TASK1_SG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wF5wszaj97Y"
      },
      "source": [
        "# MNIST using Neural Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnrWf3PCEzXL"
      },
      "source": [
        "Run the statement to import the TensorFlow library:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0trJmd6DjqBZ"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NAbSZiaoJ4z"
      },
      "source": [
        "Run the statement to load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) and to convert the samples from integers to floating-point numbers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FP5258xjs-v"
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPZ68wASog_I"
      },
      "source": [
        "Build the model with multiple convolution, pooling, dense layers and relu activation function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3IKyzTCDNGo"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Reshape((28,28,1), input_shape=(28,28)),\n",
        "  tf.keras.layers.Conv2D(5, (5,5), activation='relu'),\n",
        "  tf.keras.layers.MaxPool2D((2,2)),\n",
        "  tf.keras.layers.Conv2D(15, (5,5), activation='relu'),\n",
        "  tf.keras.layers.MaxPool2D((2,2)), \n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(120, activation='relu'),  \n",
        "  tf.keras.layers.Dense(80, activation='relu'),  \n",
        "  tf.keras.layers.Dense(40, activation='relu'),    \n",
        "  tf.keras.layers.Dense(10, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQyugpgRIyrA"
      },
      "source": [
        "Choosing our loss function, in this case crossentropy:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSkzdv8MD0tT"
      },
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9foNKHzTD2Vo"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=loss_fn,\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ix4mEL65on-w"
      },
      "source": [
        "Adjusting the model parameters to minimize the loss of our model: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7suUbJXVLqP",
        "outputId": "679a2a7d-90f2-4827-deb6-b950793b227e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 1.5402 - accuracy: 0.9171\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.4864 - accuracy: 0.9688\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 1.4800 - accuracy: 0.9761\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 1.4767 - accuracy: 0.9798\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.4751 - accuracy: 0.9813\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1ec03ca630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mDAAPFqVVgn"
      },
      "source": [
        "Checking the model's performance on the Validation-set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7dTAzgHDUh7",
        "outputId": "8cc7a2b0-5cbd-49f1-cd9d-544a0d4e2e50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "model.evaluate(x_test,  y_test, verbose=2)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 1.4754 - accuracy: 0.9819\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4754043817520142, 0.9818999767303467]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    }
  ]
}