LeNet_Digit_Recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNpWHbO8/tCBsv+oU0XLTTb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DebrishiKar1408/CRISS/blob/main/LeNet_Digit_Recognition.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": "3d15XfFdAx1t",
        "outputId": "56dba66d-d2d1-4ce0-cff2-1fee96b7b8fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "X_train shape (60000, 28, 28) X_test shape (10000, 28, 28)\n"
          ]
        }
      ],
      "source": [
        "from keras.datasets import mnist\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "print('X_train shape', X_train.shape, 'X_test shape', X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt \n",
        "import random\n",
        "plt.figure(figsize = (12,5))\n",
        "for i in range(8):\n",
        "  ind = random.randint(0, len(X_train))\n",
        "  plt.subplot(240+1+i)\n",
        "  plt.imshow(X_train[ind])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "Ykp7uCbZBDSE",
        "outputId": "3028ac70-876c-4e70-969f-b22ee18a06ac"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Preprocessing phase\n",
        "from keras.utils import to_categorical\n",
        "def preprocess_data(X_train, y_train, X_test, y_test):\n",
        "  # reshape images to the required size of Keras\n",
        "  X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)\n",
        "  X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)\n",
        "  \n",
        "  # convert image values from integers to floats\n",
        "  X_train = X_train.astype('float32')\n",
        "  X_test = X_test.astype('float32')\n",
        "  \n",
        "  # normalization\n",
        "  X_train = X_train/255.0\n",
        "  X_test_norm = X_test/255.0\n",
        "  \n",
        "  # One-hot encoding label \n",
        "  y_train = to_categorical(y_train)\n",
        "  y_test = to_categorical(y_test)\n",
        "  \n",
        "  return X_train, y_train, X_test, y_test"
      ],
      "metadata": {
        "id": "f-_570W7BLIn"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\n",
        "from keras.optimizers import SGD\n",
        "# metrics \n",
        "from keras.metrics import categorical_crossentropy\n",
        "# optimization method\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "def LeNet():\n",
        "  model = Sequential()\n",
        "  \n",
        "  # Convolutional layer  \n",
        "  model.add(Conv2D(filters = 6, kernel_size = (5,5), padding = 'same', \n",
        "                   activation = 'relu', input_shape = (28,28,1)))\n",
        "  \n",
        "  # Max-pooing layer with pooling window size is 2x2\n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  \n",
        "  # Convolutional layer \n",
        "  model.add(Conv2D(filters = 16, kernel_size = (5,5), activation = 'relu'))\n",
        "  \n",
        "  # Max-pooling layer \n",
        "  model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "  \n",
        "  # Flatten layer \n",
        "  model.add(Flatten())\n",
        "  \n",
        "  # The first fully connected layer \n",
        "  model.add(Dense(120, activation = 'relu'))\n",
        "  \n",
        "  # The output layer  \n",
        "  model.add(Dense(10, activation = 'softmax'))\n",
        "  \n",
        "  # compile the model with a loss function, a metric and an optimizer function\n",
        "  # In this case, the loss function is categorical crossentropy, \n",
        "  # we use Stochastic Gradient Descent (SGD) method with learning rate lr = 0.01 \n",
        "  # to optimize the loss function\n",
        "  # metric: accuracy \n",
        "  \n",
        "  opt = SGD(lr = 0.01)\n",
        "  model.compile(loss = categorical_crossentropy, \n",
        "                optimizer = opt, \n",
        "                metrics = ['accuracy']) \n",
        "                \n",
        "  return model"
      ],
      "metadata": {
        "id": "gsSh2wjwCMyK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LeNet_model = LeNet()\n",
        "LeNet_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkstoci4ECPe",
        "outputId": "637c0a42-1896-4ce3-db12-1e8081873583"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 6)         156       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 6)        0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 10, 10, 16)        2416      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 16)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 400)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 120)               48120     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1210      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 51,902\n",
            "Trainable params: 51,902\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/optimizers/legacy/gradient_descent.py:114: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super().__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def summary_history(history):\n",
        "  plt.figure(figsize = (10,6))\n",
        "  plt.plot(history.history['accuracy'], color = 'blue', label = 'train')\n",
        "  plt.plot(history.history['val_accuracy'], color = 'red', label = 'val')\n",
        "  plt.legend()\n",
        "  plt.title('Accuracy')\n",
        "  plt.show()\n",
        "\n",
        "def train_model(model, X_train, y_train, X_test, y_test, epochs = 50, batch_size = 128):\n",
        "  # Rescaling all training and testing data\n",
        "  X_train, y_train, X_test, y_test = preprocess_data(X_train, y_train, X_test, y_test)\n",
        "  # Fitting the model on the training set\n",
        "  history = model.fit(X_train, y_train, epochs = epochs, batch_size = batch_size, \n",
        "                      steps_per_epoch = X_train.shape[0]//batch_size, \n",
        "                      validation_data = (X_test, y_test), \n",
        "                      validation_steps = X_test.shape[0]//batch_size, verbose = 1)\n",
        "  # evaluating the model\n",
        "  _, acc = model.evaluate(X_test, y_test, verbose = 1)\n",
        "  print('%.3f' % (acc * 100.0))\n",
        "  summary_history(history)"
      ],
      "metadata": {
        "id": "HqfERzn8C3Ll"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_model(LeNet_model, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YQpz_BlJE9Yu",
        "outputId": "5f9290f4-2d6a-4185-8d90-b5b6304e6fe3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "468/468 [==============================] - 43s 90ms/step - loss: 1.1379 - accuracy: 0.6569 - val_loss: 34.6482 - val_accuracy: 0.9054\n",
            "Epoch 2/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.2738 - accuracy: 0.9199 - val_loss: 25.1213 - val_accuracy: 0.9397\n",
            "Epoch 3/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.1949 - accuracy: 0.9427 - val_loss: 19.2523 - val_accuracy: 0.9535\n",
            "Epoch 4/50\n",
            "468/468 [==============================] - 41s 87ms/step - loss: 0.1551 - accuracy: 0.9547 - val_loss: 17.2242 - val_accuracy: 0.9617\n",
            "Epoch 5/50\n",
            "468/468 [==============================] - 40s 85ms/step - loss: 0.1314 - accuracy: 0.9611 - val_loss: 13.3287 - val_accuracy: 0.9698\n",
            "Epoch 6/50\n",
            "468/468 [==============================] - 40s 84ms/step - loss: 0.1134 - accuracy: 0.9662 - val_loss: 12.3803 - val_accuracy: 0.9685\n",
            "Epoch 7/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.1019 - accuracy: 0.9695 - val_loss: 11.4343 - val_accuracy: 0.9751\n",
            "Epoch 8/50\n",
            "468/468 [==============================] - 39s 84ms/step - loss: 0.0938 - accuracy: 0.9717 - val_loss: 11.2867 - val_accuracy: 0.9749\n",
            "Epoch 9/50\n",
            "468/468 [==============================] - 39s 84ms/step - loss: 0.0858 - accuracy: 0.9739 - val_loss: 9.4423 - val_accuracy: 0.9778\n",
            "Epoch 10/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0806 - accuracy: 0.9755 - val_loss: 9.0405 - val_accuracy: 0.9788\n",
            "Epoch 11/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0754 - accuracy: 0.9772 - val_loss: 10.1730 - val_accuracy: 0.9771\n",
            "Epoch 12/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0716 - accuracy: 0.9784 - val_loss: 8.2487 - val_accuracy: 0.9807\n",
            "Epoch 13/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0677 - accuracy: 0.9794 - val_loss: 7.2210 - val_accuracy: 0.9817\n",
            "Epoch 14/50\n",
            "468/468 [==============================] - 40s 85ms/step - loss: 0.0645 - accuracy: 0.9800 - val_loss: 7.7878 - val_accuracy: 0.9819\n",
            "Epoch 15/50\n",
            "468/468 [==============================] - 42s 89ms/step - loss: 0.0614 - accuracy: 0.9816 - val_loss: 7.8745 - val_accuracy: 0.9819\n",
            "Epoch 16/50\n",
            "468/468 [==============================] - 39s 84ms/step - loss: 0.0593 - accuracy: 0.9814 - val_loss: 7.9956 - val_accuracy: 0.9823\n",
            "Epoch 17/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0561 - accuracy: 0.9828 - val_loss: 8.0664 - val_accuracy: 0.9813\n",
            "Epoch 18/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0549 - accuracy: 0.9830 - val_loss: 6.6491 - val_accuracy: 0.9837\n",
            "Epoch 19/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0532 - accuracy: 0.9835 - val_loss: 7.0322 - val_accuracy: 0.9835\n",
            "Epoch 20/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0500 - accuracy: 0.9848 - val_loss: 6.6011 - val_accuracy: 0.9841\n",
            "Epoch 21/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0497 - accuracy: 0.9843 - val_loss: 6.3881 - val_accuracy: 0.9847\n",
            "Epoch 22/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0474 - accuracy: 0.9851 - val_loss: 6.2078 - val_accuracy: 0.9853\n",
            "Epoch 23/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0462 - accuracy: 0.9859 - val_loss: 6.8782 - val_accuracy: 0.9844\n",
            "Epoch 24/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0452 - accuracy: 0.9861 - val_loss: 6.4648 - val_accuracy: 0.9852\n",
            "Epoch 25/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0440 - accuracy: 0.9865 - val_loss: 6.1491 - val_accuracy: 0.9867\n",
            "Epoch 26/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0419 - accuracy: 0.9876 - val_loss: 7.7673 - val_accuracy: 0.9835\n",
            "Epoch 27/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0422 - accuracy: 0.9867 - val_loss: 5.7622 - val_accuracy: 0.9859\n",
            "Epoch 28/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0396 - accuracy: 0.9878 - val_loss: 6.1776 - val_accuracy: 0.9856\n",
            "Epoch 29/50\n",
            "468/468 [==============================] - 40s 85ms/step - loss: 0.0397 - accuracy: 0.9875 - val_loss: 6.4847 - val_accuracy: 0.9857\n",
            "Epoch 30/50\n",
            "468/468 [==============================] - 42s 89ms/step - loss: 0.0377 - accuracy: 0.9883 - val_loss: 5.9233 - val_accuracy: 0.9867\n",
            "Epoch 31/50\n",
            "468/468 [==============================] - 40s 84ms/step - loss: 0.0376 - accuracy: 0.9880 - val_loss: 7.1468 - val_accuracy: 0.9854\n",
            "Epoch 32/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0362 - accuracy: 0.9889 - val_loss: 6.4732 - val_accuracy: 0.9858\n",
            "Epoch 33/50\n",
            "468/468 [==============================] - 40s 84ms/step - loss: 0.0348 - accuracy: 0.9891 - val_loss: 6.5133 - val_accuracy: 0.9860\n",
            "Epoch 34/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0343 - accuracy: 0.9897 - val_loss: 6.3268 - val_accuracy: 0.9864\n",
            "Epoch 35/50\n",
            "468/468 [==============================] - 39s 84ms/step - loss: 0.0338 - accuracy: 0.9889 - val_loss: 5.8730 - val_accuracy: 0.9868\n",
            "Epoch 36/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0330 - accuracy: 0.9898 - val_loss: 7.0740 - val_accuracy: 0.9863\n",
            "Epoch 37/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0325 - accuracy: 0.9898 - val_loss: 7.5196 - val_accuracy: 0.9848\n",
            "Epoch 38/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0321 - accuracy: 0.9898 - val_loss: 6.1012 - val_accuracy: 0.9871\n",
            "Epoch 39/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0307 - accuracy: 0.9901 - val_loss: 6.1045 - val_accuracy: 0.9869\n",
            "Epoch 40/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0305 - accuracy: 0.9904 - val_loss: 7.7966 - val_accuracy: 0.9860\n",
            "Epoch 41/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0285 - accuracy: 0.9911 - val_loss: 7.7133 - val_accuracy: 0.9855\n",
            "Epoch 42/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0299 - accuracy: 0.9909 - val_loss: 6.6032 - val_accuracy: 0.9873\n",
            "Epoch 43/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 6.3537 - val_accuracy: 0.9866\n",
            "Epoch 44/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0275 - accuracy: 0.9919 - val_loss: 6.4546 - val_accuracy: 0.9875\n",
            "Epoch 45/50\n",
            "468/468 [==============================] - 40s 85ms/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 6.1625 - val_accuracy: 0.9877\n",
            "Epoch 46/50\n",
            "468/468 [==============================] - 42s 90ms/step - loss: 0.0262 - accuracy: 0.9918 - val_loss: 6.4654 - val_accuracy: 0.9876\n",
            "Epoch 47/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0266 - accuracy: 0.9915 - val_loss: 6.8587 - val_accuracy: 0.9874\n",
            "Epoch 48/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0250 - accuracy: 0.9923 - val_loss: 6.3196 - val_accuracy: 0.9879\n",
            "Epoch 49/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0252 - accuracy: 0.9922 - val_loss: 6.5236 - val_accuracy: 0.9879\n",
            "Epoch 50/50\n",
            "468/468 [==============================] - 40s 86ms/step - loss: 0.0240 - accuracy: 0.9924 - val_loss: 6.8031 - val_accuracy: 0.9872\n",
            "313/313 [==============================] - 3s 9ms/step - loss: 6.7922 - accuracy: 0.9872\n",
            "98.720\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# predict labels for the test set\n",
        "y_test_pred = []\n",
        "for i in range(len(X_test)):\n",
        "  img = X_test[i]\n",
        "  img = img.reshape(1,28,28,1)\n",
        "  img = img.astype('float32')\n",
        "  img = img/255.0\n",
        "  # one-hot vector output\n",
        "  vec_p = LeNet_model.predict(img)\n",
        "  # determine the label corresponding to vector vec_p\n",
        "  y_p = np.argmax(vec_p)\n",
        "  y_test_pred.append(y_p)\n",
        "  \n",
        "# convert y_test_pred from list to array\n",
        "y_test_pred = np.asarray(y_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_9Tpr41DLSD",
        "outputId": "b18e6bb6-8e4e-477f-ac7c-21e29340af63"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
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            "1/1 [==============================] - 0s 43ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns \n",
        "\n",
        "con_mat = confusion_matrix(y_test, y_test_pred)\n",
        "\n",
        "plt.figure(figsize = (8,6))\n",
        "sns.heatmap(con_mat, linewidths = 0.1, cmap = 'Greens', linecolor = 'gray', \n",
        "            fmt = '.1f', annot = True)\n",
        "plt.xlabel('Predicted classes', fontsize = 20)\n",
        "plt.ylabel('True classes', fontsize = 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "AuujWr-mDZ6m",
        "outputId": "8e78ba92-dcde-4034-fe2f-1162cd1af655"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(70.72222222222221, 0.5, 'True classes')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# all categories\n",
        "cates = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
        "\n",
        "plt.figure(figsize = (12,7))\n",
        "for i in np.arange(8):\n",
        "  ind = random.randint(0,len(X_test))\n",
        "  img = X_test[ind]\n",
        "  img = img.reshape(1,28,28,1)\n",
        "  img = img.astype('float32')\n",
        "  img = img/255.0\n",
        "  # v_p = LeNet_model.predict_classes(img)\n",
        "  v_p = (LeNet_model.predict(img) ).astype(\"int32\")\n",
        "  plt.subplot(240+1+i)\n",
        "  plt.imshow(X_test[ind])\n",
        "  plt.title(cates[v_p[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "Wi3KvvqajI8t",
        "outputId": "48388d5b-b2e7-4aad-9005-106318fd1d88"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 62ms/step\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-04bf41cd73c8>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m240\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m   \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv_p\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1eybgCvvjRqz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}