MNIST Digits Classification(CNN)/Handwritten_Digit_Recognition_using_Convolutional_Neural_Networks_(CNNs).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": 11,
      "metadata": {
        "id": "yWZemD0gyTho"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout,BatchNormalization\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(train_images_orig, train_labels_orig), (test_images_orig, test_labels_orig) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPj8NXmZyXpw",
        "outputId": "4780163d-6273-4953-cb5f-8a7c60c23887"
      },
      "execution_count": 2,
      "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": [
        "train_images = train_images_orig.reshape((60000, 28, 28, 1))\n",
        "train_images = train_images.astype('float32') / 255"
      ],
      "metadata": {
        "id": "Vcq1R3Otyds5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = test_images_orig.reshape((10000, 28, 28, 1))\n",
        "test_images = test_images.astype('float32') / 255"
      ],
      "metadata": {
        "id": "tWZY_8jkyguC"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = to_categorical(train_labels_orig)\n",
        "test_labels = to_categorical(test_labels_orig)"
      ],
      "metadata": {
        "id": "9NsiTNa9yjgG"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "X2GE42vRyugJ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYLivO_9y31Y",
        "outputId": "5b74e586-e822-4d14-9095-160ec5ce6f01"
      },
      "execution_count": 8,
      "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",
            " batch_normalization (BatchN  (None, 13, 13, 32)       128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 5, 5, 64)         256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1152)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               147584    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 241,930\n",
            "Trainable params: 241,738\n",
            "Non-trainable params: 192\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ipj1u0BUy8Xe"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_images, train_labels, epochs=10, batch_size=32, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQd80O-2zAfc",
        "outputId": "89c25d83-543c-43c4-dc8a-52e9108cb858"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1688/1688 [==============================] - 74s 43ms/step - loss: 0.1533 - accuracy: 0.9537 - val_loss: 0.0412 - val_accuracy: 0.9897\n",
            "Epoch 2/10\n",
            "1688/1688 [==============================] - 73s 43ms/step - loss: 0.0700 - accuracy: 0.9800 - val_loss: 0.0410 - val_accuracy: 0.9892\n",
            "Epoch 3/10\n",
            "1688/1688 [==============================] - 72s 43ms/step - loss: 0.0531 - accuracy: 0.9858 - val_loss: 0.0414 - val_accuracy: 0.9897\n",
            "Epoch 4/10\n",
            "1688/1688 [==============================] - 71s 42ms/step - loss: 0.0401 - accuracy: 0.9887 - val_loss: 0.0396 - val_accuracy: 0.9908\n",
            "Epoch 5/10\n",
            "1688/1688 [==============================] - 75s 44ms/step - loss: 0.0352 - accuracy: 0.9896 - val_loss: 0.0427 - val_accuracy: 0.9907\n",
            "Epoch 6/10\n",
            "1688/1688 [==============================] - 71s 42ms/step - loss: 0.0279 - accuracy: 0.9920 - val_loss: 0.0547 - val_accuracy: 0.9900\n",
            "Epoch 7/10\n",
            "1688/1688 [==============================] - 72s 43ms/step - loss: 0.0274 - accuracy: 0.9917 - val_loss: 0.0473 - val_accuracy: 0.9915\n",
            "Epoch 8/10\n",
            "1688/1688 [==============================] - 74s 44ms/step - loss: 0.0240 - accuracy: 0.9933 - val_loss: 0.0500 - val_accuracy: 0.9892\n",
            "Epoch 9/10\n",
            "1688/1688 [==============================] - 74s 44ms/step - loss: 0.0218 - accuracy: 0.9939 - val_loss: 0.0426 - val_accuracy: 0.9930\n",
            "Epoch 10/10\n",
            "1688/1688 [==============================] - 72s 43ms/step - loss: 0.0190 - accuracy: 0.9947 - val_loss: 0.0577 - val_accuracy: 0.9907\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy = history.history['accuracy']\n",
        "\n",
        "plt.plot(range(1, len(train_accuracy) + 1), train_accuracy)\n",
        "plt.title('Training Accuracy Over Epochs')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "fCReEiYF14o-",
        "outputId": "c0b47891-bb8c-406f-d97d-821a51a4adc5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training Accuracy Over Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_accuracy = history.history['val_accuracy']\n",
        "plt.plot(range(1, len(val_accuracy) + 1), val_accuracy)\n",
        "plt.title('Validation Accuracy Over Epochs')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "5hIVFS_q2C80",
        "outputId": "031006a9-b523-4b64-fb96-ba95be479ded"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Validation Accuracy Over Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}