1_Keras_Intro_Sequential.ipynb

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  "metadata": {
    "colab": {
      "name": "1_Keras_Intro_Sequential.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jvishnuvardhan/Keras_Examples/blob/master/1_Keras_Intro_Sequential.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJ8oMnbsKjRH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "3922f5b8-0fd9-4864-af48-847112871f3f"
      },
      "source": [
        "!pip install tensorflow==2.0.0rc0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.0.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fb/4b/77f0965ec7e8a76d3dcd6a22ca8bbd2b934cd92c4ded43fef6bea5ff3258/tensorflow-2.0.0rc0-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n",
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            "Collecting tb-nightly<1.15.0a20190807,>=1.15.0a20190806 (from tensorflow==2.0.0rc0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bc/88/24b5fb7280e74c7cf65bde47c171547fd02afb3840cff41bcbe9270650f5/tb_nightly-1.15.0a20190806-py3-none-any.whl (4.3MB)\n",
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            "Collecting tf-estimator-nightly<1.14.0.dev2019080602,>=1.14.0.dev2019080601 (from tensorflow==2.0.0rc0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/21/28/f2a27a62943d5f041e4a6fd404b2d21cb7c59b2242a4e73b03d9ba166552/tf_estimator_nightly-1.14.0.dev2019080601-py2.py3-none-any.whl (501kB)\n",
            "\u001b[K     |████████████████████████████████| 501kB 56.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0rc0) (0.8.0)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0rc0) (3.1.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0rc0) (41.2.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a20190807,>=1.15.0a20190806->tensorflow==2.0.0rc0) (0.15.5)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0rc0) (2.8.0)\n",
            "Installing collected packages: tb-nightly, tf-estimator-nightly, tensorflow\n",
            "  Found existing installation: tensorflow 1.14.0\n",
            "    Uninstalling tensorflow-1.14.0:\n",
            "      Successfully uninstalled tensorflow-1.14.0\n",
            "Successfully installed tb-nightly-1.15.0a20190806 tensorflow-2.0.0rc0 tf-estimator-nightly-1.14.0.dev2019080601\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdNnWfy3Kp0g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "61898373-6598-482f-c6fb-09b5045ae23d"
      },
      "source": [
        "import tensorflow as tf\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "#load data\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",
        "# create a model\n",
        "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",
        "# compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# train the model\n",
        "model.fit(x_train, y_train, epochs=5)\n",
        "\n",
        "# Evaluate the model\n",
        "model.evaluate(x_test, y_test,verbose=0)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.2982 - accuracy: 0.9121\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.1450 - accuracy: 0.9570\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.1115 - accuracy: 0.9664\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0894 - accuracy: 0.9730\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0762 - accuracy: 0.9765\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07190327298501506, 0.9778]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94REAXJHNEnc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "7dde2c25-afaf-486c-95e5-81abb6ae0078"
      },
      "source": [
        "model.evaluate(x_test, y_test,verbose=2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/1 - 0s - loss: 0.0366 - accuracy: 0.9778\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07190327298501506, 0.9778]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDtW_elbNNW-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}