MNIST_Tensorflow.ipynb

mnist_tensorflow.ipynb

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  "metadata": {
    "colab": {
      "name": "MNIST_Tensorflow.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN2JP+1jeXpwq0iBtmY17jC",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/HarshCasper/f0003a4996f1784b8707a15692be5094/mnist_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
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        "id": "uLcFmwDGoaqZ",
        "colab_type": "code",
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          "height": 96
        },
        "outputId": "a165a7b4-5331-468a-cb4e-5f754739df7c"
      },
      "source": [
        "import tensorflow as tf\n",
        "mnist = tf.keras.datasets.mnist\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": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
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        {
          "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": "Ab3zQcNlogmk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "2f63dbc2-85b9-4811-b0b6-f17cbd6089e0"
      },
      "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, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plTIfDmUop53",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "c20f44a2-108d-4429-ddb7-81483f1104b3"
      },
      "source": [
        "model.fit(x_train, y_train, epochs=10)\n",
        "model.evaluate(x_test, y_test)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.0657 - acc: 0.9788\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 4s 71us/sample - loss: 0.0588 - acc: 0.9817\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 4s 72us/sample - loss: 0.0535 - acc: 0.9826\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 4s 72us/sample - loss: 0.0479 - acc: 0.9842\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 4s 72us/sample - loss: 0.0451 - acc: 0.9850\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.0387 - acc: 0.9869\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 4s 72us/sample - loss: 0.0387 - acc: 0.9871\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.0352 - acc: 0.9883\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.0342 - acc: 0.9885\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 4s 72us/sample - loss: 0.0324 - acc: 0.9892\n",
            "10000/10000 [==============================] - 0s 30us/sample - loss: 0.0827 - acc: 0.9791\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08271336156373436, 0.9791]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpaQMRXLorms",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
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