8_Padding_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": "__roibVneuXr"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras.layers import Dense, Conv2D, Flatten\n",
        "from keras import Sequential\n",
        "from keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data"
      ],
      "metadata": {
        "id": "blkcRROOqTAN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQnDdRqtqVPG",
        "outputId": "8c45ac27-da4c-41d1-eca6-6857c2a28942"
      },
      "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": "QEnu6wuOqxuu",
        "outputId": "76b5c9cf-5a60-41b7-abb9-8c7e2c847447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CNN model"
      ],
      "metadata": {
        "id": "Mw3ZMKLbqpk6"
      }
    },
    {
      "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(Conv2D(32, kernel_size=(3, 3), padding='valid', activation='relu'))\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='valid', activation='relu'))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "6wgF14HVqnX-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`padding = 'valid' ; that means we use no padding`"
      ],
      "metadata": {
        "id": "81AkHdu6r6KD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXidlWccrvqr",
        "outputId": "793f3b3a-511e-4923-f965-566f06f4e4e6"
      },
      "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",
            " conv2d_1 (Conv2D)           (None, 24, 24, 32)        9248      \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 22, 22, 32)        9248      \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 15488)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               1982592   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,002,698\n",
            "Trainable params: 2,002,698\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here,original image size is (28, 28). But in each layers the image size is reducing i.e. (26, 26) then (24, 24)"
      ],
      "metadata": {
        "id": "P2V0vx62r_9O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# with padding\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)))\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "K33ehxXBsasg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`padding = 'same'`"
      ],
      "metadata": {
        "id": "EYkX4mrutBFJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-E8EMYEOsyff",
        "outputId": "ebaa9d83-703b-45bc-b846-1796ccbfb483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 28, 28, 32)        9248      \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 28, 28, 32)        9248      \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               3211392   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,231,498\n",
            "Trainable params: 3,231,498\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    }
  ]
}