flower_power.ipynb

{
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  "metadata": {
    "colab": {
      "name": "flower_power.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bklooste/tensorflowcollab/blob/master/flower_power.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "i9mGezkcuIcm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Iris Flower Classifier"
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "NYJ5VWQNuIcn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3973ba35-7de1-4152-b732-3a58be879a40"
      },
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow.contrib.eager as tfe\n",
        "\n",
        "tf.enable_eager_execution()\n",
        "\n",
        "print(\"TensorFlow version: {}\".format(tf.VERSION))\n",
        "print(\"Eager execution: {}\".format(tf.executing_eagerly()))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow version: 1.13.0-rc1\n",
            "Eager execution: True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "La6xSWmEuIct",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Import and parse the training dataset"
      ]
    },
    {
      "metadata": {
        "id": "4nSjYkdpuIcv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "70e18346-4889-4d45-ed0d-39872b94850d"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "iris_train_data = pd.read_csv('http://download.tensorflow.org/data/iris_training.csv', skiprows=[0], names = ['sepal length','sepal width','petal length','petal width','Type'])\n",
        "iris_train_data['Type'] = np.where(iris_train_data['Type']==0, 'setosa', np.where(iris_train_data['Type']==1, 'versicolor', 'virginica'))\n",
        "\n",
        "train_dataset_url = \"http://download.tensorflow.org/data/iris_training.csv\"\n",
        "train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url),\n",
        "                                           origin=train_dataset_url)\n",
        "print(\"Local copy of the dataset file: {}\".format(train_dataset_fp))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://download.tensorflow.org/data/iris_training.csv\n",
            "\r8192/2194 [================================================================================================================] - 0s 0us/step\n",
            "Local copy of the dataset file: /root/.keras/datasets/iris_training.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1uABbrV8uIcy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Inspect the data"
      ]
    },
    {
      "metadata": {
        "id": "3Jy8QSUkuIc1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        },
        "outputId": "5e17da4c-2092-40ba-cbce-b75494f2f568"
      },
      "cell_type": "code",
      "source": [
        "iris_train_data"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.2</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.6</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.7</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>4.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length  sepal width  petal length  petal width        Type\n",
              "0             6.4          2.8           5.6          2.2   virginica\n",
              "1             5.0          2.3           3.3          1.0  versicolor\n",
              "2             4.9          2.5           4.5          1.7   virginica\n",
              "3             4.9          3.1           1.5          0.1      setosa\n",
              "4             5.7          3.8           1.7          0.3      setosa\n",
              "5             4.4          3.2           1.3          0.2      setosa\n",
              "6             5.4          3.4           1.5          0.4      setosa\n",
              "7             6.9          3.1           5.1          2.3   virginica\n",
              "8             6.7          3.1           4.4          1.4  versicolor\n",
              "9             5.1          3.7           1.5          0.4      setosa\n",
              "10            5.2          2.7           3.9          1.4  versicolor\n",
              "11            6.9          3.1           4.9          1.5  versicolor\n",
              "12            5.8          4.0           1.2          0.2      setosa\n",
              "13            5.4          3.9           1.7          0.4      setosa\n",
              "14            7.7          3.8           6.7          2.2   virginica\n",
              "15            6.3          3.3           4.7          1.6  versicolor\n",
              "16            6.8          3.2           5.9          2.3   virginica\n",
              "17            7.6          3.0           6.6          2.1   virginica\n",
              "18            6.4          3.2           5.3          2.3   virginica\n",
              "19            5.7          4.4           1.5          0.4      setosa\n",
              "20            6.7          3.3           5.7          2.1   virginica\n",
              "21            6.4          2.8           5.6          2.1   virginica\n",
              "22            5.4          3.9           1.3          0.4      setosa\n",
              "23            6.1          2.6           5.6          1.4   virginica\n",
              "24            7.2          3.0           5.8          1.6   virginica\n",
              "25            5.2          3.5           1.5          0.2      setosa\n",
              "26            5.8          2.6           4.0          1.2  versicolor\n",
              "27            5.9          3.0           5.1          1.8   virginica\n",
              "28            5.4          3.0           4.5          1.5  versicolor\n",
              "29            6.7          3.0           5.0          1.7  versicolor\n",
              "..            ...          ...           ...          ...         ...\n",
              "90            6.5          3.0           5.2          2.0   virginica\n",
              "91            6.1          2.8           4.7          1.2  versicolor\n",
              "92            5.1          3.5           1.4          0.3      setosa\n",
              "93            4.6          3.1           1.5          0.2      setosa\n",
              "94            6.5          3.0           5.8          2.2   virginica\n",
              "95            4.6          3.4           1.4          0.3      setosa\n",
              "96            4.6          3.2           1.4          0.2      setosa\n",
              "97            7.7          2.8           6.7          2.0   virginica\n",
              "98            5.9          3.2           4.8          1.8  versicolor\n",
              "99            5.1          3.8           1.6          0.2      setosa\n",
              "100           4.9          3.0           1.4          0.2      setosa\n",
              "101           4.9          2.4           3.3          1.0  versicolor\n",
              "102           4.5          2.3           1.3          0.3      setosa\n",
              "103           5.8          2.7           4.1          1.0  versicolor\n",
              "104           5.0          3.4           1.6          0.4      setosa\n",
              "105           5.2          3.4           1.4          0.2      setosa\n",
              "106           5.3          3.7           1.5          0.2      setosa\n",
              "107           5.0          3.6           1.4          0.2      setosa\n",
              "108           5.6          2.9           3.6          1.3  versicolor\n",
              "109           4.8          3.1           1.6          0.2      setosa\n",
              "110           6.3          2.7           4.9          1.8   virginica\n",
              "111           5.7          2.8           4.1          1.3  versicolor\n",
              "112           5.0          3.0           1.6          0.2      setosa\n",
              "113           6.3          3.3           6.0          2.5   virginica\n",
              "114           5.0          3.5           1.6          0.6      setosa\n",
              "115           5.5          2.6           4.4          1.2  versicolor\n",
              "116           5.7          3.0           4.2          1.2  versicolor\n",
              "117           4.4          2.9           1.4          0.2      setosa\n",
              "118           4.8          3.0           1.4          0.1      setosa\n",
              "119           5.5          2.4           3.7          1.0  versicolor\n",
              "\n",
              "[120 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "LJ6zk7otuIc3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Parse the dataset"
      ]
    },
    {
      "metadata": {
        "id": "-JP_1JWnuIc4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def parse_csv(line):\n",
        "  example_defaults = [[0.], [0.], [0.], [0.], [0]]  # sets field types\n",
        "  parsed_line = tf.decode_csv(line, example_defaults)\n",
        "  # First 4 fields are features, combine into single tensor\n",
        "  features = tf.reshape(parsed_line[:-1], shape=(4,))\n",
        "  # Last field is the label\n",
        "  label = tf.reshape(parsed_line[-1], shape=())\n",
        "  return features, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RU8rYIAeuIc6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create the training tf.data.Dataset"
      ]
    },
    {
      "metadata": {
        "id": "ON1pqhKPuIc7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "fdd29f9d-fa63-4c3c-d720-2fc979fa5829"
      },
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.TextLineDataset(train_dataset_fp)\n",
        "train_dataset = train_dataset.skip(1)             # skip the first header row\n",
        "train_dataset = train_dataset.map(parse_csv)      # parse each row\n",
        "train_dataset = train_dataset.shuffle(buffer_size=1000)  # randomize\n",
        "train_dataset = train_dataset.batch(32)\n",
        "\n",
        "# View a single example entry from a batch\n",
        "features, label = tfe.Iterator(train_dataset).next()\n",
        "print(\"example features:\", features[0])\n",
        "print(\"example label:\", label[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/iterator_ops.py:532: 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",
            "example features: tf.Tensor([5.  2.  3.5 1. ], shape=(4,), dtype=float32)\n",
            "example label: tf.Tensor(1, shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ftxrMmo2uIc9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "173916ad-4f4f-4801-8b76-cc79e894ff91"
      },
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.TextLineDataset(train_dataset_fp)\n",
        "train_dataset = train_dataset.skip(1)             # skip the first header row\n",
        "train_dataset = train_dataset.map(parse_csv)      # parse each row\n",
        "train_dataset = train_dataset.shuffle(buffer_size=1000)  # randomize\n",
        "train_dataset = train_dataset.batch(32)\n",
        "\n",
        "# View a single example entry from a batch\n",
        "features, label = tfe.Iterator(train_dataset).next()\n",
        "print(\"example features:\", features[0])\n",
        "print(\"example label:\", label[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "example features: tf.Tensor([5.5 3.5 1.3 0.2], shape=(4,), dtype=float32)\n",
            "example label: tf.Tensor(0, shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "k3lsmQqUuIdB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Select the type of model"
      ]
    },
    {
      "metadata": {
        "id": "CJbrUNnUuIdB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This tutorial uses a neural network to solve the Iris classification problem. \n",
        "# We will define a fully-connected neural network\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\", input_shape=(4,)),  # input shape required\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(3)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PWBn56EbuIdE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Define the loss and gradient function"
      ]
    },
    {
      "metadata": {
        "id": "zPQjKEuauIdE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def loss(model, x, y):\n",
        "  y_ = model(x)\n",
        "  return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)\n",
        "\n",
        "def grad(model, inputs, targets):\n",
        "  with tfe.GradientTape() as tape:\n",
        "    loss_value = loss(model, inputs, targets)\n",
        "  return tape.gradient(loss_value, model.variables)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pdFFFY64uIdI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create an optimizer"
      ]
    },
    {
      "metadata": {
        "id": "xXZXjrPwuIdJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sWMhIuzcuIdL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ]
    },
    {
      "metadata": {
        "id": "1HGDB4-duIdM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "ee9f5730-e11c-47ab-f21f-533235b95e23"
      },
      "cell_type": "code",
      "source": [
        "## Note: Rerunning this cell uses the same model variables\n",
        "\n",
        "# keep results for plotting\n",
        "train_loss_results = []\n",
        "train_accuracy_results = []\n",
        "\n",
        "num_epochs = 201\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  epoch_loss_avg = tfe.metrics.Mean()\n",
        "  epoch_accuracy = tfe.metrics.Accuracy()\n",
        "\n",
        "  # Training loop - using batches of 32\n",
        "  for x, y in tfe.Iterator(train_dataset):\n",
        "    # Optimize the model\n",
        "    grads = grad(model, x, y)\n",
        "    optimizer.apply_gradients(zip(grads, model.variables),\n",
        "                              global_step=tf.train.get_or_create_global_step())\n",
        "\n",
        "    # Track progress\n",
        "    epoch_loss_avg(loss(model, x, y))  # add current batch loss\n",
        "    # compare predicted label to actual label\n",
        "    epoch_accuracy(tf.argmax(model(x), axis=1, output_type=tf.int32), y)\n",
        "\n",
        "  # end epoch\n",
        "  train_loss_results.append(epoch_loss_avg.result())\n",
        "  train_accuracy_results.append(epoch_accuracy.result())\n",
        "\n",
        "  if epoch % 50 == 0:\n",
        "    print(\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(epoch,\n",
        "                                                                epoch_loss_avg.result(),\n",
        "                                                                epoch_accuracy.result()))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:209: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 000: Loss: 1.712, Accuracy: 30.000%\n",
            "Epoch 050: Loss: 0.814, Accuracy: 70.000%\n",
            "Epoch 100: Loss: 0.426, Accuracy: 86.667%\n",
            "Epoch 150: Loss: 0.280, Accuracy: 96.667%\n",
            "Epoch 200: Loss: 0.189, Accuracy: 96.667%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "i5XJOxELuIdP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualize the loss function over time"
      ]
    },
    {
      "metadata": {
        "id": "WF1TljnruIdQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "dc148c5d-4b6e-428a-f516-be10cb37f344"
      },
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\n",
        "fig.suptitle('Training Metrics')\n",
        "\n",
        "axes[0].set_ylabel(\"Loss\", fontsize=14)\n",
        "axes[0].plot(train_loss_results)\n",
        "\n",
        "axes[1].set_ylabel(\"Accuracy\", fontsize=14)\n",
        "axes[1].set_xlabel(\"Epoch\", fontsize=14)\n",
        "axes[1].plot(train_accuracy_results)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hw2zbti1WJZ0T9zthWvOmRURERCaumIVpm83Ggw8+SCAQOO0+a9eu5etf/3qsSjonCtMi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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "p3iv3CgwuIdT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluate the model's effectiveness"
      ]
    },
    {
      "metadata": {
        "id": "VkTY06PIuIdU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Setup the test dataset"
      ]
    },
    {
      "metadata": {
        "id": "zHQrVLkVuIdW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "a4843b14-c505-4ced-ed68-ca317dec8a75"
      },
      "cell_type": "code",
      "source": [
        "iris_test_data = pd.read_csv('http://download.tensorflow.org/data/iris_test.csv', skiprows=[0], names = ['sepal length','sepal width','petal length','petal width','Type'])\n",
        "iris_test_data['Type'] = np.where(iris_test_data['Type']==0, 'setosa', np.where(iris_test_data['Type']==1, 'versicolor', 'virginica'))\n",
        "\n",
        "test_url = \"http://download.tensorflow.org/data/iris_test.csv\"\n",
        "\n",
        "test_fp = tf.keras.utils.get_file(fname=os.path.basename(test_url),\n",
        "                                  origin=test_url)\n",
        "\n",
        "test_dataset = tf.data.TextLineDataset(test_fp)\n",
        "test_dataset = test_dataset.skip(1)             # skip header row\n",
        "test_dataset = test_dataset.map(parse_csv)      # parse each row with the funcition created earlier\n",
        "test_dataset = test_dataset.shuffle(1000)       # randomize\n",
        "test_dataset = test_dataset.batch(32)           # use the same batch size as the training set"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://download.tensorflow.org/data/iris_test.csv\n",
            "\r8192/573 [============================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fRDZEp9MuIdZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "52d7007b-51df-4888-a69f-136ef550a292"
      },
      "cell_type": "code",
      "source": [
        "iris_test_data"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal length  sepal width  petal length  petal width        Type\n",
              "0            5.9          3.0           4.2          1.5  versicolor\n",
              "1            6.9          3.1           5.4          2.1   virginica\n",
              "2            5.1          3.3           1.7          0.5      setosa\n",
              "3            6.0          3.4           4.5          1.6  versicolor\n",
              "4            5.5          2.5           4.0          1.3  versicolor\n",
              "5            6.2          2.9           4.3          1.3  versicolor\n",
              "6            5.5          4.2           1.4          0.2      setosa\n",
              "7            6.3          2.8           5.1          1.5   virginica\n",
              "8            5.6          3.0           4.1          1.3  versicolor\n",
              "9            6.7          2.5           5.8          1.8   virginica\n",
              "10           7.1          3.0           5.9          2.1   virginica\n",
              "11           4.3          3.0           1.1          0.1      setosa\n",
              "12           5.6          2.8           4.9          2.0   virginica\n",
              "13           5.5          2.3           4.0          1.3  versicolor\n",
              "14           6.0          2.2           4.0          1.0  versicolor\n",
              "15           5.1          3.5           1.4          0.2      setosa\n",
              "16           5.7          2.6           3.5          1.0  versicolor\n",
              "17           4.8          3.4           1.9          0.2      setosa\n",
              "18           5.1          3.4           1.5          0.2      setosa\n",
              "19           5.7          2.5           5.0          2.0   virginica\n",
              "20           5.4          3.4           1.7          0.2      setosa\n",
              "21           5.6          3.0           4.5          1.5  versicolor\n",
              "22           6.3          2.9           5.6          1.8   virginica\n",
              "23           6.3          2.5           4.9          1.5  versicolor\n",
              "24           5.8          2.7           3.9          1.2  versicolor\n",
              "25           6.1          3.0           4.6          1.4  versicolor\n",
              "26           5.2          4.1           1.5          0.1      setosa\n",
              "27           6.7          3.1           4.7          1.5  versicolor\n",
              "28           6.7          3.3           5.7          2.5   virginica\n",
              "29           6.4          2.9           4.3          1.3  versicolor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "voKr8RjFuIdd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Evaluate the model on the test dataset"
      ]
    },
    {
      "metadata": {
        "id": "JTn_G4fkuIdd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5acae606-26cf-4f3f-f65c-9408e3066f3d"
      },
      "cell_type": "code",
      "source": [
        "test_accuracy = tfe.metrics.Accuracy()\n",
        "\n",
        "for (x, y) in tfe.Iterator(test_dataset):\n",
        "  prediction = tf.argmax(model(x), axis=1, output_type=tf.int32)\n",
        "  test_accuracy(prediction, y)\n",
        "\n",
        "print(\"Test set accuracy: {:.3%}\".format(test_accuracy.result()))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set accuracy: 100.000%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "durJlMmkuIdk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Use the trained model to make predictions"
      ]
    },
    {
      "metadata": {
        "id": "7cg4avmiuIdm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c27bd960-d6f3-4207-8754-f1741222eb11"
      },
      "cell_type": "code",
      "source": [
        "class_ids = [\"Iris setosa\", \"Iris versicolor\", \"Iris virginica\"]\n",
        "\n",
        "predict_dataset = tf.convert_to_tensor([[1.1, 5.3, 13.7, 10.5]])\n",
        "\n",
        "predictions = model(predict_dataset)\n",
        "\n",
        "for i, logits in enumerate(predictions):\n",
        "  class_idx = tf.argmax(logits).numpy()\n",
        "  name = class_ids[class_idx]\n",
        "  print(\"Example {} prediction: {}\".format(i, name))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Example 0 prediction: Iris virginica\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ASoOHOIwuIdq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Expose Model "
      ]
    },
    {
      "metadata": {
        "id": "fe7019CkuIdr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### show random iris"
      ]
    },
    {
      "metadata": {
        "id": "cf_nu2Z8uIds",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "iris_test_data = pd.read_csv('http://download.tensorflow.org/data/iris_test.csv', skiprows=[0], names = ['sepal length','sepal width','petal length','petal width','Type'])\n",
        "iris_test_data['Type'] = np.where(iris_test_data['Type']==0, 'setosa', np.where(iris_test_data['Type']==1, 'versicolor', 'virginica'))\n",
        "\n",
        "def show_random_iris():\n",
        "    choices = ['setosa','versicolor','virginica']\n",
        "    random_flower = random.choice(choices)\n",
        "    flowers = {\n",
        "        'setosa': 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg/440px-Kosaciec_szczecinkowaty_Iris_setosa.jpg',\n",
        "        'versicolor': 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Iris_versicolor_3.jpg/440px-Iris_versicolor_3.jpg',\n",
        "        'virginica': 'https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/Iris_virginica.jpg/440px-Iris_virginica.jpg'\n",
        "    }\n",
        "    random_url = flowers[random_flower]\n",
        "    single_type = iris_test_data[(iris_test_data['Type']==random_flower)]\n",
        "    random_row = single_type.sample(n=1).drop(['Type'], axis=1)\n",
        "    final = random_flower, random_url, random_row\n",
        "    return(final)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hEe9VSWKuIdv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### predict flower"
      ]
    },
    {
      "metadata": {
        "id": "6GU1ewPYuIdw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def predict_flower(guess_string):\n",
        "    guess_list = eval(guess_string)\n",
        "    predict_dataset = tf.convert_to_tensor([guess_list])\n",
        "    predictions = model(predict_dataset)\n",
        "    for i, logits in enumerate(predictions):\n",
        "        class_idx = tf.argmax(logits).numpy()\n",
        "        name = class_ids[class_idx]\n",
        "    return(name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AiMh8znYuIdy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Expose via REST API endpoints"
      ]
    },
    {
      "metadata": {
        "id": "lURhUwLXuIdz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "59weywAcuId0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a83dd500-927f-4fd3-c5ad-70a6b6e0afe7"
      },
      "cell_type": "code",
      "source": [
        "# GET /get_random_flower\n",
        "\n",
        "random_result = show_random_iris()\n",
        "res = {\n",
        "    \"name\" : random_result[0],\n",
        "    \"url\" : random_result[1],\n",
        "    \"data\" : {\"keys\" : list(random_result[2]), \"values\" : random_result[2].iloc[0].tolist()}\n",
        "}\n",
        "\n",
        "print(json.dumps(res))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"name\": \"virginica\", \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/Iris_virginica.jpg/440px-Iris_virginica.jpg\", \"data\": {\"keys\": [\"sepal length\", \"sepal width\", \"petal length\", \"petal width\"], \"values\": [6.7, 3.3, 5.7, 2.5]}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kkW75lKEuId3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# GET /predict_flower\n",
        "\n",
        "# req = json.loads(REQUEST)\n",
        "# args = req['args']\n",
        "\n",
        "# guess_string = args['guess_string'][0] # first argument from URL \n",
        "\n",
        "# predicted_flower = predict_flower(guess_string)\n",
        "\n",
        "# print(json.dumps(predicted_flower))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K0gJeU_IuId_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rj0niO4PuIeB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}