Devanshu_Chatterjee.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Devanshu Chatterjee.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMQDFvfPrJAX"
      },
      "source": [
        "#Deep Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UDXCTy6rReq"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "\n",
        "data = mnist.load_data()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mzcNeQ5r0zX"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = data"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrHDVoaQtir5"
      },
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4zOp9Zv3dpn"
      },
      "source": [
        "X_train = X_train / 255\n",
        "X_test = X_test / 255"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZWyH2U93wjC",
        "outputId": "d7b97640-1f6f-499e-c819-ee3c9404d99e"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000,)\n",
            "(10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWaNVHBe4pJQ"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkJRSQ1V4xs5"
      },
      "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": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYh2cvWl5-AF"
      },
      "source": [
        "model.compile(loss= 'categorical_crossentropy', optimizer= 'adam', metrics= ['accuracy'])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lN1_krNL6XvH",
        "outputId": "2f3add1f-09f3-41c9-d799-07913c1f5de4"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 11,
      "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": "vVcV2ud06ZdQ",
        "outputId": "b7861334-e9dc-484e-ebba-3f5760d1a5e9"
      },
      "source": [
        "model.fit(X_train, y_train, epochs= 10, batch_size= 100)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.7706 - accuracy: 0.7735\n",
            "Epoch 2/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.2121 - accuracy: 0.9371\n",
            "Epoch 3/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.1651 - accuracy: 0.9516\n",
            "Epoch 4/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.1351 - accuracy: 0.9594\n",
            "Epoch 5/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.1132 - accuracy: 0.9671\n",
            "Epoch 6/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.1043 - accuracy: 0.9687\n",
            "Epoch 7/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.0907 - accuracy: 0.9733\n",
            "Epoch 8/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.0794 - accuracy: 0.9754\n",
            "Epoch 9/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.0684 - accuracy: 0.9797\n",
            "Epoch 10/10\n",
            "600/600 [==============================] - 1s 2ms/step - loss: 0.0634 - accuracy: 0.9808\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f499e0b9210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0siXe096oSL",
        "outputId": "d88269c0-e093-404b-cbe5-3d793f3ed74a"
      },
      "source": [
        "scores= model.evaluate(X_test, y_test)\n",
        "print(scores)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.1031 - accuracy: 0.9683\n",
            "[0.10313186049461365, 0.9682999849319458]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}