02-DNN-Regression-Premium.ipynb

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    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "# <!-- TITLE --> [BHP2] - Regression with a Dense Network (DNN) - Advanced code\n",
    "  <!-- DESC -->  More advanced example of DNN network code - 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 with backup and restore of the trained model. \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 and save the model\n",
    " - Restore saved model\n",
    " - Evaluate the model\n",
    " - Make some predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Import and init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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      "\n",
      "FIDLE 2020 - Practical Work Module\n",
      "Version              : 0.4.3\n",
      "Run time             : Friday 28 February 2020, 10:23:12\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 IPython.display import Markdown\n",
    "from importlib import reload\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as ooo\n",
    "\n",
    "ooo.init()\n",
    "os.makedirs('./run/models',   mode=0o750, exist_ok=True)"
   ]
  },
  {
   "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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