9_Pooling_layers_in_CNN.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rU70Vdh5xRZ0"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\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": "60ysy_bBxuma",
        "outputId": "3d191b06-2643-403f-8e93-88a16f098405"
      },
      "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": "code",
      "source": [
        "print(X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHEdQn9mxvL-",
        "outputId": "da0a3f99-a814-46ae-fa81-ca1d2301a854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# without padding\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='valid', activation='relu', input_shape=(28, 28, 1)))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding=\"valid\"))\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='valid', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding=\"valid\"))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "--qs-5PLxxzm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ic5uWFQLyelM",
        "outputId": "c700ce10-5105-448f-b43e-e8048291bdde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 800)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               102528    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 113,386\n",
            "Trainable params: 113,386\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    }
  ]
}