] Loss_D: 2.7762 Loss_G: 2.3938 D(x): 0.6903 D(G(z)): 0.4435 / 0.5027\n", + "[95/100][271/391] Loss_D: 4.1298 Loss_G: 2.7068 D(x): 0.4641 D(G(z)): 0.5511 / 0.4472\n", + "[95/100][272/391] Loss_D: 2.9028 Loss_G: 2.0169 D(x): 0.7151 D(G(z)): 0.4774 / 0.5402\n", + "[95/100][273/391] Loss_D: 2.7938 Loss_G: 2.8213 D(x): 0.6862 D(G(z)): 0.4649 / 0.4136\n", + "[95/100][274/391] Loss_D: 2.9176 Loss_G: 2.8533 D(x): 0.6636 D(G(z)): 0.4497 / 0.4008\n", + "[95/100][275/391] Loss_D: 2.7677 Loss_G: 2.2343 D(x): 0.7166 D(G(z)): 0.4232 / 0.4991\n", + "[95/100][276/391] Loss_D: 2.5600 Loss_G: 2.8851 D(x): 0.6896 D(G(z)): 0.3539 / 0.4135\n", + "[95/100][277/391] Loss_D: 2.9771 Loss_G: 2.6581 D(x): 0.6563 D(G(z)): 0.4547 / 0.4411\n", + "[95/100][278/391] Loss_D: 2.5106 Loss_G: 2.1390 D(x): 0.6599 D(G(z)): 0.3597 / 0.5196\n", + "[95/100][279/391] Loss_D: 2.5890 Loss_G: 2.8424 D(x): 0.6975 D(G(z)): 0.4131 / 0.4276\n", + "[95/100][280/391] Loss_D: 3.1059 Loss_G: 2.9363 D(x): 0.6793 D(G(z)): 0.5113 / 0.4060\n", + "[95/100][281/391] Loss_D: 2.9187 Loss_G: 2.4961 D(x): 0.6756 D(G(z)): 0.4558 / 0.4797\n", + "[95/100][282/391] Loss_D: 3.2034 Loss_G: 2.8124 D(x): 0.6096 D(G(z)): 0.4590 / 0.4347\n", + "[95/100][283/391] Loss_D: 3.0931 Loss_G: 3.6564 D(x): 0.5928 D(G(z)): 0.4085 / 0.3364\n", + "[95/100][284/391] Loss_D: 2.7947 Loss_G: 2.7275 D(x): 0.6114 D(G(z)): 0.3844 / 0.4340\n", + "[95/100][285/391] Loss_D: 2.7289 Loss_G: 2.5984 D(x): 0.7360 D(G(z)): 0.4516 / 0.4533\n", + "[95/100][286/391] Loss_D: 3.0221 Loss_G: 2.4669 D(x): 0.6806 D(G(z)): 0.4518 / 0.4676\n", + "[95/100][287/391] Loss_D: 2.9412 Loss_G: 3.2947 D(x): 0.6549 D(G(z)): 0.4130 / 0.3597\n", + "[95/100][288/391] Loss_D: 2.8529 Loss_G: 3.4287 D(x): 0.6524 D(G(z)): 0.4441 / 0.3416\n", + "[95/100][289/391] Loss_D: 2.4751 Loss_G: 3.1618 D(x): 0.7340 D(G(z)): 0.3595 / 0.3881\n", + "[95/100][290/391] Loss_D: 2.6715 Loss_G: 3.3331 D(x): 0.6891 D(G(z)): 0.3709 / 0.3555\n", + "[95/100][291/391] Loss_D: 3.1869 Loss_G: 2.3582 D(x): 0.5522 D(G(z)): 0.3436 / 0.4902\n", + "[95/100][292/391] Loss_D: 4.1732 Loss_G: 1.9493 D(x): 0.6235 D(G(z)): 0.6287 / 0.5589\n", + "[95/100][293/391] Loss_D: 2.6704 Loss_G: 2.2512 D(x): 0.7433 D(G(z)): 0.4738 / 0.4879\n", + "[95/100][294/391] Loss_D: 3.0309 Loss_G: 3.4339 D(x): 0.6277 D(G(z)): 0.4610 / 0.3596\n", + "[95/100][295/391] Loss_D: 2.4478 Loss_G: 3.0126 D(x): 0.7226 D(G(z)): 0.3474 / 0.4120\n", + "[95/100][296/391] Loss_D: 2.9916 Loss_G: 2.2816 D(x): 0.7033 D(G(z)): 0.4882 / 0.4877\n", + "[95/100][297/391] Loss_D: 2.8091 Loss_G: 3.4463 D(x): 0.6916 D(G(z)): 0.3811 / 0.3338\n", + "[95/100][298/391] Loss_D: 2.8512 Loss_G: 2.5548 D(x): 0.6984 D(G(z)): 0.4989 / 0.4749\n", + "[95/100][299/391] Loss_D: 2.9213 Loss_G: 3.2072 D(x): 0.6805 D(G(z)): 0.4276 / 0.3912\n", + "[95/100][300/391] Loss_D: 3.4608 Loss_G: 3.2404 D(x): 0.5556 D(G(z)): 0.4532 / 0.3936\n", + "[95/100][301/391] Loss_D: 3.6047 Loss_G: 2.2307 D(x): 0.6304 D(G(z)): 0.3696 / 0.5129\n", + "[95/100][302/391] Loss_D: 2.5578 Loss_G: 2.2099 D(x): 0.6532 D(G(z)): 0.3668 / 0.4968\n", + "[95/100][303/391] Loss_D: 2.9269 Loss_G: 2.6175 D(x): 0.6894 D(G(z)): 0.4881 / 0.4581\n", + "[95/100][304/391] Loss_D: 2.4510 Loss_G: 2.2771 D(x): 0.6756 D(G(z)): 0.4155 / 0.5157\n", + "[95/100][305/391] Loss_D: 2.7478 Loss_G: 2.3707 D(x): 0.7301 D(G(z)): 0.4410 / 0.4702\n", + "[95/100][306/391] Loss_D: 3.0055 Loss_G: 2.9791 D(x): 0.6294 D(G(z)): 0.4084 / 0.4021\n", + "[95/100][307/391] Loss_D: 2.8780 Loss_G: 3.0255 D(x): 0.6243 D(G(z)): 0.3962 / 0.3906\n", + "[95/100][308/391] Loss_D: 2.4325 Loss_G: 2.3854 D(x): 0.7129 D(G(z)): 0.3565 / 0.4925\n", + "[95/100][309/391] Loss_D: 2.7865 Loss_G: 3.8783 D(x): 0.6909 D(G(z)): 0.4713 / 0.3238\n", + "[95/100][310/391] Loss_D: 3.1048 Loss_G: 3.2651 D(x): 0.6116 D(G(z)): 0.4030 / 0.3721\n", + "[95/100][311/391] Loss_D: 3.3545 Loss_G: 2.8823 D(x): 0.6844 D(G(z)): 0.5107 / 0.4209\n", + "[95/100][312/391] Loss_D: 2.2882 Loss_G: 3.2046 D(x): 0.7401 D(G(z)): 0.3351 / 0.3812\n", + "[95/100][313/391] Loss_D: 2.7925 Loss_G: 3.1609 D(x): 0.6762 D(G(z)): 0.4552 / 0.3763\n", + "[95/100][314/391] Loss_D: 3.1858 Loss_G: 2.9640 D(x): 0.6462 D(G(z)): 0.5028 / 0.4055\n", + "[95/100][315/391] Loss_D: 2.6996 Loss_G: 2.8246 D(x): 0.6625 D(G(z)): 0.3686 / 0.4287\n", + "[95/100][316/391] Loss_D: 2.7296 Loss_G: 2.4921 D(x): 0.6848 D(G(z)): 0.3844 / 0.4478\n", + "[95/100][317/391] Loss_D: 2.9532 Loss_G: 3.1968 D(x): 0.5834 D(G(z)): 0.3179 / 0.3601\n", + "[95/100][318/391] Loss_D: 2.7565 Loss_G: 2.9548 D(x): 0.6408 D(G(z)): 0.4004 / 0.3968\n", + "[95/100][319/391] Loss_D: 2.7641 Loss_G: 2.4315 D(x): 0.7469 D(G(z)): 0.4908 / 0.4932\n", + "[95/100][320/391] Loss_D: 2.8938 Loss_G: 3.0501 D(x): 0.7555 D(G(z)): 0.5039 / 0.3963\n", + "[95/100][321/391] Loss_D: 3.2463 Loss_G: 3.2442 D(x): 0.6968 D(G(z)): 0.5117 / 0.3811\n", + "[95/100][322/391] Loss_D: 2.7265 Loss_G: 3.1786 D(x): 0.6508 D(G(z)): 0.3921 / 0.3860\n", + "[95/100][323/391] Loss_D: 3.4147 Loss_G: 2.7128 D(x): 0.5447 D(G(z)): 0.4399 / 0.4467\n", + "[95/100][324/391] Loss_D: 2.7882 Loss_G: 3.9295 D(x): 0.6503 D(G(z)): 0.4192 / 0.3089\n", + "[95/100][325/391] Loss_D: 2.6514 Loss_G: 3.1612 D(x): 0.6531 D(G(z)): 0.3157 / 0.3893\n", + "[95/100][326/391] Loss_D: 2.6103 Loss_G: 3.6015 D(x): 0.7692 D(G(z)): 0.4495 / 0.3348\n", + "[95/100][327/391] Loss_D: 2.8088 Loss_G: 3.5014 D(x): 0.6767 D(G(z)): 0.4276 / 0.3414\n", + "[95/100][328/391] Loss_D: 2.5445 Loss_G: 2.0569 D(x): 0.6498 D(G(z)): 0.3313 / 0.5376\n", + "[95/100][329/391] Loss_D: 2.8484 Loss_G: 3.3986 D(x): 0.6980 D(G(z)): 0.4649 / 0.3717\n", + "[95/100][330/391] Loss_D: 2.4860 Loss_G: 2.5604 D(x): 0.7543 D(G(z)): 0.3872 / 0.4573\n", + "[95/100][331/391] Loss_D: 3.4529 Loss_G: 3.9464 D(x): 0.6351 D(G(z)): 0.3553 / 0.3112\n", + "[95/100][332/391] Loss_D: 2.5251 Loss_G: 2.4876 D(x): 0.7107 D(G(z)): 0.4090 / 0.4794\n", + "[95/100][333/391] Loss_D: 3.1487 Loss_G: 3.4665 D(x): 0.6088 D(G(z)): 0.4067 / 0.3399\n", + "[95/100][334/391] Loss_D: 2.5817 Loss_G: 2.7502 D(x): 0.7055 D(G(z)): 0.4507 / 0.4350\n", + "[95/100][335/391] Loss_D: 3.1160 Loss_G: 2.5943 D(x): 0.6818 D(G(z)): 0.4991 / 0.4427\n", + "[95/100][336/391] Loss_D: 3.6379 Loss_G: 3.8916 D(x): 0.6035 D(G(z)): 0.4848 / 0.2973\n", + "[95/100][337/391] Loss_D: 2.8280 Loss_G: 2.8222 D(x): 0.6138 D(G(z)): 0.3524 / 0.4057\n", + "[95/100][338/391] Loss_D: 2.6981 Loss_G: 2.0944 D(x): 0.6403 D(G(z)): 0.4020 / 0.5413\n", + "[95/100][339/391] Loss_D: 2.7362 Loss_G: 2.5401 D(x): 0.7187 D(G(z)): 0.4428 / 0.4598\n", + "[95/100][340/391] Loss_D: 3.1979 Loss_G: 2.7646 D(x): 0.6176 D(G(z)): 0.4774 / 0.4351\n", + "[95/100][341/391] Loss_D: 2.4375 Loss_G: 2.4998 D(x): 0.7359 D(G(z)): 0.3792 / 0.4586\n", + "[95/100][342/391] Loss_D: 2.7560 Loss_G: 2.3085 D(x): 0.6856 D(G(z)): 0.4328 / 0.4803\n", + "[95/100][343/391] Loss_D: 2.9129 Loss_G: 2.9747 D(x): 0.6202 D(G(z)): 0.3269 / 0.4115\n", + "[95/100][344/391] Loss_D: 2.1434 Loss_G: 4.2495 D(x): 0.7418 D(G(z)): 0.3244 / 0.2718\n", + "[95/100][345/391] Loss_D: 3.0973 Loss_G: 2.3863 D(x): 0.7030 D(G(z)): 0.5231 / 0.4907\n", + "[95/100][346/391] Loss_D: 2.4991 Loss_G: 3.5179 D(x): 0.7327 D(G(z)): 0.3621 / 0.3430\n", + "[95/100][347/391] Loss_D: 3.2032 Loss_G: 2.8281 D(x): 0.5814 D(G(z)): 0.4260 / 0.4232\n", + "[95/100][348/391] Loss_D: 2.7902 Loss_G: 2.3225 D(x): 0.5866 D(G(z)): 0.2971 / 0.4915\n", + "[95/100][349/391] Loss_D: 2.9949 Loss_G: 2.4920 D(x): 0.6807 D(G(z)): 0.5348 / 0.4666\n", + "[95/100][350/391] Loss_D: 2.6741 Loss_G: 2.1697 D(x): 0.7389 D(G(z)): 0.4719 / 0.5266\n", + "[95/100][351/391] Loss_D: 2.9395 Loss_G: 3.0661 D(x): 0.7423 D(G(z)): 0.4752 / 0.3980\n", + "[95/100][352/391] Loss_D: 2.3983 Loss_G: 3.6592 D(x): 0.7801 D(G(z)): 0.4595 / 0.3254\n", + "[95/100][353/391] Loss_D: 2.9644 Loss_G: 2.7357 D(x): 0.6028 D(G(z)): 0.3529 / 0.4265\n", + "[95/100][354/391] Loss_D: 2.5723 Loss_G: 2.8572 D(x): 0.6409 D(G(z)): 0.3914 / 0.4127\n", + "[95/100][355/391] Loss_D: 2.8988 Loss_G: 3.2450 D(x): 0.6062 D(G(z)): 0.4168 / 0.3817\n", + "[95/100][356/391] Loss_D: 2.8385 Loss_G: 3.2205 D(x): 0.6738 D(G(z)): 0.4356 / 0.3828\n", + "[95/100][357/391] Loss_D: 3.5214 Loss_G: 2.3616 D(x): 0.5395 D(G(z)): 0.4591 / 0.4911\n", + "[95/100][358/391] Loss_D: 2.5814 Loss_G: 2.7769 D(x): 0.7000 D(G(z)): 0.4313 / 0.4233\n", + "[95/100][359/391] Loss_D: 2.4995 Loss_G: 3.1857 D(x): 0.7732 D(G(z)): 0.4298 / 0.3817\n", + "[95/100][360/391] Loss_D: 2.7149 Loss_G: 2.9378 D(x): 0.6393 D(G(z)): 0.3519 / 0.4137\n", + "[95/100][361/391] Loss_D: 3.6397 Loss_G: 2.2209 D(x): 0.7520 D(G(z)): 0.4087 / 0.5089\n", + "[95/100][362/391] Loss_D: 2.5641 Loss_G: 2.6958 D(x): 0.7132 D(G(z)): 0.3610 / 0.4375\n", + "[95/100][363/391] Loss_D: 3.0779 Loss_G: 3.1115 D(x): 0.6174 D(G(z)): 0.4293 / 0.3766\n", + "[95/100][364/391] Loss_D: 2.5967 Loss_G: 2.5802 D(x): 0.7396 D(G(z)): 0.3838 / 0.4399\n", + "[95/100][365/391] Loss_D: 2.8421 Loss_G: 2.5051 D(x): 0.6646 D(G(z)): 0.4131 / 0.4793\n", + "[95/100][366/391] Loss_D: 2.8317 Loss_G: 3.8838 D(x): 0.6542 D(G(z)): 0.4525 / 0.3014\n", + "[95/100][367/391] Loss_D: 2.9384 Loss_G: 2.1539 D(x): 0.6355 D(G(z)): 0.4015 / 0.5188\n", + "[95/100][368/391] Loss_D: 2.4366 Loss_G: 3.2295 D(x): 0.6826 D(G(z)): 0.3333 / 0.3664\n", + "[95/100][369/391] Loss_D: 3.0386 Loss_G: 2.8446 D(x): 0.6612 D(G(z)): 0.5188 / 0.4256\n", + "[95/100][370/391] Loss_D: 2.7455 Loss_G: 2.5757 D(x): 0.7535 D(G(z)): 0.4905 / 0.4700\n", + "[95/100][371/391] Loss_D: 2.3975 Loss_G: 2.5238 D(x): 0.6550 D(G(z)): 0.2631 / 0.4733\n", + "[95/100][372/391] Loss_D: 2.7008 Loss_G: 2.5092 D(x): 0.6412 D(G(z)): 0.3820 / 0.4580\n", + "[95/100][373/391] Loss_D: 2.6424 Loss_G: 2.4825 D(x): 0.6729 D(G(z)): 0.3626 / 0.4706\n", + "[95/100][374/391] Loss_D: 2.6132 Loss_G: 2.7096 D(x): 0.6770 D(G(z)): 0.3775 / 0.4223\n", + "[95/100][375/391] Loss_D: 3.0771 Loss_G: 1.9416 D(x): 0.7020 D(G(z)): 0.5003 / 0.5561\n", + "[95/100][376/391] Loss_D: 2.7347 Loss_G: 2.2387 D(x): 0.6768 D(G(z)): 0.3599 / 0.5195\n", + "[95/100][377/391] Loss_D: 2.5595 Loss_G: 2.2885 D(x): 0.7549 D(G(z)): 0.3765 / 0.5019\n", + "[95/100][378/391] Loss_D: 2.5195 Loss_G: 3.1524 D(x): 0.7305 D(G(z)): 0.4777 / 0.3724\n", + "[95/100][379/391] Loss_D: 3.2055 Loss_G: 2.3880 D(x): 0.6396 D(G(z)): 0.5034 / 0.4789\n", + "[95/100][380/391] Loss_D: 2.4194 Loss_G: 2.6310 D(x): 0.6955 D(G(z)): 0.3571 / 0.4508\n", + "[95/100][381/391] Loss_D: 2.5389 Loss_G: 2.5628 D(x): 0.7611 D(G(z)): 0.4359 / 0.4715\n", + "[95/100][382/391] Loss_D: 2.7252 Loss_G: 2.2001 D(x): 0.6749 D(G(z)): 0.4126 / 0.5108\n", + "[95/100][383/391] Loss_D: 3.0703 Loss_G: 2.5071 D(x): 0.6233 D(G(z)): 0.4233 / 0.4791\n", + "[95/100][384/391] Loss_D: 2.9156 Loss_G: 3.0970 D(x): 0.5830 D(G(z)): 0.4041 / 0.3855\n", + "[95/100][385/391] Loss_D: 3.4058 Loss_G: 3.0151 D(x): 0.7019 D(G(z)): 0.5964 / 0.3950\n", + "[95/100][386/391] Loss_D: 2.7751 Loss_G: 3.6783 D(x): 0.6657 D(G(z)): 0.3592 / 0.3301\n", + "[95/100][387/391] Loss_D: 2.8007 Loss_G: 2.0490 D(x): 0.6621 D(G(z)): 0.3787 / 0.5290\n", + "[95/100][388/391] Loss_D: 2.5483 Loss_G: 3.0192 D(x): 0.7289 D(G(z)): 0.4829 / 0.4014\n", + "[95/100][389/391] Loss_D: 3.0092 Loss_G: 3.2913 D(x): 0.6944 D(G(z)): 0.4795 / 0.3745\n", + "[95/100][390/391] Loss_D: 2.6389 Loss_G: 3.2548 D(x): 0.7044 D(G(z)): 0.4573 / 0.3891\n", + "[95/100][391/391] Loss_D: 3.7792 Loss_G: 4.0371 D(x): 0.7031 D(G(z)): 0.3540 / 0.2983\n", + "[96/100][1/391] Loss_D: 3.5484 Loss_G: 2.6238 D(x): 0.6123 D(G(z)): 0.4106 / 0.4630\n", + "[96/100][2/391] Loss_D: 3.2478 Loss_G: 2.4548 D(x): 0.5060 D(G(z)): 0.3380 / 0.4682\n", + "[96/100][3/391] Loss_D: 2.7356 Loss_G: 2.0123 D(x): 0.6159 D(G(z)): 0.3408 / 0.5326\n", + "[96/100][4/391] Loss_D: 2.3370 Loss_G: 2.6130 D(x): 0.7889 D(G(z)): 0.4412 / 0.4519\n", + "[96/100][5/391] Loss_D: 2.9239 Loss_G: 2.9252 D(x): 0.6196 D(G(z)): 0.3953 / 0.4048\n", + "[96/100][6/391] Loss_D: 3.0300 Loss_G: 3.3808 D(x): 0.7361 D(G(z)): 0.4919 / 0.3564\n", + "[96/100][7/391] Loss_D: 2.6941 Loss_G: 2.2361 D(x): 0.7011 D(G(z)): 0.3598 / 0.4944\n", + "[96/100][8/391] Loss_D: 2.5459 Loss_G: 3.0339 D(x): 0.6767 D(G(z)): 0.4253 / 0.4002\n", + "[96/100][9/391] Loss_D: 2.5338 Loss_G: 2.7220 D(x): 0.7283 D(G(z)): 0.4425 / 0.4480\n", + "[96/100][10/391] Loss_D: 3.1105 Loss_G: 2.8656 D(x): 0.6375 D(G(z)): 0.4852 / 0.4098\n", + "[96/100][11/391] Loss_D: 3.5505 Loss_G: 2.7395 D(x): 0.6276 D(G(z)): 0.5485 / 0.4424\n", + "[96/100][12/391] Loss_D: 3.3368 Loss_G: 2.9962 D(x): 0.5361 D(G(z)): 0.4002 / 0.3939\n", + "[96/100][13/391] Loss_D: 2.5191 Loss_G: 3.0606 D(x): 0.7907 D(G(z)): 0.4264 / 0.3890\n", + "[96/100][14/391] Loss_D: 3.0109 Loss_G: 2.6814 D(x): 0.5754 D(G(z)): 0.4030 / 0.4435\n", + "[96/100][15/391] Loss_D: 2.5432 Loss_G: 2.4696 D(x): 0.6286 D(G(z)): 0.3446 / 0.4652\n", + "[96/100][16/391] Loss_D: 3.0194 Loss_G: 2.3703 D(x): 0.6975 D(G(z)): 0.4734 / 0.4701\n", + "[96/100][17/391] Loss_D: 2.9102 Loss_G: 2.5697 D(x): 0.6664 D(G(z)): 0.4680 / 0.4510\n", + "[96/100][18/391] Loss_D: 2.5908 Loss_G: 3.0163 D(x): 0.6805 D(G(z)): 0.4019 / 0.3918\n", + "[96/100][19/391] Loss_D: 3.6219 Loss_G: 3.0999 D(x): 0.5784 D(G(z)): 0.5383 / 0.3918\n", + "[96/100][20/391] Loss_D: 2.7750 Loss_G: 3.2634 D(x): 0.6008 D(G(z)): 0.3528 / 0.3775\n", + "[96/100][21/391] Loss_D: 3.1983 Loss_G: 2.7163 D(x): 0.7205 D(G(z)): 0.5776 / 0.4294\n", + "[96/100][22/391] Loss_D: 2.6778 Loss_G: 4.2720 D(x): 0.7432 D(G(z)): 0.4629 / 0.2857\n", + "[96/100][23/391] Loss_D: 2.9685 Loss_G: 2.7969 D(x): 0.5876 D(G(z)): 0.3889 / 0.4258\n", + "[96/100][24/391] Loss_D: 2.6981 Loss_G: 2.3372 D(x): 0.6699 D(G(z)): 0.3985 / 0.4827\n", + "[96/100][25/391] Loss_D: 3.0175 Loss_G: 3.6731 D(x): 0.6509 D(G(z)): 0.4416 / 0.3343\n", + "[96/100][26/391] Loss_D: 2.8532 Loss_G: 3.5019 D(x): 0.7163 D(G(z)): 0.4596 / 0.3449\n", + "[96/100][27/391] Loss_D: 3.0904 Loss_G: 2.7969 D(x): 0.6756 D(G(z)): 0.4445 / 0.4299\n", + "[96/100][28/391] Loss_D: 3.4011 Loss_G: 3.2791 D(x): 0.5269 D(G(z)): 0.4007 / 0.3676\n", + "[96/100][29/391] Loss_D: 2.6694 Loss_G: 2.6566 D(x): 0.6603 D(G(z)): 0.3420 / 0.4552\n", + "[96/100][30/391] Loss_D: 2.8286 Loss_G: 3.6372 D(x): 0.7030 D(G(z)): 0.4932 / 0.3552\n", + "[96/100][31/391] Loss_D: 3.6275 Loss_G: 3.7789 D(x): 0.6871 D(G(z)): 0.4883 / 0.3361\n", + "[96/100][32/391] Loss_D: 2.6739 Loss_G: 2.6568 D(x): 0.7268 D(G(z)): 0.4191 / 0.4626\n", + "[96/100][33/391] Loss_D: 3.0631 Loss_G: 2.8274 D(x): 0.6741 D(G(z)): 0.4679 / 0.4196\n", + "[96/100][34/391] Loss_D: 2.4938 Loss_G: 2.3886 D(x): 0.6651 D(G(z)): 0.3641 / 0.4634\n", + "[96/100][35/391] Loss_D: 3.4942 Loss_G: 2.4454 D(x): 0.5404 D(G(z)): 0.4759 / 0.4589\n", + "[96/100][36/391] Loss_D: 3.2999 Loss_G: 3.1243 D(x): 0.5681 D(G(z)): 0.4202 / 0.3819\n", + "[96/100][37/391] Loss_D: 2.8077 Loss_G: 3.2723 D(x): 0.7432 D(G(z)): 0.4837 / 0.3560\n", + "[96/100][38/391] Loss_D: 3.0623 Loss_G: 2.6459 D(x): 0.5722 D(G(z)): 0.3540 / 0.4467\n", + "[96/100][39/391] Loss_D: 2.9258 Loss_G: 2.4685 D(x): 0.7067 D(G(z)): 0.4621 / 0.4700\n", + "[96/100][40/391] Loss_D: 3.0908 Loss_G: 3.0377 D(x): 0.6530 D(G(z)): 0.3982 / 0.3909\n", + "[96/100][41/391] Loss_D: 3.0983 Loss_G: 2.9486 D(x): 0.6902 D(G(z)): 0.4771 / 0.4083\n", + "[96/100][42/391] Loss_D: 2.8352 Loss_G: 2.8126 D(x): 0.6510 D(G(z)): 0.4265 / 0.4277\n", + "[96/100][43/391] Loss_D: 2.8089 Loss_G: 2.5516 D(x): 0.6858 D(G(z)): 0.4135 / 0.4745\n", + "[96/100][44/391] Loss_D: 2.8439 Loss_G: 2.7957 D(x): 0.6208 D(G(z)): 0.4281 / 0.4175\n", + "[96/100][45/391] Loss_D: 3.1239 Loss_G: 3.2408 D(x): 0.6637 D(G(z)): 0.4801 / 0.3868\n", + "[96/100][46/391] Loss_D: 2.4332 Loss_G: 2.9450 D(x): 0.7510 D(G(z)): 0.3581 / 0.3944\n", + "[96/100][47/391] Loss_D: 2.5106 Loss_G: 2.5560 D(x): 0.6921 D(G(z)): 0.3752 / 0.4564\n", + "[96/100][48/391] Loss_D: 2.3162 Loss_G: 2.8917 D(x): 0.7260 D(G(z)): 0.3655 / 0.4283\n", + "[96/100][49/391] Loss_D: 2.1014 Loss_G: 2.2308 D(x): 0.7905 D(G(z)): 0.3751 / 0.5068\n", + "[96/100][50/391] Loss_D: 2.2050 Loss_G: 2.9350 D(x): 0.7359 D(G(z)): 0.2750 / 0.4269\n", + "[96/100][51/391] Loss_D: 2.8194 Loss_G: 2.8080 D(x): 0.6439 D(G(z)): 0.3822 / 0.4297\n", + "[96/100][52/391] Loss_D: 3.1635 Loss_G: 2.7979 D(x): 0.6437 D(G(z)): 0.5021 / 0.4415\n", + "[96/100][53/391] Loss_D: 2.6580 Loss_G: 2.9440 D(x): 0.6261 D(G(z)): 0.3694 / 0.4059\n", + "[96/100][54/391] Loss_D: 2.8634 Loss_G: 2.7536 D(x): 0.6420 D(G(z)): 0.4272 / 0.4274\n", + "[96/100][55/391] Loss_D: 3.0705 Loss_G: 2.2717 D(x): 0.6068 D(G(z)): 0.4511 / 0.4996\n", + "[96/100][56/391] Loss_D: 3.2473 Loss_G: 2.7050 D(x): 0.7140 D(G(z)): 0.5470 / 0.4387\n", + "[96/100][57/391] Loss_D: 3.2985 Loss_G: 2.4651 D(x): 0.5289 D(G(z)): 0.4048 / 0.4742\n", + "[96/100][58/391] Loss_D: 2.8142 Loss_G: 3.1449 D(x): 0.6840 D(G(z)): 0.4665 / 0.3893\n", + "[96/100][59/391] Loss_D: 2.5878 Loss_G: 1.8050 D(x): 0.7216 D(G(z)): 0.4322 / 0.5920\n", + "[96/100][60/391] Loss_D: 3.1599 Loss_G: 3.3497 D(x): 0.6526 D(G(z)): 0.5033 / 0.3730\n", + "[96/100][61/391] Loss_D: 3.6877 Loss_G: 4.0651 D(x): 0.6847 D(G(z)): 0.5122 / 0.2846\n", + "[96/100][62/391] Loss_D: 3.3941 Loss_G: 3.1152 D(x): 0.5956 D(G(z)): 0.4679 / 0.3898\n", + "[96/100][63/391] Loss_D: 2.9579 Loss_G: 2.7053 D(x): 0.7292 D(G(z)): 0.5069 / 0.4378\n", + "[96/100][64/391] Loss_D: 2.7673 Loss_G: 3.1251 D(x): 0.5925 D(G(z)): 0.3528 / 0.3876\n", + "[96/100][65/391] Loss_D: 3.3652 Loss_G: 3.0465 D(x): 0.6540 D(G(z)): 0.5713 / 0.4046\n", + "[96/100][66/391] Loss_D: 3.0436 Loss_G: 2.5604 D(x): 0.5798 D(G(z)): 0.3992 / 0.4673\n", + "[96/100][67/391] Loss_D: 3.0158 Loss_G: 2.5465 D(x): 0.6846 D(G(z)): 0.3891 / 0.4760\n", + "[96/100][68/391] Loss_D: 3.0985 Loss_G: 2.6119 D(x): 0.6021 D(G(z)): 0.4643 / 0.4609\n", + "[96/100][69/391] Loss_D: 2.8638 Loss_G: 2.8993 D(x): 0.6660 D(G(z)): 0.4412 / 0.4142\n", + "[96/100][70/391] Loss_D: 2.8854 Loss_G: 2.9847 D(x): 0.7062 D(G(z)): 0.4371 / 0.4104\n", + "[96/100][71/391] Loss_D: 3.9234 Loss_G: 3.3204 D(x): 0.5259 D(G(z)): 0.5146 / 0.3622\n", + "[96/100][72/391] Loss_D: 2.2797 Loss_G: 2.2409 D(x): 0.7629 D(G(z)): 0.3620 / 0.5039\n", + "[96/100][73/391] Loss_D: 3.1219 Loss_G: 2.3706 D(x): 0.6401 D(G(z)): 0.4776 / 0.4868\n", + "[96/100][74/391] Loss_D: 3.1173 Loss_G: 3.1886 D(x): 0.6523 D(G(z)): 0.4938 / 0.3726\n", + "[96/100][75/391] Loss_D: 2.9452 Loss_G: 2.8338 D(x): 0.6240 D(G(z)): 0.4330 / 0.4106\n", + "[96/100][76/391] Loss_D: 3.4244 Loss_G: 3.2292 D(x): 0.5897 D(G(z)): 0.4966 / 0.3572\n", + "[96/100][77/391] Loss_D: 2.8083 Loss_G: 4.2098 D(x): 0.6322 D(G(z)): 0.3512 / 0.2720\n", + "[96/100][78/391] Loss_D: 4.0456 Loss_G: 1.8345 D(x): 0.4529 D(G(z)): 0.4245 / 0.5637\n", + "[96/100][79/391] Loss_D: 3.5276 Loss_G: 2.2227 D(x): 0.7406 D(G(z)): 0.6563 / 0.5125\n", + "[96/100][80/391] Loss_D: 3.5293 Loss_G: 2.3585 D(x): 0.6950 D(G(z)): 0.5652 / 0.4890\n", + "[96/100][81/391] Loss_D: 2.7851 Loss_G: 2.8701 D(x): 0.7037 D(G(z)): 0.4567 / 0.4236\n", + "[96/100][82/391] Loss_D: 3.0409 Loss_G: 2.4490 D(x): 0.6491 D(G(z)): 0.4405 / 0.4859\n", + "[96/100][83/391] Loss_D: 3.0044 Loss_G: 2.7135 D(x): 0.6467 D(G(z)): 0.4330 / 0.4504\n", + "[96/100][84/391] Loss_D: 3.2380 Loss_G: 2.9678 D(x): 0.6046 D(G(z)): 0.4828 / 0.4095\n", + "[96/100][85/391] Loss_D: 3.3978 Loss_G: 2.7590 D(x): 0.5179 D(G(z)): 0.3724 / 0.4229\n", + "[96/100][86/391] Loss_D: 3.1650 Loss_G: 2.9923 D(x): 0.6455 D(G(z)): 0.4694 / 0.3813\n", + "[96/100][87/391] Loss_D: 2.9269 Loss_G: 3.8294 D(x): 0.7738 D(G(z)): 0.4492 / 0.3025\n", + "[96/100][88/391] Loss_D: 2.5364 Loss_G: 4.1536 D(x): 0.6776 D(G(z)): 0.3815 / 0.2822\n", + "[96/100][89/391] Loss_D: 3.2401 Loss_G: 3.0851 D(x): 0.6146 D(G(z)): 0.4789 / 0.3933\n", + "[96/100][90/391] Loss_D: 2.6145 Loss_G: 2.8128 D(x): 0.6414 D(G(z)): 0.2823 / 0.4372\n", + "[96/100][91/391] Loss_D: 3.5807 Loss_G: 2.5938 D(x): 0.7000 D(G(z)): 0.5155 / 0.4575\n", + "[96/100][92/391] Loss_D: 3.0567 Loss_G: 2.5347 D(x): 0.6409 D(G(z)): 0.4584 / 0.4675\n", + "[96/100][93/391] Loss_D: 2.5892 Loss_G: 3.6680 D(x): 0.7457 D(G(z)): 0.3738 / 0.3260\n", + "[96/100][94/391] Loss_D: 2.4856 Loss_G: 3.4289 D(x): 0.6456 D(G(z)): 0.3534 / 0.3554\n", + "[96/100][95/391] Loss_D: 2.7334 Loss_G: 3.4252 D(x): 0.6888 D(G(z)): 0.4037 / 0.3432\n", + "[96/100][96/391] Loss_D: 3.0614 Loss_G: 3.3600 D(x): 0.6217 D(G(z)): 0.4093 / 0.3348\n", + "[96/100][97/391] Loss_D: 2.7835 Loss_G: 2.6620 D(x): 0.7028 D(G(z)): 0.4232 / 0.4491\n", + "[96/100][98/391] Loss_D: 2.6457 Loss_G: 3.4127 D(x): 0.7097 D(G(z)): 0.4193 / 0.3469\n", + "[96/100][99/391] Loss_D: 2.9313 Loss_G: 2.3825 D(x): 0.6912 D(G(z)): 0.4593 / 0.4886\n", + "[96/100][100/391] Loss_D: 3.2421 Loss_G: 3.6215 D(x): 0.5557 D(G(z)): 0.4001 / 0.3374\n", + "[96/100][101/391] Loss_D: 2.7904 Loss_G: 3.4458 D(x): 0.7183 D(G(z)): 0.4708 / 0.3449\n", + "[96/100][102/391] Loss_D: 2.7133 Loss_G: 2.8006 D(x): 0.6705 D(G(z)): 0.3615 / 0.4290\n", + "[96/100][103/391] Loss_D: 2.9358 Loss_G: 2.0007 D(x): 0.6326 D(G(z)): 0.4256 / 0.5442\n", + "[96/100][104/391] Loss_D: 2.5415 Loss_G: 3.6411 D(x): 0.6793 D(G(z)): 0.3877 / 0.3336\n", + "[96/100][105/391] Loss_D: 2.9102 Loss_G: 3.0260 D(x): 0.6934 D(G(z)): 0.4430 / 0.3756\n", + "[96/100][106/391] Loss_D: 2.6566 Loss_G: 3.1852 D(x): 0.7037 D(G(z)): 0.4157 / 0.3775\n", + "[96/100][107/391] Loss_D: 2.7993 Loss_G: 3.3359 D(x): 0.6715 D(G(z)): 0.3913 / 0.3429\n", + "[96/100][108/391] Loss_D: 2.8662 Loss_G: 2.4319 D(x): 0.7003 D(G(z)): 0.4735 / 0.4717\n", + "[96/100][109/391] Loss_D: 2.7329 Loss_G: 1.7071 D(x): 0.6365 D(G(z)): 0.3794 / 0.5838\n", + "[96/100][110/391] Loss_D: 2.9408 Loss_G: 3.0338 D(x): 0.6914 D(G(z)): 0.4553 / 0.3965\n", + "[96/100][111/391] Loss_D: 2.8574 Loss_G: 3.0041 D(x): 0.7615 D(G(z)): 0.4928 / 0.4030\n", + "[96/100][112/391] Loss_D: 3.1228 Loss_G: 3.2054 D(x): 0.6131 D(G(z)): 0.4556 / 0.3960\n", + "[96/100][113/391] Loss_D: 2.9703 Loss_G: 2.9346 D(x): 0.7474 D(G(z)): 0.5167 / 0.4042\n", + "[96/100][114/391] Loss_D: 2.8848 Loss_G: 2.9156 D(x): 0.5943 D(G(z)): 0.3719 / 0.4229\n", + "[96/100][115/391] Loss_D: 2.6665 Loss_G: 3.0749 D(x): 0.6769 D(G(z)): 0.3316 / 0.4032\n", + "[96/100][116/391] Loss_D: 2.3108 Loss_G: 2.9077 D(x): 0.7041 D(G(z)): 0.2731 / 0.4174\n", + "[96/100][117/391] Loss_D: 3.3597 Loss_G: 3.0847 D(x): 0.6589 D(G(z)): 0.5244 / 0.3726\n", + "[96/100][118/391] Loss_D: 2.5765 Loss_G: 2.1273 D(x): 0.7192 D(G(z)): 0.4321 / 0.5212\n", + "[96/100][119/391] Loss_D: 2.5962 Loss_G: 2.6969 D(x): 0.6691 D(G(z)): 0.3336 / 0.4465\n", + "[96/100][120/391] Loss_D: 2.8697 Loss_G: 2.6157 D(x): 0.6253 D(G(z)): 0.3896 / 0.4568\n", + "[96/100][121/391] Loss_D: 3.5758 Loss_G: 2.8380 D(x): 0.6315 D(G(z)): 0.3610 / 0.4192\n", + "[96/100][122/391] Loss_D: 3.4797 Loss_G: 2.7288 D(x): 0.5464 D(G(z)): 0.4275 / 0.4206\n", + "[96/100][123/391] Loss_D: 3.0808 Loss_G: 2.7082 D(x): 0.7221 D(G(z)): 0.5151 / 0.4307\n", + "[96/100][124/391] Loss_D: 2.8916 Loss_G: 2.3760 D(x): 0.6590 D(G(z)): 0.4544 / 0.4786\n", + "[96/100][125/391] Loss_D: 2.9449 Loss_G: 2.8827 D(x): 0.6808 D(G(z)): 0.4563 / 0.4197\n", + "[96/100][126/391] Loss_D: 2.7639 Loss_G: 1.9270 D(x): 0.6399 D(G(z)): 0.3831 / 0.5425\n", + "[96/100][127/391] Loss_D: 2.7747 Loss_G: 2.3550 D(x): 0.7257 D(G(z)): 0.4244 / 0.5035\n", + "[96/100][128/391] Loss_D: 2.8525 Loss_G: 2.7178 D(x): 0.6039 D(G(z)): 0.3764 / 0.4409\n", + "[96/100][129/391] Loss_D: 2.6808 Loss_G: 3.0563 D(x): 0.7363 D(G(z)): 0.4672 / 0.3994\n", + "[96/100][130/391] Loss_D: 2.4950 Loss_G: 3.4731 D(x): 0.7502 D(G(z)): 0.3649 / 0.3536\n", + "[96/100][131/391] Loss_D: 2.5932 Loss_G: 2.8346 D(x): 0.7786 D(G(z)): 0.4563 / 0.4363\n", + "[96/100][132/391] Loss_D: 2.4138 Loss_G: 3.0140 D(x): 0.7174 D(G(z)): 0.3209 / 0.4093\n", + "[96/100][133/391] Loss_D: 2.9849 Loss_G: 3.3676 D(x): 0.6660 D(G(z)): 0.5199 / 0.3596\n", + "[96/100][134/391] Loss_D: 2.2956 Loss_G: 3.4772 D(x): 0.7244 D(G(z)): 0.3808 / 0.3509\n", + "[96/100][135/391] Loss_D: 2.4997 Loss_G: 3.8457 D(x): 0.7153 D(G(z)): 0.3852 / 0.3016\n", + "[96/100][136/391] Loss_D: 3.4976 Loss_G: 3.0832 D(x): 0.5447 D(G(z)): 0.4457 / 0.3808\n", + "[96/100][137/391] Loss_D: 3.0165 Loss_G: 3.0909 D(x): 0.5959 D(G(z)): 0.4138 / 0.3897\n", + "[96/100][138/391] Loss_D: 2.6533 Loss_G: 2.8413 D(x): 0.6987 D(G(z)): 0.4662 / 0.4207\n", + "[96/100][139/391] Loss_D: 2.9418 Loss_G: 4.0466 D(x): 0.6623 D(G(z)): 0.4267 / 0.2973\n", + "[96/100][140/391] Loss_D: 2.5134 Loss_G: 2.9970 D(x): 0.6834 D(G(z)): 0.3566 / 0.4073\n", + "[96/100][141/391] Loss_D: 3.1953 Loss_G: 3.3860 D(x): 0.6588 D(G(z)): 0.4967 / 0.3642\n", + "[96/100][142/391] Loss_D: 2.3760 Loss_G: 3.4587 D(x): 0.7615 D(G(z)): 0.3905 / 0.3610\n", + "[96/100][143/391] Loss_D: 2.6704 Loss_G: 4.4065 D(x): 0.6902 D(G(z)): 0.4040 / 0.2680\n", + "[96/100][144/391] Loss_D: 3.2802 Loss_G: 3.5116 D(x): 0.5620 D(G(z)): 0.4450 / 0.3542\n", + "[96/100][145/391] Loss_D: 2.4141 Loss_G: 2.8826 D(x): 0.7636 D(G(z)): 0.3269 / 0.4188\n", + "[96/100][146/391] Loss_D: 2.2622 Loss_G: 2.4998 D(x): 0.7820 D(G(z)): 0.3128 / 0.4584\n", + "[96/100][147/391] Loss_D: 3.2563 Loss_G: 3.7376 D(x): 0.6241 D(G(z)): 0.4467 / 0.3337\n", + "[96/100][148/391] Loss_D: 2.5705 Loss_G: 3.0552 D(x): 0.7454 D(G(z)): 0.5053 / 0.3908\n", + "[96/100][149/391] Loss_D: 2.9263 Loss_G: 3.4446 D(x): 0.7231 D(G(z)): 0.5082 / 0.3630\n", + "[96/100][150/391] Loss_D: 2.4171 Loss_G: 3.3975 D(x): 0.7065 D(G(z)): 0.2985 / 0.3590\n", + "[96/100][151/391] Loss_D: 3.5344 Loss_G: 2.5706 D(x): 0.6632 D(G(z)): 0.3658 / 0.4660\n", + "[96/100][152/391] Loss_D: 2.9530 Loss_G: 2.8234 D(x): 0.6245 D(G(z)): 0.4362 / 0.4335\n", + "[96/100][153/391] Loss_D: 3.1620 Loss_G: 3.1186 D(x): 0.5902 D(G(z)): 0.3967 / 0.3864\n", + "[96/100][154/391] Loss_D: 2.1276 Loss_G: 2.6080 D(x): 0.7783 D(G(z)): 0.3858 / 0.4392\n", + "[96/100][155/391] Loss_D: 3.2340 Loss_G: 3.5324 D(x): 0.5897 D(G(z)): 0.3882 / 0.3457\n", + "[96/100][156/391] Loss_D: 3.0741 Loss_G: 3.5992 D(x): 0.6696 D(G(z)): 0.4912 / 0.3213\n", + "[96/100][157/391] Loss_D: 2.8213 Loss_G: 3.3454 D(x): 0.6631 D(G(z)): 0.3813 / 0.3419\n", + "[96/100][158/391] Loss_D: 2.7341 Loss_G: 2.7515 D(x): 0.6480 D(G(z)): 0.3858 / 0.4281\n", + "[96/100][159/391] Loss_D: 2.5402 Loss_G: 3.1285 D(x): 0.7145 D(G(z)): 0.3674 / 0.3971\n", + "[96/100][160/391] Loss_D: 2.8628 Loss_G: 3.0438 D(x): 0.6663 D(G(z)): 0.4474 / 0.4095\n", + "[96/100][161/391] Loss_D: 3.1645 Loss_G: 2.9201 D(x): 0.6428 D(G(z)): 0.4777 / 0.4061\n", + "[96/100][162/391] Loss_D: 2.9886 Loss_G: 3.0962 D(x): 0.6179 D(G(z)): 0.4113 / 0.3843\n", + "[96/100][163/391] Loss_D: 2.6505 Loss_G: 1.9005 D(x): 0.7501 D(G(z)): 0.4453 / 0.5621\n", + "[96/100][164/391] Loss_D: 2.2492 Loss_G: 3.6672 D(x): 0.7453 D(G(z)): 0.3732 / 0.3260\n", + "[96/100][165/391] Loss_D: 2.8785 Loss_G: 3.1114 D(x): 0.6623 D(G(z)): 0.4138 / 0.3778\n", + "[96/100][166/391] Loss_D: 3.1529 Loss_G: 3.7599 D(x): 0.6081 D(G(z)): 0.4894 / 0.3182\n", + "[96/100][167/391] Loss_D: 3.6755 Loss_G: 2.6995 D(x): 0.5682 D(G(z)): 0.5369 / 0.4391\n", + "[96/100][168/391] Loss_D: 2.9760 Loss_G: 2.7965 D(x): 0.6224 D(G(z)): 0.3900 / 0.3975\n", + "[96/100][169/391] Loss_D: 3.0226 Loss_G: 3.2739 D(x): 0.6228 D(G(z)): 0.4544 / 0.3612\n", + "[96/100][170/391] Loss_D: 2.5980 Loss_G: 2.5583 D(x): 0.7355 D(G(z)): 0.4052 / 0.4723\n", + "[96/100][171/391] Loss_D: 2.7512 Loss_G: 2.3243 D(x): 0.6377 D(G(z)): 0.3342 / 0.5077\n", + "[96/100][172/391] Loss_D: 3.0014 Loss_G: 3.0676 D(x): 0.6899 D(G(z)): 0.4775 / 0.3957\n", + "[96/100][173/391] Loss_D: 2.6574 Loss_G: 2.4360 D(x): 0.6567 D(G(z)): 0.3581 / 0.4872\n", + "[96/100][174/391] Loss_D: 2.5543 Loss_G: 1.9838 D(x): 0.6715 D(G(z)): 0.3954 / 0.5295\n", + "[96/100][175/391] Loss_D: 2.9342 Loss_G: 3.3871 D(x): 0.7091 D(G(z)): 0.4941 / 0.3567\n", + "[96/100][176/391] Loss_D: 2.7951 Loss_G: 2.0229 D(x): 0.6799 D(G(z)): 0.4029 / 0.5260\n", + "[96/100][177/391] Loss_D: 2.8456 Loss_G: 2.6151 D(x): 0.7039 D(G(z)): 0.4266 / 0.4499\n", + "[96/100][178/391] Loss_D: 2.6531 Loss_G: 2.7448 D(x): 0.6866 D(G(z)): 0.4292 / 0.4400\n", + "[96/100][179/391] Loss_D: 2.5850 Loss_G: 3.6489 D(x): 0.7134 D(G(z)): 0.4072 / 0.3273\n", + "[96/100][180/391] Loss_D: 2.9411 Loss_G: 1.8638 D(x): 0.7259 D(G(z)): 0.5035 / 0.5663\n", + "[96/100][181/391] Loss_D: 3.6471 Loss_G: 3.2875 D(x): 0.6917 D(G(z)): 0.4684 / 0.3667\n", + "[96/100][182/391] Loss_D: 2.5303 Loss_G: 2.9102 D(x): 0.7030 D(G(z)): 0.4045 / 0.4263\n", + "[96/100][183/391] Loss_D: 2.7487 Loss_G: 2.4877 D(x): 0.7086 D(G(z)): 0.4774 / 0.4791\n", + "[96/100][184/391] Loss_D: 2.7290 Loss_G: 3.0912 D(x): 0.6102 D(G(z)): 0.2868 / 0.3972\n", + "[96/100][185/391] Loss_D: 3.1024 Loss_G: 2.8122 D(x): 0.5939 D(G(z)): 0.4121 / 0.4188\n", + "[96/100][186/391] Loss_D: 2.6257 Loss_G: 2.5914 D(x): 0.6662 D(G(z)): 0.3531 / 0.4613\n", + "[96/100][187/391] Loss_D: 3.2034 Loss_G: 2.8762 D(x): 0.5319 D(G(z)): 0.3694 / 0.4041\n", + "[96/100][188/391] Loss_D: 2.4977 Loss_G: 2.8902 D(x): 0.7601 D(G(z)): 0.4837 / 0.4117\n", + "[96/100][189/391] Loss_D: 2.2766 Loss_G: 2.4758 D(x): 0.7587 D(G(z)): 0.3966 / 0.4737\n", + "[96/100][190/391] Loss_D: 2.6213 Loss_G: 1.9670 D(x): 0.6649 D(G(z)): 0.4007 / 0.5568\n", + "[96/100][191/391] Loss_D: 3.4803 Loss_G: 3.0378 D(x): 0.6185 D(G(z)): 0.4915 / 0.4073\n", + "[96/100][192/391] Loss_D: 2.2860 Loss_G: 2.6845 D(x): 0.7803 D(G(z)): 0.4054 / 0.4440\n", + "[96/100][193/391] Loss_D: 3.0621 Loss_G: 3.1165 D(x): 0.5650 D(G(z)): 0.4120 / 0.3850\n", + "[96/100][194/391] Loss_D: 3.0307 Loss_G: 2.6098 D(x): 0.6960 D(G(z)): 0.4908 / 0.4434\n", + "[96/100][195/391] Loss_D: 3.2526 Loss_G: 4.2813 D(x): 0.5807 D(G(z)): 0.4436 / 0.2647\n", + "[96/100][196/391] Loss_D: 2.8206 Loss_G: 2.6725 D(x): 0.6235 D(G(z)): 0.4373 / 0.4355\n", + "[96/100][197/391] Loss_D: 3.0821 Loss_G: 2.6760 D(x): 0.6559 D(G(z)): 0.4517 / 0.4293\n", + "[96/100][198/391] Loss_D: 3.1945 Loss_G: 3.3557 D(x): 0.6600 D(G(z)): 0.5277 / 0.3758\n", + "[96/100][199/391] Loss_D: 2.5032 Loss_G: 2.4244 D(x): 0.6798 D(G(z)): 0.3791 / 0.4736\n", + "[96/100][200/391] Loss_D: 2.6305 Loss_G: 2.9801 D(x): 0.7322 D(G(z)): 0.4433 / 0.4119\n", + "[96/100][201/391] Loss_D: 3.1219 Loss_G: 2.9059 D(x): 0.6299 D(G(z)): 0.4265 / 0.4185\n", + "[96/100][202/391] Loss_D: 2.9043 Loss_G: 3.6723 D(x): 0.6369 D(G(z)): 0.4390 / 0.3375\n", + "[96/100][203/391] Loss_D: 2.7281 Loss_G: 3.3078 D(x): 0.7074 D(G(z)): 0.4187 / 0.3623\n", + "[96/100][204/391] Loss_D: 2.5555 Loss_G: 2.3689 D(x): 0.7090 D(G(z)): 0.4405 / 0.4685\n", + "[96/100][205/391] Loss_D: 2.5384 Loss_G: 2.0971 D(x): 0.6612 D(G(z)): 0.3620 / 0.5141\n", + "[96/100][206/391] Loss_D: 3.0007 Loss_G: 4.1088 D(x): 0.5775 D(G(z)): 0.3669 / 0.2861\n", + "[96/100][207/391] Loss_D: 3.1549 Loss_G: 2.6500 D(x): 0.5594 D(G(z)): 0.3792 / 0.4218\n", + "[96/100][208/391] Loss_D: 2.5328 Loss_G: 2.2663 D(x): 0.6783 D(G(z)): 0.3914 / 0.4995\n", + "[96/100][209/391] Loss_D: 2.4176 Loss_G: 2.1536 D(x): 0.7101 D(G(z)): 0.3755 / 0.5369\n", + "[96/100][210/391] Loss_D: 2.9729 Loss_G: 1.8674 D(x): 0.7243 D(G(z)): 0.5065 / 0.5717\n", + "[96/100][211/391] Loss_D: 3.5608 Loss_G: 2.4602 D(x): 0.6867 D(G(z)): 0.4587 / 0.4799\n", + "[96/100][212/391] Loss_D: 3.2369 Loss_G: 3.1928 D(x): 0.7237 D(G(z)): 0.5287 / 0.3928\n", + "[96/100][213/391] Loss_D: 2.9213 Loss_G: 2.7484 D(x): 0.6730 D(G(z)): 0.4707 / 0.4394\n", + "[96/100][214/391] Loss_D: 2.2714 Loss_G: 2.9621 D(x): 0.6619 D(G(z)): 0.3099 / 0.4025\n", + "[96/100][215/391] Loss_D: 2.5771 Loss_G: 3.2058 D(x): 0.7322 D(G(z)): 0.3817 / 0.3849\n", + "[96/100][216/391] Loss_D: 3.1079 Loss_G: 3.1697 D(x): 0.6017 D(G(z)): 0.4438 / 0.3863\n", + "[96/100][217/391] Loss_D: 2.5997 Loss_G: 2.8440 D(x): 0.6766 D(G(z)): 0.3681 / 0.4148\n", + "[96/100][218/391] Loss_D: 2.3448 Loss_G: 3.1094 D(x): 0.7121 D(G(z)): 0.3649 / 0.3834\n", + "[96/100][219/391] Loss_D: 3.2059 Loss_G: 2.5410 D(x): 0.6952 D(G(z)): 0.5418 / 0.4611\n", + "[96/100][220/391] Loss_D: 3.0083 Loss_G: 3.1569 D(x): 0.7150 D(G(z)): 0.5088 / 0.3838\n", + "[96/100][221/391] Loss_D: 1.9894 Loss_G: 3.7573 D(x): 0.8044 D(G(z)): 0.2909 / 0.3253\n", + "[96/100][222/391] Loss_D: 2.4824 Loss_G: 3.9227 D(x): 0.7255 D(G(z)): 0.3994 / 0.3160\n", + "[96/100][223/391] Loss_D: 2.9840 Loss_G: 2.6001 D(x): 0.6837 D(G(z)): 0.4878 / 0.4689\n", + "[96/100][224/391] Loss_D: 2.3945 Loss_G: 3.0673 D(x): 0.6717 D(G(z)): 0.3323 / 0.4074\n", + "[96/100][225/391] Loss_D: 3.7851 Loss_G: 3.6141 D(x): 0.5543 D(G(z)): 0.5230 / 0.3370\n", + "[96/100][226/391] Loss_D: 2.8362 Loss_G: 3.3941 D(x): 0.6875 D(G(z)): 0.4785 / 0.3595\n", + "[96/100][227/391] Loss_D: 2.6338 Loss_G: 3.2803 D(x): 0.6650 D(G(z)): 0.3617 / 0.3564\n", + "[96/100][228/391] Loss_D: 3.8340 Loss_G: 3.2616 D(x): 0.4794 D(G(z)): 0.4200 / 0.3677\n", + "[96/100][229/391] Loss_D: 2.8929 Loss_G: 2.7228 D(x): 0.6154 D(G(z)): 0.4013 / 0.4404\n", + "[96/100][230/391] Loss_D: 2.8610 Loss_G: 3.2661 D(x): 0.6439 D(G(z)): 0.4811 / 0.3778\n", + "[96/100][231/391] Loss_D: 3.6485 Loss_G: 4.1322 D(x): 0.7640 D(G(z)): 0.6159 / 0.2912\n", + "[96/100][232/391] Loss_D: 2.8772 Loss_G: 2.0037 D(x): 0.6186 D(G(z)): 0.3823 / 0.5505\n", + "[96/100][233/391] Loss_D: 3.2271 Loss_G: 3.1973 D(x): 0.5171 D(G(z)): 0.3285 / 0.3779\n", + "[96/100][234/391] Loss_D: 2.8115 Loss_G: 2.7091 D(x): 0.6318 D(G(z)): 0.4153 / 0.4426\n", + "[96/100][235/391] Loss_D: 2.6589 Loss_G: 2.1747 D(x): 0.6423 D(G(z)): 0.3861 / 0.5157\n", + "[96/100][236/391] Loss_D: 3.2817 Loss_G: 2.0416 D(x): 0.6956 D(G(z)): 0.5578 / 0.5287\n", + "[96/100][237/391] Loss_D: 2.5440 Loss_G: 3.0468 D(x): 0.7469 D(G(z)): 0.3760 / 0.3932\n", + "[96/100][238/391] Loss_D: 2.6540 Loss_G: 2.9021 D(x): 0.6934 D(G(z)): 0.4596 / 0.4190\n", + "[96/100][239/391] Loss_D: 2.6968 Loss_G: 4.1132 D(x): 0.7008 D(G(z)): 0.4481 / 0.2961\n", + "[96/100][240/391] Loss_D: 2.5873 Loss_G: 3.7177 D(x): 0.6529 D(G(z)): 0.3268 / 0.3349\n", + "[96/100][241/391] Loss_D: 3.9497 Loss_G: 2.4932 D(x): 0.7135 D(G(z)): 0.5157 / 0.4802\n", + "[96/100][242/391] Loss_D: 2.7378 Loss_G: 2.4552 D(x): 0.7177 D(G(z)): 0.4599 / 0.4812\n", + "[96/100][243/391] Loss_D: 2.4646 Loss_G: 3.3918 D(x): 0.7534 D(G(z)): 0.3947 / 0.3754\n", + "[96/100][244/391] Loss_D: 2.6506 Loss_G: 2.6525 D(x): 0.6363 D(G(z)): 0.3659 / 0.4585\n", + "[96/100][245/391] Loss_D: 2.7687 Loss_G: 3.6679 D(x): 0.7071 D(G(z)): 0.4499 / 0.3433\n", + "[96/100][246/391] Loss_D: 2.9367 Loss_G: 3.1492 D(x): 0.6776 D(G(z)): 0.4405 / 0.3867\n", + "[96/100][247/391] Loss_D: 2.6420 Loss_G: 2.8695 D(x): 0.7115 D(G(z)): 0.4082 / 0.4084\n", + "[96/100][248/391] Loss_D: 3.0168 Loss_G: 2.4661 D(x): 0.5872 D(G(z)): 0.3575 / 0.4875\n", + "[96/100][249/391] Loss_D: 2.5269 Loss_G: 3.5831 D(x): 0.6596 D(G(z)): 0.3036 / 0.3428\n", + "[96/100][250/391] Loss_D: 2.5033 Loss_G: 2.0915 D(x): 0.7624 D(G(z)): 0.3667 / 0.5263\n", + "[96/100][251/391] Loss_D: 2.6352 Loss_G: 2.4782 D(x): 0.6732 D(G(z)): 0.3508 / 0.4847\n", + "[96/100][252/391] Loss_D: 3.2685 Loss_G: 3.1675 D(x): 0.5126 D(G(z)): 0.3192 / 0.3776\n", + "[96/100][253/391] Loss_D: 3.0432 Loss_G: 2.6658 D(x): 0.7288 D(G(z)): 0.5125 / 0.4587\n", + "[96/100][254/391] Loss_D: 3.0188 Loss_G: 3.0740 D(x): 0.6860 D(G(z)): 0.5371 / 0.3883\n", + "[96/100][255/391] Loss_D: 2.6734 Loss_G: 2.3748 D(x): 0.7040 D(G(z)): 0.3635 / 0.4816\n", + "[96/100][256/391] Loss_D: 2.5972 Loss_G: 3.0203 D(x): 0.7451 D(G(z)): 0.4359 / 0.4007\n", + "[96/100][257/391] Loss_D: 3.4662 Loss_G: 3.3076 D(x): 0.5921 D(G(z)): 0.4706 / 0.3705\n", + "[96/100][258/391] Loss_D: 2.5347 Loss_G: 3.3909 D(x): 0.7264 D(G(z)): 0.4192 / 0.3797\n", + "[96/100][259/391] Loss_D: 2.9681 Loss_G: 3.2699 D(x): 0.6726 D(G(z)): 0.4702 / 0.3651\n", + "[96/100][260/391] Loss_D: 2.5197 Loss_G: 3.4156 D(x): 0.6890 D(G(z)): 0.3640 / 0.3686\n", + "[96/100][261/391] Loss_D: 2.8707 Loss_G: 2.1397 D(x): 0.6623 D(G(z)): 0.4080 / 0.5168\n", + "[96/100][262/391] Loss_D: 2.7100 Loss_G: 2.7851 D(x): 0.6981 D(G(z)): 0.4073 / 0.4333\n", + "[96/100][263/391] Loss_D: 3.2434 Loss_G: 2.5226 D(x): 0.6122 D(G(z)): 0.4030 / 0.4642\n", + "[96/100][264/391] Loss_D: 2.5869 Loss_G: 3.0155 D(x): 0.6253 D(G(z)): 0.3530 / 0.3938\n", + "[96/100][265/391] Loss_D: 2.5728 Loss_G: 2.2714 D(x): 0.6449 D(G(z)): 0.2921 / 0.4954\n", + "[96/100][266/391] Loss_D: 3.2823 Loss_G: 2.8873 D(x): 0.7286 D(G(z)): 0.5445 / 0.4275\n", + "[96/100][267/391] Loss_D: 2.6652 Loss_G: 2.2459 D(x): 0.6952 D(G(z)): 0.4125 / 0.4899\n", + "[96/100][268/391] Loss_D: 2.6304 Loss_G: 2.0655 D(x): 0.7041 D(G(z)): 0.4660 / 0.5235\n", + "[96/100][269/391] Loss_D: 2.8952 Loss_G: 2.8655 D(x): 0.6560 D(G(z)): 0.4181 / 0.4057\n", + "[96/100][270/391] Loss_D: 2.4494 Loss_G: 2.5186 D(x): 0.6859 D(G(z)): 0.2862 / 0.4696\n", + "[96/100][271/391] Loss_D: 3.7515 Loss_G: 2.5398 D(x): 0.7181 D(G(z)): 0.5017 / 0.4712\n", + "[96/100][272/391] Loss_D: 2.5911 Loss_G: 2.7556 D(x): 0.6393 D(G(z)): 0.3034 / 0.4411\n", + "[96/100][273/391] Loss_D: 2.9204 Loss_G: 2.7959 D(x): 0.7004 D(G(z)): 0.4929 / 0.4326\n", + "[96/100][274/391] Loss_D: 2.4917 Loss_G: 2.7593 D(x): 0.7575 D(G(z)): 0.4025 / 0.4330\n", + "[96/100][275/391] Loss_D: 2.6605 Loss_G: 2.8783 D(x): 0.6692 D(G(z)): 0.3647 / 0.4262\n", + "[96/100][276/391] Loss_D: 2.5209 Loss_G: 3.6200 D(x): 0.7255 D(G(z)): 0.3731 / 0.3329\n", + "[96/100][277/391] Loss_D: 2.8021 Loss_G: 2.8513 D(x): 0.7339 D(G(z)): 0.4547 / 0.4185\n", + "[96/100][278/391] Loss_D: 2.4716 Loss_G: 3.8008 D(x): 0.7255 D(G(z)): 0.4352 / 0.3120\n", + "[96/100][279/391] Loss_D: 3.0001 Loss_G: 3.9186 D(x): 0.6666 D(G(z)): 0.4794 / 0.3083\n", + "[96/100][280/391] Loss_D: 3.3778 Loss_G: 4.4020 D(x): 0.5727 D(G(z)): 0.4540 / 0.2840\n", + "[96/100][281/391] Loss_D: 2.5184 Loss_G: 3.0024 D(x): 0.6770 D(G(z)): 0.3483 / 0.4041\n", + "[96/100][282/391] Loss_D: 2.3330 Loss_G: 2.8453 D(x): 0.7238 D(G(z)): 0.3281 / 0.4158\n", + "[96/100][283/391] Loss_D: 3.5396 Loss_G: 2.6128 D(x): 0.4997 D(G(z)): 0.4040 / 0.4327\n", + "[96/100][284/391] Loss_D: 2.5650 Loss_G: 3.1413 D(x): 0.6686 D(G(z)): 0.4155 / 0.3797\n", + "[96/100][285/391] Loss_D: 2.7788 Loss_G: 2.6934 D(x): 0.6597 D(G(z)): 0.3867 / 0.4365\n", + "[96/100][286/391] Loss_D: 3.0780 Loss_G: 3.0055 D(x): 0.7131 D(G(z)): 0.5256 / 0.3878\n", + "[96/100][287/391] Loss_D: 2.4344 Loss_G: 3.0904 D(x): 0.7545 D(G(z)): 0.3299 / 0.3977\n", + "[96/100][288/391] Loss_D: 2.8614 Loss_G: 2.1960 D(x): 0.6201 D(G(z)): 0.4259 / 0.5070\n", + "[96/100][289/391] Loss_D: 3.0857 Loss_G: 2.5170 D(x): 0.7028 D(G(z)): 0.4767 / 0.4695\n", + "[96/100][290/391] Loss_D: 3.6511 Loss_G: 2.8154 D(x): 0.5293 D(G(z)): 0.4909 / 0.4145\n", + "[96/100][291/391] Loss_D: 2.7401 Loss_G: 2.0156 D(x): 0.7666 D(G(z)): 0.4548 / 0.5492\n", + "[96/100][292/391] Loss_D: 2.7536 Loss_G: 2.5188 D(x): 0.7089 D(G(z)): 0.4135 / 0.4687\n", + "[96/100][293/391] Loss_D: 3.0892 Loss_G: 3.3560 D(x): 0.5785 D(G(z)): 0.4016 / 0.3536\n", + "[96/100][294/391] Loss_D: 2.6245 Loss_G: 2.8594 D(x): 0.7009 D(G(z)): 0.4470 / 0.4253\n", + "[96/100][295/391] Loss_D: 2.6860 Loss_G: 2.1867 D(x): 0.7067 D(G(z)): 0.3902 / 0.5037\n", + "[96/100][296/391] Loss_D: 3.0308 Loss_G: 2.6997 D(x): 0.6241 D(G(z)): 0.4522 / 0.4234\n", + "[96/100][297/391] Loss_D: 2.9209 Loss_G: 2.5035 D(x): 0.6550 D(G(z)): 0.4044 / 0.4548\n", + "[96/100][298/391] Loss_D: 2.4837 Loss_G: 3.1364 D(x): 0.7458 D(G(z)): 0.4358 / 0.3844\n", + "[96/100][299/391] Loss_D: 2.5650 Loss_G: 3.2079 D(x): 0.7757 D(G(z)): 0.4305 / 0.3736\n", + "[96/100][300/391] Loss_D: 2.7495 Loss_G: 2.0117 D(x): 0.6550 D(G(z)): 0.3578 / 0.5501\n", + "[96/100][301/391] Loss_D: 3.5069 Loss_G: 3.5927 D(x): 0.6473 D(G(z)): 0.4457 / 0.3453\n", + "[96/100][302/391] Loss_D: 2.3808 Loss_G: 2.7427 D(x): 0.7168 D(G(z)): 0.3801 / 0.4270\n", + "[96/100][303/391] Loss_D: 2.5799 Loss_G: 3.1068 D(x): 0.7128 D(G(z)): 0.3968 / 0.3961\n", + "[96/100][304/391] Loss_D: 3.0899 Loss_G: 3.1768 D(x): 0.5870 D(G(z)): 0.4592 / 0.3811\n", + "[96/100][305/391] Loss_D: 2.7397 Loss_G: 2.6265 D(x): 0.6499 D(G(z)): 0.3706 / 0.4416\n", + "[96/100][306/391] Loss_D: 3.0603 Loss_G: 2.3313 D(x): 0.6485 D(G(z)): 0.4307 / 0.4851\n", + "[96/100][307/391] Loss_D: 2.4590 Loss_G: 2.1377 D(x): 0.6932 D(G(z)): 0.3298 / 0.5071\n", + "[96/100][308/391] Loss_D: 3.1187 Loss_G: 3.0387 D(x): 0.6137 D(G(z)): 0.4794 / 0.3967\n", + "[96/100][309/391] Loss_D: 3.3829 Loss_G: 2.7028 D(x): 0.5583 D(G(z)): 0.4810 / 0.4402\n", + "[96/100][310/391] Loss_D: 2.9174 Loss_G: 2.3727 D(x): 0.6450 D(G(z)): 0.3750 / 0.5014\n", + "[96/100][311/391] Loss_D: 2.5768 Loss_G: 2.4921 D(x): 0.7792 D(G(z)): 0.3928 / 0.4623\n", + "[96/100][312/391] Loss_D: 2.6652 Loss_G: 3.6497 D(x): 0.8345 D(G(z)): 0.4846 / 0.3272\n", + "[96/100][313/391] Loss_D: 2.5044 Loss_G: 2.7363 D(x): 0.6528 D(G(z)): 0.3257 / 0.4316\n", + "[96/100][314/391] Loss_D: 2.8117 Loss_G: 2.1184 D(x): 0.7091 D(G(z)): 0.4914 / 0.5068\n", + "[96/100][315/391] Loss_D: 2.7779 Loss_G: 3.1121 D(x): 0.6642 D(G(z)): 0.4239 / 0.4018\n", + "[96/100][316/391] Loss_D: 2.7350 Loss_G: 2.3230 D(x): 0.6725 D(G(z)): 0.3903 / 0.4621\n", + "[96/100][317/391] Loss_D: 2.6624 Loss_G: 2.2069 D(x): 0.7247 D(G(z)): 0.4049 / 0.5129\n", + "[96/100][318/391] Loss_D: 3.5690 Loss_G: 2.5282 D(x): 0.5631 D(G(z)): 0.5118 / 0.4588\n", + "[96/100][319/391] Loss_D: 2.3514 Loss_G: 2.5449 D(x): 0.7239 D(G(z)): 0.3629 / 0.4683\n", + "[96/100][320/391] Loss_D: 2.4583 Loss_G: 2.8023 D(x): 0.7557 D(G(z)): 0.3519 / 0.4235\n", + "[96/100][321/391] Loss_D: 2.8194 Loss_G: 2.8298 D(x): 0.7008 D(G(z)): 0.4114 / 0.4194\n", + "[96/100][322/391] Loss_D: 2.7991 Loss_G: 2.7732 D(x): 0.7510 D(G(z)): 0.5085 / 0.4367\n", + "[96/100][323/391] Loss_D: 2.9858 Loss_G: 3.3859 D(x): 0.6341 D(G(z)): 0.4010 / 0.3504\n", + "[96/100][324/391] Loss_D: 2.5606 Loss_G: 2.8816 D(x): 0.7106 D(G(z)): 0.4403 / 0.4135\n", + "[96/100][325/391] Loss_D: 2.8803 Loss_G: 3.3188 D(x): 0.6949 D(G(z)): 0.4301 / 0.3685\n", + "[96/100][326/391] Loss_D: 2.4645 Loss_G: 2.7113 D(x): 0.6630 D(G(z)): 0.3107 / 0.4317\n", + "[96/100][327/391] Loss_D: 2.9349 Loss_G: 2.6063 D(x): 0.5669 D(G(z)): 0.3298 / 0.4638\n", + "[96/100][328/391] Loss_D: 2.6489 Loss_G: 2.6250 D(x): 0.6615 D(G(z)): 0.3799 / 0.4589\n", + "[96/100][329/391] Loss_D: 2.7024 Loss_G: 3.6507 D(x): 0.6841 D(G(z)): 0.3869 / 0.3404\n", + "[96/100][330/391] Loss_D: 3.4211 Loss_G: 2.2175 D(x): 0.5855 D(G(z)): 0.4683 / 0.5014\n", + "[96/100][331/391] Loss_D: 3.4836 Loss_G: 1.7843 D(x): 0.6803 D(G(z)): 0.4970 / 0.5833\n", + "[96/100][332/391] Loss_D: 2.6219 Loss_G: 1.9796 D(x): 0.6867 D(G(z)): 0.4289 / 0.5549\n", + "[96/100][333/391] Loss_D: 3.3617 Loss_G: 2.7441 D(x): 0.6491 D(G(z)): 0.4956 / 0.4335\n", + "[96/100][334/391] Loss_D: 2.6168 Loss_G: 2.2435 D(x): 0.6623 D(G(z)): 0.4040 / 0.5185\n", + "[96/100][335/391] Loss_D: 2.6538 Loss_G: 3.6276 D(x): 0.8254 D(G(z)): 0.4746 / 0.3228\n", + "[96/100][336/391] Loss_D: 2.8358 Loss_G: 3.4083 D(x): 0.6626 D(G(z)): 0.3707 / 0.3478\n", + "[96/100][337/391] Loss_D: 2.7720 Loss_G: 2.5468 D(x): 0.6802 D(G(z)): 0.4013 / 0.4586\n", + "[96/100][338/391] Loss_D: 3.0179 Loss_G: 3.1461 D(x): 0.6165 D(G(z)): 0.4183 / 0.3820\n", + "[96/100][339/391] Loss_D: 3.2184 Loss_G: 2.3186 D(x): 0.6191 D(G(z)): 0.4876 / 0.5032\n", + "[96/100][340/391] Loss_D: 2.3191 Loss_G: 2.7918 D(x): 0.7242 D(G(z)): 0.3133 / 0.4324\n", + "[96/100][341/391] Loss_D: 2.6787 Loss_G: 2.6153 D(x): 0.6680 D(G(z)): 0.3855 / 0.4683\n", + "[96/100][342/391] Loss_D: 3.3216 Loss_G: 2.1983 D(x): 0.6190 D(G(z)): 0.5030 / 0.5033\n", + "[96/100][343/391] Loss_D: 3.2854 Loss_G: 2.9389 D(x): 0.6626 D(G(z)): 0.5111 / 0.4074\n", + "[96/100][344/391] Loss_D: 3.3747 Loss_G: 3.6416 D(x): 0.6074 D(G(z)): 0.5076 / 0.3332\n", + "[96/100][345/391] Loss_D: 3.0434 Loss_G: 3.6537 D(x): 0.7549 D(G(z)): 0.5236 / 0.3321\n", + "[96/100][346/391] Loss_D: 3.4320 Loss_G: 3.2091 D(x): 0.5447 D(G(z)): 0.4467 / 0.3717\n", + "[96/100][347/391] Loss_D: 2.9612 Loss_G: 2.7379 D(x): 0.6230 D(G(z)): 0.3462 / 0.4355\n", + "[96/100][348/391] Loss_D: 2.5230 Loss_G: 2.6988 D(x): 0.6648 D(G(z)): 0.3091 / 0.4458\n", + "[96/100][349/391] Loss_D: 2.9997 Loss_G: 2.2286 D(x): 0.6610 D(G(z)): 0.4881 / 0.4994\n", + "[96/100][350/391] Loss_D: 2.8033 Loss_G: 2.5513 D(x): 0.7517 D(G(z)): 0.4901 / 0.4622\n", + "[96/100][351/391] Loss_D: 2.6577 Loss_G: 2.7220 D(x): 0.6426 D(G(z)): 0.3360 / 0.4442\n", + "[96/100][352/391] Loss_D: 2.8661 Loss_G: 2.7932 D(x): 0.7255 D(G(z)): 0.5109 / 0.4357\n", + "[96/100][353/391] Loss_D: 2.6503 Loss_G: 2.3210 D(x): 0.6843 D(G(z)): 0.3747 / 0.4965\n", + "[96/100][354/391] Loss_D: 2.9037 Loss_G: 2.0917 D(x): 0.6555 D(G(z)): 0.4610 / 0.5332\n", + "[96/100][355/391] Loss_D: 2.5144 Loss_G: 3.7560 D(x): 0.7471 D(G(z)): 0.4285 / 0.3089\n", + "[96/100][356/391] Loss_D: 2.9313 Loss_G: 2.6926 D(x): 0.6180 D(G(z)): 0.3962 / 0.4337\n", + "[96/100][357/391] Loss_D: 2.2564 Loss_G: 2.9505 D(x): 0.7412 D(G(z)): 0.3042 / 0.4101\n", + "[96/100][358/391] Loss_D: 2.6753 Loss_G: 3.2301 D(x): 0.6680 D(G(z)): 0.4374 / 0.3741\n", + "[96/100][359/391] Loss_D: 2.7313 Loss_G: 3.1469 D(x): 0.6900 D(G(z)): 0.4531 / 0.3945\n", + "[96/100][360/391] Loss_D: 2.9946 Loss_G: 2.4951 D(x): 0.6373 D(G(z)): 0.4697 / 0.4680\n", + "[96/100][361/391] Loss_D: 3.4893 Loss_G: 2.3213 D(x): 0.6498 D(G(z)): 0.4324 / 0.4924\n", + "[96/100][362/391] Loss_D: 2.7745 Loss_G: 3.1450 D(x): 0.6709 D(G(z)): 0.3926 / 0.3773\n", + "[96/100][363/391] Loss_D: 2.6616 Loss_G: 3.4051 D(x): 0.6537 D(G(z)): 0.3532 / 0.3555\n", + "[96/100][364/391] Loss_D: 2.5460 Loss_G: 1.8313 D(x): 0.6879 D(G(z)): 0.3105 / 0.5693\n", + "[96/100][365/391] Loss_D: 2.7045 Loss_G: 2.1826 D(x): 0.7343 D(G(z)): 0.4442 / 0.5202\n", + "[96/100][366/391] Loss_D: 2.7071 Loss_G: 3.1769 D(x): 0.7028 D(G(z)): 0.4440 / 0.3878\n", + "[96/100][367/391] Loss_D: 2.9237 Loss_G: 2.8053 D(x): 0.6972 D(G(z)): 0.4590 / 0.4179\n", + "[96/100][368/391] Loss_D: 2.6619 Loss_G: 2.2145 D(x): 0.6508 D(G(z)): 0.3745 / 0.5317\n", + "[96/100][369/391] Loss_D: 2.5221 Loss_G: 3.5592 D(x): 0.6388 D(G(z)): 0.3005 / 0.3444\n", + "[96/100][370/391] Loss_D: 2.6691 Loss_G: 2.9589 D(x): 0.6409 D(G(z)): 0.3425 / 0.4081\n", + "[96/100][371/391] Loss_D: 2.9892 Loss_G: 3.0596 D(x): 0.7487 D(G(z)): 0.4916 / 0.4125\n", + "[96/100][372/391] Loss_D: 2.6651 Loss_G: 2.5857 D(x): 0.7025 D(G(z)): 0.4561 / 0.4616\n", + "[96/100][373/391] Loss_D: 2.9315 Loss_G: 2.7721 D(x): 0.6463 D(G(z)): 0.4570 / 0.4447\n", + "[96/100][374/391] Loss_D: 2.7895 Loss_G: 2.2913 D(x): 0.7133 D(G(z)): 0.4793 / 0.4966\n", + "[96/100][375/391] Loss_D: 3.9027 Loss_G: 3.3144 D(x): 0.6946 D(G(z)): 0.6431 / 0.3716\n", + "[96/100][376/391] Loss_D: 3.0573 Loss_G: 2.4281 D(x): 0.6345 D(G(z)): 0.4123 / 0.4830\n", + "[96/100][377/391] Loss_D: 2.5699 Loss_G: 2.9715 D(x): 0.6980 D(G(z)): 0.3338 / 0.3898\n", + "[96/100][378/391] Loss_D: 2.4585 Loss_G: 3.1099 D(x): 0.6630 D(G(z)): 0.3455 / 0.3959\n", + "[96/100][379/391] Loss_D: 2.5534 Loss_G: 2.8747 D(x): 0.6705 D(G(z)): 0.3648 / 0.4297\n", + "[96/100][380/391] Loss_D: 2.7040 Loss_G: 2.7523 D(x): 0.6239 D(G(z)): 0.2914 / 0.4337\n", + "[96/100][381/391] Loss_D: 2.5844 Loss_G: 3.5806 D(x): 0.7137 D(G(z)): 0.4010 / 0.3362\n", + "[96/100][382/391] Loss_D: 2.9411 Loss_G: 2.4511 D(x): 0.6650 D(G(z)): 0.4704 / 0.4855\n", + "[96/100][383/391] Loss_D: 2.9961 Loss_G: 3.5510 D(x): 0.7439 D(G(z)): 0.4869 / 0.3336\n", + "[96/100][384/391] Loss_D: 3.3755 Loss_G: 3.4504 D(x): 0.5886 D(G(z)): 0.5062 / 0.3489\n", + "[96/100][385/391] Loss_D: 2.4563 Loss_G: 2.5815 D(x): 0.6775 D(G(z)): 0.3312 / 0.4572\n", + "[96/100][386/391] Loss_D: 2.7716 Loss_G: 2.3302 D(x): 0.6860 D(G(z)): 0.4021 / 0.4848\n", + "[96/100][387/391] Loss_D: 2.8593 Loss_G: 3.1699 D(x): 0.6843 D(G(z)): 0.4559 / 0.3784\n", + "[96/100][388/391] Loss_D: 2.6565 Loss_G: 2.9164 D(x): 0.6461 D(G(z)): 0.3589 / 0.4127\n", + "[96/100][389/391] Loss_D: 2.6948 Loss_G: 2.1359 D(x): 0.6788 D(G(z)): 0.4074 / 0.5314\n", + "[96/100][390/391] Loss_D: 2.6910 Loss_G: 3.4074 D(x): 0.7269 D(G(z)): 0.4892 / 0.3704\n", + "[96/100][391/391] Loss_D: 3.7875 Loss_G: 2.1605 D(x): 0.6813 D(G(z)): 0.3320 / 0.5470\n", + "[97/100][1/391] Loss_D: 3.4891 Loss_G: 2.8315 D(x): 0.6691 D(G(z)): 0.4843 / 0.4201\n", + "[97/100][2/391] Loss_D: 2.2197 Loss_G: 2.8353 D(x): 0.6890 D(G(z)): 0.2943 / 0.4243\n", + "[97/100][3/391] Loss_D: 2.5214 Loss_G: 2.5806 D(x): 0.7557 D(G(z)): 0.4424 / 0.4628\n", + "[97/100][4/391] Loss_D: 2.4509 Loss_G: 3.7955 D(x): 0.6942 D(G(z)): 0.4006 / 0.3095\n", + "[97/100][5/391] Loss_D: 2.7852 Loss_G: 3.1511 D(x): 0.7646 D(G(z)): 0.5001 / 0.3935\n", + "[97/100][6/391] Loss_D: 2.5713 Loss_G: 3.0504 D(x): 0.7068 D(G(z)): 0.3334 / 0.3929\n", + "[97/100][7/391] Loss_D: 2.9555 Loss_G: 3.6184 D(x): 0.6574 D(G(z)): 0.4247 / 0.3317\n", + "[97/100][8/391] Loss_D: 2.6647 Loss_G: 2.6784 D(x): 0.6085 D(G(z)): 0.3281 / 0.4551\n", + "[97/100][9/391] Loss_D: 2.5082 Loss_G: 2.4854 D(x): 0.6941 D(G(z)): 0.3789 / 0.4731\n", + "[97/100][10/391] Loss_D: 2.5625 Loss_G: 2.2801 D(x): 0.7279 D(G(z)): 0.4030 / 0.5047\n", + "[97/100][11/391] Loss_D: 3.4706 Loss_G: 4.2451 D(x): 0.5993 D(G(z)): 0.4997 / 0.2855\n", + "[97/100][12/391] Loss_D: 2.7327 Loss_G: 2.5917 D(x): 0.6470 D(G(z)): 0.3996 / 0.4523\n", + "[97/100][13/391] Loss_D: 3.1777 Loss_G: 2.3498 D(x): 0.6962 D(G(z)): 0.5181 / 0.4772\n", + "[97/100][14/391] Loss_D: 2.6777 Loss_G: 2.1580 D(x): 0.7148 D(G(z)): 0.4552 / 0.5122\n", + "[97/100][15/391] Loss_D: 2.7295 Loss_G: 2.8201 D(x): 0.6289 D(G(z)): 0.4116 / 0.4241\n", + "[97/100][16/391] Loss_D: 3.0163 Loss_G: 3.5026 D(x): 0.6379 D(G(z)): 0.4012 / 0.3485\n", + "[97/100][17/391] Loss_D: 2.9315 Loss_G: 3.2002 D(x): 0.6214 D(G(z)): 0.4311 / 0.3707\n", + "[97/100][18/391] Loss_D: 2.6909 Loss_G: 2.3957 D(x): 0.6171 D(G(z)): 0.3273 / 0.4948\n", + "[97/100][19/391] Loss_D: 2.4832 Loss_G: 2.9403 D(x): 0.6948 D(G(z)): 0.3468 / 0.4053\n", + "[97/100][20/391] Loss_D: 2.5228 Loss_G: 2.6927 D(x): 0.7625 D(G(z)): 0.4454 / 0.4445\n", + "[97/100][21/391] Loss_D: 2.9539 Loss_G: 2.2191 D(x): 0.7667 D(G(z)): 0.5660 / 0.5080\n", + "[97/100][22/391] Loss_D: 3.0775 Loss_G: 3.0175 D(x): 0.6543 D(G(z)): 0.4430 / 0.4080\n", + "[97/100][23/391] Loss_D: 2.3823 Loss_G: 3.7178 D(x): 0.7657 D(G(z)): 0.3782 / 0.3417\n", + "[97/100][24/391] Loss_D: 2.5556 Loss_G: 3.6205 D(x): 0.7111 D(G(z)): 0.3839 / 0.3369\n", + "[97/100][25/391] Loss_D: 3.2769 Loss_G: 3.4686 D(x): 0.6093 D(G(z)): 0.4904 / 0.3447\n", + "[97/100][26/391] Loss_D: 2.4390 Loss_G: 3.4831 D(x): 0.6704 D(G(z)): 0.2969 / 0.3376\n", + "[97/100][27/391] Loss_D: 3.2363 Loss_G: 3.0735 D(x): 0.5578 D(G(z)): 0.3545 / 0.3703\n", + "[97/100][28/391] Loss_D: 2.2263 Loss_G: 2.9060 D(x): 0.7535 D(G(z)): 0.3620 / 0.4154\n", + "[97/100][29/391] Loss_D: 2.6017 Loss_G: 3.9758 D(x): 0.7247 D(G(z)): 0.4172 / 0.3059\n", + "[97/100][30/391] Loss_D: 2.7837 Loss_G: 2.6783 D(x): 0.6766 D(G(z)): 0.4936 / 0.4435\n", + "[97/100][31/391] Loss_D: 3.6416 Loss_G: 2.9946 D(x): 0.6260 D(G(z)): 0.3777 / 0.4156\n", + "[97/100][32/391] Loss_D: 2.5929 Loss_G: 3.0076 D(x): 0.7108 D(G(z)): 0.3893 / 0.3917\n", + "[97/100][33/391] Loss_D: 3.3202 Loss_G: 3.0829 D(x): 0.5992 D(G(z)): 0.4798 / 0.3932\n", + "[97/100][34/391] Loss_D: 3.0549 Loss_G: 3.7140 D(x): 0.6888 D(G(z)): 0.5209 / 0.3376\n", + "[97/100][35/391] Loss_D: 2.7680 Loss_G: 3.3193 D(x): 0.7042 D(G(z)): 0.4621 / 0.3667\n", + "[97/100][36/391] Loss_D: 3.4676 Loss_G: 3.6234 D(x): 0.5191 D(G(z)): 0.3903 / 0.3192\n", + "[97/100][37/391] Loss_D: 2.9659 Loss_G: 3.0016 D(x): 0.6153 D(G(z)): 0.3645 / 0.3974\n", + "[97/100][38/391] Loss_D: 2.4772 Loss_G: 3.3388 D(x): 0.6717 D(G(z)): 0.3601 / 0.3588\n", + "[97/100][39/391] Loss_D: 2.5150 Loss_G: 2.3454 D(x): 0.7380 D(G(z)): 0.3615 / 0.4837\n", + "[97/100][40/391] Loss_D: 2.7985 Loss_G: 2.7884 D(x): 0.7435 D(G(z)): 0.4449 / 0.4432\n", + "[97/100][41/391] Loss_D: 3.2763 Loss_G: 2.9073 D(x): 0.6728 D(G(z)): 0.5170 / 0.4037\n", + "[97/100][42/391] Loss_D: 2.5611 Loss_G: 2.8366 D(x): 0.7150 D(G(z)): 0.4038 / 0.4183\n", + "[97/100][43/391] Loss_D: 2.9755 Loss_G: 4.9817 D(x): 0.6550 D(G(z)): 0.4114 / 0.2319\n", + "[97/100][44/391] Loss_D: 2.7508 Loss_G: 2.6595 D(x): 0.6792 D(G(z)): 0.4635 / 0.4344\n", + "[97/100][45/391] Loss_D: 2.8496 Loss_G: 3.2659 D(x): 0.7084 D(G(z)): 0.4468 / 0.3675\n", + "[97/100][46/391] Loss_D: 3.1193 Loss_G: 3.7879 D(x): 0.6069 D(G(z)): 0.4101 / 0.2923\n", + "[97/100][47/391] Loss_D: 3.2583 Loss_G: 3.2864 D(x): 0.6000 D(G(z)): 0.4649 / 0.3667\n", + "[97/100][48/391] Loss_D: 2.4655 Loss_G: 2.6045 D(x): 0.7045 D(G(z)): 0.3997 / 0.4373\n", + "[97/100][49/391] Loss_D: 2.9872 Loss_G: 2.6622 D(x): 0.6113 D(G(z)): 0.4292 / 0.4543\n", + "[97/100][50/391] Loss_D: 2.5896 Loss_G: 1.9283 D(x): 0.7348 D(G(z)): 0.4285 / 0.5439\n", + "[97/100][51/391] Loss_D: 2.7279 Loss_G: 2.7704 D(x): 0.6751 D(G(z)): 0.4011 / 0.4266\n", + "[97/100][52/391] Loss_D: 2.7030 Loss_G: 3.3873 D(x): 0.6617 D(G(z)): 0.4018 / 0.3603\n", + "[97/100][53/391] Loss_D: 2.9925 Loss_G: 2.9809 D(x): 0.6927 D(G(z)): 0.5178 / 0.4071\n", + "[97/100][54/391] Loss_D: 2.3446 Loss_G: 2.6151 D(x): 0.7316 D(G(z)): 0.3536 / 0.4501\n", + "[97/100][55/391] Loss_D: 2.5562 Loss_G: 3.4751 D(x): 0.6900 D(G(z)): 0.3810 / 0.3453\n", + "[97/100][56/391] Loss_D: 3.0552 Loss_G: 2.4798 D(x): 0.5326 D(G(z)): 0.2635 / 0.4629\n", + "[97/100][57/391] Loss_D: 3.4627 Loss_G: 3.2171 D(x): 0.5999 D(G(z)): 0.5121 / 0.3742\n", + "[97/100][58/391] Loss_D: 3.7911 Loss_G: 2.5587 D(x): 0.4814 D(G(z)): 0.4145 / 0.4576\n", + "[97/100][59/391] Loss_D: 2.7643 Loss_G: 2.0853 D(x): 0.6307 D(G(z)): 0.3664 / 0.5290\n", + "[97/100][60/391] Loss_D: 2.9065 Loss_G: 2.2419 D(x): 0.7807 D(G(z)): 0.4957 / 0.5005\n", + "[97/100][61/391] Loss_D: 3.8609 Loss_G: 3.4602 D(x): 0.6258 D(G(z)): 0.6007 / 0.3604\n", + "[97/100][62/391] Loss_D: 2.7659 Loss_G: 2.8403 D(x): 0.7779 D(G(z)): 0.4940 / 0.4236\n", + "[97/100][63/391] Loss_D: 3.3208 Loss_G: 3.3579 D(x): 0.5556 D(G(z)): 0.4434 / 0.3605\n", + "[97/100][64/391] Loss_D: 3.2695 Loss_G: 3.2290 D(x): 0.6729 D(G(z)): 0.5698 / 0.3803\n", + "[97/100][65/391] Loss_D: 3.3971 Loss_G: 3.3106 D(x): 0.5541 D(G(z)): 0.4135 / 0.3800\n", + "[97/100][66/391] Loss_D: 3.0463 Loss_G: 2.8581 D(x): 0.5692 D(G(z)): 0.4225 / 0.4000\n", + "[97/100][67/391] Loss_D: 3.7001 Loss_G: 2.6995 D(x): 0.5128 D(G(z)): 0.3979 / 0.4304\n", + "[97/100][68/391] Loss_D: 2.6877 Loss_G: 3.5553 D(x): 0.6881 D(G(z)): 0.4732 / 0.3423\n", + "[97/100][69/391] Loss_D: 3.1033 Loss_G: 2.5627 D(x): 0.6072 D(G(z)): 0.4181 / 0.4434\n", + "[97/100][70/391] Loss_D: 2.8504 Loss_G: 3.2740 D(x): 0.7467 D(G(z)): 0.4466 / 0.3611\n", + "[97/100][71/391] Loss_D: 2.5451 Loss_G: 2.7024 D(x): 0.7109 D(G(z)): 0.3694 / 0.4488\n", + "[97/100][72/391] Loss_D: 2.7440 Loss_G: 3.1150 D(x): 0.7244 D(G(z)): 0.4866 / 0.3924\n", + "[97/100][73/391] Loss_D: 3.2436 Loss_G: 2.5166 D(x): 0.7037 D(G(z)): 0.5410 / 0.4556\n", + "[97/100][74/391] Loss_D: 2.8565 Loss_G: 2.9110 D(x): 0.6535 D(G(z)): 0.4393 / 0.4047\n", + "[97/100][75/391] Loss_D: 2.7189 Loss_G: 3.7887 D(x): 0.6793 D(G(z)): 0.4236 / 0.3160\n", + "[97/100][76/391] Loss_D: 3.1325 Loss_G: 3.5541 D(x): 0.5356 D(G(z)): 0.3638 / 0.3436\n", + "[97/100][77/391] Loss_D: 2.5609 Loss_G: 3.1564 D(x): 0.6910 D(G(z)): 0.2594 / 0.3836\n", + "[97/100][78/391] Loss_D: 2.4097 Loss_G: 2.8621 D(x): 0.7029 D(G(z)): 0.3389 / 0.4156\n", + "[97/100][79/391] Loss_D: 2.4587 Loss_G: 3.0300 D(x): 0.7527 D(G(z)): 0.4644 / 0.4135\n", + "[97/100][80/391] Loss_D: 2.8878 Loss_G: 2.7217 D(x): 0.7293 D(G(z)): 0.4406 / 0.4449\n", + "[97/100][81/391] Loss_D: 3.6350 Loss_G: 2.7688 D(x): 0.7122 D(G(z)): 0.6167 / 0.4329\n", + "[97/100][82/391] Loss_D: 2.8919 Loss_G: 2.6531 D(x): 0.7314 D(G(z)): 0.4590 / 0.4558\n", + "[97/100][83/391] Loss_D: 2.7480 Loss_G: 3.5245 D(x): 0.6697 D(G(z)): 0.3593 / 0.3398\n", + "[97/100][84/391] Loss_D: 2.6210 Loss_G: 2.8966 D(x): 0.6421 D(G(z)): 0.3696 / 0.4140\n", + "[97/100][85/391] Loss_D: 3.0282 Loss_G: 2.6067 D(x): 0.6383 D(G(z)): 0.4434 / 0.4537\n", + "[97/100][86/391] Loss_D: 2.5253 Loss_G: 3.1475 D(x): 0.6624 D(G(z)): 0.3108 / 0.3864\n", + "[97/100][87/391] Loss_D: 3.1637 Loss_G: 2.4972 D(x): 0.7018 D(G(z)): 0.4679 / 0.4593\n", + "[97/100][88/391] Loss_D: 2.7418 Loss_G: 2.0651 D(x): 0.6094 D(G(z)): 0.3351 / 0.5186\n", + "[97/100][89/391] Loss_D: 3.0790 Loss_G: 2.4086 D(x): 0.5642 D(G(z)): 0.3709 / 0.4789\n", + "[97/100][90/391] Loss_D: 2.4391 Loss_G: 2.7193 D(x): 0.7241 D(G(z)): 0.3602 / 0.4295\n", + "[97/100][91/391] Loss_D: 3.7098 Loss_G: 2.8774 D(x): 0.7628 D(G(z)): 0.5215 / 0.4255\n", + "[97/100][92/391] Loss_D: 3.0260 Loss_G: 2.6771 D(x): 0.5964 D(G(z)): 0.4392 / 0.4433\n", + "[97/100][93/391] Loss_D: 3.1394 Loss_G: 3.3712 D(x): 0.7273 D(G(z)): 0.5510 / 0.3721\n", + "[97/100][94/391] Loss_D: 2.9413 Loss_G: 4.0655 D(x): 0.6918 D(G(z)): 0.5263 / 0.2930\n", + "[97/100][95/391] Loss_D: 2.5246 Loss_G: 3.5090 D(x): 0.7601 D(G(z)): 0.3642 / 0.3433\n", + "[97/100][96/391] Loss_D: 2.6810 Loss_G: 2.3803 D(x): 0.6830 D(G(z)): 0.4091 / 0.5022\n", + "[97/100][97/391] Loss_D: 2.8264 Loss_G: 3.4386 D(x): 0.6261 D(G(z)): 0.3224 / 0.3446\n", + "[97/100][98/391] Loss_D: 3.8212 Loss_G: 3.1982 D(x): 0.4503 D(G(z)): 0.3122 / 0.3830\n", + "[97/100][99/391] Loss_D: 3.2131 Loss_G: 2.7025 D(x): 0.6388 D(G(z)): 0.4442 / 0.4480\n", + "[97/100][100/391] Loss_D: 2.8740 Loss_G: 2.6264 D(x): 0.7200 D(G(z)): 0.4797 / 0.4516\n", + "[97/100][101/391] Loss_D: 3.1283 Loss_G: 2.5833 D(x): 0.7778 D(G(z)): 0.5586 / 0.4570\n", + "[97/100][102/391] Loss_D: 2.7778 Loss_G: 2.9554 D(x): 0.7371 D(G(z)): 0.4395 / 0.4224\n", + "[97/100][103/391] Loss_D: 3.0098 Loss_G: 2.8539 D(x): 0.5739 D(G(z)): 0.3498 / 0.4224\n", + "[97/100][104/391] Loss_D: 2.9384 Loss_G: 2.8223 D(x): 0.6356 D(G(z)): 0.4512 / 0.4420\n", + "[97/100][105/391] Loss_D: 2.9652 Loss_G: 2.6036 D(x): 0.6298 D(G(z)): 0.3647 / 0.4259\n", + "[97/100][106/391] Loss_D: 2.7901 Loss_G: 2.4297 D(x): 0.6746 D(G(z)): 0.4407 / 0.4805\n", + "[97/100][107/391] Loss_D: 3.2763 Loss_G: 2.7728 D(x): 0.6116 D(G(z)): 0.4919 / 0.4190\n", + "[97/100][108/391] Loss_D: 3.2057 Loss_G: 2.8969 D(x): 0.6261 D(G(z)): 0.4760 / 0.4140\n", + "[97/100][109/391] Loss_D: 2.1425 Loss_G: 2.2844 D(x): 0.7545 D(G(z)): 0.2753 / 0.4971\n", + "[97/100][110/391] Loss_D: 3.4834 Loss_G: 2.6916 D(x): 0.5980 D(G(z)): 0.5209 / 0.4588\n", + "[97/100][111/391] Loss_D: 2.6700 Loss_G: 2.4426 D(x): 0.7063 D(G(z)): 0.4454 / 0.4731\n", + "[97/100][112/391] Loss_D: 2.5818 Loss_G: 2.1901 D(x): 0.6701 D(G(z)): 0.4046 / 0.5103\n", + "[97/100][113/391] Loss_D: 3.1200 Loss_G: 3.6219 D(x): 0.6853 D(G(z)): 0.4887 / 0.3269\n", + "[97/100][114/391] Loss_D: 3.0817 Loss_G: 1.7531 D(x): 0.6602 D(G(z)): 0.5065 / 0.5956\n", + "[97/100][115/391] Loss_D: 2.9744 Loss_G: 2.4392 D(x): 0.5877 D(G(z)): 0.3240 / 0.4620\n", + "[97/100][116/391] Loss_D: 2.3971 Loss_G: 3.7602 D(x): 0.6840 D(G(z)): 0.3086 / 0.3192\n", + "[97/100][117/391] Loss_D: 3.2513 Loss_G: 2.2193 D(x): 0.6735 D(G(z)): 0.4801 / 0.4984\n", + "[97/100][118/391] Loss_D: 2.6224 Loss_G: 3.3900 D(x): 0.6835 D(G(z)): 0.4468 / 0.3665\n", + "[97/100][119/391] Loss_D: 2.8805 Loss_G: 3.1028 D(x): 0.7991 D(G(z)): 0.5284 / 0.3850\n", + "[97/100][120/391] Loss_D: 2.9827 Loss_G: 3.2289 D(x): 0.7397 D(G(z)): 0.5431 / 0.3856\n", + "[97/100][121/391] Loss_D: 3.5301 Loss_G: 2.9226 D(x): 0.6808 D(G(z)): 0.4198 / 0.4239\n", + "[97/100][122/391] Loss_D: 2.9317 Loss_G: 2.7248 D(x): 0.5893 D(G(z)): 0.3100 / 0.4261\n", + "[97/100][123/391] Loss_D: 2.7829 Loss_G: 2.9558 D(x): 0.7061 D(G(z)): 0.4388 / 0.4210\n", + "[97/100][124/391] Loss_D: 2.7671 Loss_G: 2.9433 D(x): 0.6404 D(G(z)): 0.4020 / 0.4117\n", + "[97/100][125/391] Loss_D: 3.1728 Loss_G: 3.2391 D(x): 0.6916 D(G(z)): 0.5146 / 0.3736\n", + "[97/100][126/391] Loss_D: 2.6954 Loss_G: 3.6074 D(x): 0.6537 D(G(z)): 0.3891 / 0.3340\n", + "[97/100][127/391] Loss_D: 3.4340 Loss_G: 2.5653 D(x): 0.5476 D(G(z)): 0.4379 / 0.4566\n", + "[97/100][128/391] Loss_D: 2.4290 Loss_G: 3.1767 D(x): 0.7143 D(G(z)): 0.4406 / 0.3807\n", + "[97/100][129/391] Loss_D: 2.5875 Loss_G: 2.4178 D(x): 0.6730 D(G(z)): 0.3423 / 0.5004\n", + "[97/100][130/391] Loss_D: 2.4094 Loss_G: 3.1118 D(x): 0.7297 D(G(z)): 0.3412 / 0.4019\n", + "[97/100][131/391] Loss_D: 2.3117 Loss_G: 1.9655 D(x): 0.7265 D(G(z)): 0.3502 / 0.5559\n", + "[97/100][132/391] Loss_D: 3.0135 Loss_G: 3.5340 D(x): 0.6403 D(G(z)): 0.4608 / 0.3480\n", + "[97/100][133/391] Loss_D: 2.9475 Loss_G: 2.2667 D(x): 0.6400 D(G(z)): 0.4434 / 0.5058\n", + "[97/100][134/391] Loss_D: 3.3307 Loss_G: 2.9470 D(x): 0.6748 D(G(z)): 0.5592 / 0.4079\n", + "[97/100][135/391] Loss_D: 3.0764 Loss_G: 3.2706 D(x): 0.6757 D(G(z)): 0.5004 / 0.3604\n", + "[97/100][136/391] Loss_D: 3.2818 Loss_G: 3.9040 D(x): 0.5825 D(G(z)): 0.4391 / 0.3025\n", + "[97/100][137/391] Loss_D: 2.8360 Loss_G: 3.3505 D(x): 0.6346 D(G(z)): 0.3784 / 0.3585\n", + "[97/100][138/391] Loss_D: 2.6591 Loss_G: 2.8808 D(x): 0.6355 D(G(z)): 0.3520 / 0.4240\n", + "[97/100][139/391] Loss_D: 3.1895 Loss_G: 2.8254 D(x): 0.5868 D(G(z)): 0.3752 / 0.4350\n", + "[97/100][140/391] Loss_D: 2.1508 Loss_G: 3.4455 D(x): 0.7949 D(G(z)): 0.3710 / 0.3572\n", + "[97/100][141/391] Loss_D: 2.6614 Loss_G: 2.7914 D(x): 0.7740 D(G(z)): 0.4557 / 0.4219\n", + "[97/100][142/391] Loss_D: 2.7445 Loss_G: 2.5749 D(x): 0.6550 D(G(z)): 0.3811 / 0.4650\n", + "[97/100][143/391] Loss_D: 2.2873 Loss_G: 2.1999 D(x): 0.7911 D(G(z)): 0.2957 / 0.5002\n", + "[97/100][144/391] Loss_D: 2.5071 Loss_G: 2.8125 D(x): 0.6950 D(G(z)): 0.3888 / 0.4149\n", + "[97/100][145/391] Loss_D: 2.5746 Loss_G: 2.7775 D(x): 0.7310 D(G(z)): 0.3224 / 0.4182\n", + "[97/100][146/391] Loss_D: 2.7265 Loss_G: 3.1132 D(x): 0.7149 D(G(z)): 0.4186 / 0.3879\n", + "[97/100][147/391] Loss_D: 2.8323 Loss_G: 2.9353 D(x): 0.7543 D(G(z)): 0.4515 / 0.3967\n", + "[97/100][148/391] Loss_D: 2.9280 Loss_G: 2.4388 D(x): 0.6177 D(G(z)): 0.4504 / 0.4719\n", + "[97/100][149/391] Loss_D: 2.8781 Loss_G: 3.1831 D(x): 0.6353 D(G(z)): 0.3728 / 0.3782\n", + "[97/100][150/391] Loss_D: 3.6224 Loss_G: 2.5261 D(x): 0.6292 D(G(z)): 0.5718 / 0.4822\n", + "[97/100][151/391] Loss_D: 3.5759 Loss_G: 3.1014 D(x): 0.6201 D(G(z)): 0.4467 / 0.3872\n", + "[97/100][152/391] Loss_D: 2.9810 Loss_G: 2.6615 D(x): 0.6010 D(G(z)): 0.4453 / 0.4513\n", + "[97/100][153/391] Loss_D: 3.0186 Loss_G: 2.6549 D(x): 0.6013 D(G(z)): 0.4116 / 0.4398\n", + "[97/100][154/391] Loss_D: 2.4283 Loss_G: 3.3568 D(x): 0.7332 D(G(z)): 0.4283 / 0.3500\n", + "[97/100][155/391] Loss_D: 2.5166 Loss_G: 3.0649 D(x): 0.7922 D(G(z)): 0.3828 / 0.4007\n", + "[97/100][156/391] Loss_D: 2.5603 Loss_G: 2.6868 D(x): 0.6957 D(G(z)): 0.3602 / 0.4426\n", + "[97/100][157/391] Loss_D: 2.7355 Loss_G: 2.7962 D(x): 0.6430 D(G(z)): 0.3099 / 0.4027\n", + "[97/100][158/391] Loss_D: 2.5137 Loss_G: 2.5502 D(x): 0.7139 D(G(z)): 0.4080 / 0.4588\n", + "[97/100][159/391] Loss_D: 2.7979 Loss_G: 2.1940 D(x): 0.7044 D(G(z)): 0.4372 / 0.5080\n", + "[97/100][160/391] Loss_D: 2.9497 Loss_G: 2.7485 D(x): 0.6330 D(G(z)): 0.4454 / 0.4400\n", + "[97/100][161/391] Loss_D: 3.2981 Loss_G: 3.1730 D(x): 0.5985 D(G(z)): 0.4597 / 0.3956\n", + "[97/100][162/391] Loss_D: 2.4164 Loss_G: 2.4688 D(x): 0.6761 D(G(z)): 0.3329 / 0.4615\n", + "[97/100][163/391] Loss_D: 2.9721 Loss_G: 3.6238 D(x): 0.6685 D(G(z)): 0.4721 / 0.3268\n", + "[97/100][164/391] Loss_D: 2.9615 Loss_G: 3.5004 D(x): 0.7446 D(G(z)): 0.5482 / 0.3514\n", + "[97/100][165/391] Loss_D: 2.2438 Loss_G: 3.4339 D(x): 0.7696 D(G(z)): 0.3482 / 0.3589\n", + "[97/100][166/391] Loss_D: 2.4970 Loss_G: 3.5065 D(x): 0.7228 D(G(z)): 0.3927 / 0.3435\n", + "[97/100][167/391] Loss_D: 3.2992 Loss_G: 2.7092 D(x): 0.5567 D(G(z)): 0.4147 / 0.4370\n", + "[97/100][168/391] Loss_D: 2.8881 Loss_G: 3.2889 D(x): 0.6483 D(G(z)): 0.4441 / 0.3639\n", + "[97/100][169/391] Loss_D: 2.9585 Loss_G: 3.1244 D(x): 0.6343 D(G(z)): 0.4621 / 0.3816\n", + "[97/100][170/391] Loss_D: 3.1825 Loss_G: 3.7991 D(x): 0.5748 D(G(z)): 0.3857 / 0.3183\n", + "[97/100][171/391] Loss_D: 3.0671 Loss_G: 3.2827 D(x): 0.7388 D(G(z)): 0.5118 / 0.3676\n", + "[97/100][172/391] Loss_D: 2.8622 Loss_G: 3.3582 D(x): 0.7757 D(G(z)): 0.4981 / 0.3522\n", + "[97/100][173/391] Loss_D: 2.9935 Loss_G: 3.5336 D(x): 0.6914 D(G(z)): 0.4916 / 0.3417\n", + "[97/100][174/391] Loss_D: 2.7430 Loss_G: 4.9224 D(x): 0.5716 D(G(z)): 0.3052 / 0.2254\n", + "[97/100][175/391] Loss_D: 2.7324 Loss_G: 3.0087 D(x): 0.6120 D(G(z)): 0.3007 / 0.3987\n", + "[97/100][176/391] Loss_D: 2.6656 Loss_G: 2.7698 D(x): 0.7353 D(G(z)): 0.4063 / 0.4290\n", + "[97/100][177/391] Loss_D: 3.1138 Loss_G: 3.0052 D(x): 0.6177 D(G(z)): 0.4400 / 0.3997\n", + "[97/100][178/391] Loss_D: 3.1079 Loss_G: 3.3847 D(x): 0.6234 D(G(z)): 0.4658 / 0.3557\n", + "[97/100][179/391] Loss_D: 2.9814 Loss_G: 2.7432 D(x): 0.7143 D(G(z)): 0.4855 / 0.4415\n", + "[97/100][180/391] Loss_D: 3.1562 Loss_G: 2.7240 D(x): 0.7292 D(G(z)): 0.5316 / 0.4472\n", + "[97/100][181/391] Loss_D: 3.5883 Loss_G: 2.8031 D(x): 0.6457 D(G(z)): 0.4851 / 0.4200\n", + "[97/100][182/391] Loss_D: 2.8339 Loss_G: 3.3908 D(x): 0.6209 D(G(z)): 0.4097 / 0.3429\n", + "[97/100][183/391] Loss_D: 2.6870 Loss_G: 3.4931 D(x): 0.6318 D(G(z)): 0.3784 / 0.3477\n", + "[97/100][184/391] Loss_D: 2.7296 Loss_G: 3.2315 D(x): 0.6882 D(G(z)): 0.4247 / 0.3778\n", + "[97/100][185/391] Loss_D: 2.9462 Loss_G: 2.1876 D(x): 0.6164 D(G(z)): 0.3795 / 0.5114\n", + "[97/100][186/391] Loss_D: 2.3913 Loss_G: 2.9791 D(x): 0.7659 D(G(z)): 0.3791 / 0.3975\n", + "[97/100][187/391] Loss_D: 2.9776 Loss_G: 3.0874 D(x): 0.5633 D(G(z)): 0.3352 / 0.3687\n", + "[97/100][188/391] Loss_D: 2.1519 Loss_G: 3.4366 D(x): 0.7456 D(G(z)): 0.3110 / 0.3395\n", + "[97/100][189/391] Loss_D: 3.8034 Loss_G: 3.0886 D(x): 0.7018 D(G(z)): 0.6479 / 0.3935\n", + "[97/100][190/391] Loss_D: 2.6272 Loss_G: 3.5569 D(x): 0.7533 D(G(z)): 0.4280 / 0.3442\n", + "[97/100][191/391] Loss_D: 3.2093 Loss_G: 3.0174 D(x): 0.7296 D(G(z)): 0.5332 / 0.4066\n", + "[97/100][192/391] Loss_D: 2.5981 Loss_G: 3.3130 D(x): 0.6619 D(G(z)): 0.3941 / 0.3800\n", + "[97/100][193/391] Loss_D: 2.2671 Loss_G: 3.0761 D(x): 0.7564 D(G(z)): 0.3531 / 0.3837\n", + "[97/100][194/391] Loss_D: 2.6518 Loss_G: 3.8185 D(x): 0.6619 D(G(z)): 0.3961 / 0.3112\n", + "[97/100][195/391] Loss_D: 2.7323 Loss_G: 3.5106 D(x): 0.6362 D(G(z)): 0.3326 / 0.3400\n", + "[97/100][196/391] Loss_D: 2.5867 Loss_G: 3.1853 D(x): 0.6628 D(G(z)): 0.3741 / 0.3841\n", + "[97/100][197/391] Loss_D: 3.0048 Loss_G: 2.8999 D(x): 0.6890 D(G(z)): 0.4390 / 0.4134\n", + "[97/100][198/391] Loss_D: 3.7984 Loss_G: 2.4125 D(x): 0.5356 D(G(z)): 0.5002 / 0.4703\n", + "[97/100][199/391] Loss_D: 2.8324 Loss_G: 2.8226 D(x): 0.6224 D(G(z)): 0.3706 / 0.4374\n", + "[97/100][200/391] Loss_D: 3.5984 Loss_G: 1.5786 D(x): 0.5252 D(G(z)): 0.4726 / 0.6158\n", + "[97/100][201/391] Loss_D: 3.3510 Loss_G: 2.0606 D(x): 0.6902 D(G(z)): 0.5401 / 0.5255\n", + "[97/100][202/391] Loss_D: 2.7794 Loss_G: 3.0577 D(x): 0.6760 D(G(z)): 0.4411 / 0.3972\n", + "[97/100][203/391] Loss_D: 3.1203 Loss_G: 2.7286 D(x): 0.6021 D(G(z)): 0.4161 / 0.4267\n", + "[97/100][204/391] Loss_D: 3.0615 Loss_G: 3.9618 D(x): 0.6488 D(G(z)): 0.5174 / 0.3108\n", + "[97/100][205/391] Loss_D: 3.0539 Loss_G: 2.5295 D(x): 0.5565 D(G(z)): 0.4082 / 0.4529\n", + "[97/100][206/391] Loss_D: 2.8816 Loss_G: 3.6650 D(x): 0.6413 D(G(z)): 0.4201 / 0.3229\n", + "[97/100][207/391] Loss_D: 2.7967 Loss_G: 2.9392 D(x): 0.6315 D(G(z)): 0.3640 / 0.3969\n", + "[97/100][208/391] Loss_D: 2.2511 Loss_G: 2.2522 D(x): 0.7311 D(G(z)): 0.3833 / 0.4954\n", + "[97/100][209/391] Loss_D: 2.9932 Loss_G: 3.3093 D(x): 0.6903 D(G(z)): 0.4963 / 0.3832\n", + "[97/100][210/391] Loss_D: 2.8438 Loss_G: 2.4203 D(x): 0.5984 D(G(z)): 0.3205 / 0.4880\n", + "[97/100][211/391] Loss_D: 3.6555 Loss_G: 2.6478 D(x): 0.7212 D(G(z)): 0.5099 / 0.4702\n", + "[97/100][212/391] Loss_D: 3.6816 Loss_G: 2.6062 D(x): 0.6250 D(G(z)): 0.5570 / 0.4451\n", + "[97/100][213/391] Loss_D: 2.6134 Loss_G: 2.5594 D(x): 0.7109 D(G(z)): 0.3941 / 0.4619\n", + "[97/100][214/391] Loss_D: 3.5160 Loss_G: 2.5545 D(x): 0.6514 D(G(z)): 0.5830 / 0.4367\n", + "[97/100][215/391] Loss_D: 2.7050 Loss_G: 3.0378 D(x): 0.7063 D(G(z)): 0.3850 / 0.3880\n", + "[97/100][216/391] Loss_D: 2.6340 Loss_G: 2.5793 D(x): 0.7149 D(G(z)): 0.4057 / 0.4474\n", + "[97/100][217/391] Loss_D: 2.7571 Loss_G: 2.7920 D(x): 0.6578 D(G(z)): 0.4066 / 0.4264\n", + "[97/100][218/391] Loss_D: 2.7164 Loss_G: 3.1660 D(x): 0.6462 D(G(z)): 0.3443 / 0.3782\n", + "[97/100][219/391] Loss_D: 2.7843 Loss_G: 3.0993 D(x): 0.6279 D(G(z)): 0.3734 / 0.3987\n", + "[97/100][220/391] Loss_D: 2.7300 Loss_G: 2.8435 D(x): 0.7301 D(G(z)): 0.4742 / 0.4380\n", + "[97/100][221/391] Loss_D: 2.8074 Loss_G: 2.5626 D(x): 0.5754 D(G(z)): 0.3559 / 0.4633\n", + "[97/100][222/391] Loss_D: 2.9208 Loss_G: 3.1445 D(x): 0.6773 D(G(z)): 0.4695 / 0.3814\n", + "[97/100][223/391] Loss_D: 3.1535 Loss_G: 2.7785 D(x): 0.6691 D(G(z)): 0.5082 / 0.4252\n", + "[97/100][224/391] Loss_D: 2.6448 Loss_G: 2.6631 D(x): 0.6731 D(G(z)): 0.3988 / 0.4563\n", + "[97/100][225/391] Loss_D: 2.5262 Loss_G: 3.7051 D(x): 0.7213 D(G(z)): 0.3966 / 0.3216\n", + "[97/100][226/391] Loss_D: 3.1403 Loss_G: 3.1223 D(x): 0.6006 D(G(z)): 0.4634 / 0.3670\n", + "[97/100][227/391] Loss_D: 2.7557 Loss_G: 2.6153 D(x): 0.6816 D(G(z)): 0.4143 / 0.4589\n", + "[97/100][228/391] Loss_D: 2.4027 Loss_G: 3.4491 D(x): 0.7501 D(G(z)): 0.3723 / 0.3469\n", + "[97/100][229/391] Loss_D: 3.0314 Loss_G: 2.2967 D(x): 0.6134 D(G(z)): 0.4095 / 0.4975\n", + "[97/100][230/391] Loss_D: 2.3556 Loss_G: 3.2664 D(x): 0.6782 D(G(z)): 0.3568 / 0.3781\n", + "[97/100][231/391] Loss_D: 2.5729 Loss_G: 3.1569 D(x): 0.6678 D(G(z)): 0.3315 / 0.3713\n", + "[97/100][232/391] Loss_D: 2.4851 Loss_G: 1.8014 D(x): 0.7355 D(G(z)): 0.3832 / 0.5733\n", + "[97/100][233/391] Loss_D: 2.1002 Loss_G: 4.0253 D(x): 0.8078 D(G(z)): 0.3402 / 0.2986\n", + "[97/100][234/391] Loss_D: 2.5358 Loss_G: 3.1718 D(x): 0.6672 D(G(z)): 0.3971 / 0.3768\n", + "[97/100][235/391] Loss_D: 2.8189 Loss_G: 2.7683 D(x): 0.6936 D(G(z)): 0.4500 / 0.4319\n", + "[97/100][236/391] Loss_D: 2.7011 Loss_G: 4.0243 D(x): 0.6814 D(G(z)): 0.4182 / 0.3028\n", + "[97/100][237/391] Loss_D: 2.4816 Loss_G: 2.5576 D(x): 0.6827 D(G(z)): 0.3315 / 0.4677\n", + "[97/100][238/391] Loss_D: 2.6116 Loss_G: 3.0534 D(x): 0.6784 D(G(z)): 0.4059 / 0.3930\n", + "[97/100][239/391] Loss_D: 2.2031 Loss_G: 2.2462 D(x): 0.7585 D(G(z)): 0.3216 / 0.5105\n", + "[97/100][240/391] Loss_D: 3.1784 Loss_G: 2.4273 D(x): 0.6691 D(G(z)): 0.5172 / 0.4789\n", + "[97/100][241/391] Loss_D: 3.7749 Loss_G: 2.2187 D(x): 0.6832 D(G(z)): 0.3517 / 0.5124\n", + "[97/100][242/391] Loss_D: 2.3550 Loss_G: 2.7196 D(x): 0.7525 D(G(z)): 0.3916 / 0.4197\n", + "[97/100][243/391] Loss_D: 2.8613 Loss_G: 2.9666 D(x): 0.6361 D(G(z)): 0.3846 / 0.3934\n", + "[97/100][244/391] Loss_D: 2.5427 Loss_G: 3.0174 D(x): 0.7832 D(G(z)): 0.4844 / 0.3977\n", + "[97/100][245/391] Loss_D: 3.2515 Loss_G: 2.6742 D(x): 0.5654 D(G(z)): 0.4306 / 0.4387\n", + "[97/100][246/391] Loss_D: 2.5246 Loss_G: 2.9064 D(x): 0.6885 D(G(z)): 0.3268 / 0.4034\n", + "[97/100][247/391] Loss_D: 2.5816 Loss_G: 3.0645 D(x): 0.7539 D(G(z)): 0.4074 / 0.3876\n", + "[97/100][248/391] Loss_D: 2.9551 Loss_G: 3.0908 D(x): 0.6625 D(G(z)): 0.4819 / 0.3960\n", + "[97/100][249/391] Loss_D: 2.9806 Loss_G: 2.6349 D(x): 0.6954 D(G(z)): 0.5359 / 0.4595\n", + "[97/100][250/391] Loss_D: 2.8557 Loss_G: 3.7092 D(x): 0.6132 D(G(z)): 0.3287 / 0.3217\n", + "[97/100][251/391] Loss_D: 3.2643 Loss_G: 2.2577 D(x): 0.5774 D(G(z)): 0.4191 / 0.5117\n", + "[97/100][252/391] Loss_D: 2.0943 Loss_G: 4.2032 D(x): 0.8204 D(G(z)): 0.3035 / 0.2951\n", + "[97/100][253/391] Loss_D: 2.4297 Loss_G: 2.4426 D(x): 0.7596 D(G(z)): 0.3264 / 0.4743\n", + "[97/100][254/391] Loss_D: 3.2785 Loss_G: 3.0508 D(x): 0.6245 D(G(z)): 0.5064 / 0.3987\n", + "[97/100][255/391] Loss_D: 2.7824 Loss_G: 2.8301 D(x): 0.6289 D(G(z)): 0.2982 / 0.4171\n", + "[97/100][256/391] Loss_D: 2.9879 Loss_G: 2.3597 D(x): 0.6875 D(G(z)): 0.4648 / 0.4815\n", + "[97/100][257/391] Loss_D: 2.6951 Loss_G: 2.5887 D(x): 0.7693 D(G(z)): 0.3972 / 0.4409\n", + "[97/100][258/391] Loss_D: 2.5109 Loss_G: 2.7091 D(x): 0.6941 D(G(z)): 0.3606 / 0.4547\n", + "[97/100][259/391] Loss_D: 2.6527 Loss_G: 2.8861 D(x): 0.7007 D(G(z)): 0.4299 / 0.4201\n", + "[97/100][260/391] Loss_D: 2.6821 Loss_G: 3.6875 D(x): 0.7051 D(G(z)): 0.4202 / 0.3290\n", + "[97/100][261/391] Loss_D: 3.0834 Loss_G: 2.8068 D(x): 0.6208 D(G(z)): 0.4119 / 0.4427\n", + "[97/100][262/391] Loss_D: 2.8181 Loss_G: 2.5983 D(x): 0.7899 D(G(z)): 0.4929 / 0.4395\n", + "[97/100][263/391] Loss_D: 2.5473 Loss_G: 2.9650 D(x): 0.7132 D(G(z)): 0.3333 / 0.4034\n", + "[97/100][264/391] Loss_D: 2.0187 Loss_G: 2.1116 D(x): 0.7466 D(G(z)): 0.3276 / 0.5307\n", + "[97/100][265/391] Loss_D: 3.5146 Loss_G: 2.1994 D(x): 0.5208 D(G(z)): 0.4344 / 0.5312\n", + "[97/100][266/391] Loss_D: 3.1519 Loss_G: 3.2869 D(x): 0.7035 D(G(z)): 0.4710 / 0.3615\n", + "[97/100][267/391] Loss_D: 2.6141 Loss_G: 2.2184 D(x): 0.6572 D(G(z)): 0.3530 / 0.5145\n", + "[97/100][268/391] Loss_D: 2.6436 Loss_G: 2.9698 D(x): 0.6749 D(G(z)): 0.4835 / 0.4061\n", + "[97/100][269/391] Loss_D: 2.9564 Loss_G: 2.4671 D(x): 0.6224 D(G(z)): 0.4085 / 0.4911\n", + "[97/100][270/391] Loss_D: 3.1115 Loss_G: 2.6562 D(x): 0.6008 D(G(z)): 0.4214 / 0.4530\n", + "[97/100][271/391] Loss_D: 3.7213 Loss_G: 2.4958 D(x): 0.6652 D(G(z)): 0.4950 / 0.4749\n", + "[97/100][272/391] Loss_D: 2.7334 Loss_G: 2.5842 D(x): 0.6528 D(G(z)): 0.4243 / 0.4646\n", + "[97/100][273/391] Loss_D: 2.2479 Loss_G: 2.7764 D(x): 0.8672 D(G(z)): 0.4055 / 0.4224\n", + "[97/100][274/391] Loss_D: 2.9012 Loss_G: 2.8912 D(x): 0.6256 D(G(z)): 0.4106 / 0.4202\n", + "[97/100][275/391] Loss_D: 2.3723 Loss_G: 2.7511 D(x): 0.7673 D(G(z)): 0.3799 / 0.4305\n", + "[97/100][276/391] Loss_D: 2.9734 Loss_G: 3.2621 D(x): 0.6592 D(G(z)): 0.4440 / 0.3517\n", + "[97/100][277/391] Loss_D: 2.4828 Loss_G: 2.1409 D(x): 0.7485 D(G(z)): 0.3260 / 0.5252\n", + "[97/100][278/391] Loss_D: 3.0627 Loss_G: 3.0987 D(x): 0.6423 D(G(z)): 0.5020 / 0.3909\n", + "[97/100][279/391] Loss_D: 2.4596 Loss_G: 3.3350 D(x): 0.7226 D(G(z)): 0.3845 / 0.3733\n", + "[97/100][280/391] Loss_D: 2.5858 Loss_G: 2.4324 D(x): 0.6772 D(G(z)): 0.3633 / 0.5011\n", + "[97/100][281/391] Loss_D: 2.7102 Loss_G: 3.2897 D(x): 0.7164 D(G(z)): 0.4248 / 0.3700\n", + "[97/100][282/391] Loss_D: 3.0905 Loss_G: 2.7979 D(x): 0.6208 D(G(z)): 0.4089 / 0.4430\n", + "[97/100][283/391] Loss_D: 2.5634 Loss_G: 2.3851 D(x): 0.6817 D(G(z)): 0.3586 / 0.4948\n", + "[97/100][284/391] Loss_D: 2.7177 Loss_G: 2.5789 D(x): 0.6595 D(G(z)): 0.4172 / 0.4504\n", + "[97/100][285/391] Loss_D: 2.9738 Loss_G: 3.0049 D(x): 0.7075 D(G(z)): 0.5074 / 0.3932\n", + "[97/100][286/391] Loss_D: 2.7005 Loss_G: 3.5719 D(x): 0.6610 D(G(z)): 0.3847 / 0.3321\n", + "[97/100][287/391] Loss_D: 3.2351 Loss_G: 3.5849 D(x): 0.6241 D(G(z)): 0.4640 / 0.3394\n", + "[97/100][288/391] Loss_D: 2.7165 Loss_G: 2.6706 D(x): 0.6271 D(G(z)): 0.3788 / 0.4427\n", + "[97/100][289/391] Loss_D: 2.7475 Loss_G: 3.3928 D(x): 0.6642 D(G(z)): 0.3755 / 0.3709\n", + "[97/100][290/391] Loss_D: 2.2020 Loss_G: 1.4381 D(x): 0.7593 D(G(z)): 0.2876 / 0.6675\n", + "[97/100][291/391] Loss_D: 2.9043 Loss_G: 2.1321 D(x): 0.7090 D(G(z)): 0.4871 / 0.5420\n", + "[97/100][292/391] Loss_D: 2.5394 Loss_G: 2.1186 D(x): 0.7670 D(G(z)): 0.3871 / 0.5314\n", + "[97/100][293/391] Loss_D: 3.1151 Loss_G: 2.9316 D(x): 0.6451 D(G(z)): 0.4658 / 0.4079\n", + "[97/100][294/391] Loss_D: 2.6313 Loss_G: 2.9289 D(x): 0.7071 D(G(z)): 0.4580 / 0.4203\n", + "[97/100][295/391] Loss_D: 2.5281 Loss_G: 4.1988 D(x): 0.6963 D(G(z)): 0.3446 / 0.2812\n", + "[97/100][296/391] Loss_D: 2.9754 Loss_G: 3.4229 D(x): 0.6158 D(G(z)): 0.3636 / 0.3545\n", + "[97/100][297/391] Loss_D: 3.3141 Loss_G: 2.9815 D(x): 0.5780 D(G(z)): 0.4082 / 0.3926\n", + "[97/100][298/391] Loss_D: 2.3416 Loss_G: 3.2623 D(x): 0.8091 D(G(z)): 0.4918 / 0.3702\n", + "[97/100][299/391] Loss_D: 2.4157 Loss_G: 2.6713 D(x): 0.7152 D(G(z)): 0.2679 / 0.4320\n", + "[97/100][300/391] Loss_D: 3.0952 Loss_G: 2.7002 D(x): 0.7368 D(G(z)): 0.5152 / 0.4351\n", + "[97/100][301/391] Loss_D: 3.6439 Loss_G: 2.3560 D(x): 0.6624 D(G(z)): 0.3471 / 0.4878\n", + "[97/100][302/391] Loss_D: 2.1503 Loss_G: 3.0953 D(x): 0.7764 D(G(z)): 0.3610 / 0.3969\n", + "[97/100][303/391] Loss_D: 3.0221 Loss_G: 3.9288 D(x): 0.6509 D(G(z)): 0.4767 / 0.3025\n", + "[97/100][304/391] Loss_D: 2.7818 Loss_G: 2.6342 D(x): 0.6641 D(G(z)): 0.4608 / 0.4407\n", + "[97/100][305/391] Loss_D: 2.8824 Loss_G: 2.5595 D(x): 0.6567 D(G(z)): 0.4346 / 0.4596\n", + "[97/100][306/391] Loss_D: 3.5048 Loss_G: 2.9882 D(x): 0.5273 D(G(z)): 0.4124 / 0.4000\n", + "[97/100][307/391] Loss_D: 2.6871 Loss_G: 2.5764 D(x): 0.6900 D(G(z)): 0.4018 / 0.4498\n", + "[97/100][308/391] Loss_D: 2.6568 Loss_G: 3.1773 D(x): 0.7559 D(G(z)): 0.5402 / 0.3884\n", + "[97/100][309/391] Loss_D: 2.4744 Loss_G: 3.0083 D(x): 0.7052 D(G(z)): 0.4235 / 0.4128\n", + "[97/100][310/391] Loss_D: 2.6206 Loss_G: 3.1333 D(x): 0.6693 D(G(z)): 0.3021 / 0.3758\n", + "[97/100][311/391] Loss_D: 2.7725 Loss_G: 2.9063 D(x): 0.7124 D(G(z)): 0.4087 / 0.4083\n", + "[97/100][312/391] Loss_D: 2.8972 Loss_G: 2.5719 D(x): 0.6895 D(G(z)): 0.4732 / 0.4695\n", + "[97/100][313/391] Loss_D: 2.7954 Loss_G: 3.2906 D(x): 0.6224 D(G(z)): 0.3612 / 0.3634\n", + "[97/100][314/391] Loss_D: 2.5235 Loss_G: 3.0467 D(x): 0.6201 D(G(z)): 0.3303 / 0.3941\n", + "[97/100][315/391] Loss_D: 2.2920 Loss_G: 2.1388 D(x): 0.7310 D(G(z)): 0.3098 / 0.5178\n", + "[97/100][316/391] Loss_D: 2.8738 Loss_G: 2.4122 D(x): 0.7396 D(G(z)): 0.4315 / 0.4761\n", + "[97/100][317/391] Loss_D: 2.8736 Loss_G: 2.6283 D(x): 0.6771 D(G(z)): 0.4209 / 0.4452\n", + "[97/100][318/391] Loss_D: 2.4849 Loss_G: 3.0186 D(x): 0.6728 D(G(z)): 0.3928 / 0.3949\n", + "[97/100][319/391] Loss_D: 2.5206 Loss_G: 2.9556 D(x): 0.7382 D(G(z)): 0.4260 / 0.3980\n", + "[97/100][320/391] Loss_D: 3.8401 Loss_G: 3.4443 D(x): 0.6830 D(G(z)): 0.6309 / 0.3586\n", + "[97/100][321/391] Loss_D: 2.7520 Loss_G: 2.4752 D(x): 0.7190 D(G(z)): 0.4180 / 0.4806\n", + "[97/100][322/391] Loss_D: 2.4399 Loss_G: 3.5163 D(x): 0.7056 D(G(z)): 0.3353 / 0.3399\n", + "[97/100][323/391] Loss_D: 3.6074 Loss_G: 3.2487 D(x): 0.6207 D(G(z)): 0.5505 / 0.3811\n", + "[97/100][324/391] Loss_D: 3.0637 Loss_G: 2.6095 D(x): 0.5641 D(G(z)): 0.3623 / 0.4530\n", + "[97/100][325/391] Loss_D: 2.5354 Loss_G: 3.2469 D(x): 0.7600 D(G(z)): 0.3916 / 0.3607\n", + "[97/100][326/391] Loss_D: 2.3608 Loss_G: 2.5346 D(x): 0.6629 D(G(z)): 0.2690 / 0.4576\n", + "[97/100][327/391] Loss_D: 2.9516 Loss_G: 3.9979 D(x): 0.6079 D(G(z)): 0.4127 / 0.2995\n", + "[97/100][328/391] Loss_D: 2.8612 Loss_G: 2.7325 D(x): 0.6924 D(G(z)): 0.4908 / 0.4431\n", + "[97/100][329/391] Loss_D: 3.3714 Loss_G: 2.4460 D(x): 0.6436 D(G(z)): 0.5217 / 0.4680\n", + "[97/100][330/391] Loss_D: 2.7225 Loss_G: 3.1962 D(x): 0.6655 D(G(z)): 0.3690 / 0.3859\n", + "[97/100][331/391] Loss_D: 3.4824 Loss_G: 2.1036 D(x): 0.5766 D(G(z)): 0.4616 / 0.5172\n", + "[97/100][332/391] Loss_D: 3.3756 Loss_G: 2.8149 D(x): 0.6745 D(G(z)): 0.5744 / 0.4324\n", + "[97/100][333/391] Loss_D: 2.6181 Loss_G: 2.9918 D(x): 0.7148 D(G(z)): 0.3524 / 0.4010\n", + "[97/100][334/391] Loss_D: 2.5938 Loss_G: 2.6305 D(x): 0.7120 D(G(z)): 0.4571 / 0.4538\n", + "[97/100][335/391] Loss_D: 2.9535 Loss_G: 3.6845 D(x): 0.7262 D(G(z)): 0.4932 / 0.3286\n", + "[97/100][336/391] Loss_D: 3.2631 Loss_G: 3.4849 D(x): 0.5735 D(G(z)): 0.3973 / 0.3510\n", + "[97/100][337/391] Loss_D: 3.1916 Loss_G: 3.4281 D(x): 0.5991 D(G(z)): 0.4373 / 0.3636\n", + "[97/100][338/391] Loss_D: 3.1546 Loss_G: 2.6822 D(x): 0.6732 D(G(z)): 0.5057 / 0.4522\n", + "[97/100][339/391] Loss_D: 2.9560 Loss_G: 2.9133 D(x): 0.6501 D(G(z)): 0.4248 / 0.4280\n", + "[97/100][340/391] Loss_D: 2.6224 Loss_G: 2.5829 D(x): 0.6799 D(G(z)): 0.3487 / 0.4712\n", + "[97/100][341/391] Loss_D: 2.5450 Loss_G: 2.5275 D(x): 0.7033 D(G(z)): 0.4043 / 0.4632\n", + "[97/100][342/391] Loss_D: 2.7469 Loss_G: 3.1948 D(x): 0.6661 D(G(z)): 0.4237 / 0.3956\n", + "[97/100][343/391] Loss_D: 2.8506 Loss_G: 2.6567 D(x): 0.6769 D(G(z)): 0.3989 / 0.4414\n", + "[97/100][344/391] Loss_D: 3.1428 Loss_G: 3.0085 D(x): 0.6404 D(G(z)): 0.4874 / 0.4024\n", + "[97/100][345/391] Loss_D: 2.4578 Loss_G: 2.3031 D(x): 0.7147 D(G(z)): 0.3415 / 0.4887\n", + "[97/100][346/391] Loss_D: 3.6806 Loss_G: 4.1139 D(x): 0.5452 D(G(z)): 0.4792 / 0.2797\n", + "[97/100][347/391] Loss_D: 2.9535 Loss_G: 3.3652 D(x): 0.6055 D(G(z)): 0.3427 / 0.3546\n", + "[97/100][348/391] Loss_D: 2.6956 Loss_G: 2.1741 D(x): 0.7091 D(G(z)): 0.4744 / 0.5154\n", + "[97/100][349/391] Loss_D: 3.1146 Loss_G: 2.7430 D(x): 0.6907 D(G(z)): 0.5091 / 0.4287\n", + "[97/100][350/391] Loss_D: 3.5176 Loss_G: 3.1289 D(x): 0.6995 D(G(z)): 0.6057 / 0.3899\n", + "[97/100][351/391] Loss_D: 2.7902 Loss_G: 3.1852 D(x): 0.6706 D(G(z)): 0.4070 / 0.3720\n", + "[97/100][352/391] Loss_D: 2.7503 Loss_G: 3.1069 D(x): 0.5648 D(G(z)): 0.2950 / 0.3797\n", + "[97/100][353/391] Loss_D: 2.7838 Loss_G: 2.6575 D(x): 0.6161 D(G(z)): 0.3498 / 0.4354\n", + "[97/100][354/391] Loss_D: 2.2971 Loss_G: 2.9009 D(x): 0.7720 D(G(z)): 0.4354 / 0.4121\n", + "[97/100][355/391] Loss_D: 2.8922 Loss_G: 2.0594 D(x): 0.7683 D(G(z)): 0.5359 / 0.5279\n", + "[97/100][356/391] Loss_D: 2.9628 Loss_G: 3.3265 D(x): 0.5642 D(G(z)): 0.3684 / 0.3560\n", + "[97/100][357/391] Loss_D: 3.2371 Loss_G: 2.7453 D(x): 0.5777 D(G(z)): 0.4351 / 0.4338\n", + "[97/100][358/391] Loss_D: 2.4053 Loss_G: 2.8604 D(x): 0.7352 D(G(z)): 0.4380 / 0.4008\n", + "[97/100][359/391] Loss_D: 2.4285 Loss_G: 3.5410 D(x): 0.6866 D(G(z)): 0.3049 / 0.3475\n", + "[97/100][360/391] Loss_D: 2.6487 Loss_G: 3.3377 D(x): 0.7511 D(G(z)): 0.4441 / 0.3668\n", + "[97/100][361/391] Loss_D: 3.6010 Loss_G: 3.2257 D(x): 0.7109 D(G(z)): 0.4662 / 0.3816\n", + "[97/100][362/391] Loss_D: 3.1189 Loss_G: 2.6604 D(x): 0.6394 D(G(z)): 0.4649 / 0.4505\n", + "[97/100][363/391] Loss_D: 3.0490 Loss_G: 2.9662 D(x): 0.6660 D(G(z)): 0.5076 / 0.4108\n", + "[97/100][364/391] Loss_D: 2.5761 Loss_G: 2.7210 D(x): 0.6960 D(G(z)): 0.3403 / 0.4331\n", + "[97/100][365/391] Loss_D: 2.7034 Loss_G: 3.1245 D(x): 0.6650 D(G(z)): 0.4018 / 0.3879\n", + "[97/100][366/391] Loss_D: 3.0354 Loss_G: 3.6466 D(x): 0.6498 D(G(z)): 0.4841 / 0.3309\n", + "[97/100][367/391] Loss_D: 2.8304 Loss_G: 3.5693 D(x): 0.6341 D(G(z)): 0.3243 / 0.3348\n", + "[97/100][368/391] Loss_D: 2.7214 Loss_G: 3.7857 D(x): 0.6189 D(G(z)): 0.2886 / 0.3059\n", + "[97/100][369/391] Loss_D: 2.3504 Loss_G: 2.4366 D(x): 0.7065 D(G(z)): 0.3423 / 0.4811\n", + "[97/100][370/391] Loss_D: 2.7027 Loss_G: 2.4426 D(x): 0.7292 D(G(z)): 0.4656 / 0.4858\n", + "[97/100][371/391] Loss_D: 2.6871 Loss_G: 2.8417 D(x): 0.6926 D(G(z)): 0.4073 / 0.4290\n", + "[97/100][372/391] Loss_D: 3.0357 Loss_G: 3.5057 D(x): 0.6248 D(G(z)): 0.4644 / 0.3346\n", + "[97/100][373/391] Loss_D: 2.3462 Loss_G: 2.8579 D(x): 0.7877 D(G(z)): 0.3565 / 0.4216\n", + "[97/100][374/391] Loss_D: 3.0240 Loss_G: 3.1606 D(x): 0.7239 D(G(z)): 0.5184 / 0.3719\n", + "[97/100][375/391] Loss_D: 2.9673 Loss_G: 3.0355 D(x): 0.6416 D(G(z)): 0.4176 / 0.3930\n", + "[97/100][376/391] Loss_D: 3.2812 Loss_G: 3.0020 D(x): 0.5617 D(G(z)): 0.3751 / 0.4007\n", + "[97/100][377/391] Loss_D: 2.7186 Loss_G: 2.5917 D(x): 0.7208 D(G(z)): 0.4328 / 0.4617\n", + "[97/100][378/391] Loss_D: 3.0400 Loss_G: 2.7927 D(x): 0.6122 D(G(z)): 0.4819 / 0.4219\n", + "[97/100][379/391] Loss_D: 3.2135 Loss_G: 3.2237 D(x): 0.5897 D(G(z)): 0.4588 / 0.3780\n", + "[97/100][380/391] Loss_D: 3.6631 Loss_G: 2.1750 D(x): 0.5966 D(G(z)): 0.5690 / 0.5151\n", + "[97/100][381/391] Loss_D: 2.8004 Loss_G: 2.2665 D(x): 0.6661 D(G(z)): 0.4149 / 0.4903\n", + "[97/100][382/391] Loss_D: 3.3465 Loss_G: 2.7862 D(x): 0.6922 D(G(z)): 0.5741 / 0.4306\n", + "[97/100][383/391] Loss_D: 2.5785 Loss_G: 2.9088 D(x): 0.6573 D(G(z)): 0.3077 / 0.4228\n", + "[97/100][384/391] Loss_D: 2.6503 Loss_G: 3.0226 D(x): 0.6879 D(G(z)): 0.4724 / 0.4041\n", + "[97/100][385/391] Loss_D: 2.6568 Loss_G: 2.8848 D(x): 0.6966 D(G(z)): 0.3988 / 0.4264\n", + "[97/100][386/391] Loss_D: 3.0334 Loss_G: 2.9763 D(x): 0.6080 D(G(z)): 0.4154 / 0.3889\n", + "[97/100][387/391] Loss_D: 3.1101 Loss_G: 3.2696 D(x): 0.5518 D(G(z)): 0.3171 / 0.3661\n", + "[97/100][388/391] Loss_D: 2.9322 Loss_G: 2.6592 D(x): 0.6813 D(G(z)): 0.4844 / 0.4299\n", + "[97/100][389/391] Loss_D: 2.8522 Loss_G: 2.5044 D(x): 0.6898 D(G(z)): 0.4411 / 0.4549\n", + "[97/100][390/391] Loss_D: 3.5709 Loss_G: 2.8884 D(x): 0.5130 D(G(z)): 0.4763 / 0.4307\n", + "[97/100][391/391] Loss_D: 3.8946 Loss_G: 2.4195 D(x): 0.7825 D(G(z)): 0.4281 / 0.4887\n", + "[98/100][1/391] Loss_D: 3.5147 Loss_G: 3.2527 D(x): 0.6775 D(G(z)): 0.4278 / 0.3811\n", + "[98/100][2/391] Loss_D: 2.5060 Loss_G: 3.7494 D(x): 0.7666 D(G(z)): 0.4447 / 0.3297\n", + "[98/100][3/391] Loss_D: 2.5186 Loss_G: 3.1873 D(x): 0.7702 D(G(z)): 0.4365 / 0.3820\n", + "[98/100][4/391] Loss_D: 2.7498 Loss_G: 3.1887 D(x): 0.6698 D(G(z)): 0.4446 / 0.3600\n", + "[98/100][5/391] Loss_D: 2.7059 Loss_G: 3.3531 D(x): 0.6552 D(G(z)): 0.3688 / 0.3411\n", + "[98/100][6/391] Loss_D: 2.7330 Loss_G: 3.4523 D(x): 0.6934 D(G(z)): 0.3892 / 0.3392\n", + "[98/100][7/391] Loss_D: 3.0550 Loss_G: 2.8520 D(x): 0.6003 D(G(z)): 0.3418 / 0.4045\n", + "[98/100][8/391] Loss_D: 2.4951 Loss_G: 2.3983 D(x): 0.6731 D(G(z)): 0.4204 / 0.4760\n", + "[98/100][9/391] Loss_D: 2.3662 Loss_G: 2.9760 D(x): 0.7307 D(G(z)): 0.3864 / 0.4213\n", + "[98/100][10/391] Loss_D: 2.5012 Loss_G: 3.7412 D(x): 0.7201 D(G(z)): 0.4075 / 0.3281\n", + "[98/100][11/391] Loss_D: 2.4925 Loss_G: 2.5951 D(x): 0.7583 D(G(z)): 0.3600 / 0.4638\n", + "[98/100][12/391] Loss_D: 2.7217 Loss_G: 2.4895 D(x): 0.6786 D(G(z)): 0.4041 / 0.4697\n", + "[98/100][13/391] Loss_D: 2.2748 Loss_G: 3.3804 D(x): 0.7071 D(G(z)): 0.2396 / 0.3564\n", + "[98/100][14/391] Loss_D: 2.9531 Loss_G: 2.2923 D(x): 0.6256 D(G(z)): 0.4843 / 0.4847\n", + "[98/100][15/391] Loss_D: 2.2052 Loss_G: 2.8416 D(x): 0.7661 D(G(z)): 0.3451 / 0.4266\n", + "[98/100][16/391] Loss_D: 3.1177 Loss_G: 4.0972 D(x): 0.7478 D(G(z)): 0.4834 / 0.3011\n", + "[98/100][17/391] Loss_D: 3.1653 Loss_G: 4.0584 D(x): 0.5784 D(G(z)): 0.4396 / 0.2826\n", + "[98/100][18/391] Loss_D: 2.9446 Loss_G: 2.7789 D(x): 0.6164 D(G(z)): 0.4302 / 0.4469\n", + "[98/100][19/391] Loss_D: 2.3016 Loss_G: 2.5678 D(x): 0.7472 D(G(z)): 0.3584 / 0.4524\n", + "[98/100][20/391] Loss_D: 3.1815 Loss_G: 2.6648 D(x): 0.6835 D(G(z)): 0.5271 / 0.4495\n", + "[98/100][21/391] Loss_D: 3.4546 Loss_G: 2.4524 D(x): 0.5441 D(G(z)): 0.4794 / 0.4639\n", + "[98/100][22/391] Loss_D: 2.8785 Loss_G: 2.8203 D(x): 0.6275 D(G(z)): 0.3839 / 0.4374\n", + "[98/100][23/391] Loss_D: 3.2490 Loss_G: 2.4915 D(x): 0.5569 D(G(z)): 0.4238 / 0.4733\n", + "[98/100][24/391] Loss_D: 2.7671 Loss_G: 3.0299 D(x): 0.7335 D(G(z)): 0.4292 / 0.3921\n", + "[98/100][25/391] Loss_D: 2.2689 Loss_G: 2.2309 D(x): 0.7528 D(G(z)): 0.3105 / 0.5144\n", + "[98/100][26/391] Loss_D: 2.6430 Loss_G: 3.3925 D(x): 0.6791 D(G(z)): 0.3870 / 0.3610\n", + "[98/100][27/391] Loss_D: 2.8886 Loss_G: 4.2034 D(x): 0.7736 D(G(z)): 0.4603 / 0.2734\n", + "[98/100][28/391] Loss_D: 3.3703 Loss_G: 3.0394 D(x): 0.5218 D(G(z)): 0.3839 / 0.3990\n", + "[98/100][29/391] Loss_D: 2.8471 Loss_G: 2.8175 D(x): 0.6301 D(G(z)): 0.3791 / 0.4271\n", + "[98/100][30/391] Loss_D: 3.0768 Loss_G: 2.2327 D(x): 0.6020 D(G(z)): 0.4689 / 0.5234\n", + "[98/100][31/391] Loss_D: 3.6227 Loss_G: 2.6179 D(x): 0.6725 D(G(z)): 0.4362 / 0.4566\n", + "[98/100][32/391] Loss_D: 3.3229 Loss_G: 2.4031 D(x): 0.6573 D(G(z)): 0.5242 / 0.4772\n", + "[98/100][33/391] Loss_D: 2.5614 Loss_G: 2.5568 D(x): 0.7729 D(G(z)): 0.4317 / 0.4402\n", + "[98/100][34/391] Loss_D: 2.6559 Loss_G: 2.6566 D(x): 0.7187 D(G(z)): 0.4682 / 0.4328\n", + "[98/100][35/391] Loss_D: 2.5993 Loss_G: 3.4421 D(x): 0.7679 D(G(z)): 0.4651 / 0.3542\n", + "[98/100][36/391] Loss_D: 3.0954 Loss_G: 3.5722 D(x): 0.6375 D(G(z)): 0.4389 / 0.3328\n", + "[98/100][37/391] Loss_D: 2.7707 Loss_G: 3.4580 D(x): 0.6096 D(G(z)): 0.3667 / 0.3582\n", + "[98/100][38/391] Loss_D: 2.3346 Loss_G: 3.3519 D(x): 0.6897 D(G(z)): 0.3314 / 0.3483\n", + "[98/100][39/391] Loss_D: 2.6874 Loss_G: 3.9926 D(x): 0.7567 D(G(z)): 0.4456 / 0.2957\n", + "[98/100][40/391] Loss_D: 2.9888 Loss_G: 3.1648 D(x): 0.5991 D(G(z)): 0.3663 / 0.3691\n", + "[98/100][41/391] Loss_D: 3.5416 Loss_G: 3.0312 D(x): 0.6052 D(G(z)): 0.5096 / 0.4027\n", + "[98/100][42/391] Loss_D: 2.6597 Loss_G: 2.2632 D(x): 0.7073 D(G(z)): 0.3874 / 0.5016\n", + "[98/100][43/391] Loss_D: 2.4896 Loss_G: 2.6871 D(x): 0.7613 D(G(z)): 0.3698 / 0.4500\n", + "[98/100][44/391] Loss_D: 2.3788 Loss_G: 2.8992 D(x): 0.6829 D(G(z)): 0.3569 / 0.4190\n", + "[98/100][45/391] Loss_D: 2.9062 Loss_G: 2.5508 D(x): 0.6311 D(G(z)): 0.3974 / 0.4683\n", + "[98/100][46/391] Loss_D: 2.5163 Loss_G: 3.2530 D(x): 0.7360 D(G(z)): 0.3626 / 0.3786\n", + "[98/100][47/391] Loss_D: 2.8539 Loss_G: 2.9616 D(x): 0.6597 D(G(z)): 0.4514 / 0.3981\n", + "[98/100][48/391] Loss_D: 3.1285 Loss_G: 3.3581 D(x): 0.5779 D(G(z)): 0.3982 / 0.3581\n", + "[98/100][49/391] Loss_D: 2.6079 Loss_G: 3.2699 D(x): 0.6996 D(G(z)): 0.4094 / 0.3840\n", + "[98/100][50/391] Loss_D: 2.5294 Loss_G: 2.4353 D(x): 0.7637 D(G(z)): 0.4376 / 0.4947\n", + "[98/100][51/391] Loss_D: 2.8785 Loss_G: 2.7503 D(x): 0.7311 D(G(z)): 0.4967 / 0.4333\n", + "[98/100][52/391] Loss_D: 2.3068 Loss_G: 3.0816 D(x): 0.6719 D(G(z)): 0.2574 / 0.3871\n", + "[98/100][53/391] Loss_D: 2.3383 Loss_G: 2.4944 D(x): 0.7304 D(G(z)): 0.3567 / 0.4653\n", + "[98/100][54/391] Loss_D: 2.4669 Loss_G: 2.9328 D(x): 0.6900 D(G(z)): 0.3584 / 0.4207\n", + "[98/100][55/391] Loss_D: 2.5600 Loss_G: 2.9591 D(x): 0.7391 D(G(z)): 0.4252 / 0.3973\n", + "[98/100][56/391] Loss_D: 2.6878 Loss_G: 2.2928 D(x): 0.6921 D(G(z)): 0.3885 / 0.5073\n", + "[98/100][57/391] Loss_D: 3.4649 Loss_G: 3.5385 D(x): 0.6072 D(G(z)): 0.5260 / 0.3391\n", + "[98/100][58/391] Loss_D: 2.4682 Loss_G: 2.7142 D(x): 0.6999 D(G(z)): 0.3924 / 0.4358\n", + "[98/100][59/391] Loss_D: 2.5948 Loss_G: 3.0798 D(x): 0.6772 D(G(z)): 0.3648 / 0.4018\n", + "[98/100][60/391] Loss_D: 2.6082 Loss_G: 3.1810 D(x): 0.7790 D(G(z)): 0.4302 / 0.3877\n", + "[98/100][61/391] Loss_D: 3.8270 Loss_G: 3.9196 D(x): 0.7379 D(G(z)): 0.4630 / 0.3135\n", + "[98/100][62/391] Loss_D: 2.9932 Loss_G: 4.7130 D(x): 0.5868 D(G(z)): 0.3810 / 0.2399\n", + "[98/100][63/391] Loss_D: 3.0750 Loss_G: 3.6937 D(x): 0.6638 D(G(z)): 0.4940 / 0.3236\n", + "[98/100][64/391] Loss_D: 2.7066 Loss_G: 3.4609 D(x): 0.6808 D(G(z)): 0.4378 / 0.3469\n", + "[98/100][65/391] Loss_D: 2.4878 Loss_G: 2.9279 D(x): 0.7425 D(G(z)): 0.3834 / 0.3973\n", + "[98/100][66/391] Loss_D: 2.8396 Loss_G: 2.4655 D(x): 0.5939 D(G(z)): 0.3694 / 0.4843\n", + "[98/100][67/391] Loss_D: 3.2339 Loss_G: 2.8971 D(x): 0.5698 D(G(z)): 0.3295 / 0.4095\n", + "[98/100][68/391] Loss_D: 2.5832 Loss_G: 3.1220 D(x): 0.6502 D(G(z)): 0.3562 / 0.3902\n", + "[98/100][69/391] Loss_D: 2.6091 Loss_G: 3.4441 D(x): 0.7421 D(G(z)): 0.4560 / 0.3546\n", + "[98/100][70/391] Loss_D: 2.6095 Loss_G: 2.8828 D(x): 0.7730 D(G(z)): 0.4234 / 0.4107\n", + "[98/100][71/391] Loss_D: 3.1884 Loss_G: 3.3986 D(x): 0.7537 D(G(z)): 0.5485 / 0.3762\n", + "[98/100][72/391] Loss_D: 2.4728 Loss_G: 4.3165 D(x): 0.6568 D(G(z)): 0.3382 / 0.2671\n", + "[98/100][73/391] Loss_D: 2.7323 Loss_G: 3.1536 D(x): 0.7088 D(G(z)): 0.4469 / 0.3753\n", + "[98/100][74/391] Loss_D: 3.6684 Loss_G: 3.7951 D(x): 0.5925 D(G(z)): 0.5393 / 0.3248\n", + "[98/100][75/391] Loss_D: 2.6005 Loss_G: 2.5868 D(x): 0.6421 D(G(z)): 0.3348 / 0.4592\n", + "[98/100][76/391] Loss_D: 2.2976 Loss_G: 3.7493 D(x): 0.7312 D(G(z)): 0.3087 / 0.3121\n", + "[98/100][77/391] Loss_D: 2.8742 Loss_G: 2.4258 D(x): 0.6184 D(G(z)): 0.3067 / 0.4774\n", + "[98/100][78/391] Loss_D: 3.0528 Loss_G: 2.7486 D(x): 0.6026 D(G(z)): 0.4490 / 0.4337\n", + "[98/100][79/391] Loss_D: 2.1722 Loss_G: 3.3942 D(x): 0.7429 D(G(z)): 0.3041 / 0.3555\n", + "[98/100][80/391] Loss_D: 2.6741 Loss_G: 2.8714 D(x): 0.7525 D(G(z)): 0.4330 / 0.4271\n", + "[98/100][81/391] Loss_D: 2.8102 Loss_G: 2.6984 D(x): 0.8172 D(G(z)): 0.5296 / 0.4417\n", + "[98/100][82/391] Loss_D: 2.8305 Loss_G: 2.7067 D(x): 0.7077 D(G(z)): 0.4343 / 0.4427\n", + "[98/100][83/391] Loss_D: 2.6528 Loss_G: 2.8252 D(x): 0.7182 D(G(z)): 0.4178 / 0.4377\n", + "[98/100][84/391] Loss_D: 2.5467 Loss_G: 4.7145 D(x): 0.6264 D(G(z)): 0.3333 / 0.2431\n", + "[98/100][85/391] Loss_D: 2.5462 Loss_G: 3.5071 D(x): 0.6985 D(G(z)): 0.3463 / 0.3450\n", + "[98/100][86/391] Loss_D: 2.7943 Loss_G: 3.4829 D(x): 0.7802 D(G(z)): 0.4632 / 0.3360\n", + "[98/100][87/391] Loss_D: 3.0156 Loss_G: 2.8752 D(x): 0.6612 D(G(z)): 0.3834 / 0.4007\n", + "[98/100][88/391] Loss_D: 2.3018 Loss_G: 2.6328 D(x): 0.7334 D(G(z)): 0.4059 / 0.4409\n", + "[98/100][89/391] Loss_D: 2.7015 Loss_G: 2.7698 D(x): 0.7447 D(G(z)): 0.4769 / 0.4337\n", + "[98/100][90/391] Loss_D: 2.3603 Loss_G: 3.1149 D(x): 0.7606 D(G(z)): 0.3709 / 0.3847\n", + "[98/100][91/391] Loss_D: 3.5116 Loss_G: 1.8809 D(x): 0.6748 D(G(z)): 0.3416 / 0.5632\n", + "[98/100][92/391] Loss_D: 2.4523 Loss_G: 3.6314 D(x): 0.7211 D(G(z)): 0.3895 / 0.3358\n", + "[98/100][93/391] Loss_D: 3.4585 Loss_G: 3.5708 D(x): 0.6489 D(G(z)): 0.5376 / 0.3437\n", + "[98/100][94/391] Loss_D: 2.6721 Loss_G: 3.3254 D(x): 0.6327 D(G(z)): 0.3522 / 0.3771\n", + "[98/100][95/391] Loss_D: 2.7633 Loss_G: 3.2113 D(x): 0.6208 D(G(z)): 0.3399 / 0.3773\n", + "[98/100][96/391] Loss_D: 2.8368 Loss_G: 3.2749 D(x): 0.6870 D(G(z)): 0.4329 / 0.3605\n", + "[98/100][97/391] Loss_D: 2.9789 Loss_G: 3.0894 D(x): 0.6657 D(G(z)): 0.4090 / 0.3890\n", + "[98/100][98/391] Loss_D: 2.4655 Loss_G: 3.6550 D(x): 0.6857 D(G(z)): 0.2808 / 0.3273\n", + "[98/100][99/391] Loss_D: 2.9687 Loss_G: 2.7163 D(x): 0.6652 D(G(z)): 0.4469 / 0.4435\n", + "[98/100][100/391] Loss_D: 2.4820 Loss_G: 2.1244 D(x): 0.7709 D(G(z)): 0.4635 / 0.5502\n", + "[98/100][101/391] Loss_D: 2.7747 Loss_G: 3.5201 D(x): 0.7036 D(G(z)): 0.4611 / 0.3472\n", + "[98/100][102/391] Loss_D: 2.5075 Loss_G: 2.7981 D(x): 0.7624 D(G(z)): 0.3888 / 0.4218\n", + "[98/100][103/391] Loss_D: 2.9185 Loss_G: 3.3697 D(x): 0.6206 D(G(z)): 0.4161 / 0.3607\n", + "[98/100][104/391] Loss_D: 2.7286 Loss_G: 3.2069 D(x): 0.6237 D(G(z)): 0.3581 / 0.3833\n", + "[98/100][105/391] Loss_D: 2.8190 Loss_G: 2.0715 D(x): 0.7022 D(G(z)): 0.4248 / 0.5281\n", + "[98/100][106/391] Loss_D: 3.2999 Loss_G: 2.6587 D(x): 0.6698 D(G(z)): 0.5297 / 0.4592\n", + "[98/100][107/391] Loss_D: 2.8647 Loss_G: 2.1952 D(x): 0.5968 D(G(z)): 0.2760 / 0.4946\n", + "[98/100][108/391] Loss_D: 2.3092 Loss_G: 2.4305 D(x): 0.7321 D(G(z)): 0.3112 / 0.4704\n", + "[98/100][109/391] Loss_D: 2.6393 Loss_G: 2.8810 D(x): 0.6606 D(G(z)): 0.3812 / 0.4212\n", + "[98/100][110/391] Loss_D: 2.9824 Loss_G: 2.2300 D(x): 0.7395 D(G(z)): 0.5287 / 0.5233\n", + "[98/100][111/391] Loss_D: 2.6576 Loss_G: 2.8569 D(x): 0.6980 D(G(z)): 0.4066 / 0.4178\n", + "[98/100][112/391] Loss_D: 3.2050 Loss_G: 2.7336 D(x): 0.6197 D(G(z)): 0.4820 / 0.4226\n", + "[98/100][113/391] Loss_D: 3.2179 Loss_G: 2.9585 D(x): 0.6350 D(G(z)): 0.4852 / 0.4122\n", + "[98/100][114/391] Loss_D: 2.7592 Loss_G: 2.9105 D(x): 0.6401 D(G(z)): 0.3935 / 0.4067\n", + "[98/100][115/391] Loss_D: 2.7751 Loss_G: 3.1864 D(x): 0.6393 D(G(z)): 0.3421 / 0.3744\n", + "[98/100][116/391] Loss_D: 3.3135 Loss_G: 2.3084 D(x): 0.6166 D(G(z)): 0.4925 / 0.4900\n", + "[98/100][117/391] Loss_D: 3.0374 Loss_G: 3.4964 D(x): 0.7090 D(G(z)): 0.4703 / 0.3416\n", + "[98/100][118/391] Loss_D: 2.7084 Loss_G: 2.5708 D(x): 0.6609 D(G(z)): 0.4177 / 0.4644\n", + "[98/100][119/391] Loss_D: 2.3485 Loss_G: 3.1221 D(x): 0.7834 D(G(z)): 0.4427 / 0.4006\n", + "[98/100][120/391] Loss_D: 2.5071 Loss_G: 2.8961 D(x): 0.6956 D(G(z)): 0.3525 / 0.4241\n", + "[98/100][121/391] Loss_D: 3.5463 Loss_G: 3.2292 D(x): 0.6935 D(G(z)): 0.4202 / 0.3804\n", + "[98/100][122/391] Loss_D: 2.4715 Loss_G: 3.5743 D(x): 0.7232 D(G(z)): 0.3672 / 0.3379\n", + "[98/100][123/391] Loss_D: 2.5676 Loss_G: 2.6316 D(x): 0.7440 D(G(z)): 0.4201 / 0.4616\n", + "[98/100][124/391] Loss_D: 2.6655 Loss_G: 4.1014 D(x): 0.6485 D(G(z)): 0.3843 / 0.2927\n", + "[98/100][125/391] Loss_D: 3.7142 Loss_G: 2.6437 D(x): 0.5136 D(G(z)): 0.4031 / 0.4382\n", + "[98/100][126/391] Loss_D: 2.7374 Loss_G: 3.3347 D(x): 0.6621 D(G(z)): 0.4363 / 0.3627\n", + "[98/100][127/391] Loss_D: 2.9219 Loss_G: 2.1018 D(x): 0.6194 D(G(z)): 0.3238 / 0.5374\n", + "[98/100][128/391] Loss_D: 3.1033 Loss_G: 1.9777 D(x): 0.6650 D(G(z)): 0.5555 / 0.5400\n", + "[98/100][129/391] Loss_D: 3.5361 Loss_G: 2.4255 D(x): 0.5541 D(G(z)): 0.5094 / 0.4944\n", + "[98/100][130/391] Loss_D: 2.8638 Loss_G: 2.6780 D(x): 0.7061 D(G(z)): 0.4604 / 0.4456\n", + "[98/100][131/391] Loss_D: 2.6015 Loss_G: 3.1817 D(x): 0.7708 D(G(z)): 0.4262 / 0.3850\n", + "[98/100][132/391] Loss_D: 2.3804 Loss_G: 3.2789 D(x): 0.7255 D(G(z)): 0.3388 / 0.3644\n", + "[98/100][133/391] Loss_D: 2.5405 Loss_G: 2.8276 D(x): 0.6224 D(G(z)): 0.3245 / 0.4252\n", + "[98/100][134/391] Loss_D: 2.3702 Loss_G: 2.1702 D(x): 0.7116 D(G(z)): 0.3685 / 0.5133\n", + "[98/100][135/391] Loss_D: 2.8584 Loss_G: 2.8242 D(x): 0.6912 D(G(z)): 0.4551 / 0.4188\n", + "[98/100][136/391] Loss_D: 3.1027 Loss_G: 1.8360 D(x): 0.7072 D(G(z)): 0.4939 / 0.5902\n", + "[98/100][137/391] Loss_D: 2.6560 Loss_G: 3.4023 D(x): 0.6418 D(G(z)): 0.2685 / 0.3377\n", + "[98/100][138/391] Loss_D: 2.3447 Loss_G: 3.8109 D(x): 0.7583 D(G(z)): 0.4455 / 0.3255\n", + "[98/100][139/391] Loss_D: 3.0764 Loss_G: 3.1036 D(x): 0.6360 D(G(z)): 0.4525 / 0.3963\n", + "[98/100][140/391] Loss_D: 2.1004 Loss_G: 2.6041 D(x): 0.7476 D(G(z)): 0.3097 / 0.4598\n", + "[98/100][141/391] Loss_D: 2.8330 Loss_G: 3.4390 D(x): 0.6455 D(G(z)): 0.4179 / 0.3574\n", + "[98/100][142/391] Loss_D: 2.5728 Loss_G: 3.5690 D(x): 0.7390 D(G(z)): 0.4548 / 0.3377\n", + "[98/100][143/391] Loss_D: 2.4750 Loss_G: 2.5016 D(x): 0.7135 D(G(z)): 0.3196 / 0.4766\n", + "[98/100][144/391] Loss_D: 2.3435 Loss_G: 2.3792 D(x): 0.7825 D(G(z)): 0.4607 / 0.5023\n", + "[98/100][145/391] Loss_D: 3.0183 Loss_G: 2.9242 D(x): 0.6114 D(G(z)): 0.3665 / 0.4059\n", + "[98/100][146/391] Loss_D: 2.3146 Loss_G: 2.9257 D(x): 0.7466 D(G(z)): 0.2908 / 0.4111\n", + "[98/100][147/391] Loss_D: 3.1707 Loss_G: 3.3247 D(x): 0.5849 D(G(z)): 0.3926 / 0.3644\n", + "[98/100][148/391] Loss_D: 2.3410 Loss_G: 2.0340 D(x): 0.7124 D(G(z)): 0.3619 / 0.5483\n", + "[98/100][149/391] Loss_D: 3.0565 Loss_G: 3.3334 D(x): 0.6877 D(G(z)): 0.4953 / 0.3687\n", + "[98/100][150/391] Loss_D: 3.0111 Loss_G: 2.4618 D(x): 0.7181 D(G(z)): 0.5321 / 0.4831\n", + "[98/100][151/391] Loss_D: 3.7853 Loss_G: 3.0861 D(x): 0.7005 D(G(z)): 0.5019 / 0.3933\n", + "[98/100][152/391] Loss_D: 2.5527 Loss_G: 2.2531 D(x): 0.7023 D(G(z)): 0.4190 / 0.5080\n", + "[98/100][153/391] Loss_D: 2.6032 Loss_G: 3.4568 D(x): 0.6720 D(G(z)): 0.3310 / 0.3605\n", + "[98/100][154/391] Loss_D: 3.7117 Loss_G: 2.3319 D(x): 0.6010 D(G(z)): 0.5735 / 0.4941\n", + "[98/100][155/391] Loss_D: 3.3602 Loss_G: 3.2050 D(x): 0.7151 D(G(z)): 0.5294 / 0.3879\n", + "[98/100][156/391] Loss_D: 3.3815 Loss_G: 3.2072 D(x): 0.5989 D(G(z)): 0.4892 / 0.3925\n", + "[98/100][157/391] Loss_D: 3.4909 Loss_G: 2.6731 D(x): 0.5922 D(G(z)): 0.5143 / 0.4343\n", + "[98/100][158/391] Loss_D: 2.9895 Loss_G: 3.8544 D(x): 0.5757 D(G(z)): 0.3412 / 0.3137\n", + "[98/100][159/391] Loss_D: 2.9524 Loss_G: 3.6605 D(x): 0.6413 D(G(z)): 0.4118 / 0.3441\n", + "[98/100][160/391] Loss_D: 2.5979 Loss_G: 3.2955 D(x): 0.7094 D(G(z)): 0.4000 / 0.3752\n", + "[98/100][161/391] Loss_D: 2.7587 Loss_G: 3.3819 D(x): 0.7119 D(G(z)): 0.4381 / 0.3712\n", + "[98/100][162/391] Loss_D: 2.7997 Loss_G: 2.9991 D(x): 0.6073 D(G(z)): 0.3766 / 0.3990\n", + "[98/100][163/391] Loss_D: 2.4210 Loss_G: 2.5534 D(x): 0.7312 D(G(z)): 0.3971 / 0.4607\n", + "[98/100][164/391] Loss_D: 2.7600 Loss_G: 2.8663 D(x): 0.6415 D(G(z)): 0.3785 / 0.4032\n", + "[98/100][165/391] Loss_D: 3.0456 Loss_G: 2.7046 D(x): 0.5919 D(G(z)): 0.4096 / 0.4246\n", + "[98/100][166/391] Loss_D: 2.5019 Loss_G: 2.5211 D(x): 0.6933 D(G(z)): 0.3673 / 0.4679\n", + "[98/100][167/391] Loss_D: 3.0420 Loss_G: 2.4313 D(x): 0.6703 D(G(z)): 0.4563 / 0.4799\n", + "[98/100][168/391] Loss_D: 2.8583 Loss_G: 2.2042 D(x): 0.7020 D(G(z)): 0.4687 / 0.5272\n", + "[98/100][169/391] Loss_D: 2.3979 Loss_G: 3.0798 D(x): 0.7130 D(G(z)): 0.3795 / 0.4023\n", + "[98/100][170/391] Loss_D: 2.4317 Loss_G: 2.3185 D(x): 0.8075 D(G(z)): 0.4162 / 0.4961\n", + "[98/100][171/391] Loss_D: 3.1051 Loss_G: 2.5520 D(x): 0.6924 D(G(z)): 0.5020 / 0.4735\n", + "[98/100][172/391] Loss_D: 2.6478 Loss_G: 3.6362 D(x): 0.7552 D(G(z)): 0.4591 / 0.3350\n", + "[98/100][173/391] Loss_D: 2.9344 Loss_G: 2.7749 D(x): 0.6474 D(G(z)): 0.4296 / 0.4322\n", + "[98/100][174/391] Loss_D: 3.8227 Loss_G: 2.7671 D(x): 0.5682 D(G(z)): 0.5615 / 0.4174\n", + "[98/100][175/391] Loss_D: 2.4706 Loss_G: 2.7808 D(x): 0.6492 D(G(z)): 0.2774 / 0.4236\n", + "[98/100][176/391] Loss_D: 2.8035 Loss_G: 2.8322 D(x): 0.7020 D(G(z)): 0.4286 / 0.4169\n", + "[98/100][177/391] Loss_D: 3.0544 Loss_G: 3.0302 D(x): 0.6029 D(G(z)): 0.3853 / 0.3889\n", + "[98/100][178/391] Loss_D: 2.8634 Loss_G: 2.8449 D(x): 0.6174 D(G(z)): 0.4056 / 0.4173\n", + "[98/100][179/391] Loss_D: 2.8922 Loss_G: 3.1394 D(x): 0.6105 D(G(z)): 0.3622 / 0.3810\n", + "[98/100][180/391] Loss_D: 2.7806 Loss_G: 2.6412 D(x): 0.7542 D(G(z)): 0.4989 / 0.4538\n", + "[98/100][181/391] Loss_D: 3.6298 Loss_G: 2.3778 D(x): 0.5901 D(G(z)): 0.3731 / 0.4914\n", + "[98/100][182/391] Loss_D: 2.8202 Loss_G: 1.8700 D(x): 0.6146 D(G(z)): 0.4084 / 0.5708\n", + "[98/100][183/391] Loss_D: 2.1032 Loss_G: 2.1875 D(x): 0.8162 D(G(z)): 0.3801 / 0.5182\n", + "[98/100][184/391] Loss_D: 2.8082 Loss_G: 2.5419 D(x): 0.6545 D(G(z)): 0.4210 / 0.4548\n", + "[98/100][185/391] Loss_D: 2.6751 Loss_G: 2.5187 D(x): 0.6709 D(G(z)): 0.3877 / 0.4562\n", + "[98/100][186/391] Loss_D: 2.6416 Loss_G: 1.8461 D(x): 0.6930 D(G(z)): 0.4134 / 0.5649\n", + "[98/100][187/391] Loss_D: 2.7729 Loss_G: 3.2430 D(x): 0.6756 D(G(z)): 0.3980 / 0.3708\n", + "[98/100][188/391] Loss_D: 2.3188 Loss_G: 2.9497 D(x): 0.8033 D(G(z)): 0.5198 / 0.4200\n", + "[98/100][189/391] Loss_D: 3.0849 Loss_G: 3.6011 D(x): 0.6243 D(G(z)): 0.4432 / 0.3433\n", + "[98/100][190/391] Loss_D: 3.2072 Loss_G: 3.2326 D(x): 0.5999 D(G(z)): 0.4656 / 0.3717\n", + "[98/100][191/391] Loss_D: 2.5975 Loss_G: 2.2628 D(x): 0.7233 D(G(z)): 0.3571 / 0.5077\n", + "[98/100][192/391] Loss_D: 2.5912 Loss_G: 2.5421 D(x): 0.6398 D(G(z)): 0.3634 / 0.4709\n", + "[98/100][193/391] Loss_D: 2.4074 Loss_G: 3.2399 D(x): 0.7696 D(G(z)): 0.4085 / 0.3754\n", + "[98/100][194/391] Loss_D: 2.6142 Loss_G: 3.1659 D(x): 0.7026 D(G(z)): 0.3923 / 0.3858\n", + "[98/100][195/391] Loss_D: 3.1479 Loss_G: 3.8261 D(x): 0.6037 D(G(z)): 0.4188 / 0.3147\n", + "[98/100][196/391] Loss_D: 2.4860 Loss_G: 2.7338 D(x): 0.6730 D(G(z)): 0.3929 / 0.4235\n", + "[98/100][197/391] Loss_D: 2.7583 Loss_G: 2.5189 D(x): 0.7385 D(G(z)): 0.4160 / 0.4451\n", + "[98/100][198/391] Loss_D: 2.8314 Loss_G: 2.7823 D(x): 0.6641 D(G(z)): 0.4497 / 0.4224\n", + "[98/100][199/391] Loss_D: 2.3072 Loss_G: 1.9869 D(x): 0.7214 D(G(z)): 0.2996 / 0.5469\n", + "[98/100][200/391] Loss_D: 2.6524 Loss_G: 2.4281 D(x): 0.6559 D(G(z)): 0.3365 / 0.4859\n", + "[98/100][201/391] Loss_D: 2.6869 Loss_G: 3.6118 D(x): 0.7383 D(G(z)): 0.4215 / 0.3334\n", + "[98/100][202/391] Loss_D: 2.6473 Loss_G: 3.0547 D(x): 0.7419 D(G(z)): 0.4609 / 0.4172\n", + "[98/100][203/391] Loss_D: 2.4878 Loss_G: 3.2006 D(x): 0.7458 D(G(z)): 0.4001 / 0.3741\n", + "[98/100][204/391] Loss_D: 2.5505 Loss_G: 2.9153 D(x): 0.7106 D(G(z)): 0.4418 / 0.4092\n", + "[98/100][205/391] Loss_D: 3.5381 Loss_G: 3.5669 D(x): 0.6206 D(G(z)): 0.5818 / 0.3321\n", + "[98/100][206/391] Loss_D: 2.7775 Loss_G: 3.1120 D(x): 0.6539 D(G(z)): 0.4193 / 0.3848\n", + "[98/100][207/391] Loss_D: 2.8640 Loss_G: 3.0079 D(x): 0.6045 D(G(z)): 0.3642 / 0.4046\n", + "[98/100][208/391] Loss_D: 2.1191 Loss_G: 2.9798 D(x): 0.7180 D(G(z)): 0.2247 / 0.3997\n", + "[98/100][209/391] Loss_D: 2.2035 Loss_G: 3.0716 D(x): 0.7261 D(G(z)): 0.2920 / 0.3981\n", + "[98/100][210/391] Loss_D: 2.4900 Loss_G: 3.9353 D(x): 0.7728 D(G(z)): 0.4148 / 0.3080\n", + "[98/100][211/391] Loss_D: 3.7628 Loss_G: 3.6782 D(x): 0.5695 D(G(z)): 0.5459 / 0.3359\n", + "[98/100][212/391] Loss_D: 3.4562 Loss_G: 4.1266 D(x): 0.6090 D(G(z)): 0.5047 / 0.2961\n", + "[98/100][213/391] Loss_D: 2.9031 Loss_G: 3.1327 D(x): 0.6723 D(G(z)): 0.4342 / 0.3901\n", + "[98/100][214/391] Loss_D: 2.1880 Loss_G: 3.1920 D(x): 0.7316 D(G(z)): 0.3751 / 0.3902\n", + "[98/100][215/391] Loss_D: 3.1240 Loss_G: 2.9827 D(x): 0.6379 D(G(z)): 0.4635 / 0.4087\n", + "[98/100][216/391] Loss_D: 2.5663 Loss_G: 3.6580 D(x): 0.6884 D(G(z)): 0.3715 / 0.3469\n", + "[98/100][217/391] Loss_D: 2.7804 Loss_G: 2.8575 D(x): 0.5925 D(G(z)): 0.2831 / 0.4189\n", + "[98/100][218/391] Loss_D: 2.6086 Loss_G: 2.9015 D(x): 0.7622 D(G(z)): 0.4862 / 0.4142\n", + "[98/100][219/391] Loss_D: 2.6209 Loss_G: 3.0458 D(x): 0.6573 D(G(z)): 0.3838 / 0.4015\n", + "[98/100][220/391] Loss_D: 2.4693 Loss_G: 3.6726 D(x): 0.7113 D(G(z)): 0.3752 / 0.3340\n", + "[98/100][221/391] Loss_D: 2.9354 Loss_G: 2.8866 D(x): 0.6651 D(G(z)): 0.4396 / 0.4352\n", + "[98/100][222/391] Loss_D: 2.4909 Loss_G: 3.1515 D(x): 0.7574 D(G(z)): 0.4100 / 0.3905\n", + "[98/100][223/391] Loss_D: 2.9335 Loss_G: 3.2952 D(x): 0.6982 D(G(z)): 0.4764 / 0.3817\n", + "[98/100][224/391] Loss_D: 2.7401 Loss_G: 3.1713 D(x): 0.6725 D(G(z)): 0.4226 / 0.3730\n", + "[98/100][225/391] Loss_D: 2.9444 Loss_G: 2.4876 D(x): 0.6132 D(G(z)): 0.3850 / 0.4645\n", + "[98/100][226/391] Loss_D: 2.6924 Loss_G: 2.3277 D(x): 0.6478 D(G(z)): 0.4000 / 0.4951\n", + "[98/100][227/391] Loss_D: 2.7253 Loss_G: 3.4898 D(x): 0.6659 D(G(z)): 0.4095 / 0.3306\n", + "[98/100][228/391] Loss_D: 2.7127 Loss_G: 3.4910 D(x): 0.6306 D(G(z)): 0.3100 / 0.3521\n", + "[98/100][229/391] Loss_D: 3.1118 Loss_G: 2.7761 D(x): 0.6912 D(G(z)): 0.5204 / 0.4407\n", + "[98/100][230/391] Loss_D: 2.7599 Loss_G: 3.3604 D(x): 0.6866 D(G(z)): 0.4916 / 0.3734\n", + "[98/100][231/391] Loss_D: 2.8535 Loss_G: 2.1177 D(x): 0.6958 D(G(z)): 0.4663 / 0.5257\n", + "[98/100][232/391] Loss_D: 2.7001 Loss_G: 3.3451 D(x): 0.6920 D(G(z)): 0.4241 / 0.3704\n", + "[98/100][233/391] Loss_D: 2.2295 Loss_G: 3.0190 D(x): 0.7224 D(G(z)): 0.2784 / 0.4070\n", + "[98/100][234/391] Loss_D: 2.6155 Loss_G: 3.2699 D(x): 0.6381 D(G(z)): 0.4050 / 0.3786\n", + "[98/100][235/391] Loss_D: 2.4929 Loss_G: 2.3852 D(x): 0.6867 D(G(z)): 0.3378 / 0.4838\n", + "[98/100][236/391] Loss_D: 2.4175 Loss_G: 3.5245 D(x): 0.7525 D(G(z)): 0.3438 / 0.3420\n", + "[98/100][237/391] Loss_D: 2.7517 Loss_G: 4.2875 D(x): 0.7607 D(G(z)): 0.4866 / 0.2692\n", + "[98/100][238/391] Loss_D: 2.7398 Loss_G: 3.3303 D(x): 0.6728 D(G(z)): 0.4292 / 0.3600\n", + "[98/100][239/391] Loss_D: 2.2831 Loss_G: 3.0645 D(x): 0.7269 D(G(z)): 0.2951 / 0.3929\n", + "[98/100][240/391] Loss_D: 2.8122 Loss_G: 2.8945 D(x): 0.7364 D(G(z)): 0.4887 / 0.4185\n", + "[98/100][241/391] Loss_D: 3.7289 Loss_G: 3.7911 D(x): 0.6305 D(G(z)): 0.4635 / 0.3166\n", + "[98/100][242/391] Loss_D: 2.4919 Loss_G: 2.6376 D(x): 0.6969 D(G(z)): 0.3923 / 0.4414\n", + "[98/100][243/391] Loss_D: 2.7684 Loss_G: 3.4780 D(x): 0.6822 D(G(z)): 0.4509 / 0.3535\n", + "[98/100][244/391] Loss_D: 2.7758 Loss_G: 2.3411 D(x): 0.6791 D(G(z)): 0.4524 / 0.4764\n", + "[98/100][245/391] Loss_D: 2.5665 Loss_G: 3.4453 D(x): 0.6581 D(G(z)): 0.3414 / 0.3485\n", + "[98/100][246/391] Loss_D: 2.4923 Loss_G: 2.5330 D(x): 0.7269 D(G(z)): 0.3562 / 0.4469\n", + "[98/100][247/391] Loss_D: 2.9756 Loss_G: 2.2501 D(x): 0.6660 D(G(z)): 0.4325 / 0.4920\n", + "[98/100][248/391] Loss_D: 2.5384 Loss_G: 2.5714 D(x): 0.6824 D(G(z)): 0.3909 / 0.4601\n", + "[98/100][249/391] Loss_D: 3.5435 Loss_G: 2.6650 D(x): 0.5077 D(G(z)): 0.4340 / 0.4545\n", + "[98/100][250/391] Loss_D: 2.9278 Loss_G: 2.4290 D(x): 0.6186 D(G(z)): 0.3571 / 0.4760\n", + "[98/100][251/391] Loss_D: 2.8766 Loss_G: 2.9154 D(x): 0.7094 D(G(z)): 0.4711 / 0.4045\n", + "[98/100][252/391] Loss_D: 3.2305 Loss_G: 2.1595 D(x): 0.6065 D(G(z)): 0.4489 / 0.5384\n", + "[98/100][253/391] Loss_D: 2.4592 Loss_G: 2.1197 D(x): 0.7620 D(G(z)): 0.3957 / 0.5321\n", + "[98/100][254/391] Loss_D: 3.0209 Loss_G: 3.0649 D(x): 0.7865 D(G(z)): 0.6011 / 0.3999\n", + "[98/100][255/391] Loss_D: 2.6149 Loss_G: 2.4378 D(x): 0.7166 D(G(z)): 0.3674 / 0.4632\n", + "[98/100][256/391] Loss_D: 2.7435 Loss_G: 3.0652 D(x): 0.5829 D(G(z)): 0.2916 / 0.3947\n", + "[98/100][257/391] Loss_D: 2.9046 Loss_G: 2.8325 D(x): 0.6433 D(G(z)): 0.3096 / 0.4366\n", + "[98/100][258/391] Loss_D: 3.0125 Loss_G: 1.9964 D(x): 0.6550 D(G(z)): 0.4855 / 0.5278\n", + "[98/100][259/391] Loss_D: 2.6800 Loss_G: 3.3333 D(x): 0.7129 D(G(z)): 0.4353 / 0.3650\n", + "[98/100][260/391] Loss_D: 3.6008 Loss_G: 2.7544 D(x): 0.6062 D(G(z)): 0.5553 / 0.4357\n", + "[98/100][261/391] Loss_D: 2.9598 Loss_G: 3.6698 D(x): 0.7598 D(G(z)): 0.4953 / 0.3276\n", + "[98/100][262/391] Loss_D: 2.9192 Loss_G: 3.3492 D(x): 0.6423 D(G(z)): 0.3757 / 0.3672\n", + "[98/100][263/391] Loss_D: 2.8110 Loss_G: 3.0343 D(x): 0.6676 D(G(z)): 0.3394 / 0.4096\n", + "[98/100][264/391] Loss_D: 2.6431 Loss_G: 2.8962 D(x): 0.6977 D(G(z)): 0.4767 / 0.4301\n", + "[98/100][265/391] Loss_D: 3.1845 Loss_G: 3.2874 D(x): 0.7141 D(G(z)): 0.5409 / 0.3634\n", + "[98/100][266/391] Loss_D: 2.7234 Loss_G: 3.2498 D(x): 0.6976 D(G(z)): 0.3590 / 0.3607\n", + "[98/100][267/391] Loss_D: 2.5158 Loss_G: 2.7341 D(x): 0.6936 D(G(z)): 0.3525 / 0.4401\n", + "[98/100][268/391] Loss_D: 3.1471 Loss_G: 3.5189 D(x): 0.5117 D(G(z)): 0.2387 / 0.3374\n", + "[98/100][269/391] Loss_D: 2.6680 Loss_G: 2.9634 D(x): 0.6645 D(G(z)): 0.3686 / 0.3937\n", + "[98/100][270/391] Loss_D: 2.5941 Loss_G: 2.3252 D(x): 0.7511 D(G(z)): 0.4136 / 0.4755\n", + "[98/100][271/391] Loss_D: 3.9009 Loss_G: 2.9772 D(x): 0.7746 D(G(z)): 0.4293 / 0.3896\n", + "[98/100][272/391] Loss_D: 3.8808 Loss_G: 3.4800 D(x): 0.6811 D(G(z)): 0.6242 / 0.3573\n", + "[98/100][273/391] Loss_D: 2.5551 Loss_G: 2.6298 D(x): 0.7137 D(G(z)): 0.3876 / 0.4620\n", + "[98/100][274/391] Loss_D: 2.3150 Loss_G: 3.6972 D(x): 0.7234 D(G(z)): 0.3815 / 0.3269\n", + "[98/100][275/391] Loss_D: 3.0557 Loss_G: 3.3761 D(x): 0.5768 D(G(z)): 0.3590 / 0.3649\n", + "[98/100][276/391] Loss_D: 2.9395 Loss_G: 3.1630 D(x): 0.6546 D(G(z)): 0.4088 / 0.3914\n", + "[98/100][277/391] Loss_D: 3.2263 Loss_G: 2.3537 D(x): 0.6352 D(G(z)): 0.4796 / 0.4734\n", + "[98/100][278/391] Loss_D: 2.7592 Loss_G: 3.0188 D(x): 0.6266 D(G(z)): 0.3935 / 0.4074\n", + "[98/100][279/391] Loss_D: 2.0428 Loss_G: 2.0082 D(x): 0.8082 D(G(z)): 0.2999 / 0.5392\n", + "[98/100][280/391] Loss_D: 3.0497 Loss_G: 3.0542 D(x): 0.6257 D(G(z)): 0.4395 / 0.3848\n", + "[98/100][281/391] Loss_D: 2.9251 Loss_G: 2.6543 D(x): 0.7774 D(G(z)): 0.5194 / 0.4460\n", + "[98/100][282/391] Loss_D: 3.4077 Loss_G: 4.2003 D(x): 0.7537 D(G(z)): 0.5962 / 0.2899\n", + "[98/100][283/391] Loss_D: 2.7863 Loss_G: 3.9271 D(x): 0.6999 D(G(z)): 0.3895 / 0.3035\n", + "[98/100][284/391] Loss_D: 2.9830 Loss_G: 3.4992 D(x): 0.5646 D(G(z)): 0.3391 / 0.3560\n", + "[98/100][285/391] Loss_D: 2.9986 Loss_G: 3.0431 D(x): 0.5451 D(G(z)): 0.2836 / 0.4015\n", + "[98/100][286/391] Loss_D: 2.8817 Loss_G: 1.6805 D(x): 0.5998 D(G(z)): 0.3565 / 0.5981\n", + "[98/100][287/391] Loss_D: 3.8189 Loss_G: 3.6675 D(x): 0.7196 D(G(z)): 0.6493 / 0.3300\n", + "[98/100][288/391] Loss_D: 2.5223 Loss_G: 4.3046 D(x): 0.6872 D(G(z)): 0.4210 / 0.2750\n", + "[98/100][289/391] Loss_D: 2.5867 Loss_G: 2.8083 D(x): 0.7093 D(G(z)): 0.3975 / 0.4268\n", + "[98/100][290/391] Loss_D: 2.7816 Loss_G: 3.6113 D(x): 0.6690 D(G(z)): 0.3850 / 0.3264\n", + "[98/100][291/391] Loss_D: 2.7120 Loss_G: 2.4365 D(x): 0.6784 D(G(z)): 0.3716 / 0.4699\n", + "[98/100][292/391] Loss_D: 2.8635 Loss_G: 3.2421 D(x): 0.7315 D(G(z)): 0.4607 / 0.3881\n", + "[98/100][293/391] Loss_D: 2.7293 Loss_G: 2.9982 D(x): 0.7284 D(G(z)): 0.4753 / 0.3993\n", + "[98/100][294/391] Loss_D: 2.4101 Loss_G: 3.3359 D(x): 0.7585 D(G(z)): 0.4311 / 0.3587\n", + "[98/100][295/391] Loss_D: 2.5474 Loss_G: 4.6606 D(x): 0.6733 D(G(z)): 0.3180 / 0.2413\n", + "[98/100][296/391] Loss_D: 2.5071 Loss_G: 2.7148 D(x): 0.6763 D(G(z)): 0.3203 / 0.4343\n", + "[98/100][297/391] Loss_D: 3.1151 Loss_G: 3.6963 D(x): 0.6738 D(G(z)): 0.4511 / 0.3203\n", + "[98/100][298/391] Loss_D: 2.8539 Loss_G: 2.7127 D(x): 0.6106 D(G(z)): 0.3725 / 0.4431\n", + "[98/100][299/391] Loss_D: 2.8631 Loss_G: 2.4120 D(x): 0.6915 D(G(z)): 0.4658 / 0.4903\n", + "[98/100][300/391] Loss_D: 2.6756 Loss_G: 2.8107 D(x): 0.6819 D(G(z)): 0.3522 / 0.4422\n", + "[98/100][301/391] Loss_D: 3.5926 Loss_G: 3.1550 D(x): 0.7212 D(G(z)): 0.4724 / 0.3902\n", + "[98/100][302/391] Loss_D: 2.7265 Loss_G: 3.0481 D(x): 0.6476 D(G(z)): 0.3806 / 0.4031\n", + "[98/100][303/391] Loss_D: 2.5328 Loss_G: 2.7355 D(x): 0.7806 D(G(z)): 0.4512 / 0.4471\n", + "[98/100][304/391] Loss_D: 2.1647 Loss_G: 3.7948 D(x): 0.6945 D(G(z)): 0.3566 / 0.3227\n", + "[98/100][305/391] Loss_D: 2.5236 Loss_G: 3.0938 D(x): 0.6378 D(G(z)): 0.2974 / 0.3972\n", + "[98/100][306/391] Loss_D: 2.6962 Loss_G: 2.7647 D(x): 0.6565 D(G(z)): 0.3795 / 0.4201\n", + "[98/100][307/391] Loss_D: 2.4601 Loss_G: 2.2458 D(x): 0.7166 D(G(z)): 0.3297 / 0.4891\n", + "[98/100][308/391] Loss_D: 2.3856 Loss_G: 2.0294 D(x): 0.7283 D(G(z)): 0.4244 / 0.5410\n", + "[98/100][309/391] Loss_D: 2.5814 Loss_G: 2.7315 D(x): 0.6685 D(G(z)): 0.3688 / 0.4440\n", + "[98/100][310/391] Loss_D: 2.9790 Loss_G: 2.0162 D(x): 0.6251 D(G(z)): 0.3659 / 0.5401\n", + "[98/100][311/391] Loss_D: 3.1597 Loss_G: 2.4697 D(x): 0.7499 D(G(z)): 0.5212 / 0.4805\n", + "[98/100][312/391] Loss_D: 2.4398 Loss_G: 2.7242 D(x): 0.7350 D(G(z)): 0.3849 / 0.4246\n", + "[98/100][313/391] Loss_D: 2.3054 Loss_G: 3.0493 D(x): 0.7562 D(G(z)): 0.3369 / 0.3892\n", + "[98/100][314/391] Loss_D: 2.6350 Loss_G: 3.6219 D(x): 0.6897 D(G(z)): 0.4635 / 0.3263\n", + "[98/100][315/391] Loss_D: 2.9276 Loss_G: 2.4371 D(x): 0.6962 D(G(z)): 0.4828 / 0.4620\n", + "[98/100][316/391] Loss_D: 2.9604 Loss_G: 3.9743 D(x): 0.5937 D(G(z)): 0.3361 / 0.3123\n", + "[98/100][317/391] Loss_D: 3.0216 Loss_G: 3.0747 D(x): 0.6228 D(G(z)): 0.4223 / 0.4026\n", + "[98/100][318/391] Loss_D: 2.8033 Loss_G: 2.6607 D(x): 0.6496 D(G(z)): 0.4198 / 0.4542\n", + "[98/100][319/391] Loss_D: 3.0546 Loss_G: 3.0415 D(x): 0.6943 D(G(z)): 0.4996 / 0.4031\n", + "[98/100][320/391] Loss_D: 3.8226 Loss_G: 2.0641 D(x): 0.5274 D(G(z)): 0.4854 / 0.5421\n", + "[98/100][321/391] Loss_D: 3.2693 Loss_G: 3.3443 D(x): 0.7421 D(G(z)): 0.5552 / 0.3628\n", + "[98/100][322/391] Loss_D: 2.4256 Loss_G: 2.5176 D(x): 0.6864 D(G(z)): 0.3636 / 0.4840\n", + "[98/100][323/391] Loss_D: 2.7555 Loss_G: 3.2686 D(x): 0.6532 D(G(z)): 0.3427 / 0.3811\n", + "[98/100][324/391] Loss_D: 2.2994 Loss_G: 2.3787 D(x): 0.7268 D(G(z)): 0.3776 / 0.4858\n", + "[98/100][325/391] Loss_D: 2.8422 Loss_G: 2.7106 D(x): 0.7347 D(G(z)): 0.4614 / 0.4271\n", + "[98/100][326/391] Loss_D: 2.3307 Loss_G: 2.6810 D(x): 0.7944 D(G(z)): 0.3532 / 0.4475\n", + "[98/100][327/391] Loss_D: 3.0603 Loss_G: 2.6686 D(x): 0.5800 D(G(z)): 0.4007 / 0.4574\n", + "[98/100][328/391] Loss_D: 2.3427 Loss_G: 2.5447 D(x): 0.7280 D(G(z)): 0.4179 / 0.4530\n", + "[98/100][329/391] Loss_D: 3.1041 Loss_G: 2.4885 D(x): 0.5675 D(G(z)): 0.3668 / 0.4755\n", + "[98/100][330/391] Loss_D: 2.9534 Loss_G: 2.4430 D(x): 0.7038 D(G(z)): 0.4746 / 0.4753\n", + "[98/100][331/391] Loss_D: 3.5327 Loss_G: 2.3218 D(x): 0.7355 D(G(z)): 0.3989 / 0.4942\n", + "[98/100][332/391] Loss_D: 2.7523 Loss_G: 3.2930 D(x): 0.7017 D(G(z)): 0.4551 / 0.3710\n", + "[98/100][333/391] Loss_D: 2.8976 Loss_G: 3.6525 D(x): 0.7035 D(G(z)): 0.4171 / 0.3150\n", + "[98/100][334/391] Loss_D: 2.0573 Loss_G: 2.8539 D(x): 0.7138 D(G(z)): 0.2829 / 0.4173\n", + "[98/100][335/391] Loss_D: 2.8996 Loss_G: 3.6559 D(x): 0.6787 D(G(z)): 0.4623 / 0.3200\n", + "[98/100][336/391] Loss_D: 2.7410 Loss_G: 3.5226 D(x): 0.7194 D(G(z)): 0.3980 / 0.3317\n", + "[98/100][337/391] Loss_D: 3.2787 Loss_G: 4.4702 D(x): 0.5946 D(G(z)): 0.4424 / 0.2499\n", + "[98/100][338/391] Loss_D: 3.0076 Loss_G: 3.0810 D(x): 0.5866 D(G(z)): 0.3493 / 0.3829\n", + "[98/100][339/391] Loss_D: 2.7340 Loss_G: 2.4497 D(x): 0.6742 D(G(z)): 0.3931 / 0.4661\n", + "[98/100][340/391] Loss_D: 3.2991 Loss_G: 2.4925 D(x): 0.6449 D(G(z)): 0.5275 / 0.4718\n", + "[98/100][341/391] Loss_D: 3.1432 Loss_G: 2.0648 D(x): 0.7037 D(G(z)): 0.5034 / 0.5411\n", + "[98/100][342/391] Loss_D: 2.9491 Loss_G: 2.4435 D(x): 0.6819 D(G(z)): 0.4553 / 0.4764\n", + "[98/100][343/391] Loss_D: 2.5866 Loss_G: 2.2038 D(x): 0.7705 D(G(z)): 0.4316 / 0.5086\n", + "[98/100][344/391] Loss_D: 2.1863 Loss_G: 3.0276 D(x): 0.7169 D(G(z)): 0.3202 / 0.4087\n", + "[98/100][345/391] Loss_D: 2.5463 Loss_G: 3.8276 D(x): 0.7393 D(G(z)): 0.3891 / 0.3079\n", + "[98/100][346/391] Loss_D: 2.7225 Loss_G: 2.9627 D(x): 0.5798 D(G(z)): 0.3153 / 0.4081\n", + "[98/100][347/391] Loss_D: 2.6688 Loss_G: 3.3193 D(x): 0.6612 D(G(z)): 0.3213 / 0.3568\n", + "[98/100][348/391] Loss_D: 3.1965 Loss_G: 2.7411 D(x): 0.5931 D(G(z)): 0.4507 / 0.4306\n", + "[98/100][349/391] Loss_D: 3.2200 Loss_G: 2.8298 D(x): 0.6234 D(G(z)): 0.4960 / 0.4117\n", + "[98/100][350/391] Loss_D: 2.8373 Loss_G: 2.7524 D(x): 0.6464 D(G(z)): 0.4433 / 0.4312\n", + "[98/100][351/391] Loss_D: 3.0366 Loss_G: 2.3429 D(x): 0.6770 D(G(z)): 0.4890 / 0.4845\n", + "[98/100][352/391] Loss_D: 2.8532 Loss_G: 2.7003 D(x): 0.7573 D(G(z)): 0.5218 / 0.4432\n", + "[98/100][353/391] Loss_D: 2.6833 Loss_G: 2.3871 D(x): 0.7094 D(G(z)): 0.4493 / 0.4857\n", + "[98/100][354/391] Loss_D: 2.2000 Loss_G: 2.4581 D(x): 0.6778 D(G(z)): 0.2696 / 0.4634\n", + "[98/100][355/391] Loss_D: 2.5551 Loss_G: 2.7828 D(x): 0.6766 D(G(z)): 0.3957 / 0.4234\n", + "[98/100][356/391] Loss_D: 2.6741 Loss_G: 3.6246 D(x): 0.6879 D(G(z)): 0.4064 / 0.3278\n", + "[98/100][357/391] Loss_D: 3.8412 Loss_G: 2.3448 D(x): 0.5382 D(G(z)): 0.5528 / 0.4670\n", + "[98/100][358/391] Loss_D: 2.6766 Loss_G: 2.6616 D(x): 0.6298 D(G(z)): 0.3443 / 0.4423\n", + "[98/100][359/391] Loss_D: 2.9525 Loss_G: 1.9672 D(x): 0.6696 D(G(z)): 0.4633 / 0.5464\n", + "[98/100][360/391] Loss_D: 2.9546 Loss_G: 4.0413 D(x): 0.6150 D(G(z)): 0.3972 / 0.3085\n", + "[98/100][361/391] Loss_D: 3.4420 Loss_G: 2.8434 D(x): 0.6190 D(G(z)): 0.4185 / 0.4202\n", + "[98/100][362/391] Loss_D: 2.8960 Loss_G: 2.4821 D(x): 0.7370 D(G(z)): 0.4829 / 0.4731\n", + "[98/100][363/391] Loss_D: 2.6776 Loss_G: 3.8641 D(x): 0.7467 D(G(z)): 0.4511 / 0.2921\n", + "[98/100][364/391] Loss_D: 3.0224 Loss_G: 3.6093 D(x): 0.6609 D(G(z)): 0.4280 / 0.3286\n", + "[98/100][365/391] Loss_D: 2.6774 Loss_G: 3.1413 D(x): 0.7084 D(G(z)): 0.4529 / 0.3834\n", + "[98/100][366/391] Loss_D: 2.2799 Loss_G: 3.5745 D(x): 0.8311 D(G(z)): 0.3882 / 0.3374\n", + "[98/100][367/391] Loss_D: 2.9048 Loss_G: 2.8985 D(x): 0.6376 D(G(z)): 0.3912 / 0.4083\n", + "[98/100][368/391] Loss_D: 3.0096 Loss_G: 2.8721 D(x): 0.5881 D(G(z)): 0.3281 / 0.4104\n", + "[98/100][369/391] Loss_D: 2.5299 Loss_G: 2.2507 D(x): 0.6172 D(G(z)): 0.3434 / 0.4938\n", + "[98/100][370/391] Loss_D: 2.8767 Loss_G: 2.9335 D(x): 0.5832 D(G(z)): 0.3580 / 0.4092\n", + "[98/100][371/391] Loss_D: 2.4408 Loss_G: 2.8033 D(x): 0.7388 D(G(z)): 0.3730 / 0.4398\n", + "[98/100][372/391] Loss_D: 2.6686 Loss_G: 2.5210 D(x): 0.7548 D(G(z)): 0.4809 / 0.4518\n", + "[98/100][373/391] Loss_D: 2.4076 Loss_G: 2.4399 D(x): 0.7702 D(G(z)): 0.3516 / 0.4689\n", + "[98/100][374/391] Loss_D: 2.5164 Loss_G: 3.1189 D(x): 0.7404 D(G(z)): 0.4374 / 0.3868\n", + "[98/100][375/391] Loss_D: 2.6055 Loss_G: 2.3458 D(x): 0.7865 D(G(z)): 0.4329 / 0.4999\n", + "[98/100][376/391] Loss_D: 3.1549 Loss_G: 3.3017 D(x): 0.6518 D(G(z)): 0.4548 / 0.3670\n", + "[98/100][377/391] Loss_D: 3.1612 Loss_G: 2.4417 D(x): 0.5637 D(G(z)): 0.3622 / 0.4708\n", + "[98/100][378/391] Loss_D: 2.9549 Loss_G: 3.3878 D(x): 0.5933 D(G(z)): 0.4360 / 0.3527\n", + "[98/100][379/391] Loss_D: 2.5309 Loss_G: 3.7687 D(x): 0.6856 D(G(z)): 0.3775 / 0.3163\n", + "[98/100][380/391] Loss_D: 3.1685 Loss_G: 2.6085 D(x): 0.5791 D(G(z)): 0.3908 / 0.4614\n", + "[98/100][381/391] Loss_D: 3.3316 Loss_G: 2.9919 D(x): 0.7736 D(G(z)): 0.5951 / 0.4173\n", + "[98/100][382/391] Loss_D: 2.2457 Loss_G: 2.8173 D(x): 0.7456 D(G(z)): 0.3593 / 0.4345\n", + "[98/100][383/391] Loss_D: 2.4347 Loss_G: 3.1010 D(x): 0.7328 D(G(z)): 0.3558 / 0.3989\n", + "[98/100][384/391] Loss_D: 2.9414 Loss_G: 2.7606 D(x): 0.6264 D(G(z)): 0.4710 / 0.4296\n", + "[98/100][385/391] Loss_D: 3.2760 Loss_G: 2.8010 D(x): 0.5857 D(G(z)): 0.4949 / 0.4117\n", + "[98/100][386/391] Loss_D: 2.7824 Loss_G: 2.5833 D(x): 0.6564 D(G(z)): 0.3682 / 0.4576\n", + "[98/100][387/391] Loss_D: 2.8612 Loss_G: 2.5804 D(x): 0.6823 D(G(z)): 0.4268 / 0.4542\n", + "[98/100][388/391] Loss_D: 2.4138 Loss_G: 2.6109 D(x): 0.6706 D(G(z)): 0.3412 / 0.4595\n", + "[98/100][389/391] Loss_D: 2.7771 Loss_G: 3.3696 D(x): 0.6364 D(G(z)): 0.3243 / 0.3655\n", + "[98/100][390/391] Loss_D: 2.7610 Loss_G: 3.1125 D(x): 0.7007 D(G(z)): 0.4496 / 0.3938\n", + "[98/100][391/391] Loss_D: 3.6624 Loss_G: 3.5019 D(x): 0.7463 D(G(z)): 0.4172 / 0.3589\n", + "[99/100][1/391] Loss_D: 3.5898 Loss_G: 2.3782 D(x): 0.7127 D(G(z)): 0.4631 / 0.4666\n", + "[99/100][2/391] Loss_D: 3.0519 Loss_G: 2.6274 D(x): 0.6260 D(G(z)): 0.4533 / 0.4575\n", + "[99/100][3/391] Loss_D: 3.0385 Loss_G: 2.8066 D(x): 0.6278 D(G(z)): 0.4474 / 0.4207\n", + "[99/100][4/391] Loss_D: 2.8373 Loss_G: 4.7295 D(x): 0.6747 D(G(z)): 0.4801 / 0.2386\n", + "[99/100][5/391] Loss_D: 2.7494 Loss_G: 2.7489 D(x): 0.6291 D(G(z)): 0.3422 / 0.4257\n", + "[99/100][6/391] Loss_D: 2.8193 Loss_G: 2.7342 D(x): 0.6587 D(G(z)): 0.3867 / 0.4221\n", + "[99/100][7/391] Loss_D: 3.0042 Loss_G: 3.1630 D(x): 0.6694 D(G(z)): 0.4099 / 0.3629\n", + "[99/100][8/391] Loss_D: 2.5558 Loss_G: 2.9061 D(x): 0.6431 D(G(z)): 0.3869 / 0.4067\n", + "[99/100][9/391] Loss_D: 2.3785 Loss_G: 3.0807 D(x): 0.7449 D(G(z)): 0.3966 / 0.3777\n", + "[99/100][10/391] Loss_D: 3.0481 Loss_G: 2.4939 D(x): 0.6700 D(G(z)): 0.5105 / 0.4609\n", + "[99/100][11/391] Loss_D: 3.1229 Loss_G: 3.1568 D(x): 0.6264 D(G(z)): 0.4208 / 0.3647\n", + "[99/100][12/391] Loss_D: 2.4161 Loss_G: 2.4569 D(x): 0.6906 D(G(z)): 0.3307 / 0.4757\n", + "[99/100][13/391] Loss_D: 2.5600 Loss_G: 2.6516 D(x): 0.6392 D(G(z)): 0.3023 / 0.4483\n", + "[99/100][14/391] Loss_D: 2.6252 Loss_G: 2.9502 D(x): 0.6667 D(G(z)): 0.4129 / 0.3958\n", + "[99/100][15/391] Loss_D: 2.5065 Loss_G: 2.4547 D(x): 0.7336 D(G(z)): 0.4227 / 0.4702\n", + "[99/100][16/391] Loss_D: 2.7550 Loss_G: 2.7071 D(x): 0.6855 D(G(z)): 0.3874 / 0.4290\n", + "[99/100][17/391] Loss_D: 2.5971 Loss_G: 2.2945 D(x): 0.6660 D(G(z)): 0.3580 / 0.4766\n", + "[99/100][18/391] Loss_D: 2.8176 Loss_G: 3.1266 D(x): 0.7037 D(G(z)): 0.4789 / 0.3933\n", + "[99/100][19/391] Loss_D: 2.1901 Loss_G: 1.5719 D(x): 0.7741 D(G(z)): 0.3549 / 0.6121\n", + "[99/100][20/391] Loss_D: 3.1837 Loss_G: 2.1380 D(x): 0.6919 D(G(z)): 0.5501 / 0.5294\n", + "[99/100][21/391] Loss_D: 2.8585 Loss_G: 3.4562 D(x): 0.5891 D(G(z)): 0.4279 / 0.3562\n", + "[99/100][22/391] Loss_D: 2.6922 Loss_G: 3.5382 D(x): 0.7217 D(G(z)): 0.4275 / 0.3366\n", + "[99/100][23/391] Loss_D: 2.8060 Loss_G: 3.0473 D(x): 0.6282 D(G(z)): 0.3604 / 0.3855\n", + "[99/100][24/391] Loss_D: 2.7179 Loss_G: 2.0565 D(x): 0.6246 D(G(z)): 0.3124 / 0.5226\n", + "[99/100][25/391] Loss_D: 2.5869 Loss_G: 2.7157 D(x): 0.7484 D(G(z)): 0.3896 / 0.4432\n", + "[99/100][26/391] Loss_D: 2.9691 Loss_G: 2.7902 D(x): 0.6447 D(G(z)): 0.4319 / 0.4316\n", + "[99/100][27/391] Loss_D: 2.7017 Loss_G: 2.7996 D(x): 0.7452 D(G(z)): 0.3845 / 0.4266\n", + "[99/100][28/391] Loss_D: 2.1741 Loss_G: 3.6760 D(x): 0.7243 D(G(z)): 0.3440 / 0.3413\n", + "[99/100][29/391] Loss_D: 2.3017 Loss_G: 2.1067 D(x): 0.7006 D(G(z)): 0.2385 / 0.5434\n", + "[99/100][30/391] Loss_D: 2.4180 Loss_G: 1.9418 D(x): 0.7311 D(G(z)): 0.4305 / 0.5326\n", + "[99/100][31/391] Loss_D: 3.6875 Loss_G: 1.3986 D(x): 0.6630 D(G(z)): 0.3498 / 0.6750\n", + "[99/100][32/391] Loss_D: 2.6201 Loss_G: 2.9752 D(x): 0.7483 D(G(z)): 0.4190 / 0.3965\n", + "[99/100][33/391] Loss_D: 2.4741 Loss_G: 2.3989 D(x): 0.7911 D(G(z)): 0.4111 / 0.5040\n", + "[99/100][34/391] Loss_D: 3.5322 Loss_G: 2.4391 D(x): 0.5869 D(G(z)): 0.5223 / 0.4759\n", + "[99/100][35/391] Loss_D: 2.6047 Loss_G: 2.4308 D(x): 0.7515 D(G(z)): 0.4373 / 0.4807\n", + "[99/100][36/391] Loss_D: 2.7547 Loss_G: 2.0225 D(x): 0.6680 D(G(z)): 0.3715 / 0.5270\n", + "[99/100][37/391] Loss_D: 2.6509 Loss_G: 2.6455 D(x): 0.6769 D(G(z)): 0.4030 / 0.4392\n", + "[99/100][38/391] Loss_D: 2.9133 Loss_G: 2.5660 D(x): 0.5714 D(G(z)): 0.3222 / 0.4502\n", + "[99/100][39/391] Loss_D: 2.8620 Loss_G: 1.8840 D(x): 0.6581 D(G(z)): 0.3760 / 0.5760\n", + "[99/100][40/391] Loss_D: 2.8944 Loss_G: 1.7934 D(x): 0.7482 D(G(z)): 0.4714 / 0.5865\n", + "[99/100][41/391] Loss_D: 3.1545 Loss_G: 2.8119 D(x): 0.7851 D(G(z)): 0.5518 / 0.4192\n", + "[99/100][42/391] Loss_D: 2.2679 Loss_G: 2.6903 D(x): 0.7987 D(G(z)): 0.3615 / 0.4371\n", + "[99/100][43/391] Loss_D: 2.7389 Loss_G: 2.6425 D(x): 0.6733 D(G(z)): 0.4060 / 0.4567\n", + "[99/100][44/391] Loss_D: 3.4094 Loss_G: 3.3574 D(x): 0.5339 D(G(z)): 0.4527 / 0.3538\n", + "[99/100][45/391] Loss_D: 2.7467 Loss_G: 2.6304 D(x): 0.6614 D(G(z)): 0.3553 / 0.4682\n", + "[99/100][46/391] Loss_D: 2.6509 Loss_G: 2.6596 D(x): 0.7602 D(G(z)): 0.4226 / 0.4417\n", + "[99/100][47/391] Loss_D: 2.8212 Loss_G: 2.2569 D(x): 0.6679 D(G(z)): 0.4082 / 0.5062\n", + "[99/100][48/391] Loss_D: 2.9180 Loss_G: 3.6512 D(x): 0.6403 D(G(z)): 0.4295 / 0.3326\n", + "[99/100][49/391] Loss_D: 2.8712 Loss_G: 2.9161 D(x): 0.6486 D(G(z)): 0.4492 / 0.4143\n", + "[99/100][50/391] Loss_D: 2.7722 Loss_G: 2.5681 D(x): 0.6753 D(G(z)): 0.4414 / 0.4685\n", + "[99/100][51/391] Loss_D: 2.7628 Loss_G: 3.0914 D(x): 0.7278 D(G(z)): 0.4631 / 0.3864\n", + "[99/100][52/391] Loss_D: 3.1551 Loss_G: 2.3768 D(x): 0.6236 D(G(z)): 0.4805 / 0.5136\n", + "[99/100][53/391] Loss_D: 2.5673 Loss_G: 3.1238 D(x): 0.7194 D(G(z)): 0.4098 / 0.3799\n", + "[99/100][54/391] Loss_D: 2.6948 Loss_G: 3.3560 D(x): 0.6420 D(G(z)): 0.3921 / 0.3556\n", + "[99/100][55/391] Loss_D: 2.6280 Loss_G: 3.2181 D(x): 0.6488 D(G(z)): 0.3712 / 0.3750\n", + "[99/100][56/391] Loss_D: 2.6084 Loss_G: 2.6319 D(x): 0.7465 D(G(z)): 0.4042 / 0.4500\n", + "[99/100][57/391] Loss_D: 2.6500 Loss_G: 2.6118 D(x): 0.7691 D(G(z)): 0.4524 / 0.4480\n", + "[99/100][58/391] Loss_D: 2.5400 Loss_G: 2.7279 D(x): 0.6794 D(G(z)): 0.4214 / 0.4320\n", + "[99/100][59/391] Loss_D: 2.8422 Loss_G: 2.8831 D(x): 0.6296 D(G(z)): 0.4040 / 0.4136\n", + "[99/100][60/391] Loss_D: 2.6892 Loss_G: 3.1803 D(x): 0.7172 D(G(z)): 0.4272 / 0.3821\n", + "[99/100][61/391] Loss_D: 3.6609 Loss_G: 2.0510 D(x): 0.6289 D(G(z)): 0.4788 / 0.5304\n", + "[99/100][62/391] Loss_D: 2.7185 Loss_G: 2.6057 D(x): 0.6819 D(G(z)): 0.4137 / 0.4564\n", + "[99/100][63/391] Loss_D: 2.5687 Loss_G: 3.4777 D(x): 0.7885 D(G(z)): 0.4356 / 0.3432\n", + "[99/100][64/391] Loss_D: 2.6518 Loss_G: 2.4338 D(x): 0.7509 D(G(z)): 0.4948 / 0.4826\n", + "[99/100][65/391] Loss_D: 2.8911 Loss_G: 2.7159 D(x): 0.5884 D(G(z)): 0.3476 / 0.4512\n", + "[99/100][66/391] Loss_D: 2.9244 Loss_G: 3.8096 D(x): 0.6823 D(G(z)): 0.4686 / 0.3223\n", + "[99/100][67/391] Loss_D: 3.0366 Loss_G: 3.5900 D(x): 0.6609 D(G(z)): 0.4036 / 0.3332\n", + "[99/100][68/391] Loss_D: 2.7732 Loss_G: 3.8348 D(x): 0.6380 D(G(z)): 0.4393 / 0.3223\n", + "[99/100][69/391] Loss_D: 2.4622 Loss_G: 3.9784 D(x): 0.7074 D(G(z)): 0.3419 / 0.3084\n", + "[99/100][70/391] Loss_D: 2.6588 Loss_G: 2.6737 D(x): 0.6650 D(G(z)): 0.3440 / 0.4406\n", + "[99/100][71/391] Loss_D: 2.6192 Loss_G: 4.0148 D(x): 0.7135 D(G(z)): 0.3634 / 0.3154\n", + "[99/100][72/391] Loss_D: 2.5593 Loss_G: 3.4032 D(x): 0.6989 D(G(z)): 0.4047 / 0.3502\n", + "[99/100][73/391] Loss_D: 2.3398 Loss_G: 3.2557 D(x): 0.6992 D(G(z)): 0.2806 / 0.3670\n", + "[99/100][74/391] Loss_D: 2.6803 Loss_G: 3.3755 D(x): 0.6539 D(G(z)): 0.3862 / 0.3657\n", + "[99/100][75/391] Loss_D: 2.4944 Loss_G: 2.6549 D(x): 0.7083 D(G(z)): 0.3697 / 0.4447\n", + "[99/100][76/391] Loss_D: 2.2834 Loss_G: 2.8129 D(x): 0.7311 D(G(z)): 0.3415 / 0.4260\n", + "[99/100][77/391] Loss_D: 2.6933 Loss_G: 2.2660 D(x): 0.6786 D(G(z)): 0.2960 / 0.5007\n", + "[99/100][78/391] Loss_D: 2.9481 Loss_G: 2.1411 D(x): 0.6541 D(G(z)): 0.4864 / 0.5395\n", + "[99/100][79/391] Loss_D: 3.0311 Loss_G: 2.9768 D(x): 0.7034 D(G(z)): 0.5433 / 0.4081\n", + "[99/100][80/391] Loss_D: 2.7223 Loss_G: 3.1784 D(x): 0.7969 D(G(z)): 0.4454 / 0.3800\n", + "[99/100][81/391] Loss_D: 3.2288 Loss_G: 2.1180 D(x): 0.6076 D(G(z)): 0.5020 / 0.5451\n", + "[99/100][82/391] Loss_D: 2.8756 Loss_G: 2.4113 D(x): 0.6448 D(G(z)): 0.3872 / 0.4773\n", + "[99/100][83/391] Loss_D: 2.8132 Loss_G: 3.3989 D(x): 0.6964 D(G(z)): 0.4447 / 0.3552\n", + "[99/100][84/391] Loss_D: 2.4180 Loss_G: 3.8601 D(x): 0.6797 D(G(z)): 0.3846 / 0.3275\n", + "[99/100][85/391] Loss_D: 2.5752 Loss_G: 3.0776 D(x): 0.7521 D(G(z)): 0.4583 / 0.3895\n", + "[99/100][86/391] Loss_D: 2.3679 Loss_G: 2.5627 D(x): 0.6905 D(G(z)): 0.2294 / 0.4466\n", + "[99/100][87/391] Loss_D: 2.6138 Loss_G: 4.0801 D(x): 0.7480 D(G(z)): 0.3464 / 0.2939\n", + "[99/100][88/391] Loss_D: 2.9640 Loss_G: 3.4657 D(x): 0.6698 D(G(z)): 0.5003 / 0.3415\n", + "[99/100][89/391] Loss_D: 2.8458 Loss_G: 3.4584 D(x): 0.5970 D(G(z)): 0.3797 / 0.3555\n", + "[99/100][90/391] Loss_D: 2.8117 Loss_G: 3.0655 D(x): 0.6642 D(G(z)): 0.4119 / 0.3974\n", + "[99/100][91/391] Loss_D: 3.6646 Loss_G: 3.1088 D(x): 0.7618 D(G(z)): 0.4350 / 0.3919\n", + "[99/100][92/391] Loss_D: 2.5842 Loss_G: 2.1417 D(x): 0.6961 D(G(z)): 0.4313 / 0.5417\n", + "[99/100][93/391] Loss_D: 2.7571 Loss_G: 2.8141 D(x): 0.6608 D(G(z)): 0.3945 / 0.4408\n", + "[99/100][94/391] Loss_D: 2.4306 Loss_G: 3.6313 D(x): 0.6842 D(G(z)): 0.3842 / 0.3345\n", + "[99/100][95/391] Loss_D: 2.2545 Loss_G: 3.6814 D(x): 0.7504 D(G(z)): 0.2901 / 0.3320\n", + "[99/100][96/391] Loss_D: 2.5897 Loss_G: 3.0249 D(x): 0.6793 D(G(z)): 0.3894 / 0.4037\n", + "[99/100][97/391] Loss_D: 2.8814 Loss_G: 2.9388 D(x): 0.7182 D(G(z)): 0.4315 / 0.3984\n", + "[99/100][98/391] Loss_D: 2.7914 Loss_G: 4.7235 D(x): 0.6483 D(G(z)): 0.3823 / 0.2414\n", + "[99/100][99/391] Loss_D: 2.8440 Loss_G: 3.2704 D(x): 0.6757 D(G(z)): 0.4297 / 0.3584\n", + "[99/100][100/391] Loss_D: 3.2442 Loss_G: 3.5588 D(x): 0.5981 D(G(z)): 0.4648 / 0.3519\n", + "[99/100][101/391] Loss_D: 2.8391 Loss_G: 2.0254 D(x): 0.7412 D(G(z)): 0.4905 / 0.5305\n", + "[99/100][102/391] Loss_D: 2.6582 Loss_G: 3.3662 D(x): 0.6861 D(G(z)): 0.3863 / 0.3626\n", + "[99/100][103/391] Loss_D: 3.4313 Loss_G: 2.9480 D(x): 0.5378 D(G(z)): 0.4454 / 0.4099\n", + "[99/100][104/391] Loss_D: 2.2603 Loss_G: 2.0530 D(x): 0.7516 D(G(z)): 0.3821 / 0.5449\n", + "[99/100][105/391] Loss_D: 3.7396 Loss_G: 2.5866 D(x): 0.5348 D(G(z)): 0.4962 / 0.4584\n", + "[99/100][106/391] Loss_D: 2.2167 Loss_G: 3.1152 D(x): 0.7177 D(G(z)): 0.2894 / 0.4000\n", + "[99/100][107/391] Loss_D: 2.7613 Loss_G: 2.7938 D(x): 0.7217 D(G(z)): 0.4394 / 0.4354\n", + "[99/100][108/391] Loss_D: 2.5162 Loss_G: 3.4274 D(x): 0.7502 D(G(z)): 0.4075 / 0.3561\n", + "[99/100][109/391] Loss_D: 2.4577 Loss_G: 3.0792 D(x): 0.7419 D(G(z)): 0.3992 / 0.4001\n", + "[99/100][110/391] Loss_D: 3.2445 Loss_G: 3.0593 D(x): 0.6316 D(G(z)): 0.5044 / 0.4031\n", + "[99/100][111/391] Loss_D: 3.0476 Loss_G: 3.4524 D(x): 0.5889 D(G(z)): 0.4104 / 0.3537\n", + "[99/100][112/391] Loss_D: 2.8812 Loss_G: 2.0141 D(x): 0.6563 D(G(z)): 0.4539 / 0.5561\n", + "[99/100][113/391] Loss_D: 2.6015 Loss_G: 2.9610 D(x): 0.7529 D(G(z)): 0.4251 / 0.4146\n", + "[99/100][114/391] Loss_D: 2.8646 Loss_G: 3.9297 D(x): 0.6393 D(G(z)): 0.4398 / 0.2975\n", + "[99/100][115/391] Loss_D: 2.4975 Loss_G: 2.0998 D(x): 0.8030 D(G(z)): 0.3980 / 0.5375\n", + "[99/100][116/391] Loss_D: 2.7177 Loss_G: 4.0201 D(x): 0.7174 D(G(z)): 0.4567 / 0.3009\n", + "[99/100][117/391] Loss_D: 2.7317 Loss_G: 2.6937 D(x): 0.6737 D(G(z)): 0.3018 / 0.4539\n", + "[99/100][118/391] Loss_D: 2.2862 Loss_G: 2.8811 D(x): 0.7076 D(G(z)): 0.2920 / 0.4038\n", + "[99/100][119/391] Loss_D: 3.0274 Loss_G: 3.2521 D(x): 0.6793 D(G(z)): 0.4744 / 0.3728\n", + "[99/100][120/391] Loss_D: 2.4778 Loss_G: 3.0791 D(x): 0.7861 D(G(z)): 0.4242 / 0.3835\n", + "[99/100][121/391] Loss_D: 3.6380 Loss_G: 4.1700 D(x): 0.5534 D(G(z)): 0.4410 / 0.3159\n", + "[99/100][122/391] Loss_D: 2.8451 Loss_G: 2.9546 D(x): 0.6911 D(G(z)): 0.4534 / 0.4276\n", + "[99/100][123/391] Loss_D: 2.9940 Loss_G: 3.6249 D(x): 0.6726 D(G(z)): 0.4382 / 0.3334\n", + "[99/100][124/391] Loss_D: 2.9621 Loss_G: 3.3448 D(x): 0.6607 D(G(z)): 0.4627 / 0.3605\n", + "[99/100][125/391] Loss_D: 2.4909 Loss_G: 2.5560 D(x): 0.7032 D(G(z)): 0.3024 / 0.4517\n", + "[99/100][126/391] Loss_D: 2.9679 Loss_G: 4.2661 D(x): 0.6390 D(G(z)): 0.4456 / 0.2720\n", + "[99/100][127/391] Loss_D: 3.0720 Loss_G: 2.8662 D(x): 0.6816 D(G(z)): 0.4434 / 0.4038\n", + "[99/100][128/391] Loss_D: 2.8517 Loss_G: 2.8000 D(x): 0.5971 D(G(z)): 0.3230 / 0.4270\n", + "[99/100][129/391] Loss_D: 2.4578 Loss_G: 3.0946 D(x): 0.6756 D(G(z)): 0.3072 / 0.3832\n", + "[99/100][130/391] Loss_D: 2.5829 Loss_G: 2.2849 D(x): 0.7536 D(G(z)): 0.3989 / 0.5102\n", + "[99/100][131/391] Loss_D: 3.4935 Loss_G: 2.5247 D(x): 0.7503 D(G(z)): 0.6162 / 0.4655\n", + "[99/100][132/391] Loss_D: 2.4724 Loss_G: 2.9953 D(x): 0.7748 D(G(z)): 0.4138 / 0.4098\n", + "[99/100][133/391] Loss_D: 3.0187 Loss_G: 3.4070 D(x): 0.6360 D(G(z)): 0.5007 / 0.3614\n", + "[99/100][134/391] Loss_D: 3.0400 Loss_G: 3.9377 D(x): 0.6449 D(G(z)): 0.4711 / 0.3059\n", + "[99/100][135/391] Loss_D: 2.8051 Loss_G: 3.5555 D(x): 0.6518 D(G(z)): 0.3771 / 0.3565\n", + "[99/100][136/391] Loss_D: 2.9080 Loss_G: 4.0208 D(x): 0.6008 D(G(z)): 0.3721 / 0.3033\n", + "[99/100][137/391] Loss_D: 2.4650 Loss_G: 3.6351 D(x): 0.6820 D(G(z)): 0.2658 / 0.3335\n", + "[99/100][138/391] Loss_D: 3.3289 Loss_G: 2.3929 D(x): 0.5270 D(G(z)): 0.3724 / 0.4761\n", + "[99/100][139/391] Loss_D: 3.0180 Loss_G: 2.7393 D(x): 0.6331 D(G(z)): 0.3918 / 0.4456\n", + "[99/100][140/391] Loss_D: 2.5052 Loss_G: 3.6764 D(x): 0.7258 D(G(z)): 0.4008 / 0.3199\n", + "[99/100][141/391] Loss_D: 2.8814 Loss_G: 3.0718 D(x): 0.7076 D(G(z)): 0.4533 / 0.4099\n", + "[99/100][142/391] Loss_D: 2.8527 Loss_G: 3.0412 D(x): 0.5982 D(G(z)): 0.4011 / 0.4000\n", + "[99/100][143/391] Loss_D: 2.5476 Loss_G: 1.9149 D(x): 0.7464 D(G(z)): 0.3944 / 0.5638\n", + "[99/100][144/391] Loss_D: 2.7731 Loss_G: 3.4746 D(x): 0.6549 D(G(z)): 0.4350 / 0.3417\n", + "[99/100][145/391] Loss_D: 2.4359 Loss_G: 2.8323 D(x): 0.7829 D(G(z)): 0.3800 / 0.4183\n", + "[99/100][146/391] Loss_D: 2.3807 Loss_G: 2.0271 D(x): 0.7739 D(G(z)): 0.3815 / 0.5524\n", + "[99/100][147/391] Loss_D: 2.8008 Loss_G: 2.9734 D(x): 0.7049 D(G(z)): 0.4167 / 0.3924\n", + "[99/100][148/391] Loss_D: 2.1780 Loss_G: 3.6792 D(x): 0.7194 D(G(z)): 0.3202 / 0.3270\n", + "[99/100][149/391] Loss_D: 2.8031 Loss_G: 2.2766 D(x): 0.6570 D(G(z)): 0.3723 / 0.5152\n", + "[99/100][150/391] Loss_D: 2.7756 Loss_G: 3.2756 D(x): 0.7060 D(G(z)): 0.4327 / 0.3901\n", + "[99/100][151/391] Loss_D: 3.6348 Loss_G: 2.4748 D(x): 0.6548 D(G(z)): 0.3530 / 0.4702\n", + "[99/100][152/391] Loss_D: 2.2766 Loss_G: 2.8985 D(x): 0.6849 D(G(z)): 0.3098 / 0.4300\n", + "[99/100][153/391] Loss_D: 2.8284 Loss_G: 2.8758 D(x): 0.8059 D(G(z)): 0.4935 / 0.4305\n", + "[99/100][154/391] Loss_D: 2.6170 Loss_G: 4.1094 D(x): 0.7322 D(G(z)): 0.4654 / 0.2806\n", + "[99/100][155/391] Loss_D: 2.9427 Loss_G: 3.0998 D(x): 0.6036 D(G(z)): 0.3529 / 0.4031\n", + "[99/100][156/391] Loss_D: 3.2055 Loss_G: 3.6708 D(x): 0.6240 D(G(z)): 0.4773 / 0.3312\n", + "[99/100][157/391] Loss_D: 2.9606 Loss_G: 2.5297 D(x): 0.6601 D(G(z)): 0.4227 / 0.4541\n", + "[99/100][158/391] Loss_D: 2.8372 Loss_G: 2.4797 D(x): 0.6874 D(G(z)): 0.4842 / 0.4801\n", + "[99/100][159/391] Loss_D: 3.1079 Loss_G: 2.8631 D(x): 0.5886 D(G(z)): 0.3512 / 0.4192\n", + "[99/100][160/391] Loss_D: 2.7128 Loss_G: 2.2645 D(x): 0.7211 D(G(z)): 0.4528 / 0.5194\n", + "[99/100][161/391] Loss_D: 2.4139 Loss_G: 2.8680 D(x): 0.7963 D(G(z)): 0.3937 / 0.4169\n", + "[99/100][162/391] Loss_D: 2.9408 Loss_G: 3.3323 D(x): 0.6722 D(G(z)): 0.4768 / 0.3643\n", + "[99/100][163/391] Loss_D: 2.7750 Loss_G: 2.5925 D(x): 0.6254 D(G(z)): 0.3848 / 0.4673\n", + "[99/100][164/391] Loss_D: 2.1753 Loss_G: 3.3309 D(x): 0.7051 D(G(z)): 0.3266 / 0.3729\n", + "[99/100][165/391] Loss_D: 3.6291 Loss_G: 2.5289 D(x): 0.7156 D(G(z)): 0.6127 / 0.4658\n", + "[99/100][166/391] Loss_D: 2.6273 Loss_G: 2.5230 D(x): 0.6149 D(G(z)): 0.3453 / 0.4687\n", + "[99/100][167/391] Loss_D: 2.7729 Loss_G: 2.9617 D(x): 0.7303 D(G(z)): 0.4280 / 0.4081\n", + "[99/100][168/391] Loss_D: 2.7211 Loss_G: 4.4141 D(x): 0.6465 D(G(z)): 0.3520 / 0.2720\n", + "[99/100][169/391] Loss_D: 2.7021 Loss_G: 2.5368 D(x): 0.7196 D(G(z)): 0.4685 / 0.4540\n", + "[99/100][170/391] Loss_D: 3.2469 Loss_G: 2.9201 D(x): 0.6385 D(G(z)): 0.5118 / 0.4202\n", + "[99/100][171/391] Loss_D: 3.3144 Loss_G: 3.5562 D(x): 0.6653 D(G(z)): 0.5426 / 0.3546\n", + "[99/100][172/391] Loss_D: 2.6296 Loss_G: 4.0233 D(x): 0.6646 D(G(z)): 0.3710 / 0.2965\n", + "[99/100][173/391] Loss_D: 2.6815 Loss_G: 3.0007 D(x): 0.6421 D(G(z)): 0.3265 / 0.4119\n", + "[99/100][174/391] Loss_D: 2.0175 Loss_G: 4.1524 D(x): 0.7417 D(G(z)): 0.3170 / 0.2943\n", + "[99/100][175/391] Loss_D: 2.4307 Loss_G: 2.8734 D(x): 0.7054 D(G(z)): 0.3289 / 0.4197\n", + "[99/100][176/391] Loss_D: 2.8628 Loss_G: 2.9657 D(x): 0.6674 D(G(z)): 0.4272 / 0.3891\n", + "[99/100][177/391] Loss_D: 2.9004 Loss_G: 2.8125 D(x): 0.6982 D(G(z)): 0.4747 / 0.4390\n", + "[99/100][178/391] Loss_D: 2.3937 Loss_G: 3.1036 D(x): 0.6831 D(G(z)): 0.3068 / 0.3882\n", + "[99/100][179/391] Loss_D: 2.6017 Loss_G: 3.2679 D(x): 0.7084 D(G(z)): 0.4077 / 0.3658\n", + "[99/100][180/391] Loss_D: 2.6318 Loss_G: 1.9600 D(x): 0.7305 D(G(z)): 0.4277 / 0.5525\n", + "[99/100][181/391] Loss_D: 3.5843 Loss_G: 2.4314 D(x): 0.6430 D(G(z)): 0.4349 / 0.4710\n", + "[99/100][182/391] Loss_D: 2.4841 Loss_G: 3.4651 D(x): 0.6752 D(G(z)): 0.3814 / 0.3476\n", + "[99/100][183/391] Loss_D: 2.3648 Loss_G: 2.1523 D(x): 0.7387 D(G(z)): 0.3847 / 0.5145\n", + "[99/100][184/391] Loss_D: 2.9774 Loss_G: 2.4812 D(x): 0.6906 D(G(z)): 0.4851 / 0.4712\n", + "[99/100][185/391] Loss_D: 3.3274 Loss_G: 3.2950 D(x): 0.5252 D(G(z)): 0.3752 / 0.3655\n", + "[99/100][186/391] Loss_D: 2.8614 Loss_G: 2.9749 D(x): 0.6146 D(G(z)): 0.4032 / 0.3774\n", + "[99/100][187/391] Loss_D: 2.3076 Loss_G: 3.2440 D(x): 0.7805 D(G(z)): 0.2541 / 0.3709\n", + "[99/100][188/391] Loss_D: 2.9002 Loss_G: 2.5117 D(x): 0.7116 D(G(z)): 0.5400 / 0.4806\n", + "[99/100][189/391] Loss_D: 2.5811 Loss_G: 2.2417 D(x): 0.6890 D(G(z)): 0.3607 / 0.5062\n", + "[99/100][190/391] Loss_D: 2.6383 Loss_G: 2.2180 D(x): 0.7638 D(G(z)): 0.4779 / 0.5121\n", + "[99/100][191/391] Loss_D: 3.4001 Loss_G: 3.5583 D(x): 0.5543 D(G(z)): 0.4160 / 0.3470\n", + "[99/100][192/391] Loss_D: 2.9389 Loss_G: 3.3976 D(x): 0.5969 D(G(z)): 0.4291 / 0.3629\n", + "[99/100][193/391] Loss_D: 2.7427 Loss_G: 2.8808 D(x): 0.7032 D(G(z)): 0.4677 / 0.4203\n", + "[99/100][194/391] Loss_D: 3.3412 Loss_G: 2.9436 D(x): 0.5459 D(G(z)): 0.3929 / 0.4106\n", + "[99/100][195/391] Loss_D: 2.9014 Loss_G: 2.9715 D(x): 0.7092 D(G(z)): 0.4698 / 0.4013\n", + "[99/100][196/391] Loss_D: 2.2582 Loss_G: 2.4104 D(x): 0.7154 D(G(z)): 0.3476 / 0.4846\n", + "[99/100][197/391] Loss_D: 2.7081 Loss_G: 2.1692 D(x): 0.6823 D(G(z)): 0.3669 / 0.4940\n", + "[99/100][198/391] Loss_D: 2.6589 Loss_G: 3.4050 D(x): 0.7503 D(G(z)): 0.4716 / 0.3447\n", + "[99/100][199/391] Loss_D: 2.8863 Loss_G: 3.4415 D(x): 0.6656 D(G(z)): 0.4886 / 0.3560\n", + "[99/100][200/391] Loss_D: 3.2054 Loss_G: 2.6406 D(x): 0.5553 D(G(z)): 0.3979 / 0.4520\n", + "[99/100][201/391] Loss_D: 2.3833 Loss_G: 2.1241 D(x): 0.7479 D(G(z)): 0.3348 / 0.5462\n", + "[99/100][202/391] Loss_D: 2.2507 Loss_G: 2.8009 D(x): 0.7308 D(G(z)): 0.3501 / 0.4171\n", + "[99/100][203/391] Loss_D: 2.6910 Loss_G: 2.5462 D(x): 0.7247 D(G(z)): 0.4452 / 0.4612\n", + "[99/100][204/391] Loss_D: 2.3531 Loss_G: 2.7128 D(x): 0.7610 D(G(z)): 0.4526 / 0.4255\n", + "[99/100][205/391] Loss_D: 2.8761 Loss_G: 2.4853 D(x): 0.6184 D(G(z)): 0.4143 / 0.4602\n", + "[99/100][206/391] Loss_D: 2.5460 Loss_G: 3.1806 D(x): 0.6384 D(G(z)): 0.3031 / 0.3823\n", + "[99/100][207/391] Loss_D: 2.8940 Loss_G: 3.5054 D(x): 0.6498 D(G(z)): 0.3915 / 0.3368\n", + "[99/100][208/391] Loss_D: 2.6688 Loss_G: 1.7802 D(x): 0.6528 D(G(z)): 0.3875 / 0.5970\n", + "[99/100][209/391] Loss_D: 2.6750 Loss_G: 2.4789 D(x): 0.7086 D(G(z)): 0.4355 / 0.4723\n", + "[99/100][210/391] Loss_D: 2.6345 Loss_G: 1.9275 D(x): 0.8139 D(G(z)): 0.4770 / 0.5462\n", + "[99/100][211/391] Loss_D: 3.7815 Loss_G: 2.7863 D(x): 0.6232 D(G(z)): 0.5196 / 0.4286\n", + "[99/100][212/391] Loss_D: 2.6567 Loss_G: 2.7601 D(x): 0.7386 D(G(z)): 0.4330 / 0.4398\n", + "[99/100][213/391] Loss_D: 3.3951 Loss_G: 2.6369 D(x): 0.6312 D(G(z)): 0.5294 / 0.4432\n", + "[99/100][214/391] Loss_D: 2.2869 Loss_G: 3.1630 D(x): 0.6897 D(G(z)): 0.3170 / 0.3731\n", + "[99/100][215/391] Loss_D: 2.6117 Loss_G: 3.0077 D(x): 0.6800 D(G(z)): 0.3645 / 0.4044\n", + "[99/100][216/391] Loss_D: 2.5044 Loss_G: 2.3094 D(x): 0.7076 D(G(z)): 0.3364 / 0.4930\n", + "[99/100][217/391] Loss_D: 2.9193 Loss_G: 2.0282 D(x): 0.6787 D(G(z)): 0.4701 / 0.5387\n", + "[99/100][218/391] Loss_D: 3.2406 Loss_G: 1.9190 D(x): 0.6134 D(G(z)): 0.5014 / 0.5423\n", + "[99/100][219/391] Loss_D: 2.5864 Loss_G: 2.7459 D(x): 0.6721 D(G(z)): 0.4136 / 0.4412\n", + "[99/100][220/391] Loss_D: 3.0486 Loss_G: 2.5217 D(x): 0.5944 D(G(z)): 0.4242 / 0.4676\n", + "[99/100][221/391] Loss_D: 2.3861 Loss_G: 2.9705 D(x): 0.8415 D(G(z)): 0.4586 / 0.4073\n", + "[99/100][222/391] Loss_D: 2.2327 Loss_G: 3.0976 D(x): 0.7741 D(G(z)): 0.3574 / 0.3988\n", + "[99/100][223/391] Loss_D: 2.6881 Loss_G: 2.6379 D(x): 0.6768 D(G(z)): 0.4119 / 0.4429\n", + "[99/100][224/391] Loss_D: 2.9614 Loss_G: 3.4822 D(x): 0.6571 D(G(z)): 0.4650 / 0.3570\n", + "[99/100][225/391] Loss_D: 2.5856 Loss_G: 3.7642 D(x): 0.6480 D(G(z)): 0.3293 / 0.3106\n", + "[99/100][226/391] Loss_D: 2.8495 Loss_G: 3.0143 D(x): 0.6875 D(G(z)): 0.4329 / 0.3980\n", + "[99/100][227/391] Loss_D: 2.8414 Loss_G: 2.6424 D(x): 0.6192 D(G(z)): 0.3483 / 0.4415\n", + "[99/100][228/391] Loss_D: 2.4474 Loss_G: 2.2117 D(x): 0.7028 D(G(z)): 0.3022 / 0.4967\n", + "[99/100][229/391] Loss_D: 2.7261 Loss_G: 4.0045 D(x): 0.7688 D(G(z)): 0.5176 / 0.3006\n", + "[99/100][230/391] Loss_D: 2.3272 Loss_G: 3.8986 D(x): 0.6924 D(G(z)): 0.3455 / 0.3183\n", + "[99/100][231/391] Loss_D: 2.6956 Loss_G: 2.6814 D(x): 0.7452 D(G(z)): 0.4309 / 0.4328\n", + "[99/100][232/391] Loss_D: 2.8879 Loss_G: 2.7009 D(x): 0.6748 D(G(z)): 0.4177 / 0.4279\n", + "[99/100][233/391] Loss_D: 2.9120 Loss_G: 2.9157 D(x): 0.6132 D(G(z)): 0.3807 / 0.4187\n", + "[99/100][234/391] Loss_D: 2.9726 Loss_G: 3.3971 D(x): 0.6183 D(G(z)): 0.4767 / 0.3696\n", + "[99/100][235/391] Loss_D: 2.8018 Loss_G: 3.1365 D(x): 0.6898 D(G(z)): 0.4575 / 0.3792\n", + "[99/100][236/391] Loss_D: 3.3634 Loss_G: 3.7297 D(x): 0.5579 D(G(z)): 0.4349 / 0.3172\n", + "[99/100][237/391] Loss_D: 2.7314 Loss_G: 2.0778 D(x): 0.7432 D(G(z)): 0.4548 / 0.5234\n", + "[99/100][238/391] Loss_D: 2.7266 Loss_G: 2.9855 D(x): 0.6570 D(G(z)): 0.4375 / 0.4030\n", + "[99/100][239/391] Loss_D: 2.3623 Loss_G: 2.8262 D(x): 0.7609 D(G(z)): 0.4038 / 0.4365\n", + "[99/100][240/391] Loss_D: 2.8147 Loss_G: 2.3750 D(x): 0.6952 D(G(z)): 0.4213 / 0.4863\n", + "[99/100][241/391] Loss_D: 3.7565 Loss_G: 2.6614 D(x): 0.6526 D(G(z)): 0.3810 / 0.4361\n", + "[99/100][242/391] Loss_D: 2.4217 Loss_G: 3.2226 D(x): 0.7067 D(G(z)): 0.3497 / 0.3759\n", + "[99/100][243/391] Loss_D: 2.7839 Loss_G: 2.7629 D(x): 0.6482 D(G(z)): 0.4241 / 0.4318\n", + "[99/100][244/391] Loss_D: 2.2812 Loss_G: 2.4831 D(x): 0.7490 D(G(z)): 0.3764 / 0.4758\n", + "[99/100][245/391] Loss_D: 2.7525 Loss_G: 2.9728 D(x): 0.7157 D(G(z)): 0.4818 / 0.4117\n", + "[99/100][246/391] Loss_D: 3.0162 Loss_G: 2.4628 D(x): 0.6145 D(G(z)): 0.3712 / 0.4569\n", + "[99/100][247/391] Loss_D: 3.0249 Loss_G: 2.7142 D(x): 0.6138 D(G(z)): 0.4055 / 0.4429\n", + "[99/100][248/391] Loss_D: 2.8186 Loss_G: 2.9039 D(x): 0.6658 D(G(z)): 0.4563 / 0.4219\n", + "[99/100][249/391] Loss_D: 2.4787 Loss_G: 1.8538 D(x): 0.7205 D(G(z)): 0.4037 / 0.5740\n", + "[99/100][250/391] Loss_D: 3.0879 Loss_G: 3.1064 D(x): 0.7191 D(G(z)): 0.5165 / 0.3842\n", + "[99/100][251/391] Loss_D: 2.7283 Loss_G: 2.3646 D(x): 0.6941 D(G(z)): 0.4155 / 0.4860\n", + "[99/100][252/391] Loss_D: 2.5735 Loss_G: 3.7607 D(x): 0.6988 D(G(z)): 0.3701 / 0.3180\n", + "[99/100][253/391] Loss_D: 3.0261 Loss_G: 2.5582 D(x): 0.5743 D(G(z)): 0.3555 / 0.4671\n", + "[99/100][254/391] Loss_D: 2.7291 Loss_G: 3.6738 D(x): 0.6985 D(G(z)): 0.4660 / 0.3252\n", + "[99/100][255/391] Loss_D: 2.3774 Loss_G: 3.5342 D(x): 0.7405 D(G(z)): 0.3265 / 0.3457\n", + "[99/100][256/391] Loss_D: 3.0864 Loss_G: 2.6490 D(x): 0.6592 D(G(z)): 0.4914 / 0.4481\n", + "[99/100][257/391] Loss_D: 2.9164 Loss_G: 3.0115 D(x): 0.7082 D(G(z)): 0.3954 / 0.3888\n", + "[99/100][258/391] Loss_D: 2.2759 Loss_G: 2.8645 D(x): 0.7243 D(G(z)): 0.2930 / 0.4144\n", + "[99/100][259/391] Loss_D: 2.5520 Loss_G: 2.6278 D(x): 0.7062 D(G(z)): 0.3750 / 0.4517\n", + "[99/100][260/391] Loss_D: 2.5386 Loss_G: 2.4710 D(x): 0.7167 D(G(z)): 0.3893 / 0.4939\n", + "[99/100][261/391] Loss_D: 3.0767 Loss_G: 4.1451 D(x): 0.6159 D(G(z)): 0.4096 / 0.2814\n", + "[99/100][262/391] Loss_D: 2.9246 Loss_G: 3.6508 D(x): 0.6948 D(G(z)): 0.4375 / 0.3173\n", + "[99/100][263/391] Loss_D: 3.5194 Loss_G: 3.3133 D(x): 0.6366 D(G(z)): 0.5458 / 0.3682\n", + "[99/100][264/391] Loss_D: 2.6655 Loss_G: 2.8885 D(x): 0.6166 D(G(z)): 0.3745 / 0.4252\n", + "[99/100][265/391] Loss_D: 2.7102 Loss_G: 3.2172 D(x): 0.6975 D(G(z)): 0.3900 / 0.3888\n", + "[99/100][266/391] Loss_D: 2.5852 Loss_G: 2.8454 D(x): 0.7335 D(G(z)): 0.3481 / 0.4302\n", + "[99/100][267/391] Loss_D: 3.1023 Loss_G: 3.6468 D(x): 0.7077 D(G(z)): 0.5224 / 0.3344\n", + "[99/100][268/391] Loss_D: 2.3430 Loss_G: 2.6453 D(x): 0.7343 D(G(z)): 0.4380 / 0.4377\n", + "[99/100][269/391] Loss_D: 2.4623 Loss_G: 3.0049 D(x): 0.7283 D(G(z)): 0.3296 / 0.4048\n", + "[99/100][270/391] Loss_D: 3.3899 Loss_G: 3.6049 D(x): 0.5536 D(G(z)): 0.4362 / 0.3453\n", + "[99/100][271/391] Loss_D: 3.7559 Loss_G: 1.9650 D(x): 0.7444 D(G(z)): 0.4232 / 0.5467\n", + "[99/100][272/391] Loss_D: 3.0620 Loss_G: 3.1842 D(x): 0.6415 D(G(z)): 0.4666 / 0.3767\n", + "[99/100][273/391] Loss_D: 2.3216 Loss_G: 3.6991 D(x): 0.7301 D(G(z)): 0.3231 / 0.3197\n", + "[99/100][274/391] Loss_D: 2.4626 Loss_G: 3.0880 D(x): 0.6569 D(G(z)): 0.3264 / 0.3935\n", + "[99/100][275/391] Loss_D: 2.8048 Loss_G: 2.3943 D(x): 0.6659 D(G(z)): 0.3774 / 0.4770\n", + "[99/100][276/391] Loss_D: 2.4394 Loss_G: 3.4573 D(x): 0.7300 D(G(z)): 0.3281 / 0.3600\n", + "[99/100][277/391] Loss_D: 2.8709 Loss_G: 2.4505 D(x): 0.7010 D(G(z)): 0.4583 / 0.4623\n", + "[99/100][278/391] Loss_D: 2.4379 Loss_G: 3.0852 D(x): 0.6716 D(G(z)): 0.3664 / 0.3882\n", + "[99/100][279/391] Loss_D: 3.0406 Loss_G: 2.4892 D(x): 0.7049 D(G(z)): 0.5266 / 0.4830\n", + "[99/100][280/391] Loss_D: 3.3227 Loss_G: 2.6531 D(x): 0.6809 D(G(z)): 0.5609 / 0.4707\n", + "[99/100][281/391] Loss_D: 3.1783 Loss_G: 2.6135 D(x): 0.6850 D(G(z)): 0.5085 / 0.4597\n", + "[99/100][282/391] Loss_D: 2.5055 Loss_G: 4.0170 D(x): 0.6778 D(G(z)): 0.3634 / 0.2974\n", + "[99/100][283/391] Loss_D: 2.6947 Loss_G: 2.8091 D(x): 0.6362 D(G(z)): 0.2716 / 0.4222\n", + "[99/100][284/391] Loss_D: 2.7078 Loss_G: 3.0644 D(x): 0.6845 D(G(z)): 0.4427 / 0.3927\n", + "[99/100][285/391] Loss_D: 2.8417 Loss_G: 3.2620 D(x): 0.6437 D(G(z)): 0.3874 / 0.3755\n", + "[99/100][286/391] Loss_D: 2.8204 Loss_G: 2.4003 D(x): 0.6335 D(G(z)): 0.3848 / 0.4855\n", + "[99/100][287/391] Loss_D: 2.9368 Loss_G: 3.2681 D(x): 0.6992 D(G(z)): 0.4676 / 0.3697\n", + "[99/100][288/391] Loss_D: 2.6707 Loss_G: 2.9379 D(x): 0.6706 D(G(z)): 0.4356 / 0.4021\n", + "[99/100][289/391] Loss_D: 3.1308 Loss_G: 2.2756 D(x): 0.6204 D(G(z)): 0.4035 / 0.4906\n", + "[99/100][290/391] Loss_D: 2.4189 Loss_G: 2.5070 D(x): 0.7608 D(G(z)): 0.3633 / 0.4735\n", + "[99/100][291/391] Loss_D: 2.8336 Loss_G: 3.2218 D(x): 0.7403 D(G(z)): 0.4812 / 0.3736\n", + "[99/100][292/391] Loss_D: 3.2110 Loss_G: 2.1461 D(x): 0.7342 D(G(z)): 0.5406 / 0.5262\n", + "[99/100][293/391] Loss_D: 3.0154 Loss_G: 2.9143 D(x): 0.6220 D(G(z)): 0.4089 / 0.4171\n", + "[99/100][294/391] Loss_D: 2.9344 Loss_G: 3.7376 D(x): 0.5705 D(G(z)): 0.3814 / 0.3293\n", + "[99/100][295/391] Loss_D: 2.7712 Loss_G: 3.0090 D(x): 0.6750 D(G(z)): 0.3971 / 0.3821\n", + "[99/100][296/391] Loss_D: 2.7483 Loss_G: 3.2321 D(x): 0.7290 D(G(z)): 0.4387 / 0.3834\n", + "[99/100][297/391] Loss_D: 2.9737 Loss_G: 3.3534 D(x): 0.6459 D(G(z)): 0.4064 / 0.3669\n", + "[99/100][298/391] Loss_D: 2.2382 Loss_G: 3.1304 D(x): 0.7656 D(G(z)): 0.4384 / 0.3961\n", + "[99/100][299/391] Loss_D: 2.7072 Loss_G: 3.8535 D(x): 0.6268 D(G(z)): 0.3006 / 0.3059\n", + "[99/100][300/391] Loss_D: 3.4512 Loss_G: 2.9965 D(x): 0.6595 D(G(z)): 0.5532 / 0.4071\n", + "[99/100][301/391] Loss_D: 3.7384 Loss_G: 3.1830 D(x): 0.6086 D(G(z)): 0.4485 / 0.3879\n", + "[99/100][302/391] Loss_D: 2.5974 Loss_G: 3.5804 D(x): 0.6450 D(G(z)): 0.3520 / 0.3296\n", + "[99/100][303/391] Loss_D: 2.4444 Loss_G: 2.6723 D(x): 0.7100 D(G(z)): 0.3287 / 0.4524\n", + "[99/100][304/391] Loss_D: 2.3496 Loss_G: 1.8821 D(x): 0.6682 D(G(z)): 0.3587 / 0.5711\n", + "[99/100][305/391] Loss_D: 2.8637 Loss_G: 2.3764 D(x): 0.6253 D(G(z)): 0.3842 / 0.4927\n", + "[99/100][306/391] Loss_D: 2.7595 Loss_G: 2.4936 D(x): 0.7547 D(G(z)): 0.4489 / 0.4670\n", + "[99/100][307/391] Loss_D: 2.4825 Loss_G: 4.1588 D(x): 0.7269 D(G(z)): 0.3379 / 0.2797\n", + "[99/100][308/391] Loss_D: 2.4204 Loss_G: 2.6206 D(x): 0.6609 D(G(z)): 0.3034 / 0.4434\n", + "[99/100][309/391] Loss_D: 2.3600 Loss_G: 3.3221 D(x): 0.7935 D(G(z)): 0.4282 / 0.3631\n", + "[99/100][310/391] Loss_D: 2.4756 Loss_G: 2.9523 D(x): 0.8333 D(G(z)): 0.4132 / 0.4041\n", + "[99/100][311/391] Loss_D: 3.4343 Loss_G: 2.3553 D(x): 0.6356 D(G(z)): 0.4994 / 0.4913\n", + "[99/100][312/391] Loss_D: 2.4332 Loss_G: 2.6524 D(x): 0.6875 D(G(z)): 0.3392 / 0.4488\n", + "[99/100][313/391] Loss_D: 3.1770 Loss_G: 3.7109 D(x): 0.6154 D(G(z)): 0.4598 / 0.3272\n", + "[99/100][314/391] Loss_D: 2.6843 Loss_G: 3.7119 D(x): 0.6012 D(G(z)): 0.3468 / 0.3142\n", + "[99/100][315/391] Loss_D: 2.4918 Loss_G: 4.1718 D(x): 0.7295 D(G(z)): 0.4169 / 0.2874\n", + "[99/100][316/391] Loss_D: 2.7151 Loss_G: 3.1201 D(x): 0.7496 D(G(z)): 0.4522 / 0.3745\n", + "[99/100][317/391] Loss_D: 2.8374 Loss_G: 2.9482 D(x): 0.7107 D(G(z)): 0.3986 / 0.3983\n", + "[99/100][318/391] Loss_D: 2.3470 Loss_G: 3.4103 D(x): 0.6919 D(G(z)): 0.3403 / 0.3583\n", + "[99/100][319/391] Loss_D: 2.7627 Loss_G: 1.9039 D(x): 0.6900 D(G(z)): 0.4496 / 0.5705\n", + "[99/100][320/391] Loss_D: 2.5175 Loss_G: 3.6983 D(x): 0.7546 D(G(z)): 0.3928 / 0.3563\n", + "[99/100][321/391] Loss_D: 2.9632 Loss_G: 3.4507 D(x): 0.6564 D(G(z)): 0.4092 / 0.3627\n", + "[99/100][322/391] Loss_D: 3.0590 Loss_G: 2.2368 D(x): 0.6364 D(G(z)): 0.4730 / 0.5094\n", + "[99/100][323/391] Loss_D: 2.7432 Loss_G: 3.4960 D(x): 0.6575 D(G(z)): 0.3704 / 0.3542\n", + "[99/100][324/391] Loss_D: 1.8523 Loss_G: 3.1894 D(x): 0.7982 D(G(z)): 0.3441 / 0.3868\n", + "[99/100][325/391] Loss_D: 3.0232 Loss_G: 3.1960 D(x): 0.6753 D(G(z)): 0.4215 / 0.3866\n", + "[99/100][326/391] Loss_D: 2.9828 Loss_G: 2.8606 D(x): 0.5626 D(G(z)): 0.3377 / 0.4252\n", + "[99/100][327/391] Loss_D: 3.6363 Loss_G: 3.0330 D(x): 0.6695 D(G(z)): 0.6176 / 0.3988\n", + "[99/100][328/391] Loss_D: 2.5559 Loss_G: 2.8637 D(x): 0.6290 D(G(z)): 0.2497 / 0.4030\n", + "[99/100][329/391] Loss_D: 2.7185 Loss_G: 2.0747 D(x): 0.7746 D(G(z)): 0.4694 / 0.5363\n", + "[99/100][330/391] Loss_D: 2.6889 Loss_G: 3.1079 D(x): 0.6738 D(G(z)): 0.3810 / 0.4024\n", + "[99/100][331/391] Loss_D: 3.4991 Loss_G: 4.3474 D(x): 0.6365 D(G(z)): 0.3617 / 0.2750\n", + "[99/100][332/391] Loss_D: 2.6085 Loss_G: 2.7777 D(x): 0.6531 D(G(z)): 0.3728 / 0.4423\n", + "[99/100][333/391] Loss_D: 2.6250 Loss_G: 3.0055 D(x): 0.7617 D(G(z)): 0.3722 / 0.3926\n", + "[99/100][334/391] Loss_D: 2.4628 Loss_G: 3.7846 D(x): 0.7417 D(G(z)): 0.4523 / 0.3288\n", + "[99/100][335/391] Loss_D: 2.7557 Loss_G: 2.3924 D(x): 0.7219 D(G(z)): 0.4531 / 0.4851\n", + "[99/100][336/391] Loss_D: 2.6010 Loss_G: 3.7978 D(x): 0.7195 D(G(z)): 0.3573 / 0.3098\n", + "[99/100][337/391] Loss_D: 2.9181 Loss_G: 3.0458 D(x): 0.6662 D(G(z)): 0.4413 / 0.3937\n", + "[99/100][338/391] Loss_D: 2.9224 Loss_G: 2.7885 D(x): 0.6502 D(G(z)): 0.4397 / 0.4277\n", + "[99/100][339/391] Loss_D: 2.8672 Loss_G: 2.7715 D(x): 0.6022 D(G(z)): 0.3385 / 0.4385\n", + "[99/100][340/391] Loss_D: 2.7795 Loss_G: 2.9354 D(x): 0.7211 D(G(z)): 0.4477 / 0.4086\n", + "[99/100][341/391] Loss_D: 2.1079 Loss_G: 3.3790 D(x): 0.7643 D(G(z)): 0.2691 / 0.3583\n", + "[99/100][342/391] Loss_D: 2.8585 Loss_G: 3.2849 D(x): 0.6793 D(G(z)): 0.4635 / 0.3679\n", + "[99/100][343/391] Loss_D: 3.1951 Loss_G: 3.6545 D(x): 0.6693 D(G(z)): 0.5060 / 0.3365\n", + "[99/100][344/391] Loss_D: 2.3822 Loss_G: 3.4158 D(x): 0.7200 D(G(z)): 0.3972 / 0.3455\n", + "[99/100][345/391] Loss_D: 2.5586 Loss_G: 2.7595 D(x): 0.6726 D(G(z)): 0.3357 / 0.4333\n", + "[99/100][346/391] Loss_D: 2.9635 Loss_G: 3.0551 D(x): 0.5771 D(G(z)): 0.3154 / 0.3966\n", + "[99/100][347/391] Loss_D: 2.9336 Loss_G: 2.4887 D(x): 0.6966 D(G(z)): 0.4175 / 0.4603\n", + "[99/100][348/391] Loss_D: 2.7630 Loss_G: 2.6160 D(x): 0.7052 D(G(z)): 0.4738 / 0.4383\n", + "[99/100][349/391] Loss_D: 2.6285 Loss_G: 3.0439 D(x): 0.7122 D(G(z)): 0.4089 / 0.4107\n", + "[99/100][350/391] Loss_D: 2.6018 Loss_G: 2.9487 D(x): 0.7026 D(G(z)): 0.4111 / 0.4209\n", + "[99/100][351/391] Loss_D: 3.3080 Loss_G: 2.9597 D(x): 0.5982 D(G(z)): 0.4584 / 0.4175\n", + "[99/100][352/391] Loss_D: 2.2643 Loss_G: 1.9144 D(x): 0.7491 D(G(z)): 0.3770 / 0.5806\n", + "[99/100][353/391] Loss_D: 2.8582 Loss_G: 3.7210 D(x): 0.6944 D(G(z)): 0.4647 / 0.3228\n", + "[99/100][354/391] Loss_D: 2.0584 Loss_G: 2.3597 D(x): 0.7440 D(G(z)): 0.3314 / 0.4729\n", + "[99/100][355/391] Loss_D: 2.5662 Loss_G: 2.6286 D(x): 0.6836 D(G(z)): 0.3975 / 0.4424\n", + "[99/100][356/391] Loss_D: 2.4625 Loss_G: 2.6663 D(x): 0.6939 D(G(z)): 0.3388 / 0.4538\n", + "[99/100][357/391] Loss_D: 2.9587 Loss_G: 4.3355 D(x): 0.6448 D(G(z)): 0.4391 / 0.2728\n", + "[99/100][358/391] Loss_D: 3.0573 Loss_G: 3.0325 D(x): 0.5949 D(G(z)): 0.4453 / 0.3970\n", + "[99/100][359/391] Loss_D: 3.7465 Loss_G: 3.0025 D(x): 0.5656 D(G(z)): 0.5254 / 0.4099\n", + "[99/100][360/391] Loss_D: 2.7518 Loss_G: 2.3511 D(x): 0.6543 D(G(z)): 0.3844 / 0.4808\n", + "[99/100][361/391] Loss_D: 3.5271 Loss_G: 2.5913 D(x): 0.6549 D(G(z)): 0.4135 / 0.4700\n", + "[99/100][362/391] Loss_D: 3.0122 Loss_G: 3.0874 D(x): 0.8099 D(G(z)): 0.5531 / 0.3861\n", + "[99/100][363/391] Loss_D: 3.0091 Loss_G: 2.5681 D(x): 0.7473 D(G(z)): 0.5335 / 0.4554\n", + "[99/100][364/391] Loss_D: 3.0686 Loss_G: 3.7919 D(x): 0.6288 D(G(z)): 0.4119 / 0.3199\n", + "[99/100][365/391] Loss_D: 3.4515 Loss_G: 4.1294 D(x): 0.5021 D(G(z)): 0.3545 / 0.2762\n", + "[99/100][366/391] Loss_D: 2.6789 Loss_G: 3.0308 D(x): 0.5889 D(G(z)): 0.2597 / 0.3886\n", + "[99/100][367/391] Loss_D: 2.6488 Loss_G: 3.1611 D(x): 0.7592 D(G(z)): 0.4179 / 0.3588\n", + "[99/100][368/391] Loss_D: 2.6654 Loss_G: 2.7927 D(x): 0.6490 D(G(z)): 0.3612 / 0.4182\n", + "[99/100][369/391] Loss_D: 2.5342 Loss_G: 2.7070 D(x): 0.6610 D(G(z)): 0.3340 / 0.4614\n", + "[99/100][370/391] Loss_D: 2.7663 Loss_G: 2.7165 D(x): 0.6419 D(G(z)): 0.4083 / 0.4248\n", + "[99/100][371/391] Loss_D: 2.9600 Loss_G: 3.0412 D(x): 0.7770 D(G(z)): 0.5343 / 0.4060\n", + "[99/100][372/391] Loss_D: 3.2901 Loss_G: 3.2643 D(x): 0.7334 D(G(z)): 0.5881 / 0.3685\n", + "[99/100][373/391] Loss_D: 2.3432 Loss_G: 3.1466 D(x): 0.7676 D(G(z)): 0.3655 / 0.3920\n", + "[99/100][374/391] Loss_D: 2.9041 Loss_G: 2.4185 D(x): 0.7376 D(G(z)): 0.4958 / 0.4927\n", + "[99/100][375/391] Loss_D: 2.6284 Loss_G: 3.0835 D(x): 0.7100 D(G(z)): 0.3756 / 0.3972\n", + "[99/100][376/391] Loss_D: 3.0054 Loss_G: 2.8496 D(x): 0.7083 D(G(z)): 0.4527 / 0.4185\n", + "[99/100][377/391] Loss_D: 2.4163 Loss_G: 3.2544 D(x): 0.7681 D(G(z)): 0.3583 / 0.3825\n", + "[99/100][378/391] Loss_D: 2.4713 Loss_G: 4.1176 D(x): 0.6444 D(G(z)): 0.1721 / 0.2795\n", + "[99/100][379/391] Loss_D: 2.5134 Loss_G: 3.3261 D(x): 0.6790 D(G(z)): 0.3826 / 0.3829\n", + "[99/100][380/391] Loss_D: 2.6084 Loss_G: 2.7601 D(x): 0.7783 D(G(z)): 0.4720 / 0.4262\n", + "[99/100][381/391] Loss_D: 2.2360 Loss_G: 3.1308 D(x): 0.7488 D(G(z)): 0.3104 / 0.3960\n", + "[99/100][382/391] Loss_D: 2.3904 Loss_G: 2.7729 D(x): 0.7098 D(G(z)): 0.3432 / 0.4318\n", + "[99/100][383/391] Loss_D: 2.4745 Loss_G: 2.4823 D(x): 0.7338 D(G(z)): 0.3611 / 0.4619\n", + "[99/100][384/391] Loss_D: 2.8849 Loss_G: 3.4696 D(x): 0.6611 D(G(z)): 0.4915 / 0.3542\n", + "[99/100][385/391] Loss_D: 2.7846 Loss_G: 2.4718 D(x): 0.6635 D(G(z)): 0.4144 / 0.4679\n", + "[99/100][386/391] Loss_D: 2.6549 Loss_G: 2.7205 D(x): 0.6765 D(G(z)): 0.3147 / 0.4409\n", + "[99/100][387/391] Loss_D: 2.4909 Loss_G: 3.1441 D(x): 0.7026 D(G(z)): 0.2992 / 0.3690\n", + "[99/100][388/391] Loss_D: 2.7734 Loss_G: 3.4487 D(x): 0.6279 D(G(z)): 0.3856 / 0.3501\n", + "[99/100][389/391] Loss_D: 2.7265 Loss_G: 3.0979 D(x): 0.6836 D(G(z)): 0.3973 / 0.3927\n", + "[99/100][390/391] Loss_D: 2.8547 Loss_G: 2.6697 D(x): 0.7344 D(G(z)): 0.5310 / 0.4545\n", + "[99/100][391/391] Loss_D: 3.7621 Loss_G: 2.8274 D(x): 0.6773 D(G(z)): 0.2414 / 0.4351\n", + "[100/100][1/391] Loss_D: 3.5876 Loss_G: 1.8612 D(x): 0.6153 D(G(z)): 0.4827 / 0.5600\n", + "[100/100][2/391] Loss_D: 3.2056 Loss_G: 3.1172 D(x): 0.7335 D(G(z)): 0.5696 / 0.3896\n", + "[100/100][3/391] Loss_D: 3.2330 Loss_G: 3.7114 D(x): 0.5978 D(G(z)): 0.4797 / 0.3246\n", + "[100/100][4/391] Loss_D: 2.2735 Loss_G: 3.5580 D(x): 0.7661 D(G(z)): 0.4240 / 0.3535\n", + "[100/100][5/391] Loss_D: 2.2682 Loss_G: 2.2827 D(x): 0.7162 D(G(z)): 0.2920 / 0.4883\n", + "[100/100][6/391] Loss_D: 2.8459 Loss_G: 3.3934 D(x): 0.6234 D(G(z)): 0.3531 / 0.3531\n", + "[100/100][7/391] Loss_D: 2.8947 Loss_G: 3.8225 D(x): 0.6711 D(G(z)): 0.3852 / 0.3170\n", + "[100/100][8/391] Loss_D: 2.0873 Loss_G: 3.2857 D(x): 0.7852 D(G(z)): 0.4125 / 0.3701\n", + "[100/100][9/391] Loss_D: 2.6645 Loss_G: 2.3471 D(x): 0.6854 D(G(z)): 0.4387 / 0.4970\n", + "[100/100][10/391] Loss_D: 2.6828 Loss_G: 3.0604 D(x): 0.6987 D(G(z)): 0.4172 / 0.3972\n", + "[100/100][11/391] Loss_D: 3.6764 Loss_G: 3.4596 D(x): 0.5187 D(G(z)): 0.4866 / 0.3525\n", + "[100/100][12/391] Loss_D: 2.6293 Loss_G: 2.8578 D(x): 0.6857 D(G(z)): 0.3841 / 0.4252\n", + "[100/100][13/391] Loss_D: 2.7501 Loss_G: 3.6327 D(x): 0.6252 D(G(z)): 0.3356 / 0.3404\n", + "[100/100][14/391] Loss_D: 2.3899 Loss_G: 2.6670 D(x): 0.7932 D(G(z)): 0.4252 / 0.4478\n", + "[100/100][15/391] Loss_D: 2.3451 Loss_G: 3.1524 D(x): 0.7256 D(G(z)): 0.3489 / 0.3792\n", + "[100/100][16/391] Loss_D: 3.5670 Loss_G: 3.0274 D(x): 0.5474 D(G(z)): 0.4219 / 0.4070\n", + "[100/100][17/391] Loss_D: 2.4808 Loss_G: 2.8960 D(x): 0.7280 D(G(z)): 0.3714 / 0.3914\n", + "[100/100][18/391] Loss_D: 2.4754 Loss_G: 2.7719 D(x): 0.7034 D(G(z)): 0.3865 / 0.4225\n", + "[100/100][19/391] Loss_D: 2.9882 Loss_G: 3.1154 D(x): 0.6916 D(G(z)): 0.4930 / 0.3956\n", + "[100/100][20/391] Loss_D: 2.4942 Loss_G: 2.2033 D(x): 0.7376 D(G(z)): 0.4247 / 0.5251\n", + "[100/100][21/391] Loss_D: 2.8321 Loss_G: 2.9254 D(x): 0.7460 D(G(z)): 0.5336 / 0.4266\n", + "[100/100][22/391] Loss_D: 2.2712 Loss_G: 2.7885 D(x): 0.7585 D(G(z)): 0.3293 / 0.4261\n", + "[100/100][23/391] Loss_D: 2.6541 Loss_G: 4.0132 D(x): 0.6476 D(G(z)): 0.3342 / 0.2898\n", + "[100/100][24/391] Loss_D: 2.8355 Loss_G: 3.8658 D(x): 0.6264 D(G(z)): 0.3838 / 0.3097\n", + "[100/100][25/391] Loss_D: 2.6001 Loss_G: 2.9988 D(x): 0.6979 D(G(z)): 0.3318 / 0.3905\n", + "[100/100][26/391] Loss_D: 2.5162 Loss_G: 3.1736 D(x): 0.6318 D(G(z)): 0.2904 / 0.3781\n", + "[100/100][27/391] Loss_D: 3.1608 Loss_G: 2.5690 D(x): 0.6980 D(G(z)): 0.4883 / 0.4615\n", + "[100/100][28/391] Loss_D: 2.5665 Loss_G: 3.3504 D(x): 0.6696 D(G(z)): 0.4128 / 0.3579\n", + "[100/100][29/391] Loss_D: 2.5716 Loss_G: 2.4444 D(x): 0.6811 D(G(z)): 0.3579 / 0.4712\n", + "[100/100][30/391] Loss_D: 2.6489 Loss_G: 3.0669 D(x): 0.6875 D(G(z)): 0.4161 / 0.4133\n", + "[100/100][31/391] Loss_D: 3.6760 Loss_G: 2.8265 D(x): 0.6881 D(G(z)): 0.4496 / 0.4254\n", + "[100/100][32/391] Loss_D: 2.5755 Loss_G: 2.1574 D(x): 0.7004 D(G(z)): 0.3856 / 0.5230\n", + "[100/100][33/391] Loss_D: 3.0027 Loss_G: 2.7976 D(x): 0.6956 D(G(z)): 0.4704 / 0.4380\n", + "[100/100][34/391] Loss_D: 2.3341 Loss_G: 3.4336 D(x): 0.8454 D(G(z)): 0.4627 / 0.3403\n", + "[100/100][35/391] Loss_D: 2.9542 Loss_G: 3.1050 D(x): 0.6377 D(G(z)): 0.4591 / 0.3972\n", + "[100/100][36/391] Loss_D: 2.6831 Loss_G: 3.8445 D(x): 0.8146 D(G(z)): 0.4668 / 0.3081\n", + "[100/100][37/391] Loss_D: 2.4732 Loss_G: 3.0603 D(x): 0.7060 D(G(z)): 0.3299 / 0.3823\n", + "[100/100][38/391] Loss_D: 2.2764 Loss_G: 2.8449 D(x): 0.7215 D(G(z)): 0.3382 / 0.4142\n", + "[100/100][39/391] Loss_D: 2.7916 Loss_G: 3.0781 D(x): 0.6463 D(G(z)): 0.3171 / 0.4098\n", + "[100/100][40/391] Loss_D: 3.1288 Loss_G: 3.3068 D(x): 0.6325 D(G(z)): 0.4127 / 0.3601\n", + "[100/100][41/391] Loss_D: 3.2388 Loss_G: 3.7187 D(x): 0.5831 D(G(z)): 0.4162 / 0.3263\n", + "[100/100][42/391] Loss_D: 3.3358 Loss_G: 3.7075 D(x): 0.6393 D(G(z)): 0.5224 / 0.3242\n", + "[100/100][43/391] Loss_D: 3.1725 Loss_G: 2.8311 D(x): 0.6981 D(G(z)): 0.5175 / 0.4328\n", + "[100/100][44/391] Loss_D: 2.4848 Loss_G: 2.7609 D(x): 0.6573 D(G(z)): 0.3702 / 0.4316\n", + "[100/100][45/391] Loss_D: 2.9054 Loss_G: 3.1479 D(x): 0.5969 D(G(z)): 0.3714 / 0.3797\n", + "[100/100][46/391] Loss_D: 2.8644 Loss_G: 3.0426 D(x): 0.6333 D(G(z)): 0.4096 / 0.3771\n", + "[100/100][47/391] Loss_D: 3.1570 Loss_G: 2.7966 D(x): 0.6410 D(G(z)): 0.4887 / 0.4033\n", + "[100/100][48/391] Loss_D: 3.3367 Loss_G: 3.0637 D(x): 0.6188 D(G(z)): 0.4949 / 0.3792\n", + "[100/100][49/391] Loss_D: 3.1006 Loss_G: 3.3043 D(x): 0.7056 D(G(z)): 0.5521 / 0.3806\n", + "[100/100][50/391] Loss_D: 2.6059 Loss_G: 3.1627 D(x): 0.7374 D(G(z)): 0.4471 / 0.3909\n", + "[100/100][51/391] Loss_D: 3.1001 Loss_G: 3.9971 D(x): 0.6268 D(G(z)): 0.4521 / 0.2921\n", + "[100/100][52/391] Loss_D: 3.0354 Loss_G: 3.0622 D(x): 0.6108 D(G(z)): 0.3856 / 0.4064\n", + "[100/100][53/391] Loss_D: 2.7492 Loss_G: 2.7815 D(x): 0.6502 D(G(z)): 0.4095 / 0.4375\n", + "[100/100][54/391] Loss_D: 2.3057 Loss_G: 2.5508 D(x): 0.7516 D(G(z)): 0.3964 / 0.4586\n", + "[100/100][55/391] Loss_D: 3.1689 Loss_G: 2.8429 D(x): 0.7392 D(G(z)): 0.5913 / 0.4328\n", + "[100/100][56/391] Loss_D: 3.1098 Loss_G: 3.4198 D(x): 0.5976 D(G(z)): 0.3915 / 0.3385\n", + "[100/100][57/391] Loss_D: 3.6004 Loss_G: 2.3949 D(x): 0.5657 D(G(z)): 0.4979 / 0.4570\n", + "[100/100][58/391] Loss_D: 2.7903 Loss_G: 3.8903 D(x): 0.6342 D(G(z)): 0.4086 / 0.3008\n", + "[100/100][59/391] Loss_D: 3.0268 Loss_G: 2.4764 D(x): 0.5548 D(G(z)): 0.2777 / 0.4886\n", + "[100/100][60/391] Loss_D: 2.8216 Loss_G: 3.6197 D(x): 0.6101 D(G(z)): 0.3438 / 0.3301\n", + "[100/100][61/391] Loss_D: 3.6201 Loss_G: 1.6301 D(x): 0.6176 D(G(z)): 0.4546 / 0.6033\n", + "[100/100][62/391] Loss_D: 3.0907 Loss_G: 3.3076 D(x): 0.7809 D(G(z)): 0.5394 / 0.3761\n", + "[100/100][63/391] Loss_D: 3.6134 Loss_G: 1.9376 D(x): 0.5332 D(G(z)): 0.4887 / 0.5562\n", + "[100/100][64/391] Loss_D: 3.0182 Loss_G: 3.1352 D(x): 0.6716 D(G(z)): 0.5239 / 0.3855\n", + "[100/100][65/391] Loss_D: 3.1522 Loss_G: 3.2602 D(x): 0.5939 D(G(z)): 0.4004 / 0.3658\n", + "[100/100][66/391] Loss_D: 2.4745 Loss_G: 2.3621 D(x): 0.7473 D(G(z)): 0.3874 / 0.4720\n", + "[100/100][67/391] Loss_D: 3.2063 Loss_G: 3.4284 D(x): 0.6203 D(G(z)): 0.4131 / 0.3445\n", + "[100/100][68/391] Loss_D: 2.3680 Loss_G: 2.9599 D(x): 0.7021 D(G(z)): 0.3572 / 0.4016\n", + "[100/100][69/391] Loss_D: 2.7366 Loss_G: 1.9400 D(x): 0.6983 D(G(z)): 0.4135 / 0.5683\n", + "[100/100][70/391] Loss_D: 2.9718 Loss_G: 3.2602 D(x): 0.6581 D(G(z)): 0.4485 / 0.3700\n", + "[100/100][71/391] Loss_D: 3.2414 Loss_G: 2.9582 D(x): 0.7336 D(G(z)): 0.5391 / 0.4237\n", + "[100/100][72/391] Loss_D: 2.3036 Loss_G: 2.3423 D(x): 0.7128 D(G(z)): 0.3335 / 0.4837\n", + "[100/100][73/391] Loss_D: 2.4623 Loss_G: 2.6827 D(x): 0.7588 D(G(z)): 0.4200 / 0.4439\n", + "[100/100][74/391] Loss_D: 2.8326 Loss_G: 2.6285 D(x): 0.6037 D(G(z)): 0.3446 / 0.4404\n", + "[100/100][75/391] Loss_D: 2.5185 Loss_G: 3.1144 D(x): 0.7466 D(G(z)): 0.4168 / 0.3770\n", + "[100/100][76/391] Loss_D: 2.7894 Loss_G: 2.9726 D(x): 0.6991 D(G(z)): 0.4548 / 0.4102\n", + "[100/100][77/391] Loss_D: 2.8646 Loss_G: 3.9332 D(x): 0.6156 D(G(z)): 0.3352 / 0.2982\n", + "[100/100][78/391] Loss_D: 2.6712 Loss_G: 3.7679 D(x): 0.6490 D(G(z)): 0.3874 / 0.3271\n", + "[100/100][79/391] Loss_D: 2.6546 Loss_G: 2.6028 D(x): 0.7038 D(G(z)): 0.4277 / 0.4635\n", + "[100/100][80/391] Loss_D: 3.0457 Loss_G: 1.9223 D(x): 0.6381 D(G(z)): 0.4553 / 0.5634\n", + "[100/100][81/391] Loss_D: 2.8718 Loss_G: 4.2125 D(x): 0.6516 D(G(z)): 0.4590 / 0.2800\n", + "[100/100][82/391] Loss_D: 2.5195 Loss_G: 2.8348 D(x): 0.6786 D(G(z)): 0.3063 / 0.4248\n", + "[100/100][83/391] Loss_D: 3.3436 Loss_G: 2.7259 D(x): 0.5883 D(G(z)): 0.4311 / 0.4369\n", + "[100/100][84/391] Loss_D: 2.5270 Loss_G: 3.1133 D(x): 0.7102 D(G(z)): 0.4393 / 0.3929\n", + "[100/100][85/391] Loss_D: 3.0016 Loss_G: 2.0599 D(x): 0.6810 D(G(z)): 0.4874 / 0.5295\n", + "[100/100][86/391] Loss_D: 2.5089 Loss_G: 3.9862 D(x): 0.7938 D(G(z)): 0.4145 / 0.2909\n", + "[100/100][87/391] Loss_D: 3.0070 Loss_G: 3.3603 D(x): 0.6771 D(G(z)): 0.3850 / 0.3522\n", + "[100/100][88/391] Loss_D: 2.0705 Loss_G: 2.9485 D(x): 0.7794 D(G(z)): 0.3338 / 0.4176\n", + "[100/100][89/391] Loss_D: 2.8064 Loss_G: 2.6665 D(x): 0.6546 D(G(z)): 0.4239 / 0.4432\n", + "[100/100][90/391] Loss_D: 2.9193 Loss_G: 2.8605 D(x): 0.6572 D(G(z)): 0.4358 / 0.4219\n", + "[100/100][91/391] Loss_D: 3.7121 Loss_G: 2.3862 D(x): 0.6459 D(G(z)): 0.5408 / 0.4992\n", + "[100/100][92/391] Loss_D: 2.8098 Loss_G: 3.3821 D(x): 0.6195 D(G(z)): 0.4165 / 0.3570\n", + "[100/100][93/391] Loss_D: 2.7046 Loss_G: 3.6837 D(x): 0.7048 D(G(z)): 0.3967 / 0.3236\n", + "[100/100][94/391] Loss_D: 3.2994 Loss_G: 2.7466 D(x): 0.6139 D(G(z)): 0.5028 / 0.4398\n", + "[100/100][95/391] Loss_D: 2.6720 Loss_G: 2.1688 D(x): 0.6368 D(G(z)): 0.3163 / 0.5206\n", + "[100/100][96/391] Loss_D: 2.4437 Loss_G: 2.9001 D(x): 0.7672 D(G(z)): 0.3992 / 0.4237\n", + "[100/100][97/391] Loss_D: 2.6619 Loss_G: 3.6074 D(x): 0.7400 D(G(z)): 0.3695 / 0.3339\n", + "[100/100][98/391] Loss_D: 2.9354 Loss_G: 4.0113 D(x): 0.6725 D(G(z)): 0.4946 / 0.3108\n", + "[100/100][99/391] Loss_D: 2.5468 Loss_G: 3.4472 D(x): 0.7545 D(G(z)): 0.4237 / 0.3590\n", + "[100/100][100/391] Loss_D: 3.0742 Loss_G: 2.9517 D(x): 0.5878 D(G(z)): 0.4136 / 0.4262\n", + "[100/100][101/391] Loss_D: 2.2532 Loss_G: 3.6718 D(x): 0.7484 D(G(z)): 0.3594 / 0.3385\n", + "[100/100][102/391] Loss_D: 2.7139 Loss_G: 2.4938 D(x): 0.6177 D(G(z)): 0.3025 / 0.4627\n", + "[100/100][103/391] Loss_D: 2.7006 Loss_G: 2.5440 D(x): 0.7607 D(G(z)): 0.4873 / 0.4775\n", + "[100/100][104/391] Loss_D: 3.1045 Loss_G: 3.6405 D(x): 0.6962 D(G(z)): 0.5249 / 0.3307\n", + "[100/100][105/391] Loss_D: 2.5897 Loss_G: 3.2787 D(x): 0.7391 D(G(z)): 0.3913 / 0.3671\n", + "[100/100][106/391] Loss_D: 2.5782 Loss_G: 4.0100 D(x): 0.6922 D(G(z)): 0.3739 / 0.3016\n", + "[100/100][107/391] Loss_D: 3.1456 Loss_G: 2.8780 D(x): 0.5927 D(G(z)): 0.4131 / 0.4220\n", + "[100/100][108/391] Loss_D: 3.0135 Loss_G: 4.3564 D(x): 0.6087 D(G(z)): 0.3346 / 0.2790\n", + "[100/100][109/391] Loss_D: 2.7333 Loss_G: 3.1678 D(x): 0.6377 D(G(z)): 0.3827 / 0.3990\n", + "[100/100][110/391] Loss_D: 2.8032 Loss_G: 2.0443 D(x): 0.6292 D(G(z)): 0.3603 / 0.5630\n", + "[100/100][111/391] Loss_D: 2.5058 Loss_G: 2.4536 D(x): 0.7800 D(G(z)): 0.4221 / 0.4775\n", + "[100/100][112/391] Loss_D: 2.8336 Loss_G: 2.5678 D(x): 0.6851 D(G(z)): 0.4499 / 0.4595\n", + "[100/100][113/391] Loss_D: 2.8700 Loss_G: 2.5196 D(x): 0.8041 D(G(z)): 0.5238 / 0.4678\n", + "[100/100][114/391] Loss_D: 2.1018 Loss_G: 2.5092 D(x): 0.7590 D(G(z)): 0.3364 / 0.4738\n", + "[100/100][115/391] Loss_D: 3.1259 Loss_G: 3.6509 D(x): 0.5773 D(G(z)): 0.3536 / 0.3338\n", + "[100/100][116/391] Loss_D: 2.9579 Loss_G: 3.5926 D(x): 0.5531 D(G(z)): 0.3003 / 0.3251\n", + "[100/100][117/391] Loss_D: 2.5716 Loss_G: 2.5277 D(x): 0.8370 D(G(z)): 0.4136 / 0.4623\n", + "[100/100][118/391] Loss_D: 2.7878 Loss_G: 3.4025 D(x): 0.6916 D(G(z)): 0.4972 / 0.3536\n", + "[100/100][119/391] Loss_D: 3.1760 Loss_G: 2.9957 D(x): 0.7001 D(G(z)): 0.5468 / 0.4069\n", + "[100/100][120/391] Loss_D: 2.5901 Loss_G: 3.1843 D(x): 0.7176 D(G(z)): 0.4274 / 0.3935\n", + "[100/100][121/391] Loss_D: 3.5296 Loss_G: 3.2682 D(x): 0.6447 D(G(z)): 0.4382 / 0.3892\n", + "[100/100][122/391] Loss_D: 2.9652 Loss_G: 4.6760 D(x): 0.5762 D(G(z)): 0.3068 / 0.2438\n", + "[100/100][123/391] Loss_D: 2.8077 Loss_G: 2.5312 D(x): 0.6334 D(G(z)): 0.3362 / 0.4474\n", + "[100/100][124/391] Loss_D: 2.6690 Loss_G: 2.7336 D(x): 0.7088 D(G(z)): 0.4591 / 0.4446\n", + "[100/100][125/391] Loss_D: 2.6864 Loss_G: 3.3830 D(x): 0.6837 D(G(z)): 0.3437 / 0.3637\n", + "[100/100][126/391] Loss_D: 2.5245 Loss_G: 2.8995 D(x): 0.6736 D(G(z)): 0.3471 / 0.4083\n", + "[100/100][127/391] Loss_D: 3.1822 Loss_G: 2.3208 D(x): 0.7128 D(G(z)): 0.5055 / 0.4994\n", + "[100/100][128/391] Loss_D: 2.1409 Loss_G: 3.2205 D(x): 0.8068 D(G(z)): 0.4365 / 0.3713\n", + "[100/100][129/391] Loss_D: 2.7926 Loss_G: 2.9943 D(x): 0.6848 D(G(z)): 0.4383 / 0.4188\n", + "[100/100][130/391] Loss_D: 2.6063 Loss_G: 2.5009 D(x): 0.7267 D(G(z)): 0.3989 / 0.4694\n", + "[100/100][131/391] Loss_D: 2.5276 Loss_G: 2.6630 D(x): 0.7305 D(G(z)): 0.4304 / 0.4434\n", + "[100/100][132/391] Loss_D: 2.9451 Loss_G: 3.9342 D(x): 0.6074 D(G(z)): 0.3703 / 0.2952\n", + "[100/100][133/391] Loss_D: 2.4545 Loss_G: 3.7852 D(x): 0.6596 D(G(z)): 0.3259 / 0.3226\n", + "[100/100][134/391] Loss_D: 2.7752 Loss_G: 2.3340 D(x): 0.6082 D(G(z)): 0.3595 / 0.4826\n", + "[100/100][135/391] Loss_D: 2.4126 Loss_G: 3.9794 D(x): 0.7614 D(G(z)): 0.3916 / 0.2962\n", + "[100/100][136/391] Loss_D: 2.6190 Loss_G: 4.5259 D(x): 0.7516 D(G(z)): 0.4154 / 0.2513\n", + "[100/100][137/391] Loss_D: 2.6229 Loss_G: 3.3505 D(x): 0.7284 D(G(z)): 0.4353 / 0.3656\n", + "[100/100][138/391] Loss_D: 3.4271 Loss_G: 2.8140 D(x): 0.5510 D(G(z)): 0.4464 / 0.4347\n", + "[100/100][139/391] Loss_D: 2.2867 Loss_G: 2.7510 D(x): 0.7356 D(G(z)): 0.2525 / 0.4332\n", + "[100/100][140/391] Loss_D: 2.8088 Loss_G: 2.0318 D(x): 0.6789 D(G(z)): 0.4497 / 0.5360\n", + "[100/100][141/391] Loss_D: 2.5508 Loss_G: 2.7531 D(x): 0.6860 D(G(z)): 0.3526 / 0.4337\n", + "[100/100][142/391] Loss_D: 2.5392 Loss_G: 3.4911 D(x): 0.6852 D(G(z)): 0.3756 / 0.3460\n", + "[100/100][143/391] Loss_D: 2.8359 Loss_G: 2.9404 D(x): 0.6464 D(G(z)): 0.3901 / 0.4098\n", + "[100/100][144/391] Loss_D: 2.9180 Loss_G: 2.9552 D(x): 0.7465 D(G(z)): 0.5230 / 0.4071\n", + "[100/100][145/391] Loss_D: 3.2434 Loss_G: 3.3861 D(x): 0.6419 D(G(z)): 0.4769 / 0.3633\n", + "[100/100][146/391] Loss_D: 3.2380 Loss_G: 3.7290 D(x): 0.6485 D(G(z)): 0.5112 / 0.3074\n", + "[100/100][147/391] Loss_D: 3.0237 Loss_G: 2.9623 D(x): 0.7030 D(G(z)): 0.4865 / 0.4141\n", + "[100/100][148/391] Loss_D: 2.6465 Loss_G: 2.0779 D(x): 0.6163 D(G(z)): 0.3210 / 0.5505\n", + "[100/100][149/391] Loss_D: 3.0277 Loss_G: 3.1338 D(x): 0.7213 D(G(z)): 0.5188 / 0.3970\n", + "[100/100][150/391] Loss_D: 3.1133 Loss_G: 3.8875 D(x): 0.6039 D(G(z)): 0.4003 / 0.3160\n", + "[100/100][151/391] Loss_D: 3.6076 Loss_G: 3.5522 D(x): 0.6893 D(G(z)): 0.3367 / 0.3342\n", + "[100/100][152/391] Loss_D: 2.6461 Loss_G: 3.0206 D(x): 0.6563 D(G(z)): 0.4142 / 0.3972\n", + "[100/100][153/391] Loss_D: 2.7282 Loss_G: 3.0359 D(x): 0.6415 D(G(z)): 0.3305 / 0.3899\n", + "[100/100][154/391] Loss_D: 2.7529 Loss_G: 2.7045 D(x): 0.7646 D(G(z)): 0.5434 / 0.4435\n", + "[100/100][155/391] Loss_D: 3.9932 Loss_G: 2.9581 D(x): 0.5226 D(G(z)): 0.5174 / 0.3932\n", + "[100/100][156/391] Loss_D: 3.0217 Loss_G: 3.0263 D(x): 0.5715 D(G(z)): 0.3512 / 0.4007\n", + "[100/100][157/391] Loss_D: 2.6135 Loss_G: 3.0805 D(x): 0.7543 D(G(z)): 0.3876 / 0.3991\n", + "[100/100][158/391] Loss_D: 2.3908 Loss_G: 1.6611 D(x): 0.7085 D(G(z)): 0.3418 / 0.6122\n", + "[100/100][159/391] Loss_D: 2.8014 Loss_G: 3.2475 D(x): 0.7227 D(G(z)): 0.4498 / 0.3770\n", + "[100/100][160/391] Loss_D: 2.9532 Loss_G: 2.0726 D(x): 0.6899 D(G(z)): 0.4675 / 0.5469\n", + "[100/100][161/391] Loss_D: 3.1601 Loss_G: 3.2921 D(x): 0.6229 D(G(z)): 0.4383 / 0.3826\n", + "[100/100][162/391] Loss_D: 2.4434 Loss_G: 3.3033 D(x): 0.7330 D(G(z)): 0.3835 / 0.3673\n", + "[100/100][163/391] Loss_D: 2.5135 Loss_G: 3.6282 D(x): 0.7562 D(G(z)): 0.4419 / 0.3404\n", + "[100/100][164/391] Loss_D: 2.2834 Loss_G: 3.6437 D(x): 0.7520 D(G(z)): 0.4029 / 0.3363\n", + "[100/100][165/391] Loss_D: 2.8195 Loss_G: 3.8875 D(x): 0.6736 D(G(z)): 0.4502 / 0.3259\n", + "[100/100][166/391] Loss_D: 2.5471 Loss_G: 3.8816 D(x): 0.7082 D(G(z)): 0.3711 / 0.3054\n", + "[100/100][167/391] Loss_D: 2.4153 Loss_G: 3.8079 D(x): 0.7269 D(G(z)): 0.3091 / 0.3030\n", + "[100/100][168/391] Loss_D: 2.6692 Loss_G: 2.9050 D(x): 0.6852 D(G(z)): 0.3708 / 0.4191\n", + "[100/100][169/391] Loss_D: 2.8022 Loss_G: 2.3682 D(x): 0.6406 D(G(z)): 0.4153 / 0.4952\n", + "[100/100][170/391] Loss_D: 2.6308 Loss_G: 3.5027 D(x): 0.6245 D(G(z)): 0.3238 / 0.3628\n", + "[100/100][171/391] Loss_D: 2.7482 Loss_G: 3.0913 D(x): 0.7157 D(G(z)): 0.4160 / 0.4094\n", + "[100/100][172/391] Loss_D: 2.8715 Loss_G: 4.3877 D(x): 0.6834 D(G(z)): 0.4641 / 0.2759\n", + "[100/100][173/391] Loss_D: 2.9721 Loss_G: 2.2274 D(x): 0.6440 D(G(z)): 0.3907 / 0.5064\n", + "[100/100][174/391] Loss_D: 2.8968 Loss_G: 3.0480 D(x): 0.7431 D(G(z)): 0.5256 / 0.4019\n", + "[100/100][175/391] Loss_D: 3.4296 Loss_G: 2.9388 D(x): 0.5664 D(G(z)): 0.4550 / 0.4041\n", + "[100/100][176/391] Loss_D: 3.4808 Loss_G: 3.2740 D(x): 0.6056 D(G(z)): 0.4980 / 0.3636\n", + "[100/100][177/391] Loss_D: 2.5961 Loss_G: 3.3584 D(x): 0.6847 D(G(z)): 0.3772 / 0.3452\n", + "[100/100][178/391] Loss_D: 2.1002 Loss_G: 2.3855 D(x): 0.7413 D(G(z)): 0.3312 / 0.4755\n", + "[100/100][179/391] Loss_D: 2.2871 Loss_G: 3.3529 D(x): 0.7327 D(G(z)): 0.2859 / 0.3601\n", + "[100/100][180/391] Loss_D: 2.4795 Loss_G: 3.1728 D(x): 0.6871 D(G(z)): 0.3369 / 0.3929\n", + "[100/100][181/391] Loss_D: 3.5278 Loss_G: 2.5834 D(x): 0.6574 D(G(z)): 0.4282 / 0.4419\n", + "[100/100][182/391] Loss_D: 2.2022 Loss_G: 2.9926 D(x): 0.6925 D(G(z)): 0.2653 / 0.3902\n", + "[100/100][183/391] Loss_D: 2.8323 Loss_G: 2.2244 D(x): 0.7110 D(G(z)): 0.5001 / 0.5123\n", + "[100/100][184/391] Loss_D: 2.3662 Loss_G: 2.9176 D(x): 0.7362 D(G(z)): 0.3613 / 0.4093\n", + "[100/100][185/391] Loss_D: 3.1057 Loss_G: 3.1349 D(x): 0.6515 D(G(z)): 0.4689 / 0.3928\n", + "[100/100][186/391] Loss_D: 2.7739 Loss_G: 2.9639 D(x): 0.7232 D(G(z)): 0.4343 / 0.4008\n", + "[100/100][187/391] Loss_D: 3.0494 Loss_G: 3.0553 D(x): 0.7271 D(G(z)): 0.5370 / 0.3899\n", + "[100/100][188/391] Loss_D: 2.5450 Loss_G: 3.5480 D(x): 0.6907 D(G(z)): 0.4185 / 0.3597\n", + "[100/100][189/391] Loss_D: 2.4174 Loss_G: 2.7111 D(x): 0.6881 D(G(z)): 0.3309 / 0.4452\n", + "[100/100][190/391] Loss_D: 2.6497 Loss_G: 2.8674 D(x): 0.6926 D(G(z)): 0.4192 / 0.4273\n", + "[100/100][191/391] Loss_D: 2.8293 Loss_G: 3.8116 D(x): 0.7106 D(G(z)): 0.4179 / 0.3104\n", + "[100/100][192/391] Loss_D: 2.3349 Loss_G: 2.8185 D(x): 0.6862 D(G(z)): 0.3554 / 0.4368\n", + "[100/100][193/391] Loss_D: 2.6797 Loss_G: 2.9433 D(x): 0.6699 D(G(z)): 0.4127 / 0.4206\n", + "[100/100][194/391] Loss_D: 3.2641 Loss_G: 1.9238 D(x): 0.6580 D(G(z)): 0.5166 / 0.5564\n", + "[100/100][195/391] Loss_D: 2.6437 Loss_G: 3.4890 D(x): 0.7025 D(G(z)): 0.3463 / 0.3408\n", + "[100/100][196/391] Loss_D: 2.7108 Loss_G: 2.8416 D(x): 0.7304 D(G(z)): 0.4840 / 0.4218\n", + "[100/100][197/391] Loss_D: 2.5007 Loss_G: 3.4431 D(x): 0.7336 D(G(z)): 0.2712 / 0.3519\n", + "[100/100][198/391] Loss_D: 2.6240 Loss_G: 2.8879 D(x): 0.6380 D(G(z)): 0.2394 / 0.4165\n", + "[100/100][199/391] Loss_D: 2.6398 Loss_G: 2.5962 D(x): 0.6985 D(G(z)): 0.4084 / 0.4540\n", + "[100/100][200/391] Loss_D: 2.6438 Loss_G: 2.0326 D(x): 0.7524 D(G(z)): 0.4597 / 0.5297\n", + "[100/100][201/391] Loss_D: 3.5841 Loss_G: 3.1301 D(x): 0.5689 D(G(z)): 0.4734 / 0.4021\n", + "[100/100][202/391] Loss_D: 3.0301 Loss_G: 2.8184 D(x): 0.6372 D(G(z)): 0.4877 / 0.4207\n", + "[100/100][203/391] Loss_D: 2.7971 Loss_G: 2.8360 D(x): 0.6553 D(G(z)): 0.3702 / 0.4305\n", + "[100/100][204/391] Loss_D: 2.3679 Loss_G: 3.3267 D(x): 0.7187 D(G(z)): 0.4304 / 0.3707\n", + "[100/100][205/391] Loss_D: 2.7801 Loss_G: 2.3337 D(x): 0.6437 D(G(z)): 0.4100 / 0.4831\n", + "[100/100][206/391] Loss_D: 2.9567 Loss_G: 3.7991 D(x): 0.5844 D(G(z)): 0.4009 / 0.3202\n", + "[100/100][207/391] Loss_D: 2.8047 Loss_G: 2.5842 D(x): 0.7047 D(G(z)): 0.4544 / 0.4685\n", + "[100/100][208/391] Loss_D: 2.8156 Loss_G: 2.6660 D(x): 0.6845 D(G(z)): 0.4714 / 0.4361\n", + "[100/100][209/391] Loss_D: 2.6693 Loss_G: 2.3263 D(x): 0.6651 D(G(z)): 0.3742 / 0.4996\n", + "[100/100][210/391] Loss_D: 2.6504 Loss_G: 2.2026 D(x): 0.6759 D(G(z)): 0.3744 / 0.5153\n", + "[100/100][211/391] Loss_D: 3.6414 Loss_G: 2.8657 D(x): 0.7173 D(G(z)): 0.4107 / 0.4221\n", + "[100/100][212/391] Loss_D: 3.2538 Loss_G: 2.9030 D(x): 0.6721 D(G(z)): 0.5196 / 0.4148\n", + "[100/100][213/391] Loss_D: 2.6112 Loss_G: 2.7832 D(x): 0.6736 D(G(z)): 0.3453 / 0.4351\n", + "[100/100][214/391] Loss_D: 2.4522 Loss_G: 3.2645 D(x): 0.7417 D(G(z)): 0.4265 / 0.3728\n", + "[100/100][215/391] Loss_D: 2.9611 Loss_G: 2.2753 D(x): 0.6904 D(G(z)): 0.4795 / 0.4967\n", + "[100/100][216/391] Loss_D: 2.6038 Loss_G: 3.2997 D(x): 0.6936 D(G(z)): 0.3704 / 0.3689\n", + "[100/100][217/391] Loss_D: 2.4960 Loss_G: 2.5945 D(x): 0.6663 D(G(z)): 0.3104 / 0.4491\n", + "[100/100][218/391] Loss_D: 3.0220 Loss_G: 3.0278 D(x): 0.5624 D(G(z)): 0.3411 / 0.4080\n", + "[100/100][219/391] Loss_D: 2.3455 Loss_G: 2.9194 D(x): 0.7480 D(G(z)): 0.3936 / 0.4120\n", + "[100/100][220/391] Loss_D: 3.1216 Loss_G: 2.9552 D(x): 0.7600 D(G(z)): 0.5464 / 0.4116\n", + "[100/100][221/391] Loss_D: 2.7208 Loss_G: 3.8267 D(x): 0.7089 D(G(z)): 0.4281 / 0.3299\n", + "[100/100][222/391] Loss_D: 2.2029 Loss_G: 2.3072 D(x): 0.7172 D(G(z)): 0.3172 / 0.4944\n", + "[100/100][223/391] Loss_D: 2.4211 Loss_G: 3.7693 D(x): 0.7371 D(G(z)): 0.3744 / 0.3237\n", + "[100/100][224/391] Loss_D: 2.7071 Loss_G: 2.4981 D(x): 0.6375 D(G(z)): 0.3769 / 0.4729\n", + "[100/100][225/391] Loss_D: 2.4712 Loss_G: 3.0262 D(x): 0.6977 D(G(z)): 0.3468 / 0.3882\n", + "[100/100][226/391] Loss_D: 2.5633 Loss_G: 3.9100 D(x): 0.7178 D(G(z)): 0.4306 / 0.3023\n", + "[100/100][227/391] Loss_D: 2.3651 Loss_G: 3.5904 D(x): 0.7090 D(G(z)): 0.2564 / 0.3496\n", + "[100/100][228/391] Loss_D: 2.5538 Loss_G: 2.1809 D(x): 0.6973 D(G(z)): 0.3810 / 0.5174\n", + "[100/100][229/391] Loss_D: 2.2520 Loss_G: 2.8189 D(x): 0.8134 D(G(z)): 0.4218 / 0.4306\n", + "[100/100][230/391] Loss_D: 2.0441 Loss_G: 3.1226 D(x): 0.7558 D(G(z)): 0.3209 / 0.3907\n", + "[100/100][231/391] Loss_D: 3.0389 Loss_G: 3.1575 D(x): 0.6094 D(G(z)): 0.4135 / 0.3969\n", + "[100/100][232/391] Loss_D: 3.2463 Loss_G: 3.2971 D(x): 0.6699 D(G(z)): 0.5394 / 0.3668\n", + "[100/100][233/391] Loss_D: 2.3844 Loss_G: 2.4776 D(x): 0.7525 D(G(z)): 0.3772 / 0.4633\n", + "[100/100][234/391] Loss_D: 2.0508 Loss_G: 3.4441 D(x): 0.6984 D(G(z)): 0.2772 / 0.3490\n", + "[100/100][235/391] Loss_D: 2.6142 Loss_G: 2.6363 D(x): 0.7272 D(G(z)): 0.4415 / 0.4514\n", + "[100/100][236/391] Loss_D: 2.7456 Loss_G: 3.8342 D(x): 0.7183 D(G(z)): 0.4568 / 0.3262\n", + "[100/100][237/391] Loss_D: 3.1665 Loss_G: 2.4883 D(x): 0.5941 D(G(z)): 0.4160 / 0.4534\n", + "[100/100][238/391] Loss_D: 2.2797 Loss_G: 2.9488 D(x): 0.7114 D(G(z)): 0.2904 / 0.4078\n", + "[100/100][239/391] Loss_D: 2.6983 Loss_G: 3.6641 D(x): 0.6897 D(G(z)): 0.4169 / 0.3395\n", + "[100/100][240/391] Loss_D: 3.0974 Loss_G: 2.3960 D(x): 0.6851 D(G(z)): 0.4988 / 0.4857\n", + "[100/100][241/391] Loss_D: 3.7110 Loss_G: 3.3261 D(x): 0.6819 D(G(z)): 0.3808 / 0.3797\n", + "[100/100][242/391] Loss_D: 2.5966 Loss_G: 4.0927 D(x): 0.8429 D(G(z)): 0.4803 / 0.2848\n", + "[100/100][243/391] Loss_D: 3.4529 Loss_G: 2.8482 D(x): 0.5685 D(G(z)): 0.4669 / 0.4232\n", + "[100/100][244/391] Loss_D: 2.3139 Loss_G: 2.3151 D(x): 0.7179 D(G(z)): 0.3843 / 0.5114\n", + "[100/100][245/391] Loss_D: 2.5296 Loss_G: 3.0631 D(x): 0.6404 D(G(z)): 0.3012 / 0.4072\n", + "[100/100][246/391] Loss_D: 2.3920 Loss_G: 2.7680 D(x): 0.7360 D(G(z)): 0.3539 / 0.4249\n", + "[100/100][247/391] Loss_D: 2.9765 Loss_G: 2.9276 D(x): 0.6358 D(G(z)): 0.4652 / 0.3969\n", + "[100/100][248/391] Loss_D: 2.7964 Loss_G: 3.7157 D(x): 0.6357 D(G(z)): 0.3930 / 0.3242\n", + "[100/100][249/391] Loss_D: 2.7417 Loss_G: 2.6430 D(x): 0.6948 D(G(z)): 0.4682 / 0.4560\n", + "[100/100][250/391] Loss_D: 2.4483 Loss_G: 3.0225 D(x): 0.7638 D(G(z)): 0.3908 / 0.4104\n", + "[100/100][251/391] Loss_D: 3.2844 Loss_G: 2.6130 D(x): 0.6263 D(G(z)): 0.4781 / 0.4387\n", + "[100/100][252/391] Loss_D: 2.8746 Loss_G: 3.5302 D(x): 0.7447 D(G(z)): 0.4826 / 0.3549\n", + "[100/100][253/391] Loss_D: 2.6170 Loss_G: 2.8727 D(x): 0.6922 D(G(z)): 0.3427 / 0.4451\n", + "[100/100][254/391] Loss_D: 2.8210 Loss_G: 4.0676 D(x): 0.6010 D(G(z)): 0.3536 / 0.2951\n", + "[100/100][255/391] Loss_D: 3.0767 Loss_G: 3.0904 D(x): 0.5677 D(G(z)): 0.2915 / 0.3847\n", + "[100/100][256/391] Loss_D: 2.7919 Loss_G: 2.6075 D(x): 0.7379 D(G(z)): 0.4665 / 0.4452\n", + "[100/100][257/391] Loss_D: 2.7727 Loss_G: 2.6627 D(x): 0.7403 D(G(z)): 0.3941 / 0.4646\n", + "[100/100][258/391] Loss_D: 2.7238 Loss_G: 2.6045 D(x): 0.7111 D(G(z)): 0.5008 / 0.4535\n", + "[100/100][259/391] Loss_D: 2.8224 Loss_G: 3.2815 D(x): 0.7228 D(G(z)): 0.4763 / 0.3675\n", + "[100/100][260/391] Loss_D: 2.8323 Loss_G: 2.7163 D(x): 0.6993 D(G(z)): 0.4697 / 0.4411\n", + "[100/100][261/391] Loss_D: 2.7144 Loss_G: 2.8006 D(x): 0.6428 D(G(z)): 0.3068 / 0.4191\n", + "[100/100][262/391] Loss_D: 2.7071 Loss_G: 3.1058 D(x): 0.7341 D(G(z)): 0.4222 / 0.3944\n", + "[100/100][263/391] Loss_D: 2.7524 Loss_G: 3.6811 D(x): 0.6663 D(G(z)): 0.3310 / 0.3291\n", + "[100/100][264/391] Loss_D: 2.1649 Loss_G: 3.4979 D(x): 0.7160 D(G(z)): 0.3552 / 0.3453\n", + "[100/100][265/391] Loss_D: 3.0272 Loss_G: 2.9860 D(x): 0.7184 D(G(z)): 0.5173 / 0.4194\n", + "[100/100][266/391] Loss_D: 3.1388 Loss_G: 4.4349 D(x): 0.6026 D(G(z)): 0.4155 / 0.2598\n", + "[100/100][267/391] Loss_D: 2.7302 Loss_G: 3.1930 D(x): 0.6555 D(G(z)): 0.4177 / 0.3885\n", + "[100/100][268/391] Loss_D: 2.7822 Loss_G: 3.5401 D(x): 0.5954 D(G(z)): 0.3443 / 0.3384\n", + "[100/100][269/391] Loss_D: 3.1918 Loss_G: 2.2426 D(x): 0.6486 D(G(z)): 0.4471 / 0.4942\n", + "[100/100][270/391] Loss_D: 3.3269 Loss_G: 2.2077 D(x): 0.6877 D(G(z)): 0.5544 / 0.5024\n", + "[100/100][271/391] Loss_D: 3.7314 Loss_G: 2.4246 D(x): 0.5406 D(G(z)): 0.4354 / 0.4918\n", + "[100/100][272/391] Loss_D: 2.0709 Loss_G: 2.2608 D(x): 0.7893 D(G(z)): 0.2902 / 0.5049\n", + "[100/100][273/391] Loss_D: 2.3940 Loss_G: 3.9284 D(x): 0.7844 D(G(z)): 0.3403 / 0.2934\n", + "[100/100][274/391] Loss_D: 3.1095 Loss_G: 2.4617 D(x): 0.6766 D(G(z)): 0.5104 / 0.4716\n", + "[100/100][275/391] Loss_D: 2.8031 Loss_G: 3.4157 D(x): 0.6854 D(G(z)): 0.4428 / 0.3609\n", + "[100/100][276/391] Loss_D: 2.8509 Loss_G: 3.1627 D(x): 0.6734 D(G(z)): 0.4225 / 0.3877\n", + "[100/100][277/391] Loss_D: 2.5828 Loss_G: 3.5799 D(x): 0.7440 D(G(z)): 0.3381 / 0.3560\n", + "[100/100][278/391] Loss_D: 2.6129 Loss_G: 2.8465 D(x): 0.7214 D(G(z)): 0.4639 / 0.4205\n", + "[100/100][279/391] Loss_D: 2.7589 Loss_G: 3.1904 D(x): 0.6948 D(G(z)): 0.4519 / 0.3874\n", + "[100/100][280/391] Loss_D: 2.8684 Loss_G: 3.8957 D(x): 0.6376 D(G(z)): 0.4052 / 0.3029\n", + "[100/100][281/391] Loss_D: 2.9801 Loss_G: 2.6733 D(x): 0.6889 D(G(z)): 0.4716 / 0.4532\n", + "[100/100][282/391] Loss_D: 2.4573 Loss_G: 3.1858 D(x): 0.7303 D(G(z)): 0.3877 / 0.3854\n", + "[100/100][283/391] Loss_D: 2.7935 Loss_G: 2.8794 D(x): 0.6193 D(G(z)): 0.3234 / 0.4250\n", + "[100/100][284/391] Loss_D: 2.0504 Loss_G: 2.9460 D(x): 0.7745 D(G(z)): 0.3782 / 0.4171\n", + "[100/100][285/391] Loss_D: 2.7562 Loss_G: 3.2811 D(x): 0.7087 D(G(z)): 0.4613 / 0.3703\n", + "[100/100][286/391] Loss_D: 2.7710 Loss_G: 3.2262 D(x): 0.6848 D(G(z)): 0.4194 / 0.3703\n", + "[100/100][287/391] Loss_D: 2.5341 Loss_G: 3.5553 D(x): 0.7239 D(G(z)): 0.3109 / 0.3480\n", + "[100/100][288/391] Loss_D: 2.7634 Loss_G: 4.2888 D(x): 0.6234 D(G(z)): 0.3821 / 0.2870\n", + "[100/100][289/391] Loss_D: 2.7842 Loss_G: 2.4319 D(x): 0.6594 D(G(z)): 0.3387 / 0.4772\n", + "[100/100][290/391] Loss_D: 3.0593 Loss_G: 3.0489 D(x): 0.6741 D(G(z)): 0.4868 / 0.3968\n", + "[100/100][291/391] Loss_D: 2.9598 Loss_G: 2.3027 D(x): 0.6667 D(G(z)): 0.4378 / 0.5070\n", + "[100/100][292/391] Loss_D: 2.3349 Loss_G: 1.9605 D(x): 0.7816 D(G(z)): 0.3442 / 0.5607\n", + "[100/100][293/391] Loss_D: 2.5734 Loss_G: 3.0118 D(x): 0.6798 D(G(z)): 0.3612 / 0.4049\n", + "[100/100][294/391] Loss_D: 3.1820 Loss_G: 3.3203 D(x): 0.6011 D(G(z)): 0.4807 / 0.3718\n", + "[100/100][295/391] Loss_D: 2.5378 Loss_G: 3.0424 D(x): 0.7757 D(G(z)): 0.4141 / 0.3954\n", + "[100/100][296/391] Loss_D: 2.5337 Loss_G: 3.0866 D(x): 0.7292 D(G(z)): 0.3751 / 0.3783\n", + "[100/100][297/391] Loss_D: 3.2493 Loss_G: 2.5716 D(x): 0.6061 D(G(z)): 0.4212 / 0.4749\n", + "[100/100][298/391] Loss_D: 3.0229 Loss_G: 2.6776 D(x): 0.6829 D(G(z)): 0.4999 / 0.4383\n", + "[100/100][299/391] Loss_D: 2.7401 Loss_G: 4.0089 D(x): 0.6805 D(G(z)): 0.4102 / 0.3041\n", + "[100/100][300/391] Loss_D: 2.9779 Loss_G: 3.2804 D(x): 0.6346 D(G(z)): 0.4239 / 0.3879\n", + "[100/100][301/391] Loss_D: 3.6039 Loss_G: 2.5379 D(x): 0.6116 D(G(z)): 0.3355 / 0.4894\n", + "[100/100][302/391] Loss_D: 2.6698 Loss_G: 3.4052 D(x): 0.6715 D(G(z)): 0.4062 / 0.3543\n", + "[100/100][303/391] Loss_D: 2.7731 Loss_G: 3.2622 D(x): 0.7365 D(G(z)): 0.4998 / 0.3822\n", + "[100/100][304/391] Loss_D: 2.7373 Loss_G: 3.6826 D(x): 0.6263 D(G(z)): 0.4051 / 0.3286\n", + "[100/100][305/391] Loss_D: 2.5272 Loss_G: 3.4537 D(x): 0.7245 D(G(z)): 0.3846 / 0.3568\n", + "[100/100][306/391] Loss_D: 2.6651 Loss_G: 3.0489 D(x): 0.7222 D(G(z)): 0.4170 / 0.4162\n", + "[100/100][307/391] Loss_D: 2.9544 Loss_G: 3.6952 D(x): 0.5968 D(G(z)): 0.3990 / 0.3205\n", + "[100/100][308/391] Loss_D: 2.5957 Loss_G: 2.6996 D(x): 0.6319 D(G(z)): 0.3020 / 0.4374\n", + "[100/100][309/391] Loss_D: 2.8936 Loss_G: 3.7811 D(x): 0.7371 D(G(z)): 0.5426 / 0.3330\n", + "[100/100][310/391] Loss_D: 2.4863 Loss_G: 2.5007 D(x): 0.7683 D(G(z)): 0.3863 / 0.4700\n", + "[100/100][311/391] Loss_D: 3.0916 Loss_G: 1.8754 D(x): 0.5283 D(G(z)): 0.2503 / 0.5722\n", + "[100/100][312/391] Loss_D: 2.5639 Loss_G: 2.0232 D(x): 0.6901 D(G(z)): 0.3776 / 0.5418\n", + "[100/100][313/391] Loss_D: 2.6387 Loss_G: 3.0752 D(x): 0.7097 D(G(z)): 0.4392 / 0.3999\n", + "[100/100][314/391] Loss_D: 2.8086 Loss_G: 3.3875 D(x): 0.7159 D(G(z)): 0.5165 / 0.3732\n", + "[100/100][315/391] Loss_D: 2.6493 Loss_G: 2.4412 D(x): 0.7101 D(G(z)): 0.4245 / 0.4859\n", + "[100/100][316/391] Loss_D: 2.8535 Loss_G: 3.2968 D(x): 0.6890 D(G(z)): 0.4389 / 0.3660\n", + "[100/100][317/391] Loss_D: 2.8523 Loss_G: 3.4819 D(x): 0.7283 D(G(z)): 0.4196 / 0.3441\n", + "[100/100][318/391] Loss_D: 2.5549 Loss_G: 4.1848 D(x): 0.6789 D(G(z)): 0.4129 / 0.2801\n", + "[100/100][319/391] Loss_D: 2.5410 Loss_G: 2.9968 D(x): 0.6733 D(G(z)): 0.3626 / 0.4121\n", + "[100/100][320/391] Loss_D: 3.1255 Loss_G: 3.8959 D(x): 0.7237 D(G(z)): 0.5238 / 0.3118\n", + "[100/100][321/391] Loss_D: 3.6141 Loss_G: 3.0085 D(x): 0.6014 D(G(z)): 0.5285 / 0.3987\n", + "[100/100][322/391] Loss_D: 3.1975 Loss_G: 4.3477 D(x): 0.5873 D(G(z)): 0.4201 / 0.2702\n", + "[100/100][323/391] Loss_D: 2.7804 Loss_G: 2.7780 D(x): 0.6768 D(G(z)): 0.3776 / 0.4178\n", + "[100/100][324/391] Loss_D: 2.2593 Loss_G: 3.1501 D(x): 0.7555 D(G(z)): 0.4064 / 0.3858\n", + "[100/100][325/391] Loss_D: 2.9619 Loss_G: 2.3227 D(x): 0.6117 D(G(z)): 0.3428 / 0.4804\n", + "[100/100][326/391] Loss_D: 3.2786 Loss_G: 2.9723 D(x): 0.6046 D(G(z)): 0.4783 / 0.4064\n", + "[100/100][327/391] Loss_D: 2.9982 Loss_G: 3.1071 D(x): 0.5890 D(G(z)): 0.4127 / 0.3839\n", + "[100/100][328/391] Loss_D: 2.7954 Loss_G: 1.6865 D(x): 0.6416 D(G(z)): 0.4214 / 0.6038\n", + "[100/100][329/391] Loss_D: 3.2223 Loss_G: 2.9577 D(x): 0.6821 D(G(z)): 0.5284 / 0.4116\n", + "[100/100][330/391] Loss_D: 2.4040 Loss_G: 2.3344 D(x): 0.7127 D(G(z)): 0.3279 / 0.4918\n", + "[100/100][331/391] Loss_D: 3.5371 Loss_G: 2.7613 D(x): 0.7468 D(G(z)): 0.4073 / 0.4252\n", + "[100/100][332/391] Loss_D: 2.6051 Loss_G: 2.7175 D(x): 0.6880 D(G(z)): 0.4022 / 0.4420\n", + "[100/100][333/391] Loss_D: 2.7465 Loss_G: 2.3363 D(x): 0.7499 D(G(z)): 0.4028 / 0.4892\n", + "[100/100][334/391] Loss_D: 2.1713 Loss_G: 3.1985 D(x): 0.7236 D(G(z)): 0.3562 / 0.3869\n", + "[100/100][335/391] Loss_D: 2.7197 Loss_G: 2.9534 D(x): 0.6029 D(G(z)): 0.2980 / 0.3996\n", + "[100/100][336/391] Loss_D: 2.4743 Loss_G: 2.1807 D(x): 0.7140 D(G(z)): 0.3323 / 0.5249\n", + "[100/100][337/391] Loss_D: 2.7129 Loss_G: 3.2961 D(x): 0.6959 D(G(z)): 0.4182 / 0.3581\n", + "[100/100][338/391] Loss_D: 2.9303 Loss_G: 2.5696 D(x): 0.6963 D(G(z)): 0.4964 / 0.4563\n", + "[100/100][339/391] Loss_D: 2.6115 Loss_G: 2.9080 D(x): 0.7952 D(G(z)): 0.4970 / 0.4383\n", + "[100/100][340/391] Loss_D: 2.9233 Loss_G: 3.8636 D(x): 0.6867 D(G(z)): 0.4727 / 0.3170\n", + "[100/100][341/391] Loss_D: 2.2707 Loss_G: 4.9629 D(x): 0.7799 D(G(z)): 0.3632 / 0.2237\n", + "[100/100][342/391] Loss_D: 2.7922 Loss_G: 3.4430 D(x): 0.7169 D(G(z)): 0.4676 / 0.3557\n", + "[100/100][343/391] Loss_D: 2.6612 Loss_G: 2.7681 D(x): 0.6808 D(G(z)): 0.3627 / 0.4446\n", + "[100/100][344/391] Loss_D: 3.2541 Loss_G: 4.9652 D(x): 0.5810 D(G(z)): 0.4422 / 0.2172\n", + "[100/100][345/391] Loss_D: 2.3038 Loss_G: 3.4191 D(x): 0.7031 D(G(z)): 0.2994 / 0.3594\n", + "[100/100][346/391] Loss_D: 2.7914 Loss_G: 2.8519 D(x): 0.6309 D(G(z)): 0.3905 / 0.4160\n", + "[100/100][347/391] Loss_D: 2.7598 Loss_G: 2.9866 D(x): 0.7325 D(G(z)): 0.4332 / 0.4062\n", + "[100/100][348/391] Loss_D: 2.6144 Loss_G: 3.9635 D(x): 0.6446 D(G(z)): 0.3507 / 0.3087\n", + "[100/100][349/391] Loss_D: 3.0911 Loss_G: 2.5143 D(x): 0.5804 D(G(z)): 0.4064 / 0.4670\n", + "[100/100][350/391] Loss_D: 2.5746 Loss_G: 1.9987 D(x): 0.7147 D(G(z)): 0.4378 / 0.5445\n", + "[100/100][351/391] Loss_D: 2.7581 Loss_G: 2.6362 D(x): 0.7592 D(G(z)): 0.4424 / 0.4489\n", + "[100/100][352/391] Loss_D: 2.8034 Loss_G: 2.1663 D(x): 0.6339 D(G(z)): 0.4284 / 0.5131\n", + "[100/100][353/391] Loss_D: 2.4298 Loss_G: 2.0544 D(x): 0.7137 D(G(z)): 0.3280 / 0.5279\n", + "[100/100][354/391] Loss_D: 2.5165 Loss_G: 2.8412 D(x): 0.7229 D(G(z)): 0.4278 / 0.4192\n", + "[100/100][355/391] Loss_D: 2.6573 Loss_G: 2.3075 D(x): 0.6838 D(G(z)): 0.4521 / 0.4907\n", + "[100/100][356/391] Loss_D: 2.7211 Loss_G: 3.2231 D(x): 0.7236 D(G(z)): 0.4667 / 0.3756\n", + "[100/100][357/391] Loss_D: 3.1393 Loss_G: 3.5680 D(x): 0.5352 D(G(z)): 0.3464 / 0.3385\n", + "[100/100][358/391] Loss_D: 3.1859 Loss_G: 2.9191 D(x): 0.5410 D(G(z)): 0.3774 / 0.4114\n", + "[100/100][359/391] Loss_D: 3.1919 Loss_G: 2.1779 D(x): 0.7273 D(G(z)): 0.5720 / 0.5225\n", + "[100/100][360/391] Loss_D: 2.9309 Loss_G: 2.7878 D(x): 0.6514 D(G(z)): 0.4384 / 0.4466\n", + "[100/100][361/391] Loss_D: 3.4801 Loss_G: 3.3842 D(x): 0.7176 D(G(z)): 0.4277 / 0.3706\n", + "[100/100][362/391] Loss_D: 2.7401 Loss_G: 3.3838 D(x): 0.7229 D(G(z)): 0.4014 / 0.3491\n", + "[100/100][363/391] Loss_D: 3.0241 Loss_G: 2.8220 D(x): 0.7606 D(G(z)): 0.5384 / 0.4267\n", + "[100/100][364/391] Loss_D: 2.6431 Loss_G: 3.8266 D(x): 0.7244 D(G(z)): 0.4183 / 0.3160\n", + "[100/100][365/391] Loss_D: 2.7717 Loss_G: 3.5561 D(x): 0.6467 D(G(z)): 0.3671 / 0.3412\n", + "[100/100][366/391] Loss_D: 3.0296 Loss_G: 2.7674 D(x): 0.5488 D(G(z)): 0.3622 / 0.4420\n", + "[100/100][367/391] Loss_D: 2.6218 Loss_G: 3.0929 D(x): 0.7032 D(G(z)): 0.3616 / 0.3877\n", + "[100/100][368/391] Loss_D: 2.6316 Loss_G: 3.2072 D(x): 0.6877 D(G(z)): 0.4004 / 0.3786\n", + "[100/100][369/391] Loss_D: 2.2341 Loss_G: 3.2685 D(x): 0.7605 D(G(z)): 0.3761 / 0.3780\n", + "[100/100][370/391] Loss_D: 2.7501 Loss_G: 3.4588 D(x): 0.6725 D(G(z)): 0.4408 / 0.3590\n", + "[100/100][371/391] Loss_D: 2.7761 Loss_G: 4.6703 D(x): 0.6561 D(G(z)): 0.4160 / 0.2474\n", + "[100/100][372/391] Loss_D: 2.7308 Loss_G: 3.4280 D(x): 0.6478 D(G(z)): 0.3890 / 0.3664\n", + "[100/100][373/391] Loss_D: 3.2333 Loss_G: 2.4757 D(x): 0.7294 D(G(z)): 0.5485 / 0.4578\n", + "[100/100][374/391] Loss_D: 2.8886 Loss_G: 3.0710 D(x): 0.5752 D(G(z)): 0.3146 / 0.3984\n", + "[100/100][375/391] Loss_D: 2.9752 Loss_G: 3.1183 D(x): 0.6718 D(G(z)): 0.4414 / 0.3854\n", + "[100/100][376/391] Loss_D: 2.5504 Loss_G: 3.8102 D(x): 0.7259 D(G(z)): 0.3447 / 0.3066\n", + "[100/100][377/391] Loss_D: 2.8814 Loss_G: 3.5837 D(x): 0.7254 D(G(z)): 0.4601 / 0.3344\n", + "[100/100][378/391] Loss_D: 2.6286 Loss_G: 3.1255 D(x): 0.6487 D(G(z)): 0.3877 / 0.3967\n", + "[100/100][379/391] Loss_D: 2.2463 Loss_G: 3.8835 D(x): 0.7403 D(G(z)): 0.3595 / 0.3125\n", + "[100/100][380/391] Loss_D: 2.3725 Loss_G: 3.0023 D(x): 0.7687 D(G(z)): 0.3869 / 0.4217\n", + "[100/100][381/391] Loss_D: 2.8461 Loss_G: 2.3684 D(x): 0.6747 D(G(z)): 0.4146 / 0.4744\n", + "[100/100][382/391] Loss_D: 2.3957 Loss_G: 3.7617 D(x): 0.6830 D(G(z)): 0.3486 / 0.3230\n", + "[100/100][383/391] Loss_D: 2.7056 Loss_G: 3.1843 D(x): 0.6907 D(G(z)): 0.4041 / 0.3740\n", + "[100/100][384/391] Loss_D: 3.1410 Loss_G: 3.0321 D(x): 0.6527 D(G(z)): 0.5234 / 0.4014\n", + "[100/100][385/391] Loss_D: 3.0333 Loss_G: 3.0557 D(x): 0.6145 D(G(z)): 0.4497 / 0.3948\n", + "[100/100][386/391] Loss_D: 2.7664 Loss_G: 3.0171 D(x): 0.7321 D(G(z)): 0.4167 / 0.3878\n", + "[100/100][387/391] Loss_D: 2.9515 Loss_G: 4.4019 D(x): 0.6776 D(G(z)): 0.4481 / 0.2593\n", + "[100/100][388/391] Loss_D: 2.7690 Loss_G: 3.2095 D(x): 0.6668 D(G(z)): 0.4365 / 0.3763\n", + "[100/100][389/391] Loss_D: 3.0740 Loss_G: 3.5476 D(x): 0.5859 D(G(z)): 0.3742 / 0.3446\n", + "[100/100][390/391] Loss_D: 2.5813 Loss_G: 3.7379 D(x): 0.6772 D(G(z)): 0.3707 / 0.3278\n", + "[100/100][391/391] Loss_D: 3.7127 Loss_G: 3.3112 D(x): 0.6844 D(G(z)): 0.4466 / 0.3768\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rOj2KZiim1Bf" + }, + "source": [ + "# save models \n", + "# if your discriminator/generator are conditional you'll want to change the inputs here\n", + "torch.jit.save(torch.jit.trace(model_G, (fixed_noise, fixed_labels)), '/content/drive/MyDrive/icl_dl_cw2/GAN2/GAN_G_model.pth')\n", + "torch.jit.save(torch.jit.trace(model_D, (fake)), '/content/drive/MyDrive/icl_dl_cw2/GAN2/GAN_D_model.pth')" + ], + "execution_count": 73, + "outputs": [] + }, + { + "source": [ + "## Part 2.1c: Results (10 Points)\n", + "This part is fairly open-ended, but not worth too much so do not go crazy. The table below shows examples of what are considered good samples. Level 3 and above will get you 10/10 points, level 2 will roughly get you 5/10 points and level 1 and below will get you 0/10 points.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

\n", + " \"Forwarding\"\n", + "
\n", + " Level 1\n", + "

\n", + "		 \n", + "

\n", + " \"Routing\"\n", + "
\n", + " Level 2\n", + "

\n", + "		 \n", + "

\n", + " \"Routing\"\n", + "
\n", + " Level 3\n", + "

\n", + "
" + ], + "cell_type": "markdown", + "metadata": { + "id": "QOjxGURDINm7" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5IQIKTdPZ-P5" + }, + "source": [ + "### Generator samples" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VIHi0HrJZ-P8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "97c5e032-d9ec-40ef-deea-9e796c599bb6" + }, + "source": [ + "input_noise = torch.randn(100, latent_vector_size, device=device)\n", + "# labels = [i for i in range(10)] *10\n", + "# labels = torch.Tensor(labels).long().to(device)\n", + "labels = torch.randint(0,10,(100,),dtype = torch.long,device = device)\n", + "with torch.no_grad():\n", + " # visualize the generated images\n", + " generated = model_G(input_noise, labels).cpu()\n", + " generated = make_grid(denorm(generated)[:100], nrow=10, padding=2, normalize=False, \n", + " range=None, scale_each=False, pad_value=0)\n", + " plt.figure(figsize=(8,8))\n", + " save_image(generated,'/content/drive/MyDrive/icl_dl_cw2/GAN2/Teaching30final.png')\n", + " show(generated) # note these are now class conditional images columns rep classes 1-10\n", + "\n", + "it = iter(loader_test)\n", + "sample_inputs, _ = next(it)\n", + "fixed_input = sample_inputs[0:64, :, :, :]\n", + "# visualize the original images of the last batch of the test set for comparison\n", + "img = make_grid(denorm(fixed_input), nrow=8, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + "plt.figure(figsize=(8,8))\n", + "show(img)" + ], + "execution_count": 109, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9tRtYjH_LbQ0" + }, + "source": [ + "## Part 2.1d: Engineering Choices (10 Points)\n", + "\n", + "Discuss the process you took to arrive at your final architecture. This should include:\n", + "\n", + "* Which empirically useful methods did you utilize\n", + "* What didn't work, what worked and what mattered most\n", + "* Are there any tricks you came across in the literature etc. which you suspect would be helpful here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HnFqwePhXeZ4" + }, + "source": [ + "**Your Answer**\n", + "\n", + "My final architecture is ACGAN, I'll talk about something I have tried to make the model much better.\n", + "\n", + "1. Flip some labels when training generator: real = fake, fake = real. It really worked, but have no idea why it works.\n", + "2. Using batchnorm.\n", + "3. Avoid sparse gradients: ReLU, MaxPool. So use leakyReLU and Conv2d + stride for downsampling, ConvTranspose2d + stride for upsampling.\n", + "4. Use soft and noisy labels. If it is real, then replace the label with a random number between 0.7 and 1.2, and if it is a fake sample, replace it with a random number between 0.0 and 0.3.\n", + "5. Use the ADAM optimizer.\n", + "6. Use dropouts.\n", + "7. Use an Embedding layer in conditional GANs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DC6ndLP5Z-P-" + }, + "source": [ + "## Part 2.2: Understanding GAN Training (5 points)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zz6oy7ixZ-P_" + }, + "source": [ + "### Loss Curves\n", + "**Your task:**\n", + "\n", + "\n", + "Plot the losses curves for the discriminator $D$ and the generator $G$ as the training progresses and explain whether the produced curves are theoretically sensible and why this is (or not) the case (x-axis: epochs, y-axis: loss).\n", + "\n", + "Make sure that the version of the notebook you deliver includes these results." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kxrUDHfBZ-QA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "708a041d-eb0a-42b5-ee71-464fea9648bb" + }, + "source": [ + "# ANSWER FOR PART 2.2 IN THIS CELL*\r\n", + "import matplotlib.pyplot as plt\r\n", + "plt.plot(list(range(0, np.array(train_losses_D).shape[0])), np.array(train_losses_D), label='loss_D')\r\n", + "plt.plot(list(range(0, np.array(train_losses_G).shape[0])), np.array(train_losses_G), label='loss_G')\r\n", + "plt.legend()\r\n", + "plt.title('Train Losses')\r\n", + "plt.show()\r\n", + "\r\n", + "# file=open('/content/drive/MyDrive/icl_dl_cw2/GAN2/train_losses_D.txt','w')\r\n", + "# file.write(str(train_losses_D));\r\n", + "# file.close()\r\n", + "\r\n", + "# file=open('/content/drive/MyDrive/icl_dl_cw2/GAN2/train_losses_G.txt','w')\r\n", + "# file.write(str(train_losses_G));\r\n", + "# file.close()" + ], + "execution_count": 110, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mZCUIHKkS0kF" + }, + "source": [ + "### Discussion\n", + "\n", + "Do your loss curves look sensible? What would you expect to see and why?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jYYOnd6YBN3k" + }, + "source": [ + "**YOUR ANSWER**\n", + "\n", + "The loss curves look sensible, at the beginning, loss_G dropped significantly and it is because the initial weights are easily optimized. After about 10 epochs, the loss of the two begins to fluctuate steadily, indicating that the two fight against each other. After 75 epochs,loss_G rises, indicating that the generation more difficult to generate images to deceive. When the generator learns to produce our images well, it's inevidable to make the job of the discriminator harder." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z_WLkdSNZ-QE" + }, + "source": [ + "## Part 2.3: Understanding Mode Collapse (5 points) \n", + "**Your task:** \n", + "\n", + "Based on the images created by your generator using the `fixed_noise` vector during training, provide a discussion on whether you noticed any mode collapse, what this behaviour may be attributed to, and explain what you did in order to cope with mode collapse." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HiPei-I0FCGM" + }, + "source": [ + "# Any additional code" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DpZuxPYUFedE" + }, + "source": [ + "### Discussion\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SynUx_QV7olI" + }, + "source": [ + "**YOUR ANSWER**\n", + "\n", + "In fact, when ACGAN is not used, mode collapse is easy to happen, and the generated pictures are not diverse enough. After trying many methods, ACGAN was finally selected to get a better generator. \n", + "\n", + "Because generating similar or the same type of pictures, the generator can better fool the discriminator, but when ACGAN is used, because the label is added, it makes it better to generate different types of pictures." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Cud7gZw2M-0U" + }, + "source": [ + "\n", + "\n", + "# TA Test Cell\n", + "TAs will run this cell to ensure that your results are reproducible, and that your models have been defined suitably. \n", + "\n", + " Please provide the input and output transformations required to make your VAE and GANs work. If your GAN generator requires more than just noise as input, also specify this below (there are two marked cells for you to inspect) \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JesvipjEFbGx" + }, + "source": [ + "# If you want to run these tests yourself, change directory:\n", + "# !cd /content/drive/MyDrive/icl_dl_cw2/" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QLlCRIn6m9ZS" + }, + "source": [ + "!pip install -q torch torchvision" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FUmBbya2nQh9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fceada4f-cffe-4f04-d595-4a69d7d172df" + }, + "source": [ + "# Do not remove anything here\n", + "import os\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader, sampler\n", + "from torchvision import datasets, transforms\n", + "from torchvision.utils import save_image, make_grid\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt \n", + "\n", + "show = lambda img: plt.imshow(np.transpose(img.cpu().numpy(), (1,2,0)))\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Do not change this cell!\n", + "torch.backends.cudnn.deterministic = True\n", + "torch.manual_seed(0)" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 1 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IFfBZsnTzQIU" + }, + "source": [ + "############# CHANGE THESE (COPY AND PASTE FROM YOUR OWN CODE) #############\n", + "vae_transform = transforms.Compose([\n", + " transforms.ToTensor(),\n", + "])\n", + "\n", + "def vae_denorm(x):\n", + " return x\n", + "\n", + "def gan_denorm(x):\n", + " x = 0.5 * (x + 1)\n", + " x = x.clamp(0, 1)\n", + " return x\n", + "\n", + "gan_latent_size = 100\n", + "\n", + "# If your generator requires something other than noise as input, please specify\n", + "# two cells down from here" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5t0CVMCFyxgU", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 417, + "referenced_widgets": [ + "208254968d00463da4fd6218773e4a83", + "c3361e8a6f02493ab6f599ecca41bb6d", + "a06338df87b349e2a79355b801ceb3a7", + "edb18eb99fc34c6b80b0176191df8a3b", + "1923dadc242949febc606e4652a0b4b5", + "ebb299f97f93402eb7acce9784955020", + "439c5b234eb84943b19eefcb5747a1f9", + "47023f26b6c643fe834a4a587a87f798", + "175bd5ac104e4246abbac3e8cf87cef9", + "84d0690198f74e638cd8e7b08e720705", + "8af14b2a15e946a4bbd0f903d113cbe5", + "8b5475fa86874f5cb311e0ff64cf8891", + "03564ee9e1a945129177cad0cf040a56", + "1516c0706ead46abb20a40eab3ff27e9", + "ccea99f12154455a99d1bc6e4d7816ff", + "4738f5763ee04734acf3306d1bf19850", + "34e4fecfa60643a9897e7125ec70cd9a", + "d2c1bd006aa54791a93257b6760221bc", + "ae63f0b072f2450d9751c4bc46803b00", + "4f40f22058744466902a762959895af0", + "8469cc2a05b94c8ebeb06e7c0ef48ed9", + "9a1b16b06f5840aaa188d0026dea345b", + "161d7f9f503942a5937e8099fe2c7953", + "b9fe179f1c0d4130942349edb1ee7023", + "d6e3a81c8fa04646ad977c2f712713bf", + "e86265fb7d4b4fb0abdb60a9d012741c", + "5a64e0bf3c23450aa585320262158182", + "c47f145ab37d4e959bf6ae4b1e1dfb37", + "21ee221a6b484ccdbf5047ba67d260a0", + "af8622099a214ca188b2053d50e85f6b", + "0e66434877534e09a9b1249c08d7f5a1", + "99ec63cc36a04a8ea6348f9dd78dc015" + ] + }, + "outputId": "f6d4e35c-4a24-40b4-e350-343c28da3f83" + }, + "source": [ + "# Load VAE Dataset\n", + "test_dat = datasets.MNIST(\"./data/\", train=False, transform=vae_transform, \n", + " download=True)\n", + "vae_loader_test = DataLoader(test_dat, batch_size=32, shuffle=False)" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HTNsHqK-mh6U" + }, + "source": [ + "############# MODIFY IF NEEDED #############\n", + "vae_input, _ = next(iter(vae_loader_test))\n", + "\n", + "# If your generator is conditional, then please modify this input suitably\n", + "input_noise = torch.randn(100, gan_latent_size, device=device)\n", + "input_labels = [7] * 100\n", + "input_labels = torch.Tensor(input_labels).long().to(device)\n", + "# input_labels = torch.randint(0,10,(100,),dtype = torch.long, device = device) # use it to generate different types of pictures\n", + "gan_input = [input_noise, input_labels] # In case you want to provide a tuple, we wrap ours" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EIg06rOqo3XA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 450 + }, + "outputId": "e53634ea-0b79-4db2-9b47-db9acc720d94" + }, + "source": [ + "# VAE Tests\n", + "# TAs will change these paths as you will have provided the model files manually\n", + "\"\"\"To TAs, you should have been creating a folder with the student uid\n", + " And the .ipynb + models in the root. Then that path is './VAE_model.pth' etc.\n", + "\"\"\"\n", + "vae = torch.jit.load('./VAE_model.pth')\n", + "vae.eval()\n", + "\n", + "# Check if VAE is convolutional\n", + "\n", + "for module in vae.children():\n", + " for layer in module.children():\n", + " if \"Conv2d\" in layer.original_name:\n", + " print(\"Used Convs\")\n", + " break\n", + "\n", + "vae_in = make_grid(vae_denorm(vae_input), nrow=8, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + "plt.figure()\n", + "plt.axis('off')\n", + "show(vae_in)\n", + "\n", + "vae_test = vae(vae_input.to(device))[0].detach()\n", + "vae_reco = make_grid(vae_denorm(vae_test), nrow=8, padding=2, normalize=False,\n", + " range=None, scale_each=False, pad_value=0)\n", + "plt.figure()\n", + "plt.axis('off')\n", + "show(vae_reco)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "9920512it [00:20, 473306.83it/s] Used Convs\n", + "Used Convs\n", + "Used Convs\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 5 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HoJLGFZSo7ON", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 500 + }, + "outputId": "ecd6cfb0-7794-4777-f4e6-dd697da97096" + }, + "source": [ + "# GAN Tests\n", + "model_G = torch.jit.load('./GAN_G_model.pth')\n", + "model_D = torch.jit.load('./GAN_D_model.pth')\n", + "[model.eval() for model in (model_G, model_D)] \n", + "\n", + "# Check that GAN doesn't have too many parameters\n", + "num_param = sum(p.numel() for p in [*model_G.parameters(),*model_D.parameters()])\n", + "\n", + "print(f\"Number of Parameters is {num_param} which is\", \"ok\" if num_param<25E+6 else \"not ok\")\n", + "\n", + "# visualize the generated images\n", + "generated = model_G(*gan_input).cpu()\n", + "generated = make_grid(gan_denorm(generated)[:100].detach(), nrow=10, padding=2, normalize=False, \n", + " range=None, scale_each=False, pad_value=0)\n", + "plt.figure(figsize=(8,8))\n", + "plt.axis('off')\n", + "show(generated)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of Parameters is 6433512 which is ok\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/CW2/dl_cw2.pdf b/CW2/dl_cw2.pdf new file mode 100644 index 0000000..5d7c2e6 Binary files /dev/null and b/CW2/dl_cw2.pdf differ diff --git a/CW2/feedback.pdf b/CW2/feedback.pdf new file mode 100644 index 0000000..613fd99 Binary files /dev/null and b/CW2/feedback.pdf differ diff --git a/CW3/CW3_pdf.pdf b/CW3/CW3_pdf.pdf new file mode 100644 index 0000000..248302b Binary files /dev/null and b/CW3/CW3_pdf.pdf differ diff --git a/CW3/Coursework_3_Full_questions.ipynb b/CW3/Coursework_3_Full_questions.ipynb new file mode 100644 index 0000000..f2c66f9 --- /dev/null +++ b/CW3/Coursework_3_Full_questions.ipynb @@ -0,0 +1,1061 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Coursework 3 - Full questions.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "zNMD5M6mQ2YY" + }, + "source": [ + "# Coursework 3: RNNs\n", + "\n", + "#### Instructions\n", + "\n", + "Please submit on CATe a zip file named *CW3_RNNs.zip* containing a version of this notebook with your answers. Write your answers in the cells below for each question.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LKehhGDF-Qte" + }, + "source": [ + "## Recurrent models coursework\n", + "\n", + "This coursework is separated into a coding and a theory component.\n", + "\n", + "For the first part, you will use the Google Speech Commands v0.02 subset that you used in the RNN tutorial: http://www.doc.ic.ac.uk/~pam213/co460_files/ \n", + "\n", + "### Part 1 - Coding\n", + "In this part you will have to:\n", + "\n", + "- Implement an LSTM\n", + "- Implement a GRU\n", + "\n", + "### Part 2 - Theory\n", + "\n", + "Here you will answer some theoretical questions about RNNs -- no detailed proofs and no programming." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qaI4P8SZ-U2j" + }, + "source": [ + "### Part 1: Coding" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bWGb-eUeXtex" + }, + "source": [ + "### Dataset\n", + "\n", + "We will be using the Google [*Speech Commands*](https://www.tensorflow.org/tutorials/sequences/audio_recognition) v0.02 [1] dataset.\n", + "\n", + "[1] Warden, P. (2018). [Speech commands: A dataset for limited-vocabulary speech recognition](https://arxiv.org/abs/1804.03209). *arXiv preprint arXiv:1804.03209.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SB5975CR7Gjg", + "outputId": "2a95c3e1-1347-4319-f376-b83691cea922" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5FWXvc8G6le4" + }, + "source": [ + "## MAKE SURE THIS POINTS INSIDE THE DATASET FOLDER.\n", + "dataset_folder = \"/content/drive/MyDrive/data/\" # this should change depending on where you have stored the data files" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SPZTUa2i6le5" + }, + "source": [ + "### Initial code before coursework questions start:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Qt3KzJzBPdHU" + }, + "source": [ + "import math\n", + "import os\n", + "import random\n", + "from collections import defaultdict\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "from torch.utils.data import Dataset\n", + "import numpy as np\n", + "from scipy.io.wavfile import read\n", + "import librosa\n", + "from matplotlib import pyplot as plt\n", + "\n", + "cuda = True if torch.cuda.is_available() else False\n", + "\n", + "Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\n" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "D_huYnDW6le6" + }, + "source": [ + "def set_seed(seed_value):\n", + " \"\"\"Set seed for reproducibility.\n", + " \"\"\"\n", + " random.seed(seed_value)\n", + " np.random.seed(seed_value)\n", + " torch.manual_seed(seed_value)\n", + " torch.cuda.manual_seed_all(seed_value)\n", + "\n", + "set_seed(42)" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QKSnqpAJLVwx" + }, + "source": [ + "class SpeechCommandsDataset(Dataset):\n", + " \"\"\"Google Speech Commands dataset.\"\"\"\n", + "\n", + " def __init__(self, root_dir, split):\n", + " \"\"\"\n", + " Args:\n", + " root_dir (string): Directory with all the data files.\n", + " split (string): In [\"train\", \"valid\", \"test\"].\n", + " \"\"\"\n", + " self.root_dir = root_dir\n", + " self.split = split\n", + "\n", + " self.number_of_classes = len(self.get_classes())\n", + "\n", + " self.class_to_file = defaultdict(list)\n", + "\n", + " self.valid_filenames = self.get_valid_filenames()\n", + " self.test_filenames = self.get_test_filenames()\n", + "\n", + " for c in self.get_classes():\n", + " file_name_list = sorted(os.listdir(self.root_dir + \"data_speech_commands_v0.02/\" + c))\n", + " for filename in file_name_list:\n", + " if split == \"train\":\n", + " if (filename not in self.valid_filenames[c]) and (filename not in self.test_filenames[c]):\n", + " self.class_to_file[c].append(filename)\n", + " elif split == \"valid\":\n", + " if filename in self.valid_filenames[c]:\n", + " self.class_to_file[c].append(filename)\n", + " elif split == \"test\":\n", + " if filename in self.test_filenames[c]:\n", + " self.class_to_file[c].append(filename)\n", + " else:\n", + " raise ValueError(\"Invalid split name.\")\n", + "\n", + " self.filepath_list = list()\n", + " self.label_list = list()\n", + " for cc, c in enumerate(self.get_classes()):\n", + " f_extension = sorted(list(self.class_to_file[c]))\n", + " l_extension = [cc for i in f_extension]\n", + " f_extension = [self.root_dir + \"data_speech_commands_v0.02/\" + c + \"/\" + filename for filename in f_extension]\n", + " self.filepath_list.extend(f_extension)\n", + " self.label_list.extend(l_extension)\n", + " self.number_of_samples = len(self.filepath_list)\n", + "\n", + " def __len__(self):\n", + " return self.number_of_samples\n", + "\n", + " def __getitem__(self, idx):\n", + " sample = np.zeros((16000, ), dtype=np.float32)\n", + "\n", + " sample_file = self.filepath_list[idx]\n", + "\n", + " sample_from_file = read(sample_file)[1]\n", + " sample[:sample_from_file.size] = sample_from_file\n", + " sample = sample.reshape((16000, ))\n", + " \n", + " sample = librosa.feature.mfcc(y=sample, sr=16000, hop_length=512, n_fft=2048).transpose().astype(np.float32)\n", + "\n", + " label = self.label_list[idx]\n", + "\n", + " return sample, label\n", + "\n", + " def get_classes(self):\n", + " return ['one', 'two', 'three']\n", + "\n", + " def get_valid_filenames(self):\n", + " class_names = self.get_classes()\n", + "\n", + " class_to_filename = defaultdict(set)\n", + " with open(self.root_dir + \"data_speech_commands_v0.02/validation_list.txt\", \"r\") as fp:\n", + " for line in fp:\n", + " clean_line = line.strip().split(\"/\")\n", + "\n", + " if clean_line[0] in class_names:\n", + " class_to_filename[clean_line[0]].add(clean_line[1])\n", + "\n", + " return class_to_filename\n", + "\n", + " def get_test_filenames(self):\n", + " class_names = self.get_classes()\n", + "\n", + " class_to_filename = defaultdict(set)\n", + " with open(self.root_dir + \"data_speech_commands_v0.02/testing_list.txt\", \"r\") as fp:\n", + " for line in fp:\n", + " clean_line = line.strip().split(\"/\")\n", + "\n", + " if clean_line[0] in class_names:\n", + " class_to_filename[clean_line[0]].add(clean_line[1])\n", + "\n", + " return class_to_filename" + ], + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vx8ptirGKa9u" + }, + "source": [ + "\n", + "train_dataset = SpeechCommandsDataset(dataset_folder,\n", + " \"train\")\n", + "valid_dataset = SpeechCommandsDataset(dataset_folder,\n", + " \"valid\")\n", + "\n", + "test_dataset = SpeechCommandsDataset(dataset_folder,\n", + " \"test\")\n", + "\n", + "batch_size = 100\n", + "\n", + "\n", + "num_epochs = 5\n", + "valid_every_n_steps = 20\n", + "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=True)\n", + "valid_loader = torch.utils.data.DataLoader(dataset=valid_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False)\n", + "\n", + "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n", + " batch_size=batch_size,\n", + " shuffle=False)" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eJwOesOQOSh9" + }, + "source": [ + "### Question 1: Finalise the LSTM and GRU cells by completing the missing code\n", + "\n", + "You are allowed to use nn.Linear." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LQu9Yxfy-Wqj" + }, + "source": [ + "class LSTMCell(nn.Module):\n", + " def __init__(self, input_size, hidden_size, bias=True):\n", + " super(LSTMCell, self).__init__()\n", + " self.input_size = input_size\n", + " self.hidden_size = hidden_size\n", + " self.bias = bias\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 1a) Complete the missing code\n", + " ########################################################################\n", + "\n", + " self.w_c_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_c_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + "\n", + " self.w_i_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_i_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + "\n", + " self.w_f_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_f_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + "\n", + " self.w_o_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_o_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + " \n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + " self.reset_parameters()\n", + "\n", + " def reset_parameters(self):\n", + " std = 1.0 / math.sqrt(self.hidden_size)\n", + " for w in self.parameters():\n", + " w.data.uniform_(-std, std)\n", + "\n", + " def forward(self, input, hx=None):\n", + " if hx is None:\n", + " hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False)\n", + " hx = (hx, hx)\n", + " \n", + " # We used hx to pack both the hidden and cell states\n", + " hx, cx = hx\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 1b) Complete the missing code\n", + " ########################################################################\n", + " c_t = torch.tanh(self.w_c_x(input) + self.w_c_h(hx))\n", + " i_t = torch.sigmoid(self.w_i_x(input) + self.w_i_h(hx))\n", + " f_t = torch.sigmoid(self.w_f_x(input) + self.w_f_h(hx))\n", + " o_t = torch.sigmoid(self.w_o_x(input) + self.w_o_h(hx))\n", + " cy = f_t * cx + i_t * c_t\n", + " hy = o_t * torch.tanh(cy)\n", + " \n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + "\n", + " return (hy, cy)\n", + "\n", + "class BasicRNNCell(nn.Module):\n", + " def __init__(self, input_size, hidden_size, bias=True, nonlinearity=\"tanh\"):\n", + " super(BasicRNNCell, self).__init__()\n", + " self.input_size = input_size\n", + " self.hidden_size = hidden_size\n", + " self.bias = bias\n", + " self.nonlinearity = nonlinearity\n", + " if self.nonlinearity not in [\"tanh\", \"relu\"]:\n", + " raise ValueError(\"Invalid nonlinearity selected for RNN.\")\n", + "\n", + " self.x2h = nn.Linear(input_size, hidden_size, bias=bias)\n", + " self.h2h = nn.Linear(hidden_size, hidden_size, bias=bias)\n", + "\n", + " self.reset_parameters()\n", + " \n", + "\n", + " def reset_parameters(self):\n", + " std = 1.0 / math.sqrt(self.hidden_size)\n", + " for w in self.parameters():\n", + " w.data.uniform_(-std, std)\n", + "\n", + " \n", + " def forward(self, input, hx=None):\n", + " if hx is None:\n", + " hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False)\n", + "\n", + " activation = getattr(nn.functional, self.nonlinearity)\n", + " hy = activation(self.x2h(input) + self.h2h(hx))\n", + "\n", + " return hy\n", + "\n", + " \n", + " \n", + "class GRUCell(nn.Module):\n", + " def __init__(self, input_size, hidden_size, bias=True):\n", + " super(GRUCell, self).__init__()\n", + " self.input_size = input_size\n", + " self.hidden_size = hidden_size\n", + " self.bias = bias\n", + "\n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 1c) Complete the missing code\n", + " ########################################################################\n", + " self.w_z_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_z_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + "\n", + " self.w_r_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_r_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + "\n", + " self.w_n_x = nn.Linear(input_size, hidden_size, bias = bias)\n", + " self.w_n_h = nn.Linear(hidden_size, hidden_size, bias = bias)\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + " self.reset_parameters()\n", + " \n", + "\n", + " def reset_parameters(self):\n", + " std = 1.0 / math.sqrt(self.hidden_size)\n", + " for w in self.parameters():\n", + " w.data.uniform_(-std, std)\n", + "\n", + " def forward(self, input, hx=None):\n", + " if hx is None:\n", + " hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False)\n", + "\n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 1d) Complete the missing code\n", + " ########################################################################\n", + " z_t = torch.sigmoid(self.w_z_x(input) + self.w_z_h(hx))\n", + " r_t = torch.sigmoid(self.w_r_x(input) + self.w_r_h(hx))\n", + " n_t = torch.tanh(self.w_n_x(input) + r_t * self.w_n_h(hx))\n", + " hy = torch.mul((1 - z_t), n_t) + torch.mul(z_t, hx)\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + " \n", + " return hy" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7kEGenNJ6le_" + }, + "source": [ + "### Question 2: Finalise the RNNModel and BidirRecurrentModel" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "F15K5FwA6lfA" + }, + "source": [ + "class RNNModel(nn.Module):\n", + " def __init__(self, mode, input_size, hidden_size, num_layers, bias, output_size):\n", + " super(RNNModel, self).__init__()\n", + " self.mode = mode\n", + " self.input_size = input_size\n", + " self.hidden_size = hidden_size\n", + " self.num_layers = num_layers\n", + " self.bias = bias\n", + " self.output_size = output_size\n", + " \n", + " self.rnn_cell_list = nn.ModuleList()\n", + " \n", + " if mode == 'LSTM':\n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2a) Complete the missing code\n", + " #\n", + " # Append the appropriate LSTM cells to rnn_cell_list\n", + " ########################################################################\n", + " self.rnn_cell_list.append(LSTMCell(self.input_size, self.hidden_size, self.bias))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(LSTMCell(self.hidden_size, self.hidden_size, self.bias))\n", + " \n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ######################################################################## \n", + "\n", + " elif mode == 'GRU':\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2b) Complete the missing code\n", + " #\n", + " # Append the appropriate GRU cells to rnn_cell_list\n", + " ########################################################################\n", + " self.rnn_cell_list.append(GRUCell(self.input_size, self.hidden_size, self.bias))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(GRUCell(self.hidden_size, self.hidden_size, self.bias)) \n", + "\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ######################################################################## \n", + " \n", + " elif mode == 'RNN_TANH':\n", + " \n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2c) Complete the missing code\n", + " #\n", + " # Append the appropriate RNN cells to rnn_cell_list\n", + " ########################################################################\n", + " self.rnn_cell_list.append(BasicRNNCell(self.input_size, self.hidden_size, self.bias, \"tanh\"))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(BasicRNNCell(self.hidden_size, self.hidden_size, self.bias, \"tanh\")) \n", + "\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + "\n", + "\n", + " \n", + " elif mode == 'RNN_RELU':\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2d) Complete the missing code\n", + " #\n", + " # Append the appropriate RNN cells to rnn_cell_list\n", + " ########################################################################\n", + " self.rnn_cell_list.append(BasicRNNCell(self.input_size, self.hidden_size, self.bias, \"relu\"))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(BasicRNNCell(self.hidden_size, self.hidden_size, self.bias, \"relu\")) \n", + "\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + "\n", + "\n", + " else:\n", + " raise ValueError(\"Invalid RNN mode selected.\")\n", + "\n", + "\n", + " self.att_fc = nn.Linear(self.hidden_size, 1)\n", + " self.fc = nn.Linear(self.hidden_size, self.output_size)\n", + "\n", + " \n", + " def forward(self, input, hx=None):\n", + "\n", + " outs = []\n", + " h0 = [None] * self.num_layers if hx is None else list(hx)\n", + " \n", + " # In this forward pass we want to create our RNN from the rnn cells,\n", + " # ..taking the hidden states from the final RNN layer and passing these \n", + " # ..through our fully connected layer (fc).\n", + " \n", + " # The multi-layered RNN should be able to run when the mode is either \n", + " # .. LSTM, GRU, RNN_TANH or RNN_RELU.\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2e) Complete the missing code\n", + " #\n", + " # HINT: You may need a special case for LSTMs\n", + " ########################################################################\n", + "\n", + " for seq in range(input.size(1)):\n", + " x = input[:, seq, :]\n", + " for l, cell in enumerate(self.rnn_cell_list):\n", + " h0[l] = cell(x, h0[l])\n", + " if self.mode == 'LSTM':\n", + " x, _ = h0[l]\n", + " else:\n", + " x = h0[l]\n", + " outs.append(x)\n", + " \n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + "\n", + " out = outs[-1].squeeze()\n", + "\n", + " out = self.fc(out)\n", + " \n", + " \n", + " return out\n", + " \n", + "\n", + "class BidirRecurrentModel(nn.Module):\n", + " def __init__(self, mode, input_size, hidden_size, num_layers, bias, output_size):\n", + " super(BidirRecurrentModel, self).__init__()\n", + " self.mode = mode\n", + " self.input_size = input_size\n", + " self.hidden_size = hidden_size\n", + " self.num_layers = num_layers\n", + " self.bias = bias\n", + " self.output_size = output_size\n", + " \n", + " self.rnn_cell_list = nn.ModuleList()\n", + " self.rnn_cell_list_rev = nn.ModuleList()\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2f) Complete the missing code\n", + " #\n", + " # Create code for the following 'mode' values:\n", + " # 'LSTM', 'GRU', 'RNN_TANH' and 'RNN_RELU'\n", + " ########################################################################\n", + " if mode == 'LSTM':\n", + " self.rnn_cell_list.append(LSTMCell(self.input_size, self.hidden_size, self.bias))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(LSTMCell(self.hidden_size, self.hidden_size, self.bias))\n", + " self.rnn_cell_list_rev.append(LSTMCell(self.input_size, self.hidden_size, self.bias))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list_rev.append(LSTMCell(self.hidden_size, self.hidden_size, self.bias))\n", + " \n", + " elif mode == 'GRU':\n", + " self.rnn_cell_list.append(GRUCell(self.input_size, self.hidden_size, self.bias))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(GRUCell(self.hidden_size, self.hidden_size, self.bias))\n", + " self.rnn_cell_list_rev.append(GRUCell(self.input_size, self.hidden_size, self.bias))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list_rev.append(GRUCell(self.hidden_size, self.hidden_size, self.bias))\n", + "\n", + " elif mode == 'RNN_TANH':\n", + " self.rnn_cell_list.append(BasicRNNCell(self.input_size, self.hidden_size, self.bias, \"tanh\"))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(BasicRNNCell(self.hidden_size, self.hidden_size, self.bias, \"tanh\"))\n", + " self.rnn_cell_list_rev.append(BasicRNNCell(self.input_size, self.hidden_size, self.bias, \"tanh\"))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list_rev.append(BasicRNNCell(self.hidden_size, self.hidden_size, self.bias, \"tanh\"))\n", + "\n", + " elif mode == 'RNN_RELU':\n", + " self.rnn_cell_list.append(BasicRNNCell(self.input_size, self.hidden_size, self.bias, \"relu\"))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list.append(BasicRNNCell(self.hidden_size, self.hidden_size, self.bias, \"relu\"))\n", + " self.rnn_cell_list_rev.append(BasicRNNCell(self.input_size, self.hidden_size, self.bias, \"relu\"))\n", + " for l in range(1, self.num_layers):\n", + " self.rnn_cell_list_rev.append(BasicRNNCell(self.hidden_size, self.hidden_size, self.bias, \"relu\"))\n", + " else:\n", + " raise ValueError(\"Invalid RNN mode selected.\")\n", + "\n", + " # x2 hidden-to-output connections \n", + " self.fc = nn.Linear(self.hidden_size*2, self.output_size) \n", + "\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + " \n", + " \n", + " def forward(self, input, hx=None):\n", + " \n", + " # In this forward pass we want to create our Bidirectional RNN from the rnn cells,\n", + " # .. taking the hidden states from the final RNN layer with their reversed counterparts\n", + " # .. before concatening these and running them through the fully connected layer (fc)\n", + " \n", + " # The multi-layered RNN should be able to run when the mode is either \n", + " # .. LSTM, GRU, RNN_TANH or RNN_RELU.\n", + " \n", + " ########################################################################\n", + " ## START OF YOUR CODE - Question 2g) Complete the missing code\n", + " ########################################################################\n", + " outs = []\n", + " outs_rev = []\n", + " h0 = [None] * self.num_layers if hx is None else list(hx)\n", + " h0_rev = [None] * self.num_layers if hx is None else list(hx)\n", + "\n", + " for seq in range(input.size(1)):\n", + " x = input[:, seq, :]\n", + " x_rev = input[:, -seq-1, :]\n", + " for l, cell in enumerate(self.rnn_cell_list):\n", + " h0[l] = cell(x, h0[l])\n", + " if self.mode == 'LSTM':\n", + " x, _ = h0[l]\n", + " else:\n", + " x = h0[l]\n", + " for l, cell_rev in enumerate(self.rnn_cell_list_rev):\n", + " h0_rev[l] = cell_rev(x_rev, h0_rev[l])\n", + " if self.mode == 'LSTM':\n", + " x_rev, _ = h0_rev[l]\n", + " else:\n", + " x_rev = h0_rev[l]\n", + " outs.append(x) \n", + " outs_rev.append(x_rev)\n", + " ########################################################################\n", + " ## END OF YOUR CODE\n", + " ########################################################################\n", + "\n", + " out = outs[-1].squeeze()\n", + " out_rev = outs_rev[0].squeeze()\n", + " out = torch.cat((out, out_rev), 1)\n", + "\n", + " out = self.fc(out)\n", + " return out" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NSGwF4XR6lfC" + }, + "source": [ + "The code below trains a network based on your code above. This should work without error:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "q5tW2k016lfC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c37dfc6b-a560-476f-b5e0-6ad450a01b80" + }, + "source": [ + "seq_dim, input_dim = train_dataset[0][0].shape\n", + "output_dim = 3\n", + "\n", + "hidden_dim = 128\n", + "layer_dim = 4\n", + "bias = True\n", + "\n", + "### Change the code below to try running different models:\n", + "# model = RNNModel(\"LSTM\", input_dim, hidden_dim, layer_dim, bias, output_dim)\n", + "model = BidirRecurrentModel(\"LSTM\", input_dim, hidden_dim, layer_dim, bias, output_dim)\n", + "\n", + "if torch.cuda.is_available():\n", + " model.cuda()\n", + " \n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "learning_rate = 0.001\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", + "\n", + "loss_list = []\n", + "iter = 0\n", + "max_v_accuracy = 0\n", + "reported_t_accuracy = 0\n", + "max_t_accuracy = 0\n", + "for epoch in range(num_epochs):\n", + " for i, (audio, labels) in enumerate(train_loader):\n", + " if torch.cuda.is_available():\n", + " audio = Variable(audio.view(-1, seq_dim, input_dim).cuda())\n", + " labels = Variable(labels.cuda())\n", + " else:\n", + " audio = Variable(audio.view(-1, seq_dim, input_dim))\n", + " labels = Variable(labels)\n", + "\n", + " optimizer.zero_grad()\n", + "\n", + " outputs = model(audio)\n", + "\n", + " loss = criterion(outputs, labels)\n", + "\n", + " if torch.cuda.is_available():\n", + " loss.cuda()\n", + "\n", + " loss.backward()\n", + "\n", + " optimizer.step()\n", + "\n", + " loss_list.append(loss.item())\n", + " iter += 1\n", + "\n", + " if iter % valid_every_n_steps == 0:\n", + " correct = 0\n", + " total = 0\n", + " for audio, labels in valid_loader:\n", + " if torch.cuda.is_available():\n", + " audio = Variable(audio.view(-1, seq_dim, input_dim).cuda())\n", + " else:\n", + " audio = Variable(audio.view(-1, seq_dim, input_dim))\n", + "\n", + " outputs = model(audio)\n", + "\n", + " _, predicted = torch.max(outputs.data, 1)\n", + "\n", + " total += labels.size(0)\n", + "\n", + " if torch.cuda.is_available():\n", + " correct += (predicted.cpu() == labels.cpu()).sum()\n", + " else:\n", + " correct += (predicted == labels).sum()\n", + "\n", + " v_accuracy = 100 * correct // total\n", + " \n", + " is_best = False\n", + " if v_accuracy >= max_v_accuracy:\n", + " max_v_accuracy = v_accuracy\n", + " is_best = True\n", + "\n", + " if is_best:\n", + " for audio, labels in test_loader:\n", + " if torch.cuda.is_available():\n", + " audio = Variable(audio.view(-1, seq_dim, input_dim).cuda())\n", + " else:\n", + " audio = Variable(audio.view(-1, seq_dim, input_dim))\n", + "\n", + " outputs = model(audio)\n", + "\n", + " _, predicted = torch.max(outputs.data, 1)\n", + "\n", + " total += labels.size(0)\n", + "\n", + " if torch.cuda.is_available():\n", + " correct += (predicted.cpu() == labels.cpu()).sum()\n", + " else:\n", + " correct += (predicted == labels).sum()\n", + "\n", + " t_accuracy = 100 * correct // total\n", + " reported_t_accuracy = t_accuracy\n", + "\n", + " print('Iteration: {}. Loss: {}. V-Accuracy: {} T-Accuracy: {}'.format(iter, loss.item(), v_accuracy, reported_t_accuracy))\n", + "\n" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Iteration: 20. Loss: 0.9298540353775024. V-Accuracy: 58 T-Accuracy: 57\n", + "Iteration: 40. Loss: 0.728527307510376. V-Accuracy: 68 T-Accuracy: 69\n", + "Iteration: 60. Loss: 0.5860320925712585. V-Accuracy: 73 T-Accuracy: 73\n", + "Iteration: 80. Loss: 0.4155048727989197. V-Accuracy: 81 T-Accuracy: 82\n", + "Iteration: 100. Loss: 0.4415952265262604. V-Accuracy: 81 T-Accuracy: 80\n", + "Iteration: 120. Loss: 0.22947010397911072. V-Accuracy: 84 T-Accuracy: 85\n", + "Iteration: 140. Loss: 0.323339581489563. V-Accuracy: 88 T-Accuracy: 88\n", + "Iteration: 160. Loss: 0.38627883791923523. V-Accuracy: 90 T-Accuracy: 91\n", + "Iteration: 180. Loss: 0.1758715659379959. V-Accuracy: 90 T-Accuracy: 91\n", + "Iteration: 200. Loss: 0.31855612993240356. V-Accuracy: 92 T-Accuracy: 92\n", + "Iteration: 220. Loss: 0.24535949528217316. V-Accuracy: 92 T-Accuracy: 93\n", + "Iteration: 240. Loss: 0.23214882612228394. V-Accuracy: 94 T-Accuracy: 93\n", + "Iteration: 260. Loss: 0.3989745080471039. V-Accuracy: 93 T-Accuracy: 93\n", + "Iteration: 280. Loss: 0.11613195389509201. V-Accuracy: 94 T-Accuracy: 94\n", + "Iteration: 300. Loss: 0.14072883129119873. V-Accuracy: 93 T-Accuracy: 94\n", + "Iteration: 320. Loss: 0.12234506011009216. V-Accuracy: 95 T-Accuracy: 95\n", + "Iteration: 340. Loss: 0.1554853469133377. V-Accuracy: 94 T-Accuracy: 95\n", + "Iteration: 360. Loss: 0.12669718265533447. V-Accuracy: 95 T-Accuracy: 95\n", + "Iteration: 380. Loss: 0.1484023630619049. V-Accuracy: 94 T-Accuracy: 95\n", + "Iteration: 400. Loss: 0.15945787727832794. V-Accuracy: 95 T-Accuracy: 95\n", + "Iteration: 420. Loss: 0.036814115941524506. V-Accuracy: 95 T-Accuracy: 96\n", + "Iteration: 440. Loss: 0.07982578128576279. V-Accuracy: 95 T-Accuracy: 96\n", + "Iteration: 460. Loss: 0.05860297754406929. V-Accuracy: 96 T-Accuracy: 96\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0H6oJyOX-W7n" + }, + "source": [ + "## Part 2: Theoretical questions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CK0guD_b-ZAR" + }, + "source": [ + "#### Theory question 1: \n", + "What is the _vanishing gradients problem_ and why does it occur? Which activation functions are more or less impacted by this, and why?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ha2y337Li6b4" + }, + "source": [ + "#### Your answers(see next cell):\n", + "* Your answer here describing vanishing gradients problem\n", + "* Two examples of activation functions more impacted by vanishing gradients\n", + "* Two examples of activation functions less impacted by vanishing gradients, why are they impacted less?" + ] + }, + { + "source": [ + "#### Answers for question 1:\n", + "\n", + "1) As for neural network like:\n", + "\n", + "![](./image/1.jpg)\n", + "\n", + "In each layer, we can get $y_{i}=\\sigma\\left(z_{i}\\right)=\\sigma\\left(w_{i} x_{i}+b_{i}\\right)$ ($\\sigma$ is activation function).\n", + "\n", + "According to Backpropagation, we can get:\n", + "\n", + "$$\\frac{\\delta C}{\\partial b_{1}}=\\frac{\\partial C}{\\partial y_{n}} \\cdot \\prod_{i=2}^{n}\\left(\\sigma^{\\prime}\\left(z_{i}\\right) w_{i}\\right)\\sigma^{\\prime}\\left(z_{1}\\right)$$\n", + "\n", + "So, if $\\sigma^{\\prime} < 1 $ and the initial value of $w < 1$, we will find with the deeper of the network, the derivative can be zero, which is called vanishing gradients problem.\n", + "\n", + "So, **sigmoid and tanh activation functions** can be more impacted by vanishing gradients, for the derivative of former is between 0 and 0.25, the latter is between 0 and 1. However, **Relu, Leaky ReLU and ELU activation functions **can be impacted less, because, when $x>0$ the derivatives are always 1, therefore, the gradient of the deep layers can also be transferred to the shallow layers.\n", + "\n", + "2) As for RNN\n", + "\n", + "![](./image/2.png)\n", + "\n", + "We can get:\n", + "\n", + "$$\\frac{\\partial \\operatorname{Loss}_{t}}{\\delta w_{h}}=\\sum_{k=0}^{t} \\frac{\\delta \\operatorname{Loss}_{t}}{\\delta L_{t}} \\frac{\\delta L_{t}}{\\delta h_{t}}\\left(\\prod_{j=k+1}^{t} \\frac{\\delta h_{j}}{\\delta h_{j-1}}\\right) \\frac{\\delta h_{k}}{\\delta w_{h}}$$\n", + "\n", + "$\\prod_{j=k+1}^{t} \\frac{\\delta h_{j}}{\\delta h_{j-1}}$ causes vanishing gradients problem, the reason is the same as 1).\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xNXeJWpn6lfE" + }, + "source": [ + "#### Theory question 2: \n", + "Why do LSTMs help address the vanishing gradient problem compared to a vanilla RNN?" + ] + }, + { + "source": [ + "#### Answers for question 2:\n", + "\n", + "$$\n", + "\\begin{array}{l}\n", + "i_{t}=\\sigma\\left(W_{i i} x_{t}+b_{i i}+W_{h i} h_{t-1}+b_{h i}\\right) \\\\\n", + "f_{t}=\\sigma\\left(W_{i f} x_{t}+b_{i f}+W_{h f} h_{t-1}+b_{h f}\\right) \\\\\n", + "o_{t}=\\sigma\\left(W_{i o} x_{t}+b_{i o}+W_{h o} h_{t-1}+b_{h o}\\right) \\\\\n", + "g_{t}=\\tanh \\left(W_{i g} x_{t}+b_{i g}+W_{h g} h_{t-1}+b_{h g}\\right) \\\\\n", + "c_{t}=f_{t} \\odot c_{t-1}+i_{t} \\odot g_{t} \\\\\n", + "h_{t}=o_{t} \\odot \\tanh \\left(c_{t}\\right)\n", + "\\end{array}\n", + "$$\n", + "\n", + "We noticed that the activation function of the first three gates is sigmoid, and this means that the output of these three gates are either close to 0 or close to 1. So as for $\\frac{\\delta c_{t}}{\\delta c_{t-1}}=f_{t} + ..., \\quad \\frac{\\delta h_{t}}{\\delta h_{t-1}}=o_{t} +...$, $f_{t}$ and $o_{t}$ are 0 or 1. When the output of gate is 1, the gradient can be well propagated in the LSTM, which greatly reduces the probability of gradient vanishing. When the gate is 0, it means that the information at the previous moment has no effect on the current moment, and it is not necessary to pass the gradient back to update the parameters." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BpefJuwe_c8o" + }, + "source": [ + "#### Theory question 3: \n", + "\n", + "The plot below shows the training curves for three models A, B, and C, trained on the same dataset up to 100 epochs. The three models are a RNN, a LSTM and a GRU, not necessarily in that order.\n", + "\n", + "* Which could plausibly be which? Why? Please explain your reasoning.\n", + "\n", + "(In the cell below please set the values for A_model, B_model and C_model to be 'RNN', 'LSTM' or 'GRU'. This needs to be exact for the automatic marking.)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FBJJMuOUV-jJ", + "scrolled": false, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "outputId": "b4a371f1-a170-4c7f-892f-455540d50793" + }, + "source": [ + "from IPython.display import Image, display\n", + "display(Image(filename='Performance by epoch.png', width=550))" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 550 + } + } + } + ] + }, + { + "source": [ + "# Answers below:\n", + "\n", + "A_model = 'GRU'\n", + "B_model = 'LSTM'\n", + "C_model = 'RNN'\n", + "\n", + "# Give your reasons below:\n", + "# See next cell." + ], + "cell_type": "code", + "metadata": { + "id": "xoULWB4S6lfF" + }, + "execution_count": null, + "outputs": [] + }, + { + "source": [ + "Model C has the worst performance in the three models, so it is RNN, for it is more susceptible to the vanishing gradient problem. \n", + "\n", + "What's more we see that model B takes longer to train to get similar performance as model A, so model B should be LSTM, for LSTMs have more paramteters than GRU." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "markdown", + "metadata": { + "id": "94yhrfZk6lfF" + }, + "source": [ + "#### Theory question 4: \n", + "\n", + "When might you choose to use each of the three different types of models?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NuLS-d3LkOUR" + }, + "source": [ + "#### Your answers:\n", + "* Type of problem when best to use vanilla RNN: We use vanilla RNN when the problem is simple and does not include long sequences of inputs.\n", + "* Type of problem to use GRU: If the task required a quite deep network, such as speech recognition, then a GRU would make sense for its efficiency, which has less paramteters.\n", + "* Type of problem to use LSTM: If we have enough data sets and computation resources, then we can use the LSTM since it contains more parameters and maybe get a better result compared to the GRU.\n" + ] + } + ] +} \ No newline at end of file diff --git a/CW3/feedback.pdf b/CW3/feedback.pdf new file mode 100644 index 0000000..baa9936 Binary files /dev/null and b/CW3/feedback.pdf differ diff --git a/CW3/image/1.jpg b/CW3/image/1.jpg new file mode 100644 index 0000000..434f4bb Binary files /dev/null and b/CW3/image/1.jpg differ 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