GAN_Fractured.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "ECS 289L SQ23 - Abdel Ibrahim"
      ],
      "metadata": {
        "id": "MJWri5Up9MrZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNS66-Jj3kZP",
        "outputId": "862394fd-a48e-4aff-fb3b-fe6999de10fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading fracture-multi-region-x-ray-data, 504652653 bytes compressed\n",
            "[==================================================] 504652653 bytes downloaded\n",
            "Downloaded and uncompressed: fracture-multi-region-x-ray-data\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'fracture-multi-region-x-ray-data:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F4854718%2F8201044%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240608%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240608T020458Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow --upgrade\n",
        "!pip install keras --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1HwrG2Vo8mX",
        "outputId": "a0ad7df1-0fe5-40cf-dd58-6f06c6bdc502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.16.1)\n",
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            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import keras\n",
        "\n",
        "dataset = keras.utils.image_dataset_from_directory('/kaggle/input/fracture-multi-region-x-ray-data/Bone_Fracture_Binary_Classification/Bone_Fracture_Binary_Classification/train/fractured', label_mode=None, image_size=(64, 64), batch_size = 32)\n",
        "dataset = dataset.map(lambda x: x / 255.0)\n"
      ],
      "metadata": {
        "id": "83ohPjLU3rCm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c107273-d40a-4e6b-cefe-f75592fc901c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4606 files.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from google.colab import files\n",
        "\n",
        "for x in dataset:\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow((x.numpy() * 255).astype(\"int32\")[0])\n",
        "    break\n"
      ],
      "metadata": {
        "id": "08mVbsWm33Mq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "948d1f43-1bc2-4682-d769-ec13c3822df6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://keras.io/examples/generative/dcgan_overriding_train_step/"
      ],
      "metadata": {
        "id": "ZDGiLHdDAWgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "import tensorflow as tf\n",
        "\n",
        "from keras import layers\n",
        "from keras import ops\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import gdown\n",
        "from zipfile import ZipFile\n",
        "\n",
        "discriminator = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=(64, 64, 3)),\n",
        "        layers.Conv2D(64, kernel_size=4, strides=2, padding=\"same\"),\n",
        "        layers.LeakyReLU(negative_slope=0.2),\n",
        "        layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
        "        layers.LeakyReLU(negative_slope=0.2),\n",
        "        layers.Conv2D(128, kernel_size=4, strides=2, padding=\"same\"),\n",
        "        layers.LeakyReLU(negative_slope=0.2),\n",
        "        layers.Flatten(),\n",
        "        layers.Dropout(0.2),\n",
        "        layers.Dense(1, activation=\"sigmoid\"),\n",
        "    ],\n",
        "    name=\"discriminator\",\n",
        ")\n",
        "discriminator.summary()"
      ],
      "metadata": {
        "id": "fkmEpBwIMj2t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "b2aefb10-1e2f-470a-a53a-96a431ef22aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"discriminator\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"discriminator\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │           \u001b[38;5;34m3,136\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu (\u001b[38;5;33mLeakyReLU\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m131,200\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_1 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │         \u001b[38;5;34m262,272\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_2 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │           \u001b[38;5;34m8,193\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,136</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,193</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m404,801\u001b[0m (1.54 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,801</span> (1.54 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m404,801\u001b[0m (1.54 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,801</span> (1.54 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "latent_dim = 128\n",
        "\n",
        "generator = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=(latent_dim,)),\n",
        "        layers.Dense(8 * 8 * 128),\n",
        "        layers.Reshape((8, 8, 128)),\n",
        "        layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding=\"same\"),\n",
        "        layers.LeakyReLU(negative_slope=0.2),\n",
        "        layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding=\"same\"),\n",
        "        layers.LeakyReLU(negative_slope=0.2),\n",
        "        layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding=\"same\"),\n",
        "        layers.LeakyReLU(negative_slope=0.2),\n",
        "        layers.Conv2D(3, kernel_size=5, padding=\"same\", activation=\"sigmoid\"),\n",
        "    ],\n",
        "    name=\"generator\",\n",
        ")\n",
        "generator.summary()"
      ],
      "metadata": {
        "id": "kon93UGaQFVS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "20df84c1-995b-44b2-9607-8bc98f052cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"generator\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"generator\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m)                │       \u001b[38;5;34m1,056,768\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ reshape (\u001b[38;5;33mReshape\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m262,272\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_3 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_1 (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m524,544\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_4 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_2 (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,097,664\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_5 (\u001b[38;5;33mLeakyReLU\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m3\u001b[0m)           │          \u001b[38;5;34m38,403\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8192</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056,768</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">524,544</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,097,664</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ leaky_re_lu_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)           │          <span style=\"color: #00af00; text-decoration-color: #00af00\">38,403</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,979,651\u001b[0m (15.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,979,651</span> (15.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,979,651\u001b[0m (15.18 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,979,651</span> (15.18 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class GAN(keras.Model):\n",
        "    def __init__(self, discriminator, generator, latent_dim):\n",
        "        super().__init__()\n",
        "        self.discriminator = discriminator\n",
        "        self.generator = generator\n",
        "        self.latent_dim = latent_dim\n",
        "        self.seed_generator = keras.random.SeedGenerator(1337)\n",
        "\n",
        "    def compile(self, d_optimizer, g_optimizer, loss_fn):\n",
        "        super().compile()\n",
        "        self.d_optimizer = d_optimizer\n",
        "        self.g_optimizer = g_optimizer\n",
        "        self.loss_fn = loss_fn\n",
        "        self.d_loss_metric = keras.metrics.Mean(name=\"d_loss\")\n",
        "        self.g_loss_metric = keras.metrics.Mean(name=\"g_loss\")\n",
        "\n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [self.d_loss_metric, self.g_loss_metric]\n",
        "\n",
        "    def train_step(self, real_images):\n",
        "        # Sample random points in the latent space\n",
        "        batch_size = ops.shape(real_images)[0]\n",
        "        random_latent_vectors = keras.random.normal(\n",
        "            shape=(batch_size, self.latent_dim), seed=self.seed_generator\n",
        "        )\n",
        "\n",
        "        # Decode them to fake images\n",
        "        generated_images = self.generator(random_latent_vectors)\n",
        "\n",
        "        # Combine them with real images\n",
        "        combined_images = ops.concatenate([generated_images, real_images], axis=0)\n",
        "\n",
        "        # Assemble labels discriminating real from fake images\n",
        "        labels = ops.concatenate(\n",
        "            [ops.ones((batch_size, 1)), ops.zeros((batch_size, 1))], axis=0\n",
        "        )\n",
        "        # Add random noise to the labels - important trick!\n",
        "        labels += 0.05 * tf.random.uniform(tf.shape(labels))\n",
        "\n",
        "        # Train the discriminator\n",
        "        with tf.GradientTape() as tape:\n",
        "            predictions = self.discriminator(combined_images)\n",
        "            d_loss = self.loss_fn(labels, predictions)\n",
        "        grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\n",
        "        self.d_optimizer.apply_gradients(\n",
        "            zip(grads, self.discriminator.trainable_weights)\n",
        "        )\n",
        "\n",
        "        # Sample random points in the latent space\n",
        "        random_latent_vectors = keras.random.normal(\n",
        "            shape=(batch_size, self.latent_dim), seed=self.seed_generator\n",
        "        )\n",
        "\n",
        "        # Assemble labels that say \"all real images\"\n",
        "        misleading_labels = ops.zeros((batch_size, 1))\n",
        "\n",
        "        # Train the generator (note that we should *not* update the weights\n",
        "        # of the discriminator)!\n",
        "        with tf.GradientTape() as tape:\n",
        "            predictions = self.discriminator(self.generator(random_latent_vectors))\n",
        "            g_loss = self.loss_fn(misleading_labels, predictions)\n",
        "        grads = tape.gradient(g_loss, self.generator.trainable_weights)\n",
        "        self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))\n",
        "\n",
        "        # Update metrics\n",
        "        self.d_loss_metric.update_state(d_loss)\n",
        "        self.g_loss_metric.update_state(g_loss)\n",
        "        return {\n",
        "            \"d_loss\": self.d_loss_metric.result(),\n",
        "            \"g_loss\": self.g_loss_metric.result(),\n",
        "        }"
      ],
      "metadata": {
        "id": "ipiIkMO3QIm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GANMonitor(keras.callbacks.Callback):\n",
        "    def __init__(self, num_img=5000, latent_dim=128):\n",
        "        self.num_img = num_img\n",
        "        self.latent_dim = latent_dim\n",
        "        self.seed_generator = keras.random.SeedGenerator(42)\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        print(epoch)\n",
        "        if epoch + 1 == 100:\n",
        "            random_latent_vectors = keras.random.normal(\n",
        "                shape=(self.num_img, self.latent_dim), seed=self.seed_generator\n",
        "            )\n",
        "            generated_images = self.model.generator(random_latent_vectors)\n",
        "            generated_images *= 255\n",
        "            generated_images.numpy()\n",
        "            for i in range(self.num_img):\n",
        "                img = keras.utils.array_to_img(generated_images[i])\n",
        "                img_path = f\"generated_images/generated_img_{epoch + 1:03d}_{i}.png\"\n",
        "                img.save(img_path)\n",
        "                files.download(img_path)"
      ],
      "metadata": {
        "id": "vVYpfqzdQLCe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 100  # In practice, use ~100 epochs\n",
        "\n",
        "gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)\n",
        "gan.compile(\n",
        "    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
        "    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\n",
        "    loss_fn=keras.losses.BinaryCrossentropy(),\n",
        ")\n",
        "\n",
        "gan.fit(\n",
        "    dataset, epochs=epochs, callbacks=[GANMonitor(num_img=5000, latent_dim=latent_dim)]\n",
        ")"
      ],
      "metadata": {
        "id": "5VRde712QLYa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1a0da46b-1a40-4041-a850-e08d92e5724e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 407ms/step - d_loss: 0.4418 - g_loss: 1.7273\n",
            "Epoch 2/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 267ms/step - d_loss: 0.2605 - g_loss: 3.3748\n",
            "Epoch 3/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4865 - g_loss: 2.2429\n",
            "Epoch 4/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5148 - g_loss: 2.7613\n",
            "Epoch 5/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4368 - g_loss: 1.6631\n",
            "Epoch 6/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6286 - g_loss: 1.8023\n",
            "Epoch 7/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6088 - g_loss: 2.7421\n",
            "Epoch 8/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.7272 - g_loss: 1.1666\n",
            "Epoch 9/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6017 - g_loss: 1.0131\n",
            "Epoch 10/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5736 - g_loss: 1.3012\n",
            "Epoch 11/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6038 - g_loss: 1.3581\n",
            "Epoch 12/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.6661 - g_loss: 1.0168\n",
            "Epoch 13/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6212 - g_loss: 0.9096\n",
            "Epoch 14/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6033 - g_loss: 1.2275\n",
            "Epoch 15/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6957 - g_loss: 0.9207\n",
            "Epoch 16/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6485 - g_loss: 1.0549\n",
            "Epoch 17/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 267ms/step - d_loss: 0.6075 - g_loss: 0.9541\n",
            "Epoch 18/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.7053 - g_loss: 0.8967\n",
            "Epoch 19/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6544 - g_loss: 0.8712\n",
            "Epoch 20/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 267ms/step - d_loss: 0.6419 - g_loss: 0.9546\n",
            "Epoch 21/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6683 - g_loss: 0.9556\n",
            "Epoch 22/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6409 - g_loss: 1.1128\n",
            "Epoch 23/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6159 - g_loss: 0.9662\n",
            "Epoch 24/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.7094 - g_loss: 0.8712\n",
            "Epoch 25/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6932 - g_loss: 0.7958\n",
            "Epoch 26/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.5680 - g_loss: 1.3126\n",
            "Epoch 27/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6482 - g_loss: 0.9984\n",
            "Epoch 28/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 267ms/step - d_loss: 0.2710 - g_loss: 3.5532\n",
            "Epoch 29/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.7173 - g_loss: 1.4864\n",
            "Epoch 30/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.7035 - g_loss: 0.8458\n",
            "Epoch 31/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.5860 - g_loss: 0.8906\n",
            "Epoch 32/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6175 - g_loss: 0.9216\n",
            "Epoch 33/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5404 - g_loss: 0.9459\n",
            "Epoch 34/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.7022 - g_loss: 0.9328\n",
            "Epoch 35/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6134 - g_loss: 1.0007\n",
            "Epoch 36/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5791 - g_loss: 1.3660\n",
            "Epoch 37/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6916 - g_loss: 1.0432\n",
            "Epoch 38/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5427 - g_loss: 1.2862\n",
            "Epoch 39/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6482 - g_loss: 0.9990\n",
            "Epoch 40/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6134 - g_loss: 0.9565\n",
            "Epoch 41/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6545 - g_loss: 1.0512\n",
            "Epoch 42/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6142 - g_loss: 1.2226\n",
            "Epoch 43/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.5842 - g_loss: 0.9832\n",
            "Epoch 44/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6186 - g_loss: 1.0794\n",
            "Epoch 45/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.6254 - g_loss: 1.5266\n",
            "Epoch 46/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.6293 - g_loss: 0.9378\n",
            "Epoch 47/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5130 - g_loss: 1.1185\n",
            "Epoch 48/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6455 - g_loss: 1.2483\n",
            "Epoch 49/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.5159 - g_loss: 1.9739\n",
            "Epoch 50/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6218 - g_loss: 1.5599\n",
            "Epoch 51/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.6740 - g_loss: 1.2446\n",
            "Epoch 52/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5464 - g_loss: 1.0278\n",
            "Epoch 53/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.5529 - g_loss: 1.1146\n",
            "Epoch 54/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5734 - g_loss: 1.2192\n",
            "Epoch 55/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6679 - g_loss: 1.2650\n",
            "Epoch 56/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.4373 - g_loss: 1.3330\n",
            "Epoch 57/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5001 - g_loss: 1.3544\n",
            "Epoch 58/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.6684 - g_loss: 1.6397\n",
            "Epoch 59/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5406 - g_loss: 1.2971\n",
            "Epoch 60/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5476 - g_loss: 1.1894\n",
            "Epoch 61/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5039 - g_loss: 1.2594\n",
            "Epoch 62/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4886 - g_loss: 1.6235\n",
            "Epoch 63/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.5631 - g_loss: 1.3500\n",
            "Epoch 64/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5980 - g_loss: 1.2703\n",
            "Epoch 65/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.4682 - g_loss: 1.3889\n",
            "Epoch 66/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4287 - g_loss: 1.4282\n",
            "Epoch 67/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.2682 - g_loss: 3.0185\n",
            "Epoch 68/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.4699 - g_loss: 1.5599\n",
            "Epoch 69/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.3990 - g_loss: 2.4747\n",
            "Epoch 70/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.5840 - g_loss: 1.5598\n",
            "Epoch 71/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.5574 - g_loss: 1.3799\n",
            "Epoch 72/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4934 - g_loss: 1.6115\n",
            "Epoch 73/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4610 - g_loss: 1.3965\n",
            "Epoch 74/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.4212 - g_loss: 1.6459\n",
            "Epoch 75/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4324 - g_loss: 1.4648\n",
            "Epoch 76/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4678 - g_loss: 1.5142\n",
            "Epoch 77/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4963 - g_loss: 1.4935\n",
            "Epoch 78/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.4159 - g_loss: 1.6978\n",
            "Epoch 79/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4305 - g_loss: 1.9615\n",
            "Epoch 80/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4238 - g_loss: 1.8522\n",
            "Epoch 81/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4581 - g_loss: 1.4177\n",
            "Epoch 82/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.5041 - g_loss: 1.3560\n",
            "Epoch 83/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 267ms/step - d_loss: 0.5158 - g_loss: 1.4954\n",
            "Epoch 84/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.2579 - g_loss: 2.9033\n",
            "Epoch 85/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.5087 - g_loss: 1.4687\n",
            "Epoch 86/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4233 - g_loss: 1.6938\n",
            "Epoch 87/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4834 - g_loss: 1.4326\n",
            "Epoch 88/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.5001 - g_loss: 1.3694\n",
            "Epoch 89/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.3510 - g_loss: 2.1625\n",
            "Epoch 90/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4289 - g_loss: 1.7079\n",
            "Epoch 91/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 267ms/step - d_loss: 0.4547 - g_loss: 1.5745\n",
            "Epoch 92/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 268ms/step - d_loss: 0.4207 - g_loss: 1.5349\n",
            "Epoch 93/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.4040 - g_loss: 1.5630\n",
            "Epoch 94/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.4365 - g_loss: 1.4650\n",
            "Epoch 95/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.4658 - g_loss: 1.4799\n",
            "Epoch 96/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 269ms/step - d_loss: 0.4133 - g_loss: 1.5766\n",
            "Epoch 97/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 267ms/step - d_loss: 0.4107 - g_loss: 1.5799\n",
            "Epoch 98/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 269ms/step - d_loss: 0.3032 - g_loss: 2.2637\n",
            "Epoch 99/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 268ms/step - d_loss: 0.3930 - g_loss: 1.5719\n",
            "Epoch 100/100\n",
            "\u001b[1m144/144\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - d_loss: 0.3453 - g_loss: 1.7876"
          ]
        },
        {
          "output_type": "error",
          "ename": "ResourceExhaustedError",
          "evalue": "Exception encountered when calling Conv2DTranspose.call().\n\n\u001b[1m{{function_node __wrapped__Conv2DBackpropInput_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[5000,64,64,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Conv2DBackpropInput]\u001b[0m\n\nArguments received by Conv2DTranspose.call():\n  • inputs=tf.Tensor(shape=(5000, 32, 32, 256), dtype=float32)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-9bae5a18fd1a>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m gan.fit(\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mGANMonitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_img\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatent_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlatent_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-8a774b6f0e68>\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m     10\u001b[0m                 \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatent_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed_generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             )\n\u001b[0;32m---> 12\u001b[0;31m             \u001b[0mgenerated_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_latent_vectors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m             \u001b[0mgenerated_images\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0;36m255\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0mgenerated_images\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m: Exception encountered when calling Conv2DTranspose.call().\n\n\u001b[1m{{function_node __wrapped__Conv2DBackpropInput_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[5000,64,64,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Conv2DBackpropInput]\u001b[0m\n\nArguments received by Conv2DTranspose.call():\n  • inputs=tf.Tensor(shape=(5000, 32, 32, 256), dtype=float32)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "drive_path = '/content/drive/MyDrive/GAN_models'\n",
        "os.makedirs(drive_path, exist_ok=True)\n",
        "\n",
        "generator_path = os.path.join(drive_path, 'generator_model.h5')\n",
        "discriminator_path = os.path.join(drive_path, 'discriminator_model.h5')\n",
        "gan.generator.save(generator_path)\n",
        "gan.discriminator.save(discriminator_path)\n",
        "\n",
        "print(f\"Models saved to {drive_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idK0wKhXNtF2",
        "outputId": "b07d9e6d-df58-4dd5-95eb-f2b6562a5524"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models saved to /content/drive/MyDrive/GAN_models\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install tensorflow --upgrade\n",
        "# !pip install keras --upgrade\n",
        "import os\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from google.colab import drive\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import string\n",
        "\n",
        "# Mount Google Drive\n",
        "# drive.mount('/content/drive')\n",
        "\n",
        "# Define the path where the models are saved in Google Drive\n",
        "drive_path = '/content/drive/MyDrive/GAN_models'\n",
        "generator_path = os.path.join(drive_path, 'generator_model.h5')\n",
        "\n",
        "# Load the generator model\n",
        "generator = keras.models.load_model(generator_path)\n",
        "\n",
        "# Generate 50 images\n",
        "latent_dim = 128  # Ensure this matches the latent_dim used during training\n",
        "num_img_to_generate = 500\n",
        "\n",
        "for j in range(20):\n",
        "    seed_generator = keras.random.SeedGenerator(42)\n",
        "\n",
        "    random_latent_vectors = keras.random.normal(\n",
        "        shape=(num_img_to_generate, latent_dim), seed=seed_generator\n",
        "    )\n",
        "    generated_images = generator(random_latent_vectors)\n",
        "    generated_images *= 255\n",
        "    generated_images = generated_images.numpy()\n",
        "\n",
        "    # Create a directory to save generated images\n",
        "    generated_images_path = os.path.join(drive_path, 'generated_images')\n",
        "    os.makedirs(generated_images_path, exist_ok=True)\n",
        "\n",
        "    def generate_random_string(length=10):\n",
        "        characters = string.ascii_letters + string.digits\n",
        "        return ''.join(random.choice(characters) for i in range(length))\n",
        "\n",
        "    # Save the generated images and display them\n",
        "\n",
        "    for i in range(num_img_to_generate):\n",
        "        random_string = generate_random_string()\n",
        "        img = keras.utils.array_to_img(generated_images[i])\n",
        "        img_path = os.path.join(generated_images_path, f\"fractured-{random_string}.png\")\n",
        "        img.save(img_path)\n",
        "\n",
        "\n",
        "\n",
        "print(f\"Generated images saved to {generated_images_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1usPXWPIVCE0",
        "outputId": "8c385d05-f202-4428-d283-9bc379696482"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated images saved to /content/drive/MyDrive/GAN_models/generated_images\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xi8VHxFLN8iW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}