is_dnn/labworks/LW1/IS_LR1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HUUZx52sc1LD"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/Colab Notebooks')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# импорт модулей\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import sklearn"
      ],
      "metadata": {
        "id": "3Y-Ux1dadqdA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка датасета\n",
        "from keras.datasets import mnist\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "id": "w25XE8ADdqP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# создание своего разбиения датасета\n",
        "from sklearn.model_selection import train_test_split\n",
        "# объединяем в один набор\n",
        "X = np.concatenate((X_train, X_test))\n",
        "y = np.concatenate((y_train, y_test))\n",
        "# разбиваем по вариантам\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "test_size = 10000,\n",
        "train_size = 60000,\n",
        "random_state = 19) #(5*4-1)"
      ],
      "metadata": {
        "id": "QcXt9zqCdqDH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод размерностей\n",
        "print('Shape of X train:', X_train.shape)\n",
        "print('Shape of y train:', y_train.shape)"
      ],
      "metadata": {
        "id": "9Cd705vod51B",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Выводим 4 изображения\n",
        "plt.figure(figsize=(10, 3))\n",
        "for i in range(4):\n",
        "    plt.subplot(1, 4, i + 1)\n",
        "    plt.imshow(X_train[i], cmap='gray')\n",
        "    plt.title(f'Label: {y_train[i]}')\n",
        "    plt.axis('off')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "vLYfI---d5rm",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# развернем каждое изображение 28*28 в вектор 784\n",
        "num_pixels = X_train.shape[1] * X_train.shape[2]\n",
        "X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n",
        "X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n",
        "print('Shape of transformed X train:', X_train.shape)"
      ],
      "metadata": {
        "id": "d0oyu59gd5fz",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# переведем метки в one-hot\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "\n",
        "print('Shape of transformed y train:', y_train.shape)\n",
        "num_classes = y_train.shape[1]"
      ],
      "metadata": {
        "id": "Q227fINPeD1A",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "metadata": {
        "id": "TzaA61smeDoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. создаем модель - объявляем ее объектом класса Sequential\n",
        "model = Sequential()\n",
        "# 2. добавляем выходной слой(скрытые слои отсутствуют)\n",
        "model.add(Dense(units=num_classes, activation='softmax'))\n",
        "# 3. компилируем модель\n",
        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Liq39zruhz0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод информации об архитектуре модели\n",
        "print(model.summary())"
      ],
      "metadata": {
        "id": "jMGGsq7piZOu",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучаем модель\n",
        "H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)"
      ],
      "metadata": {
        "id": "n_pCdxphiedM",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод графика ошибки по эпохам\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train_loss', 'val_loss'])\n",
        "plt.title('Loss by epochs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Sz_YOlsVivoR",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка качества работы модели на тестовых данных\n",
        "scores = model.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "id": "hpJALaZGnyWF",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение модели на диск\n",
        "model.save('/content/drive/MyDrive/Colab Notebooks/models/model_zero_hide.keras')"
      ],
      "metadata": {
        "id": "Z6eSmpwXn1zM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model100 = Sequential()\n",
        "model100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
        "model100.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "G1qGmPNF9afO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод информации об архитектуре модели\n",
        "print(model100.summary())"
      ],
      "metadata": {
        "id": "2WtfjJKY9abn",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучаем модель\n",
        "H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
      ],
      "metadata": {
        "id": "rPuWd80o9aYD",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод графика ошибки по эпохам\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train_loss', 'val_loss'])\n",
        "plt.title('Loss by epochs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "JLrW7S1g9aUe",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка качества работы модели на тестовых данных\n",
        "scores = model100.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "id": "8jdS02JZ9aRc",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение модели на диск\n",
        "model100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide.keras')"
      ],
      "metadata": {
        "id": "_bR3qoBy9aNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model300 = Sequential()\n",
        "model300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n",
        "model300.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "V4m3nGORGnPC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод информации об архитектуре модели\n",
        "print(model300.summary())"
      ],
      "metadata": {
        "id": "yETaYKzdA9fp",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучаем модель\n",
        "H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)"
      ],
      "metadata": {
        "id": "SFPh0Lw-A9Zq",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод графика ошибки по эпохам\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train_loss', 'val_loss'])\n",
        "plt.title('Loss by epochs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6mvOMGiLA9QE",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка качества работы модели на тестовых данных\n",
        "scores = model300.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "id": "WOJyUHP79Z86"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение модели на диск\n",
        "model300.save('/content/drive/MyDrive/Colab Notebooks/models/model300in_1hide.keras')"
      ],
      "metadata": {
        "id": "XsWc7S4aCyiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model500 = Sequential()\n",
        "model500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n",
        "model500.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "QSL-6YbkJxm0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод информации об архитектуре модели\n",
        "print(model500.summary())"
      ],
      "metadata": {
        "id": "Vs1x3ooKCybg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучаем модель\n",
        "H = model500.fit(X_train, y_train, validation_split=0.1, epochs=50)"
      ],
      "metadata": {
        "id": "x3kzDT5qCyYY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод графика ошибки по эпохам\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train_loss', 'val_loss'])\n",
        "plt.title('Loss by epochs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "FwSLP5I8CyU0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка качества работы модели на тестовых данных\n",
        "scores = model500.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "id": "5mDveUNPCyRH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение модели на диск\n",
        "model500.save('/content/drive/MyDrive/Colab Notebooks/models/model500in_1hide.keras')"
      ],
      "metadata": {
        "id": "4IEeNu1rCyNj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model10050 = Sequential()\n",
        "model10050.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
        "model10050.add(Dense(units=50,activation='sigmoid'))\n",
        "model10050.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model10050.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Ld4hMck_CyKT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод информации об архитектуре модели\n",
        "print(model10050.summary())"
      ],
      "metadata": {
        "id": "GVZLuKvqNZEK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучаем модель\n",
        "H = model10050.fit(X_train, y_train, validation_split=0.1, epochs=50)"
      ],
      "metadata": {
        "id": "UP0suqUbNY9R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод графика ошибки по эпохам\n",
        "plt.plot(H.history['loss'])\n",
        "plt.plot(H.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train_loss', 'val_loss'])\n",
        "plt.title('Loss by epochs')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "k-DhnF0SNY3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка качества работы модели на тестовых данных\n",
        "scores = model10050.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "id": "-7E0BUrMNYx9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение модели на диск\n",
        "model10050.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide_50in_2hide.keras')"
      ],
      "metadata": {
        "id": "yu11cXisCyCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model100100 = Sequential()\n",
        "model100100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
        "model100100.add(Dense(units=100,activation='sigmoid'))\n",
        "model100100.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model100100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "pTTia0gmRFaV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод информации об архитектуре модели\n",
        "print(model100100.summary())"
      ],
      "metadata": {
        "id": "XQHhKm8YRFW6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучаем модель\n",
        "H = model100100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
      ],
      "metadata": {
        "id": "oCgqwCmPRFTT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ke"
      ],
      "metadata": {
        "id": "YDdSpQO5RFPn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка качества работы модели на тестовых данных\n",
        "scores = model100100.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "id": "D_WHUHCwRFMS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение модели на диск\n",
        "model100100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide_100in_2hide.keras')"
      ],
      "metadata": {
        "id": "fkBAnNf2RFDL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# сохранение лучшей модели в папку best_model\n",
        "model100.save('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')"
      ],
      "metadata": {
        "id": "mXGyPCNdS91i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Загрузка модели с диска\n",
        "from keras.models import load_model\n",
        "model = load_model('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')"
      ],
      "metadata": {
        "id": "ILyFn-CJp1k8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод тестового изображения и результата распознавания\n",
        "n = 111\n",
        "result = model.predict(X_test[n:n+1])\n",
        "print('NN output:', result)\n",
        "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "print('Real mark: ', str(np.argmax(y_test[n])))\n",
        "print('NN answer: ', str(np.argmax(result)))"
      ],
      "metadata": {
        "id": "cCk7Do1mp-xb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод тестового изображения и результата распознавания\n",
        "n = 222\n",
        "result = model.predict(X_test[n:n+1])\n",
        "print('NN output:', result)\n",
        "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "print('Real mark: ', str(np.argmax(y_test[n])))\n",
        "print('NN answer: ', str(np.argmax(result)))"
      ],
      "metadata": {
        "id": "HrL0sv-1YosF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка собственного изображения\n",
        "from PIL import Image\n",
        "file_data = Image.open('test.png')\n",
        "file_data = file_data.convert('L') # перевод в градации серого\n",
        "test_img = np.array(file_data)"
      ],
      "metadata": {
        "id": "tfARmJMip_D8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод собственного изображения\n",
        "plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "# предобработка\n",
        "test_img = test_img / 255\n",
        "test_img = test_img.reshape(1, num_pixels)\n",
        "# распознавание\n",
        "result = model.predict(test_img)\n",
        "print('I think it\\'s ', np.argmax(result))"
      ],
      "metadata": {
        "id": "60zdtlMduHhT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка собственного изображения\n",
        "from PIL import Image\n",
        "file2_data = Image.open('test_2.png')\n",
        "file2_data = file2_data.convert('L') # перевод в градации серого\n",
        "test2_img = np.array(file2_data)"
      ],
      "metadata": {
        "id": "JcO7pbCSuvrv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод собственного изображения\n",
        "plt.imshow(test2_img, cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "# предобработка\n",
        "test2_img = test2_img / 255\n",
        "test2_img = test2_img.reshape(1, num_pixels)\n",
        "# распознавание\n",
        "result_2 = model.predict(test2_img)\n",
        "print('I think it\\'s ', np.argmax(result_2))"
      ],
      "metadata": {
        "id": "2E0evx2su4y1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка собственного изображения, повернутого на 90 градусов\n",
        "from PIL import Image\n",
        "file90_data = Image.open('test90.png')\n",
        "file90_data = file90_data.convert('L') # перевод в градации серого\n",
        "test90_img = np.array(file90_data)"
      ],
      "metadata": {
        "id": "ZsRQAhIIa_vD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод собственного изображения\n",
        "plt.imshow(test90_img, cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "# предобработка\n",
        "test90_img = test90_img / 255\n",
        "test90_img = test90_img.reshape(1, num_pixels)\n",
        "# распознавание\n",
        "result_3 = model.predict(test90_img)\n",
        "print('I think it\\'s ', np.argmax(result_3))"
      ],
      "metadata": {
        "id": "nQnk_zZMbM01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка собственного изображения, повернутого на 90 градусов\n",
        "from PIL import Image\n",
        "file902_data = Image.open('test90_2.png')\n",
        "file902_data = file902_data.convert('L') # перевод в градации серого\n",
        "test902_img = np.array(file902_data)"
      ],
      "metadata": {
        "id": "IXK_VfJqbhJ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод собственного изображения\n",
        "plt.imshow(test902_img, cmap=plt.get_cmap('gray'))\n",
        "plt.show()\n",
        "# предобработка\n",
        "test902_img = test902_img / 255\n",
        "test902_img = test902_img.reshape(1, num_pixels)\n",
        "# распознавание\n",
        "result_4 = model.predict(test902_img)\n",
        "print('I think it\\'s ', np.argmax(result_4))"
      ],
      "metadata": {
        "id": "S5WcjVtMb-bp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "n4-_iFTWXNTJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}