Barika_Khalidi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Barika Khalidi.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMVG6kVmgyIk"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "data=mnist.load_data()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9gIy4m0iiIR"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = data"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqcBOlUupP-z"
      },
      "source": [
        "X_train = X_train.reshape((X_train.shape[0], 28*28)).astype('float32')\n",
        "X_test = X_test.reshape((X_test.shape[0], 28*28)).astype('float32')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9BVrY-opP2o"
      },
      "source": [
        "X_train = X_train / 255\n",
        "X_test = X_test / 255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aQWzAvHra40",
        "outputId": "154be9a7-e6a5-45e6-fe41-d87daee244da"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "\n",
        "print(y_test.shape)\n",
        "\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test= np_utils.to_categorical(y_test)\n",
        "\n",
        "num_classes = y_test.shape[1]\n",
        "print(y_test.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000,)\n",
            "(10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55BNc1RpsEbK"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TF-M9X0LsQVG"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(32, input_dim=28*28, activation='relu'))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pq6MCmoBs1Gr"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GkpK2bXtJSv",
        "outputId": "793d0079-57e3-456d-c4da-0567b7042837"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 27,882\n",
            "Trainable params: 27,882\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaAqDKpstUPy",
        "outputId": "9824e414-df6d-4862-c284-3402e35c87ae"
      },
      "source": [
        "model.fit(X_train, y_train, epochs=10, batch_size=100)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "600/600 [==============================] - 2s 2ms/step - loss: 6.5867 - accuracy: 0.5332\n",
            "Epoch 2/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.6395 - accuracy: 0.8281\n",
            "Epoch 3/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.4590 - accuracy: 0.8784\n",
            "Epoch 4/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.3775 - accuracy: 0.8996\n",
            "Epoch 5/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.3345 - accuracy: 0.9119\n",
            "Epoch 6/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.3170 - accuracy: 0.9176\n",
            "Epoch 7/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.2838 - accuracy: 0.9266\n",
            "Epoch 8/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.2807 - accuracy: 0.9290\n",
            "Epoch 9/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.2601 - accuracy: 0.9324\n",
            "Epoch 10/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.2507 - accuracy: 0.9343\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc76f7b6690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v23GLhMTtqGW",
        "outputId": "18bb38aa-a014-437f-dfbf-3e8396cead62"
      },
      "source": [
        "scores = model.evaluate(X_test, y_test)\n",
        "print(scores)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.3080 - accuracy: 0.9308\n",
            "[0.30795958638191223, 0.9308000206947327]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}