hcr.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hcr.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ganesh-deshmukh/Google-colab-hcr/blob/master/hcr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "q5NXIxbl5cmk",
        "colab_type": "code",
        "outputId": "d6d83500-898e-4781-b9c3-782d7ee5ca9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "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\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=5)\n",
        "model.evaluate(x_test, y_test)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 15s 250us/sample - loss: 0.2207 - acc: 0.9351\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 15s 249us/sample - loss: 0.0959 - acc: 0.9703\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 14s 237us/sample - loss: 0.0687 - acc: 0.9779\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 14s 240us/sample - loss: 0.0538 - acc: 0.9834\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 14s 239us/sample - loss: 0.0423 - acc: 0.9861\n",
            "10000/10000 [==============================] - 1s 57us/sample - loss: 0.0685 - acc: 0.9807\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.06845194395067519, 0.9807]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "jW-pOBxrtTC4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Successfully trained tensorflow with accuracy of 97%**"
      ]
    },
    {
      "metadata": {
        "id": "e1eHRgmA-A3G",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "iBv2kfa6-vYh",
        "colab_type": "code",
        "outputId": "e7083035-1224-4678-e6b0-5617a6b4542a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CyU2jqwU-zGT",
        "colab_type": "code",
        "outputId": "0cf86499-7932-444e-b22e-e55456e1f529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "cell_type": "code",
      "source": [
        "!ls\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anscombe.json\t\t      mnist_test.csv\n",
            "california_housing_test.csv   mnist_train_small.csv\n",
            "california_housing_train.csv  README.md\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}