ACGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ACGAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5015aPL2Qxov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import argparse\n",
        "import datetime\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import HTML\n",
        "from IPython.display import clear_output\n",
        "from tqdm import tqdm\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.parallel\n",
        "import torch.nn.functional as F\n",
        "import torch.backends.cudnn as cudnn\n",
        "import torch.optim as optim\n",
        "import torch.utils.data\n",
        "import torchvision.datasets as datasets\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.utils as vutils\n",
        "from torchvision.utils import save_image\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import SubsetRandomSampler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvw3Zf9ZQ4Hp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# class netG(nn.Module):\n",
        "\n",
        "#     def __init__(self, nz, ngf, nc):\n",
        "\n",
        "#         super(netG, self).__init__()\n",
        "#         self.ReLU = nn.ReLU(True)\n",
        "#         self.Tanh = nn.Tanh()\n",
        "#         self.DropOut = nn.Dropout(p=0.75)\n",
        "#         #self.conv0 = nn.ConvTranspose2d(nz, ngf * 16, 4, 1, 1, bias=False)\n",
        "#         #self.BatchNorm0 = nn.BatchNorm2d(ngf * 16)\n",
        "#         self.conv1 = nn.ConvTranspose2d(nz, ngf * 8, 5, 1, 0, bias=False)\n",
        "#         self.BatchNorm1 = nn.BatchNorm2d(ngf * 8)\n",
        "\n",
        "#         self.conv2 = nn.ConvTranspose2d(ngf * 8, ngf * 4, 5, 2, 2, bias=False)\n",
        "#         self.BatchNorm2 = nn.BatchNorm2d(ngf * 4)\n",
        "\n",
        "#         self.conv3 = nn.ConvTranspose2d(ngf * 4, ngf * 2, 5, 2, 2, bias=False)\n",
        "#         self.BatchNorm3 = nn.BatchNorm2d(ngf * 2)\n",
        "\n",
        "#         self.conv4 = nn.ConvTranspose2d(ngf * 2, ngf * 1, 4, 2, 2, bias=False)\n",
        "#         self.BatchNorm4 = nn.BatchNorm2d(ngf * 1)\n",
        "\n",
        "#         self.conv5 = nn.ConvTranspose2d(ngf * 1, nc, 4, 2, 1, bias=False)\n",
        "\n",
        "#         self.apply(weights_init)\n",
        "\n",
        "\n",
        "#     def forward(self, input):\n",
        "#         #x = self.conv0(input)\n",
        "#         #x = self.BatchNorm0(x)\n",
        "#         #x = self.ReLU(x)\n",
        "#         x = self.conv1(input)\n",
        "#         x = self.BatchNorm1(x)\n",
        "#         x = self.ReLU(x)\n",
        "#         #x = self.DropOut(x)\n",
        "\n",
        "#         x = self.conv2(x)\n",
        "#         x = self.BatchNorm2(x)\n",
        "#         x = self.ReLU(x)\n",
        "#         x = self.DropOut(x)\n",
        "\n",
        "#         x = self.conv3(x)\n",
        "#         x = self.BatchNorm3(x)\n",
        "#         x = self.ReLU(x)\n",
        "#         #x = self.DropOut(x)\n",
        "\n",
        "#         x = self.conv4(x)\n",
        "#         x = self.BatchNorm4(x)\n",
        "#         x = self.ReLU(x)\n",
        "#         #x = self.DropOut(x)\n",
        "\n",
        "#         x = self.conv5(x)\n",
        "#         output = self.Tanh(x)\n",
        "#         return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccNnOS7k_N14",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class netG(nn.Module):\n",
        "\n",
        "    def __init__(self, nz, ngf, nc):\n",
        "\n",
        "        super(netG, self).__init__()\n",
        "        self.ReLU = nn.ReLU(True)\n",
        "        self.Tanh = nn.Tanh()\n",
        "        #self.DropOut = nn.Dropout(p=0.75)\n",
        "        #self.conv0 = nn.ConvTranspose2d(nz, ngf * 16, 4, 1, 1, bias=False)\n",
        "        #self.BatchNorm0 = nn.BatchNorm2d(ngf * 16)\n",
        "        self.conv1 = nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False)\n",
        "        self.BatchNorm1 = nn.BatchNorm2d(ngf * 8)\n",
        "\n",
        "        self.conv2 = nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False)\n",
        "        self.BatchNorm2 = nn.BatchNorm2d(ngf * 4)\n",
        "\n",
        "        self.conv3 = nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False)\n",
        "        self.BatchNorm3 = nn.BatchNorm2d(ngf * 2)\n",
        "\n",
        "        self.conv4 = nn.ConvTranspose2d(ngf * 2, ngf * 1, 4, 2, 1, bias=False)\n",
        "        self.BatchNorm4 = nn.BatchNorm2d(ngf * 1)\n",
        "\n",
        "        self.conv5 = nn.ConvTranspose2d(ngf * 1, nc, 4, 2, 1, bias=False)\n",
        "\n",
        "        self.apply(weights_init)\n",
        "\n",
        "\n",
        "    def forward(self, input):\n",
        "        #x = self.conv0(input)\n",
        "        #x = self.BatchNorm0(x)\n",
        "        #x = self.ReLU(x)\n",
        "        x = self.conv1(input)\n",
        "        x = self.BatchNorm1(x)\n",
        "        x = self.ReLU(x)\n",
        "        #x = self.DropOut(x)\n",
        "\n",
        "        x = self.conv2(x)\n",
        "        x = self.BatchNorm2(x)\n",
        "        x = self.ReLU(x)\n",
        "        #x = self.DropOut(x)\n",
        "\n",
        "        x = self.conv3(x)\n",
        "        x = self.BatchNorm3(x)\n",
        "        x = self.ReLU(x)\n",
        "        #x = self.DropOut(x)\n",
        "\n",
        "        x = self.conv4(x)\n",
        "        x = self.BatchNorm4(x)\n",
        "        x = self.ReLU(x)\n",
        "        #x = self.DropOut(x)\n",
        "\n",
        "        x = self.conv5(x)\n",
        "        output = self.Tanh(x)\n",
        "        return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulpxKFDPREMQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class netD(nn.Module):\n",
        "\n",
        "    def __init__(self, ndf, nc, nb_label):\n",
        "\n",
        "        super(netD, self).__init__()\n",
        "        self.LeakyReLU = nn.LeakyReLU(0.2, inplace=True)\n",
        "        self.DropOut1 = nn.Dropout(p=0.5)\n",
        "        self.DropOut2 = nn.Dropout(p=0.25)\n",
        "        self.conv1 = nn.Conv2d(nc, ndf, 4, 2, 1, bias=False)\n",
        "        self.conv2 = nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False)\n",
        "        self.BatchNorm2 = nn.BatchNorm2d(ndf * 2)\n",
        "        self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False)\n",
        "        self.BatchNorm3 = nn.BatchNorm2d(ndf * 4)\n",
        "        self.conv4 = nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False)\n",
        "        self.BatchNorm4 = nn.BatchNorm2d(ndf * 8)\n",
        "        self.conv5 = nn.Conv2d(ndf * 8, ndf * 1, 4, 1, 0, bias=False)\n",
        "        self.disc_linear = nn.Linear(ndf * 1, 1)\n",
        "        self.aux_linear = nn.Linear(ndf * 1, nb_label)\n",
        "        self.softmax = nn.Softmax()\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "        self.ndf = ndf\n",
        "        self.apply(weights_init)\n",
        "\n",
        "    def forward(self, input):\n",
        "\n",
        "        x = self.conv1(input)\n",
        "        x = self.LeakyReLU(x)\n",
        "        x = self.DropOut1(x)\n",
        "\n",
        "        x = self.conv2(x)\n",
        "        x = self.BatchNorm2(x)\n",
        "        x = self.LeakyReLU(x)\n",
        "        #x = self.DropOut(x)\n",
        "\n",
        "        x = self.conv3(x)\n",
        "        x = self.BatchNorm3(x)\n",
        "        x = self.LeakyReLU(x)\n",
        "        x = self.DropOut1(x)\n",
        "\n",
        "        x = self.conv4(x)\n",
        "        x = self.BatchNorm4(x)\n",
        "        x = self.LeakyReLU(x)\n",
        "        x = self.DropOut2(x)\n",
        "\n",
        "        x = self.conv5(x)\n",
        "        x = x.view(-1, self.ndf * 1)\n",
        "        c = self.aux_linear(x)\n",
        "        c = self.softmax(c)\n",
        "        s = self.disc_linear(x)\n",
        "        s = self.sigmoid(s)\n",
        "        return s,c"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6d4xup5RMan",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZ7QF4YbN1Pl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "028d30be-44ee-4e10-c919-d43d7efb8fe2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive') \n",
        "%cd /content/drive/My\\ Drive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MdEuFt0nkmc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4bc8adff-34c5-4d09-f24f-fe4b6b8ed6b9"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "#data_dir = '4/3AHDbc50wauQLH_Uxb1lOAm9fH5_nZ8o_gvOQGzGEebKG_8SK7yvan4div2k' #Just to check\n",
        "data_dir = 'dataset_3_classes'\n",
        "batch_size = 32\n",
        "# Number of training epochs\n",
        "num_epochs = 100\n",
        "\n",
        "#All images will be resized to this size using a transformer.\n",
        "#image_size = 64\n",
        "imageSize = 64\n",
        "\n",
        "# Number of channels in the training images. For color images this is 3\n",
        "nc = 1\n",
        "\n",
        "# Size of z latent vector (i.e. size of generator input)\n",
        "nz = 100\n",
        "\n",
        "# Size of feature maps in generator\n",
        "ngf = 64\n",
        "\n",
        "# Size of feature maps in discriminator\n",
        "ndf = 64\n",
        "\n",
        "# No of labels\n",
        "nb_label = 3\n",
        "\n",
        "# Learning rate for optimizers\n",
        "lr = 0.002\n",
        "lr_d = 0.0002\n",
        "# Beta1 hyperparam for Adam optimizers\n",
        "beta1 = 0.5\n",
        "# Beta2 hyperparam for Adam optimizers\n",
        "beta2 = 0.999\n",
        "\n",
        "real_label = 1.\n",
        "fake_label = 0.\n",
        "# Input to generator\n",
        "fixed_noise = torch.randn(64, nz, 1, 1, device=device) #batch of 64\n",
        "# Define Loss function\n",
        "s_criterion = nn.BCELoss().to(device) #For synthesizing\n",
        "c_criterion = nn.NLLLoss().to(device) #For classification\n",
        "\n",
        "input = torch.FloatTensor(batch_size, 3, imageSize, imageSize).to(device)\n",
        "noise = torch.FloatTensor(batch_size, nz, 1, 1).to(device)\n",
        "fixed_noise = torch.FloatTensor(batch_size, nz, 1, 1).normal_(0, 1).to(device)\n",
        "s_label = torch.FloatTensor(batch_size).to(device)\n",
        "c_label = torch.LongTensor(batch_size).to(device)\n",
        "\n",
        "input = Variable(input)\n",
        "s_label = Variable(s_label)\n",
        "c_label = Variable(c_label)\n",
        "noise = Variable(noise)\n",
        "fixed_noise = Variable(fixed_noise)\n",
        "fixed_noise_ = np.random.normal(0, 1, (batch_size, nz))\n",
        "random_label = np.random.randint(0, nb_label, batch_size)\n",
        "#print('fixed label:{}'.format(random_label))\n",
        "random_onehot = np.zeros((batch_size, nb_label))\n",
        "random_onehot[np.arange(batch_size), random_label] = 1\n",
        "fixed_noise_[np.arange(batch_size), :nb_label] = random_onehot[np.arange(batch_size)]\n",
        "\n",
        "\n",
        "fixed_noise_ = (torch.from_numpy(fixed_noise_))\n",
        "fixed_noise_ = fixed_noise_.resize_(batch_size, nz, 1, 1)\n",
        "fixed_noise.data.copy_(fixed_noise_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[ 0.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         [[ 1.0000]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-1.5206]],\n",
              "\n",
              "         [[ 1.9563]],\n",
              "\n",
              "         [[ 0.1167]]],\n",
              "\n",
              "\n",
              "        [[[ 1.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.3869]],\n",
              "\n",
              "         [[ 0.1829]],\n",
              "\n",
              "         [[-1.6462]]],\n",
              "\n",
              "\n",
              "        [[[ 0.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         [[ 1.0000]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.7970]],\n",
              "\n",
              "         [[ 1.6955]],\n",
              "\n",
              "         [[-0.0406]]],\n",
              "\n",
              "\n",
              "        ...,\n",
              "\n",
              "\n",
              "        [[[ 1.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.2673]],\n",
              "\n",
              "         [[-1.0765]],\n",
              "\n",
              "         [[ 0.6081]]],\n",
              "\n",
              "\n",
              "        [[[ 1.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.6802]],\n",
              "\n",
              "         [[ 0.0801]],\n",
              "\n",
              "         [[ 0.7133]]],\n",
              "\n",
              "\n",
              "        [[[ 0.0000]],\n",
              "\n",
              "         [[ 1.0000]],\n",
              "\n",
              "         [[ 0.0000]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 0.6454]],\n",
              "\n",
              "         [[-0.4559]],\n",
              "\n",
              "         [[ 0.8818]]]], device='cuda:0')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWeXmdInu38z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "2fbe11e9-2d95-49cf-a3c0-b2dffc11e675"
      },
      "source": [
        "for func in [\n",
        "    lambda: os.mkdir(os.path.join('.', 'augGAN')),\n",
        "    lambda: os.mkdir(os.path.join('.', 'augGAN/model/ACGAN')),\n",
        "    lambda: os.mkdir(os.path.join('.', 'augGAN/plots/ACGAN')),\n",
        "    lambda: os.mkdir(os.path.join('.', 'augGAN/output_images/ACGAN'))]:  # create directories\n",
        "  try:\n",
        "    func()\n",
        "  except Exception as error:\n",
        "    print(error)\n",
        "    continue\n",
        "\n",
        "METRIC_FIELDS = [\n",
        "    'train.D_x',\n",
        "    'train.D_G_z1',\n",
        "    'train.D_G_z2',\n",
        "    'train.G_losses',\n",
        "    'train.D_losses',\n",
        "]\n",
        "metrics = {field: list() for field in METRIC_FIELDS}\n",
        "\n",
        "if nc==1:\n",
        "    mu = (0.5)\n",
        "    sigma = (0.5)\n",
        "    transform = transforms.Compose([#transforms.RandomHorizontalFlip(),\n",
        "                                    transforms.Grayscale(num_output_channels=1),\n",
        "                                    transforms.Resize((64,64)),\n",
        "                                    #transforms.Scale(imageSize),\n",
        "                                    transforms.ToTensor(),\n",
        "                                    transforms.Normalize(mu, sigma)])\n",
        "elif nc==3:\n",
        "    mu = (0.5,0.5,0.5)\n",
        "    sigma = (0.5,0.5,0.5)\n",
        "    #Originally authors used just scaling\n",
        "    transform = transforms.Compose([#transforms.RandomHorizontalFlip(),\n",
        "                                    transforms.Resize((64,64)),\n",
        "                                    #transforms.Scale(imageSize),\n",
        "                                    transforms.ToTensor(),\n",
        "                                    transforms.Normalize(mu, sigma)])\n",
        "else:\n",
        "    print(\"Tranformation not defined for this option\")\n",
        "train_set = datasets.ImageFolder(os.path.join(data_dir, \"train/\"), transform=transform)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size,\n",
        "                                          shuffle=True)\n",
        "\n",
        "generator = netG(nz, ngf, nc).to(device)\n",
        "discriminator = netD(ndf, nc, nb_label).to(device)\n",
        "\n",
        "# setup optimizer\n",
        "optimizerD = optim.Adam(discriminator.parameters(), lr=lr_d, betas=(beta1, beta2))\n",
        "optimizerG = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, beta2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 17] File exists: './augGAN'\n",
            "[Errno 17] File exists: './augGAN/model/ACGAN'\n",
            "[Errno 17] File exists: './augGAN/plots/ACGAN'\n",
            "[Errno 17] File exists: './augGAN/output_images/ACGAN'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuiDzc7NMDdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7e13e54a-371a-4d3e-83ea-7805bda38da5"
      },
      "source": [
        "print(generator)\n",
        "print(discriminator)\n",
        "\n",
        "from torchsummary import summary\n",
        "\n",
        "summary(generator, (100, 1, 1))\n",
        "summary(discriminator, (1, 64, 64))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "netG(\n",
            "  (ReLU): ReLU(inplace=True)\n",
            "  (Tanh): Tanh()\n",
            "  (conv1): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
            "  (BatchNorm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv2): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (BatchNorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv3): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (BatchNorm3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv4): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (BatchNorm4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv5): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            ")\n",
            "netD(\n",
            "  (LeakyReLU): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "  (DropOut1): Dropout(p=0.5, inplace=False)\n",
            "  (DropOut2): Dropout(p=0.25, inplace=False)\n",
            "  (conv1): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (conv2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (BatchNorm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv3): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (BatchNorm3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv4): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "  (BatchNorm4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (conv5): Conv2d(512, 64, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
            "  (disc_linear): Linear(in_features=64, out_features=1, bias=True)\n",
            "  (aux_linear): Linear(in_features=64, out_features=3, bias=True)\n",
            "  (softmax): Softmax(dim=None)\n",
            "  (sigmoid): Sigmoid()\n",
            ")\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "   ConvTranspose2d-1            [-1, 512, 4, 4]         819,200\n",
            "       BatchNorm2d-2            [-1, 512, 4, 4]           1,024\n",
            "              ReLU-3            [-1, 512, 4, 4]               0\n",
            "   ConvTranspose2d-4            [-1, 256, 8, 8]       2,097,152\n",
            "       BatchNorm2d-5            [-1, 256, 8, 8]             512\n",
            "              ReLU-6            [-1, 256, 8, 8]               0\n",
            "   ConvTranspose2d-7          [-1, 128, 16, 16]         524,288\n",
            "       BatchNorm2d-8          [-1, 128, 16, 16]             256\n",
            "              ReLU-9          [-1, 128, 16, 16]               0\n",
            "  ConvTranspose2d-10           [-1, 64, 32, 32]         131,072\n",
            "      BatchNorm2d-11           [-1, 64, 32, 32]             128\n",
            "             ReLU-12           [-1, 64, 32, 32]               0\n",
            "  ConvTranspose2d-13            [-1, 1, 64, 64]           1,024\n",
            "             Tanh-14            [-1, 1, 64, 64]               0\n",
            "================================================================\n",
            "Total params: 3,574,656\n",
            "Trainable params: 3,574,656\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 2.88\n",
            "Params size (MB): 13.64\n",
            "Estimated Total Size (MB): 16.51\n",
            "----------------------------------------------------------------\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1           [-1, 64, 32, 32]           1,024\n",
            "         LeakyReLU-2           [-1, 64, 32, 32]               0\n",
            "           Dropout-3           [-1, 64, 32, 32]               0\n",
            "            Conv2d-4          [-1, 128, 16, 16]         131,072\n",
            "       BatchNorm2d-5          [-1, 128, 16, 16]             256\n",
            "         LeakyReLU-6          [-1, 128, 16, 16]               0\n",
            "            Conv2d-7            [-1, 256, 8, 8]         524,288\n",
            "       BatchNorm2d-8            [-1, 256, 8, 8]             512\n",
            "         LeakyReLU-9            [-1, 256, 8, 8]               0\n",
            "          Dropout-10            [-1, 256, 8, 8]               0\n",
            "           Conv2d-11            [-1, 512, 4, 4]       2,097,152\n",
            "      BatchNorm2d-12            [-1, 512, 4, 4]           1,024\n",
            "        LeakyReLU-13            [-1, 512, 4, 4]               0\n",
            "          Dropout-14            [-1, 512, 4, 4]               0\n",
            "           Conv2d-15             [-1, 64, 1, 1]         524,288\n",
            "           Linear-16                    [-1, 3]             195\n",
            "          Softmax-17                    [-1, 3]               0\n",
            "           Linear-18                    [-1, 1]              65\n",
            "          Sigmoid-19                    [-1, 1]               0\n",
            "================================================================\n",
            "Total params: 3,279,876\n",
            "Trainable params: 3,279,876\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.02\n",
            "Forward/backward pass size (MB): 3.00\n",
            "Params size (MB): 12.51\n",
            "Estimated Total Size (MB): 15.53\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KD04uDom4GyB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test(predict, labels):\n",
        "    correct = 0\n",
        "    pred = predict.data.max(1)[1]\n",
        "    correct = pred.eq(labels.data).cpu().sum()\n",
        "    return correct, len(labels.data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KYKwlMA4Ro5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eb2903b7-0ce1-4dae-fd95-c49be60a4b80"
      },
      "source": [
        "for epoch in range(num_epochs):\n",
        "    for i, data in enumerate(tqdm(train_loader, 0)):\n",
        "        ###########################\n",
        "        # (1) Update D network\n",
        "        ###########################\n",
        "        # train with real\n",
        "        discriminator.zero_grad()\n",
        "        img, label = data\n",
        "        batch_size = img.size(0)\n",
        "        with torch.no_grad():\n",
        "            input.resize_(img.size()).copy_(img)\n",
        "            s_label.resize_(batch_size).fill_(real_label)\n",
        "            c_label.resize_(batch_size).copy_(label)\n",
        "        s_output, c_output = discriminator(input)\n",
        "        s_errD_real = s_criterion(s_output, s_label)\n",
        "        c_errD_real = c_criterion(c_output, c_label)\n",
        "        errD_real = s_errD_real + c_errD_real\n",
        "        errD_real.backward()\n",
        "        D_x = s_output.data.mean()\n",
        "        \n",
        "        correct, length = test(c_output, c_label)\n",
        "\n",
        "        # train with fake\n",
        "        with torch.no_grad():\n",
        "            noise.resize_(batch_size, nz, 1, 1)\n",
        "            noise.normal_(0, 1)\n",
        "\n",
        "        label = np.random.randint(0, nb_label, batch_size)\n",
        "        noise_ = np.random.normal(0, 1, (batch_size, nz))\n",
        "        label_onehot = np.zeros((batch_size, nb_label))\n",
        "        label_onehot[np.arange(batch_size), label] = 1\n",
        "        noise_[np.arange(batch_size), :nb_label] = label_onehot[np.arange(batch_size)]\n",
        "        \n",
        "        noise_ = (torch.from_numpy(noise_))\n",
        "        noise_ = noise_.resize_(batch_size, nz, 1, 1)\n",
        "        noise.data.copy_(noise_)\n",
        "\n",
        "        c_label.data.resize_(batch_size).copy_(torch.from_numpy(label))\n",
        "\n",
        "        fake = generator(noise)\n",
        "        s_label.data.fill_(fake_label)\n",
        "        s_output,c_output = discriminator(fake.detach())\n",
        "        s_errD_fake = s_criterion(s_output, s_label)\n",
        "        c_errD_fake = c_criterion(c_output, c_label)\n",
        "        errD_fake = s_errD_fake + c_errD_fake\n",
        "\n",
        "        errD_fake.backward()\n",
        "        D_G_z1 = s_output.data.mean()\n",
        "        errD = s_errD_real + s_errD_fake\n",
        "        optimizerD.step()\n",
        "\n",
        "        ###########################\n",
        "        # (2) Update G network\n",
        "        ###########################\n",
        "        generator.zero_grad()\n",
        "        s_label.data.fill_(real_label)  # fake labels are real for generator cost\n",
        "        s_output,c_output = discriminator(fake)\n",
        "        s_errG = s_criterion(s_output, s_label)\n",
        "        c_errG = c_criterion(c_output, c_label)\n",
        "        \n",
        "        errG = s_errG + c_errG\n",
        "        errG.backward()\n",
        "        D_G_z2 = s_output.data.mean()\n",
        "        optimizerG.step()\n",
        "        metrics['train.G_losses'].append(errG.item())\n",
        "        metrics['train.D_losses'].append(errD.item())\n",
        "\n",
        "        print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f, Accuracy: %.4f / %.4f = %.4f'\n",
        "              % (epoch, num_epochs, i, len(train_loader),\n",
        "                 errD.data, errG.data, D_x, D_G_z1, D_G_z2,\n",
        "                 correct, length, 100.* correct / length))\n",
        "        if i % 100 == 0:\n",
        "            vutils.save_image(img,\n",
        "                    '%s/real_samples.png' % './augGAN/output_images/ACGAN', normalize=True)\n",
        "            #fake = netG(fixed_cat)\n",
        "            fake = generator(fixed_noise)\n",
        "            vutils.save_image(fake.data,\n",
        "                    '%s/fake_samples_epoch_%03d.png' % ('./augGAN/output_images/ACGAN', epoch), normalize=True)\n",
        "\n",
        "    # do checkpointing\n",
        "    #torch.save(generator.state_dict(), '%s/netG_epoch_%d.pth' % (os.path.join('.', 'augGAN/model/ACGAN'), epoch))\n",
        "    #torch.save(discriminator.state_dict(), '%s/netD_epoch_%d.pth' % (os.path.join('.', 'augGAN/model/ACGAN'), epoch))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/8 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([32])) that is different to the input size (torch.Size([32, 1])) is deprecated. Please ensure they have the same size.\n",
            "  return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][0/8] Loss_D: 1.5366 Loss_G: 0.5480 D(x): 0.4445 D(G(z)): 0.4285 / 0.4463, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:36<01:51, 18.54s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][1/8] Loss_D: 1.2114 Loss_G: 1.8489 D(x): 0.8523 D(G(z)): 0.6131 / 0.1376, Accuracy: 7.0000 / 32.0000 = 21.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:54<01:31, 18.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][2/8] Loss_D: 0.6635 Loss_G: 2.5500 D(x): 0.8345 D(G(z)): 0.3501 / 0.0722, Accuracy: 9.0000 / 32.0000 = 28.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [01:10<01:11, 17.81s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][3/8] Loss_D: 0.7254 Loss_G: 2.2284 D(x): 0.6931 D(G(z)): 0.2380 / 0.1007, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [01:28<00:53, 17.75s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][4/8] Loss_D: 0.8938 Loss_G: 2.4968 D(x): 0.7710 D(G(z)): 0.3719 / 0.0743, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [01:46<00:35, 17.68s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][5/8] Loss_D: 0.6395 Loss_G: 3.2994 D(x): 0.8588 D(G(z)): 0.3418 / 0.0386, Accuracy: 7.0000 / 32.0000 = 21.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [02:07<00:18, 18.91s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][6/8] Loss_D: 0.7416 Loss_G: 3.1950 D(x): 0.7587 D(G(z)): 0.2450 / 0.0383, Accuracy: 8.0000 / 32.0000 = 25.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([23])) that is different to the input size (torch.Size([23, 1])) is deprecated. Please ensure they have the same size.\n",
            "  return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)\n",
            "100%|██████████| 8/8 [02:21<00:00, 17.70s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0/100][7/8] Loss_D: 1.0464 Loss_G: 2.8487 D(x): 0.7016 D(G(z)): 0.4122 / 0.0610, Accuracy: 8.0000 / 23.0000 = 34.7826\n",
            "[1/100][0/8] Loss_D: 0.6512 Loss_G: 3.3506 D(x): 0.7796 D(G(z)): 0.2662 / 0.0409, Accuracy: 10.0000 / 32.0000 = 31.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:09,  1.66s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][1/8] Loss_D: 0.7474 Loss_G: 2.5412 D(x): 0.6867 D(G(z)): 0.1912 / 0.1078, Accuracy: 5.0000 / 32.0000 = 15.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][2/8] Loss_D: 1.5656 Loss_G: 4.0186 D(x): 0.7518 D(G(z)): 0.6019 / 0.0316, Accuracy: 13.0000 / 32.0000 = 40.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][3/8] Loss_D: 1.9877 Loss_G: 2.9263 D(x): 0.3422 D(G(z)): 0.3556 / 0.1146, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][4/8] Loss_D: 2.4933 Loss_G: 2.4060 D(x): 0.5042 D(G(z)): 0.6039 / 0.1177, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][5/8] Loss_D: 2.4712 Loss_G: 1.6016 D(x): 0.3029 D(G(z)): 0.4390 / 0.1871, Accuracy: 13.0000 / 32.0000 = 40.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][6/8] Loss_D: 1.6455 Loss_G: 1.4310 D(x): 0.4925 D(G(z)): 0.4227 / 0.2394, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.13s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1/100][7/8] Loss_D: 1.3819 Loss_G: 2.5260 D(x): 0.6872 D(G(z)): 0.5199 / 0.0909, Accuracy: 5.0000 / 23.0000 = 21.7391\n",
            "[2/100][0/8] Loss_D: 1.5268 Loss_G: 1.6094 D(x): 0.4756 D(G(z)): 0.3320 / 0.1765, Accuracy: 10.0000 / 32.0000 = 31.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:11,  1.84s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][1/8] Loss_D: 1.6167 Loss_G: 1.7250 D(x): 0.5551 D(G(z)): 0.4677 / 0.1560, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.54s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][2/8] Loss_D: 1.5708 Loss_G: 2.0736 D(x): 0.5563 D(G(z)): 0.4554 / 0.1206, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.39s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][3/8] Loss_D: 1.5663 Loss_G: 2.7578 D(x): 0.6007 D(G(z)): 0.5146 / 0.0731, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][4/8] Loss_D: 1.7811 Loss_G: 1.4719 D(x): 0.4769 D(G(z)): 0.4346 / 0.2205, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][5/8] Loss_D: 2.4151 Loss_G: 3.3080 D(x): 0.5217 D(G(z)): 0.7207 / 0.0431, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][6/8] Loss_D: 2.4459 Loss_G: 0.7890 D(x): 0.2005 D(G(z)): 0.3110 / 0.3740, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[2/100][7/8] Loss_D: 2.0180 Loss_G: 1.4142 D(x): 0.5913 D(G(z)): 0.6760 / 0.2412, Accuracy: 8.0000 / 23.0000 = 34.7826\n",
            "[3/100][0/8] Loss_D: 1.8285 Loss_G: 2.9230 D(x): 0.5389 D(G(z)): 0.5739 / 0.0485, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.64s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][1/8] Loss_D: 1.5001 Loss_G: 0.9874 D(x): 0.4060 D(G(z)): 0.3113 / 0.3114, Accuracy: 13.0000 / 32.0000 = 40.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][2/8] Loss_D: 2.3200 Loss_G: 2.5973 D(x): 0.6705 D(G(z)): 0.7942 / 0.0742, Accuracy: 9.0000 / 32.0000 = 28.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][3/8] Loss_D: 2.4898 Loss_G: 1.4443 D(x): 0.2969 D(G(z)): 0.5242 / 0.1964, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:04,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][4/8] Loss_D: 2.2403 Loss_G: 1.0649 D(x): 0.3408 D(G(z)): 0.5269 / 0.2904, Accuracy: 8.0000 / 32.0000 = 25.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][5/8] Loss_D: 2.3946 Loss_G: 1.8723 D(x): 0.5036 D(G(z)): 0.7403 / 0.1323, Accuracy: 9.0000 / 32.0000 = 28.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][6/8] Loss_D: 1.8679 Loss_G: 1.8895 D(x): 0.4643 D(G(z)): 0.5452 / 0.1505, Accuracy: 7.0000 / 32.0000 = 21.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[3/100][7/8] Loss_D: 2.4840 Loss_G: 1.0965 D(x): 0.3305 D(G(z)): 0.5750 / 0.2792, Accuracy: 10.0000 / 23.0000 = 43.4783\n",
            "[4/100][0/8] Loss_D: 2.3302 Loss_G: 1.6330 D(x): 0.3781 D(G(z)): 0.6290 / 0.1697, Accuracy: 9.0000 / 32.0000 = 28.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.59s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][1/8] Loss_D: 2.6442 Loss_G: 1.7034 D(x): 0.3349 D(G(z)): 0.6825 / 0.1649, Accuracy: 10.0000 / 32.0000 = 31.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][2/8] Loss_D: 2.4277 Loss_G: 1.9489 D(x): 0.3397 D(G(z)): 0.6342 / 0.1251, Accuracy: 14.0000 / 32.0000 = 43.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][3/8] Loss_D: 2.3632 Loss_G: 0.5615 D(x): 0.2350 D(G(z)): 0.4444 / 0.4546, Accuracy: 8.0000 / 32.0000 = 25.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][4/8] Loss_D: 2.6342 Loss_G: 2.2979 D(x): 0.5441 D(G(z)): 0.8205 / 0.0965, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][5/8] Loss_D: 2.0721 Loss_G: 1.9635 D(x): 0.3444 D(G(z)): 0.5331 / 0.1278, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][6/8] Loss_D: 2.3213 Loss_G: 1.3663 D(x): 0.3132 D(G(z)): 0.5576 / 0.2347, Accuracy: 14.0000 / 32.0000 = 43.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4/100][7/8] Loss_D: 2.6369 Loss_G: 1.4337 D(x): 0.3672 D(G(z)): 0.7127 / 0.2021, Accuracy: 10.0000 / 23.0000 = 43.4783\n",
            "[5/100][0/8] Loss_D: 2.6166 Loss_G: 1.5948 D(x): 0.3112 D(G(z)): 0.6915 / 0.1950, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.55s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][1/8] Loss_D: 2.9915 Loss_G: 1.3442 D(x): 0.2911 D(G(z)): 0.7285 / 0.2196, Accuracy: 14.0000 / 32.0000 = 43.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][2/8] Loss_D: 2.6037 Loss_G: 1.0670 D(x): 0.2358 D(G(z)): 0.5715 / 0.2845, Accuracy: 13.0000 / 32.0000 = 40.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][3/8] Loss_D: 1.7811 Loss_G: 1.3501 D(x): 0.4467 D(G(z)): 0.5710 / 0.2069, Accuracy: 14.0000 / 32.0000 = 43.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][4/8] Loss_D: 1.9358 Loss_G: 2.2468 D(x): 0.5494 D(G(z)): 0.6836 / 0.0911, Accuracy: 8.0000 / 32.0000 = 25.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][5/8] Loss_D: 1.8449 Loss_G: 1.8189 D(x): 0.4332 D(G(z)): 0.5201 / 0.1338, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][6/8] Loss_D: 2.4071 Loss_G: 0.6451 D(x): 0.2553 D(G(z)): 0.5171 / 0.4357, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[5/100][7/8] Loss_D: 2.3676 Loss_G: 1.3157 D(x): 0.5159 D(G(z)): 0.7695 / 0.2257, Accuracy: 12.0000 / 23.0000 = 52.1739\n",
            "[6/100][0/8] Loss_D: 1.9885 Loss_G: 1.5738 D(x): 0.3671 D(G(z)): 0.5293 / 0.1797, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.63s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][1/8] Loss_D: 2.0081 Loss_G: 1.2126 D(x): 0.3163 D(G(z)): 0.4827 / 0.2733, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.47s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][2/8] Loss_D: 2.0610 Loss_G: 1.3632 D(x): 0.4421 D(G(z)): 0.6414 / 0.2008, Accuracy: 12.0000 / 32.0000 = 37.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][3/8] Loss_D: 1.8696 Loss_G: 1.5738 D(x): 0.4467 D(G(z)): 0.5817 / 0.1796, Accuracy: 11.0000 / 32.0000 = 34.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][4/8] Loss_D: 1.6906 Loss_G: 1.4881 D(x): 0.4447 D(G(z)): 0.4764 / 0.1903, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][5/8] Loss_D: 1.8209 Loss_G: 1.5452 D(x): 0.4645 D(G(z)): 0.5878 / 0.1730, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][6/8] Loss_D: 1.8118 Loss_G: 1.0611 D(x): 0.3754 D(G(z)): 0.4633 / 0.2982, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.14s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[6/100][7/8] Loss_D: 2.0897 Loss_G: 1.1618 D(x): 0.5181 D(G(z)): 0.7052 / 0.2367, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[7/100][0/8] Loss_D: 1.9199 Loss_G: 1.5741 D(x): 0.3655 D(G(z)): 0.5102 / 0.1689, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:11,  1.93s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][1/8] Loss_D: 1.5827 Loss_G: 1.7385 D(x): 0.4922 D(G(z)): 0.5220 / 0.1640, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:08,  1.63s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][2/8] Loss_D: 1.7774 Loss_G: 1.9013 D(x): 0.5268 D(G(z)): 0.6009 / 0.1408, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:06,  1.58s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][3/8] Loss_D: 2.1508 Loss_G: 1.7864 D(x): 0.3611 D(G(z)): 0.5753 / 0.1424, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:04,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][4/8] Loss_D: 2.2275 Loss_G: 1.8338 D(x): 0.4332 D(G(z)): 0.6637 / 0.1395, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][5/8] Loss_D: 2.1330 Loss_G: 1.3233 D(x): 0.3198 D(G(z)): 0.5349 / 0.2101, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][6/8] Loss_D: 1.8298 Loss_G: 1.2969 D(x): 0.4480 D(G(z)): 0.5294 / 0.2130, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.16s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[7/100][7/8] Loss_D: 1.6258 Loss_G: 1.8470 D(x): 0.5426 D(G(z)): 0.5713 / 0.1521, Accuracy: 10.0000 / 23.0000 = 43.4783\n",
            "[8/100][0/8] Loss_D: 1.4678 Loss_G: 1.3420 D(x): 0.4830 D(G(z)): 0.4514 / 0.1978, Accuracy: 13.0000 / 32.0000 = 40.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.74s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][1/8] Loss_D: 1.5996 Loss_G: 1.6540 D(x): 0.5546 D(G(z)): 0.5487 / 0.1684, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][2/8] Loss_D: 1.9445 Loss_G: 1.3100 D(x): 0.3838 D(G(z)): 0.5229 / 0.2440, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][3/8] Loss_D: 2.3140 Loss_G: 1.6569 D(x): 0.4589 D(G(z)): 0.7175 / 0.1581, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][4/8] Loss_D: 2.5879 Loss_G: 1.5942 D(x): 0.2629 D(G(z)): 0.6105 / 0.1839, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][5/8] Loss_D: 2.4928 Loss_G: 1.3417 D(x): 0.2992 D(G(z)): 0.6192 / 0.2231, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][6/8] Loss_D: 2.1253 Loss_G: 2.5839 D(x): 0.5970 D(G(z)): 0.7184 / 0.0807, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.14s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[8/100][7/8] Loss_D: 1.2496 Loss_G: 1.9786 D(x): 0.5264 D(G(z)): 0.3359 / 0.1519, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[9/100][0/8] Loss_D: 1.4332 Loss_G: 1.8671 D(x): 0.5881 D(G(z)): 0.5172 / 0.1443, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.72s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][1/8] Loss_D: 2.2438 Loss_G: 1.8544 D(x): 0.5169 D(G(z)): 0.6861 / 0.1648, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.56s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][2/8] Loss_D: 1.7658 Loss_G: 1.5102 D(x): 0.4061 D(G(z)): 0.4477 / 0.2098, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.39s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][3/8] Loss_D: 1.7080 Loss_G: 1.0070 D(x): 0.4051 D(G(z)): 0.4476 / 0.2986, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][4/8] Loss_D: 2.0322 Loss_G: 1.3968 D(x): 0.5089 D(G(z)): 0.6666 / 0.2115, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][5/8] Loss_D: 1.9241 Loss_G: 1.2150 D(x): 0.3396 D(G(z)): 0.4412 / 0.2482, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][6/8] Loss_D: 1.6153 Loss_G: 1.3799 D(x): 0.5292 D(G(z)): 0.5260 / 0.2205, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[9/100][7/8] Loss_D: 1.6511 Loss_G: 1.0003 D(x): 0.4174 D(G(z)): 0.4535 / 0.3095, Accuracy: 14.0000 / 23.0000 = 60.8696\n",
            "[10/100][0/8] Loss_D: 1.6497 Loss_G: 1.2400 D(x): 0.4906 D(G(z)): 0.5145 / 0.2214, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:09,  1.66s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][1/8] Loss_D: 1.8023 Loss_G: 1.2248 D(x): 0.4228 D(G(z)): 0.5316 / 0.2424, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.49s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][2/8] Loss_D: 1.9841 Loss_G: 1.6976 D(x): 0.4206 D(G(z)): 0.6025 / 0.1543, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][3/8] Loss_D: 1.9995 Loss_G: 1.4110 D(x): 0.3560 D(G(z)): 0.5253 / 0.2057, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][4/8] Loss_D: 1.7086 Loss_G: 1.5533 D(x): 0.4557 D(G(z)): 0.5566 / 0.1831, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][5/8] Loss_D: 1.9987 Loss_G: 1.5864 D(x): 0.4326 D(G(z)): 0.6207 / 0.1776, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][6/8] Loss_D: 1.7895 Loss_G: 1.5542 D(x): 0.4538 D(G(z)): 0.5551 / 0.1875, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[10/100][7/8] Loss_D: 1.6580 Loss_G: 1.1487 D(x): 0.4463 D(G(z)): 0.4903 / 0.2853, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[11/100][0/8] Loss_D: 1.3924 Loss_G: 1.7898 D(x): 0.5749 D(G(z)): 0.5063 / 0.1540, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.68s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][1/8] Loss_D: 1.3730 Loss_G: 1.4480 D(x): 0.4961 D(G(z)): 0.3961 / 0.1960, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][2/8] Loss_D: 1.3772 Loss_G: 1.7639 D(x): 0.6354 D(G(z)): 0.5427 / 0.1563, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][3/8] Loss_D: 1.4676 Loss_G: 0.9522 D(x): 0.4895 D(G(z)): 0.4135 / 0.3267, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][4/8] Loss_D: 1.9012 Loss_G: 1.5924 D(x): 0.5477 D(G(z)): 0.6523 / 0.1835, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][5/8] Loss_D: 1.9950 Loss_G: 1.0454 D(x): 0.3651 D(G(z)): 0.5253 / 0.2908, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.05s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][6/8] Loss_D: 1.9322 Loss_G: 0.8739 D(x): 0.3870 D(G(z)): 0.5541 / 0.3281, Accuracy: 14.0000 / 32.0000 = 43.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[11/100][7/8] Loss_D: 1.9789 Loss_G: 0.6929 D(x): 0.3779 D(G(z)): 0.5350 / 0.3884, Accuracy: 14.0000 / 23.0000 = 60.8696\n",
            "[12/100][0/8] Loss_D: 1.7781 Loss_G: 2.0705 D(x): 0.5458 D(G(z)): 0.6383 / 0.1154, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:11,  1.86s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][1/8] Loss_D: 1.7560 Loss_G: 1.2039 D(x): 0.3676 D(G(z)): 0.3900 / 0.2424, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.55s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][2/8] Loss_D: 1.5562 Loss_G: 0.9461 D(x): 0.4531 D(G(z)): 0.4663 / 0.3311, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:06,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][3/8] Loss_D: 1.7228 Loss_G: 1.2239 D(x): 0.5397 D(G(z)): 0.6120 / 0.2387, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][4/8] Loss_D: 1.4501 Loss_G: 1.6033 D(x): 0.5130 D(G(z)): 0.4642 / 0.1834, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][5/8] Loss_D: 1.4553 Loss_G: 0.8561 D(x): 0.4443 D(G(z)): 0.4226 / 0.3380, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][6/8] Loss_D: 1.5091 Loss_G: 0.9678 D(x): 0.6086 D(G(z)): 0.5782 / 0.2905, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.15s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[12/100][7/8] Loss_D: 1.6175 Loss_G: 0.8216 D(x): 0.4511 D(G(z)): 0.4839 / 0.3607, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[13/100][0/8] Loss_D: 1.5440 Loss_G: 0.6386 D(x): 0.4762 D(G(z)): 0.4688 / 0.4363, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.78s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][1/8] Loss_D: 1.8216 Loss_G: 0.8382 D(x): 0.4784 D(G(z)): 0.5863 / 0.3589, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.55s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][2/8] Loss_D: 1.5731 Loss_G: 1.3313 D(x): 0.5413 D(G(z)): 0.5440 / 0.2004, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][3/8] Loss_D: 1.8057 Loss_G: 0.4933 D(x): 0.3387 D(G(z)): 0.3767 / 0.4579, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][4/8] Loss_D: 1.8229 Loss_G: 0.8978 D(x): 0.5525 D(G(z)): 0.6709 / 0.3230, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][5/8] Loss_D: 1.7536 Loss_G: 1.1191 D(x): 0.4411 D(G(z)): 0.5235 / 0.2559, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][6/8] Loss_D: 1.6903 Loss_G: 0.6322 D(x): 0.4158 D(G(z)): 0.4864 / 0.3972, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[13/100][7/8] Loss_D: 1.7439 Loss_G: 0.7939 D(x): 0.4654 D(G(z)): 0.5530 / 0.4000, Accuracy: 15.0000 / 23.0000 = 65.2174\n",
            "[14/100][0/8] Loss_D: 1.8384 Loss_G: 0.9939 D(x): 0.4783 D(G(z)): 0.6074 / 0.2806, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][1/8] Loss_D: 1.5493 Loss_G: 0.8581 D(x): 0.4445 D(G(z)): 0.4283 / 0.3158, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][2/8] Loss_D: 1.1091 Loss_G: 0.9564 D(x): 0.6174 D(G(z)): 0.4185 / 0.2954, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][3/8] Loss_D: 1.1139 Loss_G: 1.3044 D(x): 0.6411 D(G(z)): 0.4485 / 0.2224, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][4/8] Loss_D: 1.2495 Loss_G: 1.5828 D(x): 0.5686 D(G(z)): 0.4418 / 0.1782, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][5/8] Loss_D: 1.2619 Loss_G: 0.9823 D(x): 0.5279 D(G(z)): 0.3896 / 0.2881, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][6/8] Loss_D: 1.6739 Loss_G: 1.8887 D(x): 0.6377 D(G(z)): 0.6452 / 0.1287, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[14/100][7/8] Loss_D: 1.7740 Loss_G: 1.3224 D(x): 0.3385 D(G(z)): 0.4473 / 0.2291, Accuracy: 17.0000 / 23.0000 = 73.9130\n",
            "[15/100][0/8] Loss_D: 2.2956 Loss_G: 0.7786 D(x): 0.3182 D(G(z)): 0.5712 / 0.3786, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.64s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][1/8] Loss_D: 2.4883 Loss_G: 0.9122 D(x): 0.4046 D(G(z)): 0.7126 / 0.3177, Accuracy: 15.0000 / 32.0000 = 46.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][2/8] Loss_D: 2.2474 Loss_G: 1.9374 D(x): 0.4419 D(G(z)): 0.6905 / 0.1250, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][3/8] Loss_D: 2.1511 Loss_G: 1.0985 D(x): 0.2841 D(G(z)): 0.4451 / 0.2724, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][4/8] Loss_D: 1.7739 Loss_G: 1.2843 D(x): 0.4475 D(G(z)): 0.5503 / 0.2219, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][5/8] Loss_D: 1.3812 Loss_G: 1.6585 D(x): 0.5399 D(G(z)): 0.4776 / 0.1603, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][6/8] Loss_D: 1.3834 Loss_G: 1.3839 D(x): 0.4966 D(G(z)): 0.4116 / 0.2070, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15/100][7/8] Loss_D: 0.9996 Loss_G: 1.0891 D(x): 0.6530 D(G(z)): 0.4031 / 0.2684, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[16/100][0/8] Loss_D: 1.0028 Loss_G: 1.4052 D(x): 0.6452 D(G(z)): 0.3609 / 0.2152, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][1/8] Loss_D: 0.9211 Loss_G: 2.3637 D(x): 0.7909 D(G(z)): 0.4546 / 0.0839, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][2/8] Loss_D: 1.3287 Loss_G: 1.2895 D(x): 0.4775 D(G(z)): 0.3146 / 0.2126, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.39s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][3/8] Loss_D: 1.3629 Loss_G: 2.0303 D(x): 0.7277 D(G(z)): 0.5769 / 0.1131, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][4/8] Loss_D: 1.4395 Loss_G: 1.8787 D(x): 0.4770 D(G(z)): 0.4286 / 0.1490, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][5/8] Loss_D: 1.5279 Loss_G: 1.6616 D(x): 0.5764 D(G(z)): 0.5483 / 0.1905, Accuracy: 16.0000 / 32.0000 = 50.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][6/8] Loss_D: 1.9655 Loss_G: 2.2730 D(x): 0.5324 D(G(z)): 0.6625 / 0.0915, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[16/100][7/8] Loss_D: 2.4670 Loss_G: 1.2782 D(x): 0.2950 D(G(z)): 0.6606 / 0.2095, Accuracy: 11.0000 / 23.0000 = 47.8261\n",
            "[17/100][0/8] Loss_D: 2.4124 Loss_G: 1.4501 D(x): 0.3566 D(G(z)): 0.6441 / 0.1931, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][1/8] Loss_D: 1.7202 Loss_G: 2.6059 D(x): 0.5451 D(G(z)): 0.6138 / 0.0620, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][2/8] Loss_D: 1.4442 Loss_G: 1.6560 D(x): 0.4284 D(G(z)): 0.3598 / 0.1495, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][3/8] Loss_D: 1.4083 Loss_G: 1.3862 D(x): 0.5626 D(G(z)): 0.4668 / 0.2041, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][4/8] Loss_D: 1.3525 Loss_G: 1.5491 D(x): 0.5860 D(G(z)): 0.4730 / 0.1896, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][5/8] Loss_D: 1.1607 Loss_G: 1.8833 D(x): 0.6073 D(G(z)): 0.4259 / 0.1285, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][6/8] Loss_D: 1.7582 Loss_G: 1.2685 D(x): 0.4701 D(G(z)): 0.4504 / 0.2174, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[17/100][7/8] Loss_D: 1.9734 Loss_G: 0.6894 D(x): 0.3936 D(G(z)): 0.5125 / 0.3907, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[18/100][0/8] Loss_D: 1.9488 Loss_G: 1.1029 D(x): 0.4621 D(G(z)): 0.5963 / 0.2695, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.76s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][1/8] Loss_D: 1.5591 Loss_G: 0.8779 D(x): 0.4755 D(G(z)): 0.4639 / 0.3190, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][2/8] Loss_D: 1.7298 Loss_G: 1.1762 D(x): 0.4786 D(G(z)): 0.5727 / 0.2537, Accuracy: 17.0000 / 32.0000 = 53.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][3/8] Loss_D: 1.6336 Loss_G: 0.8113 D(x): 0.4092 D(G(z)): 0.4369 / 0.3427, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][4/8] Loss_D: 1.3832 Loss_G: 0.7069 D(x): 0.5164 D(G(z)): 0.4704 / 0.3842, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][5/8] Loss_D: 1.4418 Loss_G: 1.2122 D(x): 0.5604 D(G(z)): 0.5272 / 0.2356, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][6/8] Loss_D: 1.4963 Loss_G: 0.8183 D(x): 0.4532 D(G(z)): 0.4336 / 0.3171, Accuracy: 19.0000 / 32.0000 = 59.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[18/100][7/8] Loss_D: 1.6425 Loss_G: 0.7082 D(x): 0.4573 D(G(z)): 0.4694 / 0.4103, Accuracy: 12.0000 / 23.0000 = 52.1739\n",
            "[19/100][0/8] Loss_D: 1.6790 Loss_G: 2.0736 D(x): 0.6344 D(G(z)): 0.6509 / 0.1140, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.57s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][1/8] Loss_D: 1.8619 Loss_G: 0.3348 D(x): 0.2336 D(G(z)): 0.2301 / 0.4956, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.40s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][2/8] Loss_D: 1.7585 Loss_G: 1.4969 D(x): 0.6864 D(G(z)): 0.7152 / 0.1857, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][3/8] Loss_D: 1.2957 Loss_G: 1.4766 D(x): 0.4825 D(G(z)): 0.3608 / 0.1935, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][4/8] Loss_D: 1.3992 Loss_G: 0.8565 D(x): 0.4495 D(G(z)): 0.3752 / 0.3621, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][5/8] Loss_D: 1.3767 Loss_G: 1.3329 D(x): 0.6422 D(G(z)): 0.5740 / 0.2119, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][6/8] Loss_D: 1.2148 Loss_G: 1.2167 D(x): 0.5221 D(G(z)): 0.3880 / 0.2429, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19/100][7/8] Loss_D: 1.4742 Loss_G: 0.9177 D(x): 0.5065 D(G(z)): 0.4742 / 0.3120, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[20/100][0/8] Loss_D: 1.4207 Loss_G: 1.5032 D(x): 0.6145 D(G(z)): 0.5654 / 0.1841, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.78s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][1/8] Loss_D: 1.3597 Loss_G: 1.3114 D(x): 0.5010 D(G(z)): 0.4071 / 0.2198, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.54s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][2/8] Loss_D: 1.7512 Loss_G: 0.7919 D(x): 0.4376 D(G(z)): 0.5475 / 0.3698, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][3/8] Loss_D: 1.6487 Loss_G: 0.8395 D(x): 0.4862 D(G(z)): 0.5451 / 0.3191, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][4/8] Loss_D: 1.7713 Loss_G: 0.6752 D(x): 0.4181 D(G(z)): 0.5181 / 0.3824, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][5/8] Loss_D: 1.5068 Loss_G: 1.1816 D(x): 0.5762 D(G(z)): 0.5570 / 0.2557, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][6/8] Loss_D: 1.6076 Loss_G: 0.9710 D(x): 0.4185 D(G(z)): 0.4476 / 0.2968, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[20/100][7/8] Loss_D: 1.5006 Loss_G: 0.7180 D(x): 0.4850 D(G(z)): 0.4841 / 0.3708, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[21/100][0/8] Loss_D: 1.4594 Loss_G: 1.0073 D(x): 0.5618 D(G(z)): 0.5372 / 0.2869, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][1/8] Loss_D: 1.5397 Loss_G: 0.7239 D(x): 0.4191 D(G(z)): 0.4139 / 0.3641, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][2/8] Loss_D: 1.3707 Loss_G: 1.0376 D(x): 0.5831 D(G(z)): 0.5149 / 0.2763, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][3/8] Loss_D: 1.4988 Loss_G: 0.9261 D(x): 0.4434 D(G(z)): 0.4152 / 0.3076, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][4/8] Loss_D: 1.4475 Loss_G: 1.0242 D(x): 0.5390 D(G(z)): 0.5116 / 0.2818, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][5/8] Loss_D: 1.3116 Loss_G: 1.0191 D(x): 0.5359 D(G(z)): 0.4282 / 0.2833, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][6/8] Loss_D: 1.2603 Loss_G: 0.9858 D(x): 0.5448 D(G(z)): 0.4167 / 0.2980, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[21/100][7/8] Loss_D: 1.1710 Loss_G: 1.0410 D(x): 0.5684 D(G(z)): 0.4230 / 0.2778, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[22/100][0/8] Loss_D: 1.2314 Loss_G: 0.8882 D(x): 0.5766 D(G(z)): 0.4347 / 0.3364, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:12,  2.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][1/8] Loss_D: 1.5246 Loss_G: 1.1158 D(x): 0.5668 D(G(z)): 0.5414 / 0.2542, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:08,  1.77s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][2/8] Loss_D: 1.3593 Loss_G: 1.1324 D(x): 0.5257 D(G(z)): 0.4420 / 0.2400, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:06,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][3/8] Loss_D: 1.7173 Loss_G: 0.8611 D(x): 0.4452 D(G(z)): 0.5187 / 0.3381, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:04,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][4/8] Loss_D: 2.0355 Loss_G: 0.7420 D(x): 0.3846 D(G(z)): 0.5974 / 0.3581, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][5/8] Loss_D: 1.6949 Loss_G: 0.8328 D(x): 0.4390 D(G(z)): 0.5014 / 0.3120, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][6/8] Loss_D: 1.6751 Loss_G: 1.2427 D(x): 0.5390 D(G(z)): 0.6023 / 0.2246, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.13s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[22/100][7/8] Loss_D: 1.3999 Loss_G: 0.9196 D(x): 0.4199 D(G(z)): 0.3705 / 0.3199, Accuracy: 15.0000 / 23.0000 = 65.2174\n",
            "[23/100][0/8] Loss_D: 1.3353 Loss_G: 1.0520 D(x): 0.5632 D(G(z)): 0.4763 / 0.2922, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][1/8] Loss_D: 1.3006 Loss_G: 1.7568 D(x): 0.6294 D(G(z)): 0.5150 / 0.1544, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][2/8] Loss_D: 1.3434 Loss_G: 0.6949 D(x): 0.4299 D(G(z)): 0.3404 / 0.3543, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][3/8] Loss_D: 1.6371 Loss_G: 0.9191 D(x): 0.6063 D(G(z)): 0.6272 / 0.3207, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][4/8] Loss_D: 1.6787 Loss_G: 1.4099 D(x): 0.5151 D(G(z)): 0.5627 / 0.2018, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][5/8] Loss_D: 1.8391 Loss_G: 0.6723 D(x): 0.3023 D(G(z)): 0.3916 / 0.4041, Accuracy: 20.0000 / 32.0000 = 62.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.06s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][6/8] Loss_D: 1.6447 Loss_G: 1.6026 D(x): 0.6564 D(G(z)): 0.6589 / 0.1745, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[23/100][7/8] Loss_D: 1.3500 Loss_G: 1.7163 D(x): 0.5131 D(G(z)): 0.4468 / 0.1392, Accuracy: 18.0000 / 23.0000 = 78.2609\n",
            "[24/100][0/8] Loss_D: 1.1075 Loss_G: 0.9638 D(x): 0.5253 D(G(z)): 0.3037 / 0.3015, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.58s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][1/8] Loss_D: 1.5680 Loss_G: 1.4165 D(x): 0.6668 D(G(z)): 0.6579 / 0.1977, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.37s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][2/8] Loss_D: 1.8059 Loss_G: 1.4076 D(x): 0.4381 D(G(z)): 0.5381 / 0.2330, Accuracy: 18.0000 / 32.0000 = 56.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][3/8] Loss_D: 1.9990 Loss_G: 0.8157 D(x): 0.3434 D(G(z)): 0.4820 / 0.3709, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][4/8] Loss_D: 1.5976 Loss_G: 0.9759 D(x): 0.5441 D(G(z)): 0.5575 / 0.3051, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][5/8] Loss_D: 1.6207 Loss_G: 1.1345 D(x): 0.4702 D(G(z)): 0.5184 / 0.2488, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][6/8] Loss_D: 1.8813 Loss_G: 0.7642 D(x): 0.3481 D(G(z)): 0.4677 / 0.3615, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[24/100][7/8] Loss_D: 2.0565 Loss_G: 0.6375 D(x): 0.4313 D(G(z)): 0.6472 / 0.3860, Accuracy: 13.0000 / 23.0000 = 56.5217\n",
            "[25/100][0/8] Loss_D: 1.5237 Loss_G: 1.2673 D(x): 0.4932 D(G(z)): 0.5046 / 0.2511, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:09,  1.65s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][1/8] Loss_D: 1.6114 Loss_G: 1.0525 D(x): 0.4332 D(G(z)): 0.4665 / 0.2670, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][2/8] Loss_D: 1.5862 Loss_G: 0.5126 D(x): 0.4067 D(G(z)): 0.4407 / 0.4441, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][3/8] Loss_D: 1.3070 Loss_G: 1.4907 D(x): 0.6547 D(G(z)): 0.5529 / 0.1792, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][4/8] Loss_D: 1.1919 Loss_G: 1.1399 D(x): 0.5035 D(G(z)): 0.3194 / 0.2383, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][5/8] Loss_D: 1.3935 Loss_G: 1.1985 D(x): 0.5445 D(G(z)): 0.4947 / 0.2395, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][6/8] Loss_D: 1.5052 Loss_G: 1.5332 D(x): 0.5678 D(G(z)): 0.5462 / 0.1977, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[25/100][7/8] Loss_D: 1.5309 Loss_G: 0.9152 D(x): 0.3887 D(G(z)): 0.3623 / 0.3024, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[26/100][0/8] Loss_D: 1.4612 Loss_G: 1.5654 D(x): 0.5757 D(G(z)): 0.5481 / 0.1700, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:10,  1.67s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][1/8] Loss_D: 1.2208 Loss_G: 1.6184 D(x): 0.6321 D(G(z)): 0.4761 / 0.1808, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][2/8] Loss_D: 1.4262 Loss_G: 1.3587 D(x): 0.5097 D(G(z)): 0.4619 / 0.2375, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][3/8] Loss_D: 1.5455 Loss_G: 1.3095 D(x): 0.5252 D(G(z)): 0.5488 / 0.2247, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][4/8] Loss_D: 1.7739 Loss_G: 0.9722 D(x): 0.4364 D(G(z)): 0.5348 / 0.3081, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][5/8] Loss_D: 1.4653 Loss_G: 1.1799 D(x): 0.5259 D(G(z)): 0.4965 / 0.2498, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][6/8] Loss_D: 1.6905 Loss_G: 1.0630 D(x): 0.4538 D(G(z)): 0.5045 / 0.2775, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[26/100][7/8] Loss_D: 1.4474 Loss_G: 1.0852 D(x): 0.5733 D(G(z)): 0.5344 / 0.2487, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[27/100][0/8] Loss_D: 1.5660 Loss_G: 1.2596 D(x): 0.4841 D(G(z)): 0.4995 / 0.2370, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.61s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][1/8] Loss_D: 1.5535 Loss_G: 1.1343 D(x): 0.5079 D(G(z)): 0.5147 / 0.2682, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][2/8] Loss_D: 1.5659 Loss_G: 0.6715 D(x): 0.4138 D(G(z)): 0.4271 / 0.3903, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][3/8] Loss_D: 1.6953 Loss_G: 1.0433 D(x): 0.5239 D(G(z)): 0.5976 / 0.2744, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][4/8] Loss_D: 1.3972 Loss_G: 1.4179 D(x): 0.5166 D(G(z)): 0.4593 / 0.2077, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][5/8] Loss_D: 1.3745 Loss_G: 1.3620 D(x): 0.5118 D(G(z)): 0.4645 / 0.1993, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][6/8] Loss_D: 1.5154 Loss_G: 0.9296 D(x): 0.5281 D(G(z)): 0.5182 / 0.2962, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[27/100][7/8] Loss_D: 1.4947 Loss_G: 1.0562 D(x): 0.5140 D(G(z)): 0.5203 / 0.2592, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[28/100][0/8] Loss_D: 1.4907 Loss_G: 1.1861 D(x): 0.4919 D(G(z)): 0.4809 / 0.2421, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][1/8] Loss_D: 1.5158 Loss_G: 0.9464 D(x): 0.4894 D(G(z)): 0.4623 / 0.3109, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.40s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][2/8] Loss_D: 1.7669 Loss_G: 0.8769 D(x): 0.4472 D(G(z)): 0.5450 / 0.3371, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][3/8] Loss_D: 1.5910 Loss_G: 0.8507 D(x): 0.5093 D(G(z)): 0.5315 / 0.3240, Accuracy: 22.0000 / 32.0000 = 68.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][4/8] Loss_D: 1.6105 Loss_G: 1.1878 D(x): 0.4745 D(G(z)): 0.5112 / 0.2501, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][5/8] Loss_D: 1.7218 Loss_G: 0.8342 D(x): 0.3775 D(G(z)): 0.4755 / 0.3427, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][6/8] Loss_D: 1.5579 Loss_G: 1.2931 D(x): 0.5662 D(G(z)): 0.5844 / 0.2313, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[28/100][7/8] Loss_D: 1.6800 Loss_G: 1.1572 D(x): 0.4258 D(G(z)): 0.4699 / 0.2708, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[29/100][0/8] Loss_D: 1.5678 Loss_G: 1.1502 D(x): 0.4653 D(G(z)): 0.4770 / 0.2860, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][1/8] Loss_D: 1.3100 Loss_G: 1.5919 D(x): 0.5405 D(G(z)): 0.4514 / 0.1749, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][2/8] Loss_D: 1.1187 Loss_G: 1.2859 D(x): 0.5553 D(G(z)): 0.3407 / 0.2180, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][3/8] Loss_D: 1.0589 Loss_G: 1.5209 D(x): 0.6232 D(G(z)): 0.3889 / 0.1739, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][4/8] Loss_D: 1.0594 Loss_G: 1.6333 D(x): 0.6733 D(G(z)): 0.4361 / 0.1919, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][5/8] Loss_D: 1.1871 Loss_G: 1.4245 D(x): 0.6048 D(G(z)): 0.4310 / 0.2141, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][6/8] Loss_D: 1.3203 Loss_G: 0.9048 D(x): 0.5164 D(G(z)): 0.3972 / 0.3229, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.13s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[29/100][7/8] Loss_D: 1.9727 Loss_G: 0.5500 D(x): 0.4655 D(G(z)): 0.6317 / 0.4735, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[30/100][0/8] Loss_D: 1.4068 Loss_G: 1.1974 D(x): 0.5590 D(G(z)): 0.5152 / 0.2363, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.54s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][1/8] Loss_D: 1.5222 Loss_G: 1.0446 D(x): 0.4826 D(G(z)): 0.4472 / 0.2916, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][2/8] Loss_D: 1.4025 Loss_G: 0.8668 D(x): 0.5006 D(G(z)): 0.4285 / 0.3395, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][3/8] Loss_D: 1.6669 Loss_G: 1.1170 D(x): 0.5771 D(G(z)): 0.5986 / 0.2769, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][4/8] Loss_D: 1.3576 Loss_G: 1.1309 D(x): 0.4934 D(G(z)): 0.4255 / 0.2525, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][5/8] Loss_D: 1.6081 Loss_G: 0.8163 D(x): 0.4887 D(G(z)): 0.5164 / 0.3278, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][6/8] Loss_D: 1.7011 Loss_G: 0.9707 D(x): 0.5002 D(G(z)): 0.5454 / 0.2722, Accuracy: 21.0000 / 32.0000 = 65.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[30/100][7/8] Loss_D: 1.5384 Loss_G: 1.3510 D(x): 0.4807 D(G(z)): 0.4783 / 0.2055, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[31/100][0/8] Loss_D: 1.5454 Loss_G: 1.1003 D(x): 0.4906 D(G(z)): 0.4618 / 0.2832, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][1/8] Loss_D: 1.6618 Loss_G: 1.2485 D(x): 0.5098 D(G(z)): 0.5660 / 0.2340, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][2/8] Loss_D: 1.7618 Loss_G: 1.2913 D(x): 0.4766 D(G(z)): 0.5357 / 0.2231, Accuracy: 23.0000 / 32.0000 = 71.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][3/8] Loss_D: 1.5820 Loss_G: 1.1329 D(x): 0.4485 D(G(z)): 0.4674 / 0.2559, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][4/8] Loss_D: 1.7458 Loss_G: 1.3522 D(x): 0.5639 D(G(z)): 0.6355 / 0.2203, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][5/8] Loss_D: 1.6558 Loss_G: 0.9464 D(x): 0.3932 D(G(z)): 0.4389 / 0.2908, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][6/8] Loss_D: 1.6435 Loss_G: 1.1311 D(x): 0.5594 D(G(z)): 0.6049 / 0.2709, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[31/100][7/8] Loss_D: 1.6803 Loss_G: 1.2333 D(x): 0.4967 D(G(z)): 0.5724 / 0.2383, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[32/100][0/8] Loss_D: 1.6721 Loss_G: 0.9228 D(x): 0.4072 D(G(z)): 0.4413 / 0.3140, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][1/8] Loss_D: 1.5453 Loss_G: 1.1853 D(x): 0.5725 D(G(z)): 0.5957 / 0.2798, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][2/8] Loss_D: 1.6565 Loss_G: 1.2438 D(x): 0.4701 D(G(z)): 0.5085 / 0.2510, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][3/8] Loss_D: 1.6483 Loss_G: 0.8781 D(x): 0.4212 D(G(z)): 0.4687 / 0.3329, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][4/8] Loss_D: 1.6477 Loss_G: 1.1494 D(x): 0.5194 D(G(z)): 0.5588 / 0.2524, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][5/8] Loss_D: 1.4950 Loss_G: 0.9315 D(x): 0.4678 D(G(z)): 0.4614 / 0.2969, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][6/8] Loss_D: 1.5919 Loss_G: 0.9285 D(x): 0.4630 D(G(z)): 0.5099 / 0.3384, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[32/100][7/8] Loss_D: 1.5218 Loss_G: 1.2038 D(x): 0.5793 D(G(z)): 0.5584 / 0.2499, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[33/100][0/8] Loss_D: 1.4600 Loss_G: 1.0064 D(x): 0.4479 D(G(z)): 0.4237 / 0.2662, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.49s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][1/8] Loss_D: 1.4312 Loss_G: 0.8859 D(x): 0.4608 D(G(z)): 0.4388 / 0.3276, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][2/8] Loss_D: 1.4890 Loss_G: 1.0732 D(x): 0.5567 D(G(z)): 0.5419 / 0.2846, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][3/8] Loss_D: 1.3219 Loss_G: 1.1652 D(x): 0.5331 D(G(z)): 0.4591 / 0.2382, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][4/8] Loss_D: 1.2823 Loss_G: 1.0118 D(x): 0.5273 D(G(z)): 0.4297 / 0.2783, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][5/8] Loss_D: 1.4642 Loss_G: 1.3301 D(x): 0.5218 D(G(z)): 0.5140 / 0.2192, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][6/8] Loss_D: 1.3007 Loss_G: 1.2260 D(x): 0.5321 D(G(z)): 0.4373 / 0.2331, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[33/100][7/8] Loss_D: 1.7161 Loss_G: 0.7104 D(x): 0.3609 D(G(z)): 0.4160 / 0.4000, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[34/100][0/8] Loss_D: 1.4697 Loss_G: 1.0336 D(x): 0.5871 D(G(z)): 0.5538 / 0.2692, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.83s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][1/8] Loss_D: 1.3134 Loss_G: 1.1373 D(x): 0.5237 D(G(z)): 0.4422 / 0.2385, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.57s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][2/8] Loss_D: 1.5065 Loss_G: 0.6880 D(x): 0.4510 D(G(z)): 0.4189 / 0.3795, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][3/8] Loss_D: 1.5408 Loss_G: 1.0637 D(x): 0.5661 D(G(z)): 0.5519 / 0.2985, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][4/8] Loss_D: 1.4508 Loss_G: 1.2801 D(x): 0.5235 D(G(z)): 0.4947 / 0.2317, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][5/8] Loss_D: 1.2723 Loss_G: 0.9190 D(x): 0.5052 D(G(z)): 0.3871 / 0.3188, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][6/8] Loss_D: 1.2450 Loss_G: 1.3405 D(x): 0.6423 D(G(z)): 0.5220 / 0.2217, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[34/100][7/8] Loss_D: 1.1963 Loss_G: 1.2727 D(x): 0.5619 D(G(z)): 0.4153 / 0.2314, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[35/100][0/8] Loss_D: 1.4082 Loss_G: 0.8938 D(x): 0.5075 D(G(z)): 0.4435 / 0.2993, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.57s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][1/8] Loss_D: 1.4420 Loss_G: 1.0954 D(x): 0.5275 D(G(z)): 0.4995 / 0.2765, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.39s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][2/8] Loss_D: 1.3974 Loss_G: 1.4762 D(x): 0.5989 D(G(z)): 0.5410 / 0.1839, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][3/8] Loss_D: 1.5265 Loss_G: 0.9079 D(x): 0.4063 D(G(z)): 0.3926 / 0.3242, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][4/8] Loss_D: 1.3440 Loss_G: 1.6824 D(x): 0.6499 D(G(z)): 0.5661 / 0.1514, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][5/8] Loss_D: 1.4560 Loss_G: 1.4401 D(x): 0.4518 D(G(z)): 0.4139 / 0.1834, Accuracy: 24.0000 / 32.0000 = 75.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][6/8] Loss_D: 1.4873 Loss_G: 1.2438 D(x): 0.4761 D(G(z)): 0.4674 / 0.2655, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[35/100][7/8] Loss_D: 1.4936 Loss_G: 1.0227 D(x): 0.5323 D(G(z)): 0.5249 / 0.2950, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[36/100][0/8] Loss_D: 1.6606 Loss_G: 1.3604 D(x): 0.5272 D(G(z)): 0.5875 / 0.1921, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][1/8] Loss_D: 1.3838 Loss_G: 1.0131 D(x): 0.4372 D(G(z)): 0.3471 / 0.2894, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][2/8] Loss_D: 1.4722 Loss_G: 0.6319 D(x): 0.5163 D(G(z)): 0.5106 / 0.3700, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][3/8] Loss_D: 1.4436 Loss_G: 1.7280 D(x): 0.6254 D(G(z)): 0.5992 / 0.1481, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][4/8] Loss_D: 1.5796 Loss_G: 0.7844 D(x): 0.3289 D(G(z)): 0.3092 / 0.3841, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][5/8] Loss_D: 1.5737 Loss_G: 1.1219 D(x): 0.6029 D(G(z)): 0.5902 / 0.2703, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.06s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][6/8] Loss_D: 1.3215 Loss_G: 1.2180 D(x): 0.5284 D(G(z)): 0.4373 / 0.2329, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[36/100][7/8] Loss_D: 1.3947 Loss_G: 0.8565 D(x): 0.4875 D(G(z)): 0.4480 / 0.3499, Accuracy: 16.0000 / 23.0000 = 69.5652\n",
            "[37/100][0/8] Loss_D: 1.4939 Loss_G: 1.0361 D(x): 0.5144 D(G(z)): 0.4965 / 0.2934, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.82s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][1/8] Loss_D: 1.8401 Loss_G: 0.8314 D(x): 0.4044 D(G(z)): 0.5485 / 0.3364, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:08,  1.60s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][2/8] Loss_D: 1.5105 Loss_G: 0.8913 D(x): 0.4968 D(G(z)): 0.4910 / 0.2962, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.37s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][3/8] Loss_D: 1.1399 Loss_G: 1.0266 D(x): 0.6049 D(G(z)): 0.4303 / 0.2884, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][4/8] Loss_D: 1.3635 Loss_G: 0.9323 D(x): 0.5124 D(G(z)): 0.4469 / 0.3053, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][5/8] Loss_D: 1.1205 Loss_G: 1.2099 D(x): 0.6307 D(G(z)): 0.4489 / 0.2245, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][6/8] Loss_D: 1.2410 Loss_G: 1.3627 D(x): 0.5587 D(G(z)): 0.4382 / 0.2155, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[37/100][7/8] Loss_D: 1.2733 Loss_G: 0.7208 D(x): 0.5038 D(G(z)): 0.4067 / 0.3539, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[38/100][0/8] Loss_D: 1.2687 Loss_G: 1.2200 D(x): 0.6285 D(G(z)): 0.5253 / 0.2436, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.61s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][1/8] Loss_D: 1.2645 Loss_G: 1.3712 D(x): 0.5437 D(G(z)): 0.4327 / 0.2021, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][2/8] Loss_D: 1.4874 Loss_G: 0.7146 D(x): 0.4097 D(G(z)): 0.3771 / 0.3521, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][3/8] Loss_D: 1.4813 Loss_G: 1.1438 D(x): 0.5828 D(G(z)): 0.5657 / 0.2569, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][4/8] Loss_D: 1.5115 Loss_G: 1.5143 D(x): 0.5413 D(G(z)): 0.5370 / 0.1825, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][5/8] Loss_D: 1.4528 Loss_G: 0.7840 D(x): 0.4147 D(G(z)): 0.3559 / 0.3316, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][6/8] Loss_D: 1.6511 Loss_G: 1.0119 D(x): 0.5653 D(G(z)): 0.6035 / 0.3133, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[38/100][7/8] Loss_D: 1.7410 Loss_G: 0.9069 D(x): 0.4720 D(G(z)): 0.5790 / 0.3106, Accuracy: 18.0000 / 23.0000 = 78.2609\n",
            "[39/100][0/8] Loss_D: 1.6018 Loss_G: 0.7983 D(x): 0.4654 D(G(z)): 0.5025 / 0.3521, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][1/8] Loss_D: 1.5379 Loss_G: 1.2732 D(x): 0.5216 D(G(z)): 0.5433 / 0.2432, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][2/8] Loss_D: 1.5562 Loss_G: 0.9248 D(x): 0.4160 D(G(z)): 0.4287 / 0.3065, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][3/8] Loss_D: 1.4524 Loss_G: 1.2289 D(x): 0.5864 D(G(z)): 0.5467 / 0.2301, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][4/8] Loss_D: 0.9748 Loss_G: 1.2249 D(x): 0.5982 D(G(z)): 0.3336 / 0.2488, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][5/8] Loss_D: 1.2516 Loss_G: 1.3606 D(x): 0.6284 D(G(z)): 0.5001 / 0.2077, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][6/8] Loss_D: 1.3810 Loss_G: 1.5014 D(x): 0.5545 D(G(z)): 0.4926 / 0.1961, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[39/100][7/8] Loss_D: 1.5675 Loss_G: 1.0350 D(x): 0.4957 D(G(z)): 0.5179 / 0.2921, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[40/100][0/8] Loss_D: 1.4895 Loss_G: 1.1311 D(x): 0.5321 D(G(z)): 0.5224 / 0.2465, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.62s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][1/8] Loss_D: 1.5541 Loss_G: 1.1115 D(x): 0.5335 D(G(z)): 0.5288 / 0.2592, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.45s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][2/8] Loss_D: 1.3643 Loss_G: 1.2128 D(x): 0.5749 D(G(z)): 0.4973 / 0.2189, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][3/8] Loss_D: 1.6218 Loss_G: 0.7573 D(x): 0.4561 D(G(z)): 0.4923 / 0.3485, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][4/8] Loss_D: 1.6370 Loss_G: 1.1303 D(x): 0.5128 D(G(z)): 0.5698 / 0.2827, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][5/8] Loss_D: 1.5142 Loss_G: 1.1897 D(x): 0.4827 D(G(z)): 0.5052 / 0.2685, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][6/8] Loss_D: 1.4968 Loss_G: 0.9881 D(x): 0.4769 D(G(z)): 0.4780 / 0.3135, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.16s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[40/100][7/8] Loss_D: 1.5203 Loss_G: 1.0173 D(x): 0.5524 D(G(z)): 0.5542 / 0.2838, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[41/100][0/8] Loss_D: 1.1626 Loss_G: 1.3076 D(x): 0.5724 D(G(z)): 0.4114 / 0.2181, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.64s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][1/8] Loss_D: 1.3275 Loss_G: 1.1725 D(x): 0.5436 D(G(z)): 0.4554 / 0.2317, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][2/8] Loss_D: 1.3370 Loss_G: 0.9440 D(x): 0.4943 D(G(z)): 0.4045 / 0.2929, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][3/8] Loss_D: 1.5645 Loss_G: 0.9544 D(x): 0.4887 D(G(z)): 0.5322 / 0.2966, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][4/8] Loss_D: 1.7305 Loss_G: 1.4136 D(x): 0.4959 D(G(z)): 0.6004 / 0.1875, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][5/8] Loss_D: 1.8800 Loss_G: 0.8151 D(x): 0.3094 D(G(z)): 0.3999 / 0.3324, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][6/8] Loss_D: 1.7017 Loss_G: 1.1159 D(x): 0.4962 D(G(z)): 0.5671 / 0.2644, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[41/100][7/8] Loss_D: 1.5054 Loss_G: 1.0983 D(x): 0.5134 D(G(z)): 0.5342 / 0.2928, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[42/100][0/8] Loss_D: 1.4279 Loss_G: 1.2101 D(x): 0.5129 D(G(z)): 0.4812 / 0.2433, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.69s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][1/8] Loss_D: 1.5835 Loss_G: 0.9297 D(x): 0.4620 D(G(z)): 0.4728 / 0.2864, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.44s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][2/8] Loss_D: 1.5180 Loss_G: 0.8847 D(x): 0.4746 D(G(z)): 0.4672 / 0.3480, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][3/8] Loss_D: 1.4442 Loss_G: 1.0517 D(x): 0.5683 D(G(z)): 0.5255 / 0.2858, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][4/8] Loss_D: 1.2354 Loss_G: 0.8005 D(x): 0.4874 D(G(z)): 0.3706 / 0.3473, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][5/8] Loss_D: 1.2144 Loss_G: 0.8771 D(x): 0.5697 D(G(z)): 0.4536 / 0.3073, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][6/8] Loss_D: 1.2689 Loss_G: 1.2591 D(x): 0.5969 D(G(z)): 0.4796 / 0.2253, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[42/100][7/8] Loss_D: 1.1379 Loss_G: 1.1969 D(x): 0.5439 D(G(z)): 0.3437 / 0.2289, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[43/100][0/8] Loss_D: 1.1356 Loss_G: 0.7581 D(x): 0.5550 D(G(z)): 0.3786 / 0.3667, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.56s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][1/8] Loss_D: 1.1481 Loss_G: 1.3286 D(x): 0.6664 D(G(z)): 0.4970 / 0.2073, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][2/8] Loss_D: 1.5472 Loss_G: 0.8443 D(x): 0.4029 D(G(z)): 0.4039 / 0.3495, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][3/8] Loss_D: 1.3454 Loss_G: 1.0867 D(x): 0.5590 D(G(z)): 0.4996 / 0.2719, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][4/8] Loss_D: 1.5345 Loss_G: 0.9520 D(x): 0.4804 D(G(z)): 0.4588 / 0.3016, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][5/8] Loss_D: 1.1235 Loss_G: 1.5530 D(x): 0.6237 D(G(z)): 0.4403 / 0.1831, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][6/8] Loss_D: 1.2786 Loss_G: 1.2151 D(x): 0.5093 D(G(z)): 0.3861 / 0.2826, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[43/100][7/8] Loss_D: 1.1641 Loss_G: 1.4583 D(x): 0.7099 D(G(z)): 0.5236 / 0.1929, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[44/100][0/8] Loss_D: 1.1672 Loss_G: 1.3969 D(x): 0.5449 D(G(z)): 0.3817 / 0.2155, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.77s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][1/8] Loss_D: 1.1593 Loss_G: 1.3674 D(x): 0.5930 D(G(z)): 0.4325 / 0.2040, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][2/8] Loss_D: 1.1957 Loss_G: 2.1434 D(x): 0.6609 D(G(z)): 0.4992 / 0.1046, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][3/8] Loss_D: 1.2423 Loss_G: 0.6882 D(x): 0.4434 D(G(z)): 0.2777 / 0.3825, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][4/8] Loss_D: 1.5472 Loss_G: 1.7287 D(x): 0.7563 D(G(z)): 0.6745 / 0.1665, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][5/8] Loss_D: 1.2554 Loss_G: 1.2955 D(x): 0.4573 D(G(z)): 0.3042 / 0.2216, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][6/8] Loss_D: 1.2753 Loss_G: 1.4257 D(x): 0.6531 D(G(z)): 0.5228 / 0.2105, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.14s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[44/100][7/8] Loss_D: 1.3795 Loss_G: 1.3994 D(x): 0.5428 D(G(z)): 0.4638 / 0.1861, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[45/100][0/8] Loss_D: 1.2951 Loss_G: 1.1174 D(x): 0.5641 D(G(z)): 0.4438 / 0.2413, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.54s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][1/8] Loss_D: 1.6144 Loss_G: 0.8784 D(x): 0.4537 D(G(z)): 0.5020 / 0.2822, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][2/8] Loss_D: 1.3442 Loss_G: 1.3923 D(x): 0.5679 D(G(z)): 0.4817 / 0.1958, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][3/8] Loss_D: 1.2494 Loss_G: 1.0321 D(x): 0.5032 D(G(z)): 0.3631 / 0.2740, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][4/8] Loss_D: 1.2593 Loss_G: 1.2520 D(x): 0.6177 D(G(z)): 0.4891 / 0.2576, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][5/8] Loss_D: 1.2226 Loss_G: 1.2545 D(x): 0.5328 D(G(z)): 0.3825 / 0.2355, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][6/8] Loss_D: 1.3721 Loss_G: 1.3549 D(x): 0.5889 D(G(z)): 0.5079 / 0.2137, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[45/100][7/8] Loss_D: 1.2692 Loss_G: 0.9746 D(x): 0.5434 D(G(z)): 0.3886 / 0.3013, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[46/100][0/8] Loss_D: 1.4583 Loss_G: 1.1879 D(x): 0.5227 D(G(z)): 0.4745 / 0.2379, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:10,  1.67s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][1/8] Loss_D: 1.5383 Loss_G: 2.2661 D(x): 0.6290 D(G(z)): 0.5950 / 0.0992, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.44s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][2/8] Loss_D: 1.3588 Loss_G: 0.8685 D(x): 0.3910 D(G(z)): 0.2230 / 0.3377, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][3/8] Loss_D: 1.7853 Loss_G: 1.6633 D(x): 0.6740 D(G(z)): 0.7017 / 0.1602, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][4/8] Loss_D: 1.5750 Loss_G: 1.9594 D(x): 0.4714 D(G(z)): 0.4686 / 0.1341, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][5/8] Loss_D: 1.7033 Loss_G: 1.1134 D(x): 0.3880 D(G(z)): 0.4045 / 0.2581, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][6/8] Loss_D: 1.6785 Loss_G: 1.3728 D(x): 0.5319 D(G(z)): 0.5925 / 0.2061, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.13s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[46/100][7/8] Loss_D: 1.4621 Loss_G: 1.1721 D(x): 0.5649 D(G(z)): 0.5087 / 0.2373, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[47/100][0/8] Loss_D: 1.4994 Loss_G: 1.3612 D(x): 0.4919 D(G(z)): 0.4528 / 0.2069, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.61s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][1/8] Loss_D: 1.2304 Loss_G: 1.7864 D(x): 0.6252 D(G(z)): 0.4802 / 0.1442, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][2/8] Loss_D: 1.2033 Loss_G: 1.4283 D(x): 0.5500 D(G(z)): 0.3995 / 0.1712, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][3/8] Loss_D: 1.3851 Loss_G: 1.2267 D(x): 0.5362 D(G(z)): 0.4804 / 0.2376, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][4/8] Loss_D: 1.3597 Loss_G: 1.4014 D(x): 0.5469 D(G(z)): 0.4885 / 0.2095, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][5/8] Loss_D: 1.7312 Loss_G: 0.8088 D(x): 0.3744 D(G(z)): 0.4273 / 0.3611, Accuracy: 25.0000 / 32.0000 = 78.1250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][6/8] Loss_D: 1.6167 Loss_G: 1.0700 D(x): 0.5453 D(G(z)): 0.5806 / 0.2982, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[47/100][7/8] Loss_D: 1.5483 Loss_G: 0.8743 D(x): 0.4601 D(G(z)): 0.4707 / 0.3405, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[48/100][0/8] Loss_D: 1.4565 Loss_G: 1.1781 D(x): 0.5205 D(G(z)): 0.4846 / 0.2522, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.59s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][1/8] Loss_D: 1.2952 Loss_G: 1.1204 D(x): 0.5154 D(G(z)): 0.4128 / 0.2617, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][2/8] Loss_D: 0.9977 Loss_G: 1.3305 D(x): 0.6772 D(G(z)): 0.4311 / 0.2311, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][3/8] Loss_D: 1.3214 Loss_G: 0.8563 D(x): 0.4763 D(G(z)): 0.3726 / 0.3275, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][4/8] Loss_D: 1.2526 Loss_G: 1.1454 D(x): 0.5992 D(G(z)): 0.4846 / 0.2381, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][5/8] Loss_D: 1.2850 Loss_G: 0.8016 D(x): 0.4729 D(G(z)): 0.3589 / 0.3610, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.06s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][6/8] Loss_D: 1.2047 Loss_G: 1.2927 D(x): 0.6645 D(G(z)): 0.5209 / 0.2160, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[48/100][7/8] Loss_D: 1.4312 Loss_G: 0.8580 D(x): 0.4188 D(G(z)): 0.3431 / 0.3538, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[49/100][0/8] Loss_D: 1.3885 Loss_G: 0.8973 D(x): 0.5424 D(G(z)): 0.4965 / 0.3124, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:10,  1.74s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][1/8] Loss_D: 1.1048 Loss_G: 1.0450 D(x): 0.5901 D(G(z)): 0.3935 / 0.2886, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][2/8] Loss_D: 1.4502 Loss_G: 1.0014 D(x): 0.5337 D(G(z)): 0.5077 / 0.2708, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][3/8] Loss_D: 1.3563 Loss_G: 0.9046 D(x): 0.5022 D(G(z)): 0.4277 / 0.3021, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][4/8] Loss_D: 1.2306 Loss_G: 1.3263 D(x): 0.5670 D(G(z)): 0.4489 / 0.2375, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][5/8] Loss_D: 1.3108 Loss_G: 0.8742 D(x): 0.5371 D(G(z)): 0.4281 / 0.2980, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][6/8] Loss_D: 1.1796 Loss_G: 1.3334 D(x): 0.6317 D(G(z)): 0.4654 / 0.2297, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[49/100][7/8] Loss_D: 1.3557 Loss_G: 1.0871 D(x): 0.5173 D(G(z)): 0.4701 / 0.2726, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[50/100][0/8] Loss_D: 1.3633 Loss_G: 0.8737 D(x): 0.4648 D(G(z)): 0.3838 / 0.3224, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.75s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][1/8] Loss_D: 1.2706 Loss_G: 1.6770 D(x): 0.6930 D(G(z)): 0.5303 / 0.1937, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][2/8] Loss_D: 1.1885 Loss_G: 1.4986 D(x): 0.5499 D(G(z)): 0.3620 / 0.2002, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][3/8] Loss_D: 1.2691 Loss_G: 1.3960 D(x): 0.5730 D(G(z)): 0.4574 / 0.2111, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][4/8] Loss_D: 1.3449 Loss_G: 0.9220 D(x): 0.5394 D(G(z)): 0.4500 / 0.3069, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][5/8] Loss_D: 1.6241 Loss_G: 1.0820 D(x): 0.5341 D(G(z)): 0.5709 / 0.2738, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][6/8] Loss_D: 1.5238 Loss_G: 1.5004 D(x): 0.5383 D(G(z)): 0.5318 / 0.1959, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[50/100][7/8] Loss_D: 1.8450 Loss_G: 0.5743 D(x): 0.3381 D(G(z)): 0.4436 / 0.4310, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[51/100][0/8] Loss_D: 1.3809 Loss_G: 1.1411 D(x): 0.5929 D(G(z)): 0.5240 / 0.2484, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.61s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][1/8] Loss_D: 1.2608 Loss_G: 1.5391 D(x): 0.5772 D(G(z)): 0.4553 / 0.1845, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][2/8] Loss_D: 1.2308 Loss_G: 1.3272 D(x): 0.5259 D(G(z)): 0.3858 / 0.2229, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][3/8] Loss_D: 1.3990 Loss_G: 0.9596 D(x): 0.5040 D(G(z)): 0.4367 / 0.2877, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][4/8] Loss_D: 1.2661 Loss_G: 1.1329 D(x): 0.6414 D(G(z)): 0.5082 / 0.2245, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][5/8] Loss_D: 1.3523 Loss_G: 0.6824 D(x): 0.4636 D(G(z)): 0.3779 / 0.3478, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][6/8] Loss_D: 1.3729 Loss_G: 1.1837 D(x): 0.5862 D(G(z)): 0.5091 / 0.2351, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[51/100][7/8] Loss_D: 1.7262 Loss_G: 0.7675 D(x): 0.3915 D(G(z)): 0.4836 / 0.3834, Accuracy: 18.0000 / 23.0000 = 78.2609\n",
            "[52/100][0/8] Loss_D: 1.7512 Loss_G: 0.6416 D(x): 0.4695 D(G(z)): 0.5752 / 0.3593, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:09,  1.63s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][1/8] Loss_D: 1.6130 Loss_G: 0.9398 D(x): 0.4902 D(G(z)): 0.4938 / 0.3223, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][2/8] Loss_D: 1.3876 Loss_G: 0.9881 D(x): 0.5980 D(G(z)): 0.5372 / 0.2786, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][3/8] Loss_D: 1.5857 Loss_G: 0.7142 D(x): 0.4063 D(G(z)): 0.4323 / 0.3705, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][4/8] Loss_D: 1.6316 Loss_G: 0.9828 D(x): 0.5131 D(G(z)): 0.5733 / 0.2950, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][5/8] Loss_D: 1.5062 Loss_G: 1.0545 D(x): 0.4600 D(G(z)): 0.4652 / 0.2918, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][6/8] Loss_D: 1.4779 Loss_G: 1.2111 D(x): 0.5262 D(G(z)): 0.5139 / 0.2393, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.12s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[52/100][7/8] Loss_D: 1.0011 Loss_G: 1.2861 D(x): 0.6123 D(G(z)): 0.3782 / 0.2429, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[53/100][0/8] Loss_D: 1.2856 Loss_G: 1.0942 D(x): 0.5205 D(G(z)): 0.4214 / 0.2784, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.72s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][1/8] Loss_D: 1.2913 Loss_G: 1.2154 D(x): 0.5425 D(G(z)): 0.4439 / 0.2711, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.49s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][2/8] Loss_D: 1.2378 Loss_G: 1.0354 D(x): 0.5921 D(G(z)): 0.4645 / 0.2587, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][3/8] Loss_D: 1.3223 Loss_G: 1.3947 D(x): 0.5520 D(G(z)): 0.4664 / 0.2031, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][4/8] Loss_D: 1.2193 Loss_G: 1.5957 D(x): 0.5870 D(G(z)): 0.4219 / 0.1776, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][5/8] Loss_D: 1.3148 Loss_G: 1.5005 D(x): 0.5904 D(G(z)): 0.4711 / 0.1874, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][6/8] Loss_D: 1.5661 Loss_G: 0.6935 D(x): 0.4251 D(G(z)): 0.4194 / 0.3775, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:09<00:00,  1.13s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[53/100][7/8] Loss_D: 1.4211 Loss_G: 1.0913 D(x): 0.6828 D(G(z)): 0.6030 / 0.2493, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[54/100][0/8] Loss_D: 1.4285 Loss_G: 1.1011 D(x): 0.4615 D(G(z)): 0.4350 / 0.2521, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:09,  1.63s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][1/8] Loss_D: 1.5562 Loss_G: 1.1302 D(x): 0.5156 D(G(z)): 0.5118 / 0.2638, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.45s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][2/8] Loss_D: 1.7286 Loss_G: 0.6815 D(x): 0.3666 D(G(z)): 0.4329 / 0.3801, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][3/8] Loss_D: 1.6474 Loss_G: 1.0486 D(x): 0.4946 D(G(z)): 0.5575 / 0.3203, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][4/8] Loss_D: 1.4024 Loss_G: 1.0447 D(x): 0.5426 D(G(z)): 0.4850 / 0.2567, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][5/8] Loss_D: 1.5522 Loss_G: 1.0587 D(x): 0.4470 D(G(z)): 0.4736 / 0.2947, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][6/8] Loss_D: 1.3139 Loss_G: 0.4675 D(x): 0.4951 D(G(z)): 0.4024 / 0.3779, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[54/100][7/8] Loss_D: 1.5086 Loss_G: 0.6589 D(x): 0.5424 D(G(z)): 0.5059 / 0.3502, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[55/100][0/8] Loss_D: 1.3855 Loss_G: 1.4278 D(x): 0.6181 D(G(z)): 0.5534 / 0.2039, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][1/8] Loss_D: 1.3145 Loss_G: 0.6262 D(x): 0.4189 D(G(z)): 0.2933 / 0.3974, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.39s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][2/8] Loss_D: 1.3031 Loss_G: 0.8798 D(x): 0.6305 D(G(z)): 0.5194 / 0.3195, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][3/8] Loss_D: 1.2467 Loss_G: 1.3524 D(x): 0.6128 D(G(z)): 0.4965 / 0.2090, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][4/8] Loss_D: 1.4306 Loss_G: 0.6172 D(x): 0.4458 D(G(z)): 0.3860 / 0.3815, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][5/8] Loss_D: 1.4336 Loss_G: 1.0970 D(x): 0.6114 D(G(z)): 0.5681 / 0.2546, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][6/8] Loss_D: 1.2950 Loss_G: 1.5217 D(x): 0.5605 D(G(z)): 0.4434 / 0.1939, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[55/100][7/8] Loss_D: 1.7109 Loss_G: 0.8347 D(x): 0.3974 D(G(z)): 0.4083 / 0.3554, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[56/100][0/8] Loss_D: 1.5139 Loss_G: 0.7830 D(x): 0.5425 D(G(z)): 0.5422 / 0.3404, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.63s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][1/8] Loss_D: 1.6369 Loss_G: 1.2986 D(x): 0.5158 D(G(z)): 0.5658 / 0.2374, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][2/8] Loss_D: 1.3311 Loss_G: 0.8261 D(x): 0.4532 D(G(z)): 0.3448 / 0.3344, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][3/8] Loss_D: 1.4444 Loss_G: 1.2793 D(x): 0.6375 D(G(z)): 0.5875 / 0.2242, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][4/8] Loss_D: 1.3487 Loss_G: 1.3370 D(x): 0.5172 D(G(z)): 0.4421 / 0.2417, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][5/8] Loss_D: 1.3541 Loss_G: 0.9965 D(x): 0.5352 D(G(z)): 0.4546 / 0.2880, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][6/8] Loss_D: 1.5511 Loss_G: 0.7257 D(x): 0.4581 D(G(z)): 0.4728 / 0.3575, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[56/100][7/8] Loss_D: 1.3638 Loss_G: 0.7936 D(x): 0.6042 D(G(z)): 0.5208 / 0.3191, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[57/100][0/8] Loss_D: 1.2930 Loss_G: 1.4276 D(x): 0.5987 D(G(z)): 0.4881 / 0.1810, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.60s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][1/8] Loss_D: 1.3115 Loss_G: 0.7768 D(x): 0.4613 D(G(z)): 0.3422 / 0.3145, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][2/8] Loss_D: 1.1869 Loss_G: 0.7195 D(x): 0.5909 D(G(z)): 0.4380 / 0.3374, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][3/8] Loss_D: 1.1773 Loss_G: 1.3402 D(x): 0.6798 D(G(z)): 0.5136 / 0.2036, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][4/8] Loss_D: 1.3116 Loss_G: 1.0033 D(x): 0.4531 D(G(z)): 0.3686 / 0.3043, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][5/8] Loss_D: 1.3331 Loss_G: 1.0596 D(x): 0.5736 D(G(z)): 0.4989 / 0.2795, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][6/8] Loss_D: 1.3234 Loss_G: 1.2565 D(x): 0.5414 D(G(z)): 0.4648 / 0.2293, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.11s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[57/100][7/8] Loss_D: 1.4342 Loss_G: 0.6281 D(x): 0.4644 D(G(z)): 0.3738 / 0.3742, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[58/100][0/8] Loss_D: 1.4786 Loss_G: 1.3532 D(x): 0.6073 D(G(z)): 0.5811 / 0.2373, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][1/8] Loss_D: 1.2821 Loss_G: 1.0182 D(x): 0.5281 D(G(z)): 0.4344 / 0.2923, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][2/8] Loss_D: 1.5771 Loss_G: 0.8614 D(x): 0.4514 D(G(z)): 0.4719 / 0.3241, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][3/8] Loss_D: 1.5108 Loss_G: 1.0300 D(x): 0.5820 D(G(z)): 0.5783 / 0.2793, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][4/8] Loss_D: 1.6089 Loss_G: 0.6342 D(x): 0.3998 D(G(z)): 0.3936 / 0.4082, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][5/8] Loss_D: 1.5146 Loss_G: 0.9553 D(x): 0.5853 D(G(z)): 0.5757 / 0.2871, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][6/8] Loss_D: 1.5915 Loss_G: 0.8678 D(x): 0.4483 D(G(z)): 0.4977 / 0.3133, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[58/100][7/8] Loss_D: 1.3957 Loss_G: 0.5662 D(x): 0.4757 D(G(z)): 0.4151 / 0.4127, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[59/100][0/8] Loss_D: 1.4068 Loss_G: 0.5343 D(x): 0.5200 D(G(z)): 0.4918 / 0.4276, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.42s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][1/8] Loss_D: 1.3102 Loss_G: 0.9914 D(x): 0.5964 D(G(z)): 0.5067 / 0.3265, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][2/8] Loss_D: 1.3542 Loss_G: 0.7374 D(x): 0.4678 D(G(z)): 0.4027 / 0.3626, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][3/8] Loss_D: 1.3459 Loss_G: 1.0567 D(x): 0.5936 D(G(z)): 0.5237 / 0.2959, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][4/8] Loss_D: 1.2200 Loss_G: 0.7979 D(x): 0.5126 D(G(z)): 0.3882 / 0.3613, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][5/8] Loss_D: 1.2651 Loss_G: 0.7388 D(x): 0.5985 D(G(z)): 0.4856 / 0.3295, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][6/8] Loss_D: 1.1909 Loss_G: 0.8701 D(x): 0.5426 D(G(z)): 0.4013 / 0.3328, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[59/100][7/8] Loss_D: 1.2694 Loss_G: 0.5994 D(x): 0.5776 D(G(z)): 0.4777 / 0.3642, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[60/100][0/8] Loss_D: 1.3197 Loss_G: 0.9308 D(x): 0.5508 D(G(z)): 0.4628 / 0.2968, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][1/8] Loss_D: 1.4463 Loss_G: 0.7857 D(x): 0.4736 D(G(z)): 0.4397 / 0.3670, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][2/8] Loss_D: 1.3507 Loss_G: 0.8928 D(x): 0.5428 D(G(z)): 0.4760 / 0.3157, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][3/8] Loss_D: 1.4314 Loss_G: 0.6156 D(x): 0.4938 D(G(z)): 0.4505 / 0.3699, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][4/8] Loss_D: 1.5125 Loss_G: 1.1346 D(x): 0.5736 D(G(z)): 0.5672 / 0.2415, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][5/8] Loss_D: 1.3350 Loss_G: 0.8626 D(x): 0.4497 D(G(z)): 0.3537 / 0.3421, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][6/8] Loss_D: 1.2502 Loss_G: 0.7659 D(x): 0.5667 D(G(z)): 0.4613 / 0.3503, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[60/100][7/8] Loss_D: 1.3924 Loss_G: 0.9821 D(x): 0.6345 D(G(z)): 0.5709 / 0.2733, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[61/100][0/8] Loss_D: 1.5362 Loss_G: 0.5903 D(x): 0.4220 D(G(z)): 0.4246 / 0.3588, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.60s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][1/8] Loss_D: 1.3407 Loss_G: 0.7819 D(x): 0.5499 D(G(z)): 0.4856 / 0.3261, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.40s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][2/8] Loss_D: 1.3254 Loss_G: 0.5043 D(x): 0.5047 D(G(z)): 0.4194 / 0.4003, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][3/8] Loss_D: 1.3816 Loss_G: 0.9992 D(x): 0.6302 D(G(z)): 0.5555 / 0.2790, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][4/8] Loss_D: 1.5687 Loss_G: 0.8529 D(x): 0.4470 D(G(z)): 0.4625 / 0.3516, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][5/8] Loss_D: 1.4437 Loss_G: 0.5580 D(x): 0.5244 D(G(z)): 0.5049 / 0.4114, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][6/8] Loss_D: 1.4738 Loss_G: 0.6825 D(x): 0.4866 D(G(z)): 0.4845 / 0.3491, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[61/100][7/8] Loss_D: 1.4659 Loss_G: 0.6603 D(x): 0.4681 D(G(z)): 0.4670 / 0.3951, Accuracy: 18.0000 / 23.0000 = 78.2609\n",
            "[62/100][0/8] Loss_D: 1.3680 Loss_G: 0.9697 D(x): 0.5733 D(G(z)): 0.5213 / 0.3049, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][1/8] Loss_D: 1.4438 Loss_G: 0.9593 D(x): 0.4802 D(G(z)): 0.4318 / 0.3273, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.37s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][2/8] Loss_D: 1.3943 Loss_G: 0.7830 D(x): 0.4876 D(G(z)): 0.4315 / 0.4050, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][3/8] Loss_D: 1.4196 Loss_G: 1.1250 D(x): 0.5902 D(G(z)): 0.5543 / 0.2607, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][4/8] Loss_D: 1.2056 Loss_G: 1.1002 D(x): 0.5419 D(G(z)): 0.3951 / 0.2654, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][5/8] Loss_D: 1.4236 Loss_G: 0.5098 D(x): 0.4793 D(G(z)): 0.4332 / 0.3993, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][6/8] Loss_D: 1.3653 Loss_G: 0.9352 D(x): 0.6101 D(G(z)): 0.5395 / 0.3226, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[62/100][7/8] Loss_D: 1.3059 Loss_G: 0.9353 D(x): 0.4931 D(G(z)): 0.4012 / 0.2928, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[63/100][0/8] Loss_D: 1.4054 Loss_G: 0.4915 D(x): 0.4924 D(G(z)): 0.4594 / 0.4214, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][1/8] Loss_D: 1.3253 Loss_G: 0.8623 D(x): 0.6296 D(G(z)): 0.5404 / 0.2840, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][2/8] Loss_D: 1.5022 Loss_G: 0.8191 D(x): 0.4232 D(G(z)): 0.4300 / 0.3561, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][3/8] Loss_D: 1.4660 Loss_G: 0.8816 D(x): 0.5817 D(G(z)): 0.5730 / 0.3119, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][4/8] Loss_D: 1.6400 Loss_G: 0.6470 D(x): 0.4136 D(G(z)): 0.4781 / 0.4080, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][5/8] Loss_D: 1.5825 Loss_G: 0.7520 D(x): 0.4756 D(G(z)): 0.5056 / 0.3947, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.04s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][6/8] Loss_D: 1.3833 Loss_G: 0.9150 D(x): 0.5847 D(G(z)): 0.5179 / 0.3093, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[63/100][7/8] Loss_D: 1.5528 Loss_G: 0.8107 D(x): 0.4555 D(G(z)): 0.4593 / 0.3709, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[64/100][0/8] Loss_D: 1.4000 Loss_G: 0.6596 D(x): 0.4928 D(G(z)): 0.4339 / 0.4156, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][1/8] Loss_D: 1.3583 Loss_G: 0.8812 D(x): 0.5562 D(G(z)): 0.5015 / 0.3412, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][2/8] Loss_D: 1.4431 Loss_G: 0.7526 D(x): 0.5044 D(G(z)): 0.4900 / 0.3546, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][3/8] Loss_D: 1.2204 Loss_G: 0.8073 D(x): 0.5481 D(G(z)): 0.4270 / 0.3301, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][4/8] Loss_D: 1.4444 Loss_G: 0.6695 D(x): 0.4909 D(G(z)): 0.4583 / 0.4155, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][5/8] Loss_D: 1.4735 Loss_G: 0.4178 D(x): 0.5107 D(G(z)): 0.5152 / 0.5045, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.06s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][6/8] Loss_D: 1.4519 Loss_G: 0.8155 D(x): 0.5499 D(G(z)): 0.5432 / 0.3679, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[64/100][7/8] Loss_D: 1.3402 Loss_G: 0.6448 D(x): 0.4887 D(G(z)): 0.4227 / 0.3402, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[65/100][0/8] Loss_D: 1.3164 Loss_G: 0.6013 D(x): 0.5204 D(G(z)): 0.4482 / 0.3977, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][1/8] Loss_D: 1.3568 Loss_G: 0.8965 D(x): 0.5480 D(G(z)): 0.4861 / 0.3263, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][2/8] Loss_D: 1.4461 Loss_G: 1.1807 D(x): 0.5376 D(G(z)): 0.5015 / 0.2680, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][3/8] Loss_D: 1.1630 Loss_G: 0.7941 D(x): 0.5495 D(G(z)): 0.3916 / 0.3325, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][4/8] Loss_D: 1.3047 Loss_G: 0.7498 D(x): 0.5228 D(G(z)): 0.4340 / 0.3734, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][5/8] Loss_D: 1.3045 Loss_G: 0.7802 D(x): 0.6209 D(G(z)): 0.5180 / 0.3582, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][6/8] Loss_D: 1.2972 Loss_G: 0.8903 D(x): 0.5218 D(G(z)): 0.4189 / 0.3309, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[65/100][7/8] Loss_D: 1.4803 Loss_G: 0.5433 D(x): 0.5042 D(G(z)): 0.5086 / 0.3682, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[66/100][0/8] Loss_D: 1.4212 Loss_G: 0.9439 D(x): 0.5252 D(G(z)): 0.4835 / 0.3341, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][1/8] Loss_D: 1.4178 Loss_G: 0.7407 D(x): 0.4843 D(G(z)): 0.4682 / 0.3894, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][2/8] Loss_D: 1.4806 Loss_G: 0.7258 D(x): 0.5087 D(G(z)): 0.5044 / 0.3486, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][3/8] Loss_D: 1.4182 Loss_G: 0.6719 D(x): 0.4877 D(G(z)): 0.4626 / 0.3697, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][4/8] Loss_D: 1.3387 Loss_G: 1.2368 D(x): 0.6017 D(G(z)): 0.5299 / 0.2396, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][5/8] Loss_D: 1.5470 Loss_G: 0.4693 D(x): 0.4022 D(G(z)): 0.4091 / 0.4517, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][6/8] Loss_D: 1.3814 Loss_G: 0.4122 D(x): 0.5344 D(G(z)): 0.4916 / 0.4463, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[66/100][7/8] Loss_D: 1.4405 Loss_G: 0.6950 D(x): 0.6171 D(G(z)): 0.5805 / 0.3825, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[67/100][0/8] Loss_D: 1.5355 Loss_G: 0.8061 D(x): 0.5088 D(G(z)): 0.5253 / 0.3309, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.72s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][1/8] Loss_D: 1.3304 Loss_G: 0.6919 D(x): 0.4900 D(G(z)): 0.4031 / 0.3773, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][2/8] Loss_D: 1.4778 Loss_G: 0.5481 D(x): 0.5093 D(G(z)): 0.5118 / 0.4519, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][3/8] Loss_D: 1.3260 Loss_G: 0.8266 D(x): 0.6278 D(G(z)): 0.5478 / 0.3231, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][4/8] Loss_D: 1.4976 Loss_G: 0.6618 D(x): 0.4485 D(G(z)): 0.4510 / 0.3740, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][5/8] Loss_D: 1.5050 Loss_G: 0.5056 D(x): 0.4661 D(G(z)): 0.4702 / 0.4420, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][6/8] Loss_D: 1.4038 Loss_G: 0.9749 D(x): 0.6499 D(G(z)): 0.5986 / 0.2794, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[67/100][7/8] Loss_D: 1.4207 Loss_G: 0.8596 D(x): 0.4684 D(G(z)): 0.4364 / 0.3140, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[68/100][0/8] Loss_D: 1.3714 Loss_G: 0.5044 D(x): 0.4635 D(G(z)): 0.4019 / 0.4430, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.73s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][1/8] Loss_D: 1.5237 Loss_G: 0.4855 D(x): 0.5796 D(G(z)): 0.5930 / 0.4304, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.47s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][2/8] Loss_D: 1.2893 Loss_G: 0.8269 D(x): 0.5695 D(G(z)): 0.4748 / 0.3405, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][3/8] Loss_D: 1.4727 Loss_G: 0.5283 D(x): 0.4353 D(G(z)): 0.4316 / 0.4486, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][4/8] Loss_D: 1.4800 Loss_G: 0.7679 D(x): 0.5137 D(G(z)): 0.5160 / 0.3627, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][5/8] Loss_D: 1.6431 Loss_G: 1.0431 D(x): 0.4959 D(G(z)): 0.5681 / 0.2887, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][6/8] Loss_D: 1.6363 Loss_G: 0.6659 D(x): 0.3636 D(G(z)): 0.3982 / 0.3867, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[68/100][7/8] Loss_D: 1.4583 Loss_G: 0.6809 D(x): 0.5723 D(G(z)): 0.5473 / 0.3796, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[69/100][0/8] Loss_D: 1.4163 Loss_G: 1.0621 D(x): 0.5172 D(G(z)): 0.4904 / 0.2790, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.45s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][1/8] Loss_D: 1.3430 Loss_G: 0.6086 D(x): 0.4514 D(G(z)): 0.3837 / 0.3892, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][2/8] Loss_D: 1.1125 Loss_G: 0.8452 D(x): 0.6332 D(G(z)): 0.4490 / 0.3307, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][3/8] Loss_D: 1.3779 Loss_G: 0.6031 D(x): 0.4770 D(G(z)): 0.4297 / 0.4011, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][4/8] Loss_D: 1.4550 Loss_G: 0.6856 D(x): 0.5522 D(G(z)): 0.5386 / 0.3733, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][5/8] Loss_D: 1.4488 Loss_G: 0.5443 D(x): 0.4291 D(G(z)): 0.4174 / 0.4005, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][6/8] Loss_D: 1.5176 Loss_G: 0.3256 D(x): 0.4767 D(G(z)): 0.5061 / 0.5206, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[69/100][7/8] Loss_D: 1.5304 Loss_G: 0.5358 D(x): 0.5373 D(G(z)): 0.5715 / 0.4095, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[70/100][0/8] Loss_D: 1.3257 Loss_G: 0.8394 D(x): 0.5463 D(G(z)): 0.4797 / 0.2984, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.74s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][1/8] Loss_D: 1.3972 Loss_G: 0.5695 D(x): 0.4265 D(G(z)): 0.3812 / 0.4261, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][2/8] Loss_D: 1.3937 Loss_G: 0.5508 D(x): 0.5592 D(G(z)): 0.5218 / 0.3992, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][3/8] Loss_D: 1.2372 Loss_G: 0.7254 D(x): 0.5530 D(G(z)): 0.4389 / 0.3558, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][4/8] Loss_D: 1.3241 Loss_G: 0.8199 D(x): 0.5596 D(G(z)): 0.4948 / 0.3466, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][5/8] Loss_D: 1.2296 Loss_G: 0.7366 D(x): 0.5585 D(G(z)): 0.4409 / 0.3578, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][6/8] Loss_D: 1.3679 Loss_G: 0.5786 D(x): 0.4911 D(G(z)): 0.4331 / 0.4300, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[70/100][7/8] Loss_D: 1.3069 Loss_G: 0.7122 D(x): 0.5933 D(G(z)): 0.5168 / 0.3885, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[71/100][0/8] Loss_D: 1.3020 Loss_G: 0.6665 D(x): 0.5687 D(G(z)): 0.4842 / 0.3517, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.56s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][1/8] Loss_D: 1.3665 Loss_G: 0.6236 D(x): 0.4930 D(G(z)): 0.4466 / 0.4234, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][2/8] Loss_D: 1.2193 Loss_G: 0.8941 D(x): 0.6217 D(G(z)): 0.5003 / 0.3105, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][3/8] Loss_D: 1.3209 Loss_G: 0.7379 D(x): 0.5312 D(G(z)): 0.4538 / 0.3905, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][4/8] Loss_D: 1.3310 Loss_G: 0.7879 D(x): 0.5764 D(G(z)): 0.5059 / 0.3320, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][5/8] Loss_D: 1.5160 Loss_G: 0.7350 D(x): 0.4945 D(G(z)): 0.5031 / 0.3333, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][6/8] Loss_D: 1.5087 Loss_G: 0.5887 D(x): 0.4940 D(G(z)): 0.5075 / 0.3698, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[71/100][7/8] Loss_D: 1.3782 Loss_G: 0.7171 D(x): 0.5420 D(G(z)): 0.5067 / 0.3840, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[72/100][0/8] Loss_D: 1.3198 Loss_G: 0.5906 D(x): 0.5619 D(G(z)): 0.4776 / 0.3427, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.37s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][1/8] Loss_D: 1.2767 Loss_G: 1.0908 D(x): 0.6310 D(G(z)): 0.5424 / 0.2440, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.29s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][2/8] Loss_D: 1.4206 Loss_G: 0.6579 D(x): 0.4064 D(G(z)): 0.3588 / 0.3824, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][3/8] Loss_D: 1.4928 Loss_G: 0.6324 D(x): 0.5163 D(G(z)): 0.5195 / 0.3969, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][4/8] Loss_D: 1.5284 Loss_G: 1.0455 D(x): 0.5700 D(G(z)): 0.5815 / 0.2326, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][5/8] Loss_D: 1.5587 Loss_G: 0.7009 D(x): 0.4020 D(G(z)): 0.4008 / 0.3551, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][6/8] Loss_D: 1.4129 Loss_G: 0.8090 D(x): 0.5351 D(G(z)): 0.4984 / 0.3689, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[72/100][7/8] Loss_D: 1.4771 Loss_G: 0.9553 D(x): 0.5244 D(G(z)): 0.5309 / 0.3276, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[73/100][0/8] Loss_D: 1.4794 Loss_G: 0.7427 D(x): 0.4665 D(G(z)): 0.4734 / 0.3688, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.56s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][1/8] Loss_D: 1.5049 Loss_G: 0.7576 D(x): 0.4724 D(G(z)): 0.4860 / 0.3617, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][2/8] Loss_D: 1.4806 Loss_G: 0.8053 D(x): 0.5378 D(G(z)): 0.5272 / 0.3714, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][3/8] Loss_D: 1.4483 Loss_G: 0.8111 D(x): 0.5010 D(G(z)): 0.4936 / 0.3365, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][4/8] Loss_D: 1.3589 Loss_G: 0.6720 D(x): 0.5180 D(G(z)): 0.4657 / 0.3710, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][5/8] Loss_D: 1.3453 Loss_G: 0.8568 D(x): 0.5684 D(G(z)): 0.5173 / 0.3202, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.05s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][6/8] Loss_D: 1.3554 Loss_G: 0.9001 D(x): 0.4964 D(G(z)): 0.4377 / 0.3167, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[73/100][7/8] Loss_D: 1.3157 Loss_G: 0.4960 D(x): 0.4785 D(G(z)): 0.4036 / 0.4095, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[74/100][0/8] Loss_D: 1.5192 Loss_G: 0.7809 D(x): 0.5070 D(G(z)): 0.5345 / 0.3348, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.59s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][1/8] Loss_D: 1.4492 Loss_G: 0.9755 D(x): 0.5000 D(G(z)): 0.4989 / 0.3294, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.44s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][2/8] Loss_D: 1.4563 Loss_G: 0.5871 D(x): 0.4405 D(G(z)): 0.4248 / 0.4202, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][3/8] Loss_D: 1.3656 Loss_G: 0.8165 D(x): 0.6116 D(G(z)): 0.5587 / 0.3062, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][4/8] Loss_D: 1.5412 Loss_G: 0.4644 D(x): 0.4117 D(G(z)): 0.4359 / 0.4758, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][5/8] Loss_D: 1.4788 Loss_G: 0.6299 D(x): 0.5693 D(G(z)): 0.5634 / 0.3806, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.05s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][6/8] Loss_D: 1.4228 Loss_G: 0.9206 D(x): 0.5417 D(G(z)): 0.5217 / 0.2952, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[74/100][7/8] Loss_D: 1.3429 Loss_G: 0.5430 D(x): 0.4624 D(G(z)): 0.3897 / 0.3753, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[75/100][0/8] Loss_D: 1.4375 Loss_G: 0.5170 D(x): 0.4797 D(G(z)): 0.4763 / 0.4187, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.77s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][1/8] Loss_D: 1.3835 Loss_G: 0.8882 D(x): 0.5697 D(G(z)): 0.5394 / 0.3169, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][2/8] Loss_D: 1.2902 Loss_G: 0.7294 D(x): 0.4930 D(G(z)): 0.4060 / 0.3537, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][3/8] Loss_D: 1.3408 Loss_G: 0.6211 D(x): 0.5385 D(G(z)): 0.4804 / 0.3781, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][4/8] Loss_D: 1.4653 Loss_G: 0.5796 D(x): 0.4884 D(G(z)): 0.4857 / 0.4089, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][5/8] Loss_D: 1.4126 Loss_G: 0.7411 D(x): 0.5315 D(G(z)): 0.5022 / 0.3649, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][6/8] Loss_D: 1.4976 Loss_G: 0.4344 D(x): 0.4378 D(G(z)): 0.4450 / 0.4479, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[75/100][7/8] Loss_D: 1.2928 Loss_G: 0.7658 D(x): 0.5880 D(G(z)): 0.5067 / 0.3442, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[76/100][0/8] Loss_D: 1.2013 Loss_G: 0.9912 D(x): 0.5967 D(G(z)): 0.4601 / 0.2891, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][1/8] Loss_D: 1.4512 Loss_G: 0.5116 D(x): 0.4059 D(G(z)): 0.3663 / 0.4158, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.39s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][2/8] Loss_D: 1.4509 Loss_G: 0.8445 D(x): 0.6300 D(G(z)): 0.5999 / 0.3170, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][3/8] Loss_D: 1.3600 Loss_G: 0.8035 D(x): 0.4796 D(G(z)): 0.4273 / 0.3326, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][4/8] Loss_D: 1.3749 Loss_G: 0.8866 D(x): 0.5842 D(G(z)): 0.5247 / 0.3110, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][5/8] Loss_D: 1.4590 Loss_G: 0.5488 D(x): 0.4566 D(G(z)): 0.4433 / 0.4105, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][6/8] Loss_D: 1.3917 Loss_G: 0.6417 D(x): 0.5703 D(G(z)): 0.5310 / 0.4079, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.09s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[76/100][7/8] Loss_D: 1.4767 Loss_G: 0.6647 D(x): 0.5243 D(G(z)): 0.5300 / 0.3739, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[77/100][0/8] Loss_D: 1.3287 Loss_G: 0.7348 D(x): 0.5038 D(G(z)): 0.4461 / 0.3934, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.60s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][1/8] Loss_D: 1.2866 Loss_G: 0.8431 D(x): 0.5592 D(G(z)): 0.4757 / 0.3565, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][2/8] Loss_D: 1.3460 Loss_G: 0.7260 D(x): 0.5463 D(G(z)): 0.4857 / 0.3780, Accuracy: 26.0000 / 32.0000 = 81.2500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][3/8] Loss_D: 1.3177 Loss_G: 0.7040 D(x): 0.5272 D(G(z)): 0.4488 / 0.3868, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][4/8] Loss_D: 1.2874 Loss_G: 0.8283 D(x): 0.5948 D(G(z)): 0.5058 / 0.3225, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][5/8] Loss_D: 1.2842 Loss_G: 0.8270 D(x): 0.4949 D(G(z)): 0.4033 / 0.3559, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][6/8] Loss_D: 1.4020 Loss_G: 0.5605 D(x): 0.5045 D(G(z)): 0.4790 / 0.4023, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[77/100][7/8] Loss_D: 1.3447 Loss_G: 0.6850 D(x): 0.5551 D(G(z)): 0.5007 / 0.3956, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[78/100][0/8] Loss_D: 1.2298 Loss_G: 0.9118 D(x): 0.5412 D(G(z)): 0.4267 / 0.3176, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.65s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][1/8] Loss_D: 1.2910 Loss_G: 0.5042 D(x): 0.4970 D(G(z)): 0.4135 / 0.4372, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][2/8] Loss_D: 1.4907 Loss_G: 0.7797 D(x): 0.6029 D(G(z)): 0.5994 / 0.3411, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.27s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][3/8] Loss_D: 1.3810 Loss_G: 0.9622 D(x): 0.5188 D(G(z)): 0.4705 / 0.2793, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][4/8] Loss_D: 1.4522 Loss_G: 0.5365 D(x): 0.4367 D(G(z)): 0.4154 / 0.4066, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][5/8] Loss_D: 1.3469 Loss_G: 0.8704 D(x): 0.5786 D(G(z)): 0.5196 / 0.3268, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.03s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][6/8] Loss_D: 1.3966 Loss_G: 0.6933 D(x): 0.4665 D(G(z)): 0.4354 / 0.4061, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[78/100][7/8] Loss_D: 1.4240 Loss_G: 0.8329 D(x): 0.5870 D(G(z)): 0.5528 / 0.3718, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[79/100][0/8] Loss_D: 1.2673 Loss_G: 0.8243 D(x): 0.5874 D(G(z)): 0.4820 / 0.3135, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:11,  1.93s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][1/8] Loss_D: 1.2258 Loss_G: 0.7869 D(x): 0.5613 D(G(z)): 0.4273 / 0.3145, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:08,  1.68s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][2/8] Loss_D: 1.4248 Loss_G: 0.7330 D(x): 0.5031 D(G(z)): 0.4759 / 0.3778, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][3/8] Loss_D: 1.4629 Loss_G: 0.5650 D(x): 0.4605 D(G(z)): 0.4480 / 0.4122, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][4/8] Loss_D: 1.4041 Loss_G: 0.9258 D(x): 0.5899 D(G(z)): 0.5474 / 0.3088, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][5/8] Loss_D: 1.3559 Loss_G: 0.5209 D(x): 0.4770 D(G(z)): 0.4232 / 0.3543, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][6/8] Loss_D: 1.4161 Loss_G: 1.0130 D(x): 0.5063 D(G(z)): 0.4871 / 0.3068, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[79/100][7/8] Loss_D: 1.3455 Loss_G: 0.7212 D(x): 0.5041 D(G(z)): 0.4462 / 0.3579, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[80/100][0/8] Loss_D: 1.3791 Loss_G: 0.5294 D(x): 0.5011 D(G(z)): 0.4514 / 0.4230, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.51s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][1/8] Loss_D: 1.3454 Loss_G: 0.8985 D(x): 0.5604 D(G(z)): 0.5122 / 0.3616, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][2/8] Loss_D: 1.2571 Loss_G: 0.8153 D(x): 0.5662 D(G(z)): 0.4728 / 0.2931, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][3/8] Loss_D: 1.4874 Loss_G: 0.6660 D(x): 0.4763 D(G(z)): 0.4779 / 0.4047, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][4/8] Loss_D: 1.6062 Loss_G: 0.5341 D(x): 0.4609 D(G(z)): 0.5196 / 0.4638, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][5/8] Loss_D: 1.6007 Loss_G: 0.6763 D(x): 0.5548 D(G(z)): 0.6034 / 0.3784, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][6/8] Loss_D: 1.5251 Loss_G: 0.8753 D(x): 0.4605 D(G(z)): 0.4943 / 0.3246, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[80/100][7/8] Loss_D: 1.3938 Loss_G: 0.7184 D(x): 0.4205 D(G(z)): 0.3768 / 0.3761, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[81/100][0/8] Loss_D: 1.3532 Loss_G: 0.6498 D(x): 0.5702 D(G(z)): 0.5238 / 0.3627, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][1/8] Loss_D: 1.3283 Loss_G: 0.8131 D(x): 0.5605 D(G(z)): 0.4943 / 0.3460, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][2/8] Loss_D: 1.3933 Loss_G: 0.6646 D(x): 0.4965 D(G(z)): 0.4477 / 0.3538, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][3/8] Loss_D: 1.4193 Loss_G: 0.7209 D(x): 0.4879 D(G(z)): 0.4546 / 0.3703, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][4/8] Loss_D: 1.2606 Loss_G: 0.9250 D(x): 0.6036 D(G(z)): 0.4889 / 0.2829, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][5/8] Loss_D: 1.3199 Loss_G: 0.8773 D(x): 0.5288 D(G(z)): 0.4260 / 0.3013, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][6/8] Loss_D: 1.2975 Loss_G: 0.9696 D(x): 0.5341 D(G(z)): 0.4501 / 0.2997, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.04s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[81/100][7/8] Loss_D: 1.5960 Loss_G: 0.5222 D(x): 0.4437 D(G(z)): 0.5132 / 0.4168, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[82/100][0/8] Loss_D: 1.4196 Loss_G: 0.8752 D(x): 0.5961 D(G(z)): 0.5603 / 0.3273, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.55s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][1/8] Loss_D: 1.3846 Loss_G: 0.7099 D(x): 0.4655 D(G(z)): 0.4141 / 0.4048, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][2/8] Loss_D: 1.3154 Loss_G: 0.7304 D(x): 0.5720 D(G(z)): 0.4910 / 0.3583, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][3/8] Loss_D: 1.3976 Loss_G: 0.9756 D(x): 0.5305 D(G(z)): 0.4963 / 0.2739, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][4/8] Loss_D: 1.2303 Loss_G: 0.8202 D(x): 0.5175 D(G(z)): 0.3967 / 0.3199, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][5/8] Loss_D: 1.2698 Loss_G: 0.9524 D(x): 0.5895 D(G(z)): 0.4953 / 0.3006, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][6/8] Loss_D: 1.2092 Loss_G: 0.7668 D(x): 0.5358 D(G(z)): 0.4076 / 0.3280, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.03s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[82/100][7/8] Loss_D: 1.3199 Loss_G: 0.6133 D(x): 0.5237 D(G(z)): 0.4524 / 0.4366, Accuracy: 19.0000 / 23.0000 = 82.6087\n",
            "[83/100][0/8] Loss_D: 1.3557 Loss_G: 0.7178 D(x): 0.5585 D(G(z)): 0.5147 / 0.3401, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.47s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][1/8] Loss_D: 1.4281 Loss_G: 0.5627 D(x): 0.4813 D(G(z)): 0.4651 / 0.4401, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][2/8] Loss_D: 1.4386 Loss_G: 0.3989 D(x): 0.5048 D(G(z)): 0.4974 / 0.4346, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.16s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][3/8] Loss_D: 1.3545 Loss_G: 0.7890 D(x): 0.5938 D(G(z)): 0.5363 / 0.3251, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][4/8] Loss_D: 1.4240 Loss_G: 0.4732 D(x): 0.4252 D(G(z)): 0.3955 / 0.4327, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][5/8] Loss_D: 1.2305 Loss_G: 0.5134 D(x): 0.5833 D(G(z)): 0.4687 / 0.4211, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.06s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][6/8] Loss_D: 1.3592 Loss_G: 0.6738 D(x): 0.5600 D(G(z)): 0.5088 / 0.3738, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.04s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[83/100][7/8] Loss_D: 1.2355 Loss_G: 0.9165 D(x): 0.5709 D(G(z)): 0.4684 / 0.3179, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[84/100][0/8] Loss_D: 1.3607 Loss_G: 0.6848 D(x): 0.5149 D(G(z)): 0.4679 / 0.3801, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.46s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][1/8] Loss_D: 1.3578 Loss_G: 0.4669 D(x): 0.4880 D(G(z)): 0.4299 / 0.4331, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.31s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][2/8] Loss_D: 1.6926 Loss_G: 0.5369 D(x): 0.5118 D(G(z)): 0.5944 / 0.3815, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][3/8] Loss_D: 1.4334 Loss_G: 0.8534 D(x): 0.5398 D(G(z)): 0.5265 / 0.2925, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][4/8] Loss_D: 1.4926 Loss_G: 0.6237 D(x): 0.4040 D(G(z)): 0.4006 / 0.3926, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][5/8] Loss_D: 1.4593 Loss_G: 0.3713 D(x): 0.4799 D(G(z)): 0.4838 / 0.4734, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][6/8] Loss_D: 1.4589 Loss_G: 0.8036 D(x): 0.5882 D(G(z)): 0.5711 / 0.3288, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[84/100][7/8] Loss_D: 1.3177 Loss_G: 0.7911 D(x): 0.4978 D(G(z)): 0.4236 / 0.3305, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[85/100][0/8] Loss_D: 1.2319 Loss_G: 0.5260 D(x): 0.4999 D(G(z)): 0.3759 / 0.4103, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.71s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][1/8] Loss_D: 1.1800 Loss_G: 0.9667 D(x): 0.6156 D(G(z)): 0.4728 / 0.3008, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.49s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][2/8] Loss_D: 1.3226 Loss_G: 0.7265 D(x): 0.5162 D(G(z)): 0.4379 / 0.3492, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][3/8] Loss_D: 1.3452 Loss_G: 0.7059 D(x): 0.5378 D(G(z)): 0.4773 / 0.3458, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:06<00:03,  1.30s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][4/8] Loss_D: 1.3792 Loss_G: 1.1501 D(x): 0.5776 D(G(z)): 0.5296 / 0.2753, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][5/8] Loss_D: 1.4684 Loss_G: 0.6887 D(x): 0.4411 D(G(z)): 0.4401 / 0.3680, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][6/8] Loss_D: 1.3940 Loss_G: 0.6082 D(x): 0.4955 D(G(z)): 0.4669 / 0.4069, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.10s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[85/100][7/8] Loss_D: 1.3140 Loss_G: 0.6410 D(x): 0.5807 D(G(z)): 0.5111 / 0.3969, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[86/100][0/8] Loss_D: 1.2769 Loss_G: 0.8783 D(x): 0.5441 D(G(z)): 0.4674 / 0.3086, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][1/8] Loss_D: 1.3356 Loss_G: 0.9337 D(x): 0.5012 D(G(z)): 0.4461 / 0.3247, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][2/8] Loss_D: 1.3654 Loss_G: 0.2849 D(x): 0.4519 D(G(z)): 0.3902 / 0.5043, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.26s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][3/8] Loss_D: 1.2451 Loss_G: 0.9037 D(x): 0.6708 D(G(z)): 0.5471 / 0.3278, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][4/8] Loss_D: 1.3869 Loss_G: 0.8693 D(x): 0.4647 D(G(z)): 0.4151 / 0.3103, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][5/8] Loss_D: 1.2729 Loss_G: 1.0003 D(x): 0.5402 D(G(z)): 0.4476 / 0.2933, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][6/8] Loss_D: 1.2838 Loss_G: 0.5807 D(x): 0.4822 D(G(z)): 0.3890 / 0.4008, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.04s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[86/100][7/8] Loss_D: 1.2285 Loss_G: 0.4650 D(x): 0.6093 D(G(z)): 0.4928 / 0.4160, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[87/100][0/8] Loss_D: 1.2721 Loss_G: 0.7675 D(x): 0.5769 D(G(z)): 0.4903 / 0.3361, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:07,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][1/8] Loss_D: 1.3353 Loss_G: 0.8615 D(x): 0.5695 D(G(z)): 0.5018 / 0.3086, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][2/8] Loss_D: 1.5643 Loss_G: 0.7518 D(x): 0.4123 D(G(z)): 0.4357 / 0.3611, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][3/8] Loss_D: 1.4543 Loss_G: 0.6511 D(x): 0.4654 D(G(z)): 0.4690 / 0.4042, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][4/8] Loss_D: 1.3696 Loss_G: 0.6492 D(x): 0.5669 D(G(z)): 0.5258 / 0.3961, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][5/8] Loss_D: 1.2785 Loss_G: 0.7964 D(x): 0.5695 D(G(z)): 0.4794 / 0.3439, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][6/8] Loss_D: 1.3518 Loss_G: 0.7020 D(x): 0.4710 D(G(z)): 0.4105 / 0.3892, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.04s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[87/100][7/8] Loss_D: 1.1263 Loss_G: 0.7849 D(x): 0.6360 D(G(z)): 0.4707 / 0.3437, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[88/100][0/8] Loss_D: 1.2561 Loss_G: 0.8490 D(x): 0.5945 D(G(z)): 0.4840 / 0.3309, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:10,  1.67s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][1/8] Loss_D: 1.2370 Loss_G: 0.6786 D(x): 0.5481 D(G(z)): 0.4303 / 0.3458, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][2/8] Loss_D: 1.4904 Loss_G: 0.3871 D(x): 0.4646 D(G(z)): 0.4489 / 0.4892, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][3/8] Loss_D: 1.2969 Loss_G: 0.4930 D(x): 0.6464 D(G(z)): 0.5436 / 0.3668, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][4/8] Loss_D: 1.2928 Loss_G: 0.9411 D(x): 0.5775 D(G(z)): 0.4920 / 0.2695, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][5/8] Loss_D: 1.2416 Loss_G: 0.7738 D(x): 0.5013 D(G(z)): 0.3887 / 0.3279, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][6/8] Loss_D: 1.3252 Loss_G: 0.4309 D(x): 0.5192 D(G(z)): 0.4454 / 0.3924, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[88/100][7/8] Loss_D: 1.3593 Loss_G: 0.8117 D(x): 0.5541 D(G(z)): 0.5061 / 0.3508, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[89/100][0/8] Loss_D: 1.3090 Loss_G: 0.5263 D(x): 0.5570 D(G(z)): 0.4781 / 0.3850, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][1/8] Loss_D: 1.3585 Loss_G: 0.7034 D(x): 0.5198 D(G(z)): 0.4743 / 0.3717, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][2/8] Loss_D: 1.3227 Loss_G: 0.6818 D(x): 0.5607 D(G(z)): 0.4883 / 0.3604, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][3/8] Loss_D: 1.2745 Loss_G: 0.7902 D(x): 0.5377 D(G(z)): 0.4532 / 0.3545, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.22s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][4/8] Loss_D: 1.3861 Loss_G: 0.8318 D(x): 0.5353 D(G(z)): 0.4901 / 0.3406, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][5/8] Loss_D: 1.5328 Loss_G: 0.7291 D(x): 0.5121 D(G(z)): 0.5345 / 0.3657, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][6/8] Loss_D: 1.5209 Loss_G: 0.4927 D(x): 0.4530 D(G(z)): 0.4831 / 0.3934, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[89/100][7/8] Loss_D: 1.3983 Loss_G: 0.7477 D(x): 0.5567 D(G(z)): 0.5211 / 0.3769, Accuracy: 20.0000 / 23.0000 = 86.9565\n",
            "[90/100][0/8] Loss_D: 1.3335 Loss_G: 0.8767 D(x): 0.5159 D(G(z)): 0.4607 / 0.3039, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.64s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][1/8] Loss_D: 1.2300 Loss_G: 0.7310 D(x): 0.5704 D(G(z)): 0.4527 / 0.3397, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.53s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][2/8] Loss_D: 1.3241 Loss_G: 0.6662 D(x): 0.4845 D(G(z)): 0.4144 / 0.3994, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:05<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][3/8] Loss_D: 1.4879 Loss_G: 0.6784 D(x): 0.5309 D(G(z)): 0.5375 / 0.3690, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][4/8] Loss_D: 1.3040 Loss_G: 0.8581 D(x): 0.5385 D(G(z)): 0.4664 / 0.3066, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][5/8] Loss_D: 1.3986 Loss_G: 0.9286 D(x): 0.5360 D(G(z)): 0.5078 / 0.3249, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.04s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][6/8] Loss_D: 1.2739 Loss_G: 0.8393 D(x): 0.5318 D(G(z)): 0.4302 / 0.3331, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[90/100][7/8] Loss_D: 1.3772 Loss_G: 0.7965 D(x): 0.5122 D(G(z)): 0.4658 / 0.3678, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[91/100][0/8] Loss_D: 1.5155 Loss_G: 0.6400 D(x): 0.4888 D(G(z)): 0.5157 / 0.3867, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.70s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][1/8] Loss_D: 1.5736 Loss_G: 0.7258 D(x): 0.4962 D(G(z)): 0.5267 / 0.3634, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.45s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][2/8] Loss_D: 1.4835 Loss_G: 0.7116 D(x): 0.4997 D(G(z)): 0.5003 / 0.3697, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][3/8] Loss_D: 1.3929 Loss_G: 0.9953 D(x): 0.5194 D(G(z)): 0.4881 / 0.2988, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][4/8] Loss_D: 1.3800 Loss_G: 0.5795 D(x): 0.4245 D(G(z)): 0.3691 / 0.4020, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.05s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][5/8] Loss_D: 1.4352 Loss_G: 0.8725 D(x): 0.5621 D(G(z)): 0.5500 / 0.3280, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.03s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][6/8] Loss_D: 1.3869 Loss_G: 0.7446 D(x): 0.5097 D(G(z)): 0.4644 / 0.3703, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[91/100][7/8] Loss_D: 1.3224 Loss_G: 0.6891 D(x): 0.4876 D(G(z)): 0.4232 / 0.3713, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[92/100][0/8] Loss_D: 1.3980 Loss_G: 0.7430 D(x): 0.5490 D(G(z)): 0.5194 / 0.3473, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.65s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][1/8] Loss_D: 1.3246 Loss_G: 0.6864 D(x): 0.5068 D(G(z)): 0.4385 / 0.3573, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:04<00:07,  1.49s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][2/8] Loss_D: 1.3908 Loss_G: 0.5813 D(x): 0.5143 D(G(z)): 0.4798 / 0.4051, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.28s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][3/8] Loss_D: 1.2539 Loss_G: 0.7376 D(x): 0.5896 D(G(z)): 0.4858 / 0.3529, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][4/8] Loss_D: 1.2127 Loss_G: 0.7538 D(x): 0.5369 D(G(z)): 0.4143 / 0.3111, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:07<00:02,  1.23s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][5/8] Loss_D: 1.2234 Loss_G: 0.6793 D(x): 0.5233 D(G(z)): 0.4009 / 0.3631, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:08<00:01,  1.12s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][6/8] Loss_D: 1.4103 Loss_G: 0.8203 D(x): 0.5316 D(G(z)): 0.4983 / 0.3339, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.07s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[92/100][7/8] Loss_D: 1.3254 Loss_G: 0.3836 D(x): 0.4366 D(G(z)): 0.3526 / 0.4455, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[93/100][0/8] Loss_D: 1.2133 Loss_G: 0.7957 D(x): 0.6665 D(G(z)): 0.5319 / 0.3321, Accuracy: 27.0000 / 32.0000 = 84.3750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.45s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][1/8] Loss_D: 1.2235 Loss_G: 0.5920 D(x): 0.4883 D(G(z)): 0.3698 / 0.3872, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][2/8] Loss_D: 1.3353 Loss_G: 0.7208 D(x): 0.5380 D(G(z)): 0.4802 / 0.3615, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.25s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][3/8] Loss_D: 1.2896 Loss_G: 0.5840 D(x): 0.5352 D(G(z)): 0.4601 / 0.3800, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.15s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][4/8] Loss_D: 1.2612 Loss_G: 0.6885 D(x): 0.5575 D(G(z)): 0.4649 / 0.3556, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.13s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][5/8] Loss_D: 1.3342 Loss_G: 0.5291 D(x): 0.4940 D(G(z)): 0.4404 / 0.4107, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][6/8] Loss_D: 1.1976 Loss_G: 0.7573 D(x): 0.6355 D(G(z)): 0.5049 / 0.3502, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[93/100][7/8] Loss_D: 1.3392 Loss_G: 0.9810 D(x): 0.5805 D(G(z)): 0.5086 / 0.2840, Accuracy: 23.0000 / 23.0000 = 100.0000\n",
            "[94/100][0/8] Loss_D: 1.2467 Loss_G: 0.7775 D(x): 0.5458 D(G(z)): 0.4422 / 0.3286, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:03<00:10,  1.79s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][1/8] Loss_D: 1.5605 Loss_G: 0.4618 D(x): 0.4470 D(G(z)): 0.4879 / 0.4452, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.50s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][2/8] Loss_D: 1.3421 Loss_G: 0.7803 D(x): 0.6072 D(G(z)): 0.5415 / 0.3590, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.35s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][3/8] Loss_D: 1.1910 Loss_G: 0.9319 D(x): 0.5931 D(G(z)): 0.4590 / 0.3159, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.19s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][4/8] Loss_D: 1.3149 Loss_G: 0.8293 D(x): 0.5254 D(G(z)): 0.4423 / 0.3400, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][5/8] Loss_D: 1.3258 Loss_G: 0.7310 D(x): 0.5198 D(G(z)): 0.4540 / 0.3548, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.04s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][6/8] Loss_D: 1.3794 Loss_G: 0.7327 D(x): 0.5038 D(G(z)): 0.4670 / 0.4052, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[94/100][7/8] Loss_D: 1.1977 Loss_G: 0.8800 D(x): 0.6705 D(G(z)): 0.5228 / 0.3454, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[95/100][0/8] Loss_D: 1.2417 Loss_G: 1.1652 D(x): 0.6228 D(G(z)): 0.5035 / 0.2401, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][1/8] Loss_D: 1.4122 Loss_G: 0.8241 D(x): 0.4257 D(G(z)): 0.3703 / 0.3414, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][2/8] Loss_D: 1.3467 Loss_G: 0.4292 D(x): 0.5332 D(G(z)): 0.4778 / 0.4438, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.24s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][3/8] Loss_D: 1.2629 Loss_G: 0.7737 D(x): 0.6033 D(G(z)): 0.4982 / 0.3394, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.17s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][4/8] Loss_D: 1.3693 Loss_G: 0.9330 D(x): 0.5309 D(G(z)): 0.4923 / 0.2721, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][5/8] Loss_D: 1.4270 Loss_G: 0.8033 D(x): 0.4343 D(G(z)): 0.4181 / 0.3539, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][6/8] Loss_D: 1.3298 Loss_G: 0.9368 D(x): 0.5422 D(G(z)): 0.4845 / 0.3042, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.05s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[95/100][7/8] Loss_D: 1.2854 Loss_G: 0.6389 D(x): 0.5117 D(G(z)): 0.4267 / 0.3765, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[96/100][0/8] Loss_D: 1.2896 Loss_G: 0.6720 D(x): 0.5174 D(G(z)): 0.4279 / 0.4014, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.52s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][1/8] Loss_D: 1.3384 Loss_G: 0.7117 D(x): 0.5495 D(G(z)): 0.4882 / 0.3501, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:07,  1.41s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][2/8] Loss_D: 1.2809 Loss_G: 0.8849 D(x): 0.5517 D(G(z)): 0.4530 / 0.3007, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.32s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][3/8] Loss_D: 1.2712 Loss_G: 0.8819 D(x): 0.5793 D(G(z)): 0.4825 / 0.2762, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][4/8] Loss_D: 1.4592 Loss_G: 0.6274 D(x): 0.4156 D(G(z)): 0.3903 / 0.4237, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][5/8] Loss_D: 1.4764 Loss_G: 0.6356 D(x): 0.5557 D(G(z)): 0.5511 / 0.4039, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][6/8] Loss_D: 1.6362 Loss_G: 0.9267 D(x): 0.4709 D(G(z)): 0.5306 / 0.3067, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[96/100][7/8] Loss_D: 1.4855 Loss_G: 0.4359 D(x): 0.4085 D(G(z)): 0.3899 / 0.3852, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[97/100][0/8] Loss_D: 1.3592 Loss_G: 0.5279 D(x): 0.5304 D(G(z)): 0.4904 / 0.4349, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.61s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][1/8] Loss_D: 1.3123 Loss_G: 0.6070 D(x): 0.5332 D(G(z)): 0.4595 / 0.3764, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.38s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][2/8] Loss_D: 1.2617 Loss_G: 1.0736 D(x): 0.5401 D(G(z)): 0.4378 / 0.2999, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][3/8] Loss_D: 1.2562 Loss_G: 0.7385 D(x): 0.5214 D(G(z)): 0.4157 / 0.3798, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][4/8] Loss_D: 1.3087 Loss_G: 0.8195 D(x): 0.5737 D(G(z)): 0.4978 / 0.3350, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][5/8] Loss_D: 1.4753 Loss_G: 0.6974 D(x): 0.4816 D(G(z)): 0.4897 / 0.3555, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][6/8] Loss_D: 1.2242 Loss_G: 0.5191 D(x): 0.5030 D(G(z)): 0.3874 / 0.4477, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[97/100][7/8] Loss_D: 1.4374 Loss_G: 0.4529 D(x): 0.6173 D(G(z)): 0.5838 / 0.4156, Accuracy: 21.0000 / 23.0000 = 91.3043\n",
            "[98/100][0/8] Loss_D: 1.3710 Loss_G: 0.7417 D(x): 0.5470 D(G(z)): 0.5097 / 0.3267, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:08,  1.47s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][1/8] Loss_D: 1.2369 Loss_G: 0.6780 D(x): 0.5301 D(G(z)): 0.4116 / 0.3538, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][2/8] Loss_D: 1.3351 Loss_G: 0.7600 D(x): 0.5606 D(G(z)): 0.5019 / 0.3350, Accuracy: 28.0000 / 32.0000 = 87.5000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:04,  1.20s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][3/8] Loss_D: 1.2334 Loss_G: 0.7527 D(x): 0.5378 D(G(z)): 0.4247 / 0.3692, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.18s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][4/8] Loss_D: 1.3044 Loss_G: 0.6457 D(x): 0.5406 D(G(z)): 0.4629 / 0.3846, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.09s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][5/8] Loss_D: 1.2921 Loss_G: 0.7481 D(x): 0.5740 D(G(z)): 0.4842 / 0.3129, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][6/8] Loss_D: 1.2169 Loss_G: 0.8721 D(x): 0.5008 D(G(z)): 0.3737 / 0.3096, Accuracy: 31.0000 / 32.0000 = 96.8750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.06s/it]\n",
            "  0%|          | 0/8 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[98/100][7/8] Loss_D: 1.3429 Loss_G: 0.5577 D(x): 0.5197 D(G(z)): 0.4598 / 0.3983, Accuracy: 22.0000 / 23.0000 = 95.6522\n",
            "[99/100][0/8] Loss_D: 1.2131 Loss_G: 0.8274 D(x): 0.6301 D(G(z)): 0.4974 / 0.3111, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 25%|██▌       | 2/8 [00:02<00:09,  1.62s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][1/8] Loss_D: 1.2410 Loss_G: 0.9224 D(x): 0.5725 D(G(z)): 0.4631 / 0.2963, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 3/8 [00:03<00:06,  1.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][2/8] Loss_D: 1.4982 Loss_G: 0.6689 D(x): 0.4486 D(G(z)): 0.4599 / 0.4340, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:04<00:05,  1.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][3/8] Loss_D: 1.4409 Loss_G: 1.1543 D(x): 0.5914 D(G(z)): 0.5556 / 0.2585, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▎   | 5/8 [00:05<00:03,  1.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][4/8] Loss_D: 1.3090 Loss_G: 0.8238 D(x): 0.4925 D(G(z)): 0.4112 / 0.3149, Accuracy: 29.0000 / 32.0000 = 90.6250\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 6/8 [00:06<00:02,  1.14s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][5/8] Loss_D: 1.2368 Loss_G: 0.6140 D(x): 0.5103 D(G(z)): 0.3929 / 0.3957, Accuracy: 32.0000 / 32.0000 = 100.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 7/8 [00:07<00:01,  1.10s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][6/8] Loss_D: 1.3560 Loss_G: 0.8158 D(x): 0.6044 D(G(z)): 0.5427 / 0.3288, Accuracy: 30.0000 / 32.0000 = 93.7500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:08<00:00,  1.08s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[99/100][7/8] Loss_D: 1.3792 Loss_G: 0.5152 D(x): 0.4587 D(G(z)): 0.4199 / 0.3607, Accuracy: 22.0000 / 23.0000 = 95.6522\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0xFCy9GW441",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test2(generator, discriminator, num_epochs, metrics, loader):\n",
        "    print('Testing Block.........')\n",
        "    now = datetime.datetime.now()\n",
        "    #g_losses = metrics['train.G_losses'][-1]\n",
        "    #d_losses = metrics['train.D_losses'][-1]\n",
        "    path='augGAN/output_images/ACGAN'\n",
        "    try:\n",
        "      os.mkdir(os.path.join('.', path))\n",
        "    except Exception as error:\n",
        "      print(error)\n",
        "\n",
        "    real_batch = next(iter(loader))\n",
        "    \n",
        "    test_img_list = []\n",
        "    test_noise = torch.randn(batch_size, nz, 1, 1, device=device)\n",
        "    test_fake = generator(test_noise).detach().cpu()\n",
        "    test_img_list.append(vutils.make_grid(test_fake, padding=2, normalize=True))\n",
        "\n",
        "    fig = plt.figure(figsize=(15,15))\n",
        "    ax1 = plt.subplot(1,2,1)\n",
        "    ax1 = plt.axis(\"off\")\n",
        "    ax1 = plt.title(\"Real Images\")\n",
        "    ax1 = plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0)))\n",
        "\n",
        "    ax2 = plt.subplot(1,2,2)\n",
        "    ax2 = plt.axis(\"off\")\n",
        "    ax2 = plt.title(\"Fake Images\")\n",
        "    ax2 = plt.imshow(np.transpose(test_img_list[-1],(1,2,0)))\n",
        "    #ax2 = plt.show()\n",
        "    #fig.savefig('%s/image_%.3f_%.3f_%d_%s.png' %\n",
        "    #                (path, g_losses, d_losses, num_epochs, now.strftime(\"%Y-%m-%d_%H:%M:%S\")))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVLWLMetCrGe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot(name, train_epoch, values, path, save):\n",
        "    clear_output(wait=True)\n",
        "    plt.close('all')\n",
        "    fig = plt.figure()\n",
        "    fig = plt.ion()\n",
        "    fig = plt.subplot(1, 1, 1)\n",
        "    fig = plt.title('epoch: %s -> %s: %s' % (train_epoch, name, values[-1]))\n",
        "    fig = plt.ylabel(name)\n",
        "    fig = plt.xlabel('train_set')\n",
        "    fig = plt.plot(values)\n",
        "    fig = plt.grid()\n",
        "    get_fig = plt.gcf()\n",
        "    fig = plt.draw()  # draw the plot\n",
        "    fig = plt.pause(1)  # show it for 1 second\n",
        "    if save:\n",
        "        now = datetime.datetime.now()\n",
        "        get_fig.savefig('%s/%s_%.3f_%d_%s.png' %\n",
        "                        (path, name, train_epoch, values[-1], now.strftime(\"%Y-%m-%d_%H:%M:%S\")))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHGbk-t3imrL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def save_model(generator, discriminator, gen_optimizer, dis_optimizer, metrics, num_epochs):\n",
        "    now = datetime.datetime.now()\n",
        "    g_losses = metrics['train.G_losses'][-1]\n",
        "    d_losses = metrics['train.D_losses'][-1]\n",
        "    name = \"%+.3f_%+.3f_%d_%s.dat\" % (g_losses, d_losses, num_epochs, now.strftime(\"%Y-%m-%d_%H:%M:%S\"))\n",
        "    # fname = os.path.join('.', 'augGAN/model', name)\n",
        "    # states = {\n",
        "    #         'state_dict_generator': generator.state_dict(),\n",
        "    #         'state_dict_discriminator': discriminator.state_dict(),\n",
        "    #         'gen_optimizer': gen_optimizer.state_dict(),\n",
        "    #         'dis_optimizer': dis_optimizer.state_dict(),\n",
        "    #         'metrics': metrics,\n",
        "    #         'train_epoch': num_epochs,\n",
        "    #         'date': now.strftime(\"%Y-%m-%d_%H:%M:%S\"),\n",
        "    # }\n",
        "    # torch.save(states, fname)\n",
        "    path='augGAN/plots/ACGAN/train_%+.3f_%+.3f_%s'% (g_losses, d_losses, now.strftime(\"%Y-%m-%d_%H:%M:%S\"))\n",
        "    try:\n",
        "      os.mkdir(os.path.join('.', path))\n",
        "    except Exception as error:\n",
        "      print(error)\n",
        "\n",
        "    plot('G_losses', num_epochs, metrics['train.G_losses'], path, True)\n",
        "    plot('D_losses', num_epochs, metrics['train.D_losses'], path, True)\n",
        "    # plot('D_x', num_epochs, metrics['train.D_x'], path, True)\n",
        "    # plot('D_G_z1', num_epochs, metrics['train.D_G_z1'], path, True)\n",
        "    # plot('D_G_z2', num_epochs, metrics['train.D_G_z2'], path, True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XA-g9pAlXW9u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "37325183-2816-490f-cffc-bd0985e192bf"
      },
      "source": [
        "test2(generator, discriminator, num_epochs, metrics, train_loader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Block.........\n",
            "[Errno 17] File exists: './augGAN/output_images/ACGAN'\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZK6Q1Nv1p8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d29cdfc4-2880-4e3a-cf85-60bd5e4f5a5f"
      },
      "source": [
        "save_model(generator, discriminator, optimizerG, optimizerD, metrics, num_epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1Zz7NErSjEG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "outputId": "fee70700-19db-413a-dc76-027183a5e97b"
      },
      "source": [
        "test_batch = 16\n",
        "test_fake = 1\n",
        "\n",
        "if test_fake:\n",
        "  #check for fake image\n",
        "  test_img_list = []\n",
        "  test_noise = torch.randn(test_batch, nz, 1, 1, device=device)\n",
        "  test_img = generator(test_noise)#.detach().cpu()\n",
        "\n",
        "else:\n",
        "  #check for real image\n",
        "  test_loader = torch.utils.data.DataLoader(train_set, batch_size=test_batch,\n",
        "                                            shuffle=True)\n",
        "  data = next(iter(test_loader))\n",
        "  test_noise, test_class_lable = data\n",
        "  test_img.data.resize_(test_noise.size()).copy_(test_noise)\n",
        "  #print(data[0].size())\n",
        "  print('class label for real', test_class_lable)\n",
        "\n",
        "s_output,c_label_op = discriminator(test_img.detach().to(device))\n",
        "print('Discriminator s o/p', s_output)\n",
        "print('Discriminator c o/p', c_label_op)\n",
        "\n",
        "# label = np.random.randint(0, nb_label, batch_size)\n",
        "# c_label.data.resize_(batch_size).copy_(torch.from_numpy(label))\n",
        "# print(c_label)\n",
        "\n",
        "test_img = test_img.detach().cpu()\n",
        "test_img_list.append(vutils.make_grid(test_img, padding=2, normalize=True))\n",
        "plt.imshow(np.transpose(test_img_list[-1],(1,2,0)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Discriminator s o/p tensor([[0.3659],\n",
            "        [0.5745],\n",
            "        [0.5172],\n",
            "        [0.4262],\n",
            "        [0.3118],\n",
            "        [0.3229],\n",
            "        [0.3278],\n",
            "        [0.2713],\n",
            "        [0.5597],\n",
            "        [0.4213],\n",
            "        [0.3655],\n",
            "        [0.1485],\n",
            "        [0.5273],\n",
            "        [0.2364],\n",
            "        [0.3784],\n",
            "        [0.2657]], device='cuda:0', grad_fn=<SigmoidBackward>)\n",
            "Discriminator c o/p tensor([[9.9995e-01, 7.4573e-06, 4.4422e-05],\n",
            "        [9.9946e-01, 3.7034e-05, 4.9935e-04],\n",
            "        [1.2589e-04, 2.6322e-03, 9.9724e-01],\n",
            "        [9.9725e-01, 6.2166e-05, 2.6858e-03],\n",
            "        [1.3435e-01, 8.5405e-01, 1.1603e-02],\n",
            "        [9.6320e-04, 9.9903e-01, 4.9221e-06],\n",
            "        [3.8539e-04, 5.1185e-04, 9.9910e-01],\n",
            "        [1.5780e-03, 7.0178e-01, 2.9665e-01],\n",
            "        [5.8448e-06, 9.9944e-01, 5.5230e-04],\n",
            "        [9.2498e-02, 9.0462e-01, 2.8839e-03],\n",
            "        [6.3354e-01, 5.0523e-04, 3.6596e-01],\n",
            "        [5.2748e-03, 1.5330e-02, 9.7939e-01],\n",
            "        [1.3467e-05, 2.0089e-03, 9.9798e-01],\n",
            "        [2.2483e-04, 9.9977e-01, 3.5182e-07],\n",
            "        [6.8937e-01, 6.3833e-02, 2.4680e-01],\n",
            "        [9.5946e-05, 3.7330e-01, 6.2660e-01]], device='cuda:0',\n",
            "       grad_fn=<SoftmaxBackward>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7b0c1066d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}