FashionGAN.ipynb

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            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "73557893f11d4f9db938c2bbbbbabde8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
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            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
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            "border": null,
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            "grid_gap": null,
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            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
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            "order": null,
            "overflow": null,
            "overflow_x": null,
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            "padding": null,
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          }
        },
        "410133f36b7c42fe9e99c6c23d4ff1d5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow  tensorflow-datasets ipywidgets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0yAJtnzMGvT",
        "outputId": "fac816a2-9a4f-4244-fd2d-a2d3fb52dbad"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.5.1)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.1.3)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.21)\n",
            "Installing collected packages: jedi\n",
            "Successfully installed jedi-0.19.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "for gpu in gpus:\n",
        "  tf.config.experimental.set_memory_growth(gpu, True)"
      ],
      "metadata": {
        "id": "LNwP5hpPMRMK"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-Y7Wn8dMqyq",
        "outputId": "db6db679-75ce-4a32-ddff-effcba94186d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "ds = tfds.load('fashion_mnist', split='train')"
      ],
      "metadata": {
        "id": "IG04Oh83elNl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
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        },
        "outputId": "c7b045fa-8b45-465c-f137-7507ea1b2637"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0225398c2676412f8b82c022aea122df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd2b78fdc83144aebca017b0c4ebaf92"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf8429b4027f49529b4448bff8a11081"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b63e84e832d4880bb6da42217c5e19c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/60000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78766c81ee804b95b74929845934e67d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteAX794L/fashion_mnist-train.tfrecord*...:   0…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ebc8d00e4a94ab8bb3d02201ec15386"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40ba84fdac3a497191cfd6d9cc2b0e77"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteAX794L/fashion_mnist-test.tfrecord*...:   0%…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a098de274ba408a90df6aa61af20129"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset fashion_mnist downloaded and prepared to /root/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Viz data and build dataset"
      ],
      "metadata": {
        "id": "Y6JomaeucCYG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# set up connection aka iterator\n",
        "dataiterator = ds.as_numpy_iterator()\n",
        "# getting data out of pipeline\n",
        "dataiterator.next()"
      ],
      "metadata": {
        "id": "lDh7Jcc4b_f_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dcaae0f-947c-47d6-ff9e-9367d1549fe8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'label': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig , ax = plt.subplots(ncols=4, figsize=(20,20))\n",
        "for idx in range(4):\n",
        "  #grab an image and label\n",
        "  sample = dataiterator.next()\n",
        "  # plot the image using a specific subplot\n",
        "  ax[idx].imshow(np.squeeze(sample['image']))\n",
        "  # appending the image label as the plot title\n",
        "  ax[idx].title.set_text(sample['label'])"
      ],
      "metadata": {
        "id": "xKiY5SDBNSC8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "bef62afe-66e2-4685-bc90-cd7df560add3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scale and return images only\n",
        "def scale_images(data):\n",
        "  image = data['image']\n",
        "  return image / 255"
      ],
      "metadata": {
        "id": "8_ff9GKI_zQV"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = tfds.load('fashion_mnist', split='train')\n",
        "# running the dataset through the scale_images preprocessing step\n",
        "ds = ds.map(scale_images)\n",
        "# cache the dataset for that batch\n",
        "ds = ds.cache()\n",
        "# shuffle it up\n",
        "ds = ds.shuffle(60000)\n",
        "# batch into 128 images per sample\n",
        "ds = ds.batch(128)\n",
        "# reduce the liklihood of bottlencking\n",
        "ds = ds.prefetch(64)"
      ],
      "metadata": {
        "id": "nb-oT797AP50"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds.as_numpy_iterator().next().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFTMBAlOBj89",
        "outputId": "b3767ac4-4b1e-40ac-abbe-b1b6758e0348"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Build the Neural Network"
      ],
      "metadata": {
        "id": "Zl1U7Fyw_u2E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Import modelling components"
      ],
      "metadata": {
        "id": "TTD46kmQC3Zh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, Dense, Flatten, Reshape, LeakyReLU, Dropout, UpSampling2D"
      ],
      "metadata": {
        "id": "939t8hm882JT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Build generator"
      ],
      "metadata": {
        "id": "Uczbk0OTHdyh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.src.metrics.confusion_metrics import activations\n",
        "def build_generator():\n",
        "  model = Sequential()\n",
        "\n",
        "  # takes in random values and reshapes it to 7x7x128\n",
        "  #beginnings of a renerated image\n",
        "  model.add(Dense(7*7*128, input_dim=128))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "  model.add(Reshape((7,7,128)))\n",
        "\n",
        "  # upsampling block 1\n",
        "  model.add(UpSampling2D())\n",
        "  model.add(Conv2D(128, 5, padding='same'))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "\n",
        "  # upsampling block 2\n",
        "  model.add(UpSampling2D())\n",
        "  model.add(Conv2D(128, 5, padding='same'))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "\n",
        "  # convolutional block 1\n",
        "  model.add(Conv2D(128, 4, padding='same'))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "\n",
        "  # convolutional block 2\n",
        "  model.add(Conv2D(128, 4, padding='same'))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "\n",
        "  # conv layer to get to one channel\n",
        "  model.add(Conv2D(1, 4, padding='same', activation='sigmoid'))\n",
        "\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "fa0DdmXFHb-J"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = build_generator()\n",
        "generator.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlH8OMAkDnwf",
        "outputId": "2535bf8c-ef53-4dec-a175-df0d9d1bb180"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 6272)              809088    \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 6272)              0         \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 7, 7, 128)         0         \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2  (None, 14, 14, 128)       0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 14, 14, 128)       409728    \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 14, 14, 128)       0         \n",
            "                                                                 \n",
            " up_sampling2d_1 (UpSamplin  (None, 28, 28, 128)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 28, 28, 128)       409728    \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 28, 28, 128)       262272    \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 28, 28, 128)       262272    \n",
            "                                                                 \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 28, 28, 128)       0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 28, 28, 1)         2049      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2155137 (8.22 MB)\n",
            "Trainable params: 2155137 (8.22 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = generator.predict(np.random.randn(4, 128,  1))\n",
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CcU8QDiLOPgD",
        "outputId": "0a29fbe8-2fc5-4f96-e023-dd77cb85e8c3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 7s 7s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "        [[0.49439153],\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Grk2bXIP7yh",
        "outputId": "eef64312-d9da-427b-ed15-37be92522c8d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate new fashion\n",
        "img = generator.predict(np.random.randn(4, 128, 1))\n",
        "\n",
        "fig, ax = plt.subplots(ncols=4, figsize=(20,20))\n",
        "\n",
        "for idx, img in enumerate(img):\n",
        "  # plot the image using a specific subplot\n",
        "  ax[idx].imshow(np.squeeze(img))\n",
        "  # appending the image label as the plot title\n",
        "  ax[idx].title.set_text(idx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "vEeGmckyO9ZZ",
        "outputId": "85d4cd41-afa5-450f-c624-c5fec7c5e4b5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Build discriminator"
      ],
      "metadata": {
        "id": "I2sYEKyhJ1ul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_discriminator():\n",
        "  model = Sequential()\n",
        "\n",
        "  # first conv block\n",
        "  model.add(Conv2D(32, 5, input_shape=(28, 28, 1)))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "  model.add(Dropout(0.4))\n",
        "\n",
        "  # second conv block\n",
        "  model.add(Conv2D(64, 5))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "  model.add(Dropout(0.4))\n",
        "\n",
        "  # third conv block\n",
        "  model.add(Conv2D(128, 5))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "  model.add(Dropout(0.4))\n",
        "\n",
        "  # fourth conv block\n",
        "  model.add(Conv2D(256, 5))\n",
        "  model.add(LeakyReLU(0.2))\n",
        "  model.add(Dropout(0.4))\n",
        "\n",
        "  # flatten then pass to dense layer\n",
        "  model.add(Flatten())\n",
        "  model.add(Dropout(0.4))\n",
        "  model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "FrpBI43uIaPU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator = build_discriminator()\n",
        "discriminator.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXfSJ-qxIJ1y",
        "outputId": "06e86cff-c6bb-4e36-c627-8e98137a67dc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_5 (Conv2D)           (None, 24, 24, 32)        832       \n",
            "                                                                 \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 24, 24, 32)        0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24, 24, 32)        0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 20, 20, 64)        51264     \n",
            "                                                                 \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 20, 20, 64)        0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 20, 20, 64)        0         \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 16, 16, 128)       204928    \n",
            "                                                                 \n",
            " leaky_re_lu_7 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 12, 12, 256)       819456    \n",
            "                                                                 \n",
            " leaky_re_lu_8 (LeakyReLU)   (None, 12, 12, 256)       0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 12, 12, 256)       0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 36864)             0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 36864)             0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 36865     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1113345 (4.25 MB)\n",
            "Trainable params: 1113345 (4.25 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = generator.predict(np.random.randn(4, 128, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joL-9_ZTZJXs",
        "outputId": "c1e904cd-4956-4602-df55-983130945eac"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 38ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUImqYBsYvUV",
        "outputId": "39ff8e7f-1818-4d86-e16c-894bdf827bc2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator.predict(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBApQJDjVZ-I",
        "outputId": "025680dd-0e0f-4163-8daa-935ad1f05d52"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 314ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.5035116 ],\n",
              "       [0.50348425],\n",
              "       [0.5034957 ],\n",
              "       [0.5034362 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = img[0]\n",
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Riyn2iibYbHL",
        "outputId": "08fbd933-2b43-4a75-c4a5-dd6f2c8a05c6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator.predict(np.expand_dims(img, 0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cRVV9TZZd90",
        "outputId": "1a34241c-5413-49dc-b193-faa3d4d43636"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 206ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.50351167]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Construct training loop"
      ],
      "metadata": {
        "id": "MX5js2rPZ3S_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Setup losses and optimizers"
      ],
      "metadata": {
        "id": "XUqsnGiWis98"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import BinaryCrossentropy"
      ],
      "metadata": {
        "id": "XuiO8wJDZk2L"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g_opt = Adam(learning_rate=0.0001)\n",
        "d_opt = Adam(learning_rate=0.00001)\n",
        "g_loss = BinaryCrossentropy()\n",
        "d_loss = BinaryCrossentropy()"
      ],
      "metadata": {
        "id": "Vy5_b3zjlXic"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 build subclassed model"
      ],
      "metadata": {
        "id": "7Ku6OpXhlDc3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the base model calss to subclass our training step\n",
        "from tensorflow.keras.models import Model"
      ],
      "metadata": {
        "id": "M9UCzb96lKEa"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.normal((6, 128, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nj9clbYypBL2",
        "outputId": "70e690dd-83f2-4e25-f462-0a8eba4b8ba6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(6, 128, 1), dtype=float32, numpy=\n",
              "array([[[ 1.13301249e-02],\n",
              "        [-1.68970674e-01],\n",
              "        [-1.24835290e-01],\n",
              "        [-4.71939951e-01],\n",
              "        [-4.48197633e-01],\n",
              "        [ 7.41898239e-01],\n",
              "        [ 2.60211349e-01],\n",
              "        [-4.30939436e-01],\n",
              "        [ 2.57254422e-01],\n",
              "        [ 8.06672499e-02],\n",
              "        [ 2.32741147e-01],\n",
              "        [-3.36461455e-01],\n",
              "        [-1.20877874e+00],\n",
              "        [-2.29734614e-01],\n",
              "        [ 1.10835266e+00],\n",
              "        [-1.53839207e+00],\n",
              "        [-1.54536915e+00],\n",
              "        [ 6.54097259e-01],\n",
              "        [-1.35827303e+00],\n",
              "        [-3.08514237e-01],\n",
              "        [ 1.34547925e+00],\n",
              "        [-3.59173119e-01],\n",
              "        [ 1.57459152e+00],\n",
              "        [-1.79827392e-01],\n",
              "        [ 9.36506808e-01],\n",
              "        [-9.56138730e-01],\n",
              "        [ 1.92365396e+00],\n",
              "        [-9.14746225e-01],\n",
              "        [ 3.25581312e-01],\n",
              "        [-1.67671204e+00],\n",
              "        [ 1.52174294e+00],\n",
              "        [ 1.87217760e+00],\n",
              "        [ 9.62453842e-01],\n",
              "        [ 6.75904572e-01],\n",
              "        [ 2.65684485e+00],\n",
              "        [ 1.69890451e+00],\n",
              "        [-3.44388425e-01],\n",
              "        [-8.55774045e-01],\n",
              "        [-9.04045463e-01],\n",
              "        [ 4.55171019e-01],\n",
              "        [ 4.40067440e-01],\n",
              "        [ 1.44100219e-01],\n",
              "        [-1.24289608e+00],\n",
              "        [ 3.79722178e-01],\n",
              "        [ 1.37080443e+00],\n",
              "        [-5.05171478e-01],\n",
              "        [ 7.05295503e-01],\n",
              "        [-1.36603355e+00],\n",
              "        [-3.40876251e-01],\n",
              "        [-7.56341934e-01],\n",
              "        [-3.57232749e-01],\n",
              "        [-7.74377048e-01],\n",
              "        [-1.60447156e+00],\n",
              "        [-1.70980752e+00],\n",
              "        [-1.77755535e+00],\n",
              "        [ 3.38150375e-02],\n",
              "        [ 1.32625198e+00],\n",
              "        [-1.44177639e+00],\n",
              "        [-3.75312984e-01],\n",
              "        [-6.16228461e-01],\n",
              "        [-1.37039244e+00],\n",
              "        [ 2.88098961e-01],\n",
              "        [-2.37582162e-01],\n",
              "        [-1.75908005e+00],\n",
              "        [-2.02870750e+00],\n",
              "        [-1.51531887e+00],\n",
              "        [-3.91134173e-01],\n",
              "        [-1.87995434e+00],\n",
              "        [-2.70360142e-01],\n",
              "        [-9.07600403e-01],\n",
              "        [ 1.59974766e+00],\n",
              "        [ 1.37836003e+00],\n",
              "        [-2.20511293e+00],\n",
              "        [ 4.49255824e-01],\n",
              "        [-1.05548859e+00],\n",
              "        [-8.80309492e-02],\n",
              "        [ 1.18522847e+00],\n",
              "        [ 4.69330370e-01],\n",
              "        [-1.51082468e+00],\n",
              "        [ 4.39657271e-01],\n",
              "        [-3.26978356e-01],\n",
              "        [-1.08500290e+00],\n",
              "        [-1.42517269e+00],\n",
              "        [-5.82007289e-01],\n",
              "        [-1.00910425e+00],\n",
              "        [ 1.13482022e+00],\n",
              "        [ 2.96923190e-01],\n",
              "        [-4.30186950e-02],\n",
              "        [-1.57136548e+00],\n",
              "        [-2.17369527e-01],\n",
              "        [ 9.23380435e-01],\n",
              "        [ 2.46789885e+00],\n",
              "        [-2.52156019e-01],\n",
              "        [-5.57724953e-01],\n",
              "        [-5.31093955e-01],\n",
              "        [-1.01604569e+00],\n",
              "        [ 1.81753743e+00],\n",
              "        [ 2.14313030e-01],\n",
              "        [-4.36079860e-01],\n",
              "        [ 4.61994320e-01],\n",
              "        [ 9.54973161e-01],\n",
              "        [-1.53602198e-01],\n",
              "        [ 1.58467996e+00],\n",
              "        [ 7.29525462e-02],\n",
              "        [-5.41874349e-01],\n",
              "        [-5.86809158e-01],\n",
              "        [-1.47300708e+00],\n",
              "        [-2.10586742e-01],\n",
              "        [ 4.88409698e-01],\n",
              "        [-4.42922831e-01],\n",
              "        [-1.82379794e+00],\n",
              "        [-4.50289905e-01],\n",
              "        [ 3.86068106e-01],\n",
              "        [-1.05820203e+00],\n",
              "        [ 4.54629958e-01],\n",
              "        [ 3.07593316e-01],\n",
              "        [ 3.28324080e-01],\n",
              "        [-1.91771686e+00],\n",
              "        [ 1.47726822e+00],\n",
              "        [ 4.01493609e-01],\n",
              "        [-7.72785842e-01],\n",
              "        [-8.22329342e-01],\n",
              "        [-4.85882729e-01],\n",
              "        [-1.12688124e+00],\n",
              "        [ 6.33783638e-01],\n",
              "        [ 3.14031810e-01],\n",
              "        [-7.13438451e-01],\n",
              "        [-4.94911879e-01]],\n",
              "\n",
              "       [[ 2.02176213e+00],\n",
              "        [ 6.63247466e-01],\n",
              "        [-3.13423723e-01],\n",
              "        [-1.67239487e+00],\n",
              "        [-1.08886743e+00],\n",
              "        [ 1.02229631e+00],\n",
              "        [ 3.25144768e-01],\n",
              "        [-6.46006107e-01],\n",
              "        [ 5.66739917e-01],\n",
              "        [ 1.23246610e-01],\n",
              "        [-9.29564953e-01],\n",
              "        [ 1.05522990e+00],\n",
              "        [ 1.07135153e+00],\n",
              "        [ 8.04236308e-02],\n",
              "        [ 2.23226711e-01],\n",
              "        [-8.50941062e-01],\n",
              "        [-1.14631498e+00],\n",
              "        [-6.85897887e-01],\n",
              "        [-1.05114388e+00],\n",
              "        [-3.33597600e-01],\n",
              "        [-3.51171404e-01],\n",
              "        [-3.02170515e-01],\n",
              "        [-1.13948822e+00],\n",
              "        [-9.26921666e-02],\n",
              "        [ 9.16802764e-01],\n",
              "        [ 2.00327903e-01],\n",
              "        [ 1.06764138e+00],\n",
              "        [-1.38719738e+00],\n",
              "        [-3.91651690e-01],\n",
              "        [-3.14126462e-02],\n",
              "        [-1.56435406e+00],\n",
              "        [-2.21986145e-01],\n",
              "        [-1.39510915e-01],\n",
              "        [ 1.33617425e+00],\n",
              "        [ 8.88200700e-01],\n",
              "        [-1.91028640e-01],\n",
              "        [-5.66180229e-01],\n",
              "        [-3.47355962e-01],\n",
              "        [-2.23299921e-01],\n",
              "        [ 9.76415992e-01],\n",
              "        [-1.32382214e+00],\n",
              "        [ 1.32297099e+00],\n",
              "        [ 7.49141872e-01],\n",
              "        [-1.06315456e-01],\n",
              "        [-9.52384710e-01],\n",
              "        [-1.47258624e-01],\n",
              "        [-1.11288130e+00],\n",
              "        [-3.74550596e-02],\n",
              "        [ 2.33948007e-01],\n",
              "        [ 5.10274231e-01],\n",
              "        [-1.93061960e+00],\n",
              "        [ 1.11683917e+00],\n",
              "        [ 5.97149014e-01],\n",
              "        [-1.85212910e+00],\n",
              "        [-2.11715603e+00],\n",
              "        [-8.48837733e-01],\n",
              "        [-7.03965008e-01],\n",
              "        [ 2.28921846e-01],\n",
              "        [ 6.63849890e-01],\n",
              "        [-9.43790793e-01],\n",
              "        [ 1.99363887e+00],\n",
              "        [-5.45484781e-01],\n",
              "        [-6.86064661e-01],\n",
              "        [ 1.42685342e+00],\n",
              "        [-8.81831646e-01],\n",
              "        [ 6.37510642e-02],\n",
              "        [-4.62442428e-01],\n",
              "        [-9.40632522e-01],\n",
              "        [-1.21410131e+00],\n",
              "        [ 2.37680674e-02],\n",
              "        [ 1.24807918e+00],\n",
              "        [-5.03349900e-01],\n",
              "        [-3.99407774e-01],\n",
              "        [ 8.19758102e-02],\n",
              "        [ 1.22309422e+00],\n",
              "        [ 1.40234208e+00],\n",
              "        [-3.37838501e-01],\n",
              "        [ 2.67650747e+00],\n",
              "        [-2.14525795e+00],\n",
              "        [-5.81184804e-01],\n",
              "        [ 9.86131966e-01],\n",
              "        [ 9.30208504e-01],\n",
              "        [ 1.24018264e+00],\n",
              "        [ 4.10702705e-01],\n",
              "        [ 1.56831658e+00],\n",
              "        [-9.17493403e-01],\n",
              "        [-1.06392229e+00],\n",
              "        [-1.33852935e+00],\n",
              "        [ 1.95852256e+00],\n",
              "        [ 5.51839732e-02],\n",
              "        [-9.84697640e-01],\n",
              "        [-2.88129091e+00],\n",
              "        [-1.04656160e+00],\n",
              "        [ 4.60649788e-01],\n",
              "        [ 2.98282146e-01],\n",
              "        [-5.37101388e-01],\n",
              "        [ 4.47447091e-01],\n",
              "        [-1.37624288e+00],\n",
              "        [-9.84410763e-01],\n",
              "        [ 4.09742624e-01],\n",
              "        [ 2.96419084e-01],\n",
              "        [-3.56904745e-01],\n",
              "        [ 6.21244848e-01],\n",
              "        [ 8.50738361e-02],\n",
              "        [ 1.09719463e-01],\n",
              "        [ 1.43864381e+00],\n",
              "        [ 8.54037881e-01],\n",
              "        [ 1.40160680e+00],\n",
              "        [-1.22373021e+00],\n",
              "        [ 2.02906355e-01],\n",
              "        [ 2.78137684e+00],\n",
              "        [ 2.20302773e+00],\n",
              "        [ 3.12355310e-01],\n",
              "        [ 2.13952124e-01],\n",
              "        [-1.74726081e+00],\n",
              "        [-1.36409318e+00],\n",
              "        [ 2.15712070e+00],\n",
              "        [ 9.57781136e-01],\n",
              "        [ 7.47766674e-01],\n",
              "        [ 1.25120270e+00],\n",
              "        [-6.90281451e-01],\n",
              "        [ 2.28172302e+00],\n",
              "        [ 3.61803353e-01],\n",
              "        [ 3.88320953e-01],\n",
              "        [ 8.38063300e-01],\n",
              "        [ 8.46273780e-01],\n",
              "        [-4.39704627e-01],\n",
              "        [-1.36404502e+00]],\n",
              "\n",
              "       [[ 8.82899761e-01],\n",
              "        [ 7.54445791e-01],\n",
              "        [ 1.38641977e+00],\n",
              "        [-2.05312029e-01],\n",
              "        [-1.94874215e+00],\n",
              "        [-1.55612421e+00],\n",
              "        [ 1.65216470e+00],\n",
              "        [ 8.90257061e-01],\n",
              "        [ 5.09486735e-01],\n",
              "        [-7.05348492e-01],\n",
              "        [-2.00210050e-01],\n",
              "        [ 7.79290199e-01],\n",
              "        [ 3.54268074e-01],\n",
              "        [-1.11928940e+00],\n",
              "        [-1.19769847e+00],\n",
              "        [-1.80510795e+00],\n",
              "        [ 1.52204525e+00],\n",
              "        [ 4.43839341e-01],\n",
              "        [ 8.37854981e-01],\n",
              "        [ 1.58734187e-01],\n",
              "        [-9.97627795e-01],\n",
              "        [-8.48326087e-01],\n",
              "        [ 1.20457053e+00],\n",
              "        [ 3.54928702e-01],\n",
              "        [-4.81260300e-01],\n",
              "        [ 7.37733841e-01],\n",
              "        [-1.95574427e+00],\n",
              "        [-1.00896573e+00],\n",
              "        [-3.63780200e-01],\n",
              "        [ 9.00029242e-01],\n",
              "        [-2.02554774e+00],\n",
              "        [-3.69716287e-01],\n",
              "        [ 7.19322503e-01],\n",
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              "        [-7.82753468e-01],\n",
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              "        [ 6.90515995e-01],\n",
              "        [ 3.45204957e-02],\n",
              "        [ 4.66653705e-01],\n",
              "        [-2.02688396e-01],\n",
              "        [ 4.68052715e-01],\n",
              "        [ 9.98410061e-02],\n",
              "        [ 8.47458959e-01],\n",
              "        [ 6.90339878e-02],\n",
              "        [ 6.29015028e-01],\n",
              "        [ 2.65762687e-01],\n",
              "        [ 2.10244834e-01],\n",
              "        [ 2.05197453e-01],\n",
              "        [ 1.06475234e+00]]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FashionGAN(Model):\n",
        "  def __init__(self, generator, discriminator, *args, **kwargs):\n",
        "    super().__init__(*args, **kwargs)\n",
        "\n",
        "    # create attributes for gen and disc\n",
        "    self.generator = generator\n",
        "    self.discriminator = discriminator\n",
        "\n",
        "  def compile(self, g_opt, d_opt, g_loss, d_loss, *args, **kwargs):\n",
        "    # compile with base class\n",
        "    super().compile(*args, **kwargs)\n",
        "\n",
        "    # create attributes for losses and optimizers\n",
        "    self.g_opt = g_opt\n",
        "    self.d_opt = d_opt\n",
        "    self.g_loss = g_loss\n",
        "    self.d_loss = d_loss\n",
        "\n",
        "\n",
        "  def train_step(self, batch):\n",
        "    # get the data\n",
        "    real_images = batch\n",
        "    fake_images = self.generator(tf.random.normal((128,128,1)), training=False)\n",
        "\n",
        "    # train the discriminator\n",
        "    with tf.GradientTape() as d_tape:\n",
        "      # pass the real and fake images to the discriminator model\n",
        "      yhat_real = self.discriminator(real_images, training=True)\n",
        "      yhat_fake = self.discriminator(fake_images, training=True)\n",
        "      yhat_realfake = tf.concat([yhat_real, yhat_fake], axis=0)\n",
        "\n",
        "      # create labels for real and fake imaged\n",
        "      y_realfake = tf.concat([tf.zeros_like(yhat_real), tf.ones_like(yhat_fake)], axis=0)\n",
        "\n",
        "      # add some noise to the true output\n",
        "      noise_real = 0.15*tf.random.uniform(tf.shape(yhat_real))\n",
        "      noise_fake = -0.15*tf.random.uniform(tf.shape(yhat_fake))\n",
        "      y_realfake += tf.concat([noise_real, noise_fake], axis=0)\n",
        "\n",
        "      # calculate loss - BInaryCross\n",
        "      total_d_loss = self.d_loss(yhat_realfake, yhat_realfake)\n",
        "\n",
        "    # apply backprppagation - nn learn\n",
        "    dgrad = d_tape.gradient(total_d_loss, self.discriminator.trainable_variables)\n",
        "    self.d_opt.apply_gradients(zip(dgrad, self.discriminator.trainable_variables))\n",
        "\n",
        "\n",
        "    # train the generator\n",
        "    with tf.GradientTape() as g_tape:\n",
        "\n",
        "      # generate some new images\n",
        "      gen_images = self.generator(tf.random.normal((128, 128, 1)), training=True)\n",
        "\n",
        "      # create the predicted labels\n",
        "      predicted_labels = self.discriminator(gen_images, training=True)\n",
        "\n",
        "      # calculate loss - trick to training to fake out the discriminator\n",
        "      total_g_loss = self.g_loss(tf.zeros_like(predicted_labels), predicted_labels)\n",
        "\n",
        "    # apply backprop\n",
        "    ggrad = g_tape.gradient(total_g_loss, self.generator.trainable_variables)\n",
        "    self.g_opt.apply_gradients(zip(ggrad, self.generator.trainable_variables))\n",
        "\n",
        "    return {\"d_loss\": total_d_loss, \"g_loss\":total_g_loss}"
      ],
      "metadata": {
        "id": "KTfGMvdumTrN"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create instance of subclassed model\n",
        "fashgan = FashionGAN(generator, discriminator)"
      ],
      "metadata": {
        "id": "KLtPrBb4x9gB"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the model\n",
        "fashgan.compile(g_opt, d_opt, g_loss, d_loss)"
      ],
      "metadata": {
        "id": "SKH7U3ODyNhr"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3 Build callback"
      ],
      "metadata": {
        "id": "PKv_ToG3x5mS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from tensorflow.keras.preprocessing.image import array_to_img\n",
        "from tensorflow.keras.callbacks import Callback"
      ],
      "metadata": {
        "id": "SJvlQpDRm4-p"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ModelMonitor(Callback):\n",
        "    def __init__(self, num_img=3, latent_dim=128):\n",
        "        self.num_img = num_img\n",
        "        self.latent_dim = latent_dim\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        random_latent_vectors = tf.random.uniform((self.num_img, self.latent_dim, 1))\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 = array_to_img(generated_images[i])\n",
        "            img.save(os.path.join('/content/images/', f'generated_img_{epoch}_{i}.png'))\n"
      ],
      "metadata": {
        "id": "VL5kR2uDysKd"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "# Define the checkpoint path and filename\n",
        "checkpoint_path = '/content/generator_weights.h5/'\n",
        "\n",
        "# Create a ModelCheckpoint callback to save the weights\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    checkpoint_path,\n",
        "    save_weights_only=True,\n",
        "    save_best_only=True,\n",
        "    monitor='val_loss',  # Choose a suitable metric to monitor\n",
        "    mode='min'  # Choose 'min' or 'max' depending on the monitored metric\n",
        ")"
      ],
      "metadata": {
        "id": "r-1JRMQtIAWq"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.4 Train"
      ],
      "metadata": {
        "id": "b5yn-O_M0M4F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hist = fashgan.fit(ds, epochs=20, callbacks=[ModelMonitor(), checkpoint_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qZLyC-A0Cj3",
        "outputId": "3b76175b-03d9-4061-fdbd-9102c185ae39"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 0.0691 - g_loss: 0.0430"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 88s 165ms/step - d_loss: 0.0689 - g_loss: 0.0429\n",
            "Epoch 2/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 2.6326e-04 - g_loss: 4.8498e-17"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 79s 168ms/step - d_loss: 2.6366e-04 - g_loss: 4.8395e-17\n",
            "Epoch 3/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 7.3138e-05 - g_loss: 1.0324e-21"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 78s 167ms/step - d_loss: 7.3138e-05 - g_loss: 1.0303e-21\n",
            "Epoch 4/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 3.1341e-05 - g_loss: 1.2749e-24"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 78s 167ms/step - d_loss: 3.1326e-05 - g_loss: 1.2724e-24\n",
            "Epoch 5/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 1.5900e-05 - g_loss: 8.7797e-27"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 78s 166ms/step - d_loss: 1.5867e-05 - g_loss: 8.7614e-27\n",
            "Epoch 6/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 9.1009e-06 - g_loss: 1.4843e-28"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 78s 166ms/step - d_loss: 9.0817e-06 - g_loss: 1.4814e-28\n",
            "Epoch 7/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 5.5180e-06 - g_loss: 5.9829e-30"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 77s 165ms/step - d_loss: 5.5069e-06 - g_loss: 5.9710e-30\n",
            "Epoch 8/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 3.6164e-06 - g_loss: 2.7269e-31"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 77s 164ms/step - d_loss: 3.6093e-06 - g_loss: 2.7232e-31\n",
            "Epoch 9/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 2.1735e-06 - g_loss: 3.5643e-32"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 163ms/step - d_loss: 2.1693e-06 - g_loss: 3.5584e-32\n",
            "Epoch 10/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 1.4910e-06 - g_loss: 1.4554e-33"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 1.4879e-06 - g_loss: 1.4535e-33\n",
            "Epoch 11/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 1.1230e-06 - g_loss: 1.7292e-34"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 77s 163ms/step - d_loss: 1.1207e-06 - g_loss: 1.7256e-34\n",
            "Epoch 12/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 7.4876e-07 - g_loss: 1.3217e-35"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 7.4731e-07 - g_loss: 1.3189e-35\n",
            "Epoch 13/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 5.8601e-07 - g_loss: 2.3024e-36"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 5.8477e-07 - g_loss: 2.2979e-36\n",
            "Epoch 14/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 4.3608e-07 - g_loss: 2.5173e-37"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 4.3515e-07 - g_loss: 2.5120e-37\n",
            "Epoch 15/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 2.8270e-07 - g_loss: 2.3868e-38"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 2.8210e-07 - g_loss: 2.3817e-38\n",
            "Epoch 16/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 1.9745e-07 - g_loss: 4.0727e-39"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 1.9704e-07 - g_loss: 4.0640e-39\n",
            "Epoch 17/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 1.6711e-07 - g_loss: 3.3165e-40"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 1.6679e-07 - g_loss: 3.3094e-40\n",
            "Epoch 18/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 1.0786e-07 - g_loss: 0.0000e+00"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 163ms/step - d_loss: 1.0764e-07 - g_loss: 0.0000e+00\n",
            "Epoch 19/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 8.9818e-08 - g_loss: 0.0000e+00"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 9.6456e-08 - g_loss: 0.0000e+00\n",
            "Epoch 20/20\n",
            "469/469 [==============================] - ETA: 0s - d_loss: 6.8297e-08 - g_loss: 0.0000e+00"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r469/469 [==============================] - 76s 162ms/step - d_loss: 6.8153e-08 - g_loss: 0.0000e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Review the performance"
      ],
      "metadata": {
        "id": "DX0EIR6b1Kd4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.suptitle('Loss')\n",
        "plt.plot(hist.history['d_loss'], label='d_loss')\n",
        "plt.plot(hist.history['g_loss'], label='g_loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mpapVZdC1FF2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "b0c5ee4f-52af-4314-90b2-459e73095815"
      },
      "execution_count": 34,
      "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": [
        "## 5. Test out generator"
      ],
      "metadata": {
        "id": "kIUQZlbB8UuS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.1 Generate images"
      ],
      "metadata": {
        "id": "Px8grx738dNX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imgs = generator.predict(tf.random.normal((16, 128, 1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhOoTf0m9GmX",
        "outputId": "f5612383-baed-4e20-b5c3-d1dcebe45d84"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 684ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43kVEtIK9cQy",
        "outputId": "fc8317aa-5225-4d96-e40b-70b057611f6f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.5008549 ],\n",
              "        [0.5099414 ],\n",
              "        [0.5044675 ],\n",
              "        [0.49870974],\n",
              "        [0.50021094],\n",
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              "        [0.49988976]]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(ncols=4, nrows=4, figsize=(10,10))\n",
        "for r in range(4):\n",
        "    for c in range(4):\n",
        "        ax[r][c].imshow(imgs[(r+1)*(c+1)-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "XQtCVbYn9eST",
        "outputId": "92d34439-223a-412a-ffdc-8909f7444c93"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}