VGG16_in_Keras.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/danielsoy/ALOCC-CVPR2018/blob/master/VGG16_in_Keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOw0gPF8tApy",
        "outputId": "deccfc7a-4563-462d-ba83-dc22399767ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-18 18:11:49--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.189.128, 108.177.97.128, 108.177.125.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 68606236 (65M) [application/zip]\n",
            "Saving to: ‘cats_and_dogs_filtered.zip’\n",
            "\n",
            "cats_and_dogs_filte 100%[===================>]  65.43M  91.0MB/s    in 0.7s    \n",
            "\n",
            "2021-12-18 18:11:50 (91.0 MB/s) - ‘cats_and_dogs_filtered.zip’ saved [68606236/68606236]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate \\\n",
        "  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \\\n",
        "  -O cats_and_dogs_filtered.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuzyCdDUtApz",
        "outputId": "b15b2231-5d0c-43f0-acd4-84195ef1a5c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  cats_and_dogs_filtered.zip\n",
            "   creating: cats_and_dogs_filtered/\n",
            "  inflating: cats_and_dogs_filtered/vectorize.py  \n",
            "   creating: cats_and_dogs_filtered/validation/\n",
            "   creating: cats_and_dogs_filtered/train/\n",
            "   creating: cats_and_dogs_filtered/validation/dogs/\n",
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          ]
        }
      ],
      "source": [
        "! unzip cats_and_dogs_filtered.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jzdO4JwItAp0"
      },
      "outputs": [],
      "source": [
        "import keras,os\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CygFQBqXtAp1",
        "outputId": "d1c8dfd0-354e-4955-b2ad-6d74e7c11504"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "trdata = ImageDataGenerator()\n",
        "traindata = trdata.flow_from_directory(directory=\"cats_and_dogs_filtered/train\",target_size=(224,224))\n",
        "tsdata = ImageDataGenerator()\n",
        "testdata = tsdata.flow_from_directory(directory=\"cats_and_dogs_filtered/validation\", target_size=(224,224))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "qZnodzhutAp2"
      },
      "outputs": [],
      "source": [
        "model = Sequential()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "_epgqiS1tAp2"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "i9R9lieEtAp2"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Ovu82kXBtAp3"
      },
      "outputs": [],
      "source": [
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "8ljz_VoetAp3"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Ey2TbrUptAp4"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1uuoGoJitAp5"
      },
      "outputs": [],
      "source": [
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "NFx8s3WetAp5"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "UPFU_SGXtAp5"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "SHwHb6R9tAp6"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "qufvb8XNtAp6"
      },
      "outputs": [],
      "source": [
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ub95J4K_tAp6"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "fOW2ERmItAp6"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "zvuAg7pUtAp7"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_x0QwU4-tAp7"
      },
      "outputs": [],
      "source": [
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "cN7WN0RwtAp7"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "dDye8kx6tAp7"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "PheQ20qftAp7"
      },
      "outputs": [],
      "source": [
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Z4iYApZptAp8"
      },
      "outputs": [],
      "source": [
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "KQ-8J4mbtAp8"
      },
      "outputs": [],
      "source": [
        "model.add(Flatten())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "FT9NUfFRtAp8"
      },
      "outputs": [],
      "source": [
        "model.add(Dense(units=4096,activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "cjI_1CCHtAp8"
      },
      "outputs": [],
      "source": [
        "model.add(Dense(units=4096,activation=\"relu\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Vc7wONqGtAp9"
      },
      "outputs": [],
      "source": [
        "model.add(Dense(units=2, activation=\"softmax\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "Ta_nIYwPtAp9"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.optimizers import Adam # - Works\n",
        "\n",
        "from keras.optimizers import adam_v2\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.models import model_from_json\n",
        "import numpy\n",
        "import os\n",
        "\n",
        "opt = adam_v2.Adam(learning_rate=0.001 )\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['acc', 'mse']) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJjRuQejtAp9",
        "outputId": "5d6b89d7-1342-4161-f739-888267c970b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 112, 112, 64)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 56, 56, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 28, 28, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 14, 14, 512)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 7, 7, 512)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              102764544 \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 8194      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 134,268,738\n",
            "Trainable params: 134,268,738\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lMMLyEntAp9",
        "outputId": "f7a4fd5c-a09d-4532-8350-6dc0a7dc4c1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
          ]
        }
      ],
      "source": [
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "checkpoint = ModelCheckpoint(\"vgg16_1.h5\", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
        "early = EarlyStopping(monitor='val_acc', min_delta=0, patience=20, verbose=1, mode='auto')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ho5HLk2ktAp9",
        "outputId": "73329422-94f1-4e6c-c5e6-37458f201336"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6938 - acc: 0.4906 - mse: 0.2503\n",
            "Epoch 00001: val_acc improved from -inf to 0.51250, saving model to vgg16_1.h5\n",
            "10/10 [==============================] - 14s 1s/step - loss: 0.6938 - acc: 0.4906 - mse: 0.2503 - val_loss: 0.6928 - val_acc: 0.5125 - val_mse: 0.2498\n",
            "Epoch 2/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6923 - acc: 0.5281 - mse: 0.2496\n",
            "Epoch 00002: val_acc did not improve from 0.51250\n",
            "10/10 [==============================] - 11s 1s/step - loss: 0.6923 - acc: 0.5281 - mse: 0.2496 - val_loss: 0.6931 - val_acc: 0.5063 - val_mse: 0.2500\n",
            "Epoch 3/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6951 - acc: 0.4572 - mse: 0.2510\n",
            "Epoch 00003: val_acc did not improve from 0.51250\n",
            "10/10 [==============================] - 10s 1s/step - loss: 0.6951 - acc: 0.4572 - mse: 0.2510 - val_loss: 0.6950 - val_acc: 0.4469 - val_mse: 0.2509\n",
            "Epoch 4/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6934 - acc: 0.4969 - mse: 0.2501\n",
            "Epoch 00004: val_acc improved from 0.51250 to 0.51562, saving model to vgg16_1.h5\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.6934 - acc: 0.4969 - mse: 0.2501 - val_loss: 0.6928 - val_acc: 0.5156 - val_mse: 0.2498\n",
            "Epoch 5/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6929 - acc: 0.5156 - mse: 0.2499\n",
            "Epoch 00005: val_acc improved from 0.51562 to 0.52500, saving model to vgg16_1.h5\n",
            "10/10 [==============================] - 12s 1s/step - loss: 0.6929 - acc: 0.5156 - mse: 0.2499 - val_loss: 0.6925 - val_acc: 0.5250 - val_mse: 0.2497\n",
            "Epoch 6/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6939 - acc: 0.4781 - mse: 0.2504\n",
            "Epoch 00006: val_acc did not improve from 0.52500\n",
            "10/10 [==============================] - 11s 1s/step - loss: 0.6939 - acc: 0.4781 - mse: 0.2504 - val_loss: 0.6930 - val_acc: 0.5094 - val_mse: 0.2499\n",
            "Epoch 7/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6942 - acc: 0.4531 - mse: 0.2505\n",
            "Epoch 00007: val_acc did not improve from 0.52500\n",
            "10/10 [==============================] - 10s 1s/step - loss: 0.6942 - acc: 0.4531 - mse: 0.2505 - val_loss: 0.6930 - val_acc: 0.5125 - val_mse: 0.2499\n",
            "Epoch 8/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6932 - acc: 0.5000 - mse: 0.2500\n",
            "Epoch 00008: val_acc did not improve from 0.52500\n",
            "10/10 [==============================] - 10s 1s/step - loss: 0.6932 - acc: 0.5000 - mse: 0.2500 - val_loss: 0.6928 - val_acc: 0.5250 - val_mse: 0.2498\n",
            "Epoch 9/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6932 - acc: 0.5031 - mse: 0.2500\n",
            "Epoch 00009: val_acc did not improve from 0.52500\n",
            "10/10 [==============================] - 10s 1s/step - loss: 0.6932 - acc: 0.5031 - mse: 0.2500 - val_loss: 0.6932 - val_acc: 0.4969 - val_mse: 0.2500\n",
            "Epoch 10/10\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.6934 - acc: 0.4875 - mse: 0.2501\n",
            "Epoch 00010: val_acc did not improve from 0.52500\n",
            "10/10 [==============================] - 11s 1s/step - loss: 0.6934 - acc: 0.4875 - mse: 0.2501 - val_loss: 0.6929 - val_acc: 0.5219 - val_mse: 0.2499\n"
          ]
        }
      ],
      "source": [
        "hist = model.fit_generator(steps_per_epoch=10,generator=traindata, validation_data= testdata, validation_steps=10,epochs=10,callbacks=[checkpoint,early])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JshmbWUitAp-",
        "outputId": "f252292b-dd80-44c7-c29c-770ee8d86296"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(hist.history[\"acc\"])\n",
        "plt.plot(hist.history['val_acc'])\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title(\"model accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Accuracy\",\"Validation Accuracy\",\"loss\",\"Validation Loss\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "cvQGfeZLtAp-",
        "outputId": "98521bc4-ed18-482f-e51c-929869c635ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cat\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from keras.preprocessing import image\n",
        "img = image.load_img(\"/content/cats_and_dogs_filtered/validation/dogs/dog.2000.jpg\",target_size=(224,224))\n",
        "img = np.asarray(img)\n",
        "plt.imshow(img)\n",
        "img = np.expand_dims(img, axis=0)\n",
        "from keras.models import load_model\n",
        "saved_model = load_model(\"vgg16_1.h5\")\n",
        "output = saved_model.predict(img)\n",
        "if output[0][0] > output[0][1]:\n",
        "    print(\"cat\")\n",
        "else:\n",
        "    print('dog')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9cfKh2CtAp-"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "VGG16 in Keras.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}