{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Lz8Mphi2Qmn",
        "outputId": "cb3494f2-2de2-4756-a28b-7f64313cc366"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in c:\\users\\user\\.conda\\envs\\proj\\lib\\site-packages (2.2.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install numpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "64HDUzZ22Qmq",
        "outputId": "a565af81-c359-4593-c050-3f10178be215"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extraction complete!\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "import os\n",
        "\n",
        "# Path to your zip file\n",
        "zip_path = 'zddtpgzv63-2.zip'\n",
        "# Destination folder for extraction\n",
        "extract_to = 'dataset/'\n",
        "\n",
        "# Unzipping the file\n",
        "with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_to)\n",
        "\n",
        "print(\"Extraction complete!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "id": "KZPuvXJJQkJi"
      },
      "outputs": [],
      "source": [
        "from __future__ import print_function\n",
        "import time\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.parallel\n",
        "import torch.optim as optim\n",
        "import torch.utils.data\n",
        "import torchvision.datasets as dset\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.utils as vutils\n",
        "from torch.autograd import Variable\n",
        "import matplotlib.pyplot as plt\n",
        "from skimage.transform import resize\n",
        "import numpy as np\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from torchvision.utils import save_image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from tqdm import tqdm_notebook as tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "igVBK0cJ2Qms",
        "outputId": "cab14049-2e06-41fb-95ad-ba72336b9afe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.6.0+cpu\n",
            "0.21.0+cpu\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "id": "SI4Sr16bQkJk",
        "outputId": "5992d365-e902-44db-f05d-0344e9070450"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total dog pictures available: 4\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 25 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import random\n",
        "# Define the path to the directory containing dog images\n",
        "PATH = \"C:/Users/User/CellGAN/dataset\"\n",
        "\n",
        "# Get the list of image filenames in the directory\n",
        "images = os.listdir(PATH)\n",
        "\n",
        "# Display the count of dog pictures in the directory\n",
        "print(f'Total dog pictures available: {len(os.listdir(PATH))}')\n",
        "\n",
        "# Create subplots to display random images\n",
        "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(8,8))\n",
        "\n",
        "# Iterate through the subplots and display random dog images\n",
        "for indx, axis in enumerate(axes.flatten()):\n",
        "    PATH_TEMP = random.choice(os.listdir(PATH))\n",
        "    # Get a random index within the range of available images\n",
        "    rnd_indx = np.random.randint(0, len(os.listdir(PATH+'/'+PATH_TEMP)))\n",
        "\n",
        "    # Load and display the image using Matplotlib\n",
        "    images = os.listdir(PATH+'/'+PATH_TEMP+'/')\n",
        "    img = plt.imread(PATH+'/'+PATH_TEMP + '/'+images[rnd_indx])\n",
        "    # Resize the image to a constant size (e.g., 64x64)\n",
        "\n",
        "    img = resize(img, (400,400))\n",
        "    imgplot = axis.imshow(img)\n",
        "    axis.set_axis_off()  # Turn off the axis for better visualization\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fq2km-dJQkJl"
      },
      "outputs": [],
      "source": [
        "batch_size = 32\n",
        "image_size = 64\n",
        "\n",
        "random_transforms = [transforms.ColorJitter(), transforms.RandomRotation(degrees=20)]\n",
        "transform = transforms.Compose([transforms.Resize(64),\n",
        "                                transforms.CenterCrop(64),\n",
        "                                transforms.RandomHorizontalFlip(p=0.5),\n",
        "                                transforms.RandomApply(random_transforms, p=0.2),\n",
        "                                transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "train_data = datasets.ImageFolder(PATH, transform=transform)\n",
        "train_loader = torch.utils.data.DataLoader(train_data, shuffle=True,\n",
        "                                           batch_size=batch_size)\n",
        "\n",
        "imgs, label = next(iter(train_loader))\n",
        "imgs = imgs.numpy().transpose(0, 2, 3, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_P6c1mu4QkJl",
        "outputId": "7e0ad48e-0821-430f-df8f-92913729c148"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.96862745].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.5137255..0.8666667].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.254902].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.5764706..0.7490196].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9843137..0.4039216].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.7882353].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.32549024].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.75686276..0.8039216].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99215686].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8117647..0.9607843].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9843137].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9764706..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.827451..0.7019608].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99215686..0.41176474].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.5294118..0.45098042].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.85882354..0.7254902].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8980392..0.48235297].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8509804..0.4431373].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.90588236..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9764706].\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 25 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(5,5, figsize = (8,8))\n",
        "\n",
        "for i in range(5):\n",
        "    for j in range(5):\n",
        "        axs[i,j].imshow(imgs[5*i+j])\n",
        "        axs[i,j].axis('off')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izNh5JxHQkJm"
      },
      "source": [
        "# Weights\n",
        "### Defining the weights_init function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DnCxTfEeQkJo"
      },
      "outputs": [],
      "source": [
        "def weights_init(m):\n",
        "    \"\"\"\n",
        "    Takes as input a neural network m that will initialize all its weights.\n",
        "    \"\"\"\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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsA3L763QkJo"
      },
      "source": [
        "# Generator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jm656v9zQkJp",
        "outputId": "7fdcb59e-78bc-4d99-9820-3198fe8b246a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "G(\n",
              "  (main): Sequential(\n",
              "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
              "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (8): ReLU(inplace=True)\n",
              "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (13): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class G(nn.Module):\n",
        "    def __init__(self):\n",
        "        # Used to inherit the torch.nn Module\n",
        "        super(G, self).__init__()\n",
        "        # Meta Module - consists of different layers of Modules\n",
        "        self.main = nn.Sequential(\n",
        "                nn.ConvTranspose2d(100, 512, 4, stride=1, padding=0, bias=False),\n",
        "                nn.BatchNorm2d(512),\n",
        "                nn.ReLU(True),\n",
        "                nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1, bias=False),\n",
        "                nn.BatchNorm2d(256),\n",
        "                nn.ReLU(True),\n",
        "                nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1, bias=False),\n",
        "                nn.BatchNorm2d(128),\n",
        "                nn.ReLU(True),\n",
        "                nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1, bias=False),\n",
        "                nn.BatchNorm2d(64),\n",
        "                nn.ReLU(True),\n",
        "                nn.ConvTranspose2d(64, 3, 4, stride=2, padding=1, bias=False),\n",
        "                nn.Tanh()\n",
        "                )\n",
        "\n",
        "    def forward(self, input):\n",
        "        output = self.main(input)\n",
        "        return output\n",
        "\n",
        "# Creating the generator\n",
        "netG = G()\n",
        "netG.apply(weights_init)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efkl-ctTQkJq"
      },
      "source": [
        "# Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4v6ws7IsQkJq",
        "outputId": "1cccd816-4ddc-4c25-a340-f738906772d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "D(\n",
              "  (main): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
              "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
              "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
              "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
              "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
              "    (12): Sigmoid()\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Defining the discriminator\n",
        "class D(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(D, self).__init__()\n",
        "        self.main = nn.Sequential(\n",
        "                nn.Conv2d(3, 64, 4, stride=2, padding=1, bias=False),\n",
        "                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                nn.Conv2d(64, 128, 4, stride=2, padding=1, bias=False),\n",
        "                nn.BatchNorm2d(128),\n",
        "                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                nn.Conv2d(128, 256, 4, stride=2, padding=1, bias=False),\n",
        "                nn.BatchNorm2d(256),\n",
        "                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                nn.Conv2d(256, 512, 4, stride=2, padding=1, bias=False),\n",
        "                nn.BatchNorm2d(512),\n",
        "                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n",
        "                nn.Conv2d(512, 1, 4, stride=1, padding=0, bias=False),\n",
        "                nn.Sigmoid()\n",
        "                )\n",
        "\n",
        "    def forward(self, input):\n",
        "        output = self.main(input)\n",
        "        # .view(-1) = Flattens the output into 1D instead of 2D\n",
        "        return output.view(-1)\n",
        "\n",
        "\n",
        "# Creating the discriminator\n",
        "netD = D()\n",
        "netD.apply(weights_init)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMjgh0yCQkJr"
      },
      "source": [
        "## Another setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p-2r65W6QkJr"
      },
      "outputs": [],
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self, nz=128, channels=3):\n",
        "        super(Generator, self).__init__()\n",
        "\n",
        "        self.nz = nz\n",
        "        self.channels = channels\n",
        "\n",
        "        def convlayer(n_input, n_output, k_size=4, stride=2, padding=0):\n",
        "            block = [\n",
        "                nn.ConvTranspose2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False),\n",
        "                nn.BatchNorm2d(n_output),\n",
        "                nn.ReLU(inplace=True),\n",
        "            ]\n",
        "            return block\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            *convlayer(self.nz, 1024, 4, 1, 0), # Fully connected layer via convolution.\n",
        "            *convlayer(1024, 512, 4, 2, 1),\n",
        "            *convlayer(512, 256, 4, 2, 1),\n",
        "            *convlayer(256, 128, 4, 2, 1),\n",
        "            *convlayer(128, 64, 4, 2, 1),\n",
        "            nn.ConvTranspose2d(64, self.channels, 3, 1, 1),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, z):\n",
        "        z = z.view(-1, self.nz, 1, 1)\n",
        "        img = self.model(z)\n",
        "        return img\n",
        "\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, channels=3):\n",
        "        super(Discriminator, self).__init__()\n",
        "\n",
        "        self.channels = channels\n",
        "\n",
        "        def convlayer(n_input, n_output, k_size=4, stride=2, padding=0, bn=False):\n",
        "            block = [nn.Conv2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False)]\n",
        "            if bn:\n",
        "                block.append(nn.BatchNorm2d(n_output))\n",
        "            block.append(nn.LeakyReLU(0.2, inplace=True))\n",
        "            return block\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            *convlayer(self.channels, 32, 4, 2, 1),\n",
        "            *convlayer(32, 64, 4, 2, 1),\n",
        "            *convlayer(64, 128, 4, 2, 1, bn=True),\n",
        "            *convlayer(128, 256, 4, 2, 1, bn=True),\n",
        "            nn.Conv2d(256, 1, 4, 1, 0, bias=False),  # FC with Conv.\n",
        "        )\n",
        "\n",
        "    def forward(self, imgs):\n",
        "        logits = self.model(imgs)\n",
        "        out = torch.sigmoid(logits)\n",
        "\n",
        "        return out.view(-1, 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-66Cp737QkJr"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S5K2J7eVQkJr",
        "outputId": "798a479a-2d91-42c6-e39a-902b8f806ed3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "A subdirectory or file results already exists.\n",
            "'ls' is not recognized as an internal or external command,\n",
            "operable program or batch file.\n"
          ]
        }
      ],
      "source": [
        "!mkdir results\n",
        "!ls"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4ghP474QkJs"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bvKGxEUOQkJs",
        "outputId": "a3e79347-afd2-4f1d-f5a5-b8a0afddc9e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "batch_size = 32\n",
        "LR_G = 0.001\n",
        "LR_D = 0.0005\n",
        "\n",
        "beta1 = 0.5\n",
        "epochs = 2750\n",
        "\n",
        "real_label = 0.9\n",
        "fake_label = 0\n",
        "nz = 128\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3LES7PtQkJs"
      },
      "source": [
        "### Initialize models and optimizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wdSbdbGeQkJs"
      },
      "outputs": [],
      "source": [
        "netG = Generator(nz).to(device)\n",
        "netD = Discriminator().to(device)\n",
        "\n",
        "criterion = nn.BCELoss()\n",
        "\n",
        "optimizerD = optim.Adam(netD.parameters(), lr=LR_D, betas=(beta1, 0.999))\n",
        "optimizerG = optim.Adam(netG.parameters(), lr=LR_G, betas=(beta1, 0.999))\n",
        "\n",
        "fixed_noise = torch.randn(25, nz, 1, 1, device=device)\n",
        "\n",
        "G_losses = []\n",
        "D_losses = []\n",
        "epoch_time = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Xf09gD8QkJs"
      },
      "source": [
        "**Plot Loss per EPOCH**\n",
        "> plot_loss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "_kg_hide-output": false,
        "id": "Pzvex6moQkJs"
      },
      "outputs": [],
      "source": [
        "def plot_loss (G_losses, D_losses, epoch):\n",
        "    plt.figure(figsize=(10,5))\n",
        "    plt.title(\"Generator and Discriminator Loss - EPOCH \"+ str(epoch))\n",
        "    plt.plot(G_losses,label=\"G\")\n",
        "    plt.plot(D_losses,label=\"D\")\n",
        "    plt.xlabel(\"iterations\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M07J73ioQkJs"
      },
      "source": [
        "**Show generated images**\n",
        "> show_generated_img()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "_kg_hide-output": false,
        "id": "ZpftnVJHQkJt"
      },
      "outputs": [],
      "source": [
        "def show_generated_img(n_images=5):\n",
        "    sample = []\n",
        "    for _ in range(n_images):\n",
        "        noise = torch.randn(1, nz, 1, 1, device=device)\n",
        "        gen_image = netG(noise).to(\"cpu\").clone().detach().squeeze(0)\n",
        "        gen_image = gen_image.numpy().transpose(1, 2, 0)\n",
        "        sample.append(gen_image)\n",
        "\n",
        "    figure, axes = plt.subplots(1, len(sample), figsize = (64,64))\n",
        "    for index, axis in enumerate(axes):\n",
        "        axis.axis('off')\n",
        "        image_array = sample[index]\n",
        "        axis.imshow(image_array)\n",
        "\n",
        "    plt.show()\n",
        "    plt.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb3Auw6zQkJt"
      },
      "source": [
        "### Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "_kg_hide-output": false,
        "id": "5V3psyo6QkJt",
        "outputId": "4b9f04c7-b1d5-4722-bf15-a55bd688cbbc",
        "colab": {
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        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_10832\\4272753609.py:16: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for ii, (real_images, train_labels) in tqdm(enumerate(train_loader), total=len(train_loader)):\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92e1ddd20b08458492f752598183b457",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1/2750][20/41] Loss_D: 0.3499 Loss_G: 4.8149 D(x): 0.9204 D(G(z)): 0.0083 / 0.0048\n",
            "[1/2750][40/41] Loss_D: 1.6476 Loss_G: 7.1970 D(x): 0.7437 D(G(z)): 0.6575 / 0.0010\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d5c37a26d2245dea2cfef1441118c8b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2/2750][20/41] Loss_D: 0.6746 Loss_G: 2.0736 D(x): 0.6153 D(G(z)): 0.0883 / 0.1187\n",
            "[2/2750][40/41] Loss_D: 0.6904 Loss_G: 1.8846 D(x): 0.7213 D(G(z)): 0.2162 / 0.1309\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8fa5be0aef274d9fb62d67cb6c14ba08",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[3/2750][20/41] Loss_D: 0.7635 Loss_G: 2.6761 D(x): 0.7847 D(G(z)): 0.2913 / 0.0757\n",
            "[3/2750][40/41] Loss_D: 0.7303 Loss_G: 3.1224 D(x): 0.8121 D(G(z)): 0.2903 / 0.0353\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c40b55006cf64b2f8aa7c3859256adb5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[4/2750][20/41] Loss_D: 0.9378 Loss_G: 1.5569 D(x): 0.5634 D(G(z)): 0.2293 / 0.2115\n",
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        },
        {
          "data": {
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            "[5/2750][20/41] Loss_D: 1.1116 Loss_G: 1.1222 D(x): 0.4604 D(G(z)): 0.2645 / 0.3103\n",
            "[5/2750][40/41] Loss_D: 1.6450 Loss_G: 0.9674 D(x): 0.2443 D(G(z)): 0.1598 / 0.3710\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[6/2750][20/41] Loss_D: 1.2078 Loss_G: 1.8361 D(x): 0.5978 D(G(z)): 0.4526 / 0.1378\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[7/2750][20/41] Loss_D: 1.0269 Loss_G: 1.4792 D(x): 0.4257 D(G(z)): 0.1329 / 0.2098\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[8/2750][20/41] Loss_D: 0.8598 Loss_G: 2.9217 D(x): 0.6072 D(G(z)): 0.2322 / 0.0427\n",
            "[8/2750][40/41] Loss_D: 0.5552 Loss_G: 2.3102 D(x): 0.8225 D(G(z)): 0.1675 / 0.0817\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a73f1360c56444ba9265e28d935cad3",
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          "text": [
            "[9/2750][20/41] Loss_D: 0.8771 Loss_G: 1.6045 D(x): 0.5821 D(G(z)): 0.2292 / 0.1820\n",
            "[9/2750][40/41] Loss_D: 1.1330 Loss_G: 1.8224 D(x): 0.3528 D(G(z)): 0.0889 / 0.1454\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25fa7b418a574d59b37f6c9b783c8142",
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          "text": [
            "[10/2750][20/41] Loss_D: 0.9976 Loss_G: 1.5038 D(x): 0.4886 D(G(z)): 0.2061 / 0.2151\n",
            "[10/2750][40/41] Loss_D: 1.0304 Loss_G: 3.0694 D(x): 0.7841 D(G(z)): 0.4045 / 0.0432\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "702cf13b95824ba0be902a3088611bc9",
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          "text": [
            "[11/2750][20/41] Loss_D: 1.1805 Loss_G: 2.1130 D(x): 0.5263 D(G(z)): 0.3593 / 0.1418\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8e26d926c2fb48d8966c06b72706a933",
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          "text": [
            "[12/2750][20/41] Loss_D: 1.5860 Loss_G: 4.2049 D(x): 0.7882 D(G(z)): 0.6451 / 0.0130\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6fba0e414934f5daa452e6fc22a826b",
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          "text": [
            "[13/2750][20/41] Loss_D: 1.0499 Loss_G: 3.5611 D(x): 0.7724 D(G(z)): 0.4631 / 0.0228\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11e79d35b575494593df1701f0e76fd8",
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          "text": [
            "[14/2750][20/41] Loss_D: 0.9713 Loss_G: 2.1305 D(x): 0.6654 D(G(z)): 0.3463 / 0.1071\n",
            "[14/2750][40/41] Loss_D: 1.2032 Loss_G: 1.7341 D(x): 0.4171 D(G(z)): 0.2279 / 0.1667\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[15/2750][20/41] Loss_D: 1.1406 Loss_G: 1.9809 D(x): 0.4802 D(G(z)): 0.2715 / 0.1444\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f074126c2d174c418e6f294d8f470194",
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          "text": [
            "[16/2750][20/41] Loss_D: 1.1347 Loss_G: 2.5003 D(x): 0.7536 D(G(z)): 0.4874 / 0.0788\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0692a0373094566bfa54500f3e377a4",
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          "text": [
            "[17/2750][20/41] Loss_D: 1.5311 Loss_G: 2.7619 D(x): 0.7598 D(G(z)): 0.6425 / 0.0577\n",
            "[17/2750][40/41] Loss_D: 1.2100 Loss_G: 1.8475 D(x): 0.6926 D(G(z)): 0.5062 / 0.1462\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[18/2750][20/41] Loss_D: 1.4622 Loss_G: 1.4400 D(x): 0.3936 D(G(z)): 0.3658 / 0.2286\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "59e0e600bc194dcf8f39b35744428d29",
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          "text": [
            "[19/2750][20/41] Loss_D: 1.0836 Loss_G: 1.4625 D(x): 0.4819 D(G(z)): 0.2730 / 0.2425\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7791c6aaabe44ec3abe9d43cb6dc44ef",
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          "text": [
            "[20/2750][20/41] Loss_D: 1.4667 Loss_G: 0.9634 D(x): 0.2839 D(G(z)): 0.1606 / 0.4087\n",
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fe969a85c3940c69c36001e83fc7d1b",
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          "text": [
            "[21/2750][20/41] Loss_D: 0.9967 Loss_G: 1.4217 D(x): 0.5640 D(G(z)): 0.3026 / 0.2265\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f7f21ecf93584538b3298d74a5daad9b",
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          "text": [
            "[22/2750][20/41] Loss_D: 1.1126 Loss_G: 1.5411 D(x): 0.5110 D(G(z)): 0.3326 / 0.2061\n",
            "[22/2750][40/41] Loss_D: 1.2656 Loss_G: 1.9648 D(x): 0.5801 D(G(z)): 0.4696 / 0.1389\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5fed606a68148bd82bb19a6a527629d",
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          "text": [
            "[23/2750][20/41] Loss_D: 1.4411 Loss_G: 1.1055 D(x): 0.2856 D(G(z)): 0.1773 / 0.3449\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6fe06b5ccc72438e9e6e494000b5bd99",
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          "text": [
            "[24/2750][20/41] Loss_D: 0.9622 Loss_G: 1.9438 D(x): 0.5806 D(G(z)): 0.2749 / 0.1392\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58abbbdb401149198aac2a4bbe3ce03c",
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          "text": [
            "[25/2750][20/41] Loss_D: 1.1928 Loss_G: 2.4332 D(x): 0.6171 D(G(z)): 0.4515 / 0.0868\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[26/2750][20/41] Loss_D: 0.8218 Loss_G: 1.8797 D(x): 0.6068 D(G(z)): 0.2120 / 0.1398\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[27/2750][20/41] Loss_D: 1.1077 Loss_G: 1.2834 D(x): 0.4713 D(G(z)): 0.2556 / 0.2671\n",
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          "text": [
            "[28/2750][20/41] Loss_D: 1.0650 Loss_G: 1.7062 D(x): 0.4830 D(G(z)): 0.2459 / 0.1776\n",
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          "text": [
            "[29/2750][20/41] Loss_D: 1.0850 Loss_G: 1.7273 D(x): 0.6135 D(G(z)): 0.4011 / 0.1582\n",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11975acacf5f4d68b19ccdf3615861de",
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            "[30/2750][20/41] Loss_D: 1.0475 Loss_G: 1.6446 D(x): 0.4581 D(G(z)): 0.1951 / 0.1901\n",
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            "[31/2750][20/41] Loss_D: 1.0344 Loss_G: 1.9633 D(x): 0.4983 D(G(z)): 0.2392 / 0.1373\n",
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            "[32/2750][20/41] Loss_D: 1.0372 Loss_G: 1.8055 D(x): 0.5878 D(G(z)): 0.3523 / 0.1676\n",
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            "[33/2750][20/41] Loss_D: 1.2612 Loss_G: 1.5663 D(x): 0.3539 D(G(z)): 0.2107 / 0.1959\n",
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            "[34/2750][20/41] Loss_D: 1.3649 Loss_G: 1.1579 D(x): 0.4281 D(G(z)): 0.3392 / 0.3260\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[35/2750][20/41] Loss_D: 0.8796 Loss_G: 1.9143 D(x): 0.5873 D(G(z)): 0.2394 / 0.1386\n",
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            "[36/2750][20/41] Loss_D: 1.4261 Loss_G: 1.2278 D(x): 0.3118 D(G(z)): 0.2276 / 0.2850\n",
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            "[37/2750][20/41] Loss_D: 1.1480 Loss_G: 1.4097 D(x): 0.5446 D(G(z)): 0.3708 / 0.2490\n",
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            "[38/2750][20/41] Loss_D: 1.2485 Loss_G: 1.4976 D(x): 0.4283 D(G(z)): 0.2996 / 0.2336\n",
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            "[39/2750][20/41] Loss_D: 1.0670 Loss_G: 1.3792 D(x): 0.6176 D(G(z)): 0.3788 / 0.2633\n",
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            "[40/2750][20/41] Loss_D: 0.8033 Loss_G: 2.2757 D(x): 0.8726 D(G(z)): 0.3375 / 0.0971\n",
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            "[41/2750][20/41] Loss_D: 0.6048 Loss_G: 2.7293 D(x): 0.7146 D(G(z)): 0.1486 / 0.0674\n",
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            "[42/2750][20/41] Loss_D: 0.9874 Loss_G: 1.5358 D(x): 0.6233 D(G(z)): 0.2996 / 0.2220\n",
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            "[43/2750][20/41] Loss_D: 0.6954 Loss_G: 2.4019 D(x): 0.8014 D(G(z)): 0.2549 / 0.0808\n",
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            "[44/2750][20/41] Loss_D: 0.7016 Loss_G: 2.6524 D(x): 0.9216 D(G(z)): 0.2749 / 0.0703\n",
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            "[45/2750][20/41] Loss_D: 0.7092 Loss_G: 2.3414 D(x): 0.8352 D(G(z)): 0.2694 / 0.0927\n",
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            "[46/2750][20/41] Loss_D: 1.1204 Loss_G: 1.8720 D(x): 0.3594 D(G(z)): 0.0818 / 0.2244\n",
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            "[47/2750][20/41] Loss_D: 0.9429 Loss_G: 2.6181 D(x): 0.6086 D(G(z)): 0.2770 / 0.0811\n",
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            "[48/2750][20/41] Loss_D: 0.7049 Loss_G: 2.4059 D(x): 0.5780 D(G(z)): 0.0565 / 0.1009\n",
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            "[49/2750][20/41] Loss_D: 1.1272 Loss_G: 1.3512 D(x): 0.4724 D(G(z)): 0.2596 / 0.2643\n",
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            "[50/2750][20/41] Loss_D: 1.2418 Loss_G: 2.5049 D(x): 0.3220 D(G(z)): 0.0273 / 0.0933\n",
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            "[51/2750][20/41] Loss_D: 0.6560 Loss_G: 2.0537 D(x): 0.6852 D(G(z)): 0.1212 / 0.1204\n",
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            "[52/2750][20/41] Loss_D: 0.8486 Loss_G: 2.3999 D(x): 0.6993 D(G(z)): 0.2706 / 0.1106\n",
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            "[53/2750][20/41] Loss_D: 0.5945 Loss_G: 2.8562 D(x): 0.7422 D(G(z)): 0.1437 / 0.0628\n",
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            "[54/2750][20/41] Loss_D: 0.9899 Loss_G: 2.7351 D(x): 0.8763 D(G(z)): 0.4214 / 0.0591\n",
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            "[55/2750][20/41] Loss_D: 0.7411 Loss_G: 1.9138 D(x): 0.5786 D(G(z)): 0.0859 / 0.1532\n",
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            "[57/2750][20/41] Loss_D: 0.9288 Loss_G: 1.9122 D(x): 0.4555 D(G(z)): 0.0594 / 0.1470\n",
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            "[69/2750][20/41] Loss_D: 0.5417 Loss_G: 3.1730 D(x): 0.8687 D(G(z)): 0.1704 / 0.0345\n",
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            "[71/2750][20/41] Loss_D: 1.3533 Loss_G: 2.3456 D(x): 0.2593 D(G(z)): 0.0339 / 0.1045\n",
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            "[72/2750][20/41] Loss_D: 1.0626 Loss_G: 2.8467 D(x): 0.3962 D(G(z)): 0.0603 / 0.0835\n",
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            "[73/2750][20/41] Loss_D: 0.5725 Loss_G: 2.4031 D(x): 0.8195 D(G(z)): 0.1804 / 0.0940\n",
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            "[74/2750][20/41] Loss_D: 0.5989 Loss_G: 2.1993 D(x): 0.7383 D(G(z)): 0.1537 / 0.0980\n",
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            "[75/2750][20/41] Loss_D: 0.5802 Loss_G: 2.5428 D(x): 0.7472 D(G(z)): 0.1331 / 0.0689\n",
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            "[76/2750][20/41] Loss_D: 0.9547 Loss_G: 2.3834 D(x): 0.5320 D(G(z)): 0.1237 / 0.0900\n",
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            "[78/2750][20/41] Loss_D: 0.7820 Loss_G: 1.8314 D(x): 0.8272 D(G(z)): 0.3048 / 0.1639\n",
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            "[79/2750][20/41] Loss_D: 0.5009 Loss_G: 2.4743 D(x): 0.7244 D(G(z)): 0.0487 / 0.0832\n",
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            "[82/2750][20/41] Loss_D: 1.8487 Loss_G: 1.5980 D(x): 0.2130 D(G(z)): 0.1219 / 0.2511\n",
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            "[83/2750][20/41] Loss_D: 1.0439 Loss_G: 1.5931 D(x): 0.4595 D(G(z)): 0.1645 / 0.2182\n",
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        {
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              "model_id": "148132331ec348a8976116df974dcaa8",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[87/2750][20/41] Loss_D: 0.4055 Loss_G: 3.0567 D(x): 0.8079 D(G(z)): 0.0287 / 0.0463\n",
            "[87/2750][40/41] Loss_D: 1.4788 Loss_G: 1.7557 D(x): 0.3784 D(G(z)): 0.3361 / 0.1588\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf71149d78f84783a372a0212767babf",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[88/2750][20/41] Loss_D: 0.8134 Loss_G: 2.1267 D(x): 0.7237 D(G(z)): 0.2849 / 0.1114\n",
            "[88/2750][40/41] Loss_D: 0.9107 Loss_G: 2.1958 D(x): 0.5331 D(G(z)): 0.1792 / 0.1016\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90bff4a1682a4f8b89f5329905ee4c40",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[89/2750][20/41] Loss_D: 1.0969 Loss_G: 2.4703 D(x): 0.6305 D(G(z)): 0.3978 / 0.0799\n",
            "[89/2750][40/41] Loss_D: 0.8490 Loss_G: 2.0139 D(x): 0.8028 D(G(z)): 0.3312 / 0.1236\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cf5508adff1b483f834b5daa1f3608dc",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[90/2750][20/41] Loss_D: 1.3155 Loss_G: 3.2075 D(x): 0.8994 D(G(z)): 0.5857 / 0.0394\n",
            "[90/2750][40/41] Loss_D: 0.9105 Loss_G: 2.5009 D(x): 0.8145 D(G(z)): 0.3761 / 0.0865\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "299b9c0a523242aebf46018a41bcb551",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[91/2750][20/41] Loss_D: 0.5184 Loss_G: 2.1510 D(x): 0.8849 D(G(z)): 0.1551 / 0.1093\n",
            "[91/2750][40/41] Loss_D: 1.0847 Loss_G: 2.9371 D(x): 0.8481 D(G(z)): 0.4661 / 0.0497\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7dedab655554e439def2162c41732b2",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[92/2750][20/41] Loss_D: 0.7438 Loss_G: 1.6475 D(x): 0.5909 D(G(z)): 0.1287 / 0.2035\n",
            "[92/2750][40/41] Loss_D: 0.7616 Loss_G: 2.6448 D(x): 0.8058 D(G(z)): 0.2983 / 0.0696\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "229e8a1cd0564842a5aa7db1fcb71e0d",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[93/2750][20/41] Loss_D: 0.6222 Loss_G: 3.5735 D(x): 0.5918 D(G(z)): 0.0227 / 0.0258\n",
            "[93/2750][40/41] Loss_D: 0.9932 Loss_G: 2.2936 D(x): 0.7389 D(G(z)): 0.4085 / 0.0917\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "826ab7ba6b4c479ba736496a27325488",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[94/2750][20/41] Loss_D: 0.6984 Loss_G: 2.3093 D(x): 0.5587 D(G(z)): 0.0333 / 0.0916\n",
            "[94/2750][40/41] Loss_D: 0.5484 Loss_G: 3.1639 D(x): 0.9764 D(G(z)): 0.1074 / 0.0429\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9fa5f58ce60447384c65a24a2815a2c",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[95/2750][20/41] Loss_D: 0.5850 Loss_G: 2.2666 D(x): 0.7926 D(G(z)): 0.1669 / 0.1128\n",
            "[95/2750][40/41] Loss_D: 1.0566 Loss_G: 2.1216 D(x): 0.5484 D(G(z)): 0.2948 / 0.1104\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f2b439b265d4179bed71fd754dee7db",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[96/2750][20/41] Loss_D: 1.1387 Loss_G: 1.6977 D(x): 0.3939 D(G(z)): 0.1604 / 0.1888\n",
            "[96/2750][40/41] Loss_D: 0.7615 Loss_G: 1.8678 D(x): 0.6829 D(G(z)): 0.2400 / 0.1483\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2593d2aaf7674decb3da59c273674732",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[97/2750][20/41] Loss_D: 0.7306 Loss_G: 2.5781 D(x): 0.5658 D(G(z)): 0.0909 / 0.0819\n",
            "[97/2750][40/41] Loss_D: 0.8174 Loss_G: 2.9081 D(x): 0.8545 D(G(z)): 0.3559 / 0.0488\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bda249c4d9774f6581af39eb551fe1ea",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[98/2750][20/41] Loss_D: 0.5652 Loss_G: 3.6116 D(x): 0.6149 D(G(z)): 0.0149 / 0.0251\n",
            "[98/2750][40/41] Loss_D: 0.6512 Loss_G: 2.8711 D(x): 0.5918 D(G(z)): 0.0337 / 0.0548\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf6d2d96a0ac43abb524a935c6c79429",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[99/2750][20/41] Loss_D: 1.3000 Loss_G: 1.5177 D(x): 0.3230 D(G(z)): 0.0788 / 0.2570\n",
            "[99/2750][40/41] Loss_D: 0.7317 Loss_G: 2.0657 D(x): 0.6985 D(G(z)): 0.1951 / 0.1457\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "280131a47f714281890de0bd96e3e5ed",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[100/2750][20/41] Loss_D: 0.7129 Loss_G: 2.5141 D(x): 0.5818 D(G(z)): 0.0351 / 0.0825\n",
            "[100/2750][40/41] Loss_D: 0.6158 Loss_G: 2.2330 D(x): 0.6676 D(G(z)): 0.1054 / 0.1044\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d580fb6795249d9b63cec751f2bee76",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[101/2750][20/41] Loss_D: 0.9473 Loss_G: 3.2405 D(x): 0.4659 D(G(z)): 0.0355 / 0.0386\n",
            "[101/2750][40/41] Loss_D: 1.1709 Loss_G: 3.0924 D(x): 0.8444 D(G(z)): 0.5367 / 0.0574\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999586..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.999995..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_101.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "692b37a72fc949a2ad37fed2d48c603f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[102/2750][20/41] Loss_D: 1.2743 Loss_G: 1.9943 D(x): 0.3330 D(G(z)): 0.1000 / 0.1723\n",
            "[102/2750][40/41] Loss_D: 1.3468 Loss_G: 3.1436 D(x): 0.8993 D(G(z)): 0.5651 / 0.0479\n"
          ]
        },
        {
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            "[131/2750][20/41] Loss_D: 0.4060 Loss_G: 2.8186 D(x): 0.8689 D(G(z)): 0.0482 / 0.0574\n",
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            "[132/2750][20/41] Loss_D: 0.6531 Loss_G: 3.0542 D(x): 0.9338 D(G(z)): 0.2401 / 0.0401\n",
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            "[133/2750][20/41] Loss_D: 0.9772 Loss_G: 2.1346 D(x): 0.4435 D(G(z)): 0.1184 / 0.1182\n",
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            "[134/2750][20/41] Loss_D: 1.8129 Loss_G: 3.0102 D(x): 0.9171 D(G(z)): 0.7297 / 0.0406\n",
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            "[136/2750][20/41] Loss_D: 0.5157 Loss_G: 3.0509 D(x): 0.9243 D(G(z)): 0.1480 / 0.0400\n",
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            "[141/2750][20/41] Loss_D: 0.4002 Loss_G: 3.4402 D(x): 0.8654 D(G(z)): 0.0466 / 0.0307\n",
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            "[142/2750][20/41] Loss_D: 0.5391 Loss_G: 2.9600 D(x): 0.9488 D(G(z)): 0.1490 / 0.0486\n",
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            "[143/2750][20/41] Loss_D: 0.8472 Loss_G: 2.6285 D(x): 0.8035 D(G(z)): 0.3233 / 0.0715\n",
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            "[144/2750][20/41] Loss_D: 1.1524 Loss_G: 2.6057 D(x): 0.6975 D(G(z)): 0.4440 / 0.0676\n",
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            "[146/2750][20/41] Loss_D: 1.1570 Loss_G: 3.4881 D(x): 0.3409 D(G(z)): 0.0263 / 0.0624\n",
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            "[147/2750][20/41] Loss_D: 0.8553 Loss_G: 3.4225 D(x): 0.6842 D(G(z)): 0.0720 / 0.0301\n",
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            "[148/2750][20/41] Loss_D: 1.1814 Loss_G: 3.1026 D(x): 0.6402 D(G(z)): 0.4011 / 0.0451\n",
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            "[149/2750][20/41] Loss_D: 0.5683 Loss_G: 3.0381 D(x): 0.7426 D(G(z)): 0.1161 / 0.0418\n",
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            "[151/2750][20/41] Loss_D: 0.4669 Loss_G: 2.7072 D(x): 0.8699 D(G(z)): 0.1074 / 0.0593\n",
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            "[152/2750][20/41] Loss_D: 0.9084 Loss_G: 2.7619 D(x): 0.8585 D(G(z)): 0.3836 / 0.0750\n",
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            "[153/2750][20/41] Loss_D: 1.0004 Loss_G: 3.5234 D(x): 0.7987 D(G(z)): 0.4200 / 0.0259\n",
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            "[161/2750][20/41] Loss_D: 0.5926 Loss_G: 3.1337 D(x): 0.6213 D(G(z)): 0.0155 / 0.0402\n",
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            "[166/2750][20/41] Loss_D: 0.7383 Loss_G: 2.5506 D(x): 0.5515 D(G(z)): 0.0376 / 0.0848\n",
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            "[185/2750][20/41] Loss_D: 0.6483 Loss_G: 3.1144 D(x): 0.7805 D(G(z)): 0.1841 / 0.0445\n",
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            "[186/2750][20/41] Loss_D: 0.9486 Loss_G: 2.3392 D(x): 0.7854 D(G(z)): 0.4056 / 0.0947\n",
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            "[187/2750][20/41] Loss_D: 0.8455 Loss_G: 2.7866 D(x): 0.9334 D(G(z)): 0.3182 / 0.0536\n",
            "[187/2750][40/41] Loss_D: 0.4408 Loss_G: 3.4357 D(x): 0.9619 D(G(z)): 0.0419 / 0.0318\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[188/2750][20/41] Loss_D: 0.6085 Loss_G: 4.1158 D(x): 0.6320 D(G(z)): 0.0188 / 0.0154\n",
            "[188/2750][40/41] Loss_D: 0.5851 Loss_G: 2.2909 D(x): 0.7182 D(G(z)): 0.1101 / 0.0987\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e100397a0d14446a482c9a23c058427",
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          "text": [
            "[189/2750][20/41] Loss_D: 1.2830 Loss_G: 3.0802 D(x): 0.9587 D(G(z)): 0.5434 / 0.0433\n",
            "[189/2750][40/41] Loss_D: 0.4672 Loss_G: 3.5463 D(x): 0.8541 D(G(z)): 0.0865 / 0.0244\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "afb0d72690814fc98bb3b8adc8bb816e",
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          "text": [
            "[190/2750][20/41] Loss_D: 1.2908 Loss_G: 4.2532 D(x): 0.8910 D(G(z)): 0.5558 / 0.0222\n",
            "[190/2750][40/41] Loss_D: 1.3392 Loss_G: 3.9608 D(x): 0.2893 D(G(z)): 0.0054 / 0.0242\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c623bba69e264105952964e966de4766",
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          "text": [
            "[191/2750][20/41] Loss_D: 0.9121 Loss_G: 2.8990 D(x): 0.4688 D(G(z)): 0.0331 / 0.0729\n",
            "[191/2750][40/41] Loss_D: 0.5387 Loss_G: 2.8531 D(x): 0.6864 D(G(z)): 0.0374 / 0.0541\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f2facf197b34303ad7e6c0d04dbf8b7",
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          "text": [
            "[192/2750][20/41] Loss_D: 0.5777 Loss_G: 2.6080 D(x): 0.9190 D(G(z)): 0.1835 / 0.0675\n",
            "[192/2750][40/41] Loss_D: 0.6324 Loss_G: 2.9726 D(x): 0.7042 D(G(z)): 0.1427 / 0.0427\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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            "[193/2750][20/41] Loss_D: 0.6878 Loss_G: 2.5493 D(x): 0.8327 D(G(z)): 0.2429 / 0.0713\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[194/2750][20/41] Loss_D: 0.6358 Loss_G: 2.4252 D(x): 0.8449 D(G(z)): 0.2279 / 0.0880\n",
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          "text": [
            "[195/2750][20/41] Loss_D: 1.0943 Loss_G: 1.9852 D(x): 0.3923 D(G(z)): 0.0282 / 0.1448\n",
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          "text": [
            "[196/2750][20/41] Loss_D: 0.8293 Loss_G: 2.9160 D(x): 0.9703 D(G(z)): 0.2826 / 0.0560\n",
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          "text": [
            "[197/2750][20/41] Loss_D: 0.9169 Loss_G: 3.5131 D(x): 0.9697 D(G(z)): 0.3663 / 0.0247\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b29a06a5eca246e6b5d4c9ec39034497",
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          "text": [
            "[198/2750][20/41] Loss_D: 0.4602 Loss_G: 4.5444 D(x): 0.7625 D(G(z)): 0.0205 / 0.0097\n",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41f2334d7f344e44b98ec6d60f245e51",
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          "text": [
            "[199/2750][20/41] Loss_D: 0.5145 Loss_G: 2.5277 D(x): 0.8121 D(G(z)): 0.1024 / 0.0838\n",
            "[199/2750][40/41] Loss_D: 0.4492 Loss_G: 3.1235 D(x): 0.8200 D(G(z)): 0.0705 / 0.0455\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2915b36a177d4e7996cd9345af7ffcef",
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          "metadata": {},
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        {
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          "output_type": "stream",
          "text": [
            "[200/2750][20/41] Loss_D: 0.7303 Loss_G: 2.3691 D(x): 0.8533 D(G(z)): 0.2871 / 0.0844\n",
            "[200/2750][40/41] Loss_D: 0.6373 Loss_G: 2.6270 D(x): 0.7858 D(G(z)): 0.1928 / 0.0696\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6d372b67ff94ed4b7afdd4eae0abe76",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[201/2750][20/41] Loss_D: 0.4841 Loss_G: 2.5635 D(x): 0.7968 D(G(z)): 0.0989 / 0.0798\n",
            "[201/2750][40/41] Loss_D: 0.8685 Loss_G: 2.3988 D(x): 0.4696 D(G(z)): 0.0350 / 0.0860\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999995..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9973309].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999996..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.999999].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999959].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_201.pth\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "104dfbdc67eb46358b16db858113ef10",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[202/2750][20/41] Loss_D: 0.4806 Loss_G: 3.6350 D(x): 0.7893 D(G(z)): 0.0635 / 0.0227\n",
            "[202/2750][40/41] Loss_D: 0.3836 Loss_G: 3.6015 D(x): 0.8504 D(G(z)): 0.0233 / 0.0222\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a8e4357ced544f0834e798e5dc38e20",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[203/2750][20/41] Loss_D: 0.9650 Loss_G: 2.4458 D(x): 0.6131 D(G(z)): 0.2913 / 0.0840\n",
            "[203/2750][40/41] Loss_D: 0.5174 Loss_G: 2.8460 D(x): 0.7759 D(G(z)): 0.0891 / 0.0581\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c083a9a143244ffcb3b111415f689af2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[204/2750][20/41] Loss_D: 0.7169 Loss_G: 2.8506 D(x): 0.8115 D(G(z)): 0.2166 / 0.0563\n",
            "[204/2750][40/41] Loss_D: 0.6141 Loss_G: 2.4570 D(x): 0.6933 D(G(z)): 0.1067 / 0.0835\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f9d5d5adbc74f829ceba2b43e206a05",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[205/2750][20/41] Loss_D: 1.0232 Loss_G: 3.0322 D(x): 0.8456 D(G(z)): 0.3779 / 0.0557\n",
            "[205/2750][40/41] Loss_D: 0.7657 Loss_G: 2.6578 D(x): 0.8838 D(G(z)): 0.2965 / 0.0677\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "809a13ab9df548dfaa167fec38e1eccd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[206/2750][20/41] Loss_D: 0.8760 Loss_G: 2.9570 D(x): 0.8855 D(G(z)): 0.3929 / 0.0487\n",
            "[206/2750][40/41] Loss_D: 0.6225 Loss_G: 2.7642 D(x): 0.7913 D(G(z)): 0.0780 / 0.0578\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d2ff1f3c09842e981047c23a2983799",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[207/2750][20/41] Loss_D: 0.7861 Loss_G: 2.6929 D(x): 0.7428 D(G(z)): 0.2734 / 0.0583\n",
            "[207/2750][40/41] Loss_D: 1.0193 Loss_G: 2.5481 D(x): 0.5420 D(G(z)): 0.2867 / 0.0854\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "646f42b94ecc429ab6ccbddc95732dd8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
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            "[220/2750][20/41] Loss_D: 0.7517 Loss_G: 3.0788 D(x): 0.7511 D(G(z)): 0.2496 / 0.0517\n",
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            "[222/2750][20/41] Loss_D: 0.6281 Loss_G: 3.1951 D(x): 0.8870 D(G(z)): 0.2150 / 0.0435\n",
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            "[224/2750][20/41] Loss_D: 0.7687 Loss_G: 1.7096 D(x): 0.6141 D(G(z)): 0.1671 / 0.1718\n",
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            "[225/2750][20/41] Loss_D: 0.8056 Loss_G: 2.4701 D(x): 0.5755 D(G(z)): 0.0937 / 0.1003\n",
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            "[228/2750][20/41] Loss_D: 1.1246 Loss_G: 4.4250 D(x): 0.3528 D(G(z)): 0.0030 / 0.0135\n",
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            "[250/2750][20/41] Loss_D: 0.8023 Loss_G: 3.0144 D(x): 0.5584 D(G(z)): 0.0813 / 0.0447\n",
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            "[251/2750][20/41] Loss_D: 1.2856 Loss_G: 3.2410 D(x): 0.3172 D(G(z)): 0.0185 / 0.0496\n",
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            "[252/2750][20/41] Loss_D: 0.6703 Loss_G: 3.3813 D(x): 0.8333 D(G(z)): 0.2043 / 0.0277\n",
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            "[253/2750][20/41] Loss_D: 1.0360 Loss_G: 3.7809 D(x): 0.9607 D(G(z)): 0.4390 / 0.0218\n",
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            "[254/2750][20/41] Loss_D: 1.0737 Loss_G: 3.2065 D(x): 0.7819 D(G(z)): 0.4070 / 0.0367\n",
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            "[255/2750][20/41] Loss_D: 0.5019 Loss_G: 3.9590 D(x): 0.7594 D(G(z)): 0.0405 / 0.0180\n",
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            "[264/2750][20/41] Loss_D: 0.7072 Loss_G: 3.2019 D(x): 0.7130 D(G(z)): 0.1855 / 0.0440\n",
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          "data": {
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              "model_id": "f8d01cce969947d1b783fbe8ee381255",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[291/2750][20/41] Loss_D: 0.6733 Loss_G: 3.3881 D(x): 0.8924 D(G(z)): 0.2300 / 0.0327\n",
            "[291/2750][40/41] Loss_D: 0.6196 Loss_G: 3.0831 D(x): 0.6986 D(G(z)): 0.0717 / 0.0522\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7db4d427b3034a8b91011915e7bd4e18",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[292/2750][20/41] Loss_D: 0.6150 Loss_G: 3.7343 D(x): 0.6833 D(G(z)): 0.0517 / 0.0221\n",
            "[292/2750][40/41] Loss_D: 0.6570 Loss_G: 2.1474 D(x): 0.6909 D(G(z)): 0.1391 / 0.1164\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f621686375f4f748c7f58aa73d5fa8f",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[293/2750][20/41] Loss_D: 0.5546 Loss_G: 3.4113 D(x): 0.8245 D(G(z)): 0.1238 / 0.0291\n",
            "[293/2750][40/41] Loss_D: 1.0167 Loss_G: 2.4259 D(x): 0.4605 D(G(z)): 0.0530 / 0.1128\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "816f3c4b86374500b0bd0319e199a6d4",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[294/2750][20/41] Loss_D: 0.6043 Loss_G: 2.7937 D(x): 0.9268 D(G(z)): 0.2024 / 0.0535\n",
            "[294/2750][40/41] Loss_D: 0.4857 Loss_G: 2.8830 D(x): 0.7721 D(G(z)): 0.0544 / 0.0503\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b71955be8f994c58a3878cbd2146dc13",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[295/2750][20/41] Loss_D: 0.6073 Loss_G: 3.6556 D(x): 0.8735 D(G(z)): 0.1973 / 0.0258\n",
            "[295/2750][40/41] Loss_D: 0.4185 Loss_G: 3.5437 D(x): 0.8619 D(G(z)): 0.0516 / 0.0339\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc6488751e384e80942613da8b1893fb",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[296/2750][20/41] Loss_D: 0.6566 Loss_G: 2.9083 D(x): 0.8884 D(G(z)): 0.2355 / 0.0595\n",
            "[296/2750][40/41] Loss_D: 0.4501 Loss_G: 3.3372 D(x): 0.8690 D(G(z)): 0.0675 / 0.0317\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ecdb93dc801424d91e50f1c4a26f0e6",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[297/2750][20/41] Loss_D: 0.5257 Loss_G: 2.1470 D(x): 0.7514 D(G(z)): 0.0877 / 0.1135\n",
            "[297/2750][40/41] Loss_D: 0.5974 Loss_G: 3.0512 D(x): 0.7240 D(G(z)): 0.0584 / 0.0605\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c20dedff7356479588937f4eb6b0c3b7",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[298/2750][20/41] Loss_D: 1.0145 Loss_G: 1.7847 D(x): 0.4629 D(G(z)): 0.0733 / 0.1708\n",
            "[298/2750][40/41] Loss_D: 0.9088 Loss_G: 4.5511 D(x): 0.9523 D(G(z)): 0.3813 / 0.0100\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32ba9299ba934808a368c88531258630",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[299/2750][20/41] Loss_D: 0.5255 Loss_G: 3.1527 D(x): 0.8320 D(G(z)): 0.1103 / 0.0411\n",
            "[299/2750][40/41] Loss_D: 0.5384 Loss_G: 3.3040 D(x): 0.9384 D(G(z)): 0.1507 / 0.0379\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50560fb6b97c40da9a1f8d13af5b1470",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[300/2750][20/41] Loss_D: 0.3988 Loss_G: 4.4587 D(x): 0.8361 D(G(z)): 0.0178 / 0.0122\n",
            "[300/2750][40/41] Loss_D: 0.5601 Loss_G: 2.8905 D(x): 0.8113 D(G(z)): 0.1476 / 0.0485\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a04a5236b4048e0bbb95560e3610f76",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[301/2750][20/41] Loss_D: 0.5260 Loss_G: 3.1574 D(x): 0.8330 D(G(z)): 0.0943 / 0.0510\n",
            "[301/2750][40/41] Loss_D: 0.4639 Loss_G: 2.8986 D(x): 0.8386 D(G(z)): 0.0800 / 0.0597\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999999].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999933].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999997..0.9999991].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999981].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_301.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "455b3cf30168431686f42b770e7b06c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[302/2750][20/41] Loss_D: 0.4855 Loss_G: 3.3043 D(x): 0.7433 D(G(z)): 0.0355 / 0.0413\n",
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            "[303/2750][20/41] Loss_D: 0.9153 Loss_G: 2.6419 D(x): 0.5255 D(G(z)): 0.0909 / 0.1017\n",
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            "[319/2750][20/41] Loss_D: 1.2106 Loss_G: 1.4224 D(x): 0.5145 D(G(z)): 0.3095 / 0.2561\n",
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            "[346/2750][20/41] Loss_D: 1.0025 Loss_G: 1.4655 D(x): 0.4513 D(G(z)): 0.0547 / 0.2588\n",
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            "[347/2750][20/41] Loss_D: 1.1702 Loss_G: 3.9724 D(x): 0.9127 D(G(z)): 0.4527 / 0.0218\n",
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            "[385/2750][20/41] Loss_D: 0.5872 Loss_G: 2.7165 D(x): 0.7912 D(G(z)): 0.1492 / 0.0655\n",
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            "[386/2750][20/41] Loss_D: 0.8251 Loss_G: 4.0757 D(x): 0.9879 D(G(z)): 0.2705 / 0.0150\n",
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            "[387/2750][20/41] Loss_D: 0.9256 Loss_G: 2.7272 D(x): 0.7510 D(G(z)): 0.3044 / 0.0711\n",
            "[387/2750][40/41] Loss_D: 0.8721 Loss_G: 4.0616 D(x): 0.8702 D(G(z)): 0.3063 / 0.0181\n"
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          "text": [
            "[388/2750][20/41] Loss_D: 0.6099 Loss_G: 3.3315 D(x): 0.6594 D(G(z)): 0.0237 / 0.0388\n",
            "[388/2750][40/41] Loss_D: 0.5445 Loss_G: 3.4703 D(x): 0.8924 D(G(z)): 0.1281 / 0.0338\n"
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[389/2750][20/41] Loss_D: 0.5730 Loss_G: 2.9425 D(x): 0.7779 D(G(z)): 0.1135 / 0.0546\n",
            "[389/2750][40/41] Loss_D: 0.7385 Loss_G: 2.4675 D(x): 0.6192 D(G(z)): 0.1262 / 0.1048\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[390/2750][20/41] Loss_D: 0.4736 Loss_G: 3.5531 D(x): 0.8017 D(G(z)): 0.0624 / 0.0463\n",
            "[390/2750][40/41] Loss_D: 0.4813 Loss_G: 3.1373 D(x): 0.8017 D(G(z)): 0.0653 / 0.0399\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4efb834fd0cb4c0aad20204af0efbc13",
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          "text": [
            "[391/2750][20/41] Loss_D: 0.5448 Loss_G: 2.7826 D(x): 0.8054 D(G(z)): 0.1156 / 0.0583\n",
            "[391/2750][40/41] Loss_D: 1.1836 Loss_G: 1.8021 D(x): 0.3640 D(G(z)): 0.0343 / 0.2144\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[392/2750][20/41] Loss_D: 0.9243 Loss_G: 3.2050 D(x): 0.8344 D(G(z)): 0.2848 / 0.0492\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[393/2750][20/41] Loss_D: 0.4642 Loss_G: 3.2374 D(x): 0.9129 D(G(z)): 0.0725 / 0.0370\n",
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            "[394/2750][20/41] Loss_D: 1.1543 Loss_G: 4.7189 D(x): 0.8932 D(G(z)): 0.4703 / 0.0100\n",
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          "text": [
            "[395/2750][20/41] Loss_D: 0.7961 Loss_G: 3.7286 D(x): 0.4960 D(G(z)): 0.0081 / 0.0226\n",
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          "text": [
            "[396/2750][20/41] Loss_D: 0.6676 Loss_G: 3.3529 D(x): 0.8762 D(G(z)): 0.2258 / 0.0323\n",
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            "[397/2750][20/41] Loss_D: 0.6682 Loss_G: 2.0515 D(x): 0.6699 D(G(z)): 0.1302 / 0.1386\n",
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            "[398/2750][20/41] Loss_D: 0.4856 Loss_G: 3.8131 D(x): 0.7933 D(G(z)): 0.0828 / 0.0304\n",
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            "[399/2750][20/41] Loss_D: 0.6646 Loss_G: 3.4035 D(x): 0.7392 D(G(z)): 0.1000 / 0.0354\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
          "text": [
            "[400/2750][20/41] Loss_D: 0.4450 Loss_G: 3.7166 D(x): 0.9531 D(G(z)): 0.0507 / 0.0210\n",
            "[400/2750][40/41] Loss_D: 0.6394 Loss_G: 2.8879 D(x): 0.7737 D(G(z)): 0.1319 / 0.0611\n"
          ]
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0d03ee5c535430bb2506a509f1c57a0",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[401/2750][20/41] Loss_D: 0.4665 Loss_G: 3.1289 D(x): 0.8731 D(G(z)): 0.0755 / 0.0429\n",
            "[401/2750][40/41] Loss_D: 0.4476 Loss_G: 3.7719 D(x): 0.7564 D(G(z)): 0.0196 / 0.0251\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999994..0.9999995].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999998..0.9999991].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999958].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99658006].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_401.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f295588e423450f977109ddb0bf6a56",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[402/2750][20/41] Loss_D: 0.6264 Loss_G: 2.9895 D(x): 0.7207 D(G(z)): 0.1206 / 0.0588\n",
            "[402/2750][40/41] Loss_D: 0.4605 Loss_G: 3.5084 D(x): 0.7986 D(G(z)): 0.0590 / 0.0312\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9123764c7b744fb4b52faf903ca71765",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[403/2750][20/41] Loss_D: 0.4775 Loss_G: 3.7609 D(x): 0.8915 D(G(z)): 0.0813 / 0.0247\n",
            "[403/2750][40/41] Loss_D: 0.6467 Loss_G: 2.9551 D(x): 0.8408 D(G(z)): 0.1980 / 0.0600\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "226c6e4f5b2c4c27b37f70b4d34ca25c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[404/2750][20/41] Loss_D: 0.5863 Loss_G: 2.6999 D(x): 0.7112 D(G(z)): 0.0664 / 0.0816\n",
            "[404/2750][40/41] Loss_D: 0.4447 Loss_G: 3.1818 D(x): 0.8218 D(G(z)): 0.0543 / 0.0527\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d561792f5b2e436cb56a360b32dd0e17",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[405/2750][20/41] Loss_D: 0.4811 Loss_G: 4.3643 D(x): 0.9374 D(G(z)): 0.0980 / 0.0127\n",
            "[405/2750][40/41] Loss_D: 0.4961 Loss_G: 3.0191 D(x): 0.8044 D(G(z)): 0.0904 / 0.0626\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ceb47f22e93a444fb8ce45625147da67",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[406/2750][20/41] Loss_D: 0.6653 Loss_G: 3.8803 D(x): 0.7139 D(G(z)): 0.0206 / 0.0208\n",
            "[406/2750][40/41] Loss_D: 0.7996 Loss_G: 1.8288 D(x): 0.5523 D(G(z)): 0.0580 / 0.1869\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "735f1839f36c4db89e46bc7526a5d856",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[407/2750][20/41] Loss_D: 0.7992 Loss_G: 2.7660 D(x): 0.7690 D(G(z)): 0.2481 / 0.0746\n",
            "[407/2750][40/41] Loss_D: 0.5163 Loss_G: 3.0058 D(x): 0.7957 D(G(z)): 0.0869 / 0.0500\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "419c0dc68048441fb8a6b01a516f16f6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[408/2750][20/41] Loss_D: 0.4957 Loss_G: 3.5276 D(x): 0.7527 D(G(z)): 0.0260 / 0.0382\n",
            "[408/2750][40/41] Loss_D: 0.7031 Loss_G: 2.1833 D(x): 0.6167 D(G(z)): 0.0960 / 0.1102\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9301bba9f7bc4cbcb739f2af8d43b26c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[409/2750][20/41] Loss_D: 0.7313 Loss_G: 2.9674 D(x): 0.5865 D(G(z)): 0.0432 / 0.0757\n",
            "[409/2750][40/41] Loss_D: 0.8003 Loss_G: 3.9210 D(x): 0.8662 D(G(z)): 0.3027 / 0.0295\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11df61eed674449087445fe5a4b52a88",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[410/2750][20/41] Loss_D: 0.5214 Loss_G: 2.6905 D(x): 0.8850 D(G(z)): 0.1417 / 0.0625\n",
            "[410/2750][40/41] Loss_D: 0.8444 Loss_G: 2.4424 D(x): 0.5042 D(G(z)): 0.0293 / 0.1071\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "[440/2750][20/41] Loss_D: 0.7594 Loss_G: 3.9585 D(x): 0.9247 D(G(z)): 0.2981 / 0.0228\n",
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            "[443/2750][20/41] Loss_D: 0.4355 Loss_G: 3.8403 D(x): 0.8464 D(G(z)): 0.0533 / 0.0260\n",
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            "[444/2750][20/41] Loss_D: 1.7945 Loss_G: 1.8114 D(x): 0.2143 D(G(z)): 0.0376 / 0.1935\n",
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            "[445/2750][20/41] Loss_D: 0.6150 Loss_G: 3.2750 D(x): 0.8202 D(G(z)): 0.1243 / 0.0360\n",
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            "[446/2750][20/41] Loss_D: 0.8765 Loss_G: 3.8144 D(x): 0.8773 D(G(z)): 0.3494 / 0.0195\n",
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            "[447/2750][20/41] Loss_D: 0.7390 Loss_G: 2.1134 D(x): 0.6160 D(G(z)): 0.1119 / 0.1323\n",
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            "[448/2750][20/41] Loss_D: 0.4066 Loss_G: 3.3332 D(x): 0.8410 D(G(z)): 0.0300 / 0.0360\n",
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            "[449/2750][20/41] Loss_D: 0.5251 Loss_G: 3.0351 D(x): 0.8857 D(G(z)): 0.1301 / 0.0491\n",
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            "[451/2750][20/41] Loss_D: 0.8514 Loss_G: 2.4553 D(x): 0.5510 D(G(z)): 0.0417 / 0.0862\n",
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            "[452/2750][20/41] Loss_D: 1.1096 Loss_G: 2.2187 D(x): 0.3984 D(G(z)): 0.0282 / 0.1205\n",
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            "[453/2750][20/41] Loss_D: 0.6225 Loss_G: 2.7500 D(x): 0.8053 D(G(z)): 0.1744 / 0.0672\n",
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            "[454/2750][20/41] Loss_D: 0.6334 Loss_G: 2.6411 D(x): 0.7102 D(G(z)): 0.0867 / 0.1013\n",
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            "[456/2750][20/41] Loss_D: 0.5450 Loss_G: 3.1965 D(x): 0.8825 D(G(z)): 0.1450 / 0.0410\n",
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          "output_type": "stream",
          "text": [
            "[493/2750][20/41] Loss_D: 0.7389 Loss_G: 3.3208 D(x): 0.7995 D(G(z)): 0.2238 / 0.0418\n",
            "[493/2750][40/41] Loss_D: 0.5093 Loss_G: 3.0999 D(x): 0.9096 D(G(z)): 0.1083 / 0.0420\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3dd7c18ba7340b584c54d929d694cf3",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[494/2750][20/41] Loss_D: 0.6234 Loss_G: 4.9852 D(x): 0.8055 D(G(z)): 0.1313 / 0.0087\n",
            "[494/2750][40/41] Loss_D: 0.4462 Loss_G: 4.7198 D(x): 0.9616 D(G(z)): 0.0376 / 0.0112\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4054fbae64574b319dde16c2e750db3c",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[495/2750][20/41] Loss_D: 0.9582 Loss_G: 2.0654 D(x): 0.4529 D(G(z)): 0.0633 / 0.1683\n",
            "[495/2750][40/41] Loss_D: 0.5800 Loss_G: 3.5362 D(x): 0.9307 D(G(z)): 0.1558 / 0.0292\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c68e4edcc314e9ca7aa8b3b3880bbc6",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[496/2750][20/41] Loss_D: 0.4408 Loss_G: 3.9089 D(x): 0.9553 D(G(z)): 0.0415 / 0.0239\n",
            "[496/2750][40/41] Loss_D: 0.4911 Loss_G: 3.0043 D(x): 0.8077 D(G(z)): 0.0711 / 0.0591\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "595997364bd74e688c28a8ca93c504a8",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[497/2750][20/41] Loss_D: 0.5532 Loss_G: 2.9713 D(x): 0.7742 D(G(z)): 0.0729 / 0.0689\n",
            "[497/2750][40/41] Loss_D: 0.4760 Loss_G: 3.8762 D(x): 0.8528 D(G(z)): 0.0619 / 0.0339\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "79dcd48554d44d869ad469d40048b6ce",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[498/2750][20/41] Loss_D: 0.5119 Loss_G: 3.7188 D(x): 0.8538 D(G(z)): 0.1085 / 0.0262\n",
            "[498/2750][40/41] Loss_D: 0.4991 Loss_G: 3.7939 D(x): 0.9214 D(G(z)): 0.0896 / 0.0279\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ee14a17366b8421fa710450025d22618",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[499/2750][20/41] Loss_D: 0.6093 Loss_G: 2.5941 D(x): 0.7635 D(G(z)): 0.0895 / 0.0736\n",
            "[499/2750][40/41] Loss_D: 0.4946 Loss_G: 2.5320 D(x): 0.7805 D(G(z)): 0.0544 / 0.0834\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab19d4806d8b437c8e04ee245d218d47",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[500/2750][20/41] Loss_D: 0.6588 Loss_G: 4.0009 D(x): 0.8405 D(G(z)): 0.2036 / 0.0185\n",
            "[500/2750][40/41] Loss_D: 0.4269 Loss_G: 3.3782 D(x): 0.8373 D(G(z)): 0.0269 / 0.0307\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8bece3fd609345cda3fbf429dd18dc8b",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[501/2750][20/41] Loss_D: 0.5360 Loss_G: 2.6760 D(x): 0.7406 D(G(z)): 0.0805 / 0.0724\n",
            "[501/2750][40/41] Loss_D: 0.9278 Loss_G: 1.9272 D(x): 0.4959 D(G(z)): 0.1171 / 0.1570\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.92411846].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999998].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999999].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999999].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99454594].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_501.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26c46af8e2f4429ab48d3199534bf5f6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[502/2750][20/41] Loss_D: 0.3706 Loss_G: 5.7478 D(x): 0.8866 D(G(z)): 0.0090 / 0.0031\n",
            "[502/2750][40/41] Loss_D: 0.6328 Loss_G: 3.6193 D(x): 0.8418 D(G(z)): 0.1599 / 0.0294\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1191ed0d80a44472a56e23829ceefc3e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[503/2750][20/41] Loss_D: 0.5184 Loss_G: 4.6038 D(x): 0.7846 D(G(z)): 0.0101 / 0.0121\n",
            "[503/2750][40/41] Loss_D: 0.5337 Loss_G: 3.1509 D(x): 0.7953 D(G(z)): 0.0838 / 0.0456\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "930123fc76fa4114a0b361b357540a99",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[504/2750][20/41] Loss_D: 0.6129 Loss_G: 2.5398 D(x): 0.7023 D(G(z)): 0.0670 / 0.0820\n",
            "[504/2750][40/41] Loss_D: 0.5912 Loss_G: 3.6131 D(x): 0.6243 D(G(z)): 0.0175 / 0.0260\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8860d00147943e39344adebf6f2c53d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[505/2750][20/41] Loss_D: 0.6869 Loss_G: 2.1055 D(x): 0.7068 D(G(z)): 0.1541 / 0.1155\n",
            "[505/2750][40/41] Loss_D: 0.5480 Loss_G: 3.6922 D(x): 0.8067 D(G(z)): 0.1121 / 0.0198\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ca6849a93da47738e405cb21fac349e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[506/2750][20/41] Loss_D: 0.5110 Loss_G: 3.4887 D(x): 0.7030 D(G(z)): 0.0205 / 0.0402\n",
            "[506/2750][40/41] Loss_D: 0.5270 Loss_G: 3.2884 D(x): 0.9310 D(G(z)): 0.1055 / 0.0370\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c120fd932aef457d944c2babc8735ae3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[507/2750][20/41] Loss_D: 0.9237 Loss_G: 1.7314 D(x): 0.5038 D(G(z)): 0.1072 / 0.2078\n",
            "[507/2750][40/41] Loss_D: 0.5789 Loss_G: 3.1340 D(x): 0.7063 D(G(z)): 0.0756 / 0.0470\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "196e6ae8fb9f48caacaf7addb2b255de",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[508/2750][20/41] Loss_D: 0.4863 Loss_G: 3.4971 D(x): 0.8379 D(G(z)): 0.0674 / 0.0353\n",
            "[508/2750][40/41] Loss_D: 0.5015 Loss_G: 3.3813 D(x): 0.8568 D(G(z)): 0.0918 / 0.0387\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a92d19ed27045bd9e26c40a5c787732",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[509/2750][20/41] Loss_D: 0.9252 Loss_G: 2.7577 D(x): 0.7644 D(G(z)): 0.2684 / 0.0719\n",
            "[509/2750][40/41] Loss_D: 0.6625 Loss_G: 2.6768 D(x): 0.6208 D(G(z)): 0.0257 / 0.0730\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73bd86d796574e9c95a2807bc8d890e3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          ]
        },
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              "model_id": "1b21f8a741404a22bb62334ec2d213d2",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[593/2750][20/41] Loss_D: 0.8920 Loss_G: 3.5071 D(x): 0.7715 D(G(z)): 0.3009 / 0.0349\n",
            "[593/2750][40/41] Loss_D: 0.5066 Loss_G: 3.3485 D(x): 0.7653 D(G(z)): 0.0435 / 0.0437\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e5384ed1e9f41e58fdef61007a82378",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[594/2750][20/41] Loss_D: 0.5508 Loss_G: 2.6599 D(x): 0.7431 D(G(z)): 0.0820 / 0.0738\n",
            "[594/2750][40/41] Loss_D: 0.4800 Loss_G: 3.6153 D(x): 0.9167 D(G(z)): 0.0720 / 0.0293\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4187322cfba4491974afa60fb603f5f",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[595/2750][20/41] Loss_D: 0.4853 Loss_G: 2.7379 D(x): 0.9076 D(G(z)): 0.0992 / 0.0670\n",
            "[595/2750][40/41] Loss_D: 0.4545 Loss_G: 4.4783 D(x): 0.8892 D(G(z)): 0.0779 / 0.0107\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9b1a609c47d4649871b87fe6195c19b",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[596/2750][20/41] Loss_D: 0.4949 Loss_G: 3.1018 D(x): 0.8412 D(G(z)): 0.0879 / 0.0439\n",
            "[596/2750][40/41] Loss_D: 0.5607 Loss_G: 3.5895 D(x): 0.6997 D(G(z)): 0.0330 / 0.0329\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "730020cceb5d4aa9930da17ded0aac24",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[597/2750][20/41] Loss_D: 0.6686 Loss_G: 4.1580 D(x): 0.9173 D(G(z)): 0.1900 / 0.0144\n",
            "[597/2750][40/41] Loss_D: 0.7880 Loss_G: 3.7233 D(x): 0.9834 D(G(z)): 0.2356 / 0.0203\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd05cd3b30354c2cae143b1ef849a3f6",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[598/2750][20/41] Loss_D: 0.5441 Loss_G: 2.6934 D(x): 0.8499 D(G(z)): 0.1177 / 0.0753\n",
            "[598/2750][40/41] Loss_D: 0.6174 Loss_G: 3.0266 D(x): 0.6802 D(G(z)): 0.0853 / 0.0604\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4702e968fb7840858934ad0efa8df8fb",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[599/2750][20/41] Loss_D: 0.4467 Loss_G: 4.1500 D(x): 0.7866 D(G(z)): 0.0161 / 0.0143\n",
            "[599/2750][40/41] Loss_D: 0.6560 Loss_G: 2.6576 D(x): 0.6998 D(G(z)): 0.1126 / 0.0798\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "083820ea75ab45b59eaca3828046bd74",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[600/2750][20/41] Loss_D: 0.4584 Loss_G: 3.6475 D(x): 0.8962 D(G(z)): 0.0727 / 0.0286\n",
            "[600/2750][40/41] Loss_D: 0.5160 Loss_G: 3.1491 D(x): 0.8524 D(G(z)): 0.1197 / 0.0422\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52a84fa299ff4effb931f9303ce45ef0",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[601/2750][20/41] Loss_D: 0.5045 Loss_G: 3.4158 D(x): 0.7330 D(G(z)): 0.0303 / 0.0387\n",
            "[601/2750][40/41] Loss_D: 0.4931 Loss_G: 2.8417 D(x): 0.8898 D(G(z)): 0.0849 / 0.0562\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999998].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99999964].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99999434].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99999976].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999981].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_601.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa881ef8436643e597963d55a79d8e7b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[602/2750][20/41] Loss_D: 0.4742 Loss_G: 3.1252 D(x): 0.8275 D(G(z)): 0.0766 / 0.0435\n",
            "[602/2750][40/41] Loss_D: 0.6307 Loss_G: 3.4802 D(x): 0.8925 D(G(z)): 0.2078 / 0.0347\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "560e2182fa9f4871a9ed1178935e5dbf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[603/2750][20/41] Loss_D: 0.6983 Loss_G: 3.8406 D(x): 0.8020 D(G(z)): 0.2076 / 0.0244\n",
            "[603/2750][40/41] Loss_D: 0.3964 Loss_G: 4.7528 D(x): 0.9007 D(G(z)): 0.0170 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d1be4ae79ea341ea9c01722847017e35",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[604/2750][20/41] Loss_D: 0.8793 Loss_G: 4.0357 D(x): 0.9400 D(G(z)): 0.3358 / 0.0220\n",
            "[604/2750][40/41] Loss_D: 0.5943 Loss_G: 3.2171 D(x): 0.7833 D(G(z)): 0.0855 / 0.0412\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c896695ff5d34f3fb039c7b2366ca950",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[605/2750][20/41] Loss_D: 0.5912 Loss_G: 2.8601 D(x): 0.7349 D(G(z)): 0.0577 / 0.0699\n",
            "[605/2750][40/41] Loss_D: 0.8074 Loss_G: 2.1736 D(x): 0.5647 D(G(z)): 0.0496 / 0.1234\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92211e0f05ee4935a23d96c677294dc0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[606/2750][20/41] Loss_D: 0.7433 Loss_G: 2.7202 D(x): 0.5538 D(G(z)): 0.0253 / 0.0882\n",
            "[606/2750][40/41] Loss_D: 0.6197 Loss_G: 2.5686 D(x): 0.7368 D(G(z)): 0.1024 / 0.0826\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[689/2750][20/41] Loss_D: 0.4223 Loss_G: 3.8457 D(x): 0.8776 D(G(z)): 0.0458 / 0.0233\n",
            "[689/2750][40/41] Loss_D: 0.4834 Loss_G: 3.4105 D(x): 0.7776 D(G(z)): 0.0456 / 0.0315\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45b3e1481fb44d2f828c86ea5dde43a6",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[690/2750][20/41] Loss_D: 0.4697 Loss_G: 3.0277 D(x): 0.7779 D(G(z)): 0.0565 / 0.0524\n",
            "[690/2750][40/41] Loss_D: 0.5676 Loss_G: 3.2831 D(x): 0.8489 D(G(z)): 0.1548 / 0.0508\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c6f117089d54d7eb4f181090b9dca71",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[691/2750][20/41] Loss_D: 0.4301 Loss_G: 3.1349 D(x): 0.9358 D(G(z)): 0.0643 / 0.0419\n",
            "[691/2750][40/41] Loss_D: 0.6548 Loss_G: 2.5763 D(x): 0.6282 D(G(z)): 0.0314 / 0.0971\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e03d27b0666a43c4bb5a4fc240f2972a",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[692/2750][20/41] Loss_D: 0.8562 Loss_G: 2.7202 D(x): 0.5652 D(G(z)): 0.0372 / 0.0861\n",
            "[692/2750][40/41] Loss_D: 0.5241 Loss_G: 4.0350 D(x): 0.8116 D(G(z)): 0.0362 / 0.0170\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "543a06bcd47341ed8647fa752b68aa84",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[693/2750][20/41] Loss_D: 0.6090 Loss_G: 3.2775 D(x): 0.7571 D(G(z)): 0.0621 / 0.0472\n",
            "[693/2750][40/41] Loss_D: 0.5547 Loss_G: 3.2459 D(x): 0.9744 D(G(z)): 0.1076 / 0.0427\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dde7baabd45496cb1a22fc50835f0d4",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[694/2750][20/41] Loss_D: 0.6686 Loss_G: 3.7013 D(x): 0.5963 D(G(z)): 0.0113 / 0.0257\n",
            "[694/2750][40/41] Loss_D: 0.4338 Loss_G: 4.9034 D(x): 0.9094 D(G(z)): 0.0236 / 0.0085\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21a268186740419d8881212b58b4e461",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[695/2750][20/41] Loss_D: 0.6573 Loss_G: 3.6480 D(x): 0.8304 D(G(z)): 0.2005 / 0.0297\n",
            "[695/2750][40/41] Loss_D: 0.4569 Loss_G: 3.1318 D(x): 0.8466 D(G(z)): 0.0547 / 0.0464\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0b118a15b3004cbcae6739a4559e968d",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[696/2750][20/41] Loss_D: 0.5322 Loss_G: 3.1079 D(x): 0.8517 D(G(z)): 0.1255 / 0.0496\n",
            "[696/2750][40/41] Loss_D: 0.4472 Loss_G: 4.0731 D(x): 0.8193 D(G(z)): 0.0391 / 0.0185\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5ce584854a044704a2156fe8f7bf52aa",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[697/2750][20/41] Loss_D: 0.5992 Loss_G: 3.5240 D(x): 0.9730 D(G(z)): 0.1356 / 0.0376\n",
            "[697/2750][40/41] Loss_D: 0.8314 Loss_G: 3.6754 D(x): 0.9812 D(G(z)): 0.2657 / 0.0268\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67187ed98e234fee8c8eaecc5136ab41",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[698/2750][20/41] Loss_D: 0.6059 Loss_G: 3.6798 D(x): 0.9335 D(G(z)): 0.1837 / 0.0273\n",
            "[698/2750][40/41] Loss_D: 0.6136 Loss_G: 3.7146 D(x): 0.9488 D(G(z)): 0.1803 / 0.0250\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6b66fdf2bcc4edeba38de3d879e5fcf",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[699/2750][20/41] Loss_D: 0.5915 Loss_G: 3.4271 D(x): 0.9552 D(G(z)): 0.1313 / 0.0348\n",
            "[699/2750][40/41] Loss_D: 0.4438 Loss_G: 4.6171 D(x): 0.9131 D(G(z)): 0.0460 / 0.0137\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ad9c9ad06834870b678e046613b3c7b",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[700/2750][20/41] Loss_D: 0.6492 Loss_G: 4.1103 D(x): 0.9123 D(G(z)): 0.1762 / 0.0139\n",
            "[700/2750][40/41] Loss_D: 0.8778 Loss_G: 3.9904 D(x): 0.9475 D(G(z)): 0.3072 / 0.0181\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7f2d724238349798037ecbf9b2a8728",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[701/2750][20/41] Loss_D: 0.6399 Loss_G: 3.4935 D(x): 0.9614 D(G(z)): 0.1790 / 0.0287\n",
            "[701/2750][40/41] Loss_D: 0.4501 Loss_G: 3.9512 D(x): 0.8289 D(G(z)): 0.0268 / 0.0243\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99148256].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.97612405].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98423153].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999993].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999998].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_701.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6c81a74848043b0b6e48cdf1878750c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[702/2750][20/41] Loss_D: 0.4776 Loss_G: 3.2299 D(x): 0.8537 D(G(z)): 0.1037 / 0.0365\n",
            "[702/2750][40/41] Loss_D: 0.4538 Loss_G: 4.1687 D(x): 0.8727 D(G(z)): 0.0612 / 0.0147\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "22ccccf33a78437eb0fc81407acd56c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[703/2750][20/41] Loss_D: 0.5028 Loss_G: 3.3071 D(x): 0.8274 D(G(z)): 0.0639 / 0.0507\n",
            "[703/2750][40/41] Loss_D: 0.8270 Loss_G: 3.6034 D(x): 0.5908 D(G(z)): 0.0175 / 0.0377\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b357cd0110db41f2897d078bebb67bf7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[704/2750][20/41] Loss_D: 0.6377 Loss_G: 3.5227 D(x): 0.9756 D(G(z)): 0.1458 / 0.0348\n",
            "[704/2750][40/41] Loss_D: 0.5961 Loss_G: 3.3413 D(x): 0.7001 D(G(z)): 0.0312 / 0.0592\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0356dde1f9445349b8e6c517920320f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[705/2750][20/41] Loss_D: 0.5640 Loss_G: 3.9002 D(x): 0.9481 D(G(z)): 0.1024 / 0.0227\n",
            "[705/2750][40/41] Loss_D: 0.4337 Loss_G: 4.3385 D(x): 0.8747 D(G(z)): 0.0301 / 0.0142\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4f280fcbcb24aed9ea58fe64430a134",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[706/2750][20/41] Loss_D: 0.4124 Loss_G: 3.4753 D(x): 0.8978 D(G(z)): 0.0459 / 0.0300\n",
            "[706/2750][40/41] Loss_D: 0.7250 Loss_G: 2.8209 D(x): 0.6308 D(G(z)): 0.0376 / 0.0847\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b541c551ca0b4e699dc781ba47ded584",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[707/2750][20/41] Loss_D: 0.8747 Loss_G: 4.0661 D(x): 0.9871 D(G(z)): 0.2682 / 0.0193\n",
            "[707/2750][40/41] Loss_D: 0.5537 Loss_G: 2.6865 D(x): 0.7461 D(G(z)): 0.0539 / 0.1047\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9011799ebd4d45fa88c49c7f0063ce82",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[708/2750][20/41] Loss_D: 0.4189 Loss_G: 4.4786 D(x): 0.8714 D(G(z)): 0.0413 / 0.0185\n",
            "[708/2750][40/41] Loss_D: 0.4524 Loss_G: 4.6795 D(x): 0.8288 D(G(z)): 0.0120 / 0.0119\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1be0c0d11174ae8a88a0bd25960a2a9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[709/2750][20/41] Loss_D: 0.4426 Loss_G: 3.7849 D(x): 0.8795 D(G(z)): 0.0381 / 0.0250\n",
            "[709/2750][40/41] Loss_D: 0.9015 Loss_G: 3.8583 D(x): 0.9386 D(G(z)): 0.3126 / 0.0215\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aaf9ec44dd4a4db59cefaf3923d9fed5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[710/2750][20/41] Loss_D: 0.6289 Loss_G: 3.4724 D(x): 0.8662 D(G(z)): 0.1687 / 0.0335\n",
            "[710/2750][40/41] Loss_D: 0.5524 Loss_G: 3.8046 D(x): 0.9051 D(G(z)): 0.1264 / 0.0278\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "238d27777d65448f9b7478ac44fa5652",
              "version_major": 2,
              "version_minor": 0
            },
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            "[756/2750][20/41] Loss_D: 0.5513 Loss_G: 2.9683 D(x): 0.7647 D(G(z)): 0.0744 / 0.0576\n",
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          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[793/2750][20/41] Loss_D: 0.5175 Loss_G: 3.5368 D(x): 0.8034 D(G(z)): 0.0706 / 0.0307\n",
            "[793/2750][40/41] Loss_D: 0.4941 Loss_G: 3.7754 D(x): 0.9202 D(G(z)): 0.0835 / 0.0214\n"
          ]
        },
        {
          "data": {
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              "model_id": "b775f0b51a1943a5a3ad74955ac0f9d5",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[794/2750][20/41] Loss_D: 0.5301 Loss_G: 5.1914 D(x): 0.7557 D(G(z)): 0.0061 / 0.0078\n",
            "[794/2750][40/41] Loss_D: 0.4832 Loss_G: 3.5954 D(x): 0.8428 D(G(z)): 0.0529 / 0.0281\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6fe9fc0b18ba4047afe45240eefe400e",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[795/2750][20/41] Loss_D: 0.5143 Loss_G: 4.2307 D(x): 0.9296 D(G(z)): 0.0659 / 0.0143\n",
            "[795/2750][40/41] Loss_D: 0.7333 Loss_G: 3.4045 D(x): 0.5717 D(G(z)): 0.0211 / 0.0395\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d46501f8d5149f39166504167e92d18",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[796/2750][20/41] Loss_D: 0.4048 Loss_G: 4.4938 D(x): 0.9089 D(G(z)): 0.0160 / 0.0084\n",
            "[796/2750][40/41] Loss_D: 0.5953 Loss_G: 3.8496 D(x): 0.9202 D(G(z)): 0.1667 / 0.0191\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7fa3e4c3b09047d6b17f8976d47b8814",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[797/2750][20/41] Loss_D: 0.6385 Loss_G: 3.4614 D(x): 0.7216 D(G(z)): 0.1141 / 0.0373\n",
            "[797/2750][40/41] Loss_D: 0.5033 Loss_G: 3.6052 D(x): 0.7611 D(G(z)): 0.0445 / 0.0375\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e750ae3a9434378a778dc9c9aa0d32b",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[798/2750][20/41] Loss_D: 0.5272 Loss_G: 3.6412 D(x): 0.7248 D(G(z)): 0.0243 / 0.0310\n",
            "[798/2750][40/41] Loss_D: 0.7765 Loss_G: 2.6355 D(x): 0.5468 D(G(z)): 0.0444 / 0.0976\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f3985464a9a4e54b31b79f7c381c007",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[799/2750][20/41] Loss_D: 0.4007 Loss_G: 4.9734 D(x): 0.8781 D(G(z)): 0.0149 / 0.0102\n",
            "[799/2750][40/41] Loss_D: 0.8123 Loss_G: 3.7215 D(x): 0.9826 D(G(z)): 0.2456 / 0.0251\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74b11bc1bb254331ba55670b6aafcf2d",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[800/2750][20/41] Loss_D: 0.5493 Loss_G: 2.9925 D(x): 0.7239 D(G(z)): 0.0381 / 0.0533\n",
            "[800/2750][40/41] Loss_D: 0.5550 Loss_G: 3.4270 D(x): 0.9298 D(G(z)): 0.1256 / 0.0425\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bfa9d8b8ff3e4af6bce54f1a59bb7ab8",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[801/2750][20/41] Loss_D: 0.4530 Loss_G: 4.2048 D(x): 0.9147 D(G(z)): 0.0593 / 0.0131\n",
            "[801/2750][40/41] Loss_D: 0.6452 Loss_G: 2.2830 D(x): 0.6708 D(G(z)): 0.0789 / 0.1331\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999942].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98743796].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99998224].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99999607].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.996283].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_801.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7726447da4ee4fa0bdab07a11479deb3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[802/2750][20/41] Loss_D: 0.5126 Loss_G: 3.7500 D(x): 0.8636 D(G(z)): 0.1109 / 0.0295\n",
            "[802/2750][40/41] Loss_D: 0.4709 Loss_G: 3.8545 D(x): 0.8393 D(G(z)): 0.0508 / 0.0249\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7e01983ddd0493a8bb50a2b0e12ed3c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[803/2750][20/41] Loss_D: 0.8530 Loss_G: 4.5019 D(x): 0.9400 D(G(z)): 0.3113 / 0.0139\n",
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            "[886/2750][20/41] Loss_D: 0.5741 Loss_G: 3.1125 D(x): 0.8009 D(G(z)): 0.0953 / 0.0609\n",
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            "[887/2750][20/41] Loss_D: 0.5031 Loss_G: 3.4066 D(x): 0.8180 D(G(z)): 0.0666 / 0.0331\n",
            "[887/2750][40/41] Loss_D: 0.5285 Loss_G: 3.9395 D(x): 0.8119 D(G(z)): 0.0826 / 0.0259\n"
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            "[888/2750][20/41] Loss_D: 0.6241 Loss_G: 3.0135 D(x): 0.6484 D(G(z)): 0.0399 / 0.0678\n",
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          "text": [
            "[889/2750][20/41] Loss_D: 0.5135 Loss_G: 3.8680 D(x): 0.9073 D(G(z)): 0.1087 / 0.0205\n",
            "[889/2750][40/41] Loss_D: 0.6387 Loss_G: 4.3022 D(x): 0.9591 D(G(z)): 0.1615 / 0.0133\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2e9f81169434c0ca01906df9721f9f7",
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          "text": [
            "[890/2750][20/41] Loss_D: 1.0841 Loss_G: 5.6157 D(x): 0.9692 D(G(z)): 0.3979 / 0.0041\n",
            "[890/2750][40/41] Loss_D: 0.8535 Loss_G: 2.2735 D(x): 0.6151 D(G(z)): 0.1348 / 0.1344\n"
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37368b0b95ae4002a4c9f92e78e9b9bb",
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            "[891/2750][20/41] Loss_D: 0.5976 Loss_G: 3.0954 D(x): 0.8577 D(G(z)): 0.1222 / 0.0431\n",
            "[891/2750][40/41] Loss_D: 0.4858 Loss_G: 3.8742 D(x): 0.7833 D(G(z)): 0.0346 / 0.0233\n"
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          "text": [
            "[892/2750][20/41] Loss_D: 0.6068 Loss_G: 3.3144 D(x): 0.9417 D(G(z)): 0.1547 / 0.0426\n",
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            "[893/2750][20/41] Loss_D: 0.7975 Loss_G: 3.8749 D(x): 0.9676 D(G(z)): 0.2259 / 0.0267\n",
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            "[894/2750][20/41] Loss_D: 0.5151 Loss_G: 3.6660 D(x): 0.7258 D(G(z)): 0.0224 / 0.0332\n",
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            "[895/2750][20/41] Loss_D: 0.4856 Loss_G: 2.9371 D(x): 0.7525 D(G(z)): 0.0331 / 0.0533\n",
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            "[896/2750][20/41] Loss_D: 0.4384 Loss_G: 3.8116 D(x): 0.9030 D(G(z)): 0.0477 / 0.0201\n",
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            "[897/2750][20/41] Loss_D: 0.5358 Loss_G: 3.5601 D(x): 0.8690 D(G(z)): 0.1202 / 0.0307\n",
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          "text": [
            "[898/2750][20/41] Loss_D: 0.4531 Loss_G: 3.2531 D(x): 0.8346 D(G(z)): 0.0540 / 0.0441\n",
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            "[899/2750][20/41] Loss_D: 0.4681 Loss_G: 4.3345 D(x): 0.7453 D(G(z)): 0.0135 / 0.0135\n",
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          "text": [
            "[900/2750][20/41] Loss_D: 0.7017 Loss_G: 1.9775 D(x): 0.5811 D(G(z)): 0.0320 / 0.1730\n",
            "[900/2750][40/41] Loss_D: 0.4518 Loss_G: 3.1535 D(x): 0.8893 D(G(z)): 0.0669 / 0.0444\n"
          ]
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "372409bb897e40ca9b0c9890cda19715",
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[901/2750][20/41] Loss_D: 0.4636 Loss_G: 3.4321 D(x): 0.8700 D(G(z)): 0.0702 / 0.0342\n",
            "[901/2750][40/41] Loss_D: 0.6700 Loss_G: 2.2411 D(x): 0.7085 D(G(z)): 0.1321 / 0.1225\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98139435].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99998754].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9997558].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999906].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9815756].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_901.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09c0d7014a08405baa521895cf525fe8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[902/2750][20/41] Loss_D: 0.5345 Loss_G: 2.9979 D(x): 0.9129 D(G(z)): 0.1145 / 0.0490\n",
            "[902/2750][40/41] Loss_D: 0.8400 Loss_G: 3.8687 D(x): 0.9779 D(G(z)): 0.0932 / 0.0195\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01557e6652534697b61f9bedb9a87e5c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[903/2750][20/41] Loss_D: 0.4029 Loss_G: 4.3141 D(x): 0.8645 D(G(z)): 0.0137 / 0.0172\n",
            "[903/2750][40/41] Loss_D: 0.5401 Loss_G: 3.5858 D(x): 0.9616 D(G(z)): 0.0835 / 0.0284\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0959c58f916d459eb09d5b8f04b38d12",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "[986/2750][20/41] Loss_D: 0.4080 Loss_G: 5.0881 D(x): 0.9335 D(G(z)): 0.0229 / 0.0073\n",
            "[986/2750][40/41] Loss_D: 0.4414 Loss_G: 3.8872 D(x): 0.8866 D(G(z)): 0.0632 / 0.0289\n"
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            "[987/2750][20/41] Loss_D: 0.7514 Loss_G: 2.7020 D(x): 0.6072 D(G(z)): 0.0392 / 0.0697\n",
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            "[988/2750][20/41] Loss_D: 0.4748 Loss_G: 2.9982 D(x): 0.8917 D(G(z)): 0.0956 / 0.0444\n",
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        {
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          "text": [
            "[989/2750][20/41] Loss_D: 0.8534 Loss_G: 3.6269 D(x): 0.9566 D(G(z)): 0.3128 / 0.0335\n",
            "[989/2750][40/41] Loss_D: 0.5057 Loss_G: 4.2823 D(x): 0.9482 D(G(z)): 0.0776 / 0.0115\n"
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52f4a61dfba945329b9d31e3214ed32f",
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          "text": [
            "[990/2750][20/41] Loss_D: 0.4306 Loss_G: 3.5206 D(x): 0.9138 D(G(z)): 0.0518 / 0.0293\n",
            "[990/2750][40/41] Loss_D: 0.4281 Loss_G: 4.1699 D(x): 0.8810 D(G(z)): 0.0405 / 0.0161\n"
          ]
        },
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "24fe433730904e5bb50fc0fc92faa347",
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          "text": [
            "[991/2750][20/41] Loss_D: 0.4695 Loss_G: 3.2332 D(x): 0.9595 D(G(z)): 0.0360 / 0.0395\n",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f53c448aeab544c4b76abdb9c38b9bdd",
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          "output_type": "stream",
          "text": [
            "[992/2750][20/41] Loss_D: 0.6326 Loss_G: 4.7407 D(x): 0.9665 D(G(z)): 0.1492 / 0.0103\n",
            "[992/2750][40/41] Loss_D: 0.5432 Loss_G: 4.0234 D(x): 0.9551 D(G(z)): 0.1359 / 0.0156\n"
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37d786ad5cf64b7384fee3e7f2448b95",
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          "text": [
            "[993/2750][20/41] Loss_D: 0.4608 Loss_G: 5.6873 D(x): 0.7523 D(G(z)): 0.0033 / 0.0031\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[994/2750][20/41] Loss_D: 0.4010 Loss_G: 4.6884 D(x): 0.8691 D(G(z)): 0.0129 / 0.0079\n",
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            "application/vnd.jupyter.widget-view+json": {
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        {
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          "output_type": "stream",
          "text": [
            "[995/2750][20/41] Loss_D: 0.4151 Loss_G: 4.6226 D(x): 0.8781 D(G(z)): 0.0154 / 0.0101\n",
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          "text": [
            "[996/2750][20/41] Loss_D: 0.5783 Loss_G: 3.6070 D(x): 0.9701 D(G(z)): 0.1402 / 0.0315\n",
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          "text": [
            "[997/2750][20/41] Loss_D: 0.4122 Loss_G: 5.2398 D(x): 0.8759 D(G(z)): 0.0092 / 0.0048\n",
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          "text": [
            "[998/2750][20/41] Loss_D: 0.5979 Loss_G: 3.9780 D(x): 0.6741 D(G(z)): 0.0302 / 0.0363\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c58f731a167f46469f0e0ccbf58c7d17",
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          "text": [
            "[999/2750][20/41] Loss_D: 0.4557 Loss_G: 4.0374 D(x): 0.9199 D(G(z)): 0.0656 / 0.0225\n",
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "377a63e6d4e04fb4be58d0ba14be4819",
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        {
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          "output_type": "stream",
          "text": [
            "[1000/2750][20/41] Loss_D: 0.4637 Loss_G: 5.3467 D(x): 0.7863 D(G(z)): 0.0152 / 0.0085\n",
            "[1000/2750][40/41] Loss_D: 0.5602 Loss_G: 3.4278 D(x): 0.7441 D(G(z)): 0.0701 / 0.0370\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e85fac58ae474d9aa7c58431e6cd0619",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1001/2750][20/41] Loss_D: 0.4256 Loss_G: 4.1302 D(x): 0.8962 D(G(z)): 0.0518 / 0.0141\n",
            "[1001/2750][40/41] Loss_D: 0.3884 Loss_G: 4.5889 D(x): 0.8427 D(G(z)): 0.0123 / 0.0093\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999991].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98491067].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99579924].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999469].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99999297].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1001.pth\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b21eeeaeaaa548f598b773d7fe11c7f1",
              "version_major": 2,
              "version_minor": 0
            },
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            "[1016/2750][20/41] Loss_D: 0.6139 Loss_G: 3.4754 D(x): 0.6927 D(G(z)): 0.0326 / 0.0405\n",
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            "[1020/2750][20/41] Loss_D: 0.4831 Loss_G: 3.3017 D(x): 0.9520 D(G(z)): 0.0756 / 0.0364\n",
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            "[1023/2750][20/41] Loss_D: 0.4154 Loss_G: 3.9165 D(x): 0.8095 D(G(z)): 0.0273 / 0.0225\n",
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            "[1030/2750][20/41] Loss_D: 0.4421 Loss_G: 3.9568 D(x): 0.9178 D(G(z)): 0.0723 / 0.0245\n",
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            "[1031/2750][20/41] Loss_D: 0.7318 Loss_G: 3.1386 D(x): 0.5835 D(G(z)): 0.0274 / 0.0643\n",
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            "[1043/2750][20/41] Loss_D: 1.3503 Loss_G: 2.4830 D(x): 0.3787 D(G(z)): 0.0415 / 0.0961\n",
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            "[1057/2750][20/41] Loss_D: 0.4129 Loss_G: 4.3241 D(x): 0.8612 D(G(z)): 0.0211 / 0.0150\n",
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            "[1058/2750][20/41] Loss_D: 0.8985 Loss_G: 2.5863 D(x): 0.5650 D(G(z)): 0.0610 / 0.0848\n",
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            "[1061/2750][20/41] Loss_D: 0.3923 Loss_G: 6.0190 D(x): 0.8029 D(G(z)): 0.0051 / 0.0026\n",
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            "[1062/2750][20/41] Loss_D: 0.5065 Loss_G: 2.9586 D(x): 0.9298 D(G(z)): 0.1352 / 0.0518\n",
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            "[1065/2750][20/41] Loss_D: 0.3847 Loss_G: 4.9579 D(x): 0.8727 D(G(z)): 0.0125 / 0.0094\n",
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            "[1066/2750][20/41] Loss_D: 0.4612 Loss_G: 3.9427 D(x): 0.8255 D(G(z)): 0.0303 / 0.0206\n",
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            "[1070/2750][20/41] Loss_D: 0.6197 Loss_G: 4.2266 D(x): 0.9763 D(G(z)): 0.0921 / 0.0151\n",
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            "[1084/2750][20/41] Loss_D: 0.3630 Loss_G: 4.9344 D(x): 0.9072 D(G(z)): 0.0142 / 0.0098\n",
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            "[1085/2750][20/41] Loss_D: 0.3929 Loss_G: 5.0743 D(x): 0.8611 D(G(z)): 0.0311 / 0.0072\n",
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            "[1086/2750][20/41] Loss_D: 1.4941 Loss_G: 1.8605 D(x): 0.2906 D(G(z)): 0.0249 / 0.1911\n",
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            "[1087/2750][20/41] Loss_D: 0.5511 Loss_G: 3.1543 D(x): 0.7842 D(G(z)): 0.0795 / 0.0515\n",
            "[1087/2750][40/41] Loss_D: 0.3892 Loss_G: 4.7748 D(x): 0.8492 D(G(z)): 0.0224 / 0.0101\n"
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            "[1088/2750][20/41] Loss_D: 0.3665 Loss_G: 4.8704 D(x): 0.8751 D(G(z)): 0.0178 / 0.0120\n",
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            "[1089/2750][20/41] Loss_D: 0.4969 Loss_G: 3.0594 D(x): 0.8719 D(G(z)): 0.1348 / 0.0498\n",
            "[1089/2750][40/41] Loss_D: 1.5676 Loss_G: 2.0145 D(x): 0.2913 D(G(z)): 0.0609 / 0.2437\n"
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            "[1090/2750][20/41] Loss_D: 0.4234 Loss_G: 3.7737 D(x): 0.9235 D(G(z)): 0.0694 / 0.0233\n",
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            "[1091/2750][20/41] Loss_D: 0.3941 Loss_G: 4.3490 D(x): 0.9295 D(G(z)): 0.0293 / 0.0149\n",
            "[1091/2750][40/41] Loss_D: 0.3918 Loss_G: 3.9616 D(x): 0.9000 D(G(z)): 0.0504 / 0.0228\n"
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            "[1092/2750][20/41] Loss_D: 0.6101 Loss_G: 3.0248 D(x): 0.6793 D(G(z)): 0.0495 / 0.0841\n",
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            "[1093/2750][20/41] Loss_D: 0.8018 Loss_G: 2.1156 D(x): 0.5515 D(G(z)): 0.0544 / 0.1335\n",
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            "[1094/2750][20/41] Loss_D: 0.9922 Loss_G: 3.4926 D(x): 0.9685 D(G(z)): 0.3895 / 0.0273\n",
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            "[1095/2750][20/41] Loss_D: 0.6077 Loss_G: 4.3023 D(x): 0.9299 D(G(z)): 0.1980 / 0.0113\n",
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            "[1096/2750][20/41] Loss_D: 0.6780 Loss_G: 2.7818 D(x): 0.6520 D(G(z)): 0.0644 / 0.0725\n",
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            "[1097/2750][20/41] Loss_D: 0.3817 Loss_G: 4.5429 D(x): 0.9018 D(G(z)): 0.0193 / 0.0133\n",
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            "[1098/2750][20/41] Loss_D: 0.3761 Loss_G: 4.1411 D(x): 0.8646 D(G(z)): 0.0268 / 0.0146\n",
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            "[1099/2750][20/41] Loss_D: 0.3730 Loss_G: 5.1421 D(x): 0.8509 D(G(z)): 0.0089 / 0.0080\n",
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          "output_type": "stream",
          "text": [
            "[1100/2750][20/41] Loss_D: 0.5676 Loss_G: 2.4772 D(x): 0.9410 D(G(z)): 0.1508 / 0.0862\n",
            "[1100/2750][40/41] Loss_D: 0.3812 Loss_G: 4.1006 D(x): 0.8744 D(G(z)): 0.0193 / 0.0156\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bffdbdb60aa5477cab8f27a7105fcd9c",
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1101/2750][20/41] Loss_D: 0.4457 Loss_G: 3.2932 D(x): 0.9273 D(G(z)): 0.0889 / 0.0360\n",
            "[1101/2750][40/41] Loss_D: 0.4226 Loss_G: 4.6683 D(x): 0.7867 D(G(z)): 0.0246 / 0.0127\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99997395].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98412085].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999577].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9561252].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.95940685].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1101.pth\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "84d5a5482c64489caead593b6cfd8f9a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1102/2750][20/41] Loss_D: 0.5848 Loss_G: 3.4750 D(x): 0.8893 D(G(z)): 0.1748 / 0.0307\n",
            "[1102/2750][40/41] Loss_D: 0.6280 Loss_G: 3.1763 D(x): 0.7721 D(G(z)): 0.1146 / 0.0427\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c241a007eef04633bd3e87612689fd74",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1103/2750][20/41] Loss_D: 0.5979 Loss_G: 3.4430 D(x): 0.6804 D(G(z)): 0.0105 / 0.0351\n",
            "[1103/2750][40/41] Loss_D: 0.5933 Loss_G: 3.2137 D(x): 0.6830 D(G(z)): 0.0323 / 0.0495\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "671e4bd4953b46dc8471cfe032fad169",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1104/2750][20/41] Loss_D: 0.4118 Loss_G: 3.6834 D(x): 0.8752 D(G(z)): 0.0565 / 0.0281\n",
            "[1104/2750][40/41] Loss_D: 0.5094 Loss_G: 3.1518 D(x): 0.9533 D(G(z)): 0.1265 / 0.0464\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5c403a171abe490fa80e4138d7c4f241",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1105/2750][20/41] Loss_D: 0.5469 Loss_G: 4.4877 D(x): 0.9264 D(G(z)): 0.1227 / 0.0164\n",
            "[1105/2750][40/41] Loss_D: 0.7893 Loss_G: 2.2831 D(x): 0.6207 D(G(z)): 0.0715 / 0.1358\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c471ab38e4a34f9085c89df7664b47b3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "[1107/2750][20/41] Loss_D: 0.4396 Loss_G: 3.9295 D(x): 0.9069 D(G(z)): 0.0840 / 0.0191\n",
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            "[1108/2750][20/41] Loss_D: 0.4739 Loss_G: 3.8215 D(x): 0.9231 D(G(z)): 0.1145 / 0.0223\n",
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            "[1109/2750][20/41] Loss_D: 0.4802 Loss_G: 3.6909 D(x): 0.9285 D(G(z)): 0.1045 / 0.0268\n",
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            "[1110/2750][20/41] Loss_D: 0.5654 Loss_G: 3.8852 D(x): 0.9690 D(G(z)): 0.1216 / 0.0282\n",
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            "[1112/2750][20/41] Loss_D: 0.4444 Loss_G: 3.8027 D(x): 0.8420 D(G(z)): 0.0282 / 0.0256\n",
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            "[1113/2750][20/41] Loss_D: 0.4936 Loss_G: 3.6449 D(x): 0.9060 D(G(z)): 0.1171 / 0.0289\n",
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            "[1114/2750][20/41] Loss_D: 0.3678 Loss_G: 4.5047 D(x): 0.8993 D(G(z)): 0.0255 / 0.0126\n",
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            "[1115/2750][20/41] Loss_D: 0.3761 Loss_G: 5.6046 D(x): 0.8228 D(G(z)): 0.0076 / 0.0040\n",
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            "[1116/2750][20/41] Loss_D: 0.4341 Loss_G: 3.6446 D(x): 0.9562 D(G(z)): 0.0550 / 0.0293\n",
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            "[1117/2750][20/41] Loss_D: 0.6686 Loss_G: 2.7380 D(x): 0.9456 D(G(z)): 0.2269 / 0.0630\n",
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            "[1118/2750][20/41] Loss_D: 0.3659 Loss_G: 4.9990 D(x): 0.8837 D(G(z)): 0.0107 / 0.0064\n",
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            "[1119/2750][20/41] Loss_D: 0.7353 Loss_G: 3.6478 D(x): 0.9151 D(G(z)): 0.2772 / 0.0254\n",
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            "[1120/2750][20/41] Loss_D: 0.7039 Loss_G: 2.5560 D(x): 0.9627 D(G(z)): 0.1997 / 0.0804\n",
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            "[1126/2750][20/41] Loss_D: 0.5468 Loss_G: 2.8132 D(x): 0.9400 D(G(z)): 0.1551 / 0.0701\n",
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            "[1127/2750][20/41] Loss_D: 1.0056 Loss_G: 3.6398 D(x): 0.9523 D(G(z)): 0.2535 / 0.0232\n",
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            "[1134/2750][20/41] Loss_D: 0.4032 Loss_G: 4.6018 D(x): 0.9017 D(G(z)): 0.0327 / 0.0145\n",
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            "[1135/2750][20/41] Loss_D: 0.4568 Loss_G: 3.5807 D(x): 0.8922 D(G(z)): 0.0976 / 0.0347\n",
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            "[1136/2750][20/41] Loss_D: 0.7367 Loss_G: 3.0784 D(x): 0.9302 D(G(z)): 0.2680 / 0.0488\n",
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            "[1137/2750][20/41] Loss_D: 0.3705 Loss_G: 6.2026 D(x): 0.8782 D(G(z)): 0.0286 / 0.0045\n",
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            "[1139/2750][20/41] Loss_D: 0.5072 Loss_G: 2.9165 D(x): 0.9003 D(G(z)): 0.1110 / 0.0560\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[1140/2750][20/41] Loss_D: 0.4503 Loss_G: 4.1317 D(x): 0.8900 D(G(z)): 0.0607 / 0.0135\n",
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            "[1141/2750][20/41] Loss_D: 0.3998 Loss_G: 4.5206 D(x): 0.8388 D(G(z)): 0.0249 / 0.0134\n",
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            "[1142/2750][20/41] Loss_D: 0.3841 Loss_G: 4.1799 D(x): 0.8852 D(G(z)): 0.0323 / 0.0151\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[1143/2750][20/41] Loss_D: 0.6692 Loss_G: 3.3378 D(x): 0.6472 D(G(z)): 0.0382 / 0.0331\n",
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            "[1144/2750][20/41] Loss_D: 0.3867 Loss_G: 3.2708 D(x): 0.9054 D(G(z)): 0.0472 / 0.0427\n",
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            "[1145/2750][20/41] Loss_D: 0.3738 Loss_G: 3.8457 D(x): 0.9220 D(G(z)): 0.0306 / 0.0244\n",
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            "[1146/2750][20/41] Loss_D: 0.4167 Loss_G: 4.5391 D(x): 0.8482 D(G(z)): 0.0236 / 0.0135\n",
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            "[1147/2750][20/41] Loss_D: 0.7313 Loss_G: 2.5869 D(x): 0.6354 D(G(z)): 0.1285 / 0.0926\n",
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            "[1148/2750][20/41] Loss_D: 0.4633 Loss_G: 4.1961 D(x): 0.9125 D(G(z)): 0.0960 / 0.0132\n",
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              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1188/2750][20/41] Loss_D: 0.6270 Loss_G: 3.4638 D(x): 0.6576 D(G(z)): 0.0512 / 0.0457\n",
            "[1188/2750][40/41] Loss_D: 1.1084 Loss_G: 2.5282 D(x): 0.4034 D(G(z)): 0.0099 / 0.1281\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16bc76b32255418999b2cd2b026e6397",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1189/2750][20/41] Loss_D: 0.6816 Loss_G: 3.5517 D(x): 0.6989 D(G(z)): 0.1184 / 0.0280\n",
            "[1189/2750][40/41] Loss_D: 1.9710 Loss_G: 1.8845 D(x): 0.1759 D(G(z)): 0.0256 / 0.1801\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b05a78ada0b244a1ba83f2f58ca01ee6",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1190/2750][20/41] Loss_D: 0.5543 Loss_G: 3.5105 D(x): 0.8111 D(G(z)): 0.0986 / 0.0309\n",
            "[1190/2750][40/41] Loss_D: 0.6357 Loss_G: 3.2515 D(x): 0.6344 D(G(z)): 0.0140 / 0.0439\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dc9585edb19434a86c80541dda2648b",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1191/2750][20/41] Loss_D: 1.0931 Loss_G: 2.6069 D(x): 0.4281 D(G(z)): 0.0474 / 0.1048\n",
            "[1191/2750][40/41] Loss_D: 0.3624 Loss_G: 4.1279 D(x): 0.9000 D(G(z)): 0.0257 / 0.0201\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8b5f12caab44a62ab8ca01bc00ef2f3",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1192/2750][20/41] Loss_D: 0.6070 Loss_G: 2.8308 D(x): 0.9313 D(G(z)): 0.2117 / 0.0586\n",
            "[1192/2750][40/41] Loss_D: 0.3676 Loss_G: 4.2835 D(x): 0.8785 D(G(z)): 0.0238 / 0.0139\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8a8099993514ba08a712795961a07bd",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1193/2750][20/41] Loss_D: 0.4170 Loss_G: 4.0266 D(x): 0.8951 D(G(z)): 0.0583 / 0.0171\n",
            "[1193/2750][40/41] Loss_D: 0.4002 Loss_G: 4.5311 D(x): 0.8498 D(G(z)): 0.0418 / 0.0132\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2229c8a92dc42be8e963a32ea5170bf",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1194/2750][20/41] Loss_D: 0.5062 Loss_G: 4.2964 D(x): 0.8929 D(G(z)): 0.0715 / 0.0139\n",
            "[1194/2750][40/41] Loss_D: 1.7033 Loss_G: 1.9875 D(x): 0.2876 D(G(z)): 0.0145 / 0.2008\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7382c51f3784d238f14949d922d3483",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1195/2750][20/41] Loss_D: 0.3885 Loss_G: 4.4723 D(x): 0.9127 D(G(z)): 0.0265 / 0.0144\n",
            "[1195/2750][40/41] Loss_D: 0.4844 Loss_G: 2.5278 D(x): 0.8368 D(G(z)): 0.1115 / 0.0786\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "56b4208b5639486c9ad501f1a550f490",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1196/2750][20/41] Loss_D: 0.6815 Loss_G: 2.3234 D(x): 0.6494 D(G(z)): 0.0684 / 0.1207\n",
            "[1196/2750][40/41] Loss_D: 0.3978 Loss_G: 4.8142 D(x): 0.9041 D(G(z)): 0.0156 / 0.0096\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f7b771e815c94b9b8aec7e4c49325bad",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1197/2750][20/41] Loss_D: 0.4892 Loss_G: 5.2994 D(x): 0.7670 D(G(z)): 0.0084 / 0.0135\n",
            "[1197/2750][40/41] Loss_D: 0.5431 Loss_G: 3.4484 D(x): 0.7637 D(G(z)): 0.0531 / 0.0368\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ce63751cc6042d48c9503b8e1e44a0e",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1198/2750][20/41] Loss_D: 0.6333 Loss_G: 3.2131 D(x): 0.9541 D(G(z)): 0.2063 / 0.0383\n",
            "[1198/2750][40/41] Loss_D: 0.3832 Loss_G: 5.2180 D(x): 0.8274 D(G(z)): 0.0112 / 0.0067\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a81fa6d115e44da8f4ca5631fe275d3",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1199/2750][20/41] Loss_D: 0.4445 Loss_G: 3.6805 D(x): 0.9162 D(G(z)): 0.0840 / 0.0395\n",
            "[1199/2750][40/41] Loss_D: 0.3676 Loss_G: 5.6894 D(x): 0.8608 D(G(z)): 0.0055 / 0.0031\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0cfde1d7704b4793ae13ff097dc7b57d",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1200/2750][20/41] Loss_D: 0.3454 Loss_G: 5.2427 D(x): 0.9042 D(G(z)): 0.0081 / 0.0064\n",
            "[1200/2750][40/41] Loss_D: 0.3732 Loss_G: 4.6025 D(x): 0.8973 D(G(z)): 0.0195 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e46dfc4982e84ac8ac1aa33fae100f78",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1201/2750][20/41] Loss_D: 0.3717 Loss_G: 6.5851 D(x): 0.8748 D(G(z)): 0.0220 / 0.0042\n",
            "[1201/2750][40/41] Loss_D: 0.5128 Loss_G: 3.5285 D(x): 0.9446 D(G(z)): 0.1132 / 0.0443\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9986936].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9615348].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9991909].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9993599].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9999439].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1201.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "57a8a6d71ec249a6aaa1b4c9d38b0238",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1202/2750][20/41] Loss_D: 0.3999 Loss_G: 3.9761 D(x): 0.8570 D(G(z)): 0.0365 / 0.0194\n",
            "[1202/2750][40/41] Loss_D: 0.4140 Loss_G: 4.0217 D(x): 0.9044 D(G(z)): 0.0641 / 0.0302\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1e1eb7f023843c68da544b645fedbb5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1203/2750][20/41] Loss_D: 0.3807 Loss_G: 4.8715 D(x): 0.9289 D(G(z)): 0.0170 / 0.0096\n",
            "[1203/2750][40/41] Loss_D: 0.4042 Loss_G: 4.0918 D(x): 0.9239 D(G(z)): 0.0538 / 0.0194\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c76bc7332b6f4c7f929c4578ebbe18c7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1204/2750][20/41] Loss_D: 0.3601 Loss_G: 4.5646 D(x): 0.9012 D(G(z)): 0.0151 / 0.0110\n",
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            "[1205/2750][20/41] Loss_D: 0.4683 Loss_G: 5.1554 D(x): 0.9211 D(G(z)): 0.0971 / 0.0073\n",
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            "[1206/2750][20/41] Loss_D: 0.4301 Loss_G: 4.6134 D(x): 0.8133 D(G(z)): 0.0230 / 0.0094\n",
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          "text": [
            "[1207/2750][20/41] Loss_D: 0.4048 Loss_G: 5.6630 D(x): 0.9157 D(G(z)): 0.0092 / 0.0035\n",
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            "[1208/2750][20/41] Loss_D: 0.5539 Loss_G: 3.4769 D(x): 0.9532 D(G(z)): 0.1455 / 0.0311\n",
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            "[1209/2750][20/41] Loss_D: 0.6507 Loss_G: 4.2672 D(x): 0.8734 D(G(z)): 0.1535 / 0.0125\n",
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          "text": [
            "[1210/2750][20/41] Loss_D: 0.5866 Loss_G: 3.7039 D(x): 0.9148 D(G(z)): 0.1142 / 0.0267\n",
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          "text": [
            "[1211/2750][20/41] Loss_D: 0.5687 Loss_G: 4.4194 D(x): 0.9258 D(G(z)): 0.1004 / 0.0148\n",
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            "[1212/2750][20/41] Loss_D: 0.4182 Loss_G: 5.5967 D(x): 0.9475 D(G(z)): 0.0380 / 0.0053\n",
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            "[1213/2750][20/41] Loss_D: 0.4061 Loss_G: 3.9079 D(x): 0.9247 D(G(z)): 0.0599 / 0.0171\n",
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          "text": [
            "[1214/2750][20/41] Loss_D: 0.3509 Loss_G: 4.8311 D(x): 0.9223 D(G(z)): 0.0107 / 0.0075\n",
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            "[1215/2750][20/41] Loss_D: 0.5292 Loss_G: 3.4596 D(x): 0.9590 D(G(z)): 0.1105 / 0.0345\n",
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            "[1216/2750][20/41] Loss_D: 0.4549 Loss_G: 4.4655 D(x): 0.9157 D(G(z)): 0.0617 / 0.0121\n",
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          "text": [
            "[1217/2750][20/41] Loss_D: 0.8370 Loss_G: 4.7324 D(x): 0.9713 D(G(z)): 0.1597 / 0.0090\n",
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          "text": [
            "[1218/2750][20/41] Loss_D: 0.4185 Loss_G: 4.2653 D(x): 0.7880 D(G(z)): 0.0161 / 0.0131\n",
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            "[1219/2750][20/41] Loss_D: 0.6041 Loss_G: 3.0534 D(x): 0.6317 D(G(z)): 0.0165 / 0.0468\n",
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            "[1220/2750][20/41] Loss_D: 0.4145 Loss_G: 5.8413 D(x): 0.8220 D(G(z)): 0.0046 / 0.0042\n",
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            "[1221/2750][20/41] Loss_D: 0.3672 Loss_G: 4.9596 D(x): 0.8831 D(G(z)): 0.0263 / 0.0106\n",
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            "[1222/2750][20/41] Loss_D: 0.3671 Loss_G: 4.1633 D(x): 0.8765 D(G(z)): 0.0282 / 0.0155\n",
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            "[1223/2750][20/41] Loss_D: 0.7586 Loss_G: 4.5810 D(x): 0.9739 D(G(z)): 0.2321 / 0.0091\n",
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            "[1224/2750][20/41] Loss_D: 0.4093 Loss_G: 4.3154 D(x): 0.8683 D(G(z)): 0.0382 / 0.0156\n",
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            "[1225/2750][20/41] Loss_D: 0.5634 Loss_G: 3.3761 D(x): 0.8301 D(G(z)): 0.0880 / 0.0350\n",
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            "[1232/2750][20/41] Loss_D: 0.8910 Loss_G: 3.8664 D(x): 0.5827 D(G(z)): 0.0055 / 0.0199\n",
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            "[1240/2750][20/41] Loss_D: 0.3866 Loss_G: 3.6550 D(x): 0.8796 D(G(z)): 0.0406 / 0.0328\n",
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            "[1241/2750][20/41] Loss_D: 0.4510 Loss_G: 3.5782 D(x): 0.8375 D(G(z)): 0.0819 / 0.0304\n",
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            "[1244/2750][20/41] Loss_D: 1.2770 Loss_G: 1.4064 D(x): 0.3634 D(G(z)): 0.0667 / 0.2916\n",
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            "[1245/2750][20/41] Loss_D: 0.4906 Loss_G: 4.2032 D(x): 0.8447 D(G(z)): 0.0342 / 0.0124\n",
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            "[1246/2750][20/41] Loss_D: 0.4170 Loss_G: 4.4271 D(x): 0.9026 D(G(z)): 0.0660 / 0.0115\n",
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            "[1286/2750][20/41] Loss_D: 0.4138 Loss_G: 7.6105 D(x): 0.9187 D(G(z)): 0.0021 / 0.0007\n",
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            "[1287/2750][20/41] Loss_D: 0.5038 Loss_G: 4.2506 D(x): 0.7627 D(G(z)): 0.0208 / 0.0196\n",
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            "[1288/2750][20/41] Loss_D: 0.4426 Loss_G: 5.0687 D(x): 0.9152 D(G(z)): 0.0100 / 0.0064\n",
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            "[1289/2750][20/41] Loss_D: 0.6789 Loss_G: 3.9912 D(x): 0.6578 D(G(z)): 0.0259 / 0.0235\n",
            "[1289/2750][40/41] Loss_D: 0.5495 Loss_G: 3.7660 D(x): 0.6943 D(G(z)): 0.0161 / 0.0269\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1290/2750][20/41] Loss_D: 0.3827 Loss_G: 4.0576 D(x): 0.8988 D(G(z)): 0.0363 / 0.0207\n",
            "[1290/2750][40/41] Loss_D: 0.6790 Loss_G: 2.5486 D(x): 0.8261 D(G(z)): 0.2203 / 0.0941\n"
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffe62a182a0b41dab06b878fd45e2950",
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          "text": [
            "[1291/2750][20/41] Loss_D: 0.5663 Loss_G: 2.4155 D(x): 0.9459 D(G(z)): 0.1240 / 0.1148\n",
            "[1291/2750][40/41] Loss_D: 0.3804 Loss_G: 4.2250 D(x): 0.8562 D(G(z)): 0.0291 / 0.0131\n"
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            "application/vnd.jupyter.widget-view+json": {
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            "[1292/2750][20/41] Loss_D: 0.3934 Loss_G: 3.9991 D(x): 0.8733 D(G(z)): 0.0426 / 0.0175\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1293/2750][20/41] Loss_D: 0.9965 Loss_G: 3.4472 D(x): 0.4602 D(G(z)): 0.0117 / 0.0384\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[1294/2750][20/41] Loss_D: 0.6255 Loss_G: 3.0097 D(x): 0.6464 D(G(z)): 0.0287 / 0.0573\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1295/2750][20/41] Loss_D: 0.4104 Loss_G: 3.9812 D(x): 0.8621 D(G(z)): 0.0601 / 0.0206\n",
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            "[1296/2750][20/41] Loss_D: 0.3782 Loss_G: 3.9247 D(x): 0.8959 D(G(z)): 0.0336 / 0.0183\n",
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          "text": [
            "[1297/2750][20/41] Loss_D: 0.5726 Loss_G: 3.9351 D(x): 0.8602 D(G(z)): 0.0743 / 0.0174\n",
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          "text": [
            "[1298/2750][20/41] Loss_D: 0.3725 Loss_G: 4.6440 D(x): 0.8490 D(G(z)): 0.0231 / 0.0115\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[1299/2750][20/41] Loss_D: 0.3645 Loss_G: 4.4264 D(x): 0.8807 D(G(z)): 0.0156 / 0.0111\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
          "text": [
            "[1300/2750][20/41] Loss_D: 0.3935 Loss_G: 3.2538 D(x): 0.8859 D(G(z)): 0.0496 / 0.0442\n",
            "[1300/2750][40/41] Loss_D: 0.3463 Loss_G: 5.5430 D(x): 0.8921 D(G(z)): 0.0085 / 0.0041\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54fed62d2e554b9991d846da6a68d5e7",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1301/2750][20/41] Loss_D: 0.3580 Loss_G: 4.8010 D(x): 0.8901 D(G(z)): 0.0177 / 0.0091\n",
            "[1301/2750][40/41] Loss_D: 0.5226 Loss_G: 4.1663 D(x): 0.9006 D(G(z)): 0.1477 / 0.0150\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9977841].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999978..0.87475586].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99906796..0.9088268].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999994..0.9801889].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999804..0.8671804].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1301.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc18f70cc332484e8c371cc057b3a7df",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1302/2750][20/41] Loss_D: 0.3639 Loss_G: 4.7390 D(x): 0.8867 D(G(z)): 0.0205 / 0.0110\n",
            "[1302/2750][40/41] Loss_D: 0.5713 Loss_G: 4.0204 D(x): 0.7598 D(G(z)): 0.0189 / 0.0237\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83b05d1f77324769a940fa393f792988",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1303/2750][20/41] Loss_D: 0.4030 Loss_G: 4.3397 D(x): 0.9065 D(G(z)): 0.0582 / 0.0123\n",
            "[1303/2750][40/41] Loss_D: 0.4428 Loss_G: 4.0711 D(x): 0.9188 D(G(z)): 0.0954 / 0.0142\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b19da88e9b2c455ba9ed180f63178be8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1304/2750][20/41] Loss_D: 0.3594 Loss_G: 4.9364 D(x): 0.9026 D(G(z)): 0.0145 / 0.0077\n",
            "[1304/2750][40/41] Loss_D: 0.5182 Loss_G: 3.0398 D(x): 0.9231 D(G(z)): 0.1220 / 0.0743\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efc63f0146a04358b977f348637332db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1305/2750][20/41] Loss_D: 0.5157 Loss_G: 4.0958 D(x): 0.7741 D(G(z)): 0.0138 / 0.0146\n",
            "[1305/2750][40/41] Loss_D: 0.4543 Loss_G: 3.9663 D(x): 0.8946 D(G(z)): 0.0795 / 0.0367\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eba996e5a6c44559b227caa628bc7c62",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1306/2750][20/41] Loss_D: 0.5192 Loss_G: 3.7759 D(x): 0.7705 D(G(z)): 0.0372 / 0.0356\n",
            "[1306/2750][40/41] Loss_D: 0.9315 Loss_G: 2.6228 D(x): 0.5522 D(G(z)): 0.1286 / 0.1095\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0699bfbd7e1a419a86ecaf0837e8d9fc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1307/2750][20/41] Loss_D: 0.3890 Loss_G: 3.8148 D(x): 0.9085 D(G(z)): 0.0519 / 0.0207\n",
            "[1307/2750][40/41] Loss_D: 0.4813 Loss_G: 4.2186 D(x): 0.7576 D(G(z)): 0.0134 / 0.0169\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b466ca1940934376ba2ead83837fb915",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1308/2750][20/41] Loss_D: 0.4121 Loss_G: 3.4433 D(x): 0.9092 D(G(z)): 0.0695 / 0.0350\n",
            "[1308/2750][40/41] Loss_D: 0.3663 Loss_G: 4.6494 D(x): 0.8973 D(G(z)): 0.0249 / 0.0122\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fcf60ca0d8ee4832aa77565911aca43f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "[1310/2750][20/41] Loss_D: 0.9121 Loss_G: 2.6328 D(x): 0.5404 D(G(z)): 0.0305 / 0.1050\n",
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            "[1311/2750][20/41] Loss_D: 0.4614 Loss_G: 3.4977 D(x): 0.8769 D(G(z)): 0.0794 / 0.0345\n",
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            "[1313/2750][20/41] Loss_D: 0.4137 Loss_G: 4.0055 D(x): 0.8399 D(G(z)): 0.0255 / 0.0167\n",
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            "[1314/2750][20/41] Loss_D: 0.5172 Loss_G: 2.7914 D(x): 0.9666 D(G(z)): 0.1087 / 0.0740\n",
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            "[1315/2750][20/41] Loss_D: 0.3659 Loss_G: 5.3395 D(x): 0.8794 D(G(z)): 0.0103 / 0.0050\n",
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            "[1316/2750][20/41] Loss_D: 0.4570 Loss_G: 2.9146 D(x): 0.9194 D(G(z)): 0.0770 / 0.0550\n",
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            "[1317/2750][20/41] Loss_D: 0.4986 Loss_G: 3.0060 D(x): 0.8878 D(G(z)): 0.0779 / 0.0553\n",
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            "[1318/2750][20/41] Loss_D: 0.4803 Loss_G: 3.9706 D(x): 0.9326 D(G(z)): 0.0520 / 0.0177\n",
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            "[1319/2750][20/41] Loss_D: 0.5278 Loss_G: 3.8361 D(x): 0.9744 D(G(z)): 0.0988 / 0.0210\n",
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            "[1320/2750][20/41] Loss_D: 0.4568 Loss_G: 4.9799 D(x): 0.9249 D(G(z)): 0.0734 / 0.0072\n",
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            "[1321/2750][20/41] Loss_D: 0.3681 Loss_G: 4.4833 D(x): 0.8599 D(G(z)): 0.0209 / 0.0134\n",
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            "[1322/2750][20/41] Loss_D: 0.9773 Loss_G: 2.9379 D(x): 0.4679 D(G(z)): 0.0172 / 0.0833\n",
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            "[1323/2750][20/41] Loss_D: 0.4974 Loss_G: 3.5374 D(x): 0.9415 D(G(z)): 0.0745 / 0.0477\n",
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            "[1324/2750][20/41] Loss_D: 0.3733 Loss_G: 6.6619 D(x): 0.8737 D(G(z)): 0.0038 / 0.0028\n",
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            "[1325/2750][20/41] Loss_D: 0.5277 Loss_G: 4.1042 D(x): 0.9487 D(G(z)): 0.1287 / 0.0167\n",
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            "[1326/2750][20/41] Loss_D: 0.3934 Loss_G: 4.6645 D(x): 0.8904 D(G(z)): 0.0362 / 0.0096\n",
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            "[1327/2750][20/41] Loss_D: 0.4783 Loss_G: 3.5010 D(x): 0.9422 D(G(z)): 0.1119 / 0.0276\n",
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            "[1328/2750][20/41] Loss_D: 0.3566 Loss_G: 4.2713 D(x): 0.9148 D(G(z)): 0.0220 / 0.0150\n",
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            "[1329/2750][20/41] Loss_D: 0.3671 Loss_G: 4.6053 D(x): 0.8485 D(G(z)): 0.0153 / 0.0102\n",
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            "[1330/2750][20/41] Loss_D: 0.3962 Loss_G: 4.2208 D(x): 0.9333 D(G(z)): 0.0458 / 0.0201\n",
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            "[1331/2750][20/41] Loss_D: 0.3617 Loss_G: 5.0478 D(x): 0.8982 D(G(z)): 0.0106 / 0.0074\n",
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            "[1337/2750][20/41] Loss_D: 0.3703 Loss_G: 4.2057 D(x): 0.8775 D(G(z)): 0.0208 / 0.0131\n",
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            "[1338/2750][20/41] Loss_D: 0.7717 Loss_G: 4.1135 D(x): 0.9946 D(G(z)): 0.1745 / 0.0150\n",
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            "[1350/2750][20/41] Loss_D: 0.5902 Loss_G: 3.4963 D(x): 0.9651 D(G(z)): 0.1465 / 0.0296\n",
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            "[1351/2750][20/41] Loss_D: 0.6352 Loss_G: 3.7696 D(x): 0.8296 D(G(z)): 0.1690 / 0.0250\n",
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            "[1353/2750][20/41] Loss_D: 0.4336 Loss_G: 4.1035 D(x): 0.9273 D(G(z)): 0.0729 / 0.0187\n",
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            "[1354/2750][20/41] Loss_D: 1.3627 Loss_G: 2.2311 D(x): 0.3861 D(G(z)): 0.0183 / 0.1246\n",
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            "[1355/2750][20/41] Loss_D: 0.5384 Loss_G: 3.0253 D(x): 0.7958 D(G(z)): 0.0915 / 0.0494\n",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1391/2750][20/41] Loss_D: 0.4438 Loss_G: 3.3863 D(x): 0.9412 D(G(z)): 0.0871 / 0.0446\n",
            "[1391/2750][40/41] Loss_D: 0.3579 Loss_G: 4.9162 D(x): 0.8999 D(G(z)): 0.0234 / 0.0070\n"
          ]
        },
        {
          "data": {
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1392/2750][20/41] Loss_D: 0.4133 Loss_G: 5.4035 D(x): 0.9117 D(G(z)): 0.0396 / 0.0050\n",
            "[1392/2750][40/41] Loss_D: 0.5041 Loss_G: 3.5861 D(x): 0.9160 D(G(z)): 0.1241 / 0.0318\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25f70d353d934b999ee9f8823d6b4f71",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1393/2750][20/41] Loss_D: 0.3577 Loss_G: 5.1180 D(x): 0.8982 D(G(z)): 0.0133 / 0.0071\n",
            "[1393/2750][40/41] Loss_D: 0.3680 Loss_G: 4.4713 D(x): 0.9160 D(G(z)): 0.0280 / 0.0151\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e542f6911ed246b6bb0d64277495d5b2",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1394/2750][20/41] Loss_D: 0.3647 Loss_G: 4.4385 D(x): 0.9016 D(G(z)): 0.0202 / 0.0095\n",
            "[1394/2750][40/41] Loss_D: 0.3735 Loss_G: 5.2710 D(x): 0.8534 D(G(z)): 0.0140 / 0.0063\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc2603e853b74dbd8fcf2ed2ef55d7db",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1395/2750][20/41] Loss_D: 0.7474 Loss_G: 3.5011 D(x): 0.6234 D(G(z)): 0.0669 / 0.0339\n",
            "[1395/2750][40/41] Loss_D: 0.4940 Loss_G: 4.3736 D(x): 0.9000 D(G(z)): 0.1273 / 0.0191\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "479f444b0d0d482d85e0c88b266fe5b3",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1396/2750][20/41] Loss_D: 0.3716 Loss_G: 4.1571 D(x): 0.9073 D(G(z)): 0.0341 / 0.0145\n",
            "[1396/2750][40/41] Loss_D: 0.3692 Loss_G: 4.3539 D(x): 0.8969 D(G(z)): 0.0247 / 0.0155\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2c8e348b5b24a72a420654e67419478",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1397/2750][20/41] Loss_D: 0.6477 Loss_G: 2.7794 D(x): 0.8100 D(G(z)): 0.1380 / 0.0658\n",
            "[1397/2750][40/41] Loss_D: 0.5448 Loss_G: 4.1611 D(x): 0.9759 D(G(z)): 0.0866 / 0.0188\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a294ade7de5f4e8aa3f365b08f9e4909",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1398/2750][20/41] Loss_D: 0.3890 Loss_G: 5.4059 D(x): 0.9088 D(G(z)): 0.0104 / 0.0048\n",
            "[1398/2750][40/41] Loss_D: 1.5577 Loss_G: 5.5183 D(x): 0.9684 D(G(z)): 0.5664 / 0.0073\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5aa597367b8f40a896bf9524f9a7aa2a",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1399/2750][20/41] Loss_D: 0.4694 Loss_G: 3.7600 D(x): 0.9272 D(G(z)): 0.0971 / 0.0213\n",
            "[1399/2750][40/41] Loss_D: 1.0895 Loss_G: 3.8526 D(x): 0.4181 D(G(z)): 0.0035 / 0.0269\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fe244297b1c942ce9e6c29c018d4fb2a",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1400/2750][20/41] Loss_D: 0.3596 Loss_G: 4.7875 D(x): 0.8936 D(G(z)): 0.0209 / 0.0083\n",
            "[1400/2750][40/41] Loss_D: 0.5889 Loss_G: 3.8083 D(x): 0.9141 D(G(z)): 0.1698 / 0.0263\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bbb6cd9c3449473ba4e59064c6bb1ad0",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1401/2750][20/41] Loss_D: 0.3757 Loss_G: 4.7718 D(x): 0.9179 D(G(z)): 0.0328 / 0.0119\n",
            "[1401/2750][40/41] Loss_D: 0.3572 Loss_G: 4.7082 D(x): 0.8904 D(G(z)): 0.0154 / 0.0080\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99919].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999994..0.99741733].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999005..0.9661692].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99945587].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999654..0.96120137].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1401.pth\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9e74aeebf1d40cc87eb905d1229be16",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1402/2750][20/41] Loss_D: 0.4227 Loss_G: 3.6674 D(x): 0.8796 D(G(z)): 0.0720 / 0.0266\n",
            "[1402/2750][40/41] Loss_D: 0.4277 Loss_G: 3.9127 D(x): 0.8912 D(G(z)): 0.0761 / 0.0180\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "[1403/2750][20/41] Loss_D: 0.4792 Loss_G: 3.3744 D(x): 0.8877 D(G(z)): 0.0847 / 0.0367\n",
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          "text": [
            "[1404/2750][20/41] Loss_D: 0.5343 Loss_G: 4.1169 D(x): 0.8870 D(G(z)): 0.1098 / 0.0167\n",
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          "text": [
            "[1405/2750][20/41] Loss_D: 0.5692 Loss_G: 5.9993 D(x): 0.7013 D(G(z)): 0.0242 / 0.0061\n",
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          "text": [
            "[1406/2750][20/41] Loss_D: 0.4874 Loss_G: 3.1959 D(x): 0.9356 D(G(z)): 0.0670 / 0.0397\n",
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          "text": [
            "[1407/2750][20/41] Loss_D: 0.7221 Loss_G: 3.8312 D(x): 0.8236 D(G(z)): 0.2233 / 0.0334\n",
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          "text": [
            "[1408/2750][20/41] Loss_D: 0.9293 Loss_G: 4.2350 D(x): 0.9489 D(G(z)): 0.3849 / 0.0177\n",
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          "text": [
            "[1409/2750][20/41] Loss_D: 0.3595 Loss_G: 4.9382 D(x): 0.8878 D(G(z)): 0.0076 / 0.0059\n",
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            "[1410/2750][20/41] Loss_D: 0.8308 Loss_G: 4.4786 D(x): 0.9758 D(G(z)): 0.2921 / 0.0114\n",
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          "text": [
            "[1411/2750][20/41] Loss_D: 0.6640 Loss_G: 3.0979 D(x): 0.9588 D(G(z)): 0.1935 / 0.0440\n",
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          "text": [
            "[1412/2750][20/41] Loss_D: 0.4019 Loss_G: 4.3704 D(x): 0.9091 D(G(z)): 0.0338 / 0.0156\n",
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          "text": [
            "[1413/2750][20/41] Loss_D: 1.0952 Loss_G: 3.0543 D(x): 0.9613 D(G(z)): 0.4380 / 0.0538\n",
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          "text": [
            "[1414/2750][20/41] Loss_D: 0.3823 Loss_G: 4.5845 D(x): 0.8639 D(G(z)): 0.0227 / 0.0100\n",
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          "text": [
            "[1415/2750][20/41] Loss_D: 0.6872 Loss_G: 3.2415 D(x): 0.6338 D(G(z)): 0.0196 / 0.0484\n",
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          "text": [
            "[1416/2750][20/41] Loss_D: 0.4528 Loss_G: 3.3721 D(x): 0.9406 D(G(z)): 0.0450 / 0.0359\n",
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          "text": [
            "[1417/2750][20/41] Loss_D: 0.6314 Loss_G: 3.1461 D(x): 0.9451 D(G(z)): 0.2092 / 0.0560\n",
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          "text": [
            "[1418/2750][20/41] Loss_D: 0.4122 Loss_G: 3.7790 D(x): 0.9250 D(G(z)): 0.0542 / 0.0222\n",
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          "text": [
            "[1419/2750][20/41] Loss_D: 1.5703 Loss_G: 4.6458 D(x): 0.9591 D(G(z)): 0.5973 / 0.0122\n",
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            "[1420/2750][20/41] Loss_D: 0.4878 Loss_G: 3.8997 D(x): 0.7834 D(G(z)): 0.0477 / 0.0194\n",
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          "text": [
            "[1421/2750][20/41] Loss_D: 0.4359 Loss_G: 3.7522 D(x): 0.8862 D(G(z)): 0.0841 / 0.0287\n",
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          "text": [
            "[1422/2750][20/41] Loss_D: 0.5722 Loss_G: 4.6819 D(x): 0.7831 D(G(z)): 0.0858 / 0.0239\n",
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          "text": [
            "[1423/2750][20/41] Loss_D: 0.5132 Loss_G: 3.9393 D(x): 0.9198 D(G(z)): 0.0987 / 0.0164\n",
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            "[1424/2750][20/41] Loss_D: 0.3922 Loss_G: 4.1098 D(x): 0.9219 D(G(z)): 0.0507 / 0.0182\n",
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            "[1425/2750][20/41] Loss_D: 0.4479 Loss_G: 3.7498 D(x): 0.8973 D(G(z)): 0.0503 / 0.0264\n",
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            "[1426/2750][20/41] Loss_D: 0.4173 Loss_G: 3.5802 D(x): 0.9371 D(G(z)): 0.0466 / 0.0446\n",
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            "[1485/2750][20/41] Loss_D: 0.4361 Loss_G: 3.8691 D(x): 0.9097 D(G(z)): 0.0818 / 0.0176\n",
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            "[1486/2750][20/41] Loss_D: 0.7430 Loss_G: 3.5023 D(x): 0.5851 D(G(z)): 0.0108 / 0.0503\n",
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            "[1487/2750][20/41] Loss_D: 0.5820 Loss_G: 3.0037 D(x): 0.6940 D(G(z)): 0.0593 / 0.0590\n",
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            "[1488/2750][20/41] Loss_D: 0.7534 Loss_G: 4.2357 D(x): 0.6451 D(G(z)): 0.0226 / 0.0167\n",
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            "[1489/2750][20/41] Loss_D: 0.3755 Loss_G: 4.5147 D(x): 0.8995 D(G(z)): 0.0309 / 0.0146\n",
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            "[1490/2750][20/41] Loss_D: 0.4785 Loss_G: 3.1887 D(x): 0.8482 D(G(z)): 0.0770 / 0.0540\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[1491/2750][20/41] Loss_D: 0.3886 Loss_G: 3.8879 D(x): 0.8692 D(G(z)): 0.0468 / 0.0220\n",
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            "[1492/2750][20/41] Loss_D: 0.3512 Loss_G: 4.9234 D(x): 0.8723 D(G(z)): 0.0159 / 0.0103\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[1493/2750][20/41] Loss_D: 0.5554 Loss_G: 3.7071 D(x): 0.9380 D(G(z)): 0.1630 / 0.0449\n",
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            "[1494/2750][20/41] Loss_D: 0.3779 Loss_G: 4.8480 D(x): 0.8967 D(G(z)): 0.0313 / 0.0096\n",
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            "[1495/2750][20/41] Loss_D: 0.3734 Loss_G: 4.2827 D(x): 0.9337 D(G(z)): 0.0264 / 0.0140\n",
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            "[1496/2750][20/41] Loss_D: 0.3993 Loss_G: 3.6809 D(x): 0.8958 D(G(z)): 0.0616 / 0.0292\n",
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            "[1497/2750][20/41] Loss_D: 0.5520 Loss_G: 3.4382 D(x): 0.8075 D(G(z)): 0.0907 / 0.0356\n",
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            "[1498/2750][20/41] Loss_D: 0.3944 Loss_G: 3.8421 D(x): 0.9079 D(G(z)): 0.0555 / 0.0239\n",
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            "[1500/2750][20/41] Loss_D: 0.7806 Loss_G: 2.9141 D(x): 0.5361 D(G(z)): 0.0335 / 0.0725\n",
            "[1500/2750][40/41] Loss_D: 0.5653 Loss_G: 2.5841 D(x): 0.9333 D(G(z)): 0.1248 / 0.0838\n"
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        },
        {
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              "model_id": "d2df28ff46a64e6687cdfdddf9788743",
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          "metadata": {},
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          "text": [
            "[1501/2750][20/41] Loss_D: 0.4080 Loss_G: 3.4045 D(x): 0.8875 D(G(z)): 0.0328 / 0.0281\n",
            "[1501/2750][40/41] Loss_D: 0.4569 Loss_G: 3.6258 D(x): 0.9019 D(G(z)): 0.0797 / 0.0365\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99741346].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9992087].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9990713].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99934244].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999998..0.94304365].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1501.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba3c45b8f8dc4de08cc9915055be76ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1502/2750][20/41] Loss_D: 0.3576 Loss_G: 5.2779 D(x): 0.9079 D(G(z)): 0.0125 / 0.0054\n",
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          "text": [
            "[1503/2750][20/41] Loss_D: 0.5392 Loss_G: 3.6172 D(x): 0.9532 D(G(z)): 0.1480 / 0.0252\n",
            "[1503/2750][40/41] Loss_D: 0.3795 Loss_G: 4.4018 D(x): 0.9018 D(G(z)): 0.0239 / 0.0121\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1504/2750][20/41] Loss_D: 0.4383 Loss_G: 4.1373 D(x): 0.9342 D(G(z)): 0.0671 / 0.0288\n",
            "[1504/2750][40/41] Loss_D: 0.3520 Loss_G: 4.7202 D(x): 0.8879 D(G(z)): 0.0119 / 0.0077\n"
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          "data": {
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          "text": [
            "[1505/2750][20/41] Loss_D: 0.3993 Loss_G: 3.7144 D(x): 0.9045 D(G(z)): 0.0586 / 0.0297\n",
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          "text": [
            "[1506/2750][20/41] Loss_D: 0.7549 Loss_G: 3.6536 D(x): 0.9760 D(G(z)): 0.2286 / 0.0276\n",
            "[1506/2750][40/41] Loss_D: 0.8873 Loss_G: 3.0884 D(x): 0.5300 D(G(z)): 0.0337 / 0.0860\n"
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          "text": [
            "[1507/2750][20/41] Loss_D: 0.4704 Loss_G: 4.2218 D(x): 0.8317 D(G(z)): 0.0424 / 0.0195\n",
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          "text": [
            "[1508/2750][20/41] Loss_D: 0.8715 Loss_G: 2.1931 D(x): 0.6038 D(G(z)): 0.1598 / 0.1418\n",
            "[1508/2750][40/41] Loss_D: 0.6889 Loss_G: 2.7679 D(x): 0.6939 D(G(z)): 0.0294 / 0.0665\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1509/2750][20/41] Loss_D: 0.3713 Loss_G: 4.6185 D(x): 0.8672 D(G(z)): 0.0195 / 0.0108\n",
            "[1509/2750][40/41] Loss_D: 0.3497 Loss_G: 5.3290 D(x): 0.9028 D(G(z)): 0.0106 / 0.0081\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1510/2750][20/41] Loss_D: 0.5022 Loss_G: 3.7629 D(x): 0.9297 D(G(z)): 0.1387 / 0.0253\n",
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          "data": {
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          "text": [
            "[1511/2750][20/41] Loss_D: 0.3836 Loss_G: 3.8588 D(x): 0.9253 D(G(z)): 0.0354 / 0.0230\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1512/2750][20/41] Loss_D: 0.5782 Loss_G: 3.9179 D(x): 0.9533 D(G(z)): 0.1484 / 0.0220\n",
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          "text": [
            "[1513/2750][20/41] Loss_D: 0.4360 Loss_G: 4.1204 D(x): 0.8941 D(G(z)): 0.0920 / 0.0197\n",
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          "text": [
            "[1514/2750][20/41] Loss_D: 0.3639 Loss_G: 5.1173 D(x): 0.8844 D(G(z)): 0.0147 / 0.0081\n",
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          "text": [
            "[1515/2750][20/41] Loss_D: 0.3881 Loss_G: 4.8949 D(x): 0.8848 D(G(z)): 0.0257 / 0.0066\n",
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          "text": [
            "[1516/2750][20/41] Loss_D: 0.4877 Loss_G: 4.1024 D(x): 0.8646 D(G(z)): 0.1044 / 0.0217\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1517/2750][20/41] Loss_D: 0.3521 Loss_G: 4.5543 D(x): 0.8849 D(G(z)): 0.0152 / 0.0109\n",
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          "text": [
            "[1518/2750][20/41] Loss_D: 0.3739 Loss_G: 4.7652 D(x): 0.9014 D(G(z)): 0.0292 / 0.0078\n",
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          "text": [
            "[1519/2750][20/41] Loss_D: 0.4269 Loss_G: 3.5470 D(x): 0.9457 D(G(z)): 0.0660 / 0.0354\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1520/2750][20/41] Loss_D: 0.3834 Loss_G: 4.0326 D(x): 0.8940 D(G(z)): 0.0402 / 0.0181\n",
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          "text": [
            "[1521/2750][20/41] Loss_D: 0.3984 Loss_G: 4.1084 D(x): 0.9438 D(G(z)): 0.0470 / 0.0187\n",
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          "text": [
            "[1522/2750][20/41] Loss_D: 0.8820 Loss_G: 2.3428 D(x): 0.5603 D(G(z)): 0.0629 / 0.1350\n",
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          "text": [
            "[1523/2750][20/41] Loss_D: 0.4764 Loss_G: 3.5405 D(x): 0.8414 D(G(z)): 0.0356 / 0.0411\n",
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          "text": [
            "[1524/2750][20/41] Loss_D: 0.4250 Loss_G: 4.1173 D(x): 0.8792 D(G(z)): 0.0356 / 0.0206\n",
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          "text": [
            "[1525/2750][20/41] Loss_D: 0.6501 Loss_G: 3.4991 D(x): 0.6808 D(G(z)): 0.0378 / 0.0383\n",
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            "[1526/2750][20/41] Loss_D: 0.4157 Loss_G: 4.0510 D(x): 0.9487 D(G(z)): 0.0553 / 0.0190\n",
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            "[1527/2750][20/41] Loss_D: 0.3635 Loss_G: 4.5840 D(x): 0.8935 D(G(z)): 0.0158 / 0.0095\n",
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            "[1528/2750][20/41] Loss_D: 0.3846 Loss_G: 4.2808 D(x): 0.8926 D(G(z)): 0.0407 / 0.0174\n",
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            "[1530/2750][20/41] Loss_D: 0.3624 Loss_G: 4.1681 D(x): 0.8990 D(G(z)): 0.0250 / 0.0160\n",
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            "[1531/2750][20/41] Loss_D: 0.5058 Loss_G: 4.0718 D(x): 0.9689 D(G(z)): 0.0301 / 0.0170\n",
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            "[1539/2750][20/41] Loss_D: 0.4561 Loss_G: 3.9410 D(x): 0.9172 D(G(z)): 0.0900 / 0.0207\n",
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            "[1557/2750][20/41] Loss_D: 0.3413 Loss_G: 5.1515 D(x): 0.8864 D(G(z)): 0.0058 / 0.0046\n",
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            "[1558/2750][20/41] Loss_D: 1.1529 Loss_G: 3.4085 D(x): 0.7272 D(G(z)): 0.4264 / 0.0398\n",
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            "[1559/2750][20/41] Loss_D: 0.4729 Loss_G: 4.2117 D(x): 0.9250 D(G(z)): 0.0785 / 0.0146\n",
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            "[1560/2750][20/41] Loss_D: 0.3958 Loss_G: 5.9456 D(x): 0.9379 D(G(z)): 0.0215 / 0.0032\n",
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            "[1561/2750][20/41] Loss_D: 0.4950 Loss_G: 4.9016 D(x): 0.7513 D(G(z)): 0.0064 / 0.0098\n",
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            "[1562/2750][20/41] Loss_D: 0.6207 Loss_G: 4.3943 D(x): 0.9677 D(G(z)): 0.1391 / 0.0123\n",
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            "[1563/2750][20/41] Loss_D: 0.6444 Loss_G: 4.0681 D(x): 0.9281 D(G(z)): 0.1859 / 0.0243\n",
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            "[1564/2750][20/41] Loss_D: 0.3835 Loss_G: 4.9668 D(x): 0.8248 D(G(z)): 0.0134 / 0.0098\n",
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            "[1565/2750][20/41] Loss_D: 0.3959 Loss_G: 4.2253 D(x): 0.8909 D(G(z)): 0.0348 / 0.0163\n",
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            "[1566/2750][20/41] Loss_D: 0.4530 Loss_G: 4.0206 D(x): 0.9262 D(G(z)): 0.0909 / 0.0203\n",
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            "[1567/2750][20/41] Loss_D: 0.4563 Loss_G: 2.9170 D(x): 0.9400 D(G(z)): 0.0905 / 0.0577\n",
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            "[1568/2750][20/41] Loss_D: 1.0376 Loss_G: 4.2393 D(x): 0.9554 D(G(z)): 0.4096 / 0.0169\n",
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            "[1569/2750][20/41] Loss_D: 0.3879 Loss_G: 5.0170 D(x): 0.8211 D(G(z)): 0.0148 / 0.0065\n",
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            "[1570/2750][20/41] Loss_D: 0.5482 Loss_G: 3.6445 D(x): 0.6785 D(G(z)): 0.0152 / 0.0369\n",
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            "[1571/2750][20/41] Loss_D: 0.7223 Loss_G: 2.6347 D(x): 0.5948 D(G(z)): 0.0494 / 0.0979\n",
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            "[1572/2750][20/41] Loss_D: 0.3706 Loss_G: 4.0807 D(x): 0.9262 D(G(z)): 0.0289 / 0.0203\n",
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            "[1573/2750][20/41] Loss_D: 0.3684 Loss_G: 4.2445 D(x): 0.8970 D(G(z)): 0.0141 / 0.0142\n",
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            "[1574/2750][20/41] Loss_D: 0.5748 Loss_G: 3.9455 D(x): 0.6865 D(G(z)): 0.0110 / 0.0276\n",
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            "[1575/2750][20/41] Loss_D: 0.4286 Loss_G: 4.1622 D(x): 0.9605 D(G(z)): 0.0551 / 0.0191\n",
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            "[1576/2750][20/41] Loss_D: 0.5480 Loss_G: 4.2817 D(x): 0.7189 D(G(z)): 0.0118 / 0.0167\n",
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            "[1577/2750][20/41] Loss_D: 0.5358 Loss_G: 3.4159 D(x): 0.9599 D(G(z)): 0.0506 / 0.0372\n",
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            "[1578/2750][20/41] Loss_D: 0.7291 Loss_G: 3.1475 D(x): 0.6329 D(G(z)): 0.0321 / 0.0556\n",
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            "[1580/2750][20/41] Loss_D: 0.3978 Loss_G: 3.6835 D(x): 0.9016 D(G(z)): 0.0615 / 0.0255\n",
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            "[1584/2750][20/41] Loss_D: 0.3561 Loss_G: 5.4573 D(x): 0.9207 D(G(z)): 0.0115 / 0.0044\n",
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            "[1585/2750][20/41] Loss_D: 1.2206 Loss_G: 2.2537 D(x): 0.3790 D(G(z)): 0.0155 / 0.1526\n",
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            "[1586/2750][20/41] Loss_D: 0.3626 Loss_G: 4.9936 D(x): 0.9166 D(G(z)): 0.0263 / 0.0109\n",
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            "[1587/2750][20/41] Loss_D: 0.4926 Loss_G: 3.8369 D(x): 0.8604 D(G(z)): 0.0637 / 0.0257\n",
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            "[1588/2750][20/41] Loss_D: 0.5078 Loss_G: 3.0150 D(x): 0.9795 D(G(z)): 0.0437 / 0.0539\n",
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          "text": [
            "[1589/2750][20/41] Loss_D: 0.4700 Loss_G: 4.0014 D(x): 0.8034 D(G(z)): 0.0323 / 0.0247\n",
            "[1589/2750][40/41] Loss_D: 0.5540 Loss_G: 3.3861 D(x): 0.7085 D(G(z)): 0.0164 / 0.0383\n"
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            "application/vnd.jupyter.widget-view+json": {
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            "[1590/2750][20/41] Loss_D: 0.4755 Loss_G: 3.3007 D(x): 0.8179 D(G(z)): 0.0403 / 0.0388\n",
            "[1590/2750][40/41] Loss_D: 0.4859 Loss_G: 4.1157 D(x): 0.9458 D(G(z)): 0.0867 / 0.0208\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b91bcec6f594cc385e0120c6a37099e",
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            "[1591/2750][20/41] Loss_D: 0.4875 Loss_G: 3.2796 D(x): 0.8695 D(G(z)): 0.0853 / 0.0401\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1592/2750][20/41] Loss_D: 0.3962 Loss_G: 4.2190 D(x): 0.9158 D(G(z)): 0.0236 / 0.0136\n",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5b338e12e004cbca46442584aad2971",
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          "text": [
            "[1593/2750][20/41] Loss_D: 1.1180 Loss_G: 1.9629 D(x): 0.4576 D(G(z)): 0.0966 / 0.1576\n",
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            "[1594/2750][20/41] Loss_D: 0.3880 Loss_G: 5.0633 D(x): 0.8337 D(G(z)): 0.0230 / 0.0073\n",
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          "text": [
            "[1595/2750][20/41] Loss_D: 0.5719 Loss_G: 4.1916 D(x): 0.9063 D(G(z)): 0.1654 / 0.0175\n",
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          "text": [
            "[1596/2750][20/41] Loss_D: 0.3811 Loss_G: 5.1573 D(x): 0.8581 D(G(z)): 0.0259 / 0.0101\n",
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          "text": [
            "[1597/2750][20/41] Loss_D: 0.4007 Loss_G: 4.4117 D(x): 0.8580 D(G(z)): 0.0397 / 0.0129\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[1598/2750][20/41] Loss_D: 0.4414 Loss_G: 4.8509 D(x): 0.8002 D(G(z)): 0.0076 / 0.0080\n",
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            "[1599/2750][20/41] Loss_D: 0.3611 Loss_G: 5.4633 D(x): 0.8714 D(G(z)): 0.0072 / 0.0044\n",
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          "text": [
            "[1600/2750][20/41] Loss_D: 0.3779 Loss_G: 4.1636 D(x): 0.8651 D(G(z)): 0.0331 / 0.0188\n",
            "[1600/2750][40/41] Loss_D: 0.5308 Loss_G: 3.7288 D(x): 0.8921 D(G(z)): 0.1641 / 0.0262\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e91737b67e3a4350bbd5050b0453af2e",
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        {
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          "text": [
            "[1601/2750][20/41] Loss_D: 0.3973 Loss_G: 4.2901 D(x): 0.8328 D(G(z)): 0.0364 / 0.0143\n",
            "[1601/2750][40/41] Loss_D: 0.4687 Loss_G: 4.4344 D(x): 0.9406 D(G(z)): 0.0665 / 0.0143\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999744..0.9259158].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999958..0.8912071].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999761..0.8693983].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9943376].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9997243].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1601.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f36019cac89148f5940d669d95c173c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1602/2750][20/41] Loss_D: 0.4638 Loss_G: 4.4125 D(x): 0.8168 D(G(z)): 0.0087 / 0.0107\n",
            "[1602/2750][40/41] Loss_D: 0.3620 Loss_G: 4.9256 D(x): 0.8601 D(G(z)): 0.0151 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3d604309a804d6ba2c76bd8645c4ce3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1603/2750][20/41] Loss_D: 0.6367 Loss_G: 3.4718 D(x): 0.6515 D(G(z)): 0.0278 / 0.0374\n",
            "[1603/2750][40/41] Loss_D: 0.3614 Loss_G: 4.2339 D(x): 0.8923 D(G(z)): 0.0252 / 0.0160\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7a2c73caf17841c6b23a888ce504412f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1604/2750][20/41] Loss_D: 0.3720 Loss_G: 4.6555 D(x): 0.8436 D(G(z)): 0.0249 / 0.0129\n",
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            "[1605/2750][20/41] Loss_D: 0.5467 Loss_G: 2.9311 D(x): 0.7791 D(G(z)): 0.0475 / 0.0569\n",
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            "[1606/2750][20/41] Loss_D: 0.6085 Loss_G: 3.4192 D(x): 0.6580 D(G(z)): 0.0234 / 0.0397\n",
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            "[1607/2750][20/41] Loss_D: 1.0611 Loss_G: 3.7167 D(x): 0.9735 D(G(z)): 0.3610 / 0.0239\n",
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            "[1608/2750][20/41] Loss_D: 0.4102 Loss_G: 3.6206 D(x): 0.8940 D(G(z)): 0.0738 / 0.0299\n",
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            "[1609/2750][20/41] Loss_D: 0.6631 Loss_G: 3.0452 D(x): 0.6354 D(G(z)): 0.0309 / 0.0533\n",
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            "[1610/2750][20/41] Loss_D: 0.7235 Loss_G: 5.2685 D(x): 0.9759 D(G(z)): 0.0084 / 0.0044\n",
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            "[1611/2750][20/41] Loss_D: 0.6823 Loss_G: 4.4259 D(x): 0.9611 D(G(z)): 0.1897 / 0.0158\n",
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            "[1612/2750][20/41] Loss_D: 0.5056 Loss_G: 3.6335 D(x): 0.7528 D(G(z)): 0.0173 / 0.0263\n",
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            "[1613/2750][20/41] Loss_D: 0.4665 Loss_G: 3.8174 D(x): 0.7760 D(G(z)): 0.0308 / 0.0251\n",
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            "[1614/2750][20/41] Loss_D: 0.4277 Loss_G: 4.2793 D(x): 0.9222 D(G(z)): 0.0261 / 0.0132\n",
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            "[1615/2750][20/41] Loss_D: 0.4886 Loss_G: 3.7399 D(x): 0.8932 D(G(z)): 0.1280 / 0.0342\n",
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          "text": [
            "[1616/2750][20/41] Loss_D: 0.4435 Loss_G: 2.7300 D(x): 0.9193 D(G(z)): 0.0851 / 0.0723\n",
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            "[1617/2750][20/41] Loss_D: 0.4723 Loss_G: 4.1880 D(x): 0.8109 D(G(z)): 0.0157 / 0.0215\n",
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          "text": [
            "[1618/2750][20/41] Loss_D: 0.4083 Loss_G: 3.8837 D(x): 0.8989 D(G(z)): 0.0478 / 0.0218\n",
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          "text": [
            "[1619/2750][20/41] Loss_D: 0.5147 Loss_G: 3.1445 D(x): 0.7947 D(G(z)): 0.0429 / 0.0498\n",
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            "[1620/2750][20/41] Loss_D: 0.4615 Loss_G: 4.1940 D(x): 0.7849 D(G(z)): 0.0151 / 0.0200\n",
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          "text": [
            "[1621/2750][20/41] Loss_D: 0.6075 Loss_G: 3.4520 D(x): 0.9736 D(G(z)): 0.1310 / 0.0347\n",
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          "text": [
            "[1622/2750][20/41] Loss_D: 0.5106 Loss_G: 3.1351 D(x): 0.8993 D(G(z)): 0.1164 / 0.0384\n",
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            "[1623/2750][20/41] Loss_D: 0.4383 Loss_G: 4.0913 D(x): 0.9492 D(G(z)): 0.0498 / 0.0176\n",
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          "text": [
            "[1624/2750][20/41] Loss_D: 0.6196 Loss_G: 3.1514 D(x): 0.7233 D(G(z)): 0.0464 / 0.0528\n",
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            "[1625/2750][20/41] Loss_D: 0.4024 Loss_G: 5.1297 D(x): 0.9151 D(G(z)): 0.0170 / 0.0094\n",
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            "[1626/2750][20/41] Loss_D: 0.6757 Loss_G: 3.9218 D(x): 0.9830 D(G(z)): 0.1733 / 0.0272\n",
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            "[1632/2750][20/41] Loss_D: 0.4901 Loss_G: 3.8791 D(x): 0.9669 D(G(z)): 0.0718 / 0.0204\n",
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            "[1633/2750][20/41] Loss_D: 0.4195 Loss_G: 5.4722 D(x): 0.8991 D(G(z)): 0.0256 / 0.0072\n",
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            "[1635/2750][20/41] Loss_D: 0.6283 Loss_G: 5.6385 D(x): 0.6966 D(G(z)): 0.0015 / 0.0031\n",
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            "[1637/2750][20/41] Loss_D: 0.7613 Loss_G: 4.1515 D(x): 0.5773 D(G(z)): 0.0069 / 0.0282\n",
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            "[1640/2750][20/41] Loss_D: 0.4357 Loss_G: 3.7443 D(x): 0.8467 D(G(z)): 0.0419 / 0.0253\n",
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            "[1641/2750][20/41] Loss_D: 0.5028 Loss_G: 4.7773 D(x): 0.7425 D(G(z)): 0.0050 / 0.0096\n",
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            "[1642/2750][20/41] Loss_D: 0.4413 Loss_G: 3.7791 D(x): 0.8215 D(G(z)): 0.0262 / 0.0239\n",
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            "[1643/2750][20/41] Loss_D: 0.4736 Loss_G: 3.6831 D(x): 0.9313 D(G(z)): 0.0770 / 0.0238\n",
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            "[1644/2750][20/41] Loss_D: 0.4264 Loss_G: 3.6987 D(x): 0.9414 D(G(z)): 0.0609 / 0.0235\n",
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            "[1645/2750][20/41] Loss_D: 0.4071 Loss_G: 4.2805 D(x): 0.9075 D(G(z)): 0.0638 / 0.0119\n",
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            "[1646/2750][20/41] Loss_D: 0.4090 Loss_G: 5.2847 D(x): 0.9543 D(G(z)): 0.0203 / 0.0056\n",
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            "[1647/2750][20/41] Loss_D: 0.3571 Loss_G: 5.3929 D(x): 0.9240 D(G(z)): 0.0079 / 0.0047\n",
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            "[1648/2750][20/41] Loss_D: 0.4107 Loss_G: 4.4471 D(x): 0.9192 D(G(z)): 0.0283 / 0.0135\n",
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            "[1649/2750][20/41] Loss_D: 0.3516 Loss_G: 5.2390 D(x): 0.9001 D(G(z)): 0.0071 / 0.0058\n",
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            "[1650/2750][20/41] Loss_D: 0.3698 Loss_G: 4.4742 D(x): 0.9066 D(G(z)): 0.0241 / 0.0125\n",
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            "[1651/2750][20/41] Loss_D: 0.4435 Loss_G: 3.2736 D(x): 0.9420 D(G(z)): 0.0878 / 0.0364\n",
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            "[1652/2750][20/41] Loss_D: 0.3511 Loss_G: 4.6002 D(x): 0.9044 D(G(z)): 0.0113 / 0.0094\n",
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            "[1653/2750][20/41] Loss_D: 1.6800 Loss_G: 1.9919 D(x): 0.2468 D(G(z)): 0.0200 / 0.1894\n",
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            "[1654/2750][20/41] Loss_D: 0.7772 Loss_G: 4.5029 D(x): 0.9580 D(G(z)): 0.2812 / 0.0095\n",
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            "[1671/2750][20/41] Loss_D: 1.0262 Loss_G: 2.0307 D(x): 0.4848 D(G(z)): 0.0552 / 0.1376\n",
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            "[1672/2750][20/41] Loss_D: 0.5818 Loss_G: 3.1330 D(x): 0.9412 D(G(z)): 0.1986 / 0.0519\n",
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            "[1686/2750][20/41] Loss_D: 0.3676 Loss_G: 5.3013 D(x): 0.9087 D(G(z)): 0.0112 / 0.0059\n",
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            "[1687/2750][20/41] Loss_D: 0.4047 Loss_G: 4.8323 D(x): 0.9032 D(G(z)): 0.0125 / 0.0073\n",
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            "[1688/2750][20/41] Loss_D: 0.4146 Loss_G: 5.8712 D(x): 0.9632 D(G(z)): 0.0038 / 0.0039\n",
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            "[1689/2750][20/41] Loss_D: 0.4553 Loss_G: 3.5706 D(x): 0.9033 D(G(z)): 0.0816 / 0.0276\n",
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            "[1690/2750][20/41] Loss_D: 0.3998 Loss_G: 4.7652 D(x): 0.8649 D(G(z)): 0.0251 / 0.0115\n",
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            "[1691/2750][20/41] Loss_D: 0.4349 Loss_G: 4.3684 D(x): 0.9454 D(G(z)): 0.0130 / 0.0115\n",
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            "[1692/2750][20/41] Loss_D: 0.3444 Loss_G: 5.6384 D(x): 0.8942 D(G(z)): 0.0019 / 0.0032\n",
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            "[1693/2750][20/41] Loss_D: 0.3590 Loss_G: 5.9840 D(x): 0.8636 D(G(z)): 0.0026 / 0.0024\n",
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            "[1694/2750][20/41] Loss_D: 0.5036 Loss_G: 3.9472 D(x): 0.9534 D(G(z)): 0.0407 / 0.0239\n",
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            "[1695/2750][20/41] Loss_D: 0.4593 Loss_G: 3.3425 D(x): 0.9216 D(G(z)): 0.1064 / 0.0403\n",
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            "[1696/2750][20/41] Loss_D: 0.4725 Loss_G: 3.5266 D(x): 0.9065 D(G(z)): 0.1089 / 0.0263\n",
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            "[1697/2750][20/41] Loss_D: 0.7268 Loss_G: 3.5494 D(x): 0.5700 D(G(z)): 0.0084 / 0.0322\n",
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            "[1698/2750][20/41] Loss_D: 0.4349 Loss_G: 3.6150 D(x): 0.9236 D(G(z)): 0.0450 / 0.0234\n",
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            "[1699/2750][20/41] Loss_D: 0.6031 Loss_G: 3.5429 D(x): 0.9895 D(G(z)): 0.0908 / 0.0285\n",
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          "text": [
            "[1700/2750][20/41] Loss_D: 0.4905 Loss_G: 4.1536 D(x): 0.7823 D(G(z)): 0.0273 / 0.0221\n",
            "[1700/2750][40/41] Loss_D: 0.4352 Loss_G: 3.9642 D(x): 0.8358 D(G(z)): 0.0290 / 0.0236\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98ac9d4191174156a402a140460c8f8c",
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          "metadata": {},
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        {
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          "output_type": "stream",
          "text": [
            "[1701/2750][20/41] Loss_D: 1.2394 Loss_G: 2.0702 D(x): 0.3537 D(G(z)): 0.0078 / 0.1906\n",
            "[1701/2750][40/41] Loss_D: 0.4920 Loss_G: 4.2908 D(x): 0.8049 D(G(z)): 0.0287 / 0.0202\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.999994..0.9837062].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9879143].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99979115..0.8008511].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999976..0.9323195].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999342..0.87134093].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1701.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5053cadd14fd40a8bc8c99ae75be9ef1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1702/2750][20/41] Loss_D: 0.4089 Loss_G: 4.1448 D(x): 0.8891 D(G(z)): 0.0259 / 0.0150\n",
            "[1702/2750][40/41] Loss_D: 0.4685 Loss_G: 3.8007 D(x): 0.7915 D(G(z)): 0.0379 / 0.0257\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b3b392fadf647e7b370bb0ff7c714db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1703/2750][20/41] Loss_D: 0.4324 Loss_G: 3.7738 D(x): 0.8185 D(G(z)): 0.0313 / 0.0278\n",
            "[1703/2750][40/41] Loss_D: 0.3737 Loss_G: 5.2344 D(x): 0.8435 D(G(z)): 0.0182 / 0.0097\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0c113bfe8a3478eb90b6764bde5c896",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1704/2750][20/41] Loss_D: 0.7093 Loss_G: 3.6220 D(x): 0.9869 D(G(z)): 0.1730 / 0.0233\n",
            "[1704/2750][40/41] Loss_D: 0.4238 Loss_G: 3.8735 D(x): 0.9438 D(G(z)): 0.0599 / 0.0183\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3231c7fdaa64ca1848f4cabf41ae97f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1705/2750][20/41] Loss_D: 0.3787 Loss_G: 4.3590 D(x): 0.9068 D(G(z)): 0.0354 / 0.0151\n",
            "[1705/2750][40/41] Loss_D: 0.4072 Loss_G: 4.4078 D(x): 0.8498 D(G(z)): 0.0197 / 0.0148\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "444c3128dd894a52b16189934af594c6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1706/2750][20/41] Loss_D: 0.4634 Loss_G: 3.0702 D(x): 0.8080 D(G(z)): 0.0439 / 0.0583\n",
            "[1706/2750][40/41] Loss_D: 0.4391 Loss_G: 3.7475 D(x): 0.8254 D(G(z)): 0.0412 / 0.0299\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f79199980d81437583c4ba20dbee799e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1707/2750][20/41] Loss_D: 0.7040 Loss_G: 3.6106 D(x): 0.8603 D(G(z)): 0.0309 / 0.0328\n",
            "[1707/2750][40/41] Loss_D: 0.4652 Loss_G: 3.6322 D(x): 0.8991 D(G(z)): 0.0681 / 0.0298\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bad2b0b9a29542a780c361f9ea393d47",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1708/2750][20/41] Loss_D: 0.6081 Loss_G: 3.2704 D(x): 0.6236 D(G(z)): 0.0286 / 0.0446\n",
            "[1708/2750][40/41] Loss_D: 0.5002 Loss_G: 3.6733 D(x): 0.7649 D(G(z)): 0.0345 / 0.0334\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f72a96b5fcac402bb74d17d027212deb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1709/2750][20/41] Loss_D: 1.0107 Loss_G: 3.7406 D(x): 0.9910 D(G(z)): 0.1211 / 0.0208\n",
            "[1709/2750][40/41] Loss_D: 0.4486 Loss_G: 3.8035 D(x): 0.8028 D(G(z)): 0.0245 / 0.0242\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b5ecce8533f44fab942905a1a5c8792a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
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            "[1710/2750][20/41] Loss_D: 0.6894 Loss_G: 3.2745 D(x): 0.9564 D(G(z)): 0.2148 / 0.0418\n",
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            "[1711/2750][20/41] Loss_D: 0.4504 Loss_G: 3.6871 D(x): 0.9138 D(G(z)): 0.0874 / 0.0277\n",
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            "[1712/2750][20/41] Loss_D: 0.4498 Loss_G: 3.6340 D(x): 0.9004 D(G(z)): 0.0793 / 0.0280\n",
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            "[1713/2750][20/41] Loss_D: 0.3944 Loss_G: 4.3945 D(x): 0.8860 D(G(z)): 0.0488 / 0.0177\n",
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            "[1714/2750][20/41] Loss_D: 0.5480 Loss_G: 3.3163 D(x): 0.8050 D(G(z)): 0.1083 / 0.0545\n",
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            "[1715/2750][20/41] Loss_D: 0.3888 Loss_G: 4.2447 D(x): 0.9086 D(G(z)): 0.0387 / 0.0164\n",
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            "[1716/2750][20/41] Loss_D: 0.4302 Loss_G: 4.3922 D(x): 0.9370 D(G(z)): 0.0662 / 0.0131\n",
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            "[1717/2750][20/41] Loss_D: 0.6384 Loss_G: 3.7923 D(x): 0.9638 D(G(z)): 0.1395 / 0.0282\n",
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            "[1718/2750][20/41] Loss_D: 0.5781 Loss_G: 3.9977 D(x): 0.9274 D(G(z)): 0.1522 / 0.0182\n",
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            "[1719/2750][20/41] Loss_D: 0.4298 Loss_G: 3.2482 D(x): 0.8666 D(G(z)): 0.0397 / 0.0449\n",
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            "[1720/2750][20/41] Loss_D: 0.4123 Loss_G: 3.9697 D(x): 0.8832 D(G(z)): 0.0399 / 0.0198\n",
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            "[1721/2750][20/41] Loss_D: 0.4221 Loss_G: 4.1601 D(x): 0.8483 D(G(z)): 0.0236 / 0.0182\n",
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            "[1722/2750][20/41] Loss_D: 0.4022 Loss_G: 3.6934 D(x): 0.8417 D(G(z)): 0.0305 / 0.0267\n",
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            "[1723/2750][20/41] Loss_D: 0.4233 Loss_G: 3.6000 D(x): 0.9220 D(G(z)): 0.0745 / 0.0303\n",
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            "[1724/2750][20/41] Loss_D: 0.4702 Loss_G: 2.8342 D(x): 0.9616 D(G(z)): 0.0840 / 0.0640\n",
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            "[1725/2750][20/41] Loss_D: 0.4934 Loss_G: 2.5599 D(x): 0.9602 D(G(z)): 0.0957 / 0.0859\n",
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            "[1726/2750][20/41] Loss_D: 0.3723 Loss_G: 5.1155 D(x): 0.9395 D(G(z)): 0.0232 / 0.0084\n",
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            "[1727/2750][20/41] Loss_D: 0.6063 Loss_G: 3.5467 D(x): 0.6356 D(G(z)): 0.0128 / 0.0268\n",
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            "[1728/2750][20/41] Loss_D: 0.4570 Loss_G: 2.9874 D(x): 0.9267 D(G(z)): 0.1014 / 0.0682\n",
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            "[1729/2750][20/41] Loss_D: 0.3503 Loss_G: 5.1233 D(x): 0.8986 D(G(z)): 0.0090 / 0.0050\n",
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            "[1730/2750][20/41] Loss_D: 0.6086 Loss_G: 3.1419 D(x): 0.9462 D(G(z)): 0.1977 / 0.0407\n",
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            "[1731/2750][20/41] Loss_D: 0.3539 Loss_G: 5.9641 D(x): 0.8796 D(G(z)): 0.0113 / 0.0041\n",
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            "[1732/2750][20/41] Loss_D: 0.3558 Loss_G: 4.7621 D(x): 0.8864 D(G(z)): 0.0101 / 0.0067\n",
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            "[1738/2750][20/41] Loss_D: 0.4021 Loss_G: 3.8886 D(x): 0.9212 D(G(z)): 0.0350 / 0.0212\n",
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            "[1740/2750][20/41] Loss_D: 0.4415 Loss_G: 4.4124 D(x): 0.9387 D(G(z)): 0.0430 / 0.0185\n",
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            "[1741/2750][20/41] Loss_D: 0.4995 Loss_G: 3.9199 D(x): 0.8717 D(G(z)): 0.0948 / 0.0184\n",
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            "[1742/2750][20/41] Loss_D: 0.4003 Loss_G: 4.6207 D(x): 0.8834 D(G(z)): 0.0148 / 0.0135\n",
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            "[1743/2750][20/41] Loss_D: 0.4355 Loss_G: 3.6913 D(x): 0.9235 D(G(z)): 0.0443 / 0.0245\n",
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            "[1744/2750][20/41] Loss_D: 0.4114 Loss_G: 4.4739 D(x): 0.8119 D(G(z)): 0.0092 / 0.0142\n",
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            "[1746/2750][20/41] Loss_D: 0.5394 Loss_G: 4.0688 D(x): 0.7382 D(G(z)): 0.0176 / 0.0201\n",
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            "[1747/2750][20/41] Loss_D: 0.4633 Loss_G: 4.6116 D(x): 0.8331 D(G(z)): 0.0094 / 0.0094\n",
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            "[1749/2750][20/41] Loss_D: 0.7311 Loss_G: 3.0452 D(x): 0.9851 D(G(z)): 0.1944 / 0.0512\n",
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            "[1750/2750][20/41] Loss_D: 0.6972 Loss_G: 5.2000 D(x): 0.8951 D(G(z)): 0.0128 / 0.0066\n",
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            "[1751/2750][20/41] Loss_D: 0.5689 Loss_G: 4.4630 D(x): 0.9664 D(G(z)): 0.1191 / 0.0103\n",
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            "[1755/2750][20/41] Loss_D: 0.4218 Loss_G: 3.5126 D(x): 0.8954 D(G(z)): 0.0578 / 0.0328\n",
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            "[1765/2750][20/41] Loss_D: 0.3671 Loss_G: 4.4805 D(x): 0.8586 D(G(z)): 0.0168 / 0.0116\n",
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            "[1766/2750][20/41] Loss_D: 0.4007 Loss_G: 4.5915 D(x): 0.8633 D(G(z)): 0.0090 / 0.0087\n",
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            "[1767/2750][20/41] Loss_D: 0.4382 Loss_G: 3.8680 D(x): 0.9570 D(G(z)): 0.0282 / 0.0218\n",
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            "[1770/2750][20/41] Loss_D: 0.6256 Loss_G: 3.0690 D(x): 0.6621 D(G(z)): 0.0581 / 0.0579\n",
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            "[1771/2750][20/41] Loss_D: 0.4158 Loss_G: 4.2542 D(x): 0.9426 D(G(z)): 0.0512 / 0.0170\n",
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            "[1773/2750][20/41] Loss_D: 0.3839 Loss_G: 4.7717 D(x): 0.9158 D(G(z)): 0.0159 / 0.0108\n",
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            "[1774/2750][20/41] Loss_D: 0.5206 Loss_G: 4.6674 D(x): 0.9434 D(G(z)): 0.1090 / 0.0087\n",
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            "[1778/2750][20/41] Loss_D: 0.3634 Loss_G: 4.2646 D(x): 0.8885 D(G(z)): 0.0200 / 0.0130\n",
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            "[1791/2750][20/41] Loss_D: 0.3552 Loss_G: 4.5609 D(x): 0.8743 D(G(z)): 0.0190 / 0.0112\n",
            "[1791/2750][40/41] Loss_D: 0.4905 Loss_G: 3.2963 D(x): 0.7775 D(G(z)): 0.0602 / 0.0490\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb04fe8947594332bf31f58f5a84fd26",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1792/2750][20/41] Loss_D: 0.3756 Loss_G: 3.7341 D(x): 0.9121 D(G(z)): 0.0338 / 0.0275\n",
            "[1792/2750][40/41] Loss_D: 0.5720 Loss_G: 4.1279 D(x): 0.9389 D(G(z)): 0.1411 / 0.0186\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ff1c1d0ecedd4a4dbea2340949870d42",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1793/2750][20/41] Loss_D: 0.4038 Loss_G: 4.2348 D(x): 0.8598 D(G(z)): 0.0432 / 0.0160\n",
            "[1793/2750][40/41] Loss_D: 1.1551 Loss_G: 2.8992 D(x): 0.3878 D(G(z)): 0.0053 / 0.1073\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cc3541fda4c4cacbe816765360b4014",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1794/2750][20/41] Loss_D: 0.7875 Loss_G: 3.1772 D(x): 0.6140 D(G(z)): 0.0235 / 0.0547\n",
            "[1794/2750][40/41] Loss_D: 0.4241 Loss_G: 4.3504 D(x): 0.8798 D(G(z)): 0.0378 / 0.0125\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa173f2ba9494caf970100ff6d92ea16",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1795/2750][20/41] Loss_D: 0.3572 Loss_G: 4.9946 D(x): 0.8976 D(G(z)): 0.0178 / 0.0087\n",
            "[1795/2750][40/41] Loss_D: 0.3764 Loss_G: 4.4476 D(x): 0.8537 D(G(z)): 0.0235 / 0.0135\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3e29e964dd24ea79507a07c2465bc74",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1796/2750][20/41] Loss_D: 0.4808 Loss_G: 3.3276 D(x): 0.8889 D(G(z)): 0.0842 / 0.0447\n",
            "[1796/2750][40/41] Loss_D: 0.4304 Loss_G: 3.8461 D(x): 0.9202 D(G(z)): 0.0818 / 0.0259\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d483e223d93b4dbe9ce4896a64a4c124",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1797/2750][20/41] Loss_D: 0.4492 Loss_G: 4.3442 D(x): 0.8560 D(G(z)): 0.0263 / 0.0162\n",
            "[1797/2750][40/41] Loss_D: 0.4294 Loss_G: 4.1284 D(x): 0.8519 D(G(z)): 0.0316 / 0.0177\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7faf27a536db49949145008b847ced60",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1798/2750][20/41] Loss_D: 0.3479 Loss_G: 4.5419 D(x): 0.8826 D(G(z)): 0.0142 / 0.0099\n",
            "[1798/2750][40/41] Loss_D: 0.3873 Loss_G: 4.6601 D(x): 0.8937 D(G(z)): 0.0141 / 0.0093\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5c19616126c46a08f3e1b9fb235d895",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1799/2750][20/41] Loss_D: 0.4862 Loss_G: 3.7021 D(x): 0.9215 D(G(z)): 0.0668 / 0.0216\n",
            "[1799/2750][40/41] Loss_D: 0.5064 Loss_G: 3.8147 D(x): 0.9470 D(G(z)): 0.1150 / 0.0187\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "663c817006ed41daafe455fef2b833cc",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1800/2750][20/41] Loss_D: 0.3570 Loss_G: 4.5415 D(x): 0.9108 D(G(z)): 0.0162 / 0.0132\n",
            "[1800/2750][40/41] Loss_D: 0.4283 Loss_G: 4.5493 D(x): 0.9562 D(G(z)): 0.0311 / 0.0123\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb2630f3f6c54d149d69c61ea9a6c815",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1801/2750][20/41] Loss_D: 0.5069 Loss_G: 3.4278 D(x): 0.7281 D(G(z)): 0.0224 / 0.0376\n",
            "[1801/2750][40/41] Loss_D: 0.4647 Loss_G: 4.7755 D(x): 0.8663 D(G(z)): 0.0226 / 0.0089\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999994..0.9749646].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9934393].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9958883].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99950844..0.9029499].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.95267564].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1801.pth\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b99fe640e6044ebb230b4d40c03e52a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1802/2750][20/41] Loss_D: 0.4349 Loss_G: 5.0730 D(x): 0.8493 D(G(z)): 0.0079 / 0.0065\n",
            "[1802/2750][40/41] Loss_D: 0.6027 Loss_G: 3.4761 D(x): 0.9298 D(G(z)): 0.1663 / 0.0301\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53e5690f594b4d3589e8c47d4633eb52",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1803/2750][20/41] Loss_D: 0.6682 Loss_G: 4.0013 D(x): 0.9779 D(G(z)): 0.1057 / 0.0201\n",
            "[1803/2750][40/41] Loss_D: 0.5543 Loss_G: 4.0671 D(x): 0.9623 D(G(z)): 0.1181 / 0.0156\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae3ac60dcb734f739347cdf1a59fbf07",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1804/2750][20/41] Loss_D: 0.6663 Loss_G: 3.5015 D(x): 0.9510 D(G(z)): 0.1782 / 0.0315\n",
            "[1804/2750][40/41] Loss_D: 0.6070 Loss_G: 4.0637 D(x): 0.9590 D(G(z)): 0.1371 / 0.0146\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3df937a26a2f45ad9540aafe688dedca",
              "version_major": 2,
              "version_minor": 0
            },
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            "[1805/2750][20/41] Loss_D: 0.4156 Loss_G: 5.3908 D(x): 0.8762 D(G(z)): 0.0150 / 0.0076\n",
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            "[1806/2750][20/41] Loss_D: 0.6313 Loss_G: 4.4585 D(x): 0.6122 D(G(z)): 0.0024 / 0.0133\n",
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            "[1807/2750][20/41] Loss_D: 0.5085 Loss_G: 3.7239 D(x): 0.9201 D(G(z)): 0.1091 / 0.0239\n",
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            "[1808/2750][20/41] Loss_D: 0.4010 Loss_G: 5.0839 D(x): 0.9378 D(G(z)): 0.0089 / 0.0047\n",
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            "[1809/2750][20/41] Loss_D: 0.4659 Loss_G: 3.1218 D(x): 0.9188 D(G(z)): 0.0761 / 0.0549\n",
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            "[1810/2750][20/41] Loss_D: 0.4500 Loss_G: 4.2592 D(x): 0.9648 D(G(z)): 0.0281 / 0.0155\n",
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            "[1811/2750][20/41] Loss_D: 0.4209 Loss_G: 3.6973 D(x): 0.9113 D(G(z)): 0.0713 / 0.0245\n",
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            "[1812/2750][20/41] Loss_D: 0.4586 Loss_G: 3.6309 D(x): 0.9219 D(G(z)): 0.0707 / 0.0264\n",
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            "[1813/2750][20/41] Loss_D: 0.5275 Loss_G: 3.5189 D(x): 0.8023 D(G(z)): 0.0655 / 0.0344\n",
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            "[1814/2750][20/41] Loss_D: 0.5391 Loss_G: 3.5449 D(x): 0.9090 D(G(z)): 0.1039 / 0.0351\n",
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            "[1815/2750][20/41] Loss_D: 0.3446 Loss_G: 5.2744 D(x): 0.9106 D(G(z)): 0.0070 / 0.0046\n",
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            "[1816/2750][20/41] Loss_D: 0.3797 Loss_G: 5.1063 D(x): 0.8541 D(G(z)): 0.0104 / 0.0058\n",
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            "[1817/2750][20/41] Loss_D: 0.4496 Loss_G: 3.4275 D(x): 0.9066 D(G(z)): 0.0550 / 0.0331\n",
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            "[1818/2750][20/41] Loss_D: 0.5334 Loss_G: 3.8003 D(x): 0.9571 D(G(z)): 0.0817 / 0.0198\n",
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            "[1819/2750][20/41] Loss_D: 0.7024 Loss_G: 3.2946 D(x): 0.9515 D(G(z)): 0.1741 / 0.0363\n",
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            "[1820/2750][20/41] Loss_D: 0.4588 Loss_G: 3.5693 D(x): 0.8773 D(G(z)): 0.0760 / 0.0260\n",
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            "[1821/2750][20/41] Loss_D: 0.6488 Loss_G: 2.8107 D(x): 0.6399 D(G(z)): 0.0309 / 0.0692\n",
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            "[1822/2750][20/41] Loss_D: 0.4314 Loss_G: 4.7622 D(x): 0.8752 D(G(z)): 0.0119 / 0.0094\n",
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            "[1823/2750][20/41] Loss_D: 0.4865 Loss_G: 3.8392 D(x): 0.7451 D(G(z)): 0.0108 / 0.0216\n",
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            "[1824/2750][20/41] Loss_D: 0.4051 Loss_G: 3.7766 D(x): 0.8759 D(G(z)): 0.0299 / 0.0233\n",
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            "[1825/2750][20/41] Loss_D: 0.5797 Loss_G: 3.1572 D(x): 0.8799 D(G(z)): 0.0610 / 0.0417\n",
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            "[1826/2750][20/41] Loss_D: 0.6076 Loss_G: 3.4155 D(x): 0.7086 D(G(z)): 0.0739 / 0.0383\n",
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            "[1827/2750][20/41] Loss_D: 0.4869 Loss_G: 4.1787 D(x): 0.7353 D(G(z)): 0.0147 / 0.0168\n",
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            "[1828/2750][20/41] Loss_D: 0.4702 Loss_G: 4.0472 D(x): 0.9490 D(G(z)): 0.0580 / 0.0188\n",
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            "[1829/2750][20/41] Loss_D: 0.3737 Loss_G: 4.3901 D(x): 0.9061 D(G(z)): 0.0192 / 0.0128\n",
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            "[1842/2750][20/41] Loss_D: 0.3574 Loss_G: 4.1276 D(x): 0.8883 D(G(z)): 0.0170 / 0.0132\n",
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            "[1844/2750][20/41] Loss_D: 0.4403 Loss_G: 3.0681 D(x): 0.9281 D(G(z)): 0.0795 / 0.0534\n",
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            "[1846/2750][20/41] Loss_D: 0.3980 Loss_G: 4.2116 D(x): 0.8450 D(G(z)): 0.0401 / 0.0173\n",
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            "[1852/2750][20/41] Loss_D: 0.3651 Loss_G: 5.4231 D(x): 0.9088 D(G(z)): 0.0097 / 0.0074\n",
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            "[1860/2750][20/41] Loss_D: 0.3713 Loss_G: 4.9456 D(x): 0.8860 D(G(z)): 0.0244 / 0.0090\n",
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            "[1861/2750][20/41] Loss_D: 1.5231 Loss_G: 2.5155 D(x): 0.2967 D(G(z)): 0.0161 / 0.1117\n",
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            "[1862/2750][20/41] Loss_D: 0.3786 Loss_G: 4.3643 D(x): 0.9080 D(G(z)): 0.0337 / 0.0119\n",
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            "[1863/2750][20/41] Loss_D: 0.3866 Loss_G: 3.9610 D(x): 0.9041 D(G(z)): 0.0440 / 0.0162\n",
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            "[1864/2750][20/41] Loss_D: 0.5368 Loss_G: 4.7661 D(x): 0.9616 D(G(z)): 0.0806 / 0.0099\n",
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            "[1865/2750][20/41] Loss_D: 0.3833 Loss_G: 3.8011 D(x): 0.8600 D(G(z)): 0.0364 / 0.0217\n",
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            "[1866/2750][20/41] Loss_D: 0.4399 Loss_G: 4.3848 D(x): 0.7762 D(G(z)): 0.0109 / 0.0146\n",
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            "[1867/2750][20/41] Loss_D: 0.3627 Loss_G: 4.9549 D(x): 0.8987 D(G(z)): 0.0247 / 0.0078\n",
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            "[1868/2750][20/41] Loss_D: 0.3651 Loss_G: 4.7613 D(x): 0.9239 D(G(z)): 0.0189 / 0.0096\n",
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            "[1869/2750][20/41] Loss_D: 0.3690 Loss_G: 4.0655 D(x): 0.9010 D(G(z)): 0.0330 / 0.0179\n",
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            "[1870/2750][20/41] Loss_D: 0.3684 Loss_G: 5.5916 D(x): 0.8673 D(G(z)): 0.0087 / 0.0036\n",
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            "[1871/2750][20/41] Loss_D: 0.4292 Loss_G: 4.4702 D(x): 0.8680 D(G(z)): 0.0429 / 0.0153\n",
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            "[1872/2750][20/41] Loss_D: 0.4841 Loss_G: 3.2924 D(x): 0.7351 D(G(z)): 0.0251 / 0.0419\n",
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            "[1873/2750][20/41] Loss_D: 0.6889 Loss_G: 4.0025 D(x): 0.6047 D(G(z)): 0.0138 / 0.0258\n",
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            "[1874/2750][20/41] Loss_D: 0.3485 Loss_G: 5.3046 D(x): 0.8929 D(G(z)): 0.0082 / 0.0056\n",
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            "[1875/2750][20/41] Loss_D: 0.3584 Loss_G: 5.0546 D(x): 0.8849 D(G(z)): 0.0148 / 0.0065\n",
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            "[1876/2750][20/41] Loss_D: 0.4098 Loss_G: 4.0652 D(x): 0.9094 D(G(z)): 0.0255 / 0.0189\n",
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            "[1877/2750][20/41] Loss_D: 0.3808 Loss_G: 4.0295 D(x): 0.8685 D(G(z)): 0.0354 / 0.0161\n",
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            "[1878/2750][20/41] Loss_D: 0.4647 Loss_G: 2.8459 D(x): 0.9315 D(G(z)): 0.0998 / 0.0649\n",
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            "[1879/2750][20/41] Loss_D: 0.3711 Loss_G: 4.7007 D(x): 0.9283 D(G(z)): 0.0188 / 0.0144\n",
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            "[1880/2750][20/41] Loss_D: 0.3894 Loss_G: 4.3787 D(x): 0.8491 D(G(z)): 0.0276 / 0.0139\n",
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            "[1881/2750][20/41] Loss_D: 0.4563 Loss_G: 3.2806 D(x): 0.9243 D(G(z)): 0.1036 / 0.0412\n",
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            "[1882/2750][20/41] Loss_D: 0.8901 Loss_G: 3.3603 D(x): 0.5025 D(G(z)): 0.0130 / 0.0433\n",
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          ]
        },
        {
          "data": {
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              "model_id": "4d2e2fb431ae4e70a9eb11e241ab6858",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1887/2750][20/41] Loss_D: 0.5010 Loss_G: 3.2609 D(x): 0.7907 D(G(z)): 0.0349 / 0.0400\n",
            "[1887/2750][40/41] Loss_D: 0.4633 Loss_G: 4.3449 D(x): 0.9444 D(G(z)): 0.0944 / 0.0152\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f19657fcbb434a8d8d6bdab20b69a1ff",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1888/2750][20/41] Loss_D: 0.3633 Loss_G: 4.4073 D(x): 0.9083 D(G(z)): 0.0249 / 0.0152\n",
            "[1888/2750][40/41] Loss_D: 0.3442 Loss_G: 5.1431 D(x): 0.8826 D(G(z)): 0.0067 / 0.0047\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a456ce1dff8049e29ebd0b168db9bf08",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1889/2750][20/41] Loss_D: 0.3911 Loss_G: 2.6434 D(x): 0.9221 D(G(z)): 0.0470 / 0.0721\n",
            "[1889/2750][40/41] Loss_D: 0.4769 Loss_G: 3.4275 D(x): 0.7996 D(G(z)): 0.0284 / 0.0382\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc7537e636fd41c3b2b3ac1030320488",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1890/2750][20/41] Loss_D: 0.3703 Loss_G: 3.5902 D(x): 0.9020 D(G(z)): 0.0369 / 0.0334\n",
            "[1890/2750][40/41] Loss_D: 0.4256 Loss_G: 3.9800 D(x): 0.9181 D(G(z)): 0.0750 / 0.0180\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "33324d4fb0b842fc83e7b05d5f806c0d",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1891/2750][20/41] Loss_D: 0.3618 Loss_G: 4.7911 D(x): 0.8615 D(G(z)): 0.0092 / 0.0078\n",
            "[1891/2750][40/41] Loss_D: 0.5717 Loss_G: 2.8811 D(x): 0.9797 D(G(z)): 0.0883 / 0.0721\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "baefbafc3a2946359cd34b27cda3eaab",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1892/2750][20/41] Loss_D: 0.3605 Loss_G: 4.5797 D(x): 0.9249 D(G(z)): 0.0243 / 0.0085\n",
            "[1892/2750][40/41] Loss_D: 0.3795 Loss_G: 4.4071 D(x): 0.9055 D(G(z)): 0.0312 / 0.0190\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "14e1f61fb16c48a0b1ae0e338ddb21e7",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1893/2750][20/41] Loss_D: 0.3711 Loss_G: 4.4347 D(x): 0.8843 D(G(z)): 0.0279 / 0.0130\n",
            "[1893/2750][40/41] Loss_D: 0.3990 Loss_G: 3.4233 D(x): 0.9005 D(G(z)): 0.0344 / 0.0340\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c0d165d200ec4acbb59b5c18eff72f81",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1894/2750][20/41] Loss_D: 0.3555 Loss_G: 4.3597 D(x): 0.9033 D(G(z)): 0.0182 / 0.0130\n",
            "[1894/2750][40/41] Loss_D: 0.7872 Loss_G: 3.8169 D(x): 0.5599 D(G(z)): 0.0159 / 0.0318\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91c6859cb81a4dd98294ca10ac09c1c1",
              "version_major": 2,
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1895/2750][20/41] Loss_D: 0.3549 Loss_G: 4.8350 D(x): 0.9028 D(G(z)): 0.0143 / 0.0105\n",
            "[1895/2750][40/41] Loss_D: 0.7223 Loss_G: 3.3196 D(x): 0.8517 D(G(z)): 0.0893 / 0.0461\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a9e340cd18c4cc6b00e79e0ef518ceb",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1896/2750][20/41] Loss_D: 0.4429 Loss_G: 4.5509 D(x): 0.8199 D(G(z)): 0.0133 / 0.0126\n",
            "[1896/2750][40/41] Loss_D: 0.5524 Loss_G: 3.7123 D(x): 0.7212 D(G(z)): 0.0117 / 0.0320\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71d03ed6ff8d4e71ba17fa65a7773690",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1897/2750][20/41] Loss_D: 0.5799 Loss_G: 2.2182 D(x): 0.9496 D(G(z)): 0.1843 / 0.1128\n",
            "[1897/2750][40/41] Loss_D: 0.3602 Loss_G: 4.1154 D(x): 0.8878 D(G(z)): 0.0263 / 0.0141\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a9a0aa0c5e748e59439c11a2a397e10",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1898/2750][20/41] Loss_D: 0.7526 Loss_G: 5.0440 D(x): 0.9208 D(G(z)): 0.2918 / 0.0067\n",
            "[1898/2750][40/41] Loss_D: 1.4739 Loss_G: 2.1027 D(x): 0.3221 D(G(z)): 0.0273 / 0.2053\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed764e345cc844fcbb1cb8b48a13eaf4",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1899/2750][20/41] Loss_D: 0.4747 Loss_G: 2.9209 D(x): 0.8729 D(G(z)): 0.0701 / 0.0606\n",
            "[1899/2750][40/41] Loss_D: 0.7584 Loss_G: 3.2118 D(x): 0.9952 D(G(z)): 0.1007 / 0.0442\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "647b56754f844ef09b5dee44f4d619d1",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1900/2750][20/41] Loss_D: 0.5046 Loss_G: 3.4236 D(x): 0.9658 D(G(z)): 0.0485 / 0.0326\n",
            "[1900/2750][40/41] Loss_D: 0.5401 Loss_G: 3.5425 D(x): 0.9537 D(G(z)): 0.0930 / 0.0395\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6df69b6fc274a04966fbb3c9b947036",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1901/2750][20/41] Loss_D: 0.9188 Loss_G: 4.0055 D(x): 0.5121 D(G(z)): 0.0067 / 0.0228\n",
            "[1901/2750][40/41] Loss_D: 0.4647 Loss_G: 3.7314 D(x): 0.8829 D(G(z)): 0.0632 / 0.0277\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.999975..0.79884756].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999845..0.80680877].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999446..0.85559046].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999981..0.9652735].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99979424..0.8596707].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_1901.pth\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3aba66053fb441d90a90ed7eb779626",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1902/2750][20/41] Loss_D: 0.3989 Loss_G: 4.5058 D(x): 0.9151 D(G(z)): 0.0145 / 0.0108\n",
            "[1902/2750][40/41] Loss_D: 0.4697 Loss_G: 3.6741 D(x): 0.8119 D(G(z)): 0.0506 / 0.0280\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6e5f69ed4c94f8d919ea5016ddc1716",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1903/2750][20/41] Loss_D: 0.5438 Loss_G: 3.6328 D(x): 0.6969 D(G(z)): 0.0180 / 0.0248\n",
            "[1903/2750][40/41] Loss_D: 0.5339 Loss_G: 2.9432 D(x): 0.9168 D(G(z)): 0.1564 / 0.0519\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b9dd06e9298948dfbe88184cf7acec09",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1904/2750][20/41] Loss_D: 0.4747 Loss_G: 4.0789 D(x): 0.7654 D(G(z)): 0.0192 / 0.0164\n",
            "[1904/2750][40/41] Loss_D: 0.5422 Loss_G: 3.3888 D(x): 0.9542 D(G(z)): 0.1418 / 0.0379\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42d968b025c048f18277504116e8ccbe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1905/2750][20/41] Loss_D: 0.3854 Loss_G: 4.3667 D(x): 0.8760 D(G(z)): 0.0262 / 0.0112\n",
            "[1905/2750][40/41] Loss_D: 0.4849 Loss_G: 3.4610 D(x): 0.8503 D(G(z)): 0.0844 / 0.0332\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37b04971285e470bb0527922ee4f64b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1906/2750][20/41] Loss_D: 0.4708 Loss_G: 4.1032 D(x): 0.8015 D(G(z)): 0.0269 / 0.0178\n",
            "[1906/2750][40/41] Loss_D: 0.3609 Loss_G: 4.2286 D(x): 0.8957 D(G(z)): 0.0231 / 0.0150\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "000d080a91dc44cfa32ee0d1fb0f9e63",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1907/2750][20/41] Loss_D: 0.3486 Loss_G: 5.7030 D(x): 0.8709 D(G(z)): 0.0033 / 0.0028\n",
            "[1907/2750][40/41] Loss_D: 0.3782 Loss_G: 4.5900 D(x): 0.8744 D(G(z)): 0.0308 / 0.0110\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d20c5a9229b49b9b0791b830e5c5479",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1908/2750][20/41] Loss_D: 0.4701 Loss_G: 3.5522 D(x): 0.8105 D(G(z)): 0.0410 / 0.0314\n",
            "[1908/2750][40/41] Loss_D: 0.4094 Loss_G: 5.0169 D(x): 0.9460 D(G(z)): 0.0105 / 0.0068\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30526434428a4a9e9967f1c603610d2f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1909/2750][20/41] Loss_D: 0.3727 Loss_G: 4.3281 D(x): 0.9200 D(G(z)): 0.0246 / 0.0134\n",
            "[1909/2750][40/41] Loss_D: 0.3824 Loss_G: 3.6339 D(x): 0.8932 D(G(z)): 0.0447 / 0.0264\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f144590a6d440f0be53f749aca8731d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "[1910/2750][20/41] Loss_D: 0.3542 Loss_G: 4.4259 D(x): 0.8932 D(G(z)): 0.0146 / 0.0132\n",
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            "[1911/2750][20/41] Loss_D: 0.4719 Loss_G: 4.7085 D(x): 0.7931 D(G(z)): 0.0152 / 0.0165\n",
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            "[1912/2750][20/41] Loss_D: 0.4991 Loss_G: 4.4986 D(x): 0.7547 D(G(z)): 0.0219 / 0.0154\n",
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            "[1913/2750][20/41] Loss_D: 0.5080 Loss_G: 3.0659 D(x): 0.8191 D(G(z)): 0.0694 / 0.0533\n",
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            "[1914/2750][20/41] Loss_D: 0.3817 Loss_G: 4.4702 D(x): 0.9191 D(G(z)): 0.0197 / 0.0114\n",
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          "text": [
            "[1915/2750][20/41] Loss_D: 0.4113 Loss_G: 3.4817 D(x): 0.8782 D(G(z)): 0.0590 / 0.0273\n",
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            "[1916/2750][20/41] Loss_D: 0.3951 Loss_G: 3.7640 D(x): 0.8559 D(G(z)): 0.0469 / 0.0284\n",
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          "text": [
            "[1917/2750][20/41] Loss_D: 0.3676 Loss_G: 4.1508 D(x): 0.9054 D(G(z)): 0.0213 / 0.0155\n",
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            "[1918/2750][20/41] Loss_D: 1.0950 Loss_G: 2.5530 D(x): 0.9066 D(G(z)): 0.4970 / 0.0871\n",
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            "[1919/2750][20/41] Loss_D: 0.8937 Loss_G: 3.4965 D(x): 0.5121 D(G(z)): 0.0161 / 0.0445\n",
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            "[1920/2750][20/41] Loss_D: 0.6531 Loss_G: 3.9854 D(x): 0.6601 D(G(z)): 0.0205 / 0.0241\n",
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          "text": [
            "[1921/2750][20/41] Loss_D: 0.4124 Loss_G: 2.9201 D(x): 0.9134 D(G(z)): 0.0500 / 0.0582\n",
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          "text": [
            "[1922/2750][20/41] Loss_D: 0.7916 Loss_G: 2.6657 D(x): 0.5520 D(G(z)): 0.0502 / 0.1012\n",
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          "text": [
            "[1923/2750][20/41] Loss_D: 0.4747 Loss_G: 3.1011 D(x): 0.8999 D(G(z)): 0.0900 / 0.0509\n",
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          "text": [
            "[1924/2750][20/41] Loss_D: 0.5707 Loss_G: 2.9354 D(x): 0.7063 D(G(z)): 0.0542 / 0.0619\n",
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          "text": [
            "[1925/2750][20/41] Loss_D: 0.3910 Loss_G: 4.0466 D(x): 0.8782 D(G(z)): 0.0238 / 0.0189\n",
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            "[1926/2750][20/41] Loss_D: 0.4070 Loss_G: 3.8179 D(x): 0.9176 D(G(z)): 0.0447 / 0.0280\n",
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          "text": [
            "[1927/2750][20/41] Loss_D: 0.5887 Loss_G: 5.2954 D(x): 0.9509 D(G(z)): 0.0833 / 0.0082\n",
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          "text": [
            "[1928/2750][20/41] Loss_D: 0.4956 Loss_G: 2.9165 D(x): 0.9508 D(G(z)): 0.1008 / 0.0594\n",
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          "text": [
            "[1929/2750][20/41] Loss_D: 0.4474 Loss_G: 4.0657 D(x): 0.8505 D(G(z)): 0.0329 / 0.0204\n",
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          "text": [
            "[1930/2750][20/41] Loss_D: 0.4039 Loss_G: 4.5467 D(x): 0.9374 D(G(z)): 0.0183 / 0.0092\n",
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          "text": [
            "[1931/2750][20/41] Loss_D: 0.4054 Loss_G: 4.6948 D(x): 0.8992 D(G(z)): 0.0137 / 0.0088\n",
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          "text": [
            "[1932/2750][20/41] Loss_D: 0.4085 Loss_G: 4.3302 D(x): 0.9365 D(G(z)): 0.0144 / 0.0128\n",
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          "text": [
            "[1933/2750][20/41] Loss_D: 0.4128 Loss_G: 3.6479 D(x): 0.8066 D(G(z)): 0.0210 / 0.0277\n",
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            "[1934/2750][20/41] Loss_D: 0.3797 Loss_G: 4.0288 D(x): 0.9438 D(G(z)): 0.0287 / 0.0169\n",
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            "[1935/2750][20/41] Loss_D: 1.1704 Loss_G: 2.9984 D(x): 0.3881 D(G(z)): 0.0077 / 0.0750\n",
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            "[1936/2750][20/41] Loss_D: 0.6417 Loss_G: 2.9707 D(x): 0.7325 D(G(z)): 0.1081 / 0.0544\n",
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            "[1937/2750][20/41] Loss_D: 0.3716 Loss_G: 4.4603 D(x): 0.9062 D(G(z)): 0.0186 / 0.0118\n",
            "[1937/2750][40/41] Loss_D: 0.3761 Loss_G: 4.2115 D(x): 0.8874 D(G(z)): 0.0262 / 0.0179\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e36e86343cc4a5fbb0dac7b2ad32aff",
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        {
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          "text": [
            "[1938/2750][20/41] Loss_D: 0.3458 Loss_G: 5.0824 D(x): 0.8803 D(G(z)): 0.0098 / 0.0062\n",
            "[1938/2750][40/41] Loss_D: 0.4205 Loss_G: 3.6443 D(x): 0.9115 D(G(z)): 0.0737 / 0.0272\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e8e7d5434c784e3ebd7f9c97e5384a9d",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1939/2750][20/41] Loss_D: 0.3630 Loss_G: 4.6680 D(x): 0.9001 D(G(z)): 0.0141 / 0.0096\n",
            "[1939/2750][40/41] Loss_D: 0.7263 Loss_G: 2.7734 D(x): 0.5801 D(G(z)): 0.0222 / 0.0749\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "57316525b91c4da3ba92101a4eaf9134",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1940/2750][20/41] Loss_D: 0.8906 Loss_G: 2.6444 D(x): 0.4781 D(G(z)): 0.0209 / 0.0871\n",
            "[1940/2750][40/41] Loss_D: 0.3621 Loss_G: 4.5959 D(x): 0.8984 D(G(z)): 0.0185 / 0.0095\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a67f6434c154612b47927fe164c3ef2",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1941/2750][20/41] Loss_D: 0.7168 Loss_G: 2.9640 D(x): 0.6370 D(G(z)): 0.0293 / 0.0592\n",
            "[1941/2750][40/41] Loss_D: 0.5298 Loss_G: 3.4525 D(x): 0.8667 D(G(z)): 0.0889 / 0.0310\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4dc19e43a5324eb5985f73032b54bd20",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1942/2750][20/41] Loss_D: 0.3703 Loss_G: 4.4904 D(x): 0.8810 D(G(z)): 0.0193 / 0.0125\n",
            "[1942/2750][40/41] Loss_D: 0.3514 Loss_G: 4.8099 D(x): 0.8653 D(G(z)): 0.0102 / 0.0073\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e335f6de1c32427d934d478f821a8ae6",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1943/2750][20/41] Loss_D: 0.4393 Loss_G: 4.3210 D(x): 0.8034 D(G(z)): 0.0134 / 0.0145\n",
            "[1943/2750][40/41] Loss_D: 0.4690 Loss_G: 3.9811 D(x): 0.7594 D(G(z)): 0.0119 / 0.0184\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5bcbbfc045be4b91b4eeeb1bc9da52f3",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1944/2750][20/41] Loss_D: 0.4382 Loss_G: 3.2539 D(x): 0.8365 D(G(z)): 0.0321 / 0.0386\n",
            "[1944/2750][40/41] Loss_D: 0.3639 Loss_G: 4.5466 D(x): 0.9170 D(G(z)): 0.0193 / 0.0098\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3f8e451a06f47c0a2f097cd45f3d1d8",
              "version_major": 2,
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1945/2750][20/41] Loss_D: 0.5217 Loss_G: 3.4491 D(x): 0.9420 D(G(z)): 0.1435 / 0.0269\n",
            "[1945/2750][40/41] Loss_D: 0.3875 Loss_G: 3.7780 D(x): 0.8645 D(G(z)): 0.0301 / 0.0205\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f49ae24b4c94b388197067ca4c670cc",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1946/2750][20/41] Loss_D: 0.3806 Loss_G: 4.4179 D(x): 0.8345 D(G(z)): 0.0191 / 0.0104\n",
            "[1946/2750][40/41] Loss_D: 0.5696 Loss_G: 3.0704 D(x): 0.9323 D(G(z)): 0.1696 / 0.0667\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "49167b573467425cbe03236ce298a032",
              "version_major": 2,
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1947/2750][20/41] Loss_D: 0.4286 Loss_G: 3.6107 D(x): 0.8580 D(G(z)): 0.0420 / 0.0297\n",
            "[1947/2750][40/41] Loss_D: 0.4364 Loss_G: 3.8184 D(x): 0.8838 D(G(z)): 0.0648 / 0.0235\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ee28a5dadf9470ea29fe3ede879dc3d",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1948/2750][20/41] Loss_D: 0.3617 Loss_G: 4.8226 D(x): 0.8465 D(G(z)): 0.0107 / 0.0086\n",
            "[1948/2750][40/41] Loss_D: 0.3835 Loss_G: 3.8934 D(x): 0.8832 D(G(z)): 0.0338 / 0.0255\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e9ad4a2a3984d24ac54b85acc706f93",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1949/2750][20/41] Loss_D: 0.3736 Loss_G: 3.8737 D(x): 0.9146 D(G(z)): 0.0333 / 0.0224\n",
            "[1949/2750][40/41] Loss_D: 0.3588 Loss_G: 5.0129 D(x): 0.8483 D(G(z)): 0.0080 / 0.0059\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7bd755657bd449faac1741bb5ee8f828",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1950/2750][20/41] Loss_D: 0.4018 Loss_G: 4.6443 D(x): 0.9207 D(G(z)): 0.0145 / 0.0077\n",
            "[1950/2750][40/41] Loss_D: 0.4054 Loss_G: 3.5190 D(x): 0.8889 D(G(z)): 0.0530 / 0.0269\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9b7de101c774dbeb32181c80e5c59a3",
              "version_major": 2,
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1951/2750][20/41] Loss_D: 0.3994 Loss_G: 4.3875 D(x): 0.8838 D(G(z)): 0.0391 / 0.0116\n",
            "[1951/2750][40/41] Loss_D: 0.3529 Loss_G: 4.8167 D(x): 0.8850 D(G(z)): 0.0139 / 0.0093\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "816b38ebd49241b8b6ae99dd2af6363d",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1952/2750][20/41] Loss_D: 0.3887 Loss_G: 3.7828 D(x): 0.9074 D(G(z)): 0.0548 / 0.0311\n",
            "[1952/2750][40/41] Loss_D: 0.3941 Loss_G: 4.0100 D(x): 0.9189 D(G(z)): 0.0472 / 0.0258\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17bad798a96e4660b0e81c1f239fc495",
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        {
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          "output_type": "stream",
          "text": [
            "[1953/2750][20/41] Loss_D: 0.4197 Loss_G: 2.8530 D(x): 0.9000 D(G(z)): 0.0798 / 0.0580\n",
            "[1953/2750][40/41] Loss_D: 0.9797 Loss_G: 4.7429 D(x): 0.9549 D(G(z)): 0.3839 / 0.0113\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "887582d1dcaa44979f7547eaa6fa8ed8",
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        {
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          "output_type": "stream",
          "text": [
            "[1954/2750][20/41] Loss_D: 0.4210 Loss_G: 3.8315 D(x): 0.8972 D(G(z)): 0.0777 / 0.0241\n",
            "[1954/2750][40/41] Loss_D: 0.6942 Loss_G: 3.9164 D(x): 0.9052 D(G(z)): 0.2524 / 0.0222\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c91715a9c1574a348575751fcd6538df",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1955/2750][20/41] Loss_D: 1.0306 Loss_G: 4.1038 D(x): 0.6655 D(G(z)): 0.3191 / 0.0274\n",
            "[1955/2750][40/41] Loss_D: 0.4221 Loss_G: 4.1944 D(x): 0.9443 D(G(z)): 0.0307 / 0.0132\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e69268ee7af42fd96b6156dbf843653",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1956/2750][20/41] Loss_D: 0.9070 Loss_G: 2.4200 D(x): 0.5652 D(G(z)): 0.0950 / 0.1393\n",
            "[1956/2750][40/41] Loss_D: 0.5086 Loss_G: 3.5755 D(x): 0.7586 D(G(z)): 0.0206 / 0.0299\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "adb332b41cda497bb526546598b27a6e",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1957/2750][20/41] Loss_D: 0.4935 Loss_G: 3.6089 D(x): 0.8026 D(G(z)): 0.0419 / 0.0379\n",
            "[1957/2750][40/41] Loss_D: 0.4686 Loss_G: 4.1418 D(x): 0.9727 D(G(z)): 0.0369 / 0.0161\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58092aeb2a0c4658a3fa73a335fcb0e2",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1958/2750][20/41] Loss_D: 0.4042 Loss_G: 4.2301 D(x): 0.8870 D(G(z)): 0.0317 / 0.0154\n",
            "[1958/2750][40/41] Loss_D: 0.3636 Loss_G: 4.9497 D(x): 0.8945 D(G(z)): 0.0125 / 0.0083\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae3562773ccb46a48956565729865257",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
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          "output_type": "stream",
          "text": [
            "[1959/2750][20/41] Loss_D: 0.3930 Loss_G: 4.7900 D(x): 0.8337 D(G(z)): 0.0114 / 0.0110\n",
            "[1959/2750][40/41] Loss_D: 0.3795 Loss_G: 4.7635 D(x): 0.8020 D(G(z)): 0.0090 / 0.0087\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d65691228b3a4d558754b2196735faa9",
              "version_major": 2,
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1960/2750][20/41] Loss_D: 0.7663 Loss_G: 4.0369 D(x): 0.8803 D(G(z)): 0.2642 / 0.0211\n",
            "[1960/2750][40/41] Loss_D: 0.4802 Loss_G: 4.0514 D(x): 0.7537 D(G(z)): 0.0157 / 0.0231\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e98e8a10508402fa403f297e9e26528",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1961/2750][20/41] Loss_D: 0.3858 Loss_G: 4.6291 D(x): 0.8682 D(G(z)): 0.0362 / 0.0105\n",
            "[1961/2750][40/41] Loss_D: 0.3952 Loss_G: 4.2441 D(x): 0.8278 D(G(z)): 0.0191 / 0.0160\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d48c4bf444a4158ac79a719ab7c9280",
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        {
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          "output_type": "stream",
          "text": [
            "[1962/2750][20/41] Loss_D: 0.3886 Loss_G: 4.0552 D(x): 0.9184 D(G(z)): 0.0539 / 0.0189\n",
            "[1962/2750][40/41] Loss_D: 0.5049 Loss_G: 3.7759 D(x): 0.9454 D(G(z)): 0.0968 / 0.0267\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0daa7a5992764650aa9626b71e798269",
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          "output_type": "stream",
          "text": [
            "[1963/2750][20/41] Loss_D: 0.3617 Loss_G: 4.5611 D(x): 0.8870 D(G(z)): 0.0183 / 0.0107\n",
            "[1963/2750][40/41] Loss_D: 0.3994 Loss_G: 4.4416 D(x): 0.8873 D(G(z)): 0.0503 / 0.0101\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "063b5823ce284a9b902fd9f404406971",
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          "output_type": "stream",
          "text": [
            "[1964/2750][20/41] Loss_D: 0.5570 Loss_G: 3.3570 D(x): 0.8196 D(G(z)): 0.1090 / 0.0336\n",
            "[1964/2750][40/41] Loss_D: 0.4171 Loss_G: 4.9590 D(x): 0.9218 D(G(z)): 0.0178 / 0.0073\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1965/2750][20/41] Loss_D: 0.4452 Loss_G: 3.9441 D(x): 0.9335 D(G(z)): 0.0699 / 0.0182\n",
            "[1965/2750][40/41] Loss_D: 0.4482 Loss_G: 3.9064 D(x): 0.9458 D(G(z)): 0.0645 / 0.0267\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ea693bc31964b19abfc8b3cc5ec47bc",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[1966/2750][20/41] Loss_D: 0.4897 Loss_G: 3.6628 D(x): 0.7523 D(G(z)): 0.0206 / 0.0330\n",
            "[1966/2750][40/41] Loss_D: 0.4394 Loss_G: 4.4382 D(x): 0.9624 D(G(z)): 0.0145 / 0.0134\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9bd9ebf4091b46b9bc6998a49964a9b0",
              "version_major": 2,
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1967/2750][20/41] Loss_D: 0.4347 Loss_G: 4.2254 D(x): 0.8376 D(G(z)): 0.0314 / 0.0169\n",
            "[1967/2750][40/41] Loss_D: 0.4954 Loss_G: 3.4502 D(x): 0.9201 D(G(z)): 0.0460 / 0.0333\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "542aa3d694ee4ed291ad02b11bae390e",
              "version_major": 2,
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1968/2750][20/41] Loss_D: 0.4197 Loss_G: 4.0034 D(x): 0.9289 D(G(z)): 0.0372 / 0.0160\n",
            "[1968/2750][40/41] Loss_D: 0.5104 Loss_G: 3.9307 D(x): 0.7195 D(G(z)): 0.0125 / 0.0254\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45338305cc0f4106922e2099395fde63",
              "version_major": 2,
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            },
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1969/2750][20/41] Loss_D: 0.5136 Loss_G: 2.9118 D(x): 0.9522 D(G(z)): 0.1031 / 0.0570\n",
            "[1969/2750][40/41] Loss_D: 0.3907 Loss_G: 4.3144 D(x): 0.8746 D(G(z)): 0.0134 / 0.0146\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ffcc250453d49f78b7b12152ee7d611",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1970/2750][20/41] Loss_D: 0.3799 Loss_G: 4.3143 D(x): 0.8915 D(G(z)): 0.0198 / 0.0126\n",
            "[1970/2750][40/41] Loss_D: 0.5222 Loss_G: 3.3968 D(x): 0.7462 D(G(z)): 0.0285 / 0.0497\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21f0439bbaaa4add9435db23bd68828e",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1971/2750][20/41] Loss_D: 0.4862 Loss_G: 3.1827 D(x): 0.8020 D(G(z)): 0.0722 / 0.0472\n",
            "[1971/2750][40/41] Loss_D: 0.5200 Loss_G: 4.5105 D(x): 0.7473 D(G(z)): 0.0061 / 0.0096\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d8dda7640414a4f815f8f9e47edf068",
              "version_major": 2,
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            },
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1972/2750][20/41] Loss_D: 0.5406 Loss_G: 3.6075 D(x): 0.9505 D(G(z)): 0.1017 / 0.0269\n",
            "[1972/2750][40/41] Loss_D: 0.4085 Loss_G: 3.6012 D(x): 0.8847 D(G(z)): 0.0438 / 0.0234\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0122ff010c04cc2b5292058215efcea",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1973/2750][20/41] Loss_D: 1.0210 Loss_G: 4.1535 D(x): 0.4814 D(G(z)): 0.0031 / 0.0333\n",
            "[1973/2750][40/41] Loss_D: 0.4247 Loss_G: 3.9388 D(x): 0.9330 D(G(z)): 0.0280 / 0.0179\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad4c78a843aa496fae9bdc28b58c0369",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1974/2750][20/41] Loss_D: 0.3908 Loss_G: 4.6410 D(x): 0.9384 D(G(z)): 0.0259 / 0.0102\n",
            "[1974/2750][40/41] Loss_D: 0.8381 Loss_G: 2.0849 D(x): 0.5001 D(G(z)): 0.0461 / 0.1296\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f38861318f614048b6bce6b727f1f52d",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1975/2750][20/41] Loss_D: 0.4047 Loss_G: 4.7586 D(x): 0.8367 D(G(z)): 0.0096 / 0.0087\n",
            "[1975/2750][40/41] Loss_D: 0.4807 Loss_G: 2.8440 D(x): 0.9591 D(G(z)): 0.1018 / 0.0660\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa4b878322c44686b1b69cf97e5c70ef",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1976/2750][20/41] Loss_D: 0.3748 Loss_G: 4.5540 D(x): 0.9213 D(G(z)): 0.0212 / 0.0116\n",
            "[1976/2750][40/41] Loss_D: 0.4414 Loss_G: 3.6591 D(x): 0.9118 D(G(z)): 0.0712 / 0.0250\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e851097d53244e9ad3fe137cf177220",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1977/2750][20/41] Loss_D: 0.3704 Loss_G: 3.9915 D(x): 0.8927 D(G(z)): 0.0278 / 0.0163\n",
            "[1977/2750][40/41] Loss_D: 0.5226 Loss_G: 2.3983 D(x): 0.9485 D(G(z)): 0.1357 / 0.0941\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3127978fce140828a634968bd22012e",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1978/2750][20/41] Loss_D: 0.3549 Loss_G: 4.9417 D(x): 0.9104 D(G(z)): 0.0087 / 0.0070\n",
            "[1978/2750][40/41] Loss_D: 0.3707 Loss_G: 4.4351 D(x): 0.8690 D(G(z)): 0.0276 / 0.0112\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "231a41eb3526438da643f1fa91966b51",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1979/2750][20/41] Loss_D: 0.4778 Loss_G: 3.7498 D(x): 0.8235 D(G(z)): 0.0299 / 0.0236\n",
            "[1979/2750][40/41] Loss_D: 0.4943 Loss_G: 4.5634 D(x): 0.8138 D(G(z)): 0.0193 / 0.0156\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96d7373f2e86484f97ac01eac9c39167",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1980/2750][20/41] Loss_D: 0.3641 Loss_G: 4.5686 D(x): 0.8956 D(G(z)): 0.0162 / 0.0102\n",
            "[1980/2750][40/41] Loss_D: 0.3556 Loss_G: 5.3728 D(x): 0.8628 D(G(z)): 0.0048 / 0.0040\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60a0307ad6774d26861bae36274dfe16",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1981/2750][20/41] Loss_D: 0.3624 Loss_G: 4.8375 D(x): 0.9168 D(G(z)): 0.0155 / 0.0080\n",
            "[1981/2750][40/41] Loss_D: 0.3891 Loss_G: 4.4925 D(x): 0.9413 D(G(z)): 0.0406 / 0.0117\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a761592acb24cc2b1284923c2b71a11",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1982/2750][20/41] Loss_D: 0.5030 Loss_G: 3.7709 D(x): 0.9267 D(G(z)): 0.1203 / 0.0288\n",
            "[1982/2750][40/41] Loss_D: 0.4511 Loss_G: 4.9963 D(x): 0.7408 D(G(z)): 0.0054 / 0.0077\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7708b210d8d040cbb1acae7212534974",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1983/2750][20/41] Loss_D: 0.3985 Loss_G: 4.2214 D(x): 0.9167 D(G(z)): 0.0185 / 0.0180\n",
            "[1983/2750][40/41] Loss_D: 0.3745 Loss_G: 5.3106 D(x): 0.9146 D(G(z)): 0.0074 / 0.0044\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b879087bdb834c7396b7b749733ac08a",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1984/2750][20/41] Loss_D: 0.5213 Loss_G: 3.5491 D(x): 0.8537 D(G(z)): 0.1227 / 0.0305\n",
            "[1984/2750][40/41] Loss_D: 0.4012 Loss_G: 4.5561 D(x): 0.9170 D(G(z)): 0.0205 / 0.0093\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "31f4c51ee0bd4183b4028b11a553fd6c",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1985/2750][20/41] Loss_D: 0.4174 Loss_G: 6.4668 D(x): 0.8380 D(G(z)): 0.0017 / 0.0021\n",
            "[1985/2750][40/41] Loss_D: 0.4034 Loss_G: 4.3686 D(x): 0.9346 D(G(z)): 0.0268 / 0.0119\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37570eb5bac94a4e85717613887fbcde",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1986/2750][20/41] Loss_D: 0.4530 Loss_G: 3.4191 D(x): 0.8037 D(G(z)): 0.0373 / 0.0344\n",
            "[1986/2750][40/41] Loss_D: 0.6347 Loss_G: 3.9338 D(x): 0.9785 D(G(z)): 0.1435 / 0.0196\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b6e9e844f2a47778eff8c9d9cd45c8d",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1987/2750][20/41] Loss_D: 0.4457 Loss_G: 4.1051 D(x): 0.9462 D(G(z)): 0.0398 / 0.0168\n",
            "[1987/2750][40/41] Loss_D: 0.4314 Loss_G: 4.3768 D(x): 0.8255 D(G(z)): 0.0260 / 0.0146\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f7d7ad70495455c93ce1a994903364d",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1988/2750][20/41] Loss_D: 0.5256 Loss_G: 2.8344 D(x): 0.8712 D(G(z)): 0.0995 / 0.0714\n",
            "[1988/2750][40/41] Loss_D: 0.4500 Loss_G: 4.6420 D(x): 0.9715 D(G(z)): 0.0165 / 0.0104\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f3c232290594e6282172ec57136750a",
              "version_major": 2,
              "version_minor": 0
            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1989/2750][20/41] Loss_D: 0.3437 Loss_G: 5.3610 D(x): 0.8814 D(G(z)): 0.0033 / 0.0042\n",
            "[1989/2750][40/41] Loss_D: 0.4707 Loss_G: 4.9988 D(x): 0.7979 D(G(z)): 0.0080 / 0.0069\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a446f6b5da0c47bab6ff4faac78d336c",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1990/2750][20/41] Loss_D: 0.4514 Loss_G: 3.9681 D(x): 0.9429 D(G(z)): 0.0358 / 0.0173\n",
            "[1990/2750][40/41] Loss_D: 0.5805 Loss_G: 3.2358 D(x): 0.9537 D(G(z)): 0.1637 / 0.0381\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a015dc35e504c9ba771b2dd68e47c7f",
              "version_major": 2,
              "version_minor": 0
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1991/2750][20/41] Loss_D: 0.6325 Loss_G: 3.6558 D(x): 0.6695 D(G(z)): 0.0154 / 0.0544\n",
            "[1991/2750][40/41] Loss_D: 0.4302 Loss_G: 4.7694 D(x): 0.9047 D(G(z)): 0.0549 / 0.0102\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a63c79abaee2486c89be4279d79c41dc",
              "version_major": 2,
              "version_minor": 0
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1992/2750][20/41] Loss_D: 0.3703 Loss_G: 5.0549 D(x): 0.8294 D(G(z)): 0.0086 / 0.0092\n",
            "[1992/2750][40/41] Loss_D: 0.3806 Loss_G: 3.5493 D(x): 0.8545 D(G(z)): 0.0278 / 0.0294\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ecf195775f05457f9447f4f6bd4d4368",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1993/2750][20/41] Loss_D: 0.3918 Loss_G: 4.9552 D(x): 0.9204 D(G(z)): 0.0279 / 0.0095\n",
            "[1993/2750][40/41] Loss_D: 0.4602 Loss_G: 3.8534 D(x): 0.8764 D(G(z)): 0.0349 / 0.0199\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "089d972f234848ddadd710742970e161",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1994/2750][20/41] Loss_D: 0.3388 Loss_G: 4.9996 D(x): 0.9022 D(G(z)): 0.0069 / 0.0059\n",
            "[1994/2750][40/41] Loss_D: 0.3665 Loss_G: 4.3612 D(x): 0.8637 D(G(z)): 0.0223 / 0.0117\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "109cd01cdac44e8dbafa8b77eeba65f1",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1995/2750][20/41] Loss_D: 2.1625 Loss_G: 1.3861 D(x): 0.2086 D(G(z)): 0.0211 / 0.2907\n",
            "[1995/2750][40/41] Loss_D: 0.3597 Loss_G: 4.7713 D(x): 0.8893 D(G(z)): 0.0168 / 0.0084\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a495325417e54bb68192e13588c9fa37",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1996/2750][20/41] Loss_D: 0.6733 Loss_G: 4.2281 D(x): 0.9451 D(G(z)): 0.2309 / 0.0233\n",
            "[1996/2750][40/41] Loss_D: 0.3902 Loss_G: 4.1844 D(x): 0.8446 D(G(z)): 0.0150 / 0.0139\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d906b6db3c534275b39daa061b32bdf9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1997/2750][20/41] Loss_D: 0.3943 Loss_G: 3.3010 D(x): 0.9223 D(G(z)): 0.0377 / 0.0438\n",
            "[1997/2750][40/41] Loss_D: 0.3645 Loss_G: 4.0375 D(x): 0.8912 D(G(z)): 0.0268 / 0.0173\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5fc3af0281884015bb9a22f521761e12",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1998/2750][20/41] Loss_D: 0.3830 Loss_G: 4.9332 D(x): 0.8480 D(G(z)): 0.0122 / 0.0095\n",
            "[1998/2750][40/41] Loss_D: 0.3459 Loss_G: 5.6655 D(x): 0.8828 D(G(z)): 0.0077 / 0.0034\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8e45cd5eb58462e90354245bd4f4f91",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1999/2750][20/41] Loss_D: 0.3432 Loss_G: 4.6590 D(x): 0.8756 D(G(z)): 0.0096 / 0.0077\n",
            "[1999/2750][40/41] Loss_D: 0.8331 Loss_G: 3.1913 D(x): 0.9338 D(G(z)): 0.3574 / 0.0454\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2881f5dfa5e848949f408be9bb942a1e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2000/2750][20/41] Loss_D: 0.5818 Loss_G: 3.4430 D(x): 0.9337 D(G(z)): 0.1849 / 0.0351\n",
            "[2000/2750][40/41] Loss_D: 0.3789 Loss_G: 4.6500 D(x): 0.8858 D(G(z)): 0.0295 / 0.0138\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c575407d25374b00b9331eff7470d4ee",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2001/2750][20/41] Loss_D: 0.4045 Loss_G: 3.9578 D(x): 0.8270 D(G(z)): 0.0350 / 0.0216\n",
            "[2001/2750][40/41] Loss_D: 0.5695 Loss_G: 4.3756 D(x): 0.9762 D(G(z)): 0.0737 / 0.0133\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999994..0.91288406].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999991..0.8914103].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999794..0.9828883].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9928988].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999875..0.99574494].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2001.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e413ca0c0111401a807dada7b6b45ff5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2002/2750][20/41] Loss_D: 0.3570 Loss_G: 4.5752 D(x): 0.8678 D(G(z)): 0.0135 / 0.0109\n",
            "[2002/2750][40/41] Loss_D: 0.3869 Loss_G: 4.5452 D(x): 0.9326 D(G(z)): 0.0166 / 0.0104\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6874eb45340346b78416ecd67f6c580e",
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        {
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          "output_type": "stream",
          "text": [
            "[2003/2750][20/41] Loss_D: 0.9825 Loss_G: 5.0316 D(x): 0.9818 D(G(z)): 0.3355 / 0.0067\n",
            "[2003/2750][40/41] Loss_D: 0.5156 Loss_G: 3.6214 D(x): 0.9234 D(G(z)): 0.1517 / 0.0296\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb55012b56f24e9b8b5d9e185c2401b3",
              "version_major": 2,
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        },
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          "output_type": "stream",
          "text": [
            "[2004/2750][20/41] Loss_D: 0.4104 Loss_G: 6.0966 D(x): 0.9654 D(G(z)): 0.0021 / 0.0016\n",
            "[2004/2750][40/41] Loss_D: 0.4105 Loss_G: 3.5180 D(x): 0.9190 D(G(z)): 0.0384 / 0.0390\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da31550e745a4ebb98cf99b36b4cffa7",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2005/2750][20/41] Loss_D: 0.3930 Loss_G: 5.1564 D(x): 0.9220 D(G(z)): 0.0142 / 0.0096\n",
            "[2005/2750][40/41] Loss_D: 0.7050 Loss_G: 4.1610 D(x): 0.9131 D(G(z)): 0.0470 / 0.0138\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ca16ede76384698a5db42d5b70ea60c",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2006/2750][20/41] Loss_D: 0.4573 Loss_G: 3.7143 D(x): 0.9194 D(G(z)): 0.0802 / 0.0260\n",
            "[2006/2750][40/41] Loss_D: 0.3731 Loss_G: 4.9161 D(x): 0.8259 D(G(z)): 0.0095 / 0.0067\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8964124990cd4b6897b09afd9f29ae1e",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2007/2750][20/41] Loss_D: 0.4003 Loss_G: 3.6951 D(x): 0.8723 D(G(z)): 0.0520 / 0.0229\n",
            "[2007/2750][40/41] Loss_D: 0.3608 Loss_G: 4.5469 D(x): 0.8943 D(G(z)): 0.0186 / 0.0119\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86db5761b57f44198db2b3c1556b0230",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2008/2750][20/41] Loss_D: 0.4715 Loss_G: 3.4994 D(x): 0.9529 D(G(z)): 0.0973 / 0.0367\n",
            "[2008/2750][40/41] Loss_D: 0.3826 Loss_G: 4.0838 D(x): 0.8813 D(G(z)): 0.0384 / 0.0158\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a85ea80c3867425a8a90676eacf89bde",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2009/2750][20/41] Loss_D: 0.3760 Loss_G: 4.1920 D(x): 0.9376 D(G(z)): 0.0258 / 0.0146\n",
            "[2009/2750][40/41] Loss_D: 0.4444 Loss_G: 3.7904 D(x): 0.8547 D(G(z)): 0.0307 / 0.0239\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0116574e751949c5ab605aced4764f9c",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2010/2750][20/41] Loss_D: 0.3551 Loss_G: 4.0240 D(x): 0.8702 D(G(z)): 0.0154 / 0.0152\n",
            "[2010/2750][40/41] Loss_D: 1.0747 Loss_G: 4.4076 D(x): 0.9683 D(G(z)): 0.4264 / 0.0186\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0259f0c84b3043f0a640c66fb313125d",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2011/2750][20/41] Loss_D: 0.3782 Loss_G: 4.9170 D(x): 0.9231 D(G(z)): 0.0181 / 0.0077\n",
            "[2011/2750][40/41] Loss_D: 0.4949 Loss_G: 2.8331 D(x): 0.9082 D(G(z)): 0.1340 / 0.0598\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c914d4c8945345ed8fbdefce8acbf251",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2012/2750][20/41] Loss_D: 0.3771 Loss_G: 4.5757 D(x): 0.9156 D(G(z)): 0.0188 / 0.0127\n",
            "[2012/2750][40/41] Loss_D: 0.8818 Loss_G: 2.8948 D(x): 0.8840 D(G(z)): 0.3760 / 0.0571\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "048ddf7f8a2e4056b467193428dd97b3",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2013/2750][20/41] Loss_D: 0.5593 Loss_G: 3.6948 D(x): 0.8871 D(G(z)): 0.1294 / 0.0220\n",
            "[2013/2750][40/41] Loss_D: 0.3948 Loss_G: 4.5311 D(x): 0.8707 D(G(z)): 0.0143 / 0.0088\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bfd993e8c9234eb292bc1e82518f354c",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2014/2750][20/41] Loss_D: 0.4021 Loss_G: 4.1553 D(x): 0.8766 D(G(z)): 0.0299 / 0.0163\n",
            "[2014/2750][40/41] Loss_D: 0.5072 Loss_G: 3.7732 D(x): 0.9382 D(G(z)): 0.1205 / 0.0191\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4ac6216fa774e7291df844b04180789",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2015/2750][20/41] Loss_D: 0.4624 Loss_G: 3.8816 D(x): 0.9161 D(G(z)): 0.0727 / 0.0180\n",
            "[2015/2750][40/41] Loss_D: 0.4224 Loss_G: 4.0059 D(x): 0.9398 D(G(z)): 0.0172 / 0.0234\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f5ceb7fc63d47eaa89fdacad47dfa9b",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2016/2750][20/41] Loss_D: 0.4359 Loss_G: 3.6106 D(x): 0.9475 D(G(z)): 0.0611 / 0.0268\n",
            "[2016/2750][40/41] Loss_D: 0.4272 Loss_G: 3.6466 D(x): 0.9468 D(G(z)): 0.0425 / 0.0270\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab449de614f2489f98afdfe7ba9631f2",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2017/2750][20/41] Loss_D: 0.5777 Loss_G: 2.5853 D(x): 0.8259 D(G(z)): 0.1412 / 0.0794\n",
            "[2017/2750][40/41] Loss_D: 0.4771 Loss_G: 3.9113 D(x): 0.7192 D(G(z)): 0.0076 / 0.0198\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9af17c86c654cdf8617aade6f6f0ac8",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2018/2750][20/41] Loss_D: 0.4590 Loss_G: 3.3446 D(x): 0.9290 D(G(z)): 0.0447 / 0.0312\n",
            "[2018/2750][40/41] Loss_D: 0.4016 Loss_G: 4.8443 D(x): 0.9152 D(G(z)): 0.0333 / 0.0098\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc1e7bb678ba42408f38c292d3a216aa",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2019/2750][20/41] Loss_D: 0.4867 Loss_G: 3.4290 D(x): 0.8635 D(G(z)): 0.1014 / 0.0323\n",
            "[2019/2750][40/41] Loss_D: 0.4278 Loss_G: 4.0018 D(x): 0.9367 D(G(z)): 0.0767 / 0.0173\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b314c702630c43aa9f5c4a0ad2bde542",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2020/2750][20/41] Loss_D: 0.3571 Loss_G: 5.7268 D(x): 0.9188 D(G(z)): 0.0042 / 0.0032\n",
            "[2020/2750][40/41] Loss_D: 0.4307 Loss_G: 5.0726 D(x): 0.8566 D(G(z)): 0.0193 / 0.0084\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "46b6ad2cb11a43359b8e5e8d934efbcc",
              "version_major": 2,
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2021/2750][20/41] Loss_D: 0.3911 Loss_G: 3.7625 D(x): 0.8667 D(G(z)): 0.0415 / 0.0229\n",
            "[2021/2750][40/41] Loss_D: 0.3686 Loss_G: 4.5224 D(x): 0.8871 D(G(z)): 0.0169 / 0.0095\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "04666a76655841158b51675164307cb6",
              "version_major": 2,
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2022/2750][20/41] Loss_D: 0.4788 Loss_G: 3.8297 D(x): 0.9199 D(G(z)): 0.0802 / 0.0240\n",
            "[2022/2750][40/41] Loss_D: 0.7755 Loss_G: 4.1572 D(x): 0.9781 D(G(z)): 0.2155 / 0.0147\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "462b7fce9af14479a8973c58796167f8",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2023/2750][20/41] Loss_D: 0.5411 Loss_G: 2.7834 D(x): 0.9668 D(G(z)): 0.0740 / 0.0660\n",
            "[2023/2750][40/41] Loss_D: 0.5735 Loss_G: 3.5036 D(x): 0.9654 D(G(z)): 0.0967 / 0.0378\n"
          ]
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96a3166a285041fe9794ce2be9b5c9c0",
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          "output_type": "stream",
          "text": [
            "[2024/2750][20/41] Loss_D: 0.4167 Loss_G: 3.9737 D(x): 0.9210 D(G(z)): 0.0472 / 0.0157\n",
            "[2024/2750][40/41] Loss_D: 0.4404 Loss_G: 3.5664 D(x): 0.8269 D(G(z)): 0.0268 / 0.0249\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "10c13dd60fba45218c5728c897e5b90e",
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          "metadata": {},
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          "output_type": "stream",
          "text": [
            "[2025/2750][20/41] Loss_D: 0.4245 Loss_G: 3.6909 D(x): 0.8156 D(G(z)): 0.0198 / 0.0301\n",
            "[2025/2750][40/41] Loss_D: 0.4284 Loss_G: 3.9364 D(x): 0.8968 D(G(z)): 0.0376 / 0.0193\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce5d04a65d9b49eab2687624d68fd177",
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          "output_type": "stream",
          "text": [
            "[2026/2750][20/41] Loss_D: 0.4295 Loss_G: 3.7079 D(x): 0.8661 D(G(z)): 0.0571 / 0.0225\n",
            "[2026/2750][40/41] Loss_D: 0.4703 Loss_G: 3.5736 D(x): 0.7844 D(G(z)): 0.0290 / 0.0275\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "628492a0b8734695b26f5987bcbc6bef",
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          "output_type": "stream",
          "text": [
            "[2027/2750][20/41] Loss_D: 0.5503 Loss_G: 3.3619 D(x): 0.6776 D(G(z)): 0.0194 / 0.0408\n",
            "[2027/2750][40/41] Loss_D: 0.5944 Loss_G: 2.9659 D(x): 0.9872 D(G(z)): 0.0493 / 0.0551\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4806f0d2088e4defac7417c1989c149a",
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          "output_type": "stream",
          "text": [
            "[2028/2750][20/41] Loss_D: 0.4397 Loss_G: 3.6828 D(x): 0.9217 D(G(z)): 0.0409 / 0.0252\n",
            "[2028/2750][40/41] Loss_D: 0.4251 Loss_G: 4.2270 D(x): 0.8503 D(G(z)): 0.0264 / 0.0153\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34ef3174ed8d4c11a40f77567bc23f6d",
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          "output_type": "stream",
          "text": [
            "[2029/2750][20/41] Loss_D: 1.1125 Loss_G: 3.1257 D(x): 0.9823 D(G(z)): 0.2752 / 0.0402\n",
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            "[2030/2750][20/41] Loss_D: 0.5080 Loss_G: 3.5505 D(x): 0.7394 D(G(z)): 0.0166 / 0.0308\n",
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            "[2031/2750][20/41] Loss_D: 0.4857 Loss_G: 4.1989 D(x): 0.9774 D(G(z)): 0.0253 / 0.0140\n",
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            "[2032/2750][20/41] Loss_D: 0.4524 Loss_G: 4.5971 D(x): 0.9454 D(G(z)): 0.0224 / 0.0096\n",
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            "[2033/2750][20/41] Loss_D: 0.5352 Loss_G: 3.8561 D(x): 0.9633 D(G(z)): 0.1141 / 0.0215\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[2034/2750][20/41] Loss_D: 0.3922 Loss_G: 4.7604 D(x): 0.9060 D(G(z)): 0.0154 / 0.0088\n",
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          "text": [
            "[2035/2750][20/41] Loss_D: 0.8101 Loss_G: 4.3269 D(x): 0.9519 D(G(z)): 0.3112 / 0.0169\n",
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            "[2036/2750][20/41] Loss_D: 0.5478 Loss_G: 3.6663 D(x): 0.6689 D(G(z)): 0.0143 / 0.0263\n",
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        {
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              "model_id": "a1581e063bd54b6d83b17ef9f2f9390b",
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            "[2037/2750][20/41] Loss_D: 0.4785 Loss_G: 2.9711 D(x): 0.9371 D(G(z)): 0.0747 / 0.0615\n",
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        {
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              "model_id": "420b1d38207b4b3aaa0afc218a17b0da",
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            "[2038/2750][20/41] Loss_D: 0.3741 Loss_G: 4.7390 D(x): 0.8623 D(G(z)): 0.0078 / 0.0110\n",
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            "[2039/2750][20/41] Loss_D: 0.6223 Loss_G: 2.8818 D(x): 0.6619 D(G(z)): 0.0639 / 0.0617\n",
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            "[2040/2750][20/41] Loss_D: 0.8641 Loss_G: 2.7379 D(x): 0.4849 D(G(z)): 0.0124 / 0.0937\n",
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            "[2041/2750][20/41] Loss_D: 0.4077 Loss_G: 4.9910 D(x): 0.8100 D(G(z)): 0.0103 / 0.0081\n",
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            "[2042/2750][20/41] Loss_D: 0.4562 Loss_G: 4.4113 D(x): 0.8463 D(G(z)): 0.0205 / 0.0147\n",
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            "[2043/2750][20/41] Loss_D: 0.3963 Loss_G: 4.3123 D(x): 0.9147 D(G(z)): 0.0250 / 0.0147\n",
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            "[2044/2750][20/41] Loss_D: 0.4595 Loss_G: 3.2191 D(x): 0.7835 D(G(z)): 0.0338 / 0.0412\n",
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            "[2045/2750][20/41] Loss_D: 0.5298 Loss_G: 2.9826 D(x): 0.7256 D(G(z)): 0.0356 / 0.0576\n",
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            "[2046/2750][20/41] Loss_D: 0.3870 Loss_G: 4.4279 D(x): 0.8749 D(G(z)): 0.0173 / 0.0142\n",
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            "[2047/2750][20/41] Loss_D: 0.4720 Loss_G: 3.1578 D(x): 0.9350 D(G(z)): 0.0877 / 0.0454\n",
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            "[2048/2750][20/41] Loss_D: 0.4359 Loss_G: 3.9111 D(x): 0.9328 D(G(z)): 0.0648 / 0.0247\n",
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            "[2049/2750][20/41] Loss_D: 0.3837 Loss_G: 4.3742 D(x): 0.9198 D(G(z)): 0.0409 / 0.0114\n",
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            "[2050/2750][20/41] Loss_D: 0.3513 Loss_G: 5.0970 D(x): 0.8590 D(G(z)): 0.0062 / 0.0057\n",
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            "[2051/2750][20/41] Loss_D: 0.3911 Loss_G: 3.9866 D(x): 0.9061 D(G(z)): 0.0326 / 0.0221\n",
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            "[2052/2750][20/41] Loss_D: 0.5358 Loss_G: 2.8829 D(x): 0.8436 D(G(z)): 0.1340 / 0.0663\n",
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            "[2053/2750][20/41] Loss_D: 0.4558 Loss_G: 3.7119 D(x): 0.8046 D(G(z)): 0.0399 / 0.0258\n",
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            "[2054/2750][20/41] Loss_D: 0.4656 Loss_G: 4.6988 D(x): 0.9073 D(G(z)): 0.0143 / 0.0101\n",
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            "[2057/2750][20/41] Loss_D: 0.4489 Loss_G: 4.2518 D(x): 0.8464 D(G(z)): 0.0419 / 0.0181\n",
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            "[2058/2750][20/41] Loss_D: 0.4078 Loss_G: 4.4104 D(x): 0.8215 D(G(z)): 0.0156 / 0.0114\n",
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            "[2059/2750][20/41] Loss_D: 0.3768 Loss_G: 5.4070 D(x): 0.8708 D(G(z)): 0.0044 / 0.0040\n",
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            "[2060/2750][20/41] Loss_D: 0.4268 Loss_G: 3.7467 D(x): 0.9364 D(G(z)): 0.0435 / 0.0252\n",
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            "[2061/2750][20/41] Loss_D: 0.6344 Loss_G: 4.0923 D(x): 0.9747 D(G(z)): 0.1471 / 0.0153\n",
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            "[2062/2750][20/41] Loss_D: 0.4281 Loss_G: 3.6565 D(x): 0.9348 D(G(z)): 0.0358 / 0.0214\n",
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            "[2065/2750][20/41] Loss_D: 0.6764 Loss_G: 3.8085 D(x): 0.9405 D(G(z)): 0.2303 / 0.0206\n",
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            "[2066/2750][20/41] Loss_D: 0.4642 Loss_G: 3.8777 D(x): 0.9439 D(G(z)): 0.0875 / 0.0179\n",
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            "[2068/2750][20/41] Loss_D: 0.3881 Loss_G: 3.7023 D(x): 0.9060 D(G(z)): 0.0484 / 0.0267\n",
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            "[2069/2750][20/41] Loss_D: 0.3620 Loss_G: 4.0445 D(x): 0.9139 D(G(z)): 0.0262 / 0.0173\n",
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            "[2070/2750][20/41] Loss_D: 0.3850 Loss_G: 6.1452 D(x): 0.9392 D(G(z)): 0.0028 / 0.0018\n",
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            "[2071/2750][20/41] Loss_D: 0.3570 Loss_G: 3.5608 D(x): 0.9028 D(G(z)): 0.0191 / 0.0256\n",
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            "[2072/2750][20/41] Loss_D: 0.4190 Loss_G: 3.7348 D(x): 0.9143 D(G(z)): 0.0751 / 0.0233\n",
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            "[2073/2750][20/41] Loss_D: 0.4190 Loss_G: 4.6701 D(x): 0.9567 D(G(z)): 0.0212 / 0.0076\n",
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            "[2074/2750][20/41] Loss_D: 0.6898 Loss_G: 6.4968 D(x): 0.5893 D(G(z)): 0.0023 / 0.0017\n",
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            "[2075/2750][20/41] Loss_D: 0.5451 Loss_G: 3.4565 D(x): 0.7603 D(G(z)): 0.0700 / 0.0432\n",
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        },
        {
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            "[2084/2750][20/41] Loss_D: 0.3509 Loss_G: 4.8565 D(x): 0.9036 D(G(z)): 0.0131 / 0.0089\n",
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          "text": [
            "[2085/2750][20/41] Loss_D: 0.5548 Loss_G: 3.5892 D(x): 0.8758 D(G(z)): 0.1000 / 0.0283\n",
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          "text": [
            "[2086/2750][20/41] Loss_D: 0.5352 Loss_G: 3.9108 D(x): 0.8844 D(G(z)): 0.1182 / 0.0209\n",
            "[2086/2750][40/41] Loss_D: 0.7184 Loss_G: 4.6002 D(x): 0.9338 D(G(z)): 0.2411 / 0.0091\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d32a4b511e6a46119b8da2f76a17119f",
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          "text": [
            "[2087/2750][20/41] Loss_D: 0.3817 Loss_G: 3.8559 D(x): 0.8837 D(G(z)): 0.0379 / 0.0201\n",
            "[2087/2750][40/41] Loss_D: 0.3599 Loss_G: 5.1659 D(x): 0.8605 D(G(z)): 0.0065 / 0.0056\n"
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        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91faefb0368a4f61a5dd8024b640b3c9",
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          "text": [
            "[2088/2750][20/41] Loss_D: 0.5089 Loss_G: 2.7467 D(x): 0.8250 D(G(z)): 0.0798 / 0.0630\n",
            "[2088/2750][40/41] Loss_D: 0.3570 Loss_G: 4.1273 D(x): 0.8993 D(G(z)): 0.0216 / 0.0144\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
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        {
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          "output_type": "stream",
          "text": [
            "[2089/2750][20/41] Loss_D: 0.3457 Loss_G: 6.0096 D(x): 0.9221 D(G(z)): 0.0044 / 0.0022\n",
            "[2089/2750][40/41] Loss_D: 0.4128 Loss_G: 3.9872 D(x): 0.8862 D(G(z)): 0.0599 / 0.0189\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "624db4109c9a4a8cb25b15445db4f3c5",
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          "text": [
            "[2090/2750][20/41] Loss_D: 0.3721 Loss_G: 4.2012 D(x): 0.8852 D(G(z)): 0.0302 / 0.0160\n",
            "[2090/2750][40/41] Loss_D: 0.3543 Loss_G: 4.5386 D(x): 0.8946 D(G(z)): 0.0173 / 0.0119\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
          "text": [
            "[2091/2750][20/41] Loss_D: 0.5536 Loss_G: 3.3231 D(x): 0.8055 D(G(z)): 0.1089 / 0.0399\n",
            "[2091/2750][40/41] Loss_D: 0.5441 Loss_G: 3.1582 D(x): 0.7530 D(G(z)): 0.0464 / 0.0647\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5fde2fee3f24796b993c382e0eecfe1",
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          "output_type": "stream",
          "text": [
            "[2092/2750][20/41] Loss_D: 0.6366 Loss_G: 3.1938 D(x): 0.9539 D(G(z)): 0.1964 / 0.0410\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
          "text": [
            "[2093/2750][20/41] Loss_D: 0.8238 Loss_G: 3.3056 D(x): 0.5656 D(G(z)): 0.0097 / 0.0412\n",
            "[2093/2750][40/41] Loss_D: 0.4365 Loss_G: 4.0157 D(x): 0.8952 D(G(z)): 0.0447 / 0.0193\n"
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          "text": [
            "[2094/2750][20/41] Loss_D: 0.3660 Loss_G: 4.5315 D(x): 0.8549 D(G(z)): 0.0107 / 0.0091\n",
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          "text": [
            "[2095/2750][20/41] Loss_D: 0.3414 Loss_G: 5.5269 D(x): 0.8947 D(G(z)): 0.0058 / 0.0039\n",
            "[2095/2750][40/41] Loss_D: 0.6586 Loss_G: 4.4478 D(x): 0.9935 D(G(z)): 0.0741 / 0.0096\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0af3b26644c84a9f8f52d05e5aa3fbdb",
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          "text": [
            "[2096/2750][20/41] Loss_D: 0.5269 Loss_G: 2.9478 D(x): 0.7821 D(G(z)): 0.1032 / 0.0546\n",
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "18c970a8d8664b71b10c749c923866a4",
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          "output_type": "stream",
          "text": [
            "[2097/2750][20/41] Loss_D: 0.4408 Loss_G: 4.0348 D(x): 0.8413 D(G(z)): 0.0298 / 0.0215\n",
            "[2097/2750][40/41] Loss_D: 0.4577 Loss_G: 5.0852 D(x): 0.7676 D(G(z)): 0.0056 / 0.0083\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
          "text": [
            "[2098/2750][20/41] Loss_D: 0.4505 Loss_G: 4.0448 D(x): 0.9672 D(G(z)): 0.0447 / 0.0163\n",
            "[2098/2750][40/41] Loss_D: 0.4577 Loss_G: 3.8705 D(x): 0.8423 D(G(z)): 0.0531 / 0.0279\n"
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9ab283932e794309a98d9daba10424b5",
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          "metadata": {},
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2099/2750][20/41] Loss_D: 0.4209 Loss_G: 4.2908 D(x): 0.9630 D(G(z)): 0.0254 / 0.0120\n",
            "[2099/2750][40/41] Loss_D: 0.3601 Loss_G: 4.5476 D(x): 0.8674 D(G(z)): 0.0102 / 0.0082\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d5211504a6740c0afc889fc4315b52e",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2100/2750][20/41] Loss_D: 0.4521 Loss_G: 4.2945 D(x): 0.7520 D(G(z)): 0.0107 / 0.0135\n",
            "[2100/2750][40/41] Loss_D: 0.3661 Loss_G: 4.1580 D(x): 0.9057 D(G(z)): 0.0239 / 0.0151\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "82f0024ff69c422989fb2820425b71b4",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2101/2750][20/41] Loss_D: 0.4108 Loss_G: 3.6236 D(x): 0.9138 D(G(z)): 0.0412 / 0.0301\n",
            "[2101/2750][40/41] Loss_D: 0.3585 Loss_G: 5.7649 D(x): 0.8762 D(G(z)): 0.0045 / 0.0027\n",
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2101.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99605674].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999998..0.91592586].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999098..0.92293125].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9995987..0.8988641].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9919167].\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef8db4b57093429ab04a69219c83cbc8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2102/2750][20/41] Loss_D: 0.4252 Loss_G: 4.3342 D(x): 0.8823 D(G(z)): 0.0525 / 0.0143\n",
            "[2102/2750][40/41] Loss_D: 0.5104 Loss_G: 4.8468 D(x): 0.7760 D(G(z)): 0.0146 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "08d7140a421d4aa4bbcaec60b749e0f8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2103/2750][20/41] Loss_D: 0.3586 Loss_G: 4.4707 D(x): 0.9131 D(G(z)): 0.0223 / 0.0109\n",
            "[2103/2750][40/41] Loss_D: 0.4362 Loss_G: 3.6300 D(x): 0.9067 D(G(z)): 0.0850 / 0.0248\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8245cf37de8547c9b9ccbe6b9c1f0bf5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2104/2750][20/41] Loss_D: 0.6072 Loss_G: 2.7882 D(x): 0.9255 D(G(z)): 0.2041 / 0.0593\n",
            "[2104/2750][40/41] Loss_D: 0.5254 Loss_G: 3.4765 D(x): 0.9575 D(G(z)): 0.1301 / 0.0285\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52876ab3ea7a4377b230bc97626c688c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2105/2750][20/41] Loss_D: 0.5408 Loss_G: 3.9465 D(x): 0.9803 D(G(z)): 0.0647 / 0.0217\n",
            "[2105/2750][40/41] Loss_D: 0.4455 Loss_G: 3.4374 D(x): 0.8586 D(G(z)): 0.0446 / 0.0324\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "820aa422a2ea4449b10756507647f0ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2106/2750][20/41] Loss_D: 0.5582 Loss_G: 3.6183 D(x): 0.9411 D(G(z)): 0.1165 / 0.0478\n",
            "[2106/2750][40/41] Loss_D: 0.4419 Loss_G: 4.4309 D(x): 0.9170 D(G(z)): 0.0268 / 0.0102\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8d645321cdac4da298845e0a8775b6d0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2107/2750][20/41] Loss_D: 0.4429 Loss_G: 3.8422 D(x): 0.8470 D(G(z)): 0.0510 / 0.0213\n",
            "[2107/2750][40/41] Loss_D: 0.5661 Loss_G: 3.6865 D(x): 0.6777 D(G(z)): 0.0145 / 0.0263\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "816572cb9942486094aa3ad099816f5d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2108/2750][20/41] Loss_D: 0.4249 Loss_G: 5.0440 D(x): 0.9377 D(G(z)): 0.0142 / 0.0076\n",
            "[2108/2750][40/41] Loss_D: 0.4913 Loss_G: 4.0624 D(x): 0.8565 D(G(z)): 0.0658 / 0.0144\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "390f4f110cb040729a1d91bdf430a6ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2109/2750][20/41] Loss_D: 0.5913 Loss_G: 3.8664 D(x): 0.9862 D(G(z)): 0.0804 / 0.0211\n",
            "[2109/2750][40/41] Loss_D: 0.5424 Loss_G: 3.3047 D(x): 0.6907 D(G(z)): 0.0176 / 0.0386\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "[2191/2750][40/41] Loss_D: 0.3588 Loss_G: 4.6423 D(x): 0.8861 D(G(z)): 0.0142 / 0.0103\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2362722f6a641afa2fd3aaf2093f4ee",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2192/2750][20/41] Loss_D: 0.5466 Loss_G: 3.7028 D(x): 0.9814 D(G(z)): 0.0615 / 0.0216\n",
            "[2192/2750][40/41] Loss_D: 0.4028 Loss_G: 3.8806 D(x): 0.8960 D(G(z)): 0.0313 / 0.0185\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb20df94cb7b4a40aabc31ed2eb594ee",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2193/2750][20/41] Loss_D: 0.3563 Loss_G: 4.1824 D(x): 0.8960 D(G(z)): 0.0169 / 0.0116\n",
            "[2193/2750][40/41] Loss_D: 0.3750 Loss_G: 7.2281 D(x): 0.8231 D(G(z)): 0.0044 / 0.0020\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52b2ea36354d484f8866f1e7edcb1418",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2194/2750][20/41] Loss_D: 0.4040 Loss_G: 5.7407 D(x): 0.9336 D(G(z)): 0.0089 / 0.0058\n",
            "[2194/2750][40/41] Loss_D: 0.3817 Loss_G: 4.7016 D(x): 0.8428 D(G(z)): 0.0183 / 0.0106\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9677fa0c1cdc4ac3bca354876ff2b274",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2195/2750][20/41] Loss_D: 0.4582 Loss_G: 4.0992 D(x): 0.9554 D(G(z)): 0.0799 / 0.0158\n",
            "[2195/2750][40/41] Loss_D: 0.4220 Loss_G: 4.1356 D(x): 0.8788 D(G(z)): 0.0204 / 0.0135\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8d1e1d6e3044c90adfaf2f42d35ac73",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2196/2750][20/41] Loss_D: 0.3794 Loss_G: 5.3032 D(x): 0.8131 D(G(z)): 0.0044 / 0.0038\n",
            "[2196/2750][40/41] Loss_D: 0.4663 Loss_G: 5.1798 D(x): 0.7996 D(G(z)): 0.0235 / 0.0078\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c758689fb4e4b939f3eac8499d86908",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2197/2750][20/41] Loss_D: 0.3864 Loss_G: 5.4543 D(x): 0.8864 D(G(z)): 0.0121 / 0.0040\n",
            "[2197/2750][40/41] Loss_D: 0.3897 Loss_G: 3.8027 D(x): 0.8586 D(G(z)): 0.0386 / 0.0206\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c87b11ddc65f4765bd20b085cb3a1709",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2198/2750][20/41] Loss_D: 0.4484 Loss_G: 4.1090 D(x): 0.7652 D(G(z)): 0.0104 / 0.0131\n",
            "[2198/2750][40/41] Loss_D: 0.4977 Loss_G: 3.0779 D(x): 0.8524 D(G(z)): 0.0786 / 0.0639\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9274704c0bdf41c8b640a6a234ed5355",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2199/2750][20/41] Loss_D: 0.5496 Loss_G: 4.0983 D(x): 0.7035 D(G(z)): 0.0096 / 0.0182\n",
            "[2199/2750][40/41] Loss_D: 0.4175 Loss_G: 3.2886 D(x): 0.9038 D(G(z)): 0.0507 / 0.0382\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab752b8a76bb47cb93c1d2340cd515a9",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2200/2750][20/41] Loss_D: 0.4234 Loss_G: 4.6376 D(x): 0.8151 D(G(z)): 0.0097 / 0.0121\n",
            "[2200/2750][40/41] Loss_D: 0.5464 Loss_G: 3.4029 D(x): 0.7337 D(G(z)): 0.0443 / 0.0403\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b63f03ef5ed4d769f74826d488423e9",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2201/2750][20/41] Loss_D: 0.3524 Loss_G: 5.5765 D(x): 0.8938 D(G(z)): 0.0156 / 0.0045\n",
            "[2201/2750][40/41] Loss_D: 0.4439 Loss_G: 3.6822 D(x): 0.9469 D(G(z)): 0.0722 / 0.0282\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999999..0.93877405].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999995..0.86690277].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999964..0.9087785].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999994..0.907123].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9986177].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2201.pth\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "268f95d520ed47348a3c97d8ea14fd15",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2202/2750][20/41] Loss_D: 0.3484 Loss_G: 4.8025 D(x): 0.9039 D(G(z)): 0.0098 / 0.0074\n",
            "[2202/2750][40/41] Loss_D: 0.3575 Loss_G: 5.8884 D(x): 0.9132 D(G(z)): 0.0037 / 0.0023\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "beac610348c046929d47de3cac3ddaa7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2203/2750][20/41] Loss_D: 0.4541 Loss_G: 2.7564 D(x): 0.9165 D(G(z)): 0.0811 / 0.0773\n",
            "[2203/2750][40/41] Loss_D: 0.5510 Loss_G: 3.4010 D(x): 0.8816 D(G(z)): 0.1125 / 0.0309\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e690015b62b04cba94a97ffa82cb26bb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2204/2750][20/41] Loss_D: 0.8657 Loss_G: 2.4257 D(x): 0.5019 D(G(z)): 0.0236 / 0.0937\n",
            "[2204/2750][40/41] Loss_D: 0.3874 Loss_G: 4.3108 D(x): 0.8600 D(G(z)): 0.0229 / 0.0156\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73ca259b8bc04b8eb1e355f6137673da",
              "version_major": 2,
              "version_minor": 0
            },
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            "[2205/2750][20/41] Loss_D: 0.3775 Loss_G: 5.9057 D(x): 0.8392 D(G(z)): 0.0034 / 0.0026\n",
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            "[2206/2750][20/41] Loss_D: 0.3918 Loss_G: 3.7735 D(x): 0.8960 D(G(z)): 0.0444 / 0.0212\n",
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            "[2207/2750][20/41] Loss_D: 0.3537 Loss_G: 5.3105 D(x): 0.8817 D(G(z)): 0.0104 / 0.0066\n",
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            "[2208/2750][20/41] Loss_D: 0.3510 Loss_G: 5.7189 D(x): 0.8980 D(G(z)): 0.0034 / 0.0026\n",
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            "[2210/2750][20/41] Loss_D: 0.3467 Loss_G: 4.6711 D(x): 0.9153 D(G(z)): 0.0138 / 0.0080\n",
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            "[2211/2750][20/41] Loss_D: 0.4181 Loss_G: 4.6136 D(x): 0.8448 D(G(z)): 0.0184 / 0.0093\n",
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            "[2212/2750][20/41] Loss_D: 0.3900 Loss_G: 4.4679 D(x): 0.9084 D(G(z)): 0.0348 / 0.0106\n",
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            "[2213/2750][20/41] Loss_D: 0.4667 Loss_G: 3.9140 D(x): 0.8823 D(G(z)): 0.0327 / 0.0248\n",
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            "[2214/2750][20/41] Loss_D: 0.5406 Loss_G: 3.8155 D(x): 0.9357 D(G(z)): 0.0872 / 0.0201\n",
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            "[2215/2750][20/41] Loss_D: 0.6085 Loss_G: 3.4920 D(x): 0.7530 D(G(z)): 0.0141 / 0.0271\n",
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            "[2216/2750][20/41] Loss_D: 0.6203 Loss_G: 3.4393 D(x): 0.9832 D(G(z)): 0.1260 / 0.0272\n",
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            "[2217/2750][20/41] Loss_D: 0.3933 Loss_G: 4.7942 D(x): 0.9136 D(G(z)): 0.0086 / 0.0111\n",
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            "[2218/2750][20/41] Loss_D: 0.4341 Loss_G: 5.0108 D(x): 0.9627 D(G(z)): 0.0129 / 0.0061\n",
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            "[2219/2750][20/41] Loss_D: 0.4529 Loss_G: 4.0057 D(x): 0.9187 D(G(z)): 0.0969 / 0.0167\n",
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            "[2220/2750][20/41] Loss_D: 0.4393 Loss_G: 3.5364 D(x): 0.9391 D(G(z)): 0.0713 / 0.0266\n",
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            "[2221/2750][20/41] Loss_D: 0.3522 Loss_G: 4.3942 D(x): 0.8974 D(G(z)): 0.0189 / 0.0103\n",
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            "[2223/2750][20/41] Loss_D: 0.4364 Loss_G: 4.6714 D(x): 0.9261 D(G(z)): 0.0110 / 0.0074\n",
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            "[2224/2750][20/41] Loss_D: 0.4208 Loss_G: 3.8203 D(x): 0.9475 D(G(z)): 0.0624 / 0.0203\n",
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            "[2225/2750][20/41] Loss_D: 0.3933 Loss_G: 5.9826 D(x): 0.8398 D(G(z)): 0.0035 / 0.0027\n",
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            "[2226/2750][20/41] Loss_D: 0.4348 Loss_G: 4.0383 D(x): 0.8648 D(G(z)): 0.0182 / 0.0153\n",
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            "[2227/2750][20/41] Loss_D: 0.4191 Loss_G: 5.0284 D(x): 0.8037 D(G(z)): 0.0213 / 0.0069\n",
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            "[2228/2750][20/41] Loss_D: 0.5122 Loss_G: 3.2734 D(x): 0.7525 D(G(z)): 0.0529 / 0.0404\n",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2287/2750][20/41] Loss_D: 0.8365 Loss_G: 2.7666 D(x): 0.5129 D(G(z)): 0.0126 / 0.0733\n",
            "[2287/2750][40/41] Loss_D: 0.4756 Loss_G: 4.0551 D(x): 0.9412 D(G(z)): 0.0534 / 0.0232\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3973f51f9a8540829fad3bf69e87c9f4",
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        },
        {
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          "output_type": "stream",
          "text": [
            "[2288/2750][20/41] Loss_D: 0.4145 Loss_G: 4.2875 D(x): 0.8697 D(G(z)): 0.0193 / 0.0112\n",
            "[2288/2750][40/41] Loss_D: 0.3635 Loss_G: 4.7349 D(x): 0.8376 D(G(z)): 0.0116 / 0.0078\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e559700e9f5040a0b779c1404f81ef00",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2289/2750][20/41] Loss_D: 0.5548 Loss_G: 2.9167 D(x): 0.9171 D(G(z)): 0.1436 / 0.0545\n",
            "[2289/2750][40/41] Loss_D: 0.6911 Loss_G: 3.7554 D(x): 0.5942 D(G(z)): 0.0052 / 0.0234\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6052ebfba187464e831b772708e226f4",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2290/2750][20/41] Loss_D: 0.3646 Loss_G: 4.4486 D(x): 0.9093 D(G(z)): 0.0211 / 0.0149\n",
            "[2290/2750][40/41] Loss_D: 0.3493 Loss_G: 4.7191 D(x): 0.8724 D(G(z)): 0.0109 / 0.0070\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55d650255d044fedafc9b94e57537d3c",
              "version_major": 2,
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            },
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2291/2750][20/41] Loss_D: 0.4152 Loss_G: 5.2119 D(x): 0.9197 D(G(z)): 0.0120 / 0.0050\n",
            "[2291/2750][40/41] Loss_D: 0.3440 Loss_G: 6.1141 D(x): 0.9223 D(G(z)): 0.0029 / 0.0025\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d1ba82fbed664d3aa595fe4e40a026e1",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2292/2750][20/41] Loss_D: 0.3736 Loss_G: 3.7313 D(x): 0.9018 D(G(z)): 0.0305 / 0.0227\n",
            "[2292/2750][40/41] Loss_D: 0.3972 Loss_G: 4.7069 D(x): 0.8557 D(G(z)): 0.0112 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "456fa2dff66d43e5a1e987bc8cb60039",
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2293/2750][20/41] Loss_D: 0.3559 Loss_G: 5.6836 D(x): 0.9267 D(G(z)): 0.0121 / 0.0033\n",
            "[2293/2750][40/41] Loss_D: 0.7685 Loss_G: 1.9844 D(x): 0.5223 D(G(z)): 0.0254 / 0.1563\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ea9351dd80047d683764fee430987e0",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2294/2750][20/41] Loss_D: 0.8092 Loss_G: 4.8160 D(x): 0.4989 D(G(z)): 0.0016 / 0.0123\n",
            "[2294/2750][40/41] Loss_D: 0.4873 Loss_G: 3.7953 D(x): 0.7715 D(G(z)): 0.0144 / 0.0207\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32d235f6487b4272ba326ed5f781fe30",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2295/2750][20/41] Loss_D: 0.4188 Loss_G: 3.9828 D(x): 0.8621 D(G(z)): 0.0350 / 0.0191\n",
            "[2295/2750][40/41] Loss_D: 0.3979 Loss_G: 4.8268 D(x): 0.9242 D(G(z)): 0.0450 / 0.0075\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bad91268b0e34c22a82e8f6450de0419",
              "version_major": 2,
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2296/2750][20/41] Loss_D: 0.5460 Loss_G: 3.1144 D(x): 0.9361 D(G(z)): 0.1708 / 0.0485\n",
            "[2296/2750][40/41] Loss_D: 0.3540 Loss_G: 5.4369 D(x): 0.8708 D(G(z)): 0.0043 / 0.0036\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88d20818c07148aa98753b5b1c51c20c",
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2297/2750][20/41] Loss_D: 0.3479 Loss_G: 5.1482 D(x): 0.8705 D(G(z)): 0.0088 / 0.0051\n",
            "[2297/2750][40/41] Loss_D: 0.4523 Loss_G: 3.1637 D(x): 0.8389 D(G(z)): 0.0576 / 0.0369\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d102d9e65ce54341a20c537320c357db",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2298/2750][20/41] Loss_D: 0.4281 Loss_G: 4.1371 D(x): 0.8867 D(G(z)): 0.0706 / 0.0134\n",
            "[2298/2750][40/41] Loss_D: 0.3687 Loss_G: 5.7306 D(x): 0.8481 D(G(z)): 0.0034 / 0.0024\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63e759a198bd4f0e837aa75cc36dd860",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2299/2750][20/41] Loss_D: 0.3743 Loss_G: 4.5288 D(x): 0.8879 D(G(z)): 0.0259 / 0.0085\n",
            "[2299/2750][40/41] Loss_D: 0.4052 Loss_G: 4.0780 D(x): 0.8995 D(G(z)): 0.0684 / 0.0134\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0921c89f00c84fc9b2b30f4d2ffdadd6",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2300/2750][20/41] Loss_D: 0.3480 Loss_G: 5.8417 D(x): 0.8892 D(G(z)): 0.0057 / 0.0027\n",
            "[2300/2750][40/41] Loss_D: 0.3749 Loss_G: 6.6933 D(x): 0.9405 D(G(z)): 0.0126 / 0.0015\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ebdc2c7264c14b709f648077a87a0965",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2301/2750][20/41] Loss_D: 1.0145 Loss_G: 2.8742 D(x): 0.4180 D(G(z)): 0.0036 / 0.0766\n",
            "[2301/2750][40/41] Loss_D: 0.3935 Loss_G: 4.0717 D(x): 0.8653 D(G(z)): 0.0432 / 0.0168\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.962642].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9514507].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9756242].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999995..0.88694346].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.999999..0.90923387].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2301.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f39461aba2dc43ee99a4192cf447c97a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2302/2750][20/41] Loss_D: 0.3937 Loss_G: 4.1397 D(x): 0.8826 D(G(z)): 0.0403 / 0.0183\n",
            "[2302/2750][40/41] Loss_D: 0.3946 Loss_G: 4.2974 D(x): 0.8746 D(G(z)): 0.0489 / 0.0170\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da2e9c7800b1420eb5d34ad305862960",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2303/2750][20/41] Loss_D: 0.6272 Loss_G: 4.3498 D(x): 0.9425 D(G(z)): 0.2180 / 0.0104\n",
            "[2303/2750][40/41] Loss_D: 0.4193 Loss_G: 4.0834 D(x): 0.8749 D(G(z)): 0.0539 / 0.0146\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "23fab739166648778307620e50ba665a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2304/2750][20/41] Loss_D: 0.3839 Loss_G: 5.4117 D(x): 0.8186 D(G(z)): 0.0081 / 0.0030\n",
            "[2304/2750][40/41] Loss_D: 0.6000 Loss_G: 3.4395 D(x): 0.6655 D(G(z)): 0.0112 / 0.0380\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74ecc0d04a35409e89b02f0ec962a8fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2305/2750][20/41] Loss_D: 0.4331 Loss_G: 4.1899 D(x): 0.9225 D(G(z)): 0.0294 / 0.0130\n",
            "[2305/2750][40/41] Loss_D: 0.5242 Loss_G: 3.9587 D(x): 0.9565 D(G(z)): 0.1111 / 0.0160\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92e73a74113a4c709b4e12e7bc5d3f5d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2306/2750][20/41] Loss_D: 0.3860 Loss_G: 4.2537 D(x): 0.9031 D(G(z)): 0.0205 / 0.0123\n",
            "[2306/2750][40/41] Loss_D: 0.3769 Loss_G: 5.3077 D(x): 0.8268 D(G(z)): 0.0037 / 0.0037\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7187171d270c4760b571994181cf86ad",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2307/2750][20/41] Loss_D: 0.3933 Loss_G: 4.0848 D(x): 0.8952 D(G(z)): 0.0390 / 0.0138\n",
            "[2307/2750][40/41] Loss_D: 0.3740 Loss_G: 4.8465 D(x): 0.9288 D(G(z)): 0.0318 / 0.0089\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25f21f739dda4f1bb231cf855f3a5b80",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2308/2750][20/41] Loss_D: 0.4141 Loss_G: 3.5817 D(x): 0.8461 D(G(z)): 0.0314 / 0.0229\n",
            "[2308/2750][40/41] Loss_D: 0.3655 Loss_G: 4.8014 D(x): 0.9132 D(G(z)): 0.0125 / 0.0072\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "338eafb0904249e9af62a23aeaac539d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2309/2750][20/41] Loss_D: 0.3809 Loss_G: 3.8246 D(x): 0.9164 D(G(z)): 0.0343 / 0.0201\n",
            "[2309/2750][40/41] Loss_D: 0.4500 Loss_G: 5.4825 D(x): 0.7763 D(G(z)): 0.0032 / 0.0040\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b2b77d3f45c440ea390cc7fb2bac545",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2310/2750][20/41] Loss_D: 0.6793 Loss_G: 5.1004 D(x): 0.9970 D(G(z)): 0.0126 / 0.0067\n",
            "[2310/2750][40/41] Loss_D: 0.4403 Loss_G: 3.8393 D(x): 0.8392 D(G(z)): 0.0337 / 0.0283\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbad3879a24341c694115d1d670d7425",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2311/2750][20/41] Loss_D: 0.3995 Loss_G: 4.3073 D(x): 0.9663 D(G(z)): 0.0139 / 0.0135\n",
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            "[2312/2750][20/41] Loss_D: 0.3534 Loss_G: 4.6484 D(x): 0.8852 D(G(z)): 0.0161 / 0.0095\n",
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          "text": [
            "[2313/2750][20/41] Loss_D: 0.3812 Loss_G: 3.6750 D(x): 0.8963 D(G(z)): 0.0448 / 0.0238\n",
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            "[2314/2750][20/41] Loss_D: 0.3782 Loss_G: 4.2563 D(x): 0.8722 D(G(z)): 0.0179 / 0.0132\n",
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          "text": [
            "[2315/2750][20/41] Loss_D: 0.4082 Loss_G: 4.6444 D(x): 0.8513 D(G(z)): 0.0455 / 0.0088\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2316/2750][20/41] Loss_D: 0.4215 Loss_G: 4.3808 D(x): 0.9586 D(G(z)): 0.0420 / 0.0106\n",
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          "text": [
            "[2317/2750][20/41] Loss_D: 1.0244 Loss_G: 2.5116 D(x): 0.4278 D(G(z)): 0.0135 / 0.1027\n",
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          "text": [
            "[2318/2750][20/41] Loss_D: 0.4034 Loss_G: 4.0311 D(x): 0.8689 D(G(z)): 0.0535 / 0.0138\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2319/2750][20/41] Loss_D: 0.3495 Loss_G: 4.2695 D(x): 0.8989 D(G(z)): 0.0166 / 0.0125\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2320/2750][20/41] Loss_D: 0.3718 Loss_G: 5.5787 D(x): 0.9103 D(G(z)): 0.0279 / 0.0076\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2321/2750][20/41] Loss_D: 0.5698 Loss_G: 3.1024 D(x): 0.9322 D(G(z)): 0.1800 / 0.0413\n",
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            "[2322/2750][20/41] Loss_D: 0.4382 Loss_G: 3.9876 D(x): 0.8173 D(G(z)): 0.0158 / 0.0167\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2323/2750][20/41] Loss_D: 0.3651 Loss_G: 6.1227 D(x): 0.9397 D(G(z)): 0.0034 / 0.0019\n",
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            "application/vnd.jupyter.widget-view+json": {
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            "[2324/2750][20/41] Loss_D: 0.3909 Loss_G: 5.1510 D(x): 0.8134 D(G(z)): 0.0084 / 0.0053\n",
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          "text": [
            "[2325/2750][20/41] Loss_D: 0.3466 Loss_G: 4.9624 D(x): 0.8831 D(G(z)): 0.0090 / 0.0056\n",
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            "[2326/2750][20/41] Loss_D: 0.4476 Loss_G: 3.1650 D(x): 0.9062 D(G(z)): 0.0939 / 0.0470\n",
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            "[2327/2750][20/41] Loss_D: 0.3527 Loss_G: 4.6721 D(x): 0.8791 D(G(z)): 0.0092 / 0.0074\n",
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            "[2328/2750][20/41] Loss_D: 0.3691 Loss_G: 4.0730 D(x): 0.8716 D(G(z)): 0.0185 / 0.0176\n",
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            "[2329/2750][20/41] Loss_D: 0.3627 Loss_G: 4.7465 D(x): 0.9023 D(G(z)): 0.0225 / 0.0108\n",
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            "[2330/2750][20/41] Loss_D: 0.3481 Loss_G: 5.0338 D(x): 0.8791 D(G(z)): 0.0067 / 0.0059\n",
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            "[2331/2750][20/41] Loss_D: 0.3413 Loss_G: 4.8386 D(x): 0.8949 D(G(z)): 0.0108 / 0.0079\n",
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            "[2332/2750][20/41] Loss_D: 0.3720 Loss_G: 4.0627 D(x): 0.9022 D(G(z)): 0.0244 / 0.0136\n",
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            "[2333/2750][20/41] Loss_D: 0.3490 Loss_G: 4.7735 D(x): 0.8720 D(G(z)): 0.0092 / 0.0076\n",
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            "[2334/2750][20/41] Loss_D: 0.7165 Loss_G: 3.0193 D(x): 0.7592 D(G(z)): 0.2060 / 0.0542\n",
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            "[2384/2750][20/41] Loss_D: 0.4141 Loss_G: 4.4805 D(x): 0.8072 D(G(z)): 0.0075 / 0.0098\n",
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            "[2385/2750][20/41] Loss_D: 0.4371 Loss_G: 3.0712 D(x): 0.9035 D(G(z)): 0.0711 / 0.0584\n",
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          "text": [
            "[2393/2750][20/41] Loss_D: 0.4550 Loss_G: 4.2542 D(x): 0.8036 D(G(z)): 0.0259 / 0.0149\n",
            "[2393/2750][40/41] Loss_D: 0.4428 Loss_G: 4.0035 D(x): 0.9342 D(G(z)): 0.0851 / 0.0165\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0fe0a8b053474d48bab72df2371c2120",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2394/2750][20/41] Loss_D: 0.4763 Loss_G: 4.2074 D(x): 0.9641 D(G(z)): 0.0232 / 0.0147\n",
            "[2394/2750][40/41] Loss_D: 0.4908 Loss_G: 3.5492 D(x): 0.7016 D(G(z)): 0.0100 / 0.0242\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "44f541dfb95e40c690e70b8833d33eaf",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2395/2750][20/41] Loss_D: 0.3820 Loss_G: 4.2774 D(x): 0.8829 D(G(z)): 0.0229 / 0.0136\n",
            "[2395/2750][40/41] Loss_D: 0.4161 Loss_G: 4.7245 D(x): 0.9559 D(G(z)): 0.0118 / 0.0073\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ded7f2725f984f43a70761c83509070a",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2396/2750][20/41] Loss_D: 0.3990 Loss_G: 4.6728 D(x): 0.9111 D(G(z)): 0.0152 / 0.0080\n",
            "[2396/2750][40/41] Loss_D: 0.3894 Loss_G: 4.6543 D(x): 0.8937 D(G(z)): 0.0101 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "317987811952430f8dde6bc2adc8d231",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2397/2750][20/41] Loss_D: 0.4193 Loss_G: 4.4141 D(x): 0.9472 D(G(z)): 0.0337 / 0.0109\n",
            "[2397/2750][40/41] Loss_D: 0.4137 Loss_G: 4.3299 D(x): 0.9408 D(G(z)): 0.0258 / 0.0126\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "307b97888f044110969b27668aa87928",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2398/2750][20/41] Loss_D: 0.3751 Loss_G: 4.4749 D(x): 0.8708 D(G(z)): 0.0106 / 0.0089\n",
            "[2398/2750][40/41] Loss_D: 0.3837 Loss_G: 4.9080 D(x): 0.8437 D(G(z)): 0.0097 / 0.0076\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d88562449d954574b4385d9fb6019c9d",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2399/2750][20/41] Loss_D: 0.4905 Loss_G: 3.2230 D(x): 0.7995 D(G(z)): 0.0674 / 0.0378\n",
            "[2399/2750][40/41] Loss_D: 0.6177 Loss_G: 3.8982 D(x): 0.9945 D(G(z)): 0.0513 / 0.0214\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "257c40ec0f85466c8bfe52460b6a931d",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2400/2750][20/41] Loss_D: 0.5557 Loss_G: 3.4871 D(x): 0.6777 D(G(z)): 0.0096 / 0.0303\n",
            "[2400/2750][40/41] Loss_D: 0.3507 Loss_G: 4.3054 D(x): 0.9021 D(G(z)): 0.0122 / 0.0115\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d68357025254bb290307628f2809e36",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2401/2750][20/41] Loss_D: 0.3616 Loss_G: 4.6930 D(x): 0.8515 D(G(z)): 0.0141 / 0.0088\n",
            "[2401/2750][40/41] Loss_D: 0.4563 Loss_G: 4.0567 D(x): 0.9451 D(G(z)): 0.0944 / 0.0155\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.96867806].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99991626..0.9281039].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9916381..0.74066895].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99443036..0.74312174].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999726..0.90388906].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2401.pth\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67d85e669041409b934f194a5ed6326e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2402/2750][20/41] Loss_D: 0.3901 Loss_G: 4.7810 D(x): 0.8979 D(G(z)): 0.0450 / 0.0110\n",
            "[2402/2750][40/41] Loss_D: 0.4407 Loss_G: 3.9119 D(x): 0.8475 D(G(z)): 0.0454 / 0.0177\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9639c629ecf6419aaadbcf25336344ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2403/2750][20/41] Loss_D: 0.5061 Loss_G: 4.3044 D(x): 0.8652 D(G(z)): 0.0940 / 0.0138\n",
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            "[2404/2750][20/41] Loss_D: 0.9548 Loss_G: 2.2347 D(x): 0.4310 D(G(z)): 0.0143 / 0.1307\n",
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            "[2405/2750][20/41] Loss_D: 0.4608 Loss_G: 3.6827 D(x): 0.9376 D(G(z)): 0.0796 / 0.0217\n",
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            "[2406/2750][20/41] Loss_D: 0.4229 Loss_G: 4.0876 D(x): 0.8368 D(G(z)): 0.0317 / 0.0226\n",
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            "[2407/2750][20/41] Loss_D: 0.4340 Loss_G: 4.7470 D(x): 0.9616 D(G(z)): 0.0330 / 0.0119\n",
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            "[2408/2750][20/41] Loss_D: 0.5545 Loss_G: 3.8699 D(x): 0.9191 D(G(z)): 0.1645 / 0.0171\n",
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            "[2409/2750][20/41] Loss_D: 0.4830 Loss_G: 4.0825 D(x): 0.9606 D(G(z)): 0.0789 / 0.0167\n",
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            "[2410/2750][20/41] Loss_D: 0.4211 Loss_G: 4.9202 D(x): 0.9109 D(G(z)): 0.0143 / 0.0069\n",
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            "[2411/2750][20/41] Loss_D: 0.4194 Loss_G: 3.4697 D(x): 0.9228 D(G(z)): 0.0354 / 0.0307\n",
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            "[2412/2750][20/41] Loss_D: 0.5058 Loss_G: 4.1775 D(x): 0.9710 D(G(z)): 0.0637 / 0.0123\n",
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          "text": [
            "[2413/2750][20/41] Loss_D: 0.4104 Loss_G: 3.6769 D(x): 0.8945 D(G(z)): 0.0379 / 0.0197\n",
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            "[2414/2750][20/41] Loss_D: 0.4319 Loss_G: 4.1845 D(x): 0.9446 D(G(z)): 0.0360 / 0.0142\n",
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          "text": [
            "[2415/2750][20/41] Loss_D: 0.3926 Loss_G: 4.5860 D(x): 0.8842 D(G(z)): 0.0250 / 0.0100\n",
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          "text": [
            "[2416/2750][20/41] Loss_D: 0.6918 Loss_G: 3.0510 D(x): 0.9863 D(G(z)): 0.1327 / 0.0548\n",
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          "text": [
            "[2417/2750][20/41] Loss_D: 0.4393 Loss_G: 3.5577 D(x): 0.9204 D(G(z)): 0.0513 / 0.0252\n",
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            "[2418/2750][20/41] Loss_D: 0.4344 Loss_G: 4.2548 D(x): 0.7882 D(G(z)): 0.0170 / 0.0117\n",
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            "[2419/2750][20/41] Loss_D: 0.4054 Loss_G: 3.7706 D(x): 0.8682 D(G(z)): 0.0211 / 0.0208\n",
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            "[2420/2750][20/41] Loss_D: 0.5947 Loss_G: 2.9953 D(x): 0.9134 D(G(z)): 0.1773 / 0.0479\n",
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            "[2421/2750][20/41] Loss_D: 0.4808 Loss_G: 4.4408 D(x): 0.9473 D(G(z)): 0.0864 / 0.0091\n",
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            "[2422/2750][20/41] Loss_D: 0.4275 Loss_G: 3.6030 D(x): 0.8478 D(G(z)): 0.0358 / 0.0259\n",
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            "[2423/2750][20/41] Loss_D: 0.4583 Loss_G: 4.5097 D(x): 0.9147 D(G(z)): 0.0640 / 0.0108\n",
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            "[2425/2750][20/41] Loss_D: 0.3737 Loss_G: 4.2436 D(x): 0.8921 D(G(z)): 0.0336 / 0.0122\n",
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            "[2452/2750][20/41] Loss_D: 0.7095 Loss_G: 6.1213 D(x): 0.9412 D(G(z)): 0.2349 / 0.0019\n",
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            "[2453/2750][20/41] Loss_D: 0.6805 Loss_G: 5.4001 D(x): 0.6131 D(G(z)): 0.0028 / 0.0040\n",
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            "[2459/2750][20/41] Loss_D: 0.4051 Loss_G: 5.1976 D(x): 0.9459 D(G(z)): 0.0152 / 0.0043\n",
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            "[2471/2750][20/41] Loss_D: 0.4286 Loss_G: 3.6380 D(x): 0.8345 D(G(z)): 0.0261 / 0.0237\n",
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            "[2472/2750][20/41] Loss_D: 0.3669 Loss_G: 5.3129 D(x): 0.9313 D(G(z)): 0.0053 / 0.0034\n",
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            "[2474/2750][20/41] Loss_D: 0.3964 Loss_G: 4.1132 D(x): 0.8808 D(G(z)): 0.0217 / 0.0117\n",
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            "[2475/2750][20/41] Loss_D: 0.4465 Loss_G: 4.2276 D(x): 0.9280 D(G(z)): 0.0786 / 0.0107\n",
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            "[2476/2750][20/41] Loss_D: 0.4193 Loss_G: 4.4328 D(x): 0.9202 D(G(z)): 0.0269 / 0.0168\n",
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            "[2477/2750][20/41] Loss_D: 0.4991 Loss_G: 3.6256 D(x): 0.9240 D(G(z)): 0.1194 / 0.0240\n",
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            "[2478/2750][20/41] Loss_D: 0.4162 Loss_G: 5.0225 D(x): 0.9450 D(G(z)): 0.0271 / 0.0054\n",
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            "[2479/2750][20/41] Loss_D: 0.4722 Loss_G: 3.9075 D(x): 0.7198 D(G(z)): 0.0068 / 0.0166\n",
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            "[2480/2750][20/41] Loss_D: 0.3816 Loss_G: 4.4852 D(x): 0.9403 D(G(z)): 0.0153 / 0.0095\n",
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            "[2486/2750][20/41] Loss_D: 0.5047 Loss_G: 4.9914 D(x): 0.7223 D(G(z)): 0.0023 / 0.0059\n",
            "[2486/2750][40/41] Loss_D: 0.5966 Loss_G: 3.7778 D(x): 0.6105 D(G(z)): 0.0024 / 0.0210\n"
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          "data": {
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            "[2487/2750][20/41] Loss_D: 0.4280 Loss_G: 4.9229 D(x): 0.8030 D(G(z)): 0.0150 / 0.0093\n",
            "[2487/2750][40/41] Loss_D: 0.6303 Loss_G: 4.2297 D(x): 0.6342 D(G(z)): 0.0043 / 0.0133\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "[2488/2750][20/41] Loss_D: 0.3693 Loss_G: 4.3426 D(x): 0.8567 D(G(z)): 0.0177 / 0.0120\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2489/2750][20/41] Loss_D: 0.4045 Loss_G: 4.9274 D(x): 0.9464 D(G(z)): 0.0098 / 0.0059\n",
            "[2489/2750][40/41] Loss_D: 0.4612 Loss_G: 3.7964 D(x): 0.7723 D(G(z)): 0.0154 / 0.0221\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4f4e64a0fd74a3e8eab74f597830fa0",
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          "text": [
            "[2490/2750][20/41] Loss_D: 0.3789 Loss_G: 4.1519 D(x): 0.9359 D(G(z)): 0.0242 / 0.0135\n",
            "[2490/2750][40/41] Loss_D: 0.3730 Loss_G: 5.2504 D(x): 0.8974 D(G(z)): 0.0058 / 0.0042\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16ce70271a94453d928e39ce345a7879",
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          "text": [
            "[2491/2750][20/41] Loss_D: 0.3572 Loss_G: 5.6429 D(x): 0.9202 D(G(z)): 0.0039 / 0.0031\n",
            "[2491/2750][40/41] Loss_D: 0.3685 Loss_G: 4.5185 D(x): 0.8979 D(G(z)): 0.0129 / 0.0088\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af1ac7078a32462eb1e95c9abc972d88",
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          "text": [
            "[2492/2750][20/41] Loss_D: 0.3642 Loss_G: 4.7842 D(x): 0.8918 D(G(z)): 0.0113 / 0.0078\n",
            "[2492/2750][40/41] Loss_D: 0.4911 Loss_G: 4.2396 D(x): 0.7934 D(G(z)): 0.0545 / 0.0141\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "49e536b846c3479a8988f504ce41de0c",
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          "text": [
            "[2493/2750][20/41] Loss_D: 0.4565 Loss_G: 4.0369 D(x): 0.8107 D(G(z)): 0.0299 / 0.0165\n",
            "[2493/2750][40/41] Loss_D: 0.4967 Loss_G: 4.7903 D(x): 0.9842 D(G(z)): 0.0127 / 0.0069\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d28cf8edf924544bc1de875af8f7b24",
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          "text": [
            "[2494/2750][20/41] Loss_D: 0.4128 Loss_G: 4.9726 D(x): 0.9652 D(G(z)): 0.0143 / 0.0054\n",
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2495/2750][20/41] Loss_D: 0.3649 Loss_G: 6.3454 D(x): 0.8828 D(G(z)): 0.0033 / 0.0014\n",
            "[2495/2750][40/41] Loss_D: 0.3898 Loss_G: 6.2892 D(x): 0.9172 D(G(z)): 0.0028 / 0.0013\n"
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          "text": [
            "[2496/2750][20/41] Loss_D: 0.4588 Loss_G: 4.0505 D(x): 0.9665 D(G(z)): 0.0535 / 0.0141\n",
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2497/2750][20/41] Loss_D: 0.4554 Loss_G: 3.7451 D(x): 0.9680 D(G(z)): 0.0222 / 0.0237\n",
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        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cac39ad017ad4a3198437cca0cfbc6c8",
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          "text": [
            "[2498/2750][20/41] Loss_D: 0.4174 Loss_G: 3.8112 D(x): 0.9320 D(G(z)): 0.0497 / 0.0195\n",
            "[2498/2750][40/41] Loss_D: 0.4296 Loss_G: 4.6170 D(x): 0.9359 D(G(z)): 0.0404 / 0.0085\n"
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2499/2750][20/41] Loss_D: 0.5003 Loss_G: 3.0076 D(x): 0.7201 D(G(z)): 0.0211 / 0.0494\n",
            "[2499/2750][40/41] Loss_D: 0.3875 Loss_G: 5.8926 D(x): 0.9043 D(G(z)): 0.0032 / 0.0025\n"
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "[2500/2750][20/41] Loss_D: 0.4788 Loss_G: 3.4883 D(x): 0.9578 D(G(z)): 0.0861 / 0.0250\n",
            "[2500/2750][40/41] Loss_D: 0.5661 Loss_G: 4.2188 D(x): 0.9814 D(G(z)): 0.0998 / 0.0117\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a55a044ee354425e92f3ca7fa536e8bf",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2501/2750][20/41] Loss_D: 0.4204 Loss_G: 4.5926 D(x): 0.8495 D(G(z)): 0.0220 / 0.0105\n",
            "[2501/2750][40/41] Loss_D: 0.3829 Loss_G: 6.2469 D(x): 0.9309 D(G(z)): 0.0037 / 0.0022\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999992..0.84842974].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99739486].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98505193].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.97321194].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999965..0.95573467].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2501.pth\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf8848e4524646719cc92d20bddec362",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2502/2750][20/41] Loss_D: 0.3987 Loss_G: 4.4217 D(x): 0.8207 D(G(z)): 0.0114 / 0.0106\n",
            "[2502/2750][40/41] Loss_D: 0.5697 Loss_G: 4.7354 D(x): 0.9419 D(G(z)): 0.1323 / 0.0073\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b9db93e646784a0da8e002442c4acf4e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2503/2750][20/41] Loss_D: 0.4089 Loss_G: 5.5155 D(x): 0.9559 D(G(z)): 0.0055 / 0.0028\n",
            "[2503/2750][40/41] Loss_D: 0.4537 Loss_G: 3.9520 D(x): 0.9725 D(G(z)): 0.0281 / 0.0190\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "995f860cfe3f4ebcb166686f57b61f7f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2504/2750][20/41] Loss_D: 0.7476 Loss_G: 4.6752 D(x): 0.9754 D(G(z)): 0.1058 / 0.0191\n",
            "[2504/2750][40/41] Loss_D: 0.4433 Loss_G: 4.4664 D(x): 0.9680 D(G(z)): 0.0214 / 0.0098\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f31a463547de40a5a76b9b045be79a8b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2505/2750][20/41] Loss_D: 0.3849 Loss_G: 4.1908 D(x): 0.8910 D(G(z)): 0.0246 / 0.0132\n",
            "[2505/2750][40/41] Loss_D: 0.5672 Loss_G: 3.9052 D(x): 0.6610 D(G(z)): 0.0109 / 0.0173\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da5da6abc2604dc78c100d77ad94faad",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2506/2750][20/41] Loss_D: 0.4611 Loss_G: 5.1634 D(x): 0.9637 D(G(z)): 0.0143 / 0.0046\n",
            "[2506/2750][40/41] Loss_D: 0.3819 Loss_G: 5.0844 D(x): 0.8273 D(G(z)): 0.0089 / 0.0044\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2520e57a39ae498ebe6367314b002ae2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2507/2750][20/41] Loss_D: 0.4233 Loss_G: 4.5814 D(x): 0.7758 D(G(z)): 0.0058 / 0.0091\n",
            "[2507/2750][40/41] Loss_D: 0.4954 Loss_G: 4.1850 D(x): 0.9514 D(G(z)): 0.1129 / 0.0112\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58d16783454b4bcea3be83b8a6896cfd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2508/2750][20/41] Loss_D: 0.3566 Loss_G: 5.1051 D(x): 0.8787 D(G(z)): 0.0069 / 0.0048\n",
            "[2508/2750][40/41] Loss_D: 0.4032 Loss_G: 4.0888 D(x): 0.9395 D(G(z)): 0.0347 / 0.0134\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ea4a175c8940452589e2f165036a14b6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2509/2750][20/41] Loss_D: 0.4223 Loss_G: 4.6828 D(x): 0.9745 D(G(z)): 0.0116 / 0.0073\n",
            "[2509/2750][40/41] Loss_D: 0.4200 Loss_G: 3.6762 D(x): 0.8660 D(G(z)): 0.0396 / 0.0234\n"
          ]
        },
        {
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            "[2510/2750][20/41] Loss_D: 0.4781 Loss_G: 3.2084 D(x): 0.9155 D(G(z)): 0.1038 / 0.0371\n",
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            "[2511/2750][20/41] Loss_D: 0.4072 Loss_G: 5.3306 D(x): 0.7850 D(G(z)): 0.0028 / 0.0033\n",
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            "[2512/2750][20/41] Loss_D: 0.4604 Loss_G: 3.3443 D(x): 0.9758 D(G(z)): 0.0479 / 0.0297\n",
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            "[2513/2750][20/41] Loss_D: 0.3670 Loss_G: 5.1622 D(x): 0.9224 D(G(z)): 0.0125 / 0.0053\n",
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            "[2514/2750][20/41] Loss_D: 0.3913 Loss_G: 3.7285 D(x): 0.9180 D(G(z)): 0.0306 / 0.0196\n",
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            "[2515/2750][20/41] Loss_D: 0.3909 Loss_G: 4.3243 D(x): 0.9151 D(G(z)): 0.0165 / 0.0136\n",
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            "[2516/2750][20/41] Loss_D: 0.4869 Loss_G: 3.9827 D(x): 0.8875 D(G(z)): 0.1035 / 0.0171\n",
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            "[2517/2750][20/41] Loss_D: 0.4895 Loss_G: 3.7119 D(x): 0.9420 D(G(z)): 0.0487 / 0.0239\n",
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            "[2518/2750][20/41] Loss_D: 0.3897 Loss_G: 3.8932 D(x): 0.9047 D(G(z)): 0.0325 / 0.0164\n",
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            "[2519/2750][20/41] Loss_D: 0.3860 Loss_G: 5.1854 D(x): 0.9233 D(G(z)): 0.0151 / 0.0062\n",
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            "[2520/2750][20/41] Loss_D: 0.4041 Loss_G: 4.6893 D(x): 0.9374 D(G(z)): 0.0272 / 0.0074\n",
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            "[2521/2750][20/41] Loss_D: 0.5787 Loss_G: 3.9815 D(x): 0.9698 D(G(z)): 0.1373 / 0.0135\n",
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            "[2522/2750][20/41] Loss_D: 0.3626 Loss_G: 6.5276 D(x): 0.8518 D(G(z)): 0.0009 / 0.0010\n",
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            "[2523/2750][20/41] Loss_D: 0.4060 Loss_G: 4.4887 D(x): 0.9476 D(G(z)): 0.0152 / 0.0094\n",
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            "[2524/2750][20/41] Loss_D: 0.3730 Loss_G: 4.6277 D(x): 0.9143 D(G(z)): 0.0193 / 0.0089\n",
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            "[2525/2750][20/41] Loss_D: 0.4208 Loss_G: 3.7292 D(x): 0.9561 D(G(z)): 0.0255 / 0.0270\n",
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            "[2526/2750][20/41] Loss_D: 0.5853 Loss_G: 3.7037 D(x): 0.9860 D(G(z)): 0.0916 / 0.0210\n",
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            "[2527/2750][20/41] Loss_D: 0.3917 Loss_G: 4.5172 D(x): 0.9239 D(G(z)): 0.0224 / 0.0085\n",
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            "[2528/2750][20/41] Loss_D: 0.5515 Loss_G: 3.0294 D(x): 0.6613 D(G(z)): 0.0259 / 0.0482\n",
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            "[2529/2750][20/41] Loss_D: 0.4537 Loss_G: 4.5675 D(x): 0.9775 D(G(z)): 0.0259 / 0.0135\n",
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            "[2530/2750][20/41] Loss_D: 0.4739 Loss_G: 3.0763 D(x): 0.8019 D(G(z)): 0.0662 / 0.0446\n",
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            "[2551/2750][20/41] Loss_D: 0.4227 Loss_G: 5.3942 D(x): 0.9636 D(G(z)): 0.0096 / 0.0040\n",
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            "[2552/2750][20/41] Loss_D: 0.3841 Loss_G: 4.6660 D(x): 0.9385 D(G(z)): 0.0151 / 0.0069\n",
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            "[2553/2750][20/41] Loss_D: 0.4011 Loss_G: 4.1200 D(x): 0.8755 D(G(z)): 0.0253 / 0.0167\n",
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            "[2554/2750][20/41] Loss_D: 0.7726 Loss_G: 3.1571 D(x): 0.5007 D(G(z)): 0.0079 / 0.0427\n",
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            "[2556/2750][20/41] Loss_D: 0.3511 Loss_G: 6.0973 D(x): 0.9204 D(G(z)): 0.0045 / 0.0021\n",
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            "[2573/2750][20/41] Loss_D: 0.4003 Loss_G: 3.7024 D(x): 0.9040 D(G(z)): 0.0441 / 0.0236\n",
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            "[2574/2750][20/41] Loss_D: 0.4393 Loss_G: 4.4185 D(x): 0.9736 D(G(z)): 0.0163 / 0.0095\n",
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            "[2577/2750][20/41] Loss_D: 0.4099 Loss_G: 3.2324 D(x): 0.9020 D(G(z)): 0.0522 / 0.0357\n",
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            "[2578/2750][20/41] Loss_D: 0.3738 Loss_G: 5.0462 D(x): 0.8947 D(G(z)): 0.0108 / 0.0050\n",
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            "[2582/2750][20/41] Loss_D: 0.3853 Loss_G: 5.3163 D(x): 0.9359 D(G(z)): 0.0076 / 0.0046\n",
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            "[2583/2750][20/41] Loss_D: 0.6978 Loss_G: 3.9412 D(x): 0.9844 D(G(z)): 0.1877 / 0.0164\n",
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            "[2585/2750][20/41] Loss_D: 0.4922 Loss_G: 4.9125 D(x): 0.9775 D(G(z)): 0.0426 / 0.0092\n",
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            "[2586/2750][20/41] Loss_D: 0.3885 Loss_G: 4.2886 D(x): 0.9183 D(G(z)): 0.0190 / 0.0118\n",
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            "[2591/2750][20/41] Loss_D: 0.4209 Loss_G: 4.8019 D(x): 0.9527 D(G(z)): 0.0193 / 0.0066\n",
            "[2591/2750][40/41] Loss_D: 0.4400 Loss_G: 4.2404 D(x): 0.7890 D(G(z)): 0.0165 / 0.0168\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53bf50d523b94743b42cd4748d7eadd2",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2592/2750][20/41] Loss_D: 0.4259 Loss_G: 4.1746 D(x): 0.8952 D(G(z)): 0.0217 / 0.0135\n",
            "[2592/2750][40/41] Loss_D: 0.3753 Loss_G: 5.5605 D(x): 0.9315 D(G(z)): 0.0113 / 0.0037\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "948adc19e0f24003b4a10b58b2d3ed92",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2593/2750][20/41] Loss_D: 0.4027 Loss_G: 3.9207 D(x): 0.9517 D(G(z)): 0.0302 / 0.0166\n",
            "[2593/2750][40/41] Loss_D: 0.3990 Loss_G: 4.7207 D(x): 0.9408 D(G(z)): 0.0126 / 0.0074\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2d361d053684826b4fc6a1234d8d35e",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2594/2750][20/41] Loss_D: 0.4749 Loss_G: 4.1641 D(x): 0.7976 D(G(z)): 0.0200 / 0.0125\n",
            "[2594/2750][40/41] Loss_D: 0.4147 Loss_G: 3.4331 D(x): 0.9431 D(G(z)): 0.0403 / 0.0288\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b59e0eb962e7499888cb6cab80dd779f",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2595/2750][20/41] Loss_D: 0.5153 Loss_G: 2.9825 D(x): 0.9782 D(G(z)): 0.0708 / 0.0472\n",
            "[2595/2750][40/41] Loss_D: 0.3871 Loss_G: 4.4789 D(x): 0.8588 D(G(z)): 0.0181 / 0.0100\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c8c4d63fa294820aeeaeb526d3afc69",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2596/2750][20/41] Loss_D: 0.3652 Loss_G: 5.4805 D(x): 0.8867 D(G(z)): 0.0028 / 0.0029\n",
            "[2596/2750][40/41] Loss_D: 0.3857 Loss_G: 4.4437 D(x): 0.8620 D(G(z)): 0.0120 / 0.0092\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a548cee253b14451aa9c61ee9ab10542",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2597/2750][20/41] Loss_D: 0.4294 Loss_G: 5.2223 D(x): 0.9749 D(G(z)): 0.0089 / 0.0042\n",
            "[2597/2750][40/41] Loss_D: 0.4221 Loss_G: 2.9970 D(x): 0.8958 D(G(z)): 0.0722 / 0.0430\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43bd7aa59d3547268764113afd82d313",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2598/2750][20/41] Loss_D: 0.4763 Loss_G: 4.3199 D(x): 0.7437 D(G(z)): 0.0067 / 0.0144\n",
            "[2598/2750][40/41] Loss_D: 0.3759 Loss_G: 3.9752 D(x): 0.9314 D(G(z)): 0.0188 / 0.0166\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4fc632a680864b5b85d479f33322efde",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2599/2750][20/41] Loss_D: 0.5022 Loss_G: 3.9772 D(x): 0.9349 D(G(z)): 0.1180 / 0.0173\n",
            "[2599/2750][40/41] Loss_D: 0.4095 Loss_G: 4.5470 D(x): 0.9618 D(G(z)): 0.0181 / 0.0094\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4181f04347d64b3c9d1a135228338341",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2600/2750][20/41] Loss_D: 0.4329 Loss_G: 3.8410 D(x): 0.9227 D(G(z)): 0.0570 / 0.0217\n",
            "[2600/2750][40/41] Loss_D: 0.4011 Loss_G: 4.7531 D(x): 0.8613 D(G(z)): 0.0091 / 0.0122\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5dadfa5106754fb2ade01c4f4acfbb53",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2601/2750][20/41] Loss_D: 0.4149 Loss_G: 3.4220 D(x): 0.8443 D(G(z)): 0.0297 / 0.0316\n",
            "[2601/2750][40/41] Loss_D: 0.3885 Loss_G: 3.5947 D(x): 0.9549 D(G(z)): 0.0211 / 0.0275\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999744..0.86102134].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.99365485].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999994..0.9651701].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9998429..0.86711496].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999991..0.9796556].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2601.pth\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd70c5b1a9674354bf6e36ad954e36db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2602/2750][20/41] Loss_D: 0.3742 Loss_G: 3.9856 D(x): 0.9012 D(G(z)): 0.0204 / 0.0152\n",
            "[2602/2750][40/41] Loss_D: 0.5252 Loss_G: 4.3824 D(x): 0.6920 D(G(z)): 0.0058 / 0.0101\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b2b02db09e854902a4fb66f27ec673ca",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2603/2750][20/41] Loss_D: 0.4486 Loss_G: 3.3155 D(x): 0.7988 D(G(z)): 0.0300 / 0.0312\n",
            "[2603/2750][40/41] Loss_D: 0.3752 Loss_G: 4.3714 D(x): 0.8742 D(G(z)): 0.0179 / 0.0097\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f32a3a63852140e190de2e90b9bc5e8c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2604/2750][20/41] Loss_D: 0.4059 Loss_G: 4.5572 D(x): 0.9377 D(G(z)): 0.0179 / 0.0088\n",
            "[2604/2750][40/41] Loss_D: 0.5439 Loss_G: 3.2823 D(x): 0.6582 D(G(z)): 0.0121 / 0.0329\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af66d5b250a449cd9c66c840cb7fe4e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2605/2750][20/41] Loss_D: 0.4088 Loss_G: 4.0843 D(x): 0.8040 D(G(z)): 0.0087 / 0.0136\n",
            "[2605/2750][40/41] Loss_D: 0.4236 Loss_G: 4.0386 D(x): 0.9548 D(G(z)): 0.0391 / 0.0150\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6d921ad2a9e469f8083fc568b86d4d4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2606/2750][20/41] Loss_D: 0.4098 Loss_G: 4.7873 D(x): 0.8685 D(G(z)): 0.0106 / 0.0066\n",
            "[2606/2750][40/41] Loss_D: 0.4856 Loss_G: 3.6755 D(x): 0.7176 D(G(z)): 0.0154 / 0.0271\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d62d7b3f0d94328929a84ee5a38a633",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2607/2750][20/41] Loss_D: 0.4582 Loss_G: 4.2833 D(x): 0.9570 D(G(z)): 0.0644 / 0.0121\n",
            "[2607/2750][40/41] Loss_D: 0.3980 Loss_G: 4.0331 D(x): 0.9134 D(G(z)): 0.0274 / 0.0178\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63d1f087a35b4be5a8ad5df548898b89",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2608/2750][20/41] Loss_D: 0.4534 Loss_G: 3.6179 D(x): 0.8776 D(G(z)): 0.0517 / 0.0211\n",
            "[2608/2750][40/41] Loss_D: 0.4500 Loss_G: 4.5527 D(x): 0.7613 D(G(z)): 0.0048 / 0.0088\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f81aeb88f084dbe90b36dd3b186c78a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2609/2750][20/41] Loss_D: 0.4003 Loss_G: 4.2637 D(x): 0.8482 D(G(z)): 0.0369 / 0.0149\n",
            "[2609/2750][40/41] Loss_D: 0.4258 Loss_G: 4.2253 D(x): 0.9340 D(G(z)): 0.0480 / 0.0128\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "adfd2ed5b69149738a2bd8d2c8c47012",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
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            "[2622/2750][20/41] Loss_D: 0.5299 Loss_G: 3.6482 D(x): 0.9839 D(G(z)): 0.0407 / 0.0239\n",
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            "[2623/2750][20/41] Loss_D: 0.4162 Loss_G: 3.9213 D(x): 0.8888 D(G(z)): 0.0479 / 0.0181\n",
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            "[2624/2750][20/41] Loss_D: 0.4091 Loss_G: 5.0683 D(x): 0.8215 D(G(z)): 0.0037 / 0.0050\n",
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            "[2627/2750][20/41] Loss_D: 0.3722 Loss_G: 4.6774 D(x): 0.8645 D(G(z)): 0.0096 / 0.0089\n",
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            "[2628/2750][20/41] Loss_D: 0.4329 Loss_G: 4.6773 D(x): 0.7359 D(G(z)): 0.0055 / 0.0074\n",
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            "[2630/2750][20/41] Loss_D: 0.4671 Loss_G: 2.4635 D(x): 0.9542 D(G(z)): 0.0898 / 0.0939\n",
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            "[2638/2750][20/41] Loss_D: 0.4363 Loss_G: 4.3401 D(x): 0.9628 D(G(z)): 0.0191 / 0.0106\n",
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            "[2643/2750][20/41] Loss_D: 0.4936 Loss_G: 4.1366 D(x): 0.7155 D(G(z)): 0.0100 / 0.0149\n",
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            "[2645/2750][20/41] Loss_D: 0.3829 Loss_G: 4.6840 D(x): 0.8901 D(G(z)): 0.0113 / 0.0077\n",
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            "[2646/2750][20/41] Loss_D: 0.3761 Loss_G: 4.2567 D(x): 0.8985 D(G(z)): 0.0310 / 0.0126\n",
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            "[2647/2750][20/41] Loss_D: 0.4408 Loss_G: 4.8171 D(x): 0.9323 D(G(z)): 0.0471 / 0.0080\n",
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            "[2648/2750][20/41] Loss_D: 0.6913 Loss_G: 3.2932 D(x): 0.6142 D(G(z)): 0.0111 / 0.0566\n",
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            "[2649/2750][20/41] Loss_D: 0.3780 Loss_G: 4.8424 D(x): 0.8850 D(G(z)): 0.0137 / 0.0068\n",
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            "[2650/2750][20/41] Loss_D: 0.4617 Loss_G: 4.5042 D(x): 0.7567 D(G(z)): 0.0055 / 0.0090\n",
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            "[2651/2750][20/41] Loss_D: 0.6989 Loss_G: 4.1021 D(x): 0.5647 D(G(z)): 0.0022 / 0.0156\n",
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            "[2652/2750][20/41] Loss_D: 0.3907 Loss_G: 5.0493 D(x): 0.8323 D(G(z)): 0.0061 / 0.0062\n",
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            "[2654/2750][20/41] Loss_D: 0.5184 Loss_G: 2.5957 D(x): 0.9544 D(G(z)): 0.1018 / 0.0668\n",
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            "[2678/2750][20/41] Loss_D: 0.4737 Loss_G: 3.8177 D(x): 0.9296 D(G(z)): 0.0344 / 0.0179\n",
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            "[2682/2750][20/41] Loss_D: 0.4384 Loss_G: 4.0387 D(x): 0.8050 D(G(z)): 0.0110 / 0.0166\n",
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            "[2691/2750][20/41] Loss_D: 0.5429 Loss_G: 3.0027 D(x): 0.7643 D(G(z)): 0.0691 / 0.0476\n",
            "[2691/2750][40/41] Loss_D: 0.4161 Loss_G: 4.1567 D(x): 0.9357 D(G(z)): 0.0333 / 0.0124\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "810b1f2be95e48f598dd1f8bcb40cb64",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2692/2750][20/41] Loss_D: 0.3793 Loss_G: 5.2059 D(x): 0.8457 D(G(z)): 0.0042 / 0.0040\n",
            "[2692/2750][40/41] Loss_D: 0.4544 Loss_G: 3.4416 D(x): 0.8855 D(G(z)): 0.0865 / 0.0320\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a01ec31e632a480bb331c327bce248f0",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2693/2750][20/41] Loss_D: 0.3872 Loss_G: 4.5494 D(x): 0.9480 D(G(z)): 0.0159 / 0.0085\n",
            "[2693/2750][40/41] Loss_D: 0.4160 Loss_G: 4.6941 D(x): 0.9625 D(G(z)): 0.0224 / 0.0075\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d21bd5fad25d46fc98b051d3b09b4d94",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2694/2750][20/41] Loss_D: 0.4184 Loss_G: 6.8881 D(x): 0.7617 D(G(z)): 0.0014 / 0.0008\n",
            "[2694/2750][40/41] Loss_D: 0.4721 Loss_G: 3.6704 D(x): 0.7470 D(G(z)): 0.0142 / 0.0253\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5cb27b019ebe43ca803cd6d3c43f3c55",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2695/2750][20/41] Loss_D: 0.5062 Loss_G: 3.2287 D(x): 0.6997 D(G(z)): 0.0206 / 0.0367\n",
            "[2695/2750][40/41] Loss_D: 0.4512 Loss_G: 3.2545 D(x): 0.8603 D(G(z)): 0.0645 / 0.0340\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "72d77386ce9f44a29235999180251a11",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2696/2750][20/41] Loss_D: 0.5824 Loss_G: 3.1581 D(x): 0.9762 D(G(z)): 0.1275 / 0.0428\n",
            "[2696/2750][40/41] Loss_D: 0.3815 Loss_G: 4.0717 D(x): 0.8791 D(G(z)): 0.0260 / 0.0143\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa710eb83b394eafa3c86fdb7fc5b8a0",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2697/2750][20/41] Loss_D: 0.3909 Loss_G: 3.7990 D(x): 0.9398 D(G(z)): 0.0317 / 0.0168\n",
            "[2697/2750][40/41] Loss_D: 0.4954 Loss_G: 5.1569 D(x): 0.7093 D(G(z)): 0.0026 / 0.0045\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4bc242e16a04bf2929ca797cf14e45f",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2698/2750][20/41] Loss_D: 0.3809 Loss_G: 4.6972 D(x): 0.9357 D(G(z)): 0.0137 / 0.0078\n",
            "[2698/2750][40/41] Loss_D: 0.5999 Loss_G: 2.6302 D(x): 0.6524 D(G(z)): 0.0376 / 0.0666\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "68a182bb95af4e50b8990df2a7d3a9b8",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2699/2750][20/41] Loss_D: 0.4106 Loss_G: 4.1756 D(x): 0.8305 D(G(z)): 0.0119 / 0.0132\n",
            "[2699/2750][40/41] Loss_D: 0.3722 Loss_G: 5.1810 D(x): 0.8689 D(G(z)): 0.0048 / 0.0054\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5bd9f8b82bd7436ab846e754a9c23267",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2700/2750][20/41] Loss_D: 0.7626 Loss_G: 4.5352 D(x): 0.9732 D(G(z)): 0.2477 / 0.0112\n",
            "[2700/2750][40/41] Loss_D: 0.5433 Loss_G: 3.4396 D(x): 0.9152 D(G(z)): 0.1423 / 0.0272\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48e730fe41cb4094ab6daad7934d3f37",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2701/2750][20/41] Loss_D: 0.4327 Loss_G: 2.8716 D(x): 0.8521 D(G(z)): 0.0556 / 0.0545\n",
            "[2701/2750][40/41] Loss_D: 0.5791 Loss_G: 2.8983 D(x): 0.6605 D(G(z)): 0.0341 / 0.0605\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.98332703].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.974467].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9996463..0.6873974].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9731896].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9940296].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint saved to C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_2701.pth\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 6400x6400 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36eb7992db7c4f13964500fc6b74cf46",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2702/2750][20/41] Loss_D: 0.6565 Loss_G: 2.4734 D(x): 0.9577 D(G(z)): 0.2035 / 0.0822\n",
            "[2702/2750][40/41] Loss_D: 0.4513 Loss_G: 3.4761 D(x): 0.8604 D(G(z)): 0.0617 / 0.0306\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e02416c1fa584c959329a498cb8a5337",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2703/2750][20/41] Loss_D: 0.4892 Loss_G: 4.7085 D(x): 0.7233 D(G(z)): 0.0056 / 0.0097\n",
            "[2703/2750][40/41] Loss_D: 0.3839 Loss_G: 4.2205 D(x): 0.8409 D(G(z)): 0.0128 / 0.0125\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b842fcda15f4c8c82cd7e52f673a908",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2704/2750][20/41] Loss_D: 0.4708 Loss_G: 3.7204 D(x): 0.9718 D(G(z)): 0.0664 / 0.0208\n",
            "[2704/2750][40/41] Loss_D: 0.4315 Loss_G: 3.8985 D(x): 0.8150 D(G(z)): 0.0232 / 0.0172\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa838c778fef40d896ed11cff0c2c1b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2705/2750][20/41] Loss_D: 0.3906 Loss_G: 4.3951 D(x): 0.8617 D(G(z)): 0.0154 / 0.0106\n",
            "[2705/2750][40/41] Loss_D: 0.5830 Loss_G: 3.3138 D(x): 0.9835 D(G(z)): 0.1004 / 0.0351\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0e7761c8e7e4c9b9ea1b17c4f308330",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2706/2750][20/41] Loss_D: 0.4189 Loss_G: 3.6323 D(x): 0.9173 D(G(z)): 0.0450 / 0.0226\n",
            "[2706/2750][40/41] Loss_D: 0.4693 Loss_G: 3.7712 D(x): 0.7713 D(G(z)): 0.0208 / 0.0204\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7f6848a80fbf4546b1871698c17061d2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2707/2750][20/41] Loss_D: 0.4350 Loss_G: 4.4275 D(x): 0.8128 D(G(z)): 0.0113 / 0.0131\n",
            "[2707/2750][40/41] Loss_D: 0.4100 Loss_G: 4.9113 D(x): 0.9259 D(G(z)): 0.0348 / 0.0078\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f8d3fa563c4342fa8fdc350b2a008fa5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2708/2750][20/41] Loss_D: 0.3917 Loss_G: 4.2993 D(x): 0.8918 D(G(z)): 0.0120 / 0.0106\n",
            "[2708/2750][40/41] Loss_D: 0.3920 Loss_G: 5.5801 D(x): 0.9095 D(G(z)): 0.0048 / 0.0024\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b0b4c140a534d94986b906bcb413584",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2709/2750][20/41] Loss_D: 0.4040 Loss_G: 4.3541 D(x): 0.8676 D(G(z)): 0.0146 / 0.0114\n",
            "[2709/2750][40/41] Loss_D: 0.4061 Loss_G: 3.8977 D(x): 0.8897 D(G(z)): 0.0419 / 0.0174\n"
          ]
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "426ef9ab4fed49b48dcc82879af98e6d",
              "version_major": 2,
              "version_minor": 0
            },
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              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2737/2750][20/41] Loss_D: 0.3561 Loss_G: 4.7960 D(x): 0.9054 D(G(z)): 0.0073 / 0.0057\n",
            "[2737/2750][40/41] Loss_D: 0.7092 Loss_G: 5.2815 D(x): 0.5526 D(G(z)): 0.0035 / 0.0101\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6154e876965484f846a27fbb6d91d97",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2738/2750][20/41] Loss_D: 0.4791 Loss_G: 3.9481 D(x): 0.7584 D(G(z)): 0.0199 / 0.0187\n",
            "[2738/2750][40/41] Loss_D: 0.3700 Loss_G: 4.7635 D(x): 0.8827 D(G(z)): 0.0083 / 0.0068\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "253044c3f55e4e71af1e7fe30f60f75d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2739/2750][20/41] Loss_D: 0.5088 Loss_G: 3.7857 D(x): 0.9704 D(G(z)): 0.0721 / 0.0181\n",
            "[2739/2750][40/41] Loss_D: 0.4002 Loss_G: 3.9617 D(x): 0.9214 D(G(z)): 0.0377 / 0.0165\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c0c556c1c13949fd9fa48907a327b0c6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2740/2750][20/41] Loss_D: 0.4100 Loss_G: 3.9357 D(x): 0.8992 D(G(z)): 0.0325 / 0.0172\n",
            "[2740/2750][40/41] Loss_D: 0.4639 Loss_G: 4.8996 D(x): 0.9797 D(G(z)): 0.0112 / 0.0095\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9fb82f8ce1214859a590d3318e45cceb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2741/2750][20/41] Loss_D: 0.3985 Loss_G: 3.7936 D(x): 0.9225 D(G(z)): 0.0304 / 0.0206\n",
            "[2741/2750][40/41] Loss_D: 0.3949 Loss_G: 5.7300 D(x): 0.8698 D(G(z)): 0.0054 / 0.0026\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "24ecfadf04a545fbb82e70d5fc22032f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2742/2750][20/41] Loss_D: 0.4834 Loss_G: 3.6948 D(x): 0.8485 D(G(z)): 0.0386 / 0.0196\n",
            "[2742/2750][40/41] Loss_D: 0.3687 Loss_G: 4.8215 D(x): 0.8955 D(G(z)): 0.0096 / 0.0071\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f15d3b7941b4449bb0ca1415abc71a6a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2743/2750][20/41] Loss_D: 0.4359 Loss_G: 3.6226 D(x): 0.7914 D(G(z)): 0.0323 / 0.0254\n",
            "[2743/2750][40/41] Loss_D: 0.3773 Loss_G: 4.8700 D(x): 0.9401 D(G(z)): 0.0098 / 0.0064\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "72a42f2d38534fc0a53fe70c5e501c8a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2744/2750][20/41] Loss_D: 0.6878 Loss_G: 4.2202 D(x): 0.5812 D(G(z)): 0.0036 / 0.0142\n",
            "[2744/2750][40/41] Loss_D: 0.3995 Loss_G: 5.0561 D(x): 0.8135 D(G(z)): 0.0051 / 0.0057\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3bc6fc35d494743a8c152a5cc2e885a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2745/2750][20/41] Loss_D: 0.4961 Loss_G: 3.1238 D(x): 0.7438 D(G(z)): 0.0430 / 0.0413\n",
            "[2745/2750][40/41] Loss_D: 0.4520 Loss_G: 3.2406 D(x): 0.7994 D(G(z)): 0.0316 / 0.0375\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a137cca2a2f945ceab383a50938d8dfa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2746/2750][20/41] Loss_D: 0.3974 Loss_G: 3.7881 D(x): 0.9411 D(G(z)): 0.0240 / 0.0239\n",
            "[2746/2750][40/41] Loss_D: 0.4820 Loss_G: 3.9596 D(x): 0.9797 D(G(z)): 0.0320 / 0.0177\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef692a8ec2f141048eaa2041b7876e3c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2747/2750][20/41] Loss_D: 0.5486 Loss_G: 3.3245 D(x): 0.8759 D(G(z)): 0.1426 / 0.0331\n",
            "[2747/2750][40/41] Loss_D: 0.4216 Loss_G: 4.3886 D(x): 0.8312 D(G(z)): 0.0230 / 0.0101\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b31d720012f84d23b36168d3e998f8b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2748/2750][20/41] Loss_D: 0.3945 Loss_G: 3.9036 D(x): 0.9450 D(G(z)): 0.0173 / 0.0165\n",
            "[2748/2750][40/41] Loss_D: 0.4427 Loss_G: 2.9209 D(x): 0.8842 D(G(z)): 0.0667 / 0.0476\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "953635a70e984191acef5df0c47d577f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2749/2750][20/41] Loss_D: 0.4175 Loss_G: 3.9447 D(x): 0.9427 D(G(z)): 0.0401 / 0.0190\n",
            "[2749/2750][40/41] Loss_D: 0.4021 Loss_G: 4.4413 D(x): 0.9284 D(G(z)): 0.0243 / 0.0097\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e8e31c3161f94921848b01d28ad7719c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/41 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2750/2750][20/41] Loss_D: 0.5630 Loss_G: 3.5109 D(x): 0.6520 D(G(z)): 0.0076 / 0.0243\n",
            "[2750/2750][40/41] Loss_D: 0.4644 Loss_G: 4.6210 D(x): 0.9660 D(G(z)): 0.0301 / 0.0106\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import time\n",
        "\n",
        "# Assume 'netG', 'netD', 'optimizerG', 'optimizerD' are already defined\n",
        "\n",
        "epoch_time = []\n",
        "G_losses = []\n",
        "D_losses = []\n",
        "\n",
        "# If you want to load from a previous checkpoint, set checkpoint_path\n",
        "# checkpoint_path = 'checkpoint_epoch_250.pth'  # Path to the checkpoint file\n",
        "# epoch, G_losses, D_losses = load_checkpoint(checkpoint_path, netG, netD, optimizerG, optimizerD)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    start = time.time()\n",
        "    for ii, (real_images, train_labels) in tqdm(enumerate(train_loader), total=len(train_loader)):\n",
        "        ############################\n",
        "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
        "        ###########################\n",
        "        # train with real\n",
        "        netD.zero_grad()\n",
        "        real_images = real_images.to(device)\n",
        "        batch_size = real_images.size(0)\n",
        "        labels = torch.full((batch_size, 1), real_label, device=device)\n",
        "\n",
        "        output = netD(real_images)\n",
        "        errD_real = criterion(output, labels)\n",
        "        errD_real.backward()\n",
        "        D_x = output.mean().item()\n",
        "\n",
        "        # train with fake\n",
        "        noise = torch.randn(batch_size, nz, 1, 1, device=device)\n",
        "        fake = netG(noise)\n",
        "        labels.fill_(fake_label)\n",
        "        output = netD(fake.detach())\n",
        "        errD_fake = criterion(output, labels)\n",
        "        errD_fake.backward()\n",
        "        D_G_z1 = output.mean().item()\n",
        "        errD = errD_real + errD_fake\n",
        "        optimizerD.step()\n",
        "\n",
        "        ############################\n",
        "        # (2) Update G network: maximize log(D(G(z)))\n",
        "        ###########################\n",
        "        netG.zero_grad()\n",
        "        labels.fill_(real_label)  # fake labels are real for generator cost\n",
        "        output = netD(fake)\n",
        "        errG = criterion(output, labels)\n",
        "        errG.backward()\n",
        "        D_G_z2 = output.mean().item()\n",
        "        optimizerG.step()\n",
        "\n",
        "        # Save Losses for plotting later\n",
        "        G_losses.append(errG.item())\n",
        "        D_losses.append(errD.item())\n",
        "\n",
        "        if (ii + 1) % (len(train_loader) // 2) == 0:\n",
        "            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'\n",
        "                  % (epoch + 1, epochs, ii + 1, len(train_loader),\n",
        "                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n",
        "\n",
        "    # Save checkpoint every 250 epochs\n",
        "    if epoch % 100 == 0:\n",
        "        checkpoint = {\n",
        "            'epoch': epoch + 1,\n",
        "            'netG_state_dict': netG.state_dict(),\n",
        "            'netD_state_dict': netD.state_dict(),\n",
        "            'optimizerG_state_dict': optimizerG.state_dict(),\n",
        "            'optimizerD_state_dict': optimizerD.state_dict(),\n",
        "            'G_losses': G_losses,\n",
        "            'D_losses': D_losses\n",
        "        }\n",
        "        checkpoint_path = f\"C:/Users/User/CellGAN/checkpoints/checkpoint_epoch_{epoch + 1}.pth\"\n",
        "        torch.save(checkpoint, checkpoint_path)\n",
        "        print(f\"Checkpoint saved to {checkpoint_path}\")\n",
        "\n",
        "    # Reset losses for next epoch\n",
        "    G_losses = []\n",
        "    D_losses = []\n",
        "    if epoch % 100 == 0:\n",
        "        show_generated_img()\n",
        "    epoch_time.append(time.time() - start)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-output": true,
        "id": "rvXqRxNGQkJt",
        "outputId": "b0d13781-c92e-4c18-f119-bb57f40aa6ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">> average EPOCH duration =  64.19734229469299\n"
          ]
        }
      ],
      "source": [
        "print (\">> average EPOCH duration = \", np.mean(epoch_time))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVjNJThRQkJt"
      },
      "source": [
        "# Generation example\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "id": "MNCOPsyDQkJu",
        "outputId": "ef9e76b4-2626-48ab-dbdf-93bd6866adf3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.937363].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999998..0.9797539].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999262..0.7866993].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99998575..0.8704859].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.9583216].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9999941..0.9082884].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99999654..0.8541574].\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 6400x6400 with 7 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "show_generated_img(7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JAVQkQ6jQkJu",
        "outputId": "8e4f4e59-a7c8-4e41-d984-b609dab87f6f",
        "colab": {
          "referenced_widgets": [
            "9670a99d02754be9b2235383cd601af4"
          ]
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_10832\\3218895561.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for i_batch in tqdm(range(0, n_images, im_batch_size)):\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9670a99d02754be9b2235383cd601af4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "if not os.path.exists('C:/Users/User/CellGAN/output_images'):\n",
        "    os.mkdir('C:/Users/User/CellGAN/output_images')\n",
        "\n",
        "im_batch_size = 50\n",
        "n_images=10000\n",
        "\n",
        "for i_batch in tqdm(range(0, n_images, im_batch_size)):\n",
        "    gen_z = torch.randn(im_batch_size, nz, 1, 1, device=device)\n",
        "    gen_images = netG(gen_z)\n",
        "    images = gen_images.to(\"cpu\").clone().detach()\n",
        "    images = images.numpy().transpose(0, 2, 3, 1)\n",
        "    for i_image in range(gen_images.size(0)):\n",
        "        save_image(gen_images[i_image, :, :, :], os.path.join('C:/Users/User/CellGAN/output_images', f'image_{i_batch+i_image:05d}.png'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_kg_hide-input": true,
        "id": "Hb5i_Nf1QkJu",
        "outputId": "eb100984-d154-4ae3-c2f7-a25ad11424d6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.91042155..0.93658805].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8770315..0.9058672].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.91602206..0.4881017].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9997357..0.8200204].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6916583..0.32500598].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6855826..0.82973546].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9751814..0.6181541].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9974837..0.6124076].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.61431056..0.6502628].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.7664832..0.85206175].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92684144..0.4069027].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.5519823..0.8862192].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.45560548..0.76826066].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.5196579..0.6727999].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9995131..0.51856387].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.64466965..0.48173326].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.61378056..0.4558056].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6757228..0.7987141].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6247863..0.44822332].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.7315713..0.74387556].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9880502..0.6272824].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6300356..0.6745105].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6695571..0.7489256].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.72838247..0.7908553].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9553094..0.6327634].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8874116..0.6221519].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8630718..0.4073356].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.79524606..0.8450106].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9997868..0.83107793].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9511475..0.5310866].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99994725..0.918627].\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.6158788..0.89704645].\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2500x1600 with 32 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(25, 16))\n",
        "# display 10 images from each class\n",
        "for i, j in enumerate(images[:32]):\n",
        "    ax = fig.add_subplot(4, 8, i + 1, xticks=[], yticks=[])\n",
        "    plt.imshow(j)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xatXiw_mQkJu"
      },
      "outputs": [],
      "source": [
        "torch.save(netG.state_dict(), 'C:/Users/User/CellGAN/generator_model.pth')\n",
        "torch.save(netD.state_dict(), 'C:/Users/User/CellGAN/discriminator_model.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jzOwTfzqQnMW",
        "outputId": "044f2d02-c01e-440d-e9e9-02e802c041f4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Generating images for class 0: 100%|██████████| 500/500 [00:10<00:00, 49.73it/s]\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from tqdm import tqdm\n",
        "\n",
        "def generate_and_save_images(netG, nz, num_images_per_class=500, output_dir='C:/Users/User/CellGAN/generated_images'):\n",
        "    if not os.path.exists(output_dir):\n",
        "        os.makedirs(output_dir)\n",
        "\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    netG.to(device)\n",
        "    netG.eval()  # Set the model to evaluation mode\n",
        "    num_classes = 1 # Assuming single class case\n",
        "\n",
        "    for class_label in range(num_classes):\n",
        "        class_dir = os.path.join(output_dir, f\"class_{class_label}\")\n",
        "        os.makedirs(class_dir, exist_ok=True)\n",
        "\n",
        "        for i in tqdm(range(num_images_per_class), desc=f\"Generating images for class {class_label}\"):\n",
        "            noise = torch.randn(1, nz, 1, 1, device=device)  # Generate random noise\n",
        "            with torch.no_grad():  # No gradient calculation needed during generation\n",
        "                generated_image = netG(noise)\n",
        "            save_image(generated_image, os.path.join(class_dir, f\"image_{i}.png\"))\n",
        "generate_and_save_images(netG, nz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sR1aQ-sqQnJk"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zx0eOlWQ2Qm2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nQmVGRNo2Qm2",
        "outputId": "21471114-8185-4648-814b-350545cb07b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Checkpoint type: <class 'collections.OrderedDict'>\n",
            "Checkpoint keys: odict_keys(['model.0.weight', 'model.1.weight', 'model.1.bias', 'model.1.running_mean', 'model.1.running_var', 'model.1.num_batches_tracked', 'model.3.weight', 'model.4.weight', 'model.4.bias', 'model.4.running_mean', 'model.4.running_var', 'model.4.num_batches_tracked', 'model.6.weight', 'model.7.weight', 'model.7.bias', 'model.7.running_mean', 'model.7.running_var', 'model.7.num_batches_tracked', 'model.9.weight', 'model.10.weight', 'model.10.bias', 'model.10.running_mean', 'model.10.running_var', 'model.10.num_batches_tracked', 'model.12.weight', 'model.13.weight', 'model.13.bias', 'model.13.running_mean', 'model.13.running_var', 'model.13.num_batches_tracked', 'model.15.weight', 'model.15.bias'])\n"
          ]
        }
      ],
      "source": [
        "generator_path = \"C:/Users/User/CellGAN/generator_model.pth\"\n",
        "generator = torch.load(generator_path, map_location=device)\n",
        "\n",
        "print(\"Checkpoint type:\", type(generator))  # Check if it's a dict or state_dict\n",
        "if isinstance(generator, dict):\n",
        "    print(\"Checkpoint keys:\", generator.keys())  # Print available keys\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5GWDtynb2Qm2",
        "outputId": "c48454cc-dd38-46e6-970a-27dce9b90d15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✅ Checkpoint successfully loaded!\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Define device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Initialize the Generator model (Make sure its architecture matches the saved weights!)\n",
        "netG = Generator(nz=128, channels=3).to(device)\n",
        "\n",
        "# Load the generator model weights\n",
        "generator_path = \"C:/Users/User/CellGAN/generator_model.pth\"\n",
        "checkpoint = torch.load(generator_path, map_location=device)\n",
        "\n",
        "# Load directly since it's an OrderedDict (not wrapped inside a dictionary)\n",
        "netG.load_state_dict(checkpoint)\n",
        "\n",
        "# Set model to evaluation mode\n",
        "netG.eval()\n",
        "\n",
        "print(\"✅ Checkpoint successfully loaded!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DpY8Bgzq2Qm2",
        "outputId": "08b345c8-72eb-414a-9c60-60fd0aa7990c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Generating images for High squamous intra-epithelial lesion: 100%|██████████| 500/500 [00:11<00:00, 45.14it/s]\n",
            "Generating images for Low squamous intra-epithelial lesion: 100%|██████████| 500/500 [00:10<00:00, 48.22it/s]\n",
            "Generating images for Negative for Intraepithelial malignancy: 100%|██████████| 500/500 [00:10<00:00, 49.11it/s]\n",
            "Generating images for Squamous cell carcinoma: 100%|██████████| 500/500 [00:10<00:00, 49.21it/s]\n"
          ]
        }
      ],
      "source": [
        "#orignal modified code\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import torch\n",
        "from torchvision.utils import save_image\n",
        "\n",
        "def generate_and_save_images(netG, nz, num_images_per_class=500, output_dir='C:/Users/User/CellGAN/new64'):\n",
        "    if not os.path.exists(output_dir):\n",
        "        os.makedirs(output_dir)\n",
        "\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    netG.to(device)\n",
        "    netG.eval()  # Set the model to evaluation mode\n",
        "\n",
        "    # List of class labels\n",
        "    class_labels = [\n",
        "        \"High squamous intra-epithelial lesion\",\n",
        "        \"Low squamous intra-epithelial lesion\",\n",
        "        \"Negative for Intraepithelial malignancy\",\n",
        "        \"Squamous cell carcinoma\"\n",
        "    ]\n",
        "\n",
        "    # Number of classes\n",
        "    num_classes = len(class_labels)\n",
        "\n",
        "    for class_label in range(num_classes):\n",
        "        class_name = class_labels[class_label]\n",
        "        class_dir = os.path.join(output_dir, class_name)\n",
        "        os.makedirs(class_dir, exist_ok=True)\n",
        "\n",
        "        for i in tqdm(range(num_images_per_class), desc=f\"Generating images for {class_name}\"):\n",
        "            noise = torch.randn(1, nz, 1, 1, device=device)  # Generate random noise\n",
        "            with torch.no_grad():  # No gradient calculation needed during generation\n",
        "                generated_image = netG(noise)\n",
        "\n",
        "            # Save the generated image to the appropriate class folder\n",
        "            save_image(generated_image, os.path.join(class_dir, f\"image_{i}.png\"))\n",
        "\n",
        "# Example usage\n",
        "generate_and_save_images(netG, nz)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RK2nETVx2Qm8"
      },
      "outputs": [],
      "source": [
        "img=plt.imread()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Z3rbcTb2Qm9",
        "outputId": "fae7ffea-2398-4f32-fc02-7593e1665ff9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Images Generated Successfully!\n"
          ]
        }
      ],
      "source": [
        "netG.eval()\n",
        "\n",
        "# Define latent space size (adjust based on your model)\n",
        "LATENT_DIM = 128\n",
        "\n",
        "# Function to Generate Images\n",
        "def generate_images(generator, num_images=500, class_name=\"Generated\"):\n",
        "    os.makedirs(f\"/content/gan images/{class_name}\", exist_ok=True)\n",
        "\n",
        "    # Generate noise vector\n",
        "    noise = torch.randn(num_images, LATENT_DIM, 1, 1, device=\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "    # Generate images (PyTorch forward pass)\n",
        "    with torch.no_grad():\n",
        "        generated_images = generator(noise).cpu()\n",
        "\n",
        "    # Save images\n",
        "    for i in range(num_images):\n",
        "        img = generated_images[i].permute(1, 2, 0).numpy()  # Convert to (H, W, C)\n",
        "        img = (img - img.min()) / (img.max() - img.min())  # Normalize to [0,1]\n",
        "        plt.imshow(img)\n",
        "        plt.axis(\"off\")\n",
        "        plt.savefig(f\"/content/gan images/{class_name}/image_{i}.png\")\n",
        "        plt.close()\n",
        "\n",
        "# Generate 5 Images for Each Class\n",
        "for class_name in [\"High squamous intra-epithelial lesion\", \"Low squamous intra-epithelial lesion\",\n",
        "                   \"Negative for Intraepithelial malignancy\", \"Squamous cell carcinoma\"]:\n",
        "    generate_images(netG, num_images=500, class_name=class_name)\n",
        "\n",
        "print(\"Images Generated Successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZTVKGcss2Qm9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tVHZaIJZ2Qm9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7es8_sg2Qm9"
      },
      "outputs": [],
      "source": [
        "#classification\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B5-cwbEp2Qm9"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms\n",
        "import os\n",
        "\n",
        "# Define transformations for the images (resize and normalization)\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),  # ResNet-18 requires 224x224 input size\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # ImageNet normalization\n",
        "])\n",
        "\n",
        "# Path to the folder where your class directories are saved\n",
        "data_dir = 'C:/Users/User/CellGAN/new64'\n",
        "\n",
        "# Load dataset\n",
        "dataset = datasets.ImageFolder(root=data_dir, transform=transform)\n",
        "\n",
        "# Split dataset into train and validation sets (80% train, 20% validation)\n",
        "train_size = int(0.8 * len(dataset))\n",
        "val_size = len(dataset) - train_size\n",
        "train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
        "\n",
        "# Create DataLoader for training and validation\n",
        "batch_size = 32\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HNE3lEZ72Qm9"
      },
      "outputs": [],
      "source": [
        "from torchvision import models\n",
        "\n",
        "# Load the pre-trained ResNet-18 model with the updated 'weights' argument\n",
        "from torchvision.models import ResNet18_Weights\n",
        "\n",
        "model = models.resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)  # or ResNet18_Weights.DEFAULT\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y5-KBa0x2Qm-"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# Load the pre-trained ResNet-18 model\n",
        "model = models.resnet18(pretrained=True)\n",
        "\n",
        "# Modify the final fully connected layer to match the number of classes (4 classes in your case)\n",
        "num_ftrs = model.fc.in_features\n",
        "model.fc = nn.Linear(num_ftrs, 4)  # 4 classes in the final layer\n",
        "\n",
        "# Move the model to the device (GPU if available)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = model.to(device)\n",
        "\n",
        "# Loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yPun5NJy2Qm-",
        "outputId": "0b7120f4-7848-4606-bb6b-23e443883263"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [1/10], Train Loss: 1.5490, Train Acc: 0.2419, Val Loss: 1.4344, Val Acc: 0.2525\n",
            "Epoch [2/10], Train Loss: 1.4082, Train Acc: 0.2406, Val Loss: 1.4427, Val Acc: 0.2250\n",
            "Epoch [3/10], Train Loss: 1.4367, Train Acc: 0.2381, Val Loss: 1.4337, Val Acc: 0.2450\n",
            "Epoch [4/10], Train Loss: 1.4201, Train Acc: 0.2531, Val Loss: 1.5319, Val Acc: 0.2250\n",
            "Epoch [5/10], Train Loss: 1.4181, Train Acc: 0.2594, Val Loss: 1.4044, Val Acc: 0.2600\n",
            "Epoch [6/10], Train Loss: 1.4125, Train Acc: 0.2581, Val Loss: 1.5270, Val Acc: 0.2250\n",
            "Epoch [7/10], Train Loss: 1.4126, Train Acc: 0.2581, Val Loss: 1.4073, Val Acc: 0.2325\n",
            "Epoch [8/10], Train Loss: 1.4019, Train Acc: 0.2537, Val Loss: 1.4087, Val Acc: 0.2550\n",
            "Epoch [9/10], Train Loss: 1.4130, Train Acc: 0.2569, Val Loss: 1.4080, Val Acc: 0.2400\n",
            "Epoch [10/10], Train Loss: 1.4069, Train Acc: 0.2344, Val Loss: 1.4492, Val Acc: 0.2425\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 10  # Set the number of epochs\n",
        "\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs):\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()  # Set model to training mode\n",
        "        running_loss = 0.0\n",
        "        correct = 0\n",
        "        total = 0\n",
        "\n",
        "        # Training loop\n",
        "        for inputs, labels in train_loader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()  # Zero the gradients\n",
        "\n",
        "            # Forward pass\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "            # Backward pass\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # Calculate accuracy\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "            running_loss += loss.item()\n",
        "\n",
        "        epoch_loss = running_loss / len(train_loader)\n",
        "        epoch_acc = correct / total\n",
        "\n",
        "        # Validation loop\n",
        "        model.eval()  # Set model to evaluation mode\n",
        "        val_loss = 0.0\n",
        "        val_correct = 0\n",
        "        val_total = 0\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for inputs, labels in val_loader:\n",
        "                inputs, labels = inputs.to(device), labels.to(device)\n",
        "                outputs = model(inputs)\n",
        "                loss = criterion(outputs, labels)\n",
        "\n",
        "                _, predicted = torch.max(outputs, 1)\n",
        "                val_total += labels.size(0)\n",
        "                val_correct += (predicted == labels).sum().item()\n",
        "\n",
        "                val_loss += loss.item()\n",
        "\n",
        "        val_loss = val_loss / len(val_loader)\n",
        "        val_acc = val_correct / val_total\n",
        "\n",
        "        print(f\"Epoch [{epoch + 1}/{num_epochs}], \"\n",
        "              f\"Train Loss: {epoch_loss:.4f}, Train Acc: {epoch_acc:.4f}, \"\n",
        "              f\"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\")\n",
        "\n",
        "# Train the model\n",
        "train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SLeHnCmA2Qm-",
        "outputId": "98584037-d8b6-4074-e6b0-0b30e148b4ae"
      },
      "outputs": [
        {
          "ename": "PermissionError",
          "evalue": "[Errno 13] Permission denied: 'C:/Users/User/CellGAN/new64'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[42], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m transforms\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Load the image you want to classify\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mopen(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC:/Users/User/CellGAN/new64\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Define the same transformation as during training\u001b[39;00m\n\u001b[0;32m      8\u001b[0m transform \u001b[38;5;241m=\u001b[39m transforms\u001b[38;5;241m.\u001b[39mCompose([\n\u001b[0;32m      9\u001b[0m     transforms\u001b[38;5;241m.\u001b[39mResize((\u001b[38;5;241m224\u001b[39m, \u001b[38;5;241m224\u001b[39m)),\n\u001b[0;32m     10\u001b[0m     transforms\u001b[38;5;241m.\u001b[39mToTensor(),\n\u001b[0;32m     11\u001b[0m     transforms\u001b[38;5;241m.\u001b[39mNormalize(mean\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0.485\u001b[39m, \u001b[38;5;241m0.456\u001b[39m, \u001b[38;5;241m0.406\u001b[39m], std\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0.229\u001b[39m, \u001b[38;5;241m0.224\u001b[39m, \u001b[38;5;241m0.225\u001b[39m])\n\u001b[0;32m     12\u001b[0m ])\n",
            "File \u001b[1;32m~\\.conda\\envs\\proj\\Lib\\site-packages\\PIL\\Image.py:3465\u001b[0m, in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m   3462\u001b[0m     filename \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mfspath(fp)\n\u001b[0;32m   3464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename:\n\u001b[1;32m-> 3465\u001b[0m     fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   3466\u001b[0m     exclusive_fp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   3467\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:/Users/User/CellGAN/new64'"
          ]
        }
      ],
      "source": [
        "\n",
        "from PIL import Image\n",
        "from torchvision import transforms\n",
        "\n",
        "# Load the image you want to classify\n",
        "img = Image.open('C:/Users/User/CellGAN/new64')\n",
        "\n",
        "# Define the same transformation as during training\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Apply the transformation to the image\n",
        "img_tensor = transform(img).unsqueeze(0).to(device)  # Add batch dimension and move to device\n",
        "\n",
        "# Get predictions\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    outputs = model(img_tensor)\n",
        "    _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "# Get the predicted class label\n",
        "class_labels = [\n",
        "    \"High squamous intra-epithelial lesion\",\n",
        "    \"Low squamous intra-epithelial lesion\",\n",
        "    \"Negative for Intraepithelial malignancy\",\n",
        "    \"Squamous cell carcinoma\"\n",
        "]\n",
        "\n",
        "print(f\"Predicted class: {class_labels[predicted.item()]}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-5G_D_xt2Qm-"
      },
      "outputs": [],
      "source": []
    }
  ],
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