01-DNN-Regression.ipynb

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    "![Fidle](../fidle/img/00-Fidle-header-01.png)\n",
    "\n",
    "[<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>](#)\n",
    "\n",
    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "# <!-- TITLE --> Regression with a Dense Network (DNN)\n",
    "<!-- DESC --> A Simple regression with a Dense Neural Network (DNN) - BHPD dataset\n",
    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
    "\n",
    "## Objectives :\n",
    " - Predicts **housing prices** from a set of house features. \n",
    " - Understanding the **principle** and the **architecture** of a regression with a **dense neural network**  \n",
    "\n",
    "\n",
    "The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston.  \n",
    "Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town,  \n",
    "age of people who own the house and many other attributes...\n",
    "\n",
    "## What we're going to do :\n",
    "\n",
    " - Retrieve data\n",
    " - Preparing the data\n",
    " - Build a model\n",
    " - Train the model\n",
    " - Evaluate the result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Import and init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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      "\n",
      "FIDLE 2020 - Practical Work Module\n",
      "Version              : 0.2.9\n",
      "Run time             : Wednesday 19 February 2020, 09:49:10\n",
      "TensorFlow version   : 2.0.0\n",
      "Keras version        : 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os,sys\n",
    "\n",
    "from importlib import reload\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as ooo\n",
    "\n",
    "ooo.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Retrieve data\n",
    "\n",
    "### 2.1 - Option 1  : From Keras\n",
    "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)  "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Option 2 : From a csv file\n",
    "More fun !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "                        <th id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
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       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
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       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
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       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
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       "                        <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
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