mnist_test_data_not_normalized.ipynb

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  "metadata": {
    "colab": {
      "name": "mnist_test_data_not_normalized.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOWUM/JBXRxTSCWvWH5OUV/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jvishnuvardhan/Stackoverflow_Questions/blob/master/mnist_test_data_not_normalized.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaZlZ3g8xHXF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5WBNUP6xLfX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "346dc9b1-7653-4759-f8fb-87e7d484a422"
      },
      "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 / 1.\n",
        "# this is to demonstrate the importance of normalizing both training and testing data"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bD77zo7xL_I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fHRnra6xN9w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "be244d7a-1475-433c-f2c0-fd3c559691ec"
      },
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "model.compile(optimizer='adam',\n",
        "              loss=loss_fn,\n",
        "              metrics=['accuracy'])\n",
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2983 - accuracy: 0.9124\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1447 - accuracy: 0.9574\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1089 - accuracy: 0.9667\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0908 - accuracy: 0.9718\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0771 - accuracy: 0.9754\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7efc5fa212b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSj3lQwRxlKu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0c88fd45-2305-45ce-c408-c207b47ab745"
      },
      "source": [
        "# evaluating on test data that was not normalized results in very high loss when compared to loss during training\n",
        "model.evaluate(x_test,  y_test, verbose=2)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 13.1599 - accuracy: 0.9748\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[13.159945487976074, 0.9747999906539917]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yiZy5ZOxweA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}