regressionAutoDataset.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FhGuhbZ6M5tl"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Tutorial from : https://www.tensorflow.org/tutorials/keras/regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "AwOEIRJC6Une"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "KyPEtTqk6VdG"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EIdT9iu_Z4Rb"
   },
   "source": [
    "# Basic regression: Predict fuel efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AHp3M9ZmrIxj"
   },
   "source": [
    "In a *regression* problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a *classification* problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).\n",
    "\n",
    "This notebook uses the classic [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) Dataset and builds a model to predict the fuel efficiency of late-1970s and early 1980s automobiles. To do this, we'll provide the model with a description of many automobiles from that time period. This description includes attributes like: cylinders, displacement, horsepower, and weight.\n",
    "\n",
    "This example uses the `tf.keras` API, see [this guide](https://www.tensorflow.org/guide/keras) for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "moB4tpEHxKB3"
   },
   "outputs": [],
   "source": [
    "# Use seaborn for pairplot\n",
    "#!pip install -q seaborn\n",
    "\n",
    "# Use some functions from tensorflow_docs\n",
    "#!pip install -q git+https://github.com/tensorflow/docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9xQKvCJ85kCQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qz4HfsgRQUiV"
   },
   "outputs": [],
   "source": [
    "import tensorflow_docs as tfdocs\n",
    "import tensorflow_docs.plots\n",
    "import tensorflow_docs.modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F_72b0LCNbjx"
   },
   "source": [
    "## The Auto MPG dataset\n",
    "\n",
    "The dataset is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "### Get the data\n",
    "First download the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p9kxxgzvzlyz"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/nirupamaprv/.keras/datasets/auto-mpg.data'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
    "dataset_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nslsRLh7Zss4"
   },
   "source": [
    "Import it using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
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       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3MWuJTKEDM-f"
   },
   "source": [
    "### Clean the data\n",
    "\n",
    "The dataset contains a few unknown values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JEJHhN65a2VV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
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     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9UPN0KBHa_WI"
   },
   "source": [
    "To keep this initial tutorial simple drop those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4ZUDosChC1UN"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8XKitwaH4v8h"
   },
   "source": [
    "The `\"Origin\"` column is really categorical, not numeric. So convert that to a one-hot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ulXz4J7PAUzk"
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       "      <th>MPG</th>\n",
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       "      <td>4</td>\n",
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       "      <td>82</td>\n",
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      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Europe  Japan  USA  \n",
       "393          82       0      0    1  \n",
       "394          82       1      0    0  \n",
       "395          82       0      0    1  \n",
       "396          82       0      0    1  \n",
       "397          82       0      0    1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.get_dummies(dataset, prefix='', prefix_sep='')\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cuym4yvk76vU"
   },
   "source": [
    "### Split the data into train and test\n",
    "\n",
    "Now split the dataset into a training set and a test set.\n",
    "\n",
    "We will use the test set in the final evaluation of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qn-IGhUE7_1H"
   },
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J4ubs136WLNp"
   },
   "source": [
    "### Inspect the data\n",
    "\n",
    "Have a quick look at the joint distribution of a few pairs of columns from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gavKO_6DWRMP"
   },
   "source": [
    "Also look at the overall statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yi2FzC3T21jR"
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  \n",
       "USA              1.00     1.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Db7Auq1yXUvh"
   },
   "source": [
    "### Split features from labels\n",
    "\n",
    "Separate the target value, or \"label\", from the features. This label is the value that you will train the model to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t2sluJdCW7jN"
   },
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mRklxK5s388r"
   },
   "source": [
    "### Normalize the data\n",
    "\n",
    "Look again at the `train_stats` block above and note how different the ranges of each feature are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "It is good practice to normalize features that use different scales and ranges. Although the model *might* converge without feature normalization, it makes training more difficult, and it makes the resulting model dependent on the choice of units used in the input.\n",
    "\n",
    "Note: Although we intentionally generate these statistics from only the training dataset, these statistics will also be used to normalize the test dataset. We need to do that to project the test dataset into the same distribution that the model has been trained on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JlC5ooJrgjQF"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BuiClDk45eS4"
   },
   "source": [
    "This normalized data is what we will use to train the model.\n",
    "\n",
    "Caution: The statistics used to normalize the inputs here (mean and standard deviation) need to be applied to any other data that is fed to the model, along with the one-hot encoding that we did earlier.  That includes the test set as well as live data when the model is used in production."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SmjdzxKzEu1-"
   },
   "source": [
    "## The model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "### Build the model\n",
    "\n",
    "Let's build our model. Here, we'll use a `Sequential` model with two densely connected hidden layers, and an output layer that returns a single, continuous value. The model building steps are wrapped in a function, `build_model`, since we'll create a second model, later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c26juK7ZG8j-"
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cGbPb-PHGbhs"
   },
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sj49Og4YGULr"
   },
   "source": [
    "### Inspect the model\n",
    "\n",
    "Use the `.summary` method to print a simple description of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ReAD0n6MsFK-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vt6W50qGsJAL"
   },
   "source": [
    "Now try out the model. Take a batch of `10` examples from the training data and call `model.predict` on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-d-gBaVtGTSC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03628789],\n",
       "       [-0.14436963],\n",
       "       [-0.10247705],\n",
       "       [-0.03965861],\n",
       "       [ 0.22395234],\n",
       "       [ 0.02750303],\n",
       "       [ 0.22819081],\n",
       "       [-0.05918464],\n",
       "       [ 0.02913216],\n",
       "       [ 0.2147843 ]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QlM8KrSOsaYo"
   },
   "source": [
    "It seems to be working, and it produces a result of the expected shape and type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "### Train the model\n",
    "\n",
    "Train the model for 1000 epochs, and record the training and validation accuracy in the `history` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sD7qHCmNIOY0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 0, loss:566.5291,  mae:22.5267,  mse:566.5291,  val_loss:554.9647,  val_mae:22.2130,  val_mse:554.9647,  \n",
      "....................................................................................................\n",
      "Epoch: 100, loss:6.0907,  mae:1.7599,  mse:6.0907,  val_loss:9.0492,  val_mae:2.2249,  val_mse:9.0492,  \n",
      "....................................................................................................\n",
      "Epoch: 200, loss:5.2842,  mae:1.6126,  mse:5.2842,  val_loss:10.0233,  val_mae:2.2696,  val_mse:10.0233,  \n",
      "....................................................................................................\n",
      "Epoch: 300, loss:5.0296,  mae:1.5155,  mse:5.0296,  val_loss:9.4095,  val_mae:2.3101,  val_mse:9.4095,  \n",
      "....................................................................................................\n",
      "Epoch: 400, loss:4.6550,  mae:1.4876,  mse:4.6550,  val_loss:9.7256,  val_mae:2.4091,  val_mse:9.7256,  \n",
      "....................................................................................................\n",
      "Epoch: 500, loss:4.0940,  mae:1.3542,  mse:4.0940,  val_loss:10.1837,  val_mae:2.4161,  val_mse:10.1837,  \n",
      "....................................................................................................\n",
      "Epoch: 600, loss:3.9632,  mae:1.3156,  mse:3.9632,  val_loss:10.8424,  val_mae:2.5501,  val_mse:10.8424,  \n",
      "....................................................................................................\n",
      "Epoch: 700, loss:3.6724,  mae:1.2571,  mse:3.6724,  val_loss:11.4767,  val_mae:2.5835,  val_mse:11.4767,  \n",
      "....................................................................................................\n",
      "Epoch: 800, loss:3.3763,  mae:1.2183,  mse:3.3763,  val_loss:11.7407,  val_mae:2.6103,  val_mse:11.7407,  \n",
      "....................................................................................................\n",
      "Epoch: 900, loss:2.9666,  mae:1.1527,  mse:2.9666,  val_loss:12.8266,  val_mae:2.7631,  val_mse:12.8266,  \n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[tfdocs.modeling.EpochDots()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tQm3pc0FYPQB"
   },
   "source": [
    "Visualize the model's training progress using the stats stored in the `history` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Xj91b-dymEy"
   },
   "outputs": [
    {
     "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>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.655242</td>\n",
       "      <td>1.097418</td>\n",
       "      <td>2.655242</td>\n",
       "      <td>13.454863</td>\n",
       "      <td>2.742705</td>\n",
       "      <td>13.454863</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.674859</td>\n",
       "      <td>1.107523</td>\n",
       "      <td>2.674859</td>\n",
       "      <td>13.336629</td>\n",
       "      <td>2.800503</td>\n",
       "      <td>13.336629</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>3.021471</td>\n",
       "      <td>1.124143</td>\n",
       "      <td>3.021471</td>\n",
       "      <td>13.477475</td>\n",
       "      <td>2.784582</td>\n",
       "      <td>13.477475</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.835783</td>\n",
       "      <td>1.099548</td>\n",
       "      <td>2.835783</td>\n",
       "      <td>13.526041</td>\n",
       "      <td>2.785244</td>\n",
       "      <td>13.526041</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.601588</td>\n",
       "      <td>1.017053</td>\n",
       "      <td>2.601588</td>\n",
       "      <td>13.494969</td>\n",
       "      <td>2.745609</td>\n",
       "      <td>13.494970</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  2.655242  1.097418  2.655242  13.454863  2.742705  13.454863    995\n",
       "996  2.674859  1.107523  2.674859  13.336629  2.800503  13.336629    996\n",
       "997  3.021471  1.124143  3.021471  13.477475  2.784582  13.477475    997\n",
       "998  2.835783  1.099548  2.835783  13.526041  2.785244  13.526041    998\n",
       "999  2.601588  1.017053  2.601588  13.494969  2.745609  13.494970    999"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czYtZS9A6D-X"
   },
   "outputs": [],
   "source": [
    "plotter = tfdocs.plots.HistoryPlotter(smoothing_std=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nMCWKskbUTvG"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'MAE [MPG]')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.plot({'Basic': history}, metric = \"mae\")\n",
    "plt.ylim([0, 10])\n",
    "plt.ylabel('MAE [MPG]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N9u74b1tXMd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'MSE [MPG^2]')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.plot({'Basic': history}, metric = \"mse\")\n",
    "plt.ylim([0, 20])\n",
    "plt.ylabel('MSE [MPG^2]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AqsuANc11FYv"
   },
   "source": [
    "This graph shows little improvement, or even degradation in the validation error after about 100 epochs. Let's update the `model.fit` call to automatically stop training when the validation score doesn't improve. We'll use an *EarlyStopping callback* that tests a training condition for  every epoch. If a set amount of epochs elapses without showing improvement, then automatically stop the training.\n",
    "\n",
    "You can learn more about this callback [here](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fdMZuhUgzMZ4",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 0, loss:582.7007,  mae:22.8053,  mse:582.7007,  val_loss:576.7985,  val_mae:22.6294,  val_mse:576.7985,  \n",
      "....................................................."
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "# The patience parameter is the amount of epochs to check for improvement\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "early_history = model.fit(normed_train_data, train_labels, \n",
    "                    epochs=EPOCHS, validation_split = 0.2, verbose=0, \n",
    "                    callbacks=[early_stop, tfdocs.modeling.EpochDots()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LcopvQh3X-kX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'MAE [MPG]')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter.plot({'Early Stopping': early_history}, metric = \"mae\")\n",
    "plt.ylim([0, 10])\n",
    "plt.ylabel('MAE [MPG]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3St8-DmrX8P4"
   },
   "source": [
    "The graph shows that on the validation set, the average error is usually around +/- 2 MPG. Is this good? We'll leave that decision up to you.\n",
    "\n",
    "Let's see how well the model generalizes by using the **test** set, which we did not use when training the model.  This tells us how well we can expect the model to predict when we use it in the real world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jl_yNr5n1kms"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 5.7511 - mae: 1.8565 - mse: 5.7511\n",
      "Testing set Mean Abs Error:  1.86 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ft603OzXuEZC"
   },
   "source": [
    "### Make predictions\n",
    "\n",
    "Finally, predict MPG values using data in the testing set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xe7RXH3N3CWU"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "lims = [0, 50]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "_ = plt.plot(lims, lims)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "19wyogbOSU5t"
   },
   "source": [
    "It looks like our model predicts reasonably well. Let's take a look at the error distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f-OHX4DiXd8x"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m0CB5tBjSU5w"
   },
   "source": [
    "It's not quite gaussian, but we might expect that because the number of samples is very small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook introduced a few techniques to handle a regression problem.\n",
    "\n",
    "* Mean Squared Error (MSE) is a common loss function used for regression problems (different loss functions are used for classification problems).\n",
    "* Similarly, evaluation metrics used for regression differ from classification. A common regression metric is Mean Absolute Error (MAE).\n",
    "* When numeric input data features have values with different ranges, each feature should be scaled independently to the same range.\n",
    "* If there is not much training data, one technique is to prefer a small network with few hidden layers to avoid overfitting.\n",
    "* Early stopping is a useful technique to prevent overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "regression.ipynb",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
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}