10_CNN___LeNet_5.ipynb

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    "colab": {
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cIinO7MzsMr-"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras.layers import Dense, Conv2D, Flatten, AveragePooling2D\n",
        "from keras import Sequential\n",
        "from keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2nnjirCswNc",
        "outputId": "be23ce14-ad47-4458-ce58-07697bd509fc"
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LeNet Model"
      ],
      "metadata": {
        "id": "U4iL1djts-AA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "# one\n",
        "model.add(Conv2D(6, kernel_size=(5, 5), padding='valid', activation='tanh', input_shape=(32, 32, 1)))\n",
        "model.add(AveragePooling2D(pool_size=(2, 2), strides=2, padding='valid'))\n",
        "\n",
        "# two\n",
        "model.add(Conv2D(16, kernel_size=(5, 5), padding='valid', activation='tanh'))\n",
        "model.add(AveragePooling2D(pool_size=(2, 2), strides=2, padding='valid'))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "# Three\n",
        "model.add(Dense(120, activation='tanh'))\n",
        "# Four\n",
        "model.add(Dense(84, activation='tanh'))\n",
        "# Five\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "asRnUGWOtBYe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLDQcU0ouhub",
        "outputId": "871587d4-dd7b-44ba-8287-554f327e99b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 28, 28, 6)         156       \n",
            "                                                                 \n",
            " average_pooling2d_2 (Averag  (None, 14, 14, 6)        0         \n",
            " ePooling2D)                                                     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 10, 10, 16)        2416      \n",
            "                                                                 \n",
            " average_pooling2d_3 (Averag  (None, 5, 5, 16)         0         \n",
            " ePooling2D)                                                     \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 400)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 120)               48120     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 84)                10164     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                850       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 61,706\n",
            "Trainable params: 61,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    }
  ]
}