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- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "\n",
- "# <!-- TITLE --> [BHP1] - 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 theses informations : \n",
- "\n",
- " - CRIM: This is the per capita crime rate by town\n",
- " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
- " - INDUS: This is the proportion of non-retail business acres per town\n",
- " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
- " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
- " - RM: This is the average number of rooms per dwelling\n",
- " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
- " - DIS: This is the weighted distances to five Boston employment centers\n",
- " - RAD: This is the index of accessibility to radial highways\n",
- " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
- " - PTRATIO: This is the pupil-teacher ratio by town\n",
- " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
- " - LSTAT: This is the percentage lower status of the population\n",
- " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\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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- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
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- "Version : 0.6.1 DEV\n",
- "Notebook id : BHP1\n",
- "Run time : Wednesday 16 December 2020, 21:07:33\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : ~/datasets/fidle\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\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",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('BHP1')"
- ]
- },
- {
- "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": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "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": 3,
- "metadata": {},
- "outputs": [
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- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_5edb565e_3fda_11eb_9239_15d9bb000c39row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f4cc01fb110>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Missing Data : 0 Shape is : (506, 14)\n"
- ]
- }
- ],
- "source": [
- "data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
- "\n",
- "display(data.head(5).style.format(\"{0:.2f}\").set_caption(\"Few lines of the dataset :\"))\n",
- "print('Missing Data : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preparing the data\n",
- "### 3.1 - Split data\n",
- "We will use 70% of the data for training and 30% for validation. \n",
- "The dataset is **shuffled** and shared between **learning** and **testing**. \n",
- "x will be input data and y the expected output"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Original data shape was : (506, 14)\n",
- "x_train : (354, 13) y_train : (354,)\n",
- "x_test : (152, 13) y_test : (152,)\n"
- ]
- }
- ],
- "source": [
- "# ---- Suffle and Split => train, test\n",
- "#\n",
- "data_train = data.sample(frac=0.7, axis=0)\n",
- "data_test = data.drop(data_train.index)\n",
- "\n",
- "# ---- Split => x,y (medv is price)\n",
- "#\n",
- "x_train = data_train.drop('medv', axis=1)\n",
- "y_train = data_train['medv']\n",
- "x_test = data_test.drop('medv', axis=1)\n",
- "y_test = data_test['medv']\n",
- "\n",
- "print('Original data shape was : ',data.shape)\n",
- "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
- "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Data normalization\n",
- "**Note :** \n",
- " - All input data must be normalized, train and test. \n",
- " - To do this we will **subtract the mean** and **divide by the standard deviation**. \n",
- " - But test data should not be used in any way, even for normalization. \n",
- " - The mean and the standard deviation will therefore only be calculated with the train data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col0\" class=\"data row1 col0\" >3.83</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col1\" class=\"data row1 col1\" >11.78</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col2\" class=\"data row1 col2\" >10.98</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col6\" class=\"data row1 col6\" >67.78</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col7\" class=\"data row1 col7\" >3.77</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col8\" class=\"data row1 col8\" >9.58</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col9\" class=\"data row1 col9\" >405.92</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col10\" class=\"data row1 col10\" >18.43</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col11\" class=\"data row1 col11\" >356.45</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row1_col12\" class=\"data row1 col12\" >12.26</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col0\" class=\"data row2 col0\" >9.54</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col1\" class=\"data row2 col1\" >23.96</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col2\" class=\"data row2 col2\" >6.69</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col3\" class=\"data row2 col3\" >0.27</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col5\" class=\"data row2 col5\" >0.72</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col6\" class=\"data row2 col6\" >27.86</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col7\" class=\"data row2 col7\" >1.99</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col8\" class=\"data row2 col8\" >8.73</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col9\" class=\"data row2 col9\" >168.10</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col10\" class=\"data row2 col10\" >2.23</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col11\" class=\"data row2 col11\" >92.60</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row2_col12\" class=\"data row2 col12\" >7.06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col2\" class=\"data row3 col2\" >0.74</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col2\" class=\"data row4 col2\" >5.15</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col6\" class=\"data row4 col6\" >45.02</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col7\" class=\"data row4 col7\" >2.11</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col10\" class=\"data row4 col10\" >16.92</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col11\" class=\"data row4 col11\" >375.38</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row4_col12\" class=\"data row4 col12\" >6.74</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col0\" class=\"data row5 col0\" >0.28</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col2\" class=\"data row5 col2\" >8.56</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col5\" class=\"data row5 col5\" >6.19</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col6\" class=\"data row5 col6\" >74.70</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col7\" class=\"data row5 col7\" >3.36</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col9\" class=\"data row5 col9\" >329.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col10\" class=\"data row5 col10\" >19.05</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col11\" class=\"data row5 col11\" >391.88</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row5_col12\" class=\"data row5 col12\" >10.38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col0\" class=\"data row6 col0\" >3.68</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col1\" class=\"data row6 col1\" >16.25</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col6\" class=\"data row6 col6\" >93.45</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col7\" class=\"data row6 col7\" >5.07</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col11\" class=\"data row6 col11\" >396.27</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row6_col12\" class=\"data row6 col12\" >16.40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col5\" class=\"data row7 col5\" >8.72</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_64b6e886_3fda_11eb_9239_15d9bb000c39row7_col12\" class=\"data row7 col12\" >36.98</td>\n",
- " </tr>\n",
- " </tbody></table>"
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- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col10\" class=\"data row1 col10\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col2\" class=\"data row3 col2\" >-1.53</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col3\" class=\"data row3 col3\" >-0.29</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col4\" class=\"data row3 col4\" >-1.43</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col5\" class=\"data row3 col5\" >-3.80</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col6\" class=\"data row3 col6\" >-2.33</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col7\" class=\"data row3 col7\" >-1.32</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col10\" class=\"data row3 col10\" >-2.61</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col11\" class=\"data row3 col11\" >-3.82</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row3_col12\" class=\"data row3 col12\" >-1.49</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col0\" class=\"data row4 col0\" >-0.39</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col3\" class=\"data row4 col3\" >-0.29</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col4\" class=\"data row4 col4\" >-0.88</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col6\" class=\"data row4 col6\" >-0.82</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col7\" class=\"data row4 col7\" >-0.83</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col8\" class=\"data row4 col8\" >-0.64</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col10\" class=\"data row4 col10\" >-0.67</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col11\" class=\"data row4 col11\" >0.20</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col0\" class=\"data row5 col0\" >-0.37</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col2\" class=\"data row5 col2\" >-0.36</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col3\" class=\"data row5 col3\" >-0.29</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col4\" class=\"data row5 col4\" >-0.14</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col5\" class=\"data row5 col5\" >-0.15</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col6\" class=\"data row5 col6\" >0.25</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col7\" class=\"data row5 col7\" >-0.20</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col8\" class=\"data row5 col8\" >-0.52</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col10\" class=\"data row5 col10\" >0.28</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row5_col12\" class=\"data row5 col12\" >-0.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col0\" class=\"data row6 col0\" >-0.02</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col1\" class=\"data row6 col1\" >0.19</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col2\" class=\"data row6 col2\" >1.06</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col3\" class=\"data row6 col3\" >-0.29</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col4\" class=\"data row6 col4\" >0.61</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col5\" class=\"data row6 col5\" >0.46</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col6\" class=\"data row6 col6\" >0.92</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col7\" class=\"data row6 col7\" >0.65</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col8\" class=\"data row6 col8\" >1.65</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col9\" class=\"data row6 col9\" >1.55</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col10\" class=\"data row6 col10\" >0.79</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row6_col12\" class=\"data row6 col12\" >0.59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col0\" class=\"data row7 col0\" >8.92</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col1\" class=\"data row7 col1\" >3.68</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col2\" class=\"data row7 col2\" >2.50</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col3\" class=\"data row7 col3\" >3.48</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col4\" class=\"data row7 col4\" >2.76</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col5\" class=\"data row7 col5\" >3.36</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col6\" class=\"data row7 col6\" >1.16</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col7\" class=\"data row7 col7\" >3.48</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col8\" class=\"data row7 col8\" >1.65</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col9\" class=\"data row7 col9\" >1.81</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col10\" class=\"data row7 col10\" >1.60</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
- " <td id=\"T_64bdd7d6_3fda_11eb_9239_15d9bb000c39row7_col12\" class=\"data row7 col12\" >3.50</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f4c48281390>"
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- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39level0_row0\" class=\"row_heading level0 row0\" >388</th>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col0\" class=\"data row0 col0\" >1.10</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col1\" class=\"data row0 col1\" >-0.49</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col2\" class=\"data row0 col2\" >1.06</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col3\" class=\"data row0 col3\" >-0.29</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col4\" class=\"data row0 col4\" >1.27</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col5\" class=\"data row0 col5\" >-1.97</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col6\" class=\"data row0 col6\" >1.16</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col7\" class=\"data row0 col7\" >-1.09</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col8\" class=\"data row0 col8\" >1.65</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col9\" class=\"data row0 col9\" >1.55</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col10\" class=\"data row0 col10\" >0.79</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col11\" class=\"data row0 col11\" >0.18</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row0_col12\" class=\"data row0 col12\" >2.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39level0_row1\" class=\"row_heading level0 row1\" >400</th>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col0\" class=\"data row1 col0\" >2.22</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col1\" class=\"data row1 col1\" >-0.49</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col2\" class=\"data row1 col2\" >1.06</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col3\" class=\"data row1 col3\" >-0.29</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col4\" class=\"data row1 col4\" >1.21</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col5\" class=\"data row1 col5\" >-0.44</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col6\" class=\"data row1 col6\" >1.16</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col7\" class=\"data row1 col7\" >-1.09</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col8\" class=\"data row1 col8\" >1.65</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col9\" class=\"data row1 col9\" >1.55</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col10\" class=\"data row1 col10\" >0.79</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col11\" class=\"data row1 col11\" >0.44</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row1_col12\" class=\"data row1 col12\" >2.06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39level0_row2\" class=\"row_heading level0 row2\" >85</th>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col0\" class=\"data row2 col0\" >-0.39</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col1\" class=\"data row2 col1\" >-0.49</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col2\" class=\"data row2 col2\" >-0.97</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col3\" class=\"data row2 col3\" >-0.29</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col4\" class=\"data row2 col4\" >-0.91</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col5\" class=\"data row2 col5\" >0.46</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col6\" class=\"data row2 col6\" >-0.42</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col7\" class=\"data row2 col7\" >0.34</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col8\" class=\"data row2 col8\" >-0.75</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col9\" class=\"data row2 col9\" >-0.95</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col10\" class=\"data row2 col10\" >0.03</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col11\" class=\"data row2 col11\" >0.39</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row2_col12\" class=\"data row2 col12\" >-0.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39level0_row3\" class=\"row_heading level0 row3\" >335</th>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col2\" class=\"data row3 col2\" >-0.86</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col3\" class=\"data row3 col3\" >-0.29</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col4\" class=\"data row3 col4\" >-0.34</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col5\" class=\"data row3 col5\" >-0.37</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col6\" class=\"data row3 col6\" >-1.19</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col7\" class=\"data row3 col7\" >1.11</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col8\" class=\"data row3 col8\" >-0.52</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col9\" class=\"data row3 col9\" >-1.08</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col10\" class=\"data row3 col10\" >0.79</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col11\" class=\"data row3 col11\" >0.44</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row3_col12\" class=\"data row3 col12\" >-0.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39level0_row4\" class=\"row_heading level0 row4\" >94</th>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col1\" class=\"data row4 col1\" >0.68</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col2\" class=\"data row4 col2\" >0.61</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col3\" class=\"data row4 col3\" >-0.29</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col4\" class=\"data row4 col4\" >-0.78</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col5\" class=\"data row4 col5\" >-0.07</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col6\" class=\"data row4 col6\" >0.34</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col7\" class=\"data row4 col7\" >-0.08</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col8\" class=\"data row4 col8\" >-0.64</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col9\" class=\"data row4 col9\" >-0.81</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col10\" class=\"data row4 col10\" >-0.10</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col11\" class=\"data row4 col11\" >0.44</td>\n",
- " <td id=\"T_64beac06_3fda_11eb_9239_15d9bb000c39row4_col12\" class=\"data row4 col12\" >-0.24</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f4cc024ff90>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
- "\n",
- "mean = x_train.mean()\n",
- "std = x_train.std()\n",
- "x_train = (x_train - mean) / std\n",
- "x_test = (x_test - mean) / std\n",
- "\n",
- "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
- "display(x_train.head(5).style.format(\"{0:.2f}\").set_caption(\"Few lines of the dataset :\"))\n",
- "\n",
- "x_train, y_train = np.array(x_train), np.array(y_train)\n",
- "x_test, y_test = np.array(x_test), np.array(y_test)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build a model\n",
- "About informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- " def get_model_v1(shape):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
- " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
- " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
- " model.add(keras.layers.Dense(1, name='Output'))\n",
- " \n",
- " model.compile(optimizer = 'rmsprop',\n",
- " loss = 'mse',\n",
- " metrics = ['mae', 'mse'] )\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential_1\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model=get_model_v1( (13,) )\n",
- "\n",
- "model.summary()\n",
- "\n",
- "# img=keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)\n",
- "# display(img)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 354 samples, validate on 152 samples\n",
- "Epoch 1/100\n",
- "354/354 [==============================] - 1s 2ms/sample - loss: 559.3389 - mae: 21.3539 - mse: 559.3389 - val_loss: 376.8441 - val_mae: 17.5978 - val_mse: 376.8441\n",
- "Epoch 2/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 366.9889 - mae: 16.6631 - mse: 366.9888 - val_loss: 190.5160 - val_mae: 11.8520 - val_mse: 190.5160\n",
- "Epoch 3/100\n",
- "354/354 [==============================] - 0s 244us/sample - loss: 168.8779 - mae: 10.5596 - mse: 168.8779 - val_loss: 64.1663 - val_mae: 6.5603 - val_mse: 64.1663\n",
- "Epoch 4/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 66.9357 - mae: 6.3509 - mse: 66.9357 - val_loss: 35.1695 - val_mae: 4.6727 - val_mse: 35.1694\n",
- "Epoch 5/100\n",
- "354/354 [==============================] - 0s 230us/sample - loss: 39.4804 - mae: 4.6161 - mse: 39.4804 - val_loss: 25.0917 - val_mae: 3.7680 - val_mse: 25.0917\n",
- "Epoch 6/100\n",
- "354/354 [==============================] - 0s 252us/sample - loss: 28.5192 - mae: 3.8081 - mse: 28.5192 - val_loss: 19.6559 - val_mae: 3.2363 - val_mse: 19.6559\n",
- "Epoch 7/100\n",
- "354/354 [==============================] - 0s 229us/sample - loss: 23.1326 - mae: 3.3960 - mse: 23.1326 - val_loss: 17.9289 - val_mae: 2.9819 - val_mse: 17.9289\n",
- "Epoch 8/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 20.1564 - mae: 3.1458 - mse: 20.1564 - val_loss: 17.1985 - val_mae: 2.9429 - val_mse: 17.1985\n",
- "Epoch 9/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 17.9801 - mae: 2.9005 - mse: 17.9801 - val_loss: 16.4553 - val_mae: 2.8830 - val_mse: 16.4553\n",
- "Epoch 10/100\n",
- "354/354 [==============================] - 0s 229us/sample - loss: 16.9272 - mae: 2.8096 - mse: 16.9272 - val_loss: 15.8260 - val_mae: 2.7384 - val_mse: 15.8260\n",
- "Epoch 11/100\n",
- "354/354 [==============================] - 0s 251us/sample - loss: 15.8887 - mae: 2.6495 - mse: 15.8887 - val_loss: 15.7330 - val_mae: 2.7499 - val_mse: 15.7330\n",
- "Epoch 12/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 14.7554 - mae: 2.5771 - mse: 14.7554 - val_loss: 15.6936 - val_mae: 2.7338 - val_mse: 15.6936\n",
- "Epoch 13/100\n",
- "354/354 [==============================] - 0s 247us/sample - loss: 13.8885 - mae: 2.5541 - mse: 13.8885 - val_loss: 15.1870 - val_mae: 2.6825 - val_mse: 15.1870\n",
- "Epoch 14/100\n",
- "354/354 [==============================] - 0s 245us/sample - loss: 13.2875 - mae: 2.4147 - mse: 13.2875 - val_loss: 15.4071 - val_mae: 2.7051 - val_mse: 15.4071\n",
- "Epoch 15/100\n",
- "354/354 [==============================] - 0s 218us/sample - loss: 12.5419 - mae: 2.3772 - mse: 12.5419 - val_loss: 14.6967 - val_mae: 2.6135 - val_mse: 14.6967\n",
- "Epoch 16/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 12.0579 - mae: 2.3855 - mse: 12.0579 - val_loss: 15.0117 - val_mae: 2.6226 - val_mse: 15.0117\n",
- "Epoch 17/100\n",
- "354/354 [==============================] - 0s 226us/sample - loss: 11.8239 - mae: 2.3227 - mse: 11.8239 - val_loss: 14.6878 - val_mae: 2.5761 - val_mse: 14.6878\n",
- "Epoch 18/100\n",
- "354/354 [==============================] - 0s 253us/sample - loss: 11.4843 - mae: 2.2879 - mse: 11.4843 - val_loss: 15.0906 - val_mae: 2.6017 - val_mse: 15.0906\n",
- "Epoch 19/100\n",
- "354/354 [==============================] - 0s 229us/sample - loss: 11.1887 - mae: 2.2663 - mse: 11.1887 - val_loss: 15.1687 - val_mae: 2.5992 - val_mse: 15.1687\n",
- "Epoch 20/100\n",
- "354/354 [==============================] - 0s 251us/sample - loss: 10.8531 - mae: 2.2715 - mse: 10.8531 - val_loss: 15.5461 - val_mae: 2.6520 - val_mse: 15.5461\n",
- "Epoch 21/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 10.5002 - mae: 2.2545 - mse: 10.5002 - val_loss: 15.6734 - val_mae: 2.7218 - val_mse: 15.6734\n",
- "Epoch 22/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 10.3560 - mae: 2.2152 - mse: 10.3560 - val_loss: 15.1688 - val_mae: 2.6291 - val_mse: 15.1688\n",
- "Epoch 23/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 10.1000 - mae: 2.1553 - mse: 10.1000 - val_loss: 15.0622 - val_mae: 2.5826 - val_mse: 15.0622\n",
- "Epoch 24/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 9.6788 - mae: 2.1429 - mse: 9.6788 - val_loss: 16.3673 - val_mae: 2.7813 - val_mse: 16.3673\n",
- "Epoch 25/100\n",
- "354/354 [==============================] - 0s 233us/sample - loss: 9.9303 - mae: 2.1358 - mse: 9.9303 - val_loss: 15.3259 - val_mae: 2.6056 - val_mse: 15.3259\n",
- "Epoch 26/100\n",
- "354/354 [==============================] - 0s 223us/sample - loss: 9.5239 - mae: 2.1137 - mse: 9.5239 - val_loss: 15.0186 - val_mae: 2.5824 - val_mse: 15.0186\n",
- "Epoch 27/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 9.6209 - mae: 2.1253 - mse: 9.6209 - val_loss: 16.8419 - val_mae: 2.7562 - val_mse: 16.8419\n",
- "Epoch 28/100\n",
- "354/354 [==============================] - 0s 238us/sample - loss: 9.1132 - mae: 2.0803 - mse: 9.1132 - val_loss: 15.3359 - val_mae: 2.5663 - val_mse: 15.3359\n",
- "Epoch 29/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 9.0402 - mae: 2.0878 - mse: 9.0402 - val_loss: 15.5912 - val_mae: 2.6013 - val_mse: 15.5912\n",
- "Epoch 30/100\n",
- "354/354 [==============================] - 0s 254us/sample - loss: 8.7160 - mae: 2.0564 - mse: 8.7160 - val_loss: 15.5916 - val_mae: 2.6276 - val_mse: 15.5916\n",
- "Epoch 31/100\n",
- "354/354 [==============================] - 0s 244us/sample - loss: 8.9422 - mae: 2.0629 - mse: 8.9422 - val_loss: 15.0898 - val_mae: 2.5457 - val_mse: 15.0898\n",
- "Epoch 32/100\n",
- "354/354 [==============================] - 0s 221us/sample - loss: 8.4271 - mae: 2.0411 - mse: 8.4271 - val_loss: 16.0204 - val_mae: 2.5948 - val_mse: 16.0204\n",
- "Epoch 33/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 8.4956 - mae: 2.0368 - mse: 8.4956 - val_loss: 16.0425 - val_mae: 2.6195 - val_mse: 16.0425\n",
- "Epoch 34/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 8.4621 - mae: 2.0366 - mse: 8.4621 - val_loss: 15.2432 - val_mae: 2.5632 - val_mse: 15.2432\n",
- "Epoch 35/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 8.0943 - mae: 2.0001 - mse: 8.0943 - val_loss: 15.3228 - val_mae: 2.6081 - val_mse: 15.3228\n",
- "Epoch 36/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 8.1738 - mae: 1.9585 - mse: 8.1738 - val_loss: 15.5203 - val_mae: 2.6234 - val_mse: 15.5203\n",
- "Epoch 37/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 8.1451 - mae: 2.0027 - mse: 8.1451 - val_loss: 15.4533 - val_mae: 2.5483 - val_mse: 15.4533\n",
- "Epoch 38/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 7.6986 - mae: 1.9325 - mse: 7.6986 - val_loss: 16.2505 - val_mae: 2.7286 - val_mse: 16.2505\n",
- "Epoch 39/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 7.8702 - mae: 1.9740 - mse: 7.8702 - val_loss: 15.7567 - val_mae: 2.5586 - val_mse: 15.7567\n",
- "Epoch 40/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 7.5809 - mae: 1.9462 - mse: 7.5809 - val_loss: 15.5753 - val_mae: 2.5962 - val_mse: 15.5753\n",
- "Epoch 41/100\n",
- "354/354 [==============================] - 0s 214us/sample - loss: 7.6618 - mae: 1.9305 - mse: 7.6618 - val_loss: 15.0298 - val_mae: 2.5522 - val_mse: 15.0298\n",
- "Epoch 42/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 7.3388 - mae: 1.8815 - mse: 7.3388 - val_loss: 15.4820 - val_mae: 2.5426 - val_mse: 15.4820\n",
- "Epoch 43/100\n",
- "354/354 [==============================] - 0s 229us/sample - loss: 7.1564 - mae: 1.8520 - mse: 7.1564 - val_loss: 15.6216 - val_mae: 2.5706 - val_mse: 15.6215\n",
- "Epoch 44/100\n",
- "354/354 [==============================] - 0s 233us/sample - loss: 7.1698 - mae: 1.9118 - mse: 7.1698 - val_loss: 16.1703 - val_mae: 2.5756 - val_mse: 16.1703\n",
- "Epoch 45/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 6.9308 - mae: 1.8668 - mse: 6.9308 - val_loss: 16.0441 - val_mae: 2.6813 - val_mse: 16.0441\n",
- "Epoch 46/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 7.1931 - mae: 1.8628 - mse: 7.1931 - val_loss: 15.5112 - val_mae: 2.5444 - val_mse: 15.5112\n",
- "Epoch 47/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 6.9758 - mae: 1.8562 - mse: 6.9758 - val_loss: 15.4856 - val_mae: 2.5616 - val_mse: 15.4856\n",
- "Epoch 48/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 6.8062 - mae: 1.8246 - mse: 6.8062 - val_loss: 15.2317 - val_mae: 2.5851 - val_mse: 15.2317\n",
- "Epoch 49/100\n",
- "354/354 [==============================] - 0s 238us/sample - loss: 6.7735 - mae: 1.8314 - mse: 6.7735 - val_loss: 15.5211 - val_mae: 2.5131 - val_mse: 15.5211\n",
- "Epoch 50/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 6.5600 - mae: 1.7998 - mse: 6.5600 - val_loss: 16.7930 - val_mae: 2.7328 - val_mse: 16.7930\n",
- "Epoch 51/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 6.6818 - mae: 1.8058 - mse: 6.6818 - val_loss: 15.4193 - val_mae: 2.5365 - val_mse: 15.4193\n",
- "Epoch 52/100\n",
- "354/354 [==============================] - 0s 223us/sample - loss: 6.5178 - mae: 1.8086 - mse: 6.5178 - val_loss: 15.5370 - val_mae: 2.5626 - val_mse: 15.5370\n",
- "Epoch 53/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 6.4316 - mae: 1.7979 - mse: 6.4316 - val_loss: 16.0008 - val_mae: 2.6065 - val_mse: 16.0008\n",
- "Epoch 54/100\n",
- "354/354 [==============================] - 0s 231us/sample - loss: 6.4092 - mae: 1.7621 - mse: 6.4092 - val_loss: 16.4983 - val_mae: 2.6283 - val_mse: 16.4983\n",
- "Epoch 55/100\n",
- "354/354 [==============================] - 0s 251us/sample - loss: 6.2204 - mae: 1.7612 - mse: 6.2204 - val_loss: 15.1744 - val_mae: 2.5993 - val_mse: 15.1744\n",
- "Epoch 56/100\n",
- "354/354 [==============================] - 0s 248us/sample - loss: 6.2116 - mae: 1.7550 - mse: 6.2116 - val_loss: 17.5214 - val_mae: 2.7019 - val_mse: 17.5214\n",
- "Epoch 57/100\n",
- "354/354 [==============================] - 0s 231us/sample - loss: 6.0831 - mae: 1.7581 - mse: 6.0831 - val_loss: 15.1197 - val_mae: 2.5460 - val_mse: 15.1197\n",
- "Epoch 58/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 6.1441 - mae: 1.7428 - mse: 6.1441 - val_loss: 15.4464 - val_mae: 2.6310 - val_mse: 15.4464\n",
- "Epoch 59/100\n",
- "354/354 [==============================] - 0s 235us/sample - loss: 6.1997 - mae: 1.7502 - mse: 6.1997 - val_loss: 15.0488 - val_mae: 2.5226 - val_mse: 15.0488\n",
- "Epoch 60/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 5.9617 - mae: 1.7260 - mse: 5.9617 - val_loss: 15.9566 - val_mae: 2.5740 - val_mse: 15.9566\n",
- "Epoch 61/100\n",
- "354/354 [==============================] - 0s 225us/sample - loss: 6.0531 - mae: 1.7497 - mse: 6.0531 - val_loss: 15.5786 - val_mae: 2.5930 - val_mse: 15.5786\n",
- "Epoch 62/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 5.8327 - mae: 1.7298 - mse: 5.8327 - val_loss: 14.8878 - val_mae: 2.5330 - val_mse: 14.8878\n",
- "Epoch 63/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 5.7329 - mae: 1.6732 - mse: 5.7329 - val_loss: 15.1598 - val_mae: 2.5538 - val_mse: 15.1598\n",
- "Epoch 64/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 5.6597 - mae: 1.6803 - mse: 5.6597 - val_loss: 15.4771 - val_mae: 2.5157 - val_mse: 15.4771\n",
- "Epoch 65/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 5.7236 - mae: 1.6856 - mse: 5.7236 - val_loss: 15.5129 - val_mae: 2.5381 - val_mse: 15.5129\n",
- "Epoch 66/100\n",
- "354/354 [==============================] - 0s 230us/sample - loss: 5.6596 - mae: 1.7112 - mse: 5.6596 - val_loss: 16.1541 - val_mae: 2.6061 - val_mse: 16.1541\n",
- "Epoch 67/100\n",
- "354/354 [==============================] - 0s 226us/sample - loss: 5.5383 - mae: 1.6830 - mse: 5.5383 - val_loss: 15.0659 - val_mae: 2.5452 - val_mse: 15.0659\n",
- "Epoch 68/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 5.6108 - mae: 1.6500 - mse: 5.6108 - val_loss: 15.6278 - val_mae: 2.5266 - val_mse: 15.6278\n",
- "Epoch 69/100\n",
- "354/354 [==============================] - 0s 245us/sample - loss: 5.4007 - mae: 1.6510 - mse: 5.4007 - val_loss: 15.2597 - val_mae: 2.5669 - val_mse: 15.2597\n",
- "Epoch 70/100\n",
- "354/354 [==============================] - 0s 228us/sample - loss: 5.2480 - mae: 1.6030 - mse: 5.2480 - val_loss: 17.3470 - val_mae: 2.6816 - val_mse: 17.3470\n",
- "Epoch 71/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 5.4217 - mae: 1.6316 - mse: 5.4217 - val_loss: 16.0206 - val_mae: 2.5512 - val_mse: 16.0206\n",
- "Epoch 72/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 5.2485 - mae: 1.6410 - mse: 5.2485 - val_loss: 15.1011 - val_mae: 2.5455 - val_mse: 15.1011\n",
- "Epoch 73/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 5.2745 - mae: 1.6167 - mse: 5.2745 - val_loss: 16.6134 - val_mae: 2.5947 - val_mse: 16.6134\n",
- "Epoch 74/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 5.2549 - mae: 1.6431 - mse: 5.2549 - val_loss: 15.7825 - val_mae: 2.5709 - val_mse: 15.7825\n",
- "Epoch 75/100\n",
- "354/354 [==============================] - 0s 212us/sample - loss: 5.1067 - mae: 1.5820 - mse: 5.1067 - val_loss: 15.0070 - val_mae: 2.5135 - val_mse: 15.0070\n",
- "Epoch 76/100\n",
- "354/354 [==============================] - 0s 233us/sample - loss: 5.2849 - mae: 1.5994 - mse: 5.2849 - val_loss: 15.3024 - val_mae: 2.4977 - val_mse: 15.3024\n",
- "Epoch 77/100\n",
- "354/354 [==============================] - 0s 221us/sample - loss: 4.9876 - mae: 1.6140 - mse: 4.9876 - val_loss: 15.3205 - val_mae: 2.5436 - val_mse: 15.3205\n",
- "Epoch 78/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 5.0753 - mae: 1.5947 - mse: 5.0753 - val_loss: 15.6210 - val_mae: 2.5967 - val_mse: 15.6210\n",
- "Epoch 79/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 4.9119 - mae: 1.5924 - mse: 4.9119 - val_loss: 14.7212 - val_mae: 2.5015 - val_mse: 14.7212\n",
- "Epoch 80/100\n",
- "354/354 [==============================] - 0s 232us/sample - loss: 5.0702 - mae: 1.6201 - mse: 5.0702 - val_loss: 15.3674 - val_mae: 2.5486 - val_mse: 15.3674\n",
- "Epoch 81/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 4.7804 - mae: 1.5636 - mse: 4.7804 - val_loss: 15.0296 - val_mae: 2.5425 - val_mse: 15.0296\n",
- "Epoch 82/100\n",
- "354/354 [==============================] - 0s 212us/sample - loss: 4.7741 - mae: 1.5668 - mse: 4.7741 - val_loss: 14.8257 - val_mae: 2.5242 - val_mse: 14.8257\n",
- "Epoch 83/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 4.7994 - mae: 1.5658 - mse: 4.7994 - val_loss: 15.4970 - val_mae: 2.6127 - val_mse: 15.4970\n",
- "Epoch 84/100\n",
- "354/354 [==============================] - 0s 221us/sample - loss: 4.6561 - mae: 1.5640 - mse: 4.6561 - val_loss: 14.4621 - val_mae: 2.5372 - val_mse: 14.4621\n",
- "Epoch 85/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 4.7807 - mae: 1.5391 - mse: 4.7807 - val_loss: 15.3561 - val_mae: 2.5788 - val_mse: 15.3561\n",
- "Epoch 86/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 4.5408 - mae: 1.5199 - mse: 4.5408 - val_loss: 15.7730 - val_mae: 2.6125 - val_mse: 15.7730\n",
- "Epoch 87/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 4.6009 - mae: 1.5031 - mse: 4.6009 - val_loss: 16.4323 - val_mae: 2.7032 - val_mse: 16.4323\n",
- "Epoch 88/100\n",
- "354/354 [==============================] - 0s 235us/sample - loss: 4.6478 - mae: 1.5411 - mse: 4.6478 - val_loss: 16.0993 - val_mae: 2.6815 - val_mse: 16.0993\n",
- "Epoch 89/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 4.4097 - mae: 1.4889 - mse: 4.4097 - val_loss: 15.5798 - val_mae: 2.6420 - val_mse: 15.5798\n",
- "Epoch 90/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 4.3853 - mae: 1.5146 - mse: 4.3853 - val_loss: 14.2581 - val_mae: 2.5210 - val_mse: 14.2581\n",
- "Epoch 91/100\n",
- "354/354 [==============================] - 0s 225us/sample - loss: 4.5299 - mae: 1.5295 - mse: 4.5299 - val_loss: 14.3628 - val_mae: 2.4900 - val_mse: 14.3628\n",
- "Epoch 92/100\n",
- "354/354 [==============================] - 0s 248us/sample - loss: 4.3293 - mae: 1.4698 - mse: 4.3293 - val_loss: 15.2263 - val_mae: 2.6003 - val_mse: 15.2263\n",
- "Epoch 93/100\n",
- "354/354 [==============================] - 0s 230us/sample - loss: 4.3929 - mae: 1.4918 - mse: 4.3929 - val_loss: 14.5684 - val_mae: 2.5611 - val_mse: 14.5684\n",
- "Epoch 94/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 4.2871 - mae: 1.4765 - mse: 4.2871 - val_loss: 14.1033 - val_mae: 2.4800 - val_mse: 14.1033\n",
- "Epoch 95/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 4.3250 - mae: 1.4728 - mse: 4.3250 - val_loss: 14.8172 - val_mae: 2.5070 - val_mse: 14.8172\n",
- "Epoch 96/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 4.2478 - mae: 1.4617 - mse: 4.2478 - val_loss: 13.8714 - val_mae: 2.4700 - val_mse: 13.8714\n",
- "Epoch 97/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 4.2061 - mae: 1.4586 - mse: 4.2061 - val_loss: 14.8290 - val_mae: 2.5543 - val_mse: 14.8290\n",
- "Epoch 98/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 4.1120 - mae: 1.4495 - mse: 4.1120 - val_loss: 15.1588 - val_mae: 2.5921 - val_mse: 15.1588\n",
- "Epoch 99/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 4.0187 - mae: 1.4488 - mse: 4.0187 - val_loss: 15.8335 - val_mae: 2.6448 - val_mse: 15.8335\n",
- "Epoch 100/100\n",
- "354/354 [==============================] - 0s 237us/sample - loss: 4.0992 - mae: 1.4641 - mse: 4.0992 - val_loss: 14.8246 - val_mae: 2.5098 - val_mse: 14.8246\n"
- ]
- }
- ],
- "source": [
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = 100,\n",
- " batch_size = 10,\n",
- " verbose = 1,\n",
- " validation_data = (x_test, y_test))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Model evaluation\n",
- "MAE = Mean Absolute Error (between the labels and predictions) \n",
- "A mae equal to 3 represents an average error in prediction of $3k."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 14.8246\n",
- "x_test / mae : 2.5098\n",
- "x_test / mse : 14.8246\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
- "print('x_test / mse : {:5.4f}'.format(score[2]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Training history\n",
- "What was the best result during our training ?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "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>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",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>559.338897</td>\n",
- " <td>21.353868</td>\n",
- " <td>559.338867</td>\n",
- " <td>376.844083</td>\n",
- " <td>17.597807</td>\n",
- " <td>376.844086</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>366.988856</td>\n",
- " <td>16.663086</td>\n",
- " <td>366.988831</td>\n",
- " <td>190.516033</td>\n",
- " <td>11.851984</td>\n",
- " <td>190.516037</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>168.877884</td>\n",
- " <td>10.559620</td>\n",
- " <td>168.877884</td>\n",
- " <td>64.166264</td>\n",
- " <td>6.560343</td>\n",
- " <td>64.166260</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>66.935664</td>\n",
- " <td>6.350907</td>\n",
- " <td>66.935661</td>\n",
- " <td>35.169455</td>\n",
- " <td>4.672655</td>\n",
- " <td>35.169449</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>39.480374</td>\n",
- " <td>4.616073</td>\n",
- " <td>39.480385</td>\n",
- " <td>25.091733</td>\n",
- " <td>3.767965</td>\n",
- " <td>25.091730</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>95</th>\n",
- " <td>4.247794</td>\n",
- " <td>1.461652</td>\n",
- " <td>4.247793</td>\n",
- " <td>13.871438</td>\n",
- " <td>2.470020</td>\n",
- " <td>13.871437</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>96</th>\n",
- " <td>4.206060</td>\n",
- " <td>1.458569</td>\n",
- " <td>4.206059</td>\n",
- " <td>14.828999</td>\n",
- " <td>2.554330</td>\n",
- " <td>14.829000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>97</th>\n",
- " <td>4.111996</td>\n",
- " <td>1.449539</td>\n",
- " <td>4.111996</td>\n",
- " <td>15.158840</td>\n",
- " <td>2.592129</td>\n",
- " <td>15.158841</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>98</th>\n",
- " <td>4.018689</td>\n",
- " <td>1.448834</td>\n",
- " <td>4.018689</td>\n",
- " <td>15.833544</td>\n",
- " <td>2.644831</td>\n",
- " <td>15.833545</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>99</th>\n",
- " <td>4.099164</td>\n",
- " <td>1.464135</td>\n",
- " <td>4.099164</td>\n",
- " <td>14.824627</td>\n",
- " <td>2.509779</td>\n",
- " <td>14.824627</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>100 rows × 6 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " loss mae mse val_loss val_mae val_mse\n",
- "0 559.338897 21.353868 559.338867 376.844083 17.597807 376.844086\n",
- "1 366.988856 16.663086 366.988831 190.516033 11.851984 190.516037\n",
- "2 168.877884 10.559620 168.877884 64.166264 6.560343 64.166260\n",
- "3 66.935664 6.350907 66.935661 35.169455 4.672655 35.169449\n",
- "4 39.480374 4.616073 39.480385 25.091733 3.767965 25.091730\n",
- ".. ... ... ... ... ... ...\n",
- "95 4.247794 1.461652 4.247793 13.871438 2.470020 13.871437\n",
- "96 4.206060 1.458569 4.206059 14.828999 2.554330 14.829000\n",
- "97 4.111996 1.449539 4.111996 15.158840 2.592129 15.158841\n",
- "98 4.018689 1.448834 4.018689 15.833544 2.644831 15.833545\n",
- "99 4.099164 1.464135 4.099164 14.824627 2.509779 14.824627\n",
- "\n",
- "[100 rows x 6 columns]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df=pd.DataFrame(data=history.history)\n",
- "display(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min( val_mae ) : 2.4700\n"
- ]
- }
- ],
- "source": [
- "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/BHP1-01-history_0</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
- " 'MAE' :['mae', 'val_mae'],\n",
- " 'LOSS':['loss','val_loss']}, save_as='01-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - Make a prediction\n",
- "The data must be normalized with the parameters (mean, std) previously used."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_data = [ 1.26425925, -0.48522739, 1.0436489 , -0.23112788, 1.37120745,\n",
- " -2.14308942, 1.13489104, -1.06802005, 1.71189006, 1.57042287,\n",
- " 0.77859951, 0.14769795, 2.7585581 ]\n",
- "real_price = 10.4\n",
- "\n",
- "my_data=np.array(my_data).reshape(1,13)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prediction : 0.04 K$\n",
- "Reality : 10.40 K$\n"
- ]
- }
- ],
- "source": [
- "\n",
- "predictions = model.predict( my_data )\n",
- "print(\"Prediction : {:.2f} K$\".format(predictions[0][0]))\n",
- "print(\"Reality : {:.2f} K$\".format(real_price))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 16 December 2020, 21:12:10\n",
- "Duration is : 00:04:37 856ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/BHPD /02-DNN-Regression-Premium.ipynb b/BHPD /02-DNN-Regression-Premium.ipynb
deleted file mode 100644
index 35dc1ecca51de514f3d856dbbd550183c8d0d475..0000000000000000000000000000000000000000
--- a/BHPD /02-DNN-Regression-Premium.ipynb
+++ /dev/null
@@ -1,1298 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<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 these information :\n",
- "\n",
- " - CRIM: This is the per capita crime rate by town\n",
- " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
- " - INDUS: This is the proportion of non-retail business acres per town\n",
- " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
- " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
- " - RM: This is the average number of rooms per dwelling\n",
- " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
- " - DIS: This is the weighted distances to five Boston employment centers\n",
- " - RAD: This is the index of accessibility to radial highways\n",
- " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
- " - PTRATIO: This is the pupil-teacher ratio by town\n",
- " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
- " - LSTAT: This is the percentage lower status of the population\n",
- " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\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": [
- {
- "data": {
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- "metadata": {},
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- },
- {
- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : BHP2\n",
- "Run time : Wednesday 16 December 2020, 21:13:48\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : ~/datasets/fidle\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\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 pwk\n",
- "\n",
- "datasets_dir = pwk.init('BHP2')"
- ]
- },
- {
- "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": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "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": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_3553ad76_3fdb_11eb_ab9b_cb8a5f6c0018row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fa28b888990>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Missing Data : 0 Shape is : (506, 14)\n"
- ]
- }
- ],
- "source": [
- "data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
- "\n",
- "display(data.head(5).style.format(\"{0:.2f}\"))\n",
- "print('Missing Data : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preparing the data\n",
- "### 3.1 - Split data\n",
- "We will use 80% of the data for training and 20% for validation. \n",
- "x will be input data and y the expected output"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Original data shape was : (506, 14)\n",
- "x_train : (354, 13) y_train : (354,)\n",
- "x_test : (152, 13) y_test : (152,)\n"
- ]
- }
- ],
- "source": [
- "# ---- Split => train, test\n",
- "#\n",
- "data_train = data.sample(frac=0.7, axis=0)\n",
- "data_test = data.drop(data_train.index)\n",
- "\n",
- "# ---- Split => x,y (medv is price)\n",
- "#\n",
- "x_train = data_train.drop('medv', axis=1)\n",
- "y_train = data_train['medv']\n",
- "x_test = data_test.drop('medv', axis=1)\n",
- "y_test = data_test['medv']\n",
- "\n",
- "print('Original data shape was : ',data.shape)\n",
- "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
- "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Data normalization\n",
- "**Note :** \n",
- " - All input data must be normalized, train and test. \n",
- " - To do this we will subtract the mean and divide by the standard deviation. \n",
- " - But test data should not be used in any way, even for normalization. \n",
- " - The mean and the standard deviation will therefore only be calculated with the train data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col0\" class=\"data row1 col0\" >3.82</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col1\" class=\"data row1 col1\" >11.53</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col2\" class=\"data row1 col2\" >11.32</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col5\" class=\"data row1 col5\" >6.25</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col6\" class=\"data row1 col6\" >67.99</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col7\" class=\"data row1 col7\" >3.80</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col8\" class=\"data row1 col8\" >9.33</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col9\" class=\"data row1 col9\" >403.71</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col10\" class=\"data row1 col10\" >18.41</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col11\" class=\"data row1 col11\" >355.95</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row1_col12\" class=\"data row1 col12\" >12.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col0\" class=\"data row2 col0\" >9.14</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col1\" class=\"data row2 col1\" >23.50</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col2\" class=\"data row2 col2\" >6.89</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col3\" class=\"data row2 col3\" >0.26</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col5\" class=\"data row2 col5\" >0.67</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col6\" class=\"data row2 col6\" >29.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col7\" class=\"data row2 col7\" >2.06</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col8\" class=\"data row2 col8\" >8.62</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col9\" class=\"data row2 col9\" >168.39</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col10\" class=\"data row2 col10\" >2.22</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col11\" class=\"data row2 col11\" >92.01</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row2_col12\" class=\"data row2 col12\" >7.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col5\" class=\"data row3 col5\" >3.86</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col2\" class=\"data row4 col2\" >5.40</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col5\" class=\"data row4 col5\" >5.88</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col6\" class=\"data row4 col6\" >42.15</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col7\" class=\"data row4 col7\" >2.11</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col9\" class=\"data row4 col9\" >277.50</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col10\" class=\"data row4 col10\" >16.90</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col11\" class=\"data row4 col11\" >374.69</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row4_col12\" class=\"data row4 col12\" >7.18</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col0\" class=\"data row5 col0\" >0.23</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col2\" class=\"data row5 col2\" >9.90</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col5\" class=\"data row5 col5\" >6.17</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col6\" class=\"data row5 col6\" >76.85</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col7\" class=\"data row5 col7\" >3.24</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col9\" class=\"data row5 col9\" >329.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col10\" class=\"data row5 col10\" >19.05</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col11\" class=\"data row5 col11\" >391.60</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row5_col12\" class=\"data row5 col12\" >11.66</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col0\" class=\"data row6 col0\" >3.28</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col5\" class=\"data row6 col5\" >6.56</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col6\" class=\"data row6 col6\" >94.47</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col7\" class=\"data row6 col7\" >5.23</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col8\" class=\"data row6 col8\" >20.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col11\" class=\"data row6 col11\" >396.27</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row6_col12\" class=\"data row6 col12\" >16.96</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col5\" class=\"data row7 col5\" >8.30</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col7\" class=\"data row7 col7\" >10.59</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_355b97de_3fdb_11eb_ab9b_cb8a5f6c0018row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
- " </tr>\n",
- " </tbody></table>"
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- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col0\" class=\"data row3 col0\" >-0.42</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col2\" class=\"data row3 col2\" >-1.58</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col4\" class=\"data row3 col4\" >-1.47</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col5\" class=\"data row3 col5\" >-3.54</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col6\" class=\"data row3 col6\" >-2.24</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col7\" class=\"data row3 col7\" >-1.30</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col8\" class=\"data row3 col8\" >-0.97</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col9\" class=\"data row3 col9\" >-1.28</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col10\" class=\"data row3 col10\" >-2.62</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col11\" class=\"data row3 col11\" >-3.87</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row3_col12\" class=\"data row3 col12\" >-1.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col0\" class=\"data row4 col0\" >-0.41</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col2\" class=\"data row4 col2\" >-0.86</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col5\" class=\"data row4 col5\" >-0.55</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col6\" class=\"data row4 col6\" >-0.89</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col8\" class=\"data row4 col8\" >-0.62</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col9\" class=\"data row4 col9\" >-0.75</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col10\" class=\"data row4 col10\" >-0.68</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col11\" class=\"data row4 col11\" >0.20</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col3\" class=\"data row5 col3\" >-0.28</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col4\" class=\"data row5 col4\" >-0.14</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col6\" class=\"data row5 col6\" >0.31</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col7\" class=\"data row5 col7\" >-0.27</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col8\" class=\"data row5 col8\" >-0.50</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col9\" class=\"data row5 col9\" >-0.44</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col10\" class=\"data row5 col10\" >0.29</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row5_col12\" class=\"data row5 col12\" >-0.16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col0\" class=\"data row6 col0\" >-0.06</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col1\" class=\"data row6 col1\" >0.04</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col2\" class=\"data row6 col2\" >0.98</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col3\" class=\"data row6 col3\" >-0.28</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col5\" class=\"data row6 col5\" >0.47</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col6\" class=\"data row6 col6\" >0.91</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col7\" class=\"data row6 col7\" >0.69</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col8\" class=\"data row6 col8\" >1.24</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col9\" class=\"data row6 col9\" >1.56</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col10\" class=\"data row6 col10\" >0.81</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row6_col12\" class=\"data row6 col12\" >0.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col0\" class=\"data row7 col0\" >9.32</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col1\" class=\"data row7 col1\" >3.76</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col2\" class=\"data row7 col2\" >2.38</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col3\" class=\"data row7 col3\" >3.55</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col4\" class=\"data row7 col4\" >2.74</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col5\" class=\"data row7 col5\" >3.04</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col6\" class=\"data row7 col6\" >1.10</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col7\" class=\"data row7 col7\" >3.29</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col8\" class=\"data row7 col8\" >1.70</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col9\" class=\"data row7 col9\" >1.82</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col10\" class=\"data row7 col10\" >1.62</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col11\" class=\"data row7 col11\" >0.45</td>\n",
- " <td id=\"T_3564e172_3fdb_11eb_ab9b_cb8a5f6c0018row7_col12\" class=\"data row7 col12\" >3.47</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fa28b6de2d0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
- "\n",
- "mean = x_train.mean()\n",
- "std = x_train.std()\n",
- "x_train = (x_train - mean) / std\n",
- "x_test = (x_test - mean) / std\n",
- "\n",
- "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
- "\n",
- "x_train, y_train = np.array(x_train), np.array(y_train)\n",
- "x_test, y_test = np.array(x_test), np.array(y_test)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build a model\n",
- "More informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- " def get_model_v1(shape):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
- " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
- " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
- " model.add(keras.layers.Dense(1, name='Output'))\n",
- " \n",
- " model.compile(optimizer = 'rmsprop',\n",
- " loss = 'mse',\n",
- " metrics = ['mae', 'mse'] )\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model=get_model_v1( (13,) )\n",
- "\n",
- "model.summary()\n",
- "# img=keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)\n",
- "# display(img)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
- "\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.3 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 354 samples, validate on 152 samples\n",
- "Epoch 1/100\n",
- "354/354 [==============================] - 1s 2ms/sample - loss: 483.3541 - mae: 19.9419 - mse: 483.3542 - val_loss: 402.4295 - val_mae: 17.7721 - val_mse: 402.4295\n",
- "Epoch 2/100\n",
- "354/354 [==============================] - 0s 252us/sample - loss: 265.9092 - mae: 13.9827 - mse: 265.9091 - val_loss: 170.4736 - val_mae: 10.3814 - val_mse: 170.4736\n",
- "Epoch 3/100\n",
- "354/354 [==============================] - 0s 264us/sample - loss: 103.1428 - mae: 7.9022 - mse: 103.1428 - val_loss: 76.5217 - val_mae: 6.4929 - val_mse: 76.5217\n",
- "Epoch 4/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 53.7278 - mae: 5.3582 - mse: 53.7278 - val_loss: 42.3464 - val_mae: 4.8395 - val_mse: 42.3464\n",
- "Epoch 5/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 34.1679 - mae: 4.1669 - mse: 34.1679 - val_loss: 28.7698 - val_mae: 3.9412 - val_mse: 28.7698\n",
- "Epoch 6/100\n",
- "354/354 [==============================] - 0s 238us/sample - loss: 25.6470 - mae: 3.4843 - mse: 25.6470 - val_loss: 23.4439 - val_mae: 3.5135 - val_mse: 23.4439\n",
- "Epoch 7/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 21.8655 - mae: 3.2492 - mse: 21.8655 - val_loss: 21.6451 - val_mae: 3.3260 - val_mse: 21.6451\n",
- "Epoch 8/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 19.6529 - mae: 2.9502 - mse: 19.6529 - val_loss: 21.1501 - val_mae: 3.3495 - val_mse: 21.1501\n",
- "Epoch 9/100\n",
- "354/354 [==============================] - 0s 231us/sample - loss: 17.8505 - mae: 2.8674 - mse: 17.8505 - val_loss: 20.3661 - val_mae: 3.2418 - val_mse: 20.3661\n",
- "Epoch 10/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 16.6143 - mae: 2.7924 - mse: 16.6143 - val_loss: 18.6808 - val_mae: 2.9841 - val_mse: 18.6808\n",
- "Epoch 11/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 15.2757 - mae: 2.6240 - mse: 15.2757 - val_loss: 19.3353 - val_mae: 3.1216 - val_mse: 19.3353\n",
- "Epoch 12/100\n",
- "354/354 [==============================] - 0s 240us/sample - loss: 14.6014 - mae: 2.5722 - mse: 14.6014 - val_loss: 18.6065 - val_mae: 3.0238 - val_mse: 18.6065\n",
- "Epoch 13/100\n",
- "354/354 [==============================] - 0s 253us/sample - loss: 13.9226 - mae: 2.5732 - mse: 13.9226 - val_loss: 17.2032 - val_mae: 2.8274 - val_mse: 17.2032\n",
- "Epoch 14/100\n",
- "354/354 [==============================] - 0s 245us/sample - loss: 13.1008 - mae: 2.4556 - mse: 13.1008 - val_loss: 17.0273 - val_mae: 2.8188 - val_mse: 17.0273\n",
- "Epoch 15/100\n",
- "354/354 [==============================] - 0s 241us/sample - loss: 12.5638 - mae: 2.3986 - mse: 12.5638 - val_loss: 18.1385 - val_mae: 2.9360 - val_mse: 18.1385\n",
- "Epoch 16/100\n",
- "354/354 [==============================] - 0s 227us/sample - loss: 12.0747 - mae: 2.3770 - mse: 12.0747 - val_loss: 19.0295 - val_mae: 3.1033 - val_mse: 19.0295\n",
- "Epoch 17/100\n",
- "354/354 [==============================] - 0s 232us/sample - loss: 11.7585 - mae: 2.3485 - mse: 11.7585 - val_loss: 16.8025 - val_mae: 2.7537 - val_mse: 16.8025\n",
- "Epoch 18/100\n",
- "354/354 [==============================] - 0s 238us/sample - loss: 11.1622 - mae: 2.3130 - mse: 11.1622 - val_loss: 16.1754 - val_mae: 2.6902 - val_mse: 16.1754\n",
- "Epoch 19/100\n",
- "354/354 [==============================] - 0s 238us/sample - loss: 11.0410 - mae: 2.2970 - mse: 11.0410 - val_loss: 16.0992 - val_mae: 2.6674 - val_mse: 16.0992\n",
- "Epoch 20/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 10.7863 - mae: 2.2871 - mse: 10.7863 - val_loss: 16.7388 - val_mae: 2.8191 - val_mse: 16.7388\n",
- "Epoch 21/100\n",
- "354/354 [==============================] - 0s 212us/sample - loss: 10.7021 - mae: 2.2414 - mse: 10.7021 - val_loss: 16.6998 - val_mae: 2.7544 - val_mse: 16.6998\n",
- "Epoch 22/100\n",
- "354/354 [==============================] - 0s 254us/sample - loss: 10.2228 - mae: 2.2079 - mse: 10.2228 - val_loss: 16.0634 - val_mae: 2.6520 - val_mse: 16.0634\n",
- "Epoch 23/100\n",
- "354/354 [==============================] - 0s 248us/sample - loss: 10.0609 - mae: 2.1896 - mse: 10.0609 - val_loss: 15.8375 - val_mae: 2.6662 - val_mse: 15.8375\n",
- "Epoch 24/100\n",
- "354/354 [==============================] - 0s 223us/sample - loss: 9.7379 - mae: 2.1534 - mse: 9.7379 - val_loss: 17.0298 - val_mae: 2.7464 - val_mse: 17.0298\n",
- "Epoch 25/100\n",
- "354/354 [==============================] - 0s 229us/sample - loss: 9.4700 - mae: 2.1768 - mse: 9.4700 - val_loss: 16.0757 - val_mae: 2.6375 - val_mse: 16.0757\n",
- "Epoch 26/100\n",
- "354/354 [==============================] - 0s 225us/sample - loss: 9.3435 - mae: 2.1333 - mse: 9.3435 - val_loss: 18.3934 - val_mae: 2.8681 - val_mse: 18.3934\n",
- "Epoch 27/100\n",
- "354/354 [==============================] - 0s 246us/sample - loss: 9.0069 - mae: 2.0935 - mse: 9.0069 - val_loss: 15.4444 - val_mae: 2.6123 - val_mse: 15.4444\n",
- "Epoch 28/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 8.9357 - mae: 2.0635 - mse: 8.9357 - val_loss: 15.7287 - val_mae: 2.5909 - val_mse: 15.7287\n",
- "Epoch 29/100\n",
- "354/354 [==============================] - 0s 218us/sample - loss: 8.8445 - mae: 2.0849 - mse: 8.8445 - val_loss: 16.8335 - val_mae: 2.7164 - val_mse: 16.8335\n",
- "Epoch 30/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 8.7798 - mae: 2.0682 - mse: 8.7798 - val_loss: 14.9682 - val_mae: 2.5153 - val_mse: 14.9682\n",
- "Epoch 31/100\n",
- "354/354 [==============================] - 0s 216us/sample - loss: 8.6369 - mae: 2.0122 - mse: 8.6369 - val_loss: 17.8983 - val_mae: 2.9533 - val_mse: 17.8983\n",
- "Epoch 32/100\n",
- "354/354 [==============================] - 0s 211us/sample - loss: 8.3828 - mae: 2.0524 - mse: 8.3828 - val_loss: 16.0668 - val_mae: 2.7383 - val_mse: 16.0668\n",
- "Epoch 33/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 8.5447 - mae: 2.0082 - mse: 8.5447 - val_loss: 15.1219 - val_mae: 2.5224 - val_mse: 15.1219\n",
- "Epoch 34/100\n",
- "354/354 [==============================] - 0s 226us/sample - loss: 8.1540 - mae: 1.9673 - mse: 8.1540 - val_loss: 16.8374 - val_mae: 2.7148 - val_mse: 16.8374\n",
- "Epoch 35/100\n",
- "354/354 [==============================] - 0s 248us/sample - loss: 8.0155 - mae: 1.9871 - mse: 8.0155 - val_loss: 14.6871 - val_mae: 2.5105 - val_mse: 14.6871\n",
- "Epoch 36/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 8.1472 - mae: 1.9709 - mse: 8.1472 - val_loss: 14.6178 - val_mae: 2.4975 - val_mse: 14.6178\n",
- "Epoch 37/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 7.8367 - mae: 1.9733 - mse: 7.8367 - val_loss: 15.5460 - val_mae: 2.5704 - val_mse: 15.5460\n",
- "Epoch 38/100\n",
- "354/354 [==============================] - 0s 223us/sample - loss: 7.7893 - mae: 1.9585 - mse: 7.7893 - val_loss: 15.2375 - val_mae: 2.6453 - val_mse: 15.2375\n",
- "Epoch 39/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 7.5407 - mae: 1.9485 - mse: 7.5407 - val_loss: 16.9275 - val_mae: 2.7329 - val_mse: 16.9275\n",
- "Epoch 40/100\n",
- "354/354 [==============================] - 0s 211us/sample - loss: 7.5565 - mae: 1.9030 - mse: 7.5565 - val_loss: 15.2341 - val_mae: 2.6018 - val_mse: 15.2341\n",
- "Epoch 41/100\n",
- "354/354 [==============================] - 0s 216us/sample - loss: 7.7118 - mae: 1.9763 - mse: 7.7118 - val_loss: 15.7800 - val_mae: 2.6320 - val_mse: 15.7800\n",
- "Epoch 42/100\n",
- "354/354 [==============================] - 0s 237us/sample - loss: 7.4267 - mae: 1.9107 - mse: 7.4267 - val_loss: 14.6142 - val_mae: 2.5046 - val_mse: 14.6142\n",
- "Epoch 43/100\n",
- "354/354 [==============================] - 0s 254us/sample - loss: 7.4060 - mae: 1.9014 - mse: 7.4060 - val_loss: 14.1551 - val_mae: 2.4665 - val_mse: 14.1551\n",
- "Epoch 44/100\n",
- "354/354 [==============================] - 0s 214us/sample - loss: 7.3371 - mae: 1.9142 - mse: 7.3371 - val_loss: 14.2481 - val_mae: 2.4548 - val_mse: 14.2481\n",
- "Epoch 45/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 7.0906 - mae: 1.8327 - mse: 7.0906 - val_loss: 15.6930 - val_mae: 2.6193 - val_mse: 15.6930\n",
- "Epoch 46/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 6.9100 - mae: 1.8281 - mse: 6.9100 - val_loss: 19.2019 - val_mae: 2.9945 - val_mse: 19.2019\n",
- "Epoch 47/100\n",
- "354/354 [==============================] - 0s 218us/sample - loss: 7.2151 - mae: 1.8930 - mse: 7.2151 - val_loss: 15.6496 - val_mae: 2.6456 - val_mse: 15.6496\n",
- "Epoch 48/100\n",
- "354/354 [==============================] - 0s 223us/sample - loss: 7.0622 - mae: 1.8432 - mse: 7.0622 - val_loss: 15.2120 - val_mae: 2.5867 - val_mse: 15.2120\n",
- "Epoch 49/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 6.7430 - mae: 1.8479 - mse: 6.7430 - val_loss: 16.1004 - val_mae: 2.6940 - val_mse: 16.1004\n",
- "Epoch 50/100\n",
- "354/354 [==============================] - 0s 216us/sample - loss: 6.7931 - mae: 1.8296 - mse: 6.7931 - val_loss: 16.1302 - val_mae: 2.7490 - val_mse: 16.1302\n",
- "Epoch 51/100\n",
- "354/354 [==============================] - 0s 214us/sample - loss: 6.9212 - mae: 1.8488 - mse: 6.9212 - val_loss: 15.2317 - val_mae: 2.5733 - val_mse: 15.2317\n",
- "Epoch 52/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 6.6125 - mae: 1.8240 - mse: 6.6125 - val_loss: 13.7314 - val_mae: 2.4453 - val_mse: 13.7314\n",
- "Epoch 53/100\n",
- "354/354 [==============================] - 0s 226us/sample - loss: 6.8553 - mae: 1.8228 - mse: 6.8553 - val_loss: 15.6613 - val_mae: 2.6350 - val_mse: 15.6613\n",
- "Epoch 54/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 6.4486 - mae: 1.8099 - mse: 6.4486 - val_loss: 16.3183 - val_mae: 2.7530 - val_mse: 16.3183\n",
- "Epoch 55/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 6.5164 - mae: 1.8011 - mse: 6.5164 - val_loss: 15.5147 - val_mae: 2.6820 - val_mse: 15.5147\n",
- "Epoch 56/100\n",
- "354/354 [==============================] - 0s 243us/sample - loss: 6.5868 - mae: 1.8040 - mse: 6.5868 - val_loss: 13.6455 - val_mae: 2.4098 - val_mse: 13.6455\n",
- "Epoch 57/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 6.1876 - mae: 1.7420 - mse: 6.1876 - val_loss: 14.3190 - val_mae: 2.4943 - val_mse: 14.3190\n",
- "Epoch 58/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 6.1500 - mae: 1.7502 - mse: 6.1500 - val_loss: 16.6569 - val_mae: 2.8489 - val_mse: 16.6569\n",
- "Epoch 59/100\n",
- "354/354 [==============================] - 0s 231us/sample - loss: 6.2243 - mae: 1.7922 - mse: 6.2243 - val_loss: 15.1053 - val_mae: 2.6139 - val_mse: 15.1053\n",
- "Epoch 60/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 5.9984 - mae: 1.7819 - mse: 5.9984 - val_loss: 13.0936 - val_mae: 2.4477 - val_mse: 13.0936\n",
- "Epoch 61/100\n",
- "354/354 [==============================] - 0s 227us/sample - loss: 6.2950 - mae: 1.8000 - mse: 6.2950 - val_loss: 13.9017 - val_mae: 2.4648 - val_mse: 13.9017\n",
- "Epoch 62/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 6.0324 - mae: 1.7703 - mse: 6.0324 - val_loss: 15.0329 - val_mae: 2.5613 - val_mse: 15.0329\n",
- "Epoch 63/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 5.9684 - mae: 1.7423 - mse: 5.9684 - val_loss: 12.7976 - val_mae: 2.3946 - val_mse: 12.7976\n",
- "Epoch 64/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 5.7571 - mae: 1.6761 - mse: 5.7571 - val_loss: 15.2138 - val_mae: 2.6756 - val_mse: 15.2138\n",
- "Epoch 65/100\n",
- "354/354 [==============================] - 0s 230us/sample - loss: 5.9879 - mae: 1.7234 - mse: 5.9879 - val_loss: 13.5897 - val_mae: 2.4875 - val_mse: 13.5897\n",
- "Epoch 66/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 5.8570 - mae: 1.7208 - mse: 5.8570 - val_loss: 13.1858 - val_mae: 2.3988 - val_mse: 13.1858\n",
- "Epoch 67/100\n",
- "354/354 [==============================] - 0s 212us/sample - loss: 5.7453 - mae: 1.7049 - mse: 5.7453 - val_loss: 13.7194 - val_mae: 2.4507 - val_mse: 13.7194\n",
- "Epoch 68/100\n",
- "354/354 [==============================] - 0s 210us/sample - loss: 5.6848 - mae: 1.6871 - mse: 5.6848 - val_loss: 14.4627 - val_mae: 2.5663 - val_mse: 14.4627\n",
- "Epoch 69/100\n",
- "354/354 [==============================] - 0s 207us/sample - loss: 5.8216 - mae: 1.6898 - mse: 5.8216 - val_loss: 13.4539 - val_mae: 2.4769 - val_mse: 13.4539\n",
- "Epoch 70/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 5.5027 - mae: 1.6663 - mse: 5.5027 - val_loss: 13.4271 - val_mae: 2.4337 - val_mse: 13.4271\n",
- "Epoch 71/100\n",
- "354/354 [==============================] - 0s 218us/sample - loss: 5.4858 - mae: 1.6510 - mse: 5.4858 - val_loss: 13.6147 - val_mae: 2.4432 - val_mse: 13.6147\n",
- "Epoch 72/100\n",
- "354/354 [==============================] - 0s 208us/sample - loss: 5.4131 - mae: 1.6451 - mse: 5.4131 - val_loss: 13.2387 - val_mae: 2.4175 - val_mse: 13.2387\n",
- "Epoch 73/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 5.3593 - mae: 1.6671 - mse: 5.3593 - val_loss: 12.5856 - val_mae: 2.4682 - val_mse: 12.5856\n",
- "Epoch 74/100\n",
- "354/354 [==============================] - 0s 221us/sample - loss: 5.3946 - mae: 1.6290 - mse: 5.3946 - val_loss: 13.6171 - val_mae: 2.4803 - val_mse: 13.6171\n",
- "Epoch 75/100\n",
- "354/354 [==============================] - 0s 224us/sample - loss: 5.2424 - mae: 1.6341 - mse: 5.2424 - val_loss: 14.7231 - val_mae: 2.6550 - val_mse: 14.7231\n",
- "Epoch 76/100\n",
- "354/354 [==============================] - 0s 221us/sample - loss: 5.1533 - mae: 1.5881 - mse: 5.1533 - val_loss: 13.4304 - val_mae: 2.5766 - val_mse: 13.4304\n",
- "Epoch 77/100\n",
- "354/354 [==============================] - 0s 216us/sample - loss: 5.2420 - mae: 1.6104 - mse: 5.2420 - val_loss: 12.8703 - val_mae: 2.3890 - val_mse: 12.8703\n",
- "Epoch 78/100\n",
- "354/354 [==============================] - 0s 214us/sample - loss: 4.9934 - mae: 1.5716 - mse: 4.9934 - val_loss: 13.8570 - val_mae: 2.5972 - val_mse: 13.8570\n",
- "Epoch 79/100\n",
- "354/354 [==============================] - 0s 222us/sample - loss: 5.0281 - mae: 1.5918 - mse: 5.0281 - val_loss: 12.6817 - val_mae: 2.3643 - val_mse: 12.6817\n",
- "Epoch 80/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 5.2042 - mae: 1.6049 - mse: 5.2042 - val_loss: 12.6017 - val_mae: 2.3721 - val_mse: 12.6017\n",
- "Epoch 81/100\n",
- "354/354 [==============================] - 0s 227us/sample - loss: 4.9504 - mae: 1.5701 - mse: 4.9504 - val_loss: 12.4663 - val_mae: 2.3832 - val_mse: 12.4663\n",
- "Epoch 82/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 4.8695 - mae: 1.6113 - mse: 4.8695 - val_loss: 12.4286 - val_mae: 2.3672 - val_mse: 12.4286\n",
- "Epoch 83/100\n",
- "354/354 [==============================] - 0s 239us/sample - loss: 4.9597 - mae: 1.5901 - mse: 4.9597 - val_loss: 11.9593 - val_mae: 2.3090 - val_mse: 11.9593\n",
- "Epoch 84/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 4.7727 - mae: 1.5529 - mse: 4.7727 - val_loss: 15.0728 - val_mae: 2.6187 - val_mse: 15.0728\n",
- "Epoch 85/100\n",
- "354/354 [==============================] - 0s 217us/sample - loss: 4.7613 - mae: 1.5471 - mse: 4.7613 - val_loss: 12.1164 - val_mae: 2.3669 - val_mse: 12.1164\n",
- "Epoch 86/100\n",
- "354/354 [==============================] - 0s 225us/sample - loss: 4.7107 - mae: 1.5415 - mse: 4.7107 - val_loss: 12.4001 - val_mae: 2.3728 - val_mse: 12.4001\n",
- "Epoch 87/100\n",
- "354/354 [==============================] - 0s 234us/sample - loss: 4.6553 - mae: 1.5038 - mse: 4.6553 - val_loss: 12.9030 - val_mae: 2.4729 - val_mse: 12.9030\n",
- "Epoch 88/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 4.7791 - mae: 1.5290 - mse: 4.7791 - val_loss: 11.3780 - val_mae: 2.2924 - val_mse: 11.3780\n",
- "Epoch 89/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 4.6260 - mae: 1.4847 - mse: 4.6260 - val_loss: 12.4903 - val_mae: 2.3655 - val_mse: 12.4903\n",
- "Epoch 90/100\n",
- "354/354 [==============================] - 0s 220us/sample - loss: 4.6343 - mae: 1.4983 - mse: 4.6343 - val_loss: 11.9263 - val_mae: 2.3201 - val_mse: 11.9263\n",
- "Epoch 91/100\n",
- "354/354 [==============================] - 0s 219us/sample - loss: 4.4519 - mae: 1.4970 - mse: 4.4519 - val_loss: 11.5961 - val_mae: 2.3123 - val_mse: 11.5961\n",
- "Epoch 92/100\n",
- "354/354 [==============================] - 0s 209us/sample - loss: 4.6032 - mae: 1.4873 - mse: 4.6032 - val_loss: 12.5865 - val_mae: 2.3583 - val_mse: 12.5865\n",
- "Epoch 93/100\n",
- "354/354 [==============================] - 0s 215us/sample - loss: 4.5848 - mae: 1.5171 - mse: 4.5848 - val_loss: 11.5866 - val_mae: 2.2889 - val_mse: 11.5866\n",
- "Epoch 94/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 4.4029 - mae: 1.4931 - mse: 4.4029 - val_loss: 12.1418 - val_mae: 2.3795 - val_mse: 12.1418\n",
- "Epoch 95/100\n",
- "354/354 [==============================] - 0s 225us/sample - loss: 4.3969 - mae: 1.4670 - mse: 4.3969 - val_loss: 14.1345 - val_mae: 2.5131 - val_mse: 14.1345\n",
- "Epoch 96/100\n",
- "354/354 [==============================] - 0s 236us/sample - loss: 4.2738 - mae: 1.4875 - mse: 4.2738 - val_loss: 12.2324 - val_mae: 2.3689 - val_mse: 12.2324\n",
- "Epoch 97/100\n",
- "354/354 [==============================] - 0s 238us/sample - loss: 4.3581 - mae: 1.4904 - mse: 4.3581 - val_loss: 12.9004 - val_mae: 2.4411 - val_mse: 12.9004\n",
- "Epoch 98/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 4.2009 - mae: 1.4409 - mse: 4.2009 - val_loss: 11.3523 - val_mae: 2.2606 - val_mse: 11.3523\n",
- "Epoch 99/100\n",
- "354/354 [==============================] - 0s 228us/sample - loss: 4.1062 - mae: 1.3971 - mse: 4.1062 - val_loss: 11.6835 - val_mae: 2.2600 - val_mse: 11.6835\n",
- "Epoch 100/100\n",
- "354/354 [==============================] - 0s 213us/sample - loss: 4.2882 - mae: 1.4424 - mse: 4.2882 - val_loss: 11.6418 - val_mae: 2.2779 - val_mse: 11.6418\n"
- ]
- }
- ],
- "source": [
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = 100,\n",
- " batch_size = 10,\n",
- " verbose = 1,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [savemodel_callback])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Model evaluation\n",
- "MAE = Mean Absolute Error (between the labels and predictions) \n",
- "A mae equal to 3 represents an average error in prediction of $3k."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 11.6418\n",
- "x_test / mae : 2.2779\n",
- "x_test / mse : 11.6418\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
- "print('x_test / mse : {:5.4f}'.format(score[2]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Training history\n",
- "What was the best result during our training ?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min( val_mae ) : 2.2600\n"
- ]
- }
- ],
- "source": [
- "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/BHP2-01-history_0</div>"
- ],
- "text/plain": [
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- },
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\n",
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
- " 'MAE' :['mae', 'val_mae'],\n",
- " 'LOSS':['loss','val_loss']}, save_as='01-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - Restore a model :"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.1 - Reload model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "Loaded.\n"
- ]
- }
- ],
- "source": [
- "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
- "loaded_model.summary()\n",
- "print(\"Loaded.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.2 - Evaluate it :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 11.3523\n",
- "x_test / mae : 2.2606\n",
- "x_test / mse : 11.3523\n"
- ]
- }
- ],
- "source": [
- "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
- "print('x_test / mse : {:5.4f}'.format(score[2]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.3 - Make a prediction"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_data = [ 1.26425925, -0.48522739, 1.0436489 , -0.23112788, 1.37120745,\n",
- " -2.14308942, 1.13489104, -1.06802005, 1.71189006, 1.57042287,\n",
- " 0.77859951, 0.14769795, 2.7585581 ]\n",
- "real_price = 10.4\n",
- "\n",
- "my_data=np.array(my_data).reshape(1,13)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prediction : 9.11 K$ Reality : 10.40 K$\n"
- ]
- }
- ],
- "source": [
- "predictions = loaded_model.predict( my_data )\n",
- "print(\"Prediction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], real_price))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 16 December 2020, 21:13:59\n",
- "Duration is : 00:00:11 697ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b8e33dc7b73b6f36c22c182fc6dc470102cde752
--- /dev/null
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -0,0 +1,1463 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "\n",
+ "# <!-- TITLE --> [BHPD1] - 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 Prices 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 theses informations : \n",
+ "\n",
+ " - CRIM: This is the per capita crime rate by town\n",
+ " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
+ " - INDUS: This is the proportion of non-retail business acres per town\n",
+ " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
+ " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
+ " - RM: This is the average number of rooms per dwelling\n",
+ " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
+ " - DIS: This is the weighted distances to five Boston employment centers\n",
+ " - RAD: This is the index of accessibility to radial highways\n",
+ " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
+ " - PTRATIO: This is the pupil-teacher ratio by town\n",
+ " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
+ " - LSTAT: This is the percentage lower status of the population\n",
+ " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\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,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 1.2b1 DEV\n",
+ "Notebook id : BHPD1\n",
+ "Run time : Friday 8 January 2021, 01:09:13\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\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",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('BHPD1')"
+ ]
+ },
+ {
+ "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": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "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": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_41981_\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_41981_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_41981_row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_41981_row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_41981_row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_41981_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_41981_row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_41981_row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_41981_row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_41981_row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_41981_row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_41981_row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_41981_row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_41981_row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_41981_row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_41981_row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_41981_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_41981_row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_41981_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_41981_row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_41981_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_41981_row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_41981_row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_41981_row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_41981_row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_41981_row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_41981_row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_41981_row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_41981_row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_41981_row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_41981_row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_41981_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_41981_row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_41981_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_41981_row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_41981_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_41981_row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_41981_row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_41981_row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_41981_row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_41981_row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_41981_row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_41981_row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_41981_row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_41981_row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_41981_row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_41981_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_41981_row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_41981_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_41981_row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_41981_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_41981_row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_41981_row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_41981_row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_41981_row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_41981_row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_41981_row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_41981_row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_41981_row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_41981_row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_41981_row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_41981_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_41981_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_41981_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_41981_row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_41981_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_41981_row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_41981_row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_41981_row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_41981_row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_41981_row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_41981_row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_41981_row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_41981_row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_41981_row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_41981_row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f0105877a10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Missing Data : 0 Shape is : (506, 14)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
+ "\n",
+ "display(data.head(5).style.format(\"{0:.2f}\").set_caption(\"Few lines of the dataset :\"))\n",
+ "print('Missing Data : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preparing the data\n",
+ "### 3.1 - Split data\n",
+ "We will use 70% of the data for training and 30% for validation. \n",
+ "The dataset is **shuffled** and shared between **learning** and **testing**. \n",
+ "x will be input data and y the expected output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data shape was : (506, 14)\n",
+ "x_train : (354, 13) y_train : (354,)\n",
+ "x_test : (152, 13) y_test : (152,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Suffle and Split => train, test\n",
+ "#\n",
+ "data_train = data.sample(frac=0.7, axis=0)\n",
+ "data_test = data.drop(data_train.index)\n",
+ "\n",
+ "# ---- Split => x,y (medv is price)\n",
+ "#\n",
+ "x_train = data_train.drop('medv', axis=1)\n",
+ "y_train = data_train['medv']\n",
+ "x_test = data_test.drop('medv', axis=1)\n",
+ "y_test = data_test['medv']\n",
+ "\n",
+ "print('Original data shape was : ',data.shape)\n",
+ "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
+ "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 - Data normalization\n",
+ "**Note :** \n",
+ " - All input data must be normalized, train and test. \n",
+ " - To do this we will **subtract the mean** and **divide by the standard deviation**. \n",
+ " - But test data should not be used in any way, even for normalization. \n",
+ " - The mean and the standard deviation will therefore only be calculated with the train data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_b4ec0_\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_b4ec0_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_b4ec0_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_b4ec0_row1_col0\" class=\"data row1 col0\" >3.37</td>\n",
+ " <td id=\"T_b4ec0_row1_col1\" class=\"data row1 col1\" >11.25</td>\n",
+ " <td id=\"T_b4ec0_row1_col2\" class=\"data row1 col2\" >11.05</td>\n",
+ " <td id=\"T_b4ec0_row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
+ " <td id=\"T_b4ec0_row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
+ " <td id=\"T_b4ec0_row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_b4ec0_row1_col6\" class=\"data row1 col6\" >68.01</td>\n",
+ " <td id=\"T_b4ec0_row1_col7\" class=\"data row1 col7\" >3.77</td>\n",
+ " <td id=\"T_b4ec0_row1_col8\" class=\"data row1 col8\" >9.68</td>\n",
+ " <td id=\"T_b4ec0_row1_col9\" class=\"data row1 col9\" >409.00</td>\n",
+ " <td id=\"T_b4ec0_row1_col10\" class=\"data row1 col10\" >18.39</td>\n",
+ " <td id=\"T_b4ec0_row1_col11\" class=\"data row1 col11\" >355.75</td>\n",
+ " <td id=\"T_b4ec0_row1_col12\" class=\"data row1 col12\" >12.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_b4ec0_row2_col0\" class=\"data row2 col0\" >7.50</td>\n",
+ " <td id=\"T_b4ec0_row2_col1\" class=\"data row2 col1\" >23.19</td>\n",
+ " <td id=\"T_b4ec0_row2_col2\" class=\"data row2 col2\" >6.73</td>\n",
+ " <td id=\"T_b4ec0_row2_col3\" class=\"data row2 col3\" >0.26</td>\n",
+ " <td id=\"T_b4ec0_row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
+ " <td id=\"T_b4ec0_row2_col5\" class=\"data row2 col5\" >0.74</td>\n",
+ " <td id=\"T_b4ec0_row2_col6\" class=\"data row2 col6\" >28.85</td>\n",
+ " <td id=\"T_b4ec0_row2_col7\" class=\"data row2 col7\" >2.03</td>\n",
+ " <td id=\"T_b4ec0_row2_col8\" class=\"data row2 col8\" >8.80</td>\n",
+ " <td id=\"T_b4ec0_row2_col9\" class=\"data row2 col9\" >169.89</td>\n",
+ " <td id=\"T_b4ec0_row2_col10\" class=\"data row2 col10\" >2.22</td>\n",
+ " <td id=\"T_b4ec0_row2_col11\" class=\"data row2 col11\" >90.11</td>\n",
+ " <td id=\"T_b4ec0_row2_col12\" class=\"data row2 col12\" >7.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_b4ec0_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_b4ec0_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
+ " <td id=\"T_b4ec0_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_b4ec0_row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_b4ec0_row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_b4ec0_row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
+ " <td id=\"T_b4ec0_row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_b4ec0_row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
+ " <td id=\"T_b4ec0_row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_b4ec0_row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_b4ec0_row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_b4ec0_row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_b4ec0_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_b4ec0_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_b4ec0_row4_col5\" class=\"data row4 col5\" >5.90</td>\n",
+ " <td id=\"T_b4ec0_row4_col6\" class=\"data row4 col6\" >42.32</td>\n",
+ " <td id=\"T_b4ec0_row4_col7\" class=\"data row4 col7\" >2.12</td>\n",
+ " <td id=\"T_b4ec0_row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_b4ec0_row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
+ " <td id=\"T_b4ec0_row4_col10\" class=\"data row4 col10\" >16.90</td>\n",
+ " <td id=\"T_b4ec0_row4_col11\" class=\"data row4 col11\" >374.46</td>\n",
+ " <td id=\"T_b4ec0_row4_col12\" class=\"data row4 col12\" >6.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_b4ec0_row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
+ " <td id=\"T_b4ec0_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_b4ec0_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_b4ec0_row5_col5\" class=\"data row5 col5\" >6.20</td>\n",
+ " <td id=\"T_b4ec0_row5_col6\" class=\"data row5 col6\" >77.70</td>\n",
+ " <td id=\"T_b4ec0_row5_col7\" class=\"data row5 col7\" >3.21</td>\n",
+ " <td id=\"T_b4ec0_row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_b4ec0_row5_col9\" class=\"data row5 col9\" >329.50</td>\n",
+ " <td id=\"T_b4ec0_row5_col10\" class=\"data row5 col10\" >18.90</td>\n",
+ " <td id=\"T_b4ec0_row5_col11\" class=\"data row5 col11\" >390.88</td>\n",
+ " <td id=\"T_b4ec0_row5_col12\" class=\"data row5 col12\" >11.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_b4ec0_row6_col0\" class=\"data row6 col0\" >3.76</td>\n",
+ " <td id=\"T_b4ec0_row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
+ " <td id=\"T_b4ec0_row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_b4ec0_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_b4ec0_row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_b4ec0_row6_col5\" class=\"data row6 col5\" >6.60</td>\n",
+ " <td id=\"T_b4ec0_row6_col6\" class=\"data row6 col6\" >93.90</td>\n",
+ " <td id=\"T_b4ec0_row6_col7\" class=\"data row6 col7\" >5.19</td>\n",
+ " <td id=\"T_b4ec0_row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_b4ec0_row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_b4ec0_row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_b4ec0_row6_col11\" class=\"data row6 col11\" >395.56</td>\n",
+ " <td id=\"T_b4ec0_row6_col12\" class=\"data row6 col12\" >16.86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_b4ec0_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_b4ec0_row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
+ " <td id=\"T_b4ec0_row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_b4ec0_row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_b4ec0_row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_b4ec0_row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_b4ec0_row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_b4ec0_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_b4ec0_row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
+ " <td id=\"T_b4ec0_row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_b4ec0_row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_b4ec0_row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_b4ec0_row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_b4ec0_row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f010572c1d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_be30a_\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_be30a_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_be30a_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_be30a_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_be30a_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
+ " <td id=\"T_be30a_row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+ " <td id=\"T_be30a_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_be30a_row1_col10\" class=\"data row1 col10\" >-0.00</td>\n",
+ " <td id=\"T_be30a_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+ " <td id=\"T_be30a_row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_be30a_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_be30a_row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_be30a_row3_col0\" class=\"data row3 col0\" >-0.45</td>\n",
+ " <td id=\"T_be30a_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_be30a_row3_col2\" class=\"data row3 col2\" >-1.46</td>\n",
+ " <td id=\"T_be30a_row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
+ " <td id=\"T_be30a_row3_col4\" class=\"data row3 col4\" >-1.49</td>\n",
+ " <td id=\"T_be30a_row3_col5\" class=\"data row3 col5\" >-3.72</td>\n",
+ " <td id=\"T_be30a_row3_col6\" class=\"data row3 col6\" >-2.26</td>\n",
+ " <td id=\"T_be30a_row3_col7\" class=\"data row3 col7\" >-1.29</td>\n",
+ " <td id=\"T_be30a_row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
+ " <td id=\"T_be30a_row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
+ " <td id=\"T_be30a_row3_col10\" class=\"data row3 col10\" >-2.61</td>\n",
+ " <td id=\"T_be30a_row3_col11\" class=\"data row3 col11\" >-3.94</td>\n",
+ " <td id=\"T_be30a_row3_col12\" class=\"data row3 col12\" >-1.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_be30a_row4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
+ " <td id=\"T_be30a_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_be30a_row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
+ " <td id=\"T_be30a_row4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
+ " <td id=\"T_be30a_row4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
+ " <td id=\"T_be30a_row4_col5\" class=\"data row4 col5\" >-0.55</td>\n",
+ " <td id=\"T_be30a_row4_col6\" class=\"data row4 col6\" >-0.89</td>\n",
+ " <td id=\"T_be30a_row4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
+ " <td id=\"T_be30a_row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
+ " <td id=\"T_be30a_row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
+ " <td id=\"T_be30a_row4_col10\" class=\"data row4 col10\" >-0.67</td>\n",
+ " <td id=\"T_be30a_row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
+ " <td id=\"T_be30a_row4_col12\" class=\"data row4 col12\" >-0.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_be30a_row5_col0\" class=\"data row5 col0\" >-0.41</td>\n",
+ " <td id=\"T_be30a_row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
+ " <td id=\"T_be30a_row5_col2\" class=\"data row5 col2\" >-0.20</td>\n",
+ " <td id=\"T_be30a_row5_col3\" class=\"data row5 col3\" >-0.28</td>\n",
+ " <td id=\"T_be30a_row5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
+ " <td id=\"T_be30a_row5_col5\" class=\"data row5 col5\" >-0.14</td>\n",
+ " <td id=\"T_be30a_row5_col6\" class=\"data row5 col6\" >0.34</td>\n",
+ " <td id=\"T_be30a_row5_col7\" class=\"data row5 col7\" >-0.28</td>\n",
+ " <td id=\"T_be30a_row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
+ " <td id=\"T_be30a_row5_col9\" class=\"data row5 col9\" >-0.47</td>\n",
+ " <td id=\"T_be30a_row5_col10\" class=\"data row5 col10\" >0.23</td>\n",
+ " <td id=\"T_be30a_row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
+ " <td id=\"T_be30a_row5_col12\" class=\"data row5 col12\" >-0.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_be30a_row6_col0\" class=\"data row6 col0\" >0.05</td>\n",
+ " <td id=\"T_be30a_row6_col1\" class=\"data row6 col1\" >0.05</td>\n",
+ " <td id=\"T_be30a_row6_col2\" class=\"data row6 col2\" >1.05</td>\n",
+ " <td id=\"T_be30a_row6_col3\" class=\"data row6 col3\" >-0.28</td>\n",
+ " <td id=\"T_be30a_row6_col4\" class=\"data row6 col4\" >0.61</td>\n",
+ " <td id=\"T_be30a_row6_col5\" class=\"data row6 col5\" >0.40</td>\n",
+ " <td id=\"T_be30a_row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_be30a_row6_col7\" class=\"data row6 col7\" >0.70</td>\n",
+ " <td id=\"T_be30a_row6_col8\" class=\"data row6 col8\" >1.63</td>\n",
+ " <td id=\"T_be30a_row6_col9\" class=\"data row6 col9\" >1.51</td>\n",
+ " <td id=\"T_be30a_row6_col10\" class=\"data row6 col10\" >0.82</td>\n",
+ " <td id=\"T_be30a_row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
+ " <td id=\"T_be30a_row6_col12\" class=\"data row6 col12\" >0.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_be30a_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_be30a_row7_col0\" class=\"data row7 col0\" >9.35</td>\n",
+ " <td id=\"T_be30a_row7_col1\" class=\"data row7 col1\" >3.83</td>\n",
+ " <td id=\"T_be30a_row7_col2\" class=\"data row7 col2\" >2.48</td>\n",
+ " <td id=\"T_be30a_row7_col3\" class=\"data row7 col3\" >3.55</td>\n",
+ " <td id=\"T_be30a_row7_col4\" class=\"data row7 col4\" >2.78</td>\n",
+ " <td id=\"T_be30a_row7_col5\" class=\"data row7 col5\" >3.36</td>\n",
+ " <td id=\"T_be30a_row7_col6\" class=\"data row7 col6\" >1.11</td>\n",
+ " <td id=\"T_be30a_row7_col7\" class=\"data row7 col7\" >3.41</td>\n",
+ " <td id=\"T_be30a_row7_col8\" class=\"data row7 col8\" >1.63</td>\n",
+ " <td id=\"T_be30a_row7_col9\" class=\"data row7 col9\" >1.78</td>\n",
+ " <td id=\"T_be30a_row7_col10\" class=\"data row7 col10\" >1.63</td>\n",
+ " <td id=\"T_be30a_row7_col11\" class=\"data row7 col11\" >0.46</td>\n",
+ " <td id=\"T_be30a_row7_col12\" class=\"data row7 col12\" >3.49</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f01bb4b2a10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_38be7_\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_38be7_level0_row0\" class=\"row_heading level0 row0\" >357</th>\n",
+ " <td id=\"T_38be7_row0_col0\" class=\"data row0 col0\" >0.06</td>\n",
+ " <td id=\"T_38be7_row0_col1\" class=\"data row0 col1\" >-0.49</td>\n",
+ " <td id=\"T_38be7_row0_col2\" class=\"data row0 col2\" >1.05</td>\n",
+ " <td id=\"T_38be7_row0_col3\" class=\"data row0 col3\" >3.55</td>\n",
+ " <td id=\"T_38be7_row0_col4\" class=\"data row0 col4\" >1.89</td>\n",
+ " <td id=\"T_38be7_row0_col5\" class=\"data row0 col5\" >0.12</td>\n",
+ " <td id=\"T_38be7_row0_col6\" class=\"data row0 col6\" >0.80</td>\n",
+ " <td id=\"T_38be7_row0_col7\" class=\"data row0 col7\" >-0.62</td>\n",
+ " <td id=\"T_38be7_row0_col8\" class=\"data row0 col8\" >1.63</td>\n",
+ " <td id=\"T_38be7_row0_col9\" class=\"data row0 col9\" >1.51</td>\n",
+ " <td id=\"T_38be7_row0_col10\" class=\"data row0 col10\" >0.82</td>\n",
+ " <td id=\"T_38be7_row0_col11\" class=\"data row0 col11\" >0.39</td>\n",
+ " <td id=\"T_38be7_row0_col12\" class=\"data row0 col12\" >0.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_38be7_level0_row1\" class=\"row_heading level0 row1\" >274</th>\n",
+ " <td id=\"T_38be7_row1_col0\" class=\"data row1 col0\" >-0.44</td>\n",
+ " <td id=\"T_38be7_row1_col1\" class=\"data row1 col1\" >1.24</td>\n",
+ " <td id=\"T_38be7_row1_col2\" class=\"data row1 col2\" >-0.69</td>\n",
+ " <td id=\"T_38be7_row1_col3\" class=\"data row1 col3\" >3.55</td>\n",
+ " <td id=\"T_38be7_row1_col4\" class=\"data row1 col4\" >-0.95</td>\n",
+ " <td id=\"T_38be7_row1_col5\" class=\"data row1 col5\" >0.61</td>\n",
+ " <td id=\"T_38be7_row1_col6\" class=\"data row1 col6\" >-1.22</td>\n",
+ " <td id=\"T_38be7_row1_col7\" class=\"data row1 col7\" >0.15</td>\n",
+ " <td id=\"T_38be7_row1_col8\" class=\"data row1 col8\" >-0.65</td>\n",
+ " <td id=\"T_38be7_row1_col9\" class=\"data row1 col9\" >-0.91</td>\n",
+ " <td id=\"T_38be7_row1_col10\" class=\"data row1 col10\" >-0.35</td>\n",
+ " <td id=\"T_38be7_row1_col11\" class=\"data row1 col11\" >0.46</td>\n",
+ " <td id=\"T_38be7_row1_col12\" class=\"data row1 col12\" >-1.25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_38be7_level0_row2\" class=\"row_heading level0 row2\" >412</th>\n",
+ " <td id=\"T_38be7_row2_col0\" class=\"data row2 col0\" >2.06</td>\n",
+ " <td id=\"T_38be7_row2_col1\" class=\"data row2 col1\" >-0.49</td>\n",
+ " <td id=\"T_38be7_row2_col2\" class=\"data row2 col2\" >1.05</td>\n",
+ " <td id=\"T_38be7_row2_col3\" class=\"data row2 col3\" >-0.28</td>\n",
+ " <td id=\"T_38be7_row2_col4\" class=\"data row2 col4\" >0.37</td>\n",
+ " <td id=\"T_38be7_row2_col5\" class=\"data row2 col5\" >-2.27</td>\n",
+ " <td id=\"T_38be7_row2_col6\" class=\"data row2 col6\" >1.11</td>\n",
+ " <td id=\"T_38be7_row2_col7\" class=\"data row2 col7\" >-1.09</td>\n",
+ " <td id=\"T_38be7_row2_col8\" class=\"data row2 col8\" >1.63</td>\n",
+ " <td id=\"T_38be7_row2_col9\" class=\"data row2 col9\" >1.51</td>\n",
+ " <td id=\"T_38be7_row2_col10\" class=\"data row2 col10\" >0.82</td>\n",
+ " <td id=\"T_38be7_row2_col11\" class=\"data row2 col11\" >-3.63</td>\n",
+ " <td id=\"T_38be7_row2_col12\" class=\"data row2 col12\" >2.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_38be7_level0_row3\" class=\"row_heading level0 row3\" >474</th>\n",
+ " <td id=\"T_38be7_row3_col0\" class=\"data row3 col0\" >0.62</td>\n",
+ " <td id=\"T_38be7_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_38be7_row3_col2\" class=\"data row3 col2\" >1.05</td>\n",
+ " <td id=\"T_38be7_row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
+ " <td id=\"T_38be7_row3_col4\" class=\"data row3 col4\" >0.26</td>\n",
+ " <td id=\"T_38be7_row3_col5\" class=\"data row3 col5\" >-1.19</td>\n",
+ " <td id=\"T_38be7_row3_col6\" class=\"data row3 col6\" >0.95</td>\n",
+ " <td id=\"T_38be7_row3_col7\" class=\"data row3 col7\" >-0.66</td>\n",
+ " <td id=\"T_38be7_row3_col8\" class=\"data row3 col8\" >1.63</td>\n",
+ " <td id=\"T_38be7_row3_col9\" class=\"data row3 col9\" >1.51</td>\n",
+ " <td id=\"T_38be7_row3_col10\" class=\"data row3 col10\" >0.82</td>\n",
+ " <td id=\"T_38be7_row3_col11\" class=\"data row3 col11\" >-0.04</td>\n",
+ " <td id=\"T_38be7_row3_col12\" class=\"data row3 col12\" >0.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_38be7_level0_row4\" class=\"row_heading level0 row4\" >216</th>\n",
+ " <td id=\"T_38be7_row4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
+ " <td id=\"T_38be7_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_38be7_row4_col2\" class=\"data row4 col2\" >0.42</td>\n",
+ " <td id=\"T_38be7_row4_col3\" class=\"data row4 col3\" >3.55</td>\n",
+ " <td id=\"T_38be7_row4_col4\" class=\"data row4 col4\" >-0.04</td>\n",
+ " <td id=\"T_38be7_row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
+ " <td id=\"T_38be7_row4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
+ " <td id=\"T_38be7_row4_col7\" class=\"data row4 col7\" >-0.32</td>\n",
+ " <td id=\"T_38be7_row4_col8\" class=\"data row4 col8\" >-0.53</td>\n",
+ " <td id=\"T_38be7_row4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
+ " <td id=\"T_38be7_row4_col10\" class=\"data row4 col10\" >-0.90</td>\n",
+ " <td id=\"T_38be7_row4_col11\" class=\"data row4 col11\" >0.41</td>\n",
+ " <td id=\"T_38be7_row4_col12\" class=\"data row4 col12\" >0.12</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f010572c090>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
+ "\n",
+ "mean = x_train.mean()\n",
+ "std = x_train.std()\n",
+ "x_train = (x_train - mean) / std\n",
+ "x_test = (x_test - mean) / std\n",
+ "\n",
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
+ "display(x_train.head(5).style.format(\"{0:.2f}\").set_caption(\"Few lines of the dataset :\"))\n",
+ "\n",
+ "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+ "x_test, y_test = np.array(x_test), np.array(y_test)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build a model\n",
+ "About informations about : \n",
+ " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+ " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+ " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " def get_model_v1(shape):\n",
+ " \n",
+ " model = keras.models.Sequential()\n",
+ " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
+ " model.add(keras.layers.Dense(1, name='Output'))\n",
+ " \n",
+ " model.compile(optimizer = 'rmsprop',\n",
+ " loss = 'mse',\n",
+ " metrics = ['mae', 'mse'] )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model=get_model_v1( (13,) )\n",
+ "\n",
+ "model.summary()\n",
+ "\n",
+ "# img=keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)\n",
+ "# display(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n",
+ "36/36 [==============================] - 0s 5ms/step - loss: 528.6602 - mae: 20.8095 - mse: 528.6602 - val_loss: 382.0872 - val_mae: 17.4028 - val_mse: 382.0872\n",
+ "Epoch 2/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 324.0841 - mae: 15.5158 - mse: 324.0841 - val_loss: 186.1084 - val_mae: 11.6776 - val_mse: 186.1084\n",
+ "Epoch 3/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 137.9440 - mae: 9.1263 - mse: 137.9440 - val_loss: 73.6228 - val_mae: 6.5124 - val_mse: 73.6228\n",
+ "Epoch 4/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 58.4952 - mae: 5.7129 - mse: 58.4952 - val_loss: 46.9296 - val_mae: 4.9677 - val_mse: 46.9296\n",
+ "Epoch 5/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 34.6571 - mae: 4.3862 - mse: 34.6571 - val_loss: 36.0974 - val_mae: 4.0957 - val_mse: 36.0974\n",
+ "Epoch 6/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 25.0004 - mae: 3.6589 - mse: 25.0004 - val_loss: 31.2137 - val_mae: 3.7278 - val_mse: 31.2137\n",
+ "Epoch 7/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 21.0519 - mae: 3.3354 - mse: 21.0519 - val_loss: 28.1914 - val_mae: 3.4788 - val_mse: 28.1914\n",
+ "Epoch 8/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 18.5185 - mae: 3.1083 - mse: 18.5185 - val_loss: 25.0752 - val_mae: 3.1716 - val_mse: 25.0752\n",
+ "Epoch 9/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 16.6720 - mae: 2.8630 - mse: 16.6720 - val_loss: 23.5365 - val_mae: 3.0271 - val_mse: 23.5365\n",
+ "Epoch 10/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 15.6488 - mae: 2.7623 - mse: 15.6488 - val_loss: 21.7859 - val_mae: 2.9200 - val_mse: 21.7859\n",
+ "Epoch 11/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 14.5972 - mae: 2.6339 - mse: 14.5972 - val_loss: 20.7650 - val_mae: 2.8556 - val_mse: 20.7650\n",
+ "Epoch 12/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 13.8285 - mae: 2.5940 - mse: 13.8285 - val_loss: 20.2548 - val_mae: 2.7733 - val_mse: 20.2548\n",
+ "Epoch 13/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 13.3525 - mae: 2.5089 - mse: 13.3525 - val_loss: 19.2741 - val_mae: 2.7488 - val_mse: 19.2741\n",
+ "Epoch 14/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.9928 - mae: 2.4619 - mse: 12.9928 - val_loss: 19.1163 - val_mae: 2.8952 - val_mse: 19.1163\n",
+ "Epoch 15/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.6822 - mae: 2.4667 - mse: 12.6822 - val_loss: 18.0593 - val_mae: 2.7012 - val_mse: 18.0593\n",
+ "Epoch 16/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.0337 - mae: 2.4042 - mse: 12.0337 - val_loss: 18.4064 - val_mae: 2.7333 - val_mse: 18.4064\n",
+ "Epoch 17/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.9141 - mae: 2.3476 - mse: 11.9141 - val_loss: 17.7628 - val_mae: 2.7539 - val_mse: 17.7628\n",
+ "Epoch 18/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.4745 - mae: 2.3208 - mse: 11.4745 - val_loss: 17.0482 - val_mae: 2.6804 - val_mse: 17.0482\n",
+ "Epoch 19/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.3928 - mae: 2.3049 - mse: 11.3928 - val_loss: 16.8050 - val_mae: 2.6693 - val_mse: 16.8050\n",
+ "Epoch 20/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.3859 - mae: 2.3019 - mse: 11.3859 - val_loss: 16.5509 - val_mae: 2.6351 - val_mse: 16.5509\n",
+ "Epoch 21/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.7268 - mae: 2.2695 - mse: 10.7268 - val_loss: 16.8328 - val_mae: 2.5932 - val_mse: 16.8328\n",
+ "Epoch 22/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.8960 - mae: 2.2326 - mse: 10.8960 - val_loss: 16.8741 - val_mae: 2.7190 - val_mse: 16.8741\n",
+ "Epoch 23/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.6260 - mae: 2.2117 - mse: 10.6260 - val_loss: 16.9705 - val_mae: 2.8508 - val_mse: 16.9705\n",
+ "Epoch 24/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.3208 - mae: 2.2318 - mse: 10.3208 - val_loss: 15.7215 - val_mae: 2.5971 - val_mse: 15.7215\n",
+ "Epoch 25/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.1904 - mae: 2.1686 - mse: 10.1904 - val_loss: 15.5120 - val_mae: 2.5987 - val_mse: 15.5120\n",
+ "Epoch 26/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.9797 - mae: 2.1702 - mse: 9.9797 - val_loss: 15.5233 - val_mae: 2.6006 - val_mse: 15.5233\n",
+ "Epoch 27/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.0867 - mae: 2.1720 - mse: 10.0867 - val_loss: 15.4992 - val_mae: 2.6143 - val_mse: 15.4992\n",
+ "Epoch 28/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.8478 - mae: 2.1402 - mse: 9.8478 - val_loss: 15.3322 - val_mae: 2.5853 - val_mse: 15.3322\n",
+ "Epoch 29/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.7488 - mae: 2.1326 - mse: 9.7488 - val_loss: 15.3041 - val_mae: 2.6232 - val_mse: 15.3041\n",
+ "Epoch 30/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.4161 - mae: 2.0990 - mse: 9.4161 - val_loss: 15.3295 - val_mae: 2.5953 - val_mse: 15.3295\n",
+ "Epoch 31/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.3843 - mae: 2.0823 - mse: 9.3843 - val_loss: 15.5947 - val_mae: 2.6750 - val_mse: 15.5947\n",
+ "Epoch 32/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.2416 - mae: 2.0897 - mse: 9.2416 - val_loss: 15.2306 - val_mae: 2.5839 - val_mse: 15.2306\n",
+ "Epoch 33/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.1938 - mae: 2.0604 - mse: 9.1938 - val_loss: 14.7121 - val_mae: 2.5371 - val_mse: 14.7121\n",
+ "Epoch 34/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.9643 - mae: 2.0460 - mse: 8.9643 - val_loss: 14.8987 - val_mae: 2.5898 - val_mse: 14.8987\n",
+ "Epoch 35/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.7968 - mae: 2.0337 - mse: 8.7968 - val_loss: 14.7443 - val_mae: 2.5886 - val_mse: 14.7443\n",
+ "Epoch 36/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6585 - mae: 1.9865 - mse: 8.6585 - val_loss: 15.1119 - val_mae: 2.6940 - val_mse: 15.1119\n",
+ "Epoch 37/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.5932 - mae: 2.0139 - mse: 8.5932 - val_loss: 14.4391 - val_mae: 2.5568 - val_mse: 14.4391\n",
+ "Epoch 38/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6503 - mae: 1.9818 - mse: 8.6503 - val_loss: 14.6089 - val_mae: 2.5932 - val_mse: 14.6089\n",
+ "Epoch 39/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.4741 - mae: 1.9945 - mse: 8.4741 - val_loss: 14.4245 - val_mae: 2.5198 - val_mse: 14.4245\n",
+ "Epoch 40/100\n",
+ "36/36 [==============================] - 0s 4ms/step - loss: 8.3792 - mae: 1.9386 - mse: 8.3792 - val_loss: 14.8579 - val_mae: 2.5530 - val_mse: 14.8579\n",
+ "Epoch 41/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.2416 - mae: 1.9460 - mse: 8.2416 - val_loss: 14.1700 - val_mae: 2.4763 - val_mse: 14.1700\n",
+ "Epoch 42/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.1441 - mae: 1.9615 - mse: 8.1441 - val_loss: 14.7666 - val_mae: 2.5614 - val_mse: 14.7666\n",
+ "Epoch 43/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.9628 - mae: 1.9341 - mse: 7.9628 - val_loss: 14.9515 - val_mae: 2.5567 - val_mse: 14.9515\n",
+ "Epoch 44/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8643 - mae: 1.9443 - mse: 7.8643 - val_loss: 14.1425 - val_mae: 2.4835 - val_mse: 14.1425\n",
+ "Epoch 45/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8650 - mae: 1.9313 - mse: 7.8650 - val_loss: 14.2290 - val_mae: 2.4958 - val_mse: 14.2290\n",
+ "Epoch 46/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.7619 - mae: 1.8989 - mse: 7.7619 - val_loss: 14.1142 - val_mae: 2.5040 - val_mse: 14.1142\n",
+ "Epoch 47/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6118 - mae: 1.8890 - mse: 7.6118 - val_loss: 13.9982 - val_mae: 2.5404 - val_mse: 13.9982\n",
+ "Epoch 48/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.4609 - mae: 1.8513 - mse: 7.4609 - val_loss: 13.7267 - val_mae: 2.4774 - val_mse: 13.7267\n",
+ "Epoch 49/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.4310 - mae: 1.8845 - mse: 7.4310 - val_loss: 13.6127 - val_mae: 2.5150 - val_mse: 13.6127\n",
+ "Epoch 50/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6582 - mae: 1.8754 - mse: 7.6582 - val_loss: 13.6260 - val_mae: 2.5100 - val_mse: 13.6260\n",
+ "Epoch 51/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3610 - mae: 1.8299 - mse: 7.3610 - val_loss: 13.9227 - val_mae: 2.4919 - val_mse: 13.9227\n",
+ "Epoch 52/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3116 - mae: 1.8142 - mse: 7.3116 - val_loss: 13.3471 - val_mae: 2.4560 - val_mse: 13.3471\n",
+ "Epoch 53/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.1248 - mae: 1.8402 - mse: 7.1248 - val_loss: 13.7073 - val_mae: 2.4522 - val_mse: 13.7073\n",
+ "Epoch 54/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.1053 - mae: 1.8392 - mse: 7.1053 - val_loss: 13.7225 - val_mae: 2.5354 - val_mse: 13.7225\n",
+ "Epoch 55/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0583 - mae: 1.7580 - mse: 7.0583 - val_loss: 13.2049 - val_mae: 2.4446 - val_mse: 13.2049\n",
+ "Epoch 56/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7665 - mae: 1.7663 - mse: 6.7665 - val_loss: 14.4253 - val_mae: 2.5627 - val_mse: 14.4253\n",
+ "Epoch 57/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.8827 - mae: 1.7752 - mse: 6.8827 - val_loss: 13.2658 - val_mae: 2.5167 - val_mse: 13.2658\n",
+ "Epoch 58/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.8156 - mae: 1.7930 - mse: 6.8156 - val_loss: 13.7665 - val_mae: 2.5500 - val_mse: 13.7665\n",
+ "Epoch 59/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.8988 - mae: 1.7702 - mse: 6.8988 - val_loss: 13.4498 - val_mae: 2.4387 - val_mse: 13.4498\n",
+ "Epoch 60/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7312 - mae: 1.7800 - mse: 6.7312 - val_loss: 13.7434 - val_mae: 2.4560 - val_mse: 13.7434\n",
+ "Epoch 61/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7225 - mae: 1.7679 - mse: 6.7225 - val_loss: 13.0107 - val_mae: 2.4511 - val_mse: 13.0107\n",
+ "Epoch 62/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.5523 - mae: 1.7499 - mse: 6.5523 - val_loss: 13.6302 - val_mae: 2.5602 - val_mse: 13.6302\n",
+ "Epoch 63/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.6796 - mae: 1.7420 - mse: 6.6796 - val_loss: 12.8770 - val_mae: 2.4025 - val_mse: 12.8770\n",
+ "Epoch 64/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4967 - mae: 1.7323 - mse: 6.4967 - val_loss: 12.8915 - val_mae: 2.4153 - val_mse: 12.8915\n",
+ "Epoch 65/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1840 - mae: 1.7119 - mse: 6.1840 - val_loss: 15.0107 - val_mae: 2.5778 - val_mse: 15.0107\n",
+ "Epoch 66/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3673 - mae: 1.7264 - mse: 6.3673 - val_loss: 12.7434 - val_mae: 2.4700 - val_mse: 12.7434\n",
+ "Epoch 67/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.0817 - mae: 1.6988 - mse: 6.0817 - val_loss: 13.4032 - val_mae: 2.4337 - val_mse: 13.4032\n",
+ "Epoch 68/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2663 - mae: 1.6922 - mse: 6.2663 - val_loss: 12.6261 - val_mae: 2.4417 - val_mse: 12.6261\n",
+ "Epoch 69/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1028 - mae: 1.6903 - mse: 6.1028 - val_loss: 12.7853 - val_mae: 2.5243 - val_mse: 12.7853\n",
+ "Epoch 70/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1497 - mae: 1.7011 - mse: 6.1497 - val_loss: 12.2234 - val_mae: 2.3462 - val_mse: 12.2234\n",
+ "Epoch 71/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1940 - mae: 1.6558 - mse: 6.1940 - val_loss: 12.8567 - val_mae: 2.4579 - val_mse: 12.8567\n",
+ "Epoch 72/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.9576 - mae: 1.6938 - mse: 5.9576 - val_loss: 12.7185 - val_mae: 2.4336 - val_mse: 12.7185\n",
+ "Epoch 73/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.9010 - mae: 1.6688 - mse: 5.9010 - val_loss: 12.8730 - val_mae: 2.4404 - val_mse: 12.8730\n",
+ "Epoch 74/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.7729 - mae: 1.6628 - mse: 5.7729 - val_loss: 12.9853 - val_mae: 2.5059 - val_mse: 12.9853\n",
+ "Epoch 75/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.7743 - mae: 1.6401 - mse: 5.7743 - val_loss: 12.4382 - val_mae: 2.3861 - val_mse: 12.4382\n",
+ "Epoch 76/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5220 - mae: 1.6460 - mse: 5.5220 - val_loss: 12.8593 - val_mae: 2.3853 - val_mse: 12.8593\n",
+ "Epoch 77/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6033 - mae: 1.6650 - mse: 5.6033 - val_loss: 12.7523 - val_mae: 2.4240 - val_mse: 12.7523\n",
+ "Epoch 78/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5523 - mae: 1.6175 - mse: 5.5523 - val_loss: 12.2824 - val_mae: 2.4211 - val_mse: 12.2824\n",
+ "Epoch 79/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5198 - mae: 1.5948 - mse: 5.5198 - val_loss: 13.4955 - val_mae: 2.4499 - val_mse: 13.4955\n",
+ "Epoch 80/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6490 - mae: 1.6285 - mse: 5.6490 - val_loss: 12.1719 - val_mae: 2.4192 - val_mse: 12.1719\n",
+ "Epoch 81/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6101 - mae: 1.6227 - mse: 5.6101 - val_loss: 12.1732 - val_mae: 2.3812 - val_mse: 12.1732\n",
+ "Epoch 82/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5147 - mae: 1.5905 - mse: 5.5147 - val_loss: 12.5534 - val_mae: 2.5524 - val_mse: 12.5534\n",
+ "Epoch 83/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3745 - mae: 1.5853 - mse: 5.3745 - val_loss: 11.7885 - val_mae: 2.3590 - val_mse: 11.7885\n",
+ "Epoch 84/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2953 - mae: 1.5975 - mse: 5.2953 - val_loss: 12.1622 - val_mae: 2.3873 - val_mse: 12.1622\n",
+ "Epoch 85/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.4370 - mae: 1.5739 - mse: 5.4370 - val_loss: 12.4707 - val_mae: 2.5029 - val_mse: 12.4707\n",
+ "Epoch 86/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3153 - mae: 1.5867 - mse: 5.3153 - val_loss: 12.1893 - val_mae: 2.3688 - val_mse: 12.1893\n",
+ "Epoch 87/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2006 - mae: 1.5596 - mse: 5.2006 - val_loss: 11.8706 - val_mae: 2.4313 - val_mse: 11.8706\n",
+ "Epoch 88/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3182 - mae: 1.5731 - mse: 5.3182 - val_loss: 12.0165 - val_mae: 2.4454 - val_mse: 12.0165\n",
+ "Epoch 89/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.1297 - mae: 1.5592 - mse: 5.1297 - val_loss: 11.9012 - val_mae: 2.3429 - val_mse: 11.9012\n",
+ "Epoch 90/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.1805 - mae: 1.5531 - mse: 5.1805 - val_loss: 12.1738 - val_mae: 2.3710 - val_mse: 12.1738\n",
+ "Epoch 91/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.1514 - mae: 1.5627 - mse: 5.1514 - val_loss: 11.3544 - val_mae: 2.2900 - val_mse: 11.3544\n",
+ "Epoch 92/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.1515 - mae: 1.5425 - mse: 5.1515 - val_loss: 11.1602 - val_mae: 2.2967 - val_mse: 11.1602\n",
+ "Epoch 93/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 4.9348 - mae: 1.5161 - mse: 4.9348 - val_loss: 11.2760 - val_mae: 2.2890 - val_mse: 11.2760\n",
+ "Epoch 94/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.8446 - mae: 1.4886 - mse: 4.8446 - val_loss: 11.6856 - val_mae: 2.3227 - val_mse: 11.6856\n",
+ "Epoch 95/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0610 - mae: 1.5577 - mse: 5.0610 - val_loss: 11.3523 - val_mae: 2.3237 - val_mse: 11.3523\n",
+ "Epoch 96/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9049 - mae: 1.4806 - mse: 4.9049 - val_loss: 11.9970 - val_mae: 2.5045 - val_mse: 11.9970\n",
+ "Epoch 97/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9395 - mae: 1.5305 - mse: 4.9395 - val_loss: 11.6265 - val_mae: 2.3327 - val_mse: 11.6265\n",
+ "Epoch 98/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9233 - mae: 1.5680 - mse: 4.9233 - val_loss: 11.5942 - val_mae: 2.3879 - val_mse: 11.5942\n",
+ "Epoch 99/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.6708 - mae: 1.5022 - mse: 4.6708 - val_loss: 12.3912 - val_mae: 2.5328 - val_mse: 12.3912\n",
+ "Epoch 100/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.5990 - mae: 1.4997 - mse: 4.5990 - val_loss: 11.2350 - val_mae: 2.3192 - val_mse: 11.2350\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = 100,\n",
+ " batch_size = 10,\n",
+ " verbose = 1,\n",
+ " validation_data = (x_test, y_test))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Model evaluation\n",
+ "MAE = Mean Absolute Error (between the labels and predictions) \n",
+ "A mae equal to 3 represents an average error in prediction of $3k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 11.2350\n",
+ "x_test / mae : 2.3192\n",
+ "x_test / mse : 11.2350\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Training history\n",
+ "What was the best result during our training ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "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",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>528.660156</td>\n",
+ " <td>20.809525</td>\n",
+ " <td>528.660156</td>\n",
+ " <td>382.087250</td>\n",
+ " <td>17.402838</td>\n",
+ " <td>382.087250</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>324.084076</td>\n",
+ " <td>15.515764</td>\n",
+ " <td>324.084076</td>\n",
+ " <td>186.108353</td>\n",
+ " <td>11.677550</td>\n",
+ " <td>186.108353</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>137.944000</td>\n",
+ " <td>9.126263</td>\n",
+ " <td>137.944000</td>\n",
+ " <td>73.622841</td>\n",
+ " <td>6.512352</td>\n",
+ " <td>73.622841</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>58.495167</td>\n",
+ " <td>5.712880</td>\n",
+ " <td>58.495167</td>\n",
+ " <td>46.929630</td>\n",
+ " <td>4.967716</td>\n",
+ " <td>46.929630</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>34.657124</td>\n",
+ " <td>4.386191</td>\n",
+ " <td>34.657124</td>\n",
+ " <td>36.097355</td>\n",
+ " <td>4.095750</td>\n",
+ " <td>36.097355</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>95</th>\n",
+ " <td>4.904853</td>\n",
+ " <td>1.480641</td>\n",
+ " <td>4.904853</td>\n",
+ " <td>11.996970</td>\n",
+ " <td>2.504455</td>\n",
+ " <td>11.996970</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96</th>\n",
+ " <td>4.939476</td>\n",
+ " <td>1.530525</td>\n",
+ " <td>4.939476</td>\n",
+ " <td>11.626506</td>\n",
+ " <td>2.332702</td>\n",
+ " <td>11.626506</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>97</th>\n",
+ " <td>4.923304</td>\n",
+ " <td>1.568031</td>\n",
+ " <td>4.923304</td>\n",
+ " <td>11.594161</td>\n",
+ " <td>2.387874</td>\n",
+ " <td>11.594161</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>98</th>\n",
+ " <td>4.670831</td>\n",
+ " <td>1.502215</td>\n",
+ " <td>4.670831</td>\n",
+ " <td>12.391187</td>\n",
+ " <td>2.532793</td>\n",
+ " <td>12.391187</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>99</th>\n",
+ " <td>4.598972</td>\n",
+ " <td>1.499657</td>\n",
+ " <td>4.598972</td>\n",
+ " <td>11.235036</td>\n",
+ " <td>2.319229</td>\n",
+ " <td>11.235036</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>100 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " loss mae mse val_loss val_mae val_mse\n",
+ "0 528.660156 20.809525 528.660156 382.087250 17.402838 382.087250\n",
+ "1 324.084076 15.515764 324.084076 186.108353 11.677550 186.108353\n",
+ "2 137.944000 9.126263 137.944000 73.622841 6.512352 73.622841\n",
+ "3 58.495167 5.712880 58.495167 46.929630 4.967716 46.929630\n",
+ "4 34.657124 4.386191 34.657124 36.097355 4.095750 36.097355\n",
+ ".. ... ... ... ... ... ...\n",
+ "95 4.904853 1.480641 4.904853 11.996970 2.504455 11.996970\n",
+ "96 4.939476 1.530525 4.939476 11.626506 2.332702 11.626506\n",
+ "97 4.923304 1.568031 4.923304 11.594161 2.387874 11.594161\n",
+ "98 4.670831 1.502215 4.670831 12.391187 2.532793 12.391187\n",
+ "99 4.598972 1.499657 4.598972 11.235036 2.319229 11.235036\n",
+ "\n",
+ "[100 rows x 6 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df=pd.DataFrame(data=history.history)\n",
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "min( val_mae ) : 2.2890\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/BHPD1-01-history_0</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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\n",
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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ " 'MAE' :['mae', 'val_mae'],\n",
+ " 'LOSS':['loss','val_loss']}, save_as='01-history')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 7 - Make a prediction\n",
+ "The data must be normalized with the parameters (mean, std) previously used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "my_data = [ 1.26425925, -0.48522739, 1.0436489 , -0.23112788, 1.37120745,\n",
+ " -2.14308942, 1.13489104, -1.06802005, 1.71189006, 1.57042287,\n",
+ " 0.77859951, 0.14769795, 2.7585581 ]\n",
+ "real_price = 10.4\n",
+ "\n",
+ "my_data=np.array(my_data).reshape(1,13)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction : 12.02 K$\n",
+ "Reality : 10.40 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "predictions = model.predict( my_data )\n",
+ "print(\"Prediction : {:.2f} K$\".format(predictions[0][0]))\n",
+ "print(\"Reality : {:.2f} K$\".format(real_price))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Friday 8 January 2021, 01:09:24\n",
+ "Duration is : 00:00:11 984ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/BHPD/02-DNN-Regression-Premium.ipynb b/BHPD/02-DNN-Regression-Premium.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d19c52703e4cc617132c4a2497079d6214536ef2
--- /dev/null
+++ b/BHPD/02-DNN-Regression-Premium.ipynb
@@ -0,0 +1,1298 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [BHPD2] - 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 Prices 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 these information :\n",
+ "\n",
+ " - CRIM: This is the per capita crime rate by town\n",
+ " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
+ " - INDUS: This is the proportion of non-retail business acres per town\n",
+ " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
+ " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
+ " - RM: This is the average number of rooms per dwelling\n",
+ " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
+ " - DIS: This is the weighted distances to five Boston employment centers\n",
+ " - RAD: This is the index of accessibility to radial highways\n",
+ " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
+ " - PTRATIO: This is the pupil-teacher ratio by town\n",
+ " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
+ " - LSTAT: This is the percentage lower status of the population\n",
+ " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\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": [
+ {
+ "data": {
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+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 1.2b1 DEV\n",
+ "Notebook id : BHPD2\n",
+ "Run time : Friday 8 January 2021, 01:10:28\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\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 pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('BHPD2')"
+ ]
+ },
+ {
+ "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": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "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": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_f13fa_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_f13fa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_f13fa_row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_f13fa_row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_f13fa_row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_f13fa_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_f13fa_row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_f13fa_row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_f13fa_row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_f13fa_row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_f13fa_row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_f13fa_row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_f13fa_row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_f13fa_row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_f13fa_row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f13fa_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_f13fa_row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_f13fa_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_f13fa_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_f13fa_row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_f13fa_row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_f13fa_row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_f13fa_row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_f13fa_row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_f13fa_row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_f13fa_row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_f13fa_row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_f13fa_row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f13fa_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_f13fa_row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_f13fa_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_f13fa_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_f13fa_row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_f13fa_row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_f13fa_row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_f13fa_row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_f13fa_row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_f13fa_row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_f13fa_row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_f13fa_row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_f13fa_row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f13fa_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_f13fa_row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_f13fa_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_f13fa_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_f13fa_row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_f13fa_row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_f13fa_row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_f13fa_row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_f13fa_row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_f13fa_row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_f13fa_row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_f13fa_row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_f13fa_row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_f13fa_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_f13fa_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_f13fa_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_f13fa_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_f13fa_row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_f13fa_row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_f13fa_row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_f13fa_row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_f13fa_row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_f13fa_row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_f13fa_row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_f13fa_row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_f13fa_row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_f13fa_row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f07873c2c10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Missing Data : 0 Shape is : (506, 14)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
+ "\n",
+ "display(data.head(5).style.format(\"{0:.2f}\"))\n",
+ "print('Missing Data : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preparing the data\n",
+ "### 3.1 - Split data\n",
+ "We will use 80% of the data for training and 20% for validation. \n",
+ "x will be input data and y the expected output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data shape was : (506, 14)\n",
+ "x_train : (354, 13) y_train : (354,)\n",
+ "x_test : (152, 13) y_test : (152,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Split => train, test\n",
+ "#\n",
+ "data_train = data.sample(frac=0.7, axis=0)\n",
+ "data_test = data.drop(data_train.index)\n",
+ "\n",
+ "# ---- Split => x,y (medv is price)\n",
+ "#\n",
+ "x_train = data_train.drop('medv', axis=1)\n",
+ "y_train = data_train['medv']\n",
+ "x_test = data_test.drop('medv', axis=1)\n",
+ "y_test = data_test['medv']\n",
+ "\n",
+ "print('Original data shape was : ',data.shape)\n",
+ "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
+ "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 - Data normalization\n",
+ "**Note :** \n",
+ " - All input data must be normalized, train and test. \n",
+ " - To do this we will subtract the mean and divide by the standard deviation. \n",
+ " - But test data should not be used in any way, even for normalization. \n",
+ " - The mean and the standard deviation will therefore only be calculated with the train data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_fc6dd_\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_fc6dd_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_fc6dd_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_fc6dd_row1_col0\" class=\"data row1 col0\" >3.71</td>\n",
+ " <td id=\"T_fc6dd_row1_col1\" class=\"data row1 col1\" >11.71</td>\n",
+ " <td id=\"T_fc6dd_row1_col2\" class=\"data row1 col2\" >11.18</td>\n",
+ " <td id=\"T_fc6dd_row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_fc6dd_row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_fc6dd_row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_fc6dd_row1_col6\" class=\"data row1 col6\" >68.24</td>\n",
+ " <td id=\"T_fc6dd_row1_col7\" class=\"data row1 col7\" >3.82</td>\n",
+ " <td id=\"T_fc6dd_row1_col8\" class=\"data row1 col8\" >9.72</td>\n",
+ " <td id=\"T_fc6dd_row1_col9\" class=\"data row1 col9\" >413.70</td>\n",
+ " <td id=\"T_fc6dd_row1_col10\" class=\"data row1 col10\" >18.46</td>\n",
+ " <td id=\"T_fc6dd_row1_col11\" class=\"data row1 col11\" >355.85</td>\n",
+ " <td id=\"T_fc6dd_row1_col12\" class=\"data row1 col12\" >12.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_fc6dd_row2_col0\" class=\"data row2 col0\" >9.08</td>\n",
+ " <td id=\"T_fc6dd_row2_col1\" class=\"data row2 col1\" >24.05</td>\n",
+ " <td id=\"T_fc6dd_row2_col2\" class=\"data row2 col2\" >6.79</td>\n",
+ " <td id=\"T_fc6dd_row2_col3\" class=\"data row2 col3\" >0.24</td>\n",
+ " <td id=\"T_fc6dd_row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_fc6dd_row2_col5\" class=\"data row2 col5\" >0.73</td>\n",
+ " <td id=\"T_fc6dd_row2_col6\" class=\"data row2 col6\" >28.03</td>\n",
+ " <td id=\"T_fc6dd_row2_col7\" class=\"data row2 col7\" >2.13</td>\n",
+ " <td id=\"T_fc6dd_row2_col8\" class=\"data row2 col8\" >8.77</td>\n",
+ " <td id=\"T_fc6dd_row2_col9\" class=\"data row2 col9\" >168.30</td>\n",
+ " <td id=\"T_fc6dd_row2_col10\" class=\"data row2 col10\" >2.22</td>\n",
+ " <td id=\"T_fc6dd_row2_col11\" class=\"data row2 col11\" >93.00</td>\n",
+ " <td id=\"T_fc6dd_row2_col12\" class=\"data row2 col12\" >7.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_fc6dd_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_fc6dd_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_fc6dd_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_fc6dd_row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_fc6dd_row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
+ " <td id=\"T_fc6dd_row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
+ " <td id=\"T_fc6dd_row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_fc6dd_row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_fc6dd_row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_fc6dd_row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_fc6dd_row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_fc6dd_row4_col0\" class=\"data row4 col0\" >0.09</td>\n",
+ " <td id=\"T_fc6dd_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_fc6dd_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_fc6dd_row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_fc6dd_row4_col6\" class=\"data row4 col6\" >43.47</td>\n",
+ " <td id=\"T_fc6dd_row4_col7\" class=\"data row4 col7\" >2.07</td>\n",
+ " <td id=\"T_fc6dd_row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_fc6dd_row4_col9\" class=\"data row4 col9\" >284.00</td>\n",
+ " <td id=\"T_fc6dd_row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_fc6dd_row4_col11\" class=\"data row4 col11\" >375.24</td>\n",
+ " <td id=\"T_fc6dd_row4_col12\" class=\"data row4 col12\" >6.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_fc6dd_row5_col0\" class=\"data row5 col0\" >0.28</td>\n",
+ " <td id=\"T_fc6dd_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_fc6dd_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_fc6dd_row5_col5\" class=\"data row5 col5\" >6.21</td>\n",
+ " <td id=\"T_fc6dd_row5_col6\" class=\"data row5 col6\" >77.15</td>\n",
+ " <td id=\"T_fc6dd_row5_col7\" class=\"data row5 col7\" >3.32</td>\n",
+ " <td id=\"T_fc6dd_row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_fc6dd_row5_col9\" class=\"data row5 col9\" >341.00</td>\n",
+ " <td id=\"T_fc6dd_row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_fc6dd_row5_col11\" class=\"data row5 col11\" >391.29</td>\n",
+ " <td id=\"T_fc6dd_row5_col12\" class=\"data row5 col12\" >11.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_fc6dd_row6_col0\" class=\"data row6 col0\" >3.69</td>\n",
+ " <td id=\"T_fc6dd_row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
+ " <td id=\"T_fc6dd_row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_fc6dd_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_fc6dd_row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
+ " <td id=\"T_fc6dd_row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
+ " <td id=\"T_fc6dd_row6_col6\" class=\"data row6 col6\" >93.55</td>\n",
+ " <td id=\"T_fc6dd_row6_col7\" class=\"data row6 col7\" >5.21</td>\n",
+ " <td id=\"T_fc6dd_row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_fc6dd_row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_fc6dd_row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_fc6dd_row6_col11\" class=\"data row6 col11\" >396.30</td>\n",
+ " <td id=\"T_fc6dd_row6_col12\" class=\"data row6 col12\" >16.72</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_fc6dd_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_fc6dd_row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_fc6dd_row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_fc6dd_row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_fc6dd_row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_fc6dd_row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_fc6dd_row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_fc6dd_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_fc6dd_row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_fc6dd_row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_fc6dd_row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_fc6dd_row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_fc6dd_row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_fc6dd_row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f06d332fcd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_30f2b_\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_30f2b_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_30f2b_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_30f2b_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+ " <td id=\"T_30f2b_row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_30f2b_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_30f2b_row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_30f2b_row3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
+ " <td id=\"T_30f2b_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_30f2b_row3_col2\" class=\"data row3 col2\" >-1.58</td>\n",
+ " <td id=\"T_30f2b_row3_col3\" class=\"data row3 col3\" >-0.25</td>\n",
+ " <td id=\"T_30f2b_row3_col4\" class=\"data row3 col4\" >-1.46</td>\n",
+ " <td id=\"T_30f2b_row3_col5\" class=\"data row3 col5\" >-3.77</td>\n",
+ " <td id=\"T_30f2b_row3_col6\" class=\"data row3 col6\" >-2.22</td>\n",
+ " <td id=\"T_30f2b_row3_col7\" class=\"data row3 col7\" >-1.26</td>\n",
+ " <td id=\"T_30f2b_row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
+ " <td id=\"T_30f2b_row3_col9\" class=\"data row3 col9\" >-1.35</td>\n",
+ " <td id=\"T_30f2b_row3_col10\" class=\"data row3 col10\" >-2.64</td>\n",
+ " <td id=\"T_30f2b_row3_col11\" class=\"data row3 col11\" >-3.82</td>\n",
+ " <td id=\"T_30f2b_row3_col12\" class=\"data row3 col12\" >-1.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_30f2b_row4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
+ " <td id=\"T_30f2b_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_30f2b_row4_col2\" class=\"data row4 col2\" >-0.88</td>\n",
+ " <td id=\"T_30f2b_row4_col3\" class=\"data row4 col3\" >-0.25</td>\n",
+ " <td id=\"T_30f2b_row4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
+ " <td id=\"T_30f2b_row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
+ " <td id=\"T_30f2b_row4_col6\" class=\"data row4 col6\" >-0.88</td>\n",
+ " <td id=\"T_30f2b_row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
+ " <td id=\"T_30f2b_row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
+ " <td id=\"T_30f2b_row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
+ " <td id=\"T_30f2b_row4_col10\" class=\"data row4 col10\" >-0.48</td>\n",
+ " <td id=\"T_30f2b_row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
+ " <td id=\"T_30f2b_row4_col12\" class=\"data row4 col12\" >-0.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_30f2b_row5_col0\" class=\"data row5 col0\" >-0.38</td>\n",
+ " <td id=\"T_30f2b_row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
+ " <td id=\"T_30f2b_row5_col2\" class=\"data row5 col2\" >-0.22</td>\n",
+ " <td id=\"T_30f2b_row5_col3\" class=\"data row5 col3\" >-0.25</td>\n",
+ " <td id=\"T_30f2b_row5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
+ " <td id=\"T_30f2b_row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
+ " <td id=\"T_30f2b_row5_col6\" class=\"data row5 col6\" >0.32</td>\n",
+ " <td id=\"T_30f2b_row5_col7\" class=\"data row5 col7\" >-0.23</td>\n",
+ " <td id=\"T_30f2b_row5_col8\" class=\"data row5 col8\" >-0.54</td>\n",
+ " <td id=\"T_30f2b_row5_col9\" class=\"data row5 col9\" >-0.43</td>\n",
+ " <td id=\"T_30f2b_row5_col10\" class=\"data row5 col10\" >0.29</td>\n",
+ " <td id=\"T_30f2b_row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_30f2b_row5_col12\" class=\"data row5 col12\" >-0.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_30f2b_row6_col0\" class=\"data row6 col0\" >-0.00</td>\n",
+ " <td id=\"T_30f2b_row6_col1\" class=\"data row6 col1\" >0.03</td>\n",
+ " <td id=\"T_30f2b_row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
+ " <td id=\"T_30f2b_row6_col3\" class=\"data row6 col3\" >-0.25</td>\n",
+ " <td id=\"T_30f2b_row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
+ " <td id=\"T_30f2b_row6_col5\" class=\"data row6 col5\" >0.45</td>\n",
+ " <td id=\"T_30f2b_row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_30f2b_row6_col7\" class=\"data row6 col7\" >0.65</td>\n",
+ " <td id=\"T_30f2b_row6_col8\" class=\"data row6 col8\" >1.63</td>\n",
+ " <td id=\"T_30f2b_row6_col9\" class=\"data row6 col9\" >1.50</td>\n",
+ " <td id=\"T_30f2b_row6_col10\" class=\"data row6 col10\" >0.79</td>\n",
+ " <td id=\"T_30f2b_row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_30f2b_row6_col12\" class=\"data row6 col12\" >0.56</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_30f2b_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_30f2b_row7_col0\" class=\"data row7 col0\" >9.39</td>\n",
+ " <td id=\"T_30f2b_row7_col1\" class=\"data row7 col1\" >3.67</td>\n",
+ " <td id=\"T_30f2b_row7_col2\" class=\"data row7 col2\" >2.44</td>\n",
+ " <td id=\"T_30f2b_row7_col3\" class=\"data row7 col3\" >3.98</td>\n",
+ " <td id=\"T_30f2b_row7_col4\" class=\"data row7 col4\" >2.67</td>\n",
+ " <td id=\"T_30f2b_row7_col5\" class=\"data row7 col5\" >3.42</td>\n",
+ " <td id=\"T_30f2b_row7_col6\" class=\"data row7 col6\" >1.13</td>\n",
+ " <td id=\"T_30f2b_row7_col7\" class=\"data row7 col7\" >3.90</td>\n",
+ " <td id=\"T_30f2b_row7_col8\" class=\"data row7 col8\" >1.63</td>\n",
+ " <td id=\"T_30f2b_row7_col9\" class=\"data row7 col9\" >1.77</td>\n",
+ " <td id=\"T_30f2b_row7_col10\" class=\"data row7 col10\" >1.60</td>\n",
+ " <td id=\"T_30f2b_row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
+ " <td id=\"T_30f2b_row7_col12\" class=\"data row7 col12\" >3.47</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f06d14fcb90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
+ "\n",
+ "mean = x_train.mean()\n",
+ "std = x_train.std()\n",
+ "x_train = (x_train - mean) / std\n",
+ "x_test = (x_test - mean) / std\n",
+ "\n",
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
+ "\n",
+ "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+ "x_test, y_test = np.array(x_test), np.array(y_test)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build a model\n",
+ "More informations about : \n",
+ " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+ " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+ " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " def get_model_v1(shape):\n",
+ " \n",
+ " model = keras.models.Sequential()\n",
+ " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
+ " model.add(keras.layers.Dense(1, name='Output'))\n",
+ " \n",
+ " model.compile(optimizer = 'rmsprop',\n",
+ " loss = 'mse',\n",
+ " metrics = ['mae', 'mse'] )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model=get_model_v1( (13,) )\n",
+ "\n",
+ "model.summary()\n",
+ "# img=keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)\n",
+ "# display(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Add callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = \"./run/models/best_model.h5\"\n",
+ "\n",
+ "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.3 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n",
+ "36/36 [==============================] - 0s 7ms/step - loss: 475.3738 - mae: 19.9912 - mse: 475.3738 - val_loss: 355.5190 - val_mae: 17.1932 - val_mse: 355.5190\n",
+ "Epoch 2/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 226.4147 - mae: 13.0131 - mse: 226.4147 - val_loss: 129.1039 - val_mae: 9.0031 - val_mse: 129.1039\n",
+ "Epoch 3/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 75.2953 - mae: 6.7032 - mse: 75.2953 - val_loss: 54.9836 - val_mae: 5.2806 - val_mse: 54.9836\n",
+ "Epoch 4/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 37.4691 - mae: 4.6232 - mse: 37.4691 - val_loss: 38.6827 - val_mae: 4.1977 - val_mse: 38.6827\n",
+ "Epoch 5/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 26.8464 - mae: 3.8138 - mse: 26.8464 - val_loss: 33.8882 - val_mae: 3.7787 - val_mse: 33.8882\n",
+ "Epoch 6/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 21.8734 - mae: 3.4106 - mse: 21.8734 - val_loss: 29.8489 - val_mae: 3.6882 - val_mse: 29.8489\n",
+ "Epoch 7/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 19.1155 - mae: 3.1659 - mse: 19.1155 - val_loss: 27.4722 - val_mae: 3.4667 - val_mse: 27.4722\n",
+ "Epoch 8/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 17.0937 - mae: 2.9284 - mse: 17.0937 - val_loss: 26.4015 - val_mae: 3.3250 - val_mse: 26.4015\n",
+ "Epoch 9/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 15.7820 - mae: 2.7847 - mse: 15.7820 - val_loss: 25.4634 - val_mae: 3.1832 - val_mse: 25.4634\n",
+ "Epoch 10/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 14.7705 - mae: 2.6322 - mse: 14.7705 - val_loss: 24.3008 - val_mae: 3.1048 - val_mse: 24.3008\n",
+ "Epoch 11/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 14.0468 - mae: 2.5779 - mse: 14.0468 - val_loss: 23.5067 - val_mae: 3.1615 - val_mse: 23.5067\n",
+ "Epoch 12/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 13.2634 - mae: 2.4898 - mse: 13.2634 - val_loss: 22.6733 - val_mae: 3.0316 - val_mse: 22.6733\n",
+ "Epoch 13/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 12.8004 - mae: 2.4238 - mse: 12.8004 - val_loss: 22.1356 - val_mae: 2.9750 - val_mse: 22.1356\n",
+ "Epoch 14/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.2295 - mae: 2.3461 - mse: 12.2295 - val_loss: 21.9092 - val_mae: 3.1515 - val_mse: 21.9092\n",
+ "Epoch 15/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.0662 - mae: 2.3527 - mse: 12.0662 - val_loss: 20.9063 - val_mae: 2.9685 - val_mse: 20.9063\n",
+ "Epoch 16/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.6152 - mae: 2.2743 - mse: 11.6152 - val_loss: 20.2121 - val_mae: 2.8589 - val_mse: 20.2121\n",
+ "Epoch 17/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 11.1531 - mae: 2.2281 - mse: 11.1531 - val_loss: 19.7149 - val_mae: 2.8922 - val_mse: 19.7149\n",
+ "Epoch 18/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.8126 - mae: 2.2231 - mse: 10.8126 - val_loss: 19.5988 - val_mae: 2.8813 - val_mse: 19.5988\n",
+ "Epoch 19/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.9167 - mae: 2.2329 - mse: 10.9167 - val_loss: 19.9177 - val_mae: 2.8580 - val_mse: 19.9177\n",
+ "Epoch 20/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.6797 - mae: 2.1405 - mse: 10.6797 - val_loss: 19.1656 - val_mae: 2.8241 - val_mse: 19.1656\n",
+ "Epoch 21/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.3777 - mae: 2.1111 - mse: 10.3777 - val_loss: 19.2481 - val_mae: 2.9912 - val_mse: 19.2481\n",
+ "Epoch 22/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 10.2719 - mae: 2.1766 - mse: 10.2719 - val_loss: 18.8485 - val_mae: 2.7967 - val_mse: 18.8485\n",
+ "Epoch 23/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.1286 - mae: 2.1058 - mse: 10.1286 - val_loss: 18.0448 - val_mae: 2.9200 - val_mse: 18.0448\n",
+ "Epoch 24/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.2453 - mae: 2.1320 - mse: 10.2453 - val_loss: 18.4625 - val_mae: 2.7887 - val_mse: 18.4625\n",
+ "Epoch 25/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.9561 - mae: 2.1024 - mse: 9.9561 - val_loss: 17.8922 - val_mae: 2.7371 - val_mse: 17.8922\n",
+ "Epoch 26/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.8956 - mae: 2.0484 - mse: 9.8956 - val_loss: 18.6694 - val_mae: 2.8163 - val_mse: 18.6694\n",
+ "Epoch 27/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.7229 - mae: 2.0378 - mse: 9.7229 - val_loss: 17.7471 - val_mae: 2.7123 - val_mse: 17.7471\n",
+ "Epoch 28/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.8073 - mae: 2.0552 - mse: 9.8073 - val_loss: 17.5750 - val_mae: 2.7652 - val_mse: 17.5750\n",
+ "Epoch 29/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.2723 - mae: 2.0088 - mse: 9.2723 - val_loss: 17.8873 - val_mae: 2.8168 - val_mse: 17.8873\n",
+ "Epoch 30/100\n",
+ "36/36 [==============================] - 0s 4ms/step - loss: 9.4324 - mae: 1.9978 - mse: 9.4324 - val_loss: 17.4225 - val_mae: 2.7191 - val_mse: 17.4225\n",
+ "Epoch 31/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.4192 - mae: 2.0191 - mse: 9.4192 - val_loss: 16.5583 - val_mae: 2.7164 - val_mse: 16.5583\n",
+ "Epoch 32/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.0246 - mae: 2.0058 - mse: 9.0246 - val_loss: 16.5136 - val_mae: 2.7170 - val_mse: 16.5136\n",
+ "Epoch 33/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.2024 - mae: 2.0063 - mse: 9.2024 - val_loss: 16.3303 - val_mae: 2.8191 - val_mse: 16.3303\n",
+ "Epoch 34/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.1927 - mae: 2.0216 - mse: 9.1927 - val_loss: 16.3692 - val_mae: 2.6946 - val_mse: 16.3692\n",
+ "Epoch 35/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.9978 - mae: 1.9756 - mse: 8.9978 - val_loss: 16.4107 - val_mae: 2.7557 - val_mse: 16.4107\n",
+ "Epoch 36/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.7153 - mae: 1.9554 - mse: 8.7153 - val_loss: 16.7948 - val_mae: 2.7352 - val_mse: 16.7948\n",
+ "Epoch 37/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.0232 - mae: 1.9594 - mse: 9.0232 - val_loss: 16.4719 - val_mae: 2.6820 - val_mse: 16.4719\n",
+ "Epoch 38/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.5107 - mae: 1.9278 - mse: 8.5107 - val_loss: 15.8009 - val_mae: 2.6720 - val_mse: 15.8009\n",
+ "Epoch 39/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.8051 - mae: 1.9010 - mse: 8.8051 - val_loss: 16.0620 - val_mae: 2.6799 - val_mse: 16.0620\n",
+ "Epoch 40/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.2687 - mae: 1.9202 - mse: 8.2687 - val_loss: 19.2407 - val_mae: 2.9441 - val_mse: 19.2407\n",
+ "Epoch 41/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6178 - mae: 1.9433 - mse: 8.6178 - val_loss: 16.4289 - val_mae: 2.6687 - val_mse: 16.4289\n",
+ "Epoch 42/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.2694 - mae: 1.9159 - mse: 8.2694 - val_loss: 15.6580 - val_mae: 2.7538 - val_mse: 15.6580\n",
+ "Epoch 43/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.3998 - mae: 1.8863 - mse: 8.3998 - val_loss: 15.3973 - val_mae: 2.7006 - val_mse: 15.3973\n",
+ "Epoch 44/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.4504 - mae: 1.8948 - mse: 8.4504 - val_loss: 15.3550 - val_mae: 2.6351 - val_mse: 15.3550\n",
+ "Epoch 45/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.1592 - mae: 1.8847 - mse: 8.1592 - val_loss: 16.1119 - val_mae: 2.6115 - val_mse: 16.1119\n",
+ "Epoch 46/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.0892 - mae: 1.8970 - mse: 8.0892 - val_loss: 15.5314 - val_mae: 2.6214 - val_mse: 15.5314\n",
+ "Epoch 47/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.0762 - mae: 1.8906 - mse: 8.0762 - val_loss: 15.6544 - val_mae: 2.6558 - val_mse: 15.6544\n",
+ "Epoch 48/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.9513 - mae: 1.8809 - mse: 7.9513 - val_loss: 16.9534 - val_mae: 2.6809 - val_mse: 16.9534\n",
+ "Epoch 49/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8678 - mae: 1.8491 - mse: 7.8678 - val_loss: 16.0355 - val_mae: 2.6036 - val_mse: 16.0355\n",
+ "Epoch 50/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.9786 - mae: 1.8659 - mse: 7.9786 - val_loss: 15.8552 - val_mae: 2.6041 - val_mse: 15.8552\n",
+ "Epoch 51/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.5612 - mae: 1.8174 - mse: 7.5612 - val_loss: 15.5496 - val_mae: 2.6545 - val_mse: 15.5496\n",
+ "Epoch 52/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.4924 - mae: 1.8578 - mse: 7.4924 - val_loss: 16.1388 - val_mae: 2.6079 - val_mse: 16.1388\n",
+ "Epoch 53/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.7586 - mae: 1.8298 - mse: 7.7586 - val_loss: 17.2166 - val_mae: 2.6738 - val_mse: 17.2166\n",
+ "Epoch 54/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.7121 - mae: 1.8394 - mse: 7.7121 - val_loss: 16.0234 - val_mae: 2.5735 - val_mse: 16.0234\n",
+ "Epoch 55/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.5455 - mae: 1.8023 - mse: 7.5455 - val_loss: 15.8189 - val_mae: 2.7010 - val_mse: 15.8189\n",
+ "Epoch 56/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.5965 - mae: 1.8203 - mse: 7.5965 - val_loss: 15.8920 - val_mae: 2.6231 - val_mse: 15.8920\n",
+ "Epoch 57/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.4273 - mae: 1.7775 - mse: 7.4273 - val_loss: 16.4608 - val_mae: 2.8454 - val_mse: 16.4608\n",
+ "Epoch 58/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.4376 - mae: 1.8135 - mse: 7.4376 - val_loss: 15.9365 - val_mae: 2.7168 - val_mse: 15.9365\n",
+ "Epoch 59/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.4878 - mae: 1.7901 - mse: 7.4878 - val_loss: 14.4604 - val_mae: 2.6034 - val_mse: 14.4604\n",
+ "Epoch 60/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.1747 - mae: 1.7831 - mse: 7.1747 - val_loss: 15.3341 - val_mae: 2.5616 - val_mse: 15.3341\n",
+ "Epoch 61/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0954 - mae: 1.7754 - mse: 7.0954 - val_loss: 14.5495 - val_mae: 2.7065 - val_mse: 14.5495\n",
+ "Epoch 62/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0930 - mae: 1.7892 - mse: 7.0930 - val_loss: 14.7401 - val_mae: 2.6647 - val_mse: 14.7401\n",
+ "Epoch 63/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3064 - mae: 1.7758 - mse: 7.3064 - val_loss: 15.2798 - val_mae: 2.6231 - val_mse: 15.2798\n",
+ "Epoch 64/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.1116 - mae: 1.7659 - mse: 7.1116 - val_loss: 14.2042 - val_mae: 2.5114 - val_mse: 14.2042\n",
+ "Epoch 65/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0160 - mae: 1.7421 - mse: 7.0160 - val_loss: 15.3414 - val_mae: 2.5520 - val_mse: 15.3414\n",
+ "Epoch 66/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.9254 - mae: 1.7089 - mse: 6.9254 - val_loss: 15.8074 - val_mae: 2.6835 - val_mse: 15.8074\n",
+ "Epoch 67/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0619 - mae: 1.7703 - mse: 7.0619 - val_loss: 14.7753 - val_mae: 2.5483 - val_mse: 14.7753\n",
+ "Epoch 68/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7564 - mae: 1.7313 - mse: 6.7564 - val_loss: 14.5842 - val_mae: 2.6013 - val_mse: 14.5842\n",
+ "Epoch 69/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7918 - mae: 1.7587 - mse: 6.7918 - val_loss: 14.8742 - val_mae: 2.5747 - val_mse: 14.8742\n",
+ "Epoch 70/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7486 - mae: 1.7229 - mse: 6.7486 - val_loss: 14.0923 - val_mae: 2.6278 - val_mse: 14.0923\n",
+ "Epoch 71/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.8134 - mae: 1.7418 - mse: 6.8134 - val_loss: 15.0033 - val_mae: 2.5960 - val_mse: 15.0033\n",
+ "Epoch 72/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7563 - mae: 1.7649 - mse: 6.7563 - val_loss: 14.5488 - val_mae: 2.5460 - val_mse: 14.5488\n",
+ "Epoch 73/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.6271 - mae: 1.7036 - mse: 6.6271 - val_loss: 13.5843 - val_mae: 2.5701 - val_mse: 13.5843\n",
+ "Epoch 74/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.6084 - mae: 1.7073 - mse: 6.6084 - val_loss: 13.7134 - val_mae: 2.5716 - val_mse: 13.7134\n",
+ "Epoch 75/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4696 - mae: 1.7316 - mse: 6.4696 - val_loss: 14.4993 - val_mae: 2.5646 - val_mse: 14.4993\n",
+ "Epoch 76/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3220 - mae: 1.6540 - mse: 6.3220 - val_loss: 14.6173 - val_mae: 2.4900 - val_mse: 14.6173\n",
+ "Epoch 77/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3876 - mae: 1.6780 - mse: 6.3876 - val_loss: 14.2370 - val_mae: 2.5747 - val_mse: 14.2370\n",
+ "Epoch 78/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3408 - mae: 1.6560 - mse: 6.3408 - val_loss: 13.6508 - val_mae: 2.6113 - val_mse: 13.6508\n",
+ "Epoch 79/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.9661 - mae: 1.6637 - mse: 5.9661 - val_loss: 16.3465 - val_mae: 2.6276 - val_mse: 16.3465\n",
+ "Epoch 80/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.2598 - mae: 1.6500 - mse: 6.2598 - val_loss: 13.5719 - val_mae: 2.5735 - val_mse: 13.5719\n",
+ "Epoch 81/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1077 - mae: 1.6426 - mse: 6.1077 - val_loss: 13.8778 - val_mae: 2.5090 - val_mse: 13.8778\n",
+ "Epoch 82/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1184 - mae: 1.6258 - mse: 6.1184 - val_loss: 14.0033 - val_mae: 2.5179 - val_mse: 14.0033\n",
+ "Epoch 83/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.8503 - mae: 1.6440 - mse: 5.8503 - val_loss: 13.9289 - val_mae: 2.5396 - val_mse: 13.9289\n",
+ "Epoch 84/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 6.1745 - mae: 1.6489 - mse: 6.1745 - val_loss: 13.1696 - val_mae: 2.4742 - val_mse: 13.1696\n",
+ "Epoch 85/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.0069 - mae: 1.6452 - mse: 6.0069 - val_loss: 12.9157 - val_mae: 2.5818 - val_mse: 12.9157\n",
+ "Epoch 86/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.7613 - mae: 1.6045 - mse: 5.7613 - val_loss: 13.3368 - val_mae: 2.5157 - val_mse: 13.3368\n",
+ "Epoch 87/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.9914 - mae: 1.6307 - mse: 5.9914 - val_loss: 13.1113 - val_mae: 2.5445 - val_mse: 13.1113\n",
+ "Epoch 88/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6568 - mae: 1.6085 - mse: 5.6568 - val_loss: 13.8029 - val_mae: 2.5412 - val_mse: 13.8029\n",
+ "Epoch 89/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.8280 - mae: 1.5581 - mse: 5.8280 - val_loss: 13.3723 - val_mae: 2.5053 - val_mse: 13.3723\n",
+ "Epoch 90/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.7126 - mae: 1.5804 - mse: 5.7126 - val_loss: 12.9745 - val_mae: 2.5436 - val_mse: 12.9745\n",
+ "Epoch 91/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6580 - mae: 1.5735 - mse: 5.6580 - val_loss: 12.8074 - val_mae: 2.5277 - val_mse: 12.8074\n",
+ "Epoch 92/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.7264 - mae: 1.5897 - mse: 5.7264 - val_loss: 14.1068 - val_mae: 2.4713 - val_mse: 14.1068\n",
+ "Epoch 93/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.6612 - mae: 1.5688 - mse: 5.6612 - val_loss: 13.0306 - val_mae: 2.5313 - val_mse: 13.0306\n",
+ "Epoch 94/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5369 - mae: 1.5979 - mse: 5.5369 - val_loss: 13.2463 - val_mae: 2.5441 - val_mse: 13.2463\n",
+ "Epoch 95/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3318 - mae: 1.5423 - mse: 5.3318 - val_loss: 14.2846 - val_mae: 2.4916 - val_mse: 14.2846\n",
+ "Epoch 96/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5833 - mae: 1.5621 - mse: 5.5833 - val_loss: 12.9900 - val_mae: 2.5152 - val_mse: 12.9900\n",
+ "Epoch 97/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.3842 - mae: 1.5510 - mse: 5.3842 - val_loss: 12.7831 - val_mae: 2.4384 - val_mse: 12.7831\n",
+ "Epoch 98/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3732 - mae: 1.5228 - mse: 5.3732 - val_loss: 13.5497 - val_mae: 2.4686 - val_mse: 13.5497\n",
+ "Epoch 99/100\n",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2501 - mae: 1.5620 - mse: 5.2501 - val_loss: 13.1683 - val_mae: 2.5256 - val_mse: 13.1683\n",
+ "Epoch 100/100\n",
+ "36/36 [==============================] - 0s 3ms/step - loss: 5.4235 - mae: 1.5454 - mse: 5.4235 - val_loss: 12.4521 - val_mae: 2.5606 - val_mse: 12.4521\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = 100,\n",
+ " batch_size = 10,\n",
+ " verbose = 1,\n",
+ " validation_data = (x_test, y_test),\n",
+ " callbacks = [savemodel_callback])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Model evaluation\n",
+ "MAE = Mean Absolute Error (between the labels and predictions) \n",
+ "A mae equal to 3 represents an average error in prediction of $3k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 12.4521\n",
+ "x_test / mae : 2.5606\n",
+ "x_test / mse : 12.4521\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Training history\n",
+ "What was the best result during our training ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "min( val_mae ) : 2.4384\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/BHPD2-01-history_0</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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\n",
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DOHX4al8erRwpIiJRVzfBAbgQ2BM4d4DH9wMagcdLPPZEsA0Hh3lAHngqXNA5lwEWFZWtin6DI/Ou2r9eRERkm9VFcDCznYFvApc455YMUGx2sF1e4rHCvjlF5ducc10DlJ9mZg0D1OdsM3tmqxWvUP8Wh9xwH15ERGTE1UVwAK4FXsMPZBxIS7AtFQQyRWUK90uVHah8L+fcD51zBw5SlyFJaIyDiIhEXM0HR5rZqcDRwHznXM8gRTuCbWOJx5qKyhTuzxjgWKXKj7hwV0U2p64KERGJnpoGBzNrxLcy3A28aWa7BQ8VuhwmBvvagBVFj4UV9oW7MVYAbzezxhLdFXPw3Rjd2/oaKqHBkSIiEnW17qpoBqYDxwMvh24PBo+fGvx8JrAY3/VwcInjHBRsw+MSnsa/vneHC5pZE3BAUdmq0HRMERGJulp3VbQDHymxfzqQxk/N/DHwF+fcZjO7E/hnM9vfOfccgJm14oPFy/SfQXEr8BX8uhCPhPafhR/bcPPwvpSt0wJQIiISdTUNDsGYhl8W7zeznYK7/3DOhR//MvB+4D4zWwhsxAeBOcDxhcWfgmMvNrNrgHPN7A58d8he+JUjH6LKiz+BrlUhIiLRV+sWh4o4514xs0OA7wBfAhqAZ4FjnXP3l3jKBcAS4Gx8d0gbcDVwkXOu6p/c4RaHbo1xEBGRCKrL4BCs5WADPPYCcFKZx8nhl64utXx11SXUVSEiIhFX68GRY4q6KkREJOoUHKooEetrRMnlHXmntRxERCRaFByqyMy0loOIiESagkOVaUqmiIhEmYJDlWmcg4iIRJmCQ5Ul4n3jHDQlU0REokbBocrUVSEiIlGm4FBlul6FiIhEmYJDlSU0q0JERCJMwaHKwoMjs3kFBxERiRYFhyrTOg4iIhJlCg5V1u9CVxrjICIiEaPgUGX9uioUHEREJGIUHKpMXRUiIhJlCg5VltB0TBERiTAFhyrrvwCUro4pIiLRouBQZbpWhYiIRFmi1hUYM175Gyx5iXe/8gZLulp4sXEmPdlcrWslIiJSEQWHannuCfjdbRwC/H3SwT44qKtCREQiRl0V1ZJI9t5NOt/SoK4KERGJGgWHakn2BYcGlwU0HVNERKJHwaFaEg29d5POBwYtACUiIlGj4FAtSXVViIhI9Ck4VEuyr8Wht6tCwUFERCJGwaFaSg2O1BgHERGJGAWHagkFhwbUVSEiItGk4FAtyfDgSAUHERGJJgWHalFXhYiIjAIKDtVSYh2HbF7BQUREokXBoVoSJboq1OIgIiIRo+BQLVrHQURERgEFh2rRtSpERGQUUHCollItDuqqEBGRiFFwqJZwi4PWcRARkYhScKiWRHjJaR8cdJErERGJGgWHatHgSBERGQUUHKqlODg4pzEOIiISOQoO1RKLQ8yf7jiOOHmyOVfjSomIiFRGwaGaiq5Xoa4KERGJGgWHagpfITMIDs6p1UFERKJDwaGaSiwClc0rOIiISHQoOFRTqUtra4CkiIhEiIJDNRV1VYCmZIqISLQoOFRTuMUBLQIlIiLRo+BQTbpehYiIRJyCQzX166rIAtCtFgcREYkQBYdqUouDiIhEnIJDNZWcjqngICIi0aHgUE2lZlWoxUFERCKk5sHBzPYws5vN7AUz22BmHWb2opldaWazBij/GzNbZ2btZvaImR05wLFjZnZhcLyMmb1hZleY2biRf2UllFrHQWMcREQkQhK1rgAwF5gF/BpYBmSBfYGzgQVmdoBz7i0AM9sVeCwoczmwATgLuNfMPuicu7/o2AuB84NjXwHsFfz8DjM7yjlX3U/tUl0VCg4iIhIhNQ8Ozrn/Bf63eL+ZPQzcBpyBDwkAlwGTgHc55xYF5W4CngeuMbM9XXDxBzPbGzgPuMM5d3LouK8B3wcWALeMyIsaSHLLWRXqqhARkSipeVfFIJYG28kAQffCicCDhdAA4JzbDPwI2B2YF3r+KYABVxUd9wagAzh1JCo9qMSWC0BpOqaIiERJ3QQHM2sys2lmNtfMjgauDx66O9juBzQCj5d4+hPBNhwc5gF54KlwQedcBlhUVLY6SkzHVFeFiIhESd0EB+BMYDXwBnAvvkviVOfcI8Hjs4Pt8hLPLeybE9o3G2hzznUNUH6amTWUeAwzO9vMnqms+mXQtSpERCTi6ik4/Ab4APAh4BJgPTA99HhLsC0VBDJFZQr3S5UdqHwv59wPnXMHbrXGldLVMUVEJOJqPjiywDm3DD+rAuA3ZvYr4Gkza3bOXYYflwC+u6JYU7DtCO3rAGYM8OtKlR95mlUhIiIRV08tDv045/4C/BlIBbtWBNs5JYoX9oW7MVbguyNKBY05+G6M7uGoa9nUVSEiIhFXt8Eh0AxMCe4vxnc9HFyi3EHBNjwu4Wn863t3uKCZNQEHFJWtDnVViIhIxNU8OJjZzAH2HwHsQzBjIph2eSdwuJntHyrXih9Y+TL9Z1DcCjjggqJDn4Uf23Dz8LyCCvSbVRGs46AWBxERiZB6GONwbbC09B/wazc0Ae/CL9C0Cfi3UNkvA+8H7jOzhcBGfBCYAxxfWPwJwDm32MyuAc41szvw0zoLK0c+RLUXf4KSYxwUHEREJErqITj8AjgdOA0/i8LhA8T1wPecc68XCjrnXjGzQ4DvAF8CGoBngWNLLDcNvrVhCX756uOBNuBq4KKqLzcN/VscUHAQEZHoqXlwcM7dhl9autzyLwAnlVk2h79GxRVDq90wKzE4MptzA5UWERGpOzUf4zCmaHCkiIhEnIJDNWmMg4iIRJyCQzVpHQcREYk4BYdqKtVVoeAgIiIRouBQTSWujqkxDiIiEiUKDtXUr6vCLwCla1WIiEiUKDhUk7oqREQk4oZlHYdUKrU/cARgwCPpdLr614GIglKDI9VVISIiEVJWi0MqlZqfSqVuSqVSB5V47GL86o1XAP8BPJlKpa4c1lqOFuHgQA6cU4uDiIhESrldFR8BPgq8EN6ZSqXeB1wE5PEXjboOWAN8PpVKHTeM9RwdYjGI9zXyJMgrOIiISKSUGxwOBp5Mp9MbivZ/Bn9tifPT6fQn0+n054D5QBb41PBVcxQp6q5QcBARkSgpNzjMxl+2utiRQDtwQ2FHOp1+EbgXmLfNtRuNiqZkaoyDiIhESbnBYSqwKrwjlUrNBGYCj6XT6WxR+ZeB7ba9eqNQ0cwKTccUEZEoKTc4dLJlEHhnsP1zifJd+O4KKVa0loO6KkREJErKDQ4vAh9MpVLh6ZvH48c3PFai/PbAym2s2+iU2LKrwjldWltERKKh3HUcfglcDvxPKpW6Dtgd+DSwAfh9ifKHUDQDQwJFYxwckHeOuFnt6iQiIlKmcoPD1cApwLHAMcE+A/5vOp3OhAumUqn3ADsFz5FiAywCFW/QIp4iIlL/yvq0SqfTXfhplhcB9+DXbDghnU7/oETxA4D/Bu4cpjqOLuHBkfjg0K1xDiIiEhFlLzmdTqfbgUvLKHc9cP22VGpUS2x5hUzNrBARkahQ+3i19Rvj4CeeaC0HERGJim2+yFUqlUoC5+AXgzLgIeCaoHtDiiX6uioadIVMERGJmHIvcvXJVCr1eiqVen/R/hjwW2AhcCJwAvA94A9FUzelIFmqq0LTMUVEJBrK7ar4ADAeeLBo/ynBY6uAM4GPAU8CB+Gna0qxEmMc1OIgIiJRUW5weCd+aelc0f5T8YtAfTKdTv9nOp2+HTgav77DR4evmqNI0ZLTAN3Z4tMqIiJSn8oNDtsBr5bY/15gVTqdvr+wI51ObwbuAvbZ9uqNQiXWcVBXhYiIREW5wWEC/iqYvVKp1G747os/lii/DJi0TTUbrdRVISIiEVZucFgH7Fy0r3DZ7FIXuUoAm4daqVEtPDiSvpUjRUREoqDc4PBn4PhUKjUrtG8BfnzDQyXKvw1d5Kq0ftMx/ToOWgBKRESiotwpkz/GD3p8PJVK3YG/yNVxwCvpdLpfV0UwDfN9+KWppViJ6ZjqqhARkago91oVtwM/AnYALsCHhg3AWSWKnwBMpvRVM0XBQUREIqzsJafT6fTZwKHAF/FrNuydTqdLdVN0ABcC/zMsNRxtSsyq6NYYBxERiYiKVndMp9OPAY9tpcy9wL3bUqlRTRe5EhGRCNNFrqqtxAJQ6qoQEZGoqPh6EqlU6mDgbOAQYDZ+ZsVK4FHghnQ6/fiw1nC06ddVoatjiohItJQdHIKrYKaBf8FfBTNst+B2eiqV+k8glU6ne4atlqNJv8GRPjCoq0JERKKikhaHH+OvTbEOP8Pi98Ab+BAxF3+xq0/jg0UDcPqw1nS0CK3j0LsAlIKDiIhERFnBIZVKHYUPDX8CTkin028WFXkRuD+VSl2Jv8z2qalU6qZ0Ov2/w1rb0SBZoqtCwUFERCKi3MGRZ+GvVfFPJUJDr3Q6vQr4J6CT0ms8SMmrYyo4iIhINJQbHA4GfpdOp5dvrWBQ5i78lTOlWIl1HLp6dFltERGJhnKDwwzg5QqO+wowvfLqjAEl1nHIdGdrVRsREZGKlBscOvCX0C7XeCBTeXXGgBJdFRm1OIiISESUGxxeBo6s4LhHUFkLxdjRr8XBtzQoOIiISFSUGxzuAvZKpVKprRVMpVKfBd6On10hxUpc5CrTreAgIiLRUO46Dt8HzgP+/1QqNRv4Xjqd3hAukEqlJgBfwF8Eay1w9XBWdNQID44kD86pxUFERCKjrOCQTqfXp1Kpk/EtD18G/jWVSv0JvwCUw19u+11AI34q5ofT6fS6kalyxJn58JD1C2smyZHp0eBIERGJhkouq/0w8B7gAaAJf62KBcApwf0m4EHgoAEuty0FRTMr1FUhIiJRUelltf8GHJVKpXYCDgVm4ZecXgk8mk6nXwNIpVJNQEM6nd44vNUdJZLJ3jknSZdjs7oqREQkIiq+OiZAOp1eAiwZpMi1wGlDPf6oV7QIVC7v6MnlScZ1lXMREalvI/lJVXwFTSkouQiUWh1ERKT+1fwrrpntbmaXmNkTZrbazDaZ2SIz+6qZjStRfg8z+42ZrTOzdjN7xMxKrjFhZjEzu9DMXjSzjJm9YWZXlDpuVZVcBEoDJEVEpP7VPDjgL8N9IfAP4BL8lM6/A5cCj5lZc6Ggme0KPIa/dsblQdlW4F4zO6rEsRcCVwJ/w08nvR04H7jTzGr32hNbXiFTLQ4iIhIF9TAG4ZfAZc658LoQ15nZy8BXgU8DPwj2XwZMAt7lnFsEYGY3Ac8D15jZns45F+zfGx8W7nDOnVw4sJm9hl+XYgFwywi+roGVWgRKAyRFRCQCat7i4Jx7pig0FNwabPcBCLoXTgQeLISG4PmbgR8BuwPzQs8/BT/O4qqi496Av/bGqcNQ/aHR9SpERCSiah4cBjE32K4KtvvhF5h6vETZJ4JtODjMA/LAU+GCzrkMsKiobHX1Wz1SV8gUEZHoKKurIpVKVfXrsJnFgYuALH3dCbOD7fISTynsmxPaNxtoc851DVD+vWbW4JzrLvH7zwbOPuecc4ZS/a0rNatCLQ4iIhIB5bY42BBu2+Iq4CDgIufc34N9LcG2VBDIFJUp3C9VdqDyvZxzP3TOHVh2bSsV6qpo0HRMERGJkHKvVVG1Lg0z+xZwLvBD59xloYc6gm1jiac1FZUp3J8xwK8pVb561OIgIiIRVVdjHMzsYuBrwE+AzxY9vCLYzmFLhX3hbowVwDQzKxU05uC7MbbopqiKkrMqNMZBRETqX90EBzP7BvAN4CbgzMK0ypDF+K6Hg0s8/aBg+0xo39P41/fuot/TBBxQVLa6Sqzj0KWuChERiYC6CA5mdhFwMfAz4FPOuXxxmWDa5Z3A4Wa2f+i5rcCZwMv0n0FxK/6S3xcUHeos/NiGm4fvFVRI6ziIiEhE1XwBKDP7HPBN4HXgfuDjZv3GVq5yzv0+uP9l4P3AfWa2ENiIDwJzgOPDrRTOucVmdg1wrpndAdwN7IVfOfIharX4E2gdBxERiayaBwf61lPYAfhpiccfAn4P4Jx7xcwOAb4DfAloAJ4FjnXO3V/iuRfgr+J5NnA80AZcjZ+tsUWrRtUUXR0TNKtCRESioebBwTl3BnBGBeVfAE4qs2wOuCK41Y/wrAo0OFJERKKjLsY4jDnqqhARkYhScKgFXR1TREQiSsGhFrQAlIiIRJSCQy2U6qrQRa5ERCQCFBxqIVliVoVaHEREJAIUHGpBXRUiIhJRCg61ULKrQsFBRETqn4JDLZRYAKonlyeXL748h4iISH1RcKiFcHCwvpYGLQIlIiL1TsGhFkKDIxtdKDiou0JEROqcgkMtJPrGODTQd8kMDZAUEZF6p+BQCyUuqw1qcRARkfqn4FAL/YJD37gGjXEQEZF6p+BQCyXWcQB1VYiISP1TcKiF0DoO8XxfWOhSV4WIiNQ5BYdaiCd67yZcDnN+/Qa1OIiISL1TcKgFMy07LSIikaTgUCvhZafRFTJFRCQaFBxqpd+y0z4wqMVBRETqnYJDrZRYy0HrOIiISL1TcKiVUlfIVIuDiIjUOQWHWilxhUwtACUiIvVOwaFWSs2qUFeFiIjUOQWHWklqcKSIiESPgkOtJDTGQUREokfBoVbUVSEiIhGk4FArSQ2OFBGR6FFwqJXwOg6oq0JERKJBwaFW1FUhIiIRpOBQK6EFoBo0OFJERCJCwaFWSrQ4dPXkyAeX2BYREalHCg61EgoOTZbvvd+tVgcREaljCg61EuqqaI71BQd1V4iISD1TcKiVUItDs/V1T2iApIiI1DMFh1oJtTiEuyrU4iAiIvVMwaFWkuEWh76woEWgRESknik41ErzuN67rfmu3vvqqhARkXqm4FArEyb13c129N5XV4WIiNQzBYdamTC59+74ns2999XiICIi9UzBoVbGT+y929rd3ntfYxxERKSeKTjUSmtfcGju7iDm/MwKdVWIiEg9U3ColUQCWicAYDgm5DOAuipERKS+KTjU0vhJvXcn5/wASbU4iIhIPVNwqKXQzIpJCg4iIhIBCg61FGpxmJTrBCDTrcGRIiJSvxQcainc4pBXi4OIiNQ/BYda6tdVUWhxUHAQEZH6peBQS6FFoDQ4UkREokDBoZbCYxzyQYuDFoASEZE6VvPgYGZfNrPbzexVM3NmtmQr5fcws9+Y2TozazezR8zsyAHKxszsQjN70cwyZvaGmV1hZuNKla+6CX2LQPXOqlBXhYiI1LGaBwfg28CRwD+AdYMVNLNdgceAg4HLgS8ArcC9ZnZUiacsBK4E/gacB9wOnA/caWa1f+2hroreMQ7qqhARkTqWqHUFgF2dc68CmNlf8UFgIJcBk4B3OecWBc+5CXgeuMbM9nTOuWD/3viwcIdz7uTCAczsNeD7wALglmF/NZUoXgDKOboUHEREpI7V/Ft3ITRsTdC9cCLwYCE0BM/fDPwI2B2YF3rKKYABVxUd6gagAzh1yJUeLk3N0NAIQAM5WlyPuipERKSu1Tw4VGA/oBF4vMRjTwTbcHCYB+SBp8IFnXMZYFFR2dopWj0y05MlaDQRERGpO1EKDrOD7fISjxX2zSkq3+ac6xqg/DQzayj1i8zsbDN7Zsg1rUS/cQ4d5B305PJV+dUiIiKVilJwaAm2pYJApqhM4X6psgOV7+Wc+6Fz7sCKazgU40MzK/JaBEpEROpblIJDR7BtLPFYU1GZwv1SZQcqXxtaBEpERCIkSsFhRbCdU+Kxwr5wN8YKfHdEqfAwB9+N0T2M9RsaXehKREQiJErBYTG+6+HgEo8dFGzD4xKexr++d4cLmlkTcEBR2drRpbVFRCRCIhMcgmmXdwKHm9n+hf1m1gqcCbxM/xkUtwIOuKDoUGfhxzbcPJL1LVu/K2RqESgREalvNV8AysxOA3YMfpwONJjZ14KflzrnfhYq/mXg/cB9ZrYQ2IgPAnOA411oHqNzbrGZXQOca2Z3AHcDe+FXjnyIWi/+VFC8CBQaHCkiIvWr5sEB+DRwWNG+bwXbh4De4OCce8XMDgG+A3wJaACeBY51zt1f4tgXAEuAs4HjgTbgauAi51x9zHlUV4WIiERIzYODc+7wCsu/AJxUZtkccEVwq08luyo0OFJEROpTZMY4jFrjJkBwva3x+S4SLqeuChERqVsKDrUWi/VbBGpirlNdFSIiUrcUHOpBKDhMznWoxUFEROqWgkM9CF+vIt+pMQ4iIlK3FBzqwRZXyFSLg4iI1CcFh3rQLzh0qqtCRETqloJDPQhfryLfQUeXuipERKQ+KTjUg6IrZK5Y117DyoiIiAxMwaEeTOibVTEp18myNe305OpjYUsREZEwBYd6EJ5Vkesgl3csX6NWBxERqT8KDvWg3xgHv+z0krc21agyIiIiA1NwqAdFsyrMOZasVnAQEZH6o+BQD5IN0NwCQII8rfkutTiIiEhdUnCoF0VTMtXiICIi9UjBoV4UdVe8ua6DTLfWcxARkfqi4FAvwi0OuQ4c8Hrb5ppVR0REpBQFh3pRtAgUoO4KERGpOwoO9SLcVaEpmSIiUqcUHOrF+PDqkYUWB3VViIhIfVFwqBcTp/Te3blnDQBL1eIgIiJ1RsGhXuy2N8TjALy9602271lL26YMmzp7alwxERGRPgoO9WLiZDjg4N4fj9v0PABLNUBSRETqiIJDPZn/wd67H2h/gaTLamaFiIjUFQWHerLXO2DadgCMz3dxaPs/NLNCRETqioJDPYnF4H19rQ7Hbf6ruipERKSuKDjUm0M+gIv5f5b9ulbQ/cZSnHM1rpSIiIin4FBvJk2F/Q/q/XF+2yLWtXfVsEIiIiJ9FBzqkB12XO/9o9pfZOmKtTWsjYiISB8Fh3r09newodlfu2JiPkPP04/UuEIiIiKegkM9isVZ/vZDe3/c/5Fb4MkHalghERERT8GhTuUOOZpNsUYAGrNdcMN34cYroStT45qJiMhYpuBQp3bfaxe+vdNHWZ7ou/gVj94Hl54Pr/+jdhUTEZExTcGhTjUm4xxz4hGcO2sBf2jZve+Bla/DpefBbTdAprN2FRQRkTFJwaGOHbb3LHbfdRbfnXY0V0x9P92xhH8gn4f7fgUXfQYWPVHbSoqIyJii4FDHzIzzPrgPyUSc+1rfzjkzF7Bmdqj1Ye1b8IOL4bIL4aG7oF2rTIqIyMhScKhzc6e28pH37gLAsuRkzm49nvZPfB5aJ/QV+scL8LOr4d8+DulvwQN3wovPwcb1tam0iIiMWolaV0C2bsEhu/HAX1ewcl0Hm7uy/H8rp/OVr11L629vgsfvh1zOF8z2wLN/9LeC1okwcw7MmAPbzYHtZsOsHWC7uZDQP7+IiFTGdB2EgaVSKQeQTqdrXRWefuUtvvaLp3t/njW5ha9/+J3sOs7BUw/5ALHk5fIPGE/A7B1g7s4+REzbDqbOgKnbwaQpEIuPwKsQEZEIsEEfVHAYWD0FB4D//MOL3PrHvqmYyXiMcz+4N8ccsD1mBiteh788CcuXwsql/ufuIVznIp6AKdN9mJg2098fPwnGT/Tbca3Q0AjJBr9taFLrhYjI6DFocNC7fYT8y5F7stvMiSy88y90dGfpyeVZ+NvFPPy3lfzTu3fmwN22JzZ7h74n5POwbjWsWg6rVsBby/395UtgzVsD/6JcFlav9LdymPmAsd1cmDXXb6dMg8nT/UW7Wif4MiIiEnlqcRhEvbU4FCxbs5lLf/ksr73VfxbF7CktnHDgThy17xwmtDQMfpCOzT5ALFsCbSuhbZUPE2tWwaYNw1vhRNK3Vowb78dcjGv1rRXxBMTjvlukpxsyHX5tiu4MNLXAlBm+tWPKdGhs8kEonweX989tGQfN46Cl1R+/pXV46z2a5HPw98U+xG2/S61rIyL1TV0VQ1WvwQEg05Pj2nuf594/v0Hxv2DMjH12mMzBe8zkvbtvx8zJLZUdvCsDbW/6ENG2Ctav8WFi03q/bd8M2W7o7oaeLv9hXw9/R80tfozGtO18iOho9wGpYzNksz5cTJjku1sam2Bdm5/SuuYt2LzRt47MmO1v02f54yUSkGjw255u/1q7MtDVCclGH4bGjYfW8f75k6fXV7dNLgdP/AHu/i/f2gSw617w/pPgnYfWV12HIpfzoSi5laAsIpVQcBiqeg4OBSvWtvPbPy3l3kVvsDmTLVlm1uQW3jZrIm+bNZHdZ01kl+0mbL1FohI93fDWClj5Bry5zN9fvwbWt/kP586O4ftd9S4W8y0l02f61hWzvlssDsmkb4FJJH2ricuDw29xkHd+65z/UOxs97eOdj9epaEBGlugqdkHmwmTfGCZNA0mTvbP6+7y4WbNKvj9r30ILGXyNHj3YTB9th8YO2U6TJjsQ1WyYeDupc52WLvaB672Tb4VY8Ik/9zWCUHds77+mQ5441VY+jIseQXefN2HuhlzgpA2ywevROi8NDZBYzM0Bduebti4Lrit939fK16HFUt9GMpl/bHm7uxbU2Zu3/cakkmIJ/35LbRYZXtCQXi9fw1NLT5YtgbhcvaO/nxUk3M+kLZvgq4u/2/S2FTdOoyUN5fBnx/zM7v2P8i3NEo9U3AYqigEh4JMd5Y//HUF9z33Bi8uW79FK0SxCc1Jtp/WyvZTW5k1uYXpE5qYNqE52DbRkBjG/9hdGf+NfvMGv23f5FsAcln/bTEXfGNsCj4QGxp9mbWrg9tb/s3eYv6DORb3P3cWWhTa/YfKUAaCjiXNLdDT48/d1pgFH7wN/rwbftvTNXaC4JTpsOvbYZc9feAojPtpe9MHnKnBTKQpM/zfbU+3P7eFv82N6/3f/MYN/vzN3N7PZJq1gw9LK5b2dReuXgntG/umVvfWYQbM2t5/4La0+t+bDLWAdWX8rbvLPz51Rl+dMp2weoUPWm+t9P/XCi1k48b7oFeYqt3UXPocdHf1tcptXOfrVwiFzvlg19wSdBu29rXqJRt8mb88CQ/8Fv72bP/zevjx8L4P+vLFCqE5l/X3Y7G+m8Xqa7xUV8aH0qYyWnWd81+k2jf5f8+GxpGv39ApOAxVlIJD2NrNGZ546S0e//ub/Pm1NfTk8hUfY/K4RmZMbGbGxGamTWhiQnOS8c1Jxjc3MKG5gUnjGpg8rpEJLUnisTpYR8w5/ybdFnSvdHX2jX8Y1+q/3W/a0PetNdPpv6kX3mhbJ/qBpG8Fb7Rtb/qumMIHQbYnCDfN/s2ysck/3r7Jv+Fv3ujfFNa11fpMbGncePjAh+DIk/yHzUN3wYN3+XMxGlgsaLGRIZs0FSZO8eGiEOo724c+3ql5nP+gH2w120TSh6JC118m47tAi8NTWLKhb9zTlBl+6nhjk5/Z1dDof2e4i7Ir40NWstG31sVi/v//ujbfKrpxnT9my3j/PtFSuAVBqGWcP24i6Y8TT8DqN30L2tKXfYuXc771bvYOMGtHmDzV/z/rCbpzN60PWmPf6Lu6cSzmA+SOb/OtZC3jQkHdYP3avsC2Ya3//c3jfEhrHudbFydP7xuEPnHKcLfiKDgMVVSDQ1h3NsfS1Zt5acV6Xl65gZdXbuCNNe109Qzyn7MCBoxrStKQiJGMx0jEYzQkYrQ2FYKGDxvjm0L3m5O0NCZoaUjQ0pigucH3s+fyjlw+Ty7vSMRjNCZiNCTjxOrpG8bWdHf5/+yrV/R9M8/n+5rvsz193/rzuf7f5jGIFbo2gm9YTS19g0AbmvrGlGQ6/PE3rA26hdbAhnX+OY3Bm2hjE+yyF8w/dstvRNkeeO7Jvhk2hTep9k3+zW2wVonwm/e4CT44bVzvbx2bfN0LA18TSZg5179B7vQ2353Q2dEX0Fav9K8lfF4KXS2ZTv+Bkkj2dYVMmBS8Se/ob7N28N0Ry5fCslf9lWPXru574y6EPgt9a40ngu6VyX2DdjMdQffFBn8ul75cm0vYNzT2dd2sWeX/dkYDi8FeB/huq03ra12b0cdi8Mnz4X3HDtsRB3sw4iOjZGsaEvHe8Q0Feedo25jhjTWbWda2mVUbOlm9MUPbxgyrN3ayZlPGd7WXwQGbM2U0fW+DZDxGU0Oc5oYETUm/bUjEiMeNeCxGPGYkYv5+Mm4k4jFiZmTzebI5Ry4YN9CUTNDUEKcp6W+xmGFmxMwPKE3EYySDAJSMx8g75295R95BS2OCSS0NTGxpYOK4BmJm9OT87+jJ5SmEcEtMglmTaEjEGdfkQ1I81v//YeG4BX4YhFUvJCWS8K5D/a2UfC4Y/NpNv7EXsfjwTK/d7e3b9vxiOwXBZLjkcj6I/OMFWPqKD2HTZvoxGdNm+jBSmIW05q2+FqlCV0JjU9+6J+Mn+sdXvtE3NqOz3XddzN0J5uzkA1Chib8g29M3dmj1yr4wVAhEiYa+8SANDT70FELg2tU+hGw3xw/0nTHLf+tu3+S/iW/e6MutWu6PPdC3/Fgs9O1+auibdxwwH+56x+Fs9uFx0/q+wDN+ov8wO+w437XT0w3PPAz/+9+DL1hXCHhmfTOpCgG8nhQCaa70+LItjBvvWzTa3hze1+LyvtW0StTiMIjR0OIwFLl8nraNGd7a0MmqDZ2sa+9iU2dP721DRxfr27tZ397Fxs6RDQ2jRUtDgkTcB42ebJ7sAMksETMaEnEfYBKx3pDTFISlrp4cme4cHd1ZurM5WhoSTAjCzITmBnJ5R2d3lkxPjkxPjqZkvDfoTAoGxLZ3ZenoytLZncUw3/oTtPwk40Y278gGgagQnpyjNxg1NSRoDgJYY9AiFIsZcbPeAATBVxaDeBDqEjEjHvdBL2bWu3XOkXP+d+bzjmQizvimJOOaErQ2JUnEt+wKyztHV0+Orp4cPbk8seB3x4LwVQiBiSAcFvSGuyi1Yo20XM5/kLVvhkS8r7WooWloq8jm874VqqMDpk73YaOYcz4UdXX2df01NvlwE48PHEw72n2X4pq3/Hbjet9CVbjlsn1dlC2tvmsxm/Utdd3d/vHxE32rVaF7pqfHt5R1bPbnoDAYuXNz36DkcGth6wQfUnfcDebu4l/f6hW+1WvFUn+cZKNvCWto9K19M+f6cDg+GDCd6fCtY0tf8V0YhZCe7YZszndFTJnuu1EnTd1yoPSGtcGssNV+u3EdfO3q4QzP6qoYqrEaHCqRy+fZnMmSDT4Qe3J5unpybM70sClTCBvdfpvpYVNHN5syPXR0ZenoztLZ5T8EjcIHjP8QyuYc3dkc3dlR0lQrQ9YXMiAWM3J5V9HfRTxmvcHHhfYVutaS8RhmfV8AHX1BKR9sGxJxmhvjNCcTNDf6EBjvF5j6v8/m8nm6s3m6sjl6sr77rSER9116QatW79978PrisZj/oh3z9cnlXL9WMx8g+1reCnV24O+Y+V6vIEAVAl9v61awP9zKFo9bUJdYb6uYWdB7Zr4lLxGcp0TQmpeI+ft+bJOvW2+wDI5d+HdrDrojxzX5YJrPOzI9OR9uu3OY0ftvkAj+HcIju+Mxo6khvsU4qu5sjs7uHNlcvrduyd5/yzEYCrM9fQPHh8fY7aowsxjweeAzwE7AauA24CLnXHsNqzZqxGMxJg7n1M4ieefoyeb7vdl0Bqtm+jdU/6aczflv8bnCNu/8G13whugg+Lbe9208/Kaad663y6EQgAofVPGYYRibMj1s6OhmQ6ilpfBmmozFiMWs9xutA7qzedozPbR3lW7GLHxLLnxg5RXiS8o7Rz439HOTK9G648fT5KDMsT7tXVnW6R2jZhJBgAAj050dsMUO6B0b1ZiMk4zH6AkCXHfQOtWYjPe2sBXCTE8u33vrC3L+/SMWhFYL/r8Cvd2XLghnhTK9rW9B4CpsGxOx3pbEeMzo7vHvaYVgmYwbyUKwDLpagd6P70Jd4kFgzQXvi+H3qwWH7MquM6vTXTGqgwOwEDgf+DVwBbBX8PM7zOwo5zQUu97FzGgM3gRGMqCMpLxzdHb5sBMeQ1H8zcg5RzbvW1p6sv4ba6Y7S2fQPdGdzfV2W7Q0JEgmYnR0ZX2Y6ehmU2c3iXisdxxIYzJOZ7d/fH17Nxs6uohZ/64JwLf+BF0XPYVvcLG+sSLhN8583ncRdAYhrKsnR94RfOt0veGn99t7bzdEX8jL5X1YywWBLfxGHYsZ3dk8mzM9bM700J7pGXC8TWMiRmMy3tuVUfiGXQiSPbl8ydAg0ZPNuwHXqSnWlc3Tlc2zaYBu1M5u31oBo2v69vv3naPgsK3MbG/gPOAO59zJof2vAd8HFgC31Kh6MobEzBjXVKKft4iZ+W8e8RjU9RTv6imEqXwQTHJB0Ggsc7ZN3vmA0vuNMdhfGMdR+LZWYKFm9sI3TMOCpvEsnd05OrqyZPP53paqXN5RvHBKLBir0pj03zQN6Mn5MFj4llh4biFQFX4uHDcedAkUvvUWmuczQT2cc/3qWNzFYkZvN0os6D8ohDvnIBsEq0J3SD7o9yh06OTz9D7mW/T6Wvl6cnlyOdfX7REa/FuoQy4IzB3dWdozvqUvZn6MTKHbxbDekOcHGPf/d8jlHJme7BbhMREzmhsTJOOxvucPMnZoLBjKtPuhGrXBATgF39BzVdH+G4DvAKei4CBS1wphiiF23cbMiMW3DBiFgNZcdiPW1oOfDC4c4CrhnO9KKHQvNjfEB1ygLu8c3T053+rQE3QDBC1TjQk/jqKrJxeMr/KteTELxkcErYGFsFro9gyPdfEtav27JQp1zLu+oFp4bmGQcWHsl+9izfe2ojYVulOC4NOdzdEdBIBwz2WuEODyPrDFY9bXehls95g9aSj/LEMymoPDPCAPPBXe6ZzLmNmi4HEREamC4inJ5TKzYGDp1tNjzMy3aAwSCBPxWFktgDKwOljyb8TMBtqcc6U6spYD08ys5J+XmZ1tZs+MaO1EREQiaDQHhxYGHv2SCZXZgnPuh865A0ekViIiIhE2moNDBwMPMWsKlREREZEyjebgsALfHVEqPMzBd2N0V7lOIiIikTaag8PT+Nf37vBOM2sCDgA0hkFERKRCozk43IqfXX1B0f6z8GMbbq52hURERKJu1E7HdM4tNrNrgHPN7A7gbvpWjnwIreEgIiJSsVEbHAIXAEuAs4HjgTbgavy1KrTctIiISIVGdXBwzuXw16i4otZ1ERERGQ1G8xgHERERGWYKDiIiIlK2Ud1VMVxSqVStqyAiIlItLp1OD3hxEbU4iIiISNnMubF7/fJaMLNndB2MbafzODx0HoeHzuPw0HkcHiN9HtXiICIiImVTcBAREZGyKThU3w9rXYFRQudxeOg8Dg+dx+Gh8zg8RvQ8aoyDiIiIlE0tDiIiIlI2BQcREREpm4LDCDOzmJldaGYvmlnGzN4wsyvMbFyt61aPzGx3M7vEzJ4ws9VmtsnMFpnZV0udMzPbw8x+Y2brzKzdzB4xsyNrUfd6ZmYtZvaamTkz+0GJx3UeB2FmU8zsP8zsleD/8Woze8DM3ldUTudxAGbWamZfMbPFwf/rNjN7zMzOMDMrKjvmz6OZfdnMbjezV4P/t0u2Ur7sc7atn0taOXLkLcRfyvvX+IttFS7t/Q4zO0pX6dzCvwCfA/4HuBnoAY4ALgU+amYHOec6AcxsV+AxIAtcDmwAzgLuNbMPOufur0H969UlwLRSD+g8Ds7MdgQeBFqBHwMvAROB/YA5oXI6jwMwsxjwO+C9wE/xVyluAU4BfoJ/X/xiUFbn0fs2sBZ4Fpg0WMEhnLNt+1xyzuk2QjdgbyAP/Kpo/3mAAz5e6zrW2w04EJhYYv+lwTk7N7TvNiAHHBDa1wosBf5OMPh3rN+AdwZvKP8anMMfFD2u8zj4+XsEeAOYtZVyOo8Dn5uDg7+9hUX7G4BXgfU6j1ucs11C9/8KLBmkbNnnbDg+l9RVMbJOAQy4qmj/DUAHcGq1K1TvnHPPOOc2lHjo1mC7D0DQpHYi8KBzblHo+ZuBHwG7A/NGtrb1z8zi+L+3e4A7Sjyu8zgIM5sPHApc7pxbaWZJM2spUU7ncXATgu2K8E7nXDfQBrSDzmOYc+7VcsoN4Zxt8+eSgsPImodPdk+FdzrnMsAixsh/gGEyN9iuCrb7AY3A4yXKPhFsdX7hQmBP4NwBHtd5HNxxwfZ1M7sT6ATazewlMwu/weo8Du4pYD3w72b2ETPbIeiTvwx4F3BxUE7nsXKVnrNt/lxScBhZs4E251xXiceWA9PMrKHKdYqc4FvzRfjm9luC3bOD7fISTynsm1PisTHDzHYGvglc4pxbMkAxncfB7RFsbwCmAKcDnwa6gZ+Z2aeCx3UeB+GcW4f/VrwW36y+FHgRP57pZOfcDUFRncfKVXrOtvlzSYMjR1YLUOofByATKtNdnepE1lXAQcBXnHN/D/YVmotLnd9MUZmx6lrgNeDKQcroPA5ufLDdBBwRNK1jZr/G981/28x+is5jOTbj++r/Bz+Qbwo+ONxiZic5536PzuNQVHrOtvlzScFhZHUAMwZ4rClURgZgZt/CN7P/0Dl3WeihwnlrLPG0MX9ug2b0o4H5zrmeQYrqPA6uM9j+ohAawH+DNrP/AT6Jb5XQeRyEme2LDwsXOueuC+3/BT5M3BDMDNB5rFyl52ybP5fUVTGyVuCbfUr9g87BNxeptWEAZnYx8DX8dK3PFj1cGGRVqtmysK9U092oF/y9XQncDbxpZruZ2W7AjkGRicG+Seg8bs2yYPtmicdWBtvJ6DxuzYX4D6Xbwzudcx3AXfi/zZ3QeRyKSs/ZNn8uKTiMrKfx5/jd4Z1m1gQcADxTgzpFgpl9A/gGcBNwpgvmC4Usxje3HVzi6QcF27F6fpuB6cDxwMuh24PB46cGP5+JzuPWFAaQzS3xWGHfW+g8bk3hAyxe4rFEaKvzWLlKz9m2fy7Veq7qaL4B+zL4fNlTa13HerzhB0I6fGiIDVLudvzc5f1D+wpzl19ijMz3LnFeksCHS9zOCc7r74Kfd9d53Oq5nAxsxLc8tIb2z8L32b8U2qfzOPB5XBj87f170f5J+G/Aa4GEzuOA529r6ziUfc6G43NJV8ccYWZ2Nb6P/tf4puPCCl1/BI50WjmyHzP7HPAD4HXg6/g/8LBVzg+iImh+fwq/uuRC/Bv8Wfj/GMc75+6tVr2jwMx2wg+WvMY5d25ov87jIMzsbOB64HngP/GLFp2DDw//xzl3X1BO53EAweqbz+KD2M34978p+POzE/A551w6KKvzCJjZafR1L56H/7u7Ivh5qXPuZ6GyFZ2zbf5cqnWSGu03fNPcv+FX7+rC9zVdSejbi279zteN+NQ70O3BovJ7Af+NnyPeATwKHFXr11GPN/wb9BYrR+o8lnXu/hk/J74dP8PiPuAQnceKzuGu+OWmlwUfcBuBh4F/1nkseb4eLPd9sNJztq2fS2pxEBERkbJpcKSIiIiUTcFBREREyqbgICIiImVTcBAREZGyKTiIiIhI2RQcREREpGwKDiIiIlI2XR1TREa9VCp1Mf7aJ0ek0+kHa1sbkWhTcBCRrUqlUuWsFKcPZZExQMFBRCrxzUEeW1KtSohI7Sg4iEjZ0un0xbWug4jUloKDiAy78JgC/BX+LgD2xF8g6rfAV9Lp9Jslnvc2/FVR3w9MB9qA+4FvpdPpl0uUj+OvAngasA/+CoLL8RcI+u4Az/kw8O9B+Qz+glX/lk6nl2/DSxYZMzSrQkRG0oXAdcBzwFX4q/F9CngslUpNDxdMpVLzgGeAU4Gngf/AX5HyE8AzqVTqwKLyDcA9wLXA9sAtwPeBPwEfAg4pUZ8U8HN8t8o1wF+BjwH3p1Kpxm19sSJjgVocRKRsQUtCKZl0Ov2dEvs/CLwnnU7/OXSMhfgWiO8Anw72GXATMAE4NZ1O3xwq/zHgv4Cfp1Kpt6fT6Xzw0MXAUcCdwEfS6XRX6DmNwbGKHQvMS6fTi0NlbwFOAU4CbhvotYuIpxYHEanENwa4fWmA8j8Lh4bAxcAG4OOhb/nvxXdlPB4ODQDpdPpW4FFgD+BQ6O2iSAGdwGfDoSF4Tlc6nV5doj7fD4eGwA3B9t0DvAYRCVGLg4iULZ1OW4VPeajEMTakUqlFwGHAXsAi4J3Bw38Y4Dh/wIeGdwAP40PGRODJdDq9ooL6PFNi3xvBdnIFxxEZs9TiICIjadUA+wsDIycWbVcOUL6wf1LRttIBjetL7MsG23iFxxIZkxQcRGQkbTfA/pnBdkPRdmaJsgCzisqtD7ZzhlwzERkSBQcRGUmHFe9IpVITgQPwUyFfCHYXxkEcPsBxCvufDbYv4sPDfqlUava2V1NEyqXgICIj6bRUKvWOon0X47smfhEa1PhH/FTNQ4N1FnoFP88HXsIPkiSdTueANNAMXFc8lTKVSjUUT/cUkeGhwZEiUrZBpmMC/CadTi8q2vc74I+pVOo2/DiFQ4PbEkIzMdLptEulUqcDvwduTaVS/41vVdgD+Cf8wlGfDE3FBL/89XuAE4CXUqnUb4Ny2wNHA18AbhzCyxSRQSg4iEglvjHIY0vwMyTCFgK/xq/b8DFgM/7D/CvpdPqtcMF0Ov1ksAjU1/DrM5yAXznyF/iVI/9eVL47lUodC3wW+CRwOmDAiuB3PlrpixORrTPnyrnonYhI+XQZa5HRS2McREREpGwKDiIiIlI2BQcREREpm8Y4iIiISNnU4iAiIiJlU3AQERGRsik4iIiISNkUHERERKRsCg4iIiJSNgUHERERKdv/A4EnN76M5nxYAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ " 'MAE' :['mae', 'val_mae'],\n",
+ " 'LOSS':['loss','val_loss']}, save_as='01-history')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 7 - Restore a model :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.1 - Reload model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "Loaded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
+ "loaded_model.summary()\n",
+ "print(\"Loaded.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.2 - Evaluate it :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 12.4521\n",
+ "x_test / mae : 2.5606\n",
+ "x_test / mse : 12.4521\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.3 - Make a prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "my_data = [ 1.26425925, -0.48522739, 1.0436489 , -0.23112788, 1.37120745,\n",
+ " -2.14308942, 1.13489104, -1.06802005, 1.71189006, 1.57042287,\n",
+ " 0.77859951, 0.14769795, 2.7585581 ]\n",
+ "real_price = 10.4\n",
+ "\n",
+ "my_data=np.array(my_data).reshape(1,13)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction : 10.59 K$ Reality : 10.40 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "predictions = loaded_model.predict( my_data )\n",
+ "print(\"Prediction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], real_price))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Friday 8 January 2021, 01:10:39\n",
+ "Duration is : 00:00:12 582ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.ipynb b/README.ipynb
index 49bd3ae6ab01d893e97e1002ab2f418cfc901c27..4b422afaad2a3c5f0e6b2a9652eb0c6440cf7fed 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -5,10 +5,10 @@
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+ "shell.execute_reply": "2021-01-08T00:45:39.084633Z"
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"source_hidden": true
@@ -59,40 +59,307 @@
"## Jupyter notebooks\n",
"\n",
"<!-- INDEX_BEGIN -->\n",
- "| | |\n",
- "|--|--|\n",
- "|LINR1| [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br>Direct determination of linear regression |\n",
- "|GRAD1| [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br>An example of gradient descent in the simple case of a linear regression.|\n",
- "|POLR1| [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br>Illustration of the problem of complexity with the polynomial regression|\n",
- "|LOGR1| [Logistic regression, with sklearn](LinearReg/04-Logistic-Regression.ipynb)<br>Logistic Regression using Sklearn|\n",
- "|PER57| [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)<br>A simple perceptron, with the IRIS dataset.|\n",
- "|MNIST1| [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)<br>Example of classification with a fully connected neural network|\n",
- "|GTSRB1| [CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|\n",
- "|GTSRB2| [CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br>Episode 2 : First convolutions and first results|\n",
- "|GTSRB3| [CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br>Episode 3 : Monitoring and analysing training, managing checkpoints|\n",
- "|GTSRB4| [CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br>Episode 4 : Improving the results with data augmentation|\n",
- "|GTSRB5| [CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br>Episode 5 : A lot of models, a lot of datasets and a lot of results.|\n",
- "|GTSRB6| [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)<br>Episode 6 : Run Full convolution notebook as a batch|\n",
- "|GTSRB7| [CNN with GTSRB dataset - Show reports](GTSRB/07-Show-report.ipynb)<br>Episode 7 : Displaying a jobs report|\n",
- "|GTSRB10| [OAR batch submission](GTSRB/batch_oar.sh)<br>Bash script for OAR batch submission of GTSRB notebook |\n",
- "|GTSRB11| [SLURM batch script](GTSRB/batch_slurm.sh)<br>Bash script for SLURM batch submission of GTSRB notebooks |\n",
- "|IMDB1| [Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br>A very classical example of word embedding for text classification (sentiment analysis)|\n",
- "|IMDB2| [Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br>Example of reusing a previously saved model|\n",
- "|IMDB3| [Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br>Still the same problem, but with a network combining embedding and LSTM|\n",
- "|SYNOP1| [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|\n",
- "|SYNOP2| [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)<br>Episode 2 : Training session and first predictions|\n",
- "|SYNOP3| [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)<br>Episode 3: Attempt to predict in the longer term |\n",
- "|VAE1| [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)<br>Building a simple model with the MNIST dataset|\n",
- "|VAE2| [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)<br>Visualization and analysis of latent space|\n",
- "|VAE3| [About the CelebA dataset](VAE/05-About-CelebA.ipynb)<br>Presentation of the CelebA dataset and problems related to its size|\n",
- "|VAE6| [Preparation of the CelebA dataset](VAE/06-Prepare-CelebA-datasets.ipynb)<br>Preparation of a clustered dataset, batchable|\n",
- "|VAE7| [Checking the clustered CelebA dataset](VAE/07-Check-CelebA.ipynb)<br>Check the clustered dataset|\n",
- "|VAE8| [Variational AutoEncoder (VAE) with CelebA](VAE/08-VAE-with-CelebA.ipynb)<br>Building a VAE and train it, using a data generator|\n",
- "|VAE9| [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/09-VAE-withCelebA-post.ipynb)<br>Exploring latent space of our trained models|\n",
- "|VAE10| [SLURM batch script](VAE/batch_slurm.sh)<br>Bash script for SLURM batch submission of VAE notebooks |\n",
- "|ACTF1| [Activation functions](Misc/Activation-Functions.ipynb)<br>Some activation functions, with their derivatives.|\n",
- "|NP1| [A short introduction to Numpy](Misc/Numpy.ipynb)<br>Numpy is an essential tool for the Scientific Python.|\n",
- "|TSB1| [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)<br>4 ways to use Tensorboard from the Jupyter environment|\n",
+ "<style>\n",
+ "\n",
+ ".fid_line{\n",
+ " padding-top: 10px\n",
+ "}\n",
+ "\n",
+ ".fid_id { \n",
+ " font-size:1.em;\n",
+ " color:black;\n",
+ " font-weight: bold; \n",
+ " padding:0px;\n",
+ " margin-left: 20px;\n",
+ " display: inline-block;\n",
+ " width: 60px;\n",
+ " }\n",
+ "\n",
+ ".fid_desc { \n",
+ " font-size:1.em;\n",
+ " padding:0px;\n",
+ " margin-left: 85px;\n",
+ " display: inline-block;\n",
+ " width: 600px;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.fid_section { \n",
+ " font-size:1.2em;\n",
+ " color:black;\n",
+ " margin-left: 0px;\n",
+ " margin-top: 12px;\n",
+ " margin-bottom:8px;\n",
+ " border-bottom: solid;\n",
+ " border-block-width: 1px;\n",
+ " border-block-color: #dadada;\n",
+ " width: 700px;\n",
+ " }\n",
+ "\n",
+ "</style>\n",
+ "<div class=\"fid_section\">Linear and logistic regression</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/01-Linear-Regression.ipynb\">LINR1</a>\n",
+ " </span> <a href=\"LinearReg/01-Linear-Regression.ipynb\">Linear regression with direct resolution</a><br>\n",
+ " <span class=\"fid_desc\">Direct determination of linear regression </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/02-Gradient-descent.ipynb\">GRAD1</a>\n",
+ " </span> <a href=\"LinearReg/02-Gradient-descent.ipynb\">Linear regression with gradient descent</a><br>\n",
+ " <span class=\"fid_desc\">An example of gradient descent in the simple case of a linear regression.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/03-Polynomial-Regression.ipynb\">POLR1</a>\n",
+ " </span> <a href=\"LinearReg/03-Polynomial-Regression.ipynb\">Complexity Syndrome</a><br>\n",
+ " <span class=\"fid_desc\">Illustration of the problem of complexity with the polynomial regression</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/04-Logistic-Regression.ipynb\">LOGR1</a>\n",
+ " </span> <a href=\"LinearReg/04-Logistic-Regression.ipynb\">Logistic regression, with sklearn</a><br>\n",
+ " <span class=\"fid_desc\">Logistic Regression using Sklearn</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Perceptron Model 1957</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IRIS/01-Simple-Perceptron.ipynb\">PER57</a>\n",
+ " </span> <a href=\"IRIS/01-Simple-Perceptron.ipynb\">Perceptron Model 1957</a><br>\n",
+ " <span class=\"fid_desc\">A simple perceptron, with the IRIS dataset.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Basic regression using DNN</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"BHPD/01-DNN-Regression.ipynb\">BHPD1</a>\n",
+ " </span> <a href=\"BHPD/01-DNN-Regression.ipynb\">Regression with a Dense Network (DNN)</a><br>\n",
+ " <span class=\"fid_desc\">A Simple regression with a Dense Neural Network (DNN) - BHPD dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"BHPD/02-DNN-Regression-Premium.ipynb\">BHPD2</a>\n",
+ " </span> <a href=\"BHPD/02-DNN-Regression-Premium.ipynb\">Regression with a Dense Network (DNN) - Advanced code</a><br>\n",
+ " <span class=\"fid_desc\">More advanced example of DNN network code - BHPD dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Basic classification using a DNN</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"MNIST/01-DNN-MNIST.ipynb\">MNIST1</a>\n",
+ " </span> <a href=\"MNIST/01-DNN-MNIST.ipynb\">Simple classification with DNN</a><br>\n",
+ " <span class=\"fid_desc\">Example of classification with a fully connected neural network</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Images classification with Convolutional Neural Networks (CNN)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/01-Preparation-of-data.ipynb\">GTSRB1</a>\n",
+ " </span> <a href=\"GTSRB/01-Preparation-of-data.ipynb\">CNN with GTSRB dataset - Data analysis and preparation</a><br>\n",
+ " <span class=\"fid_desc\">Episode 1 : Data analysis and creation of a usable dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/02-First-convolutions.ipynb\">GTSRB2</a>\n",
+ " </span> <a href=\"GTSRB/02-First-convolutions.ipynb\">CNN with GTSRB dataset - First convolutions</a><br>\n",
+ " <span class=\"fid_desc\">Episode 2 : First convolutions and first results</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/03-Tracking-and-visualizing.ipynb\">GTSRB3</a>\n",
+ " </span> <a href=\"GTSRB/03-Tracking-and-visualizing.ipynb\">CNN with GTSRB dataset - Monitoring </a><br>\n",
+ " <span class=\"fid_desc\">Episode 3 : Monitoring and analysing training, managing checkpoints</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/04-Data-augmentation.ipynb\">GTSRB4</a>\n",
+ " </span> <a href=\"GTSRB/04-Data-augmentation.ipynb\">CNN with GTSRB dataset - Data augmentation </a><br>\n",
+ " <span class=\"fid_desc\">Episode 4 : Improving the results with data augmentation</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/05-Full-convolutions.ipynb\">GTSRB5</a>\n",
+ " </span> <a href=\"GTSRB/05-Full-convolutions.ipynb\">CNN with GTSRB dataset - Full convolutions </a><br>\n",
+ " <span class=\"fid_desc\">Episode 5 : A lot of models, a lot of datasets and a lot of results.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/06-Notebook-as-a-batch.ipynb\">GTSRB6</a>\n",
+ " </span> <a href=\"GTSRB/06-Notebook-as-a-batch.ipynb\">Full convolutions as a batch</a><br>\n",
+ " <span class=\"fid_desc\">Episode 6 : Run Full convolution notebook as a batch</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/07-Show-report.ipynb\">GTSRB7</a>\n",
+ " </span> <a href=\"GTSRB/07-Show-report.ipynb\">CNN with GTSRB dataset - Show reports</a><br>\n",
+ " <span class=\"fid_desc\">Episode 7 : Displaying a jobs report</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/batch_oar.sh\">GTSRB10</a>\n",
+ " </span> <a href=\"GTSRB/batch_oar.sh\">OAR batch submission</a><br>\n",
+ " <span class=\"fid_desc\">Bash script for OAR batch submission of GTSRB notebook </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/batch_slurm.sh\">GTSRB11</a>\n",
+ " </span> <a href=\"GTSRB/batch_slurm.sh\">SLURM batch script</a><br>\n",
+ " <span class=\"fid_desc\">Bash script for SLURM batch submission of GTSRB notebooks </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Sentiment analysis with word embedding</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IMDB/01-Embedding-Keras.ipynb\">IMDB1</a>\n",
+ " </span> <a href=\"IMDB/01-Embedding-Keras.ipynb\">Text embedding with IMDB</a><br>\n",
+ " <span class=\"fid_desc\">A very classical example of word embedding for text classification (sentiment analysis)</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IMDB/02-Prediction.ipynb\">IMDB2</a>\n",
+ " </span> <a href=\"IMDB/02-Prediction.ipynb\">Text embedding with IMDB - Reloaded</a><br>\n",
+ " <span class=\"fid_desc\">Example of reusing a previously saved model</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IMDB/03-LSTM-Keras.ipynb\">IMDB3</a>\n",
+ " </span> <a href=\"IMDB/03-LSTM-Keras.ipynb\">Text embedding/LSTM model with IMDB</a><br>\n",
+ " <span class=\"fid_desc\">Still the same problem, but with a network combining embedding and LSTM</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Time series with Recurrent Neural Network (RNN)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"SYNOP/01-Preparation-of-data.ipynb\">SYNOP1</a>\n",
+ " </span> <a href=\"SYNOP/01-Preparation-of-data.ipynb\">Time series with RNN - Preparation of data</a><br>\n",
+ " <span class=\"fid_desc\">Episode 1 : Data analysis and creation of a usable dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"SYNOP/02-First-predictions.ipynb\">SYNOP2</a>\n",
+ " </span> <a href=\"SYNOP/02-First-predictions.ipynb\">Time series with RNN - Try a prediction</a><br>\n",
+ " <span class=\"fid_desc\">Episode 2 : Training session and first predictions</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"SYNOP/03-12h-predictions.ipynb\">SYNOP3</a>\n",
+ " </span> <a href=\"SYNOP/03-12h-predictions.ipynb\">Time series with RNN - 12h predictions</a><br>\n",
+ " <span class=\"fid_desc\">Episode 3: Attempt to predict in the longer term </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Unsupervised learning with an autoencoder neural network (AE)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"AE/01-AE-with-MNIST.ipynb\">AE1</a>\n",
+ " </span> <a href=\"AE/01-AE-with-MNIST.ipynb\">AutoEncoder (AE) with MNIST</a><br>\n",
+ " <span class=\"fid_desc\">Episode 1 : Model construction and Training</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"AE/02-AE-with-MNIST-post.ipynb\">AE2</a>\n",
+ " </span> <a href=\"AE/02-AE-with-MNIST-post.ipynb\">AutoEncoder (AE) with MNIST - Analysis</a><br>\n",
+ " <span class=\"fid_desc\">Episode 2 : Exploring our denoiser</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Generative network with Variational Autoencoder (VAE)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/01-VAE-with-MNIST.ipynb\">VAE1</a>\n",
+ " </span> <a href=\"VAE/01-VAE-with-MNIST.ipynb\">Variational AutoEncoder (VAE) with MNIST</a><br>\n",
+ " <span class=\"fid_desc\">Building a simple model with the MNIST dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/02-VAE-with-MNIST-post.ipynb\">VAE2</a>\n",
+ " </span> <a href=\"VAE/02-VAE-with-MNIST-post.ipynb\">Variational AutoEncoder (VAE) with MNIST - Analysis</a><br>\n",
+ " <span class=\"fid_desc\">Visualization and analysis of latent space</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/05-About-CelebA.ipynb\">VAE3</a>\n",
+ " </span> <a href=\"VAE/05-About-CelebA.ipynb\">About the CelebA dataset</a><br>\n",
+ " <span class=\"fid_desc\">Presentation of the CelebA dataset and problems related to its size</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/06-Prepare-CelebA-datasets.ipynb\">VAE6</a>\n",
+ " </span> <a href=\"VAE/06-Prepare-CelebA-datasets.ipynb\">Preparation of the CelebA dataset</a><br>\n",
+ " <span class=\"fid_desc\">Preparation of a clustered dataset, batchable</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/07-Check-CelebA.ipynb\">VAE7</a>\n",
+ " </span> <a href=\"VAE/07-Check-CelebA.ipynb\">Checking the clustered CelebA dataset</a><br>\n",
+ " <span class=\"fid_desc\">Check the clustered dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/08-VAE-with-CelebA==1090048==.ipynb\">VAE8</a>\n",
+ " </span> <a href=\"VAE/08-VAE-with-CelebA==1090048==.ipynb\">Variational AutoEncoder (VAE) with CelebA (small)</a><br>\n",
+ " <span class=\"fid_desc\">Variational AutoEncoder (VAE) with CelebA (small res. 128x128)</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/09-VAE-withCelebA-post.ipynb\">VAE9</a>\n",
+ " </span> <a href=\"VAE/09-VAE-withCelebA-post.ipynb\">Variational AutoEncoder (VAE) with CelebA - Analysis</a><br>\n",
+ " <span class=\"fid_desc\">Exploring latent space of our trained models</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/batch_slurm.sh\">VAE10</a>\n",
+ " </span> <a href=\"VAE/batch_slurm.sh\">SLURM batch script</a><br>\n",
+ " <span class=\"fid_desc\">Bash script for SLURM batch submission of VAE notebooks </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Miscellaneous</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"Misc/Activation-Functions.ipynb\">ACTF1</a>\n",
+ " </span> <a href=\"Misc/Activation-Functions.ipynb\">Activation functions</a><br>\n",
+ " <span class=\"fid_desc\">Some activation functions, with their derivatives.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"Misc/Numpy.ipynb\">NP1</a>\n",
+ " </span> <a href=\"Misc/Numpy.ipynb\">A short introduction to Numpy</a><br>\n",
+ " <span class=\"fid_desc\">Numpy is an essential tool for the Scientific Python.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"Misc/Using-Tensorboard.ipynb\">TSB1</a>\n",
+ " </span> <a href=\"Misc/Using-Tensorboard.ipynb\">Tensorboard with/from Jupyter </a><br>\n",
+ " <span class=\"fid_desc\">4 ways to use Tensorboard from the Jupyter environment</span>\n",
+ " </div>\n",
+ " \n",
"<!-- INDEX_END -->\n",
"\n",
"\n",
diff --git a/README.md b/README.md
index 5aa8de75abe6c264348fc2f7fdebe2be70603d80..3ba2058ce995a8fe788d3b7b763092fd1b016c87 100644
--- a/README.md
+++ b/README.md
@@ -39,40 +39,307 @@ Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble
## Jupyter notebooks
<!-- INDEX_BEGIN -->
-| | |
-|--|--|
-|LINR1| [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br>Direct determination of linear regression |
-|GRAD1| [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br>An example of gradient descent in the simple case of a linear regression.|
-|POLR1| [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br>Illustration of the problem of complexity with the polynomial regression|
-|LOGR1| [Logistic regression, with sklearn](LinearReg/04-Logistic-Regression.ipynb)<br>Logistic Regression using Sklearn|
-|PER57| [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)<br>A simple perceptron, with the IRIS dataset.|
-|MNIST1| [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)<br>Example of classification with a fully connected neural network|
-|GTSRB1| [CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|
-|GTSRB2| [CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br>Episode 2 : First convolutions and first results|
-|GTSRB3| [CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br>Episode 3 : Monitoring and analysing training, managing checkpoints|
-|GTSRB4| [CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br>Episode 4 : Improving the results with data augmentation|
-|GTSRB5| [CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br>Episode 5 : A lot of models, a lot of datasets and a lot of results.|
-|GTSRB6| [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)<br>Episode 6 : Run Full convolution notebook as a batch|
-|GTSRB7| [CNN with GTSRB dataset - Show reports](GTSRB/07-Show-report.ipynb)<br>Episode 7 : Displaying a jobs report|
-|GTSRB10| [OAR batch submission](GTSRB/batch_oar.sh)<br>Bash script for OAR batch submission of GTSRB notebook |
-|GTSRB11| [SLURM batch script](GTSRB/batch_slurm.sh)<br>Bash script for SLURM batch submission of GTSRB notebooks |
-|IMDB1| [Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br>A very classical example of word embedding for text classification (sentiment analysis)|
-|IMDB2| [Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br>Example of reusing a previously saved model|
-|IMDB3| [Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br>Still the same problem, but with a network combining embedding and LSTM|
-|SYNOP1| [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|
-|SYNOP2| [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)<br>Episode 2 : Training session and first predictions|
-|SYNOP3| [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)<br>Episode 3: Attempt to predict in the longer term |
-|VAE1| [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)<br>Building a simple model with the MNIST dataset|
-|VAE2| [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)<br>Visualization and analysis of latent space|
-|VAE3| [About the CelebA dataset](VAE/05-About-CelebA.ipynb)<br>Presentation of the CelebA dataset and problems related to its size|
-|VAE6| [Preparation of the CelebA dataset](VAE/06-Prepare-CelebA-datasets.ipynb)<br>Preparation of a clustered dataset, batchable|
-|VAE7| [Checking the clustered CelebA dataset](VAE/07-Check-CelebA.ipynb)<br>Check the clustered dataset|
-|VAE8| [Variational AutoEncoder (VAE) with CelebA](VAE/08-VAE-with-CelebA.ipynb)<br>Building a VAE and train it, using a data generator|
-|VAE9| [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/09-VAE-withCelebA-post.ipynb)<br>Exploring latent space of our trained models|
-|VAE10| [SLURM batch script](VAE/batch_slurm.sh)<br>Bash script for SLURM batch submission of VAE notebooks |
-|ACTF1| [Activation functions](Misc/Activation-Functions.ipynb)<br>Some activation functions, with their derivatives.|
-|NP1| [A short introduction to Numpy](Misc/Numpy.ipynb)<br>Numpy is an essential tool for the Scientific Python.|
-|TSB1| [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)<br>4 ways to use Tensorboard from the Jupyter environment|
+<style>
+
+.fid_line{
+ padding-top: 10px
+}
+
+.fid_id {
+ font-size:1.em;
+ color:black;
+ font-weight: bold;
+ padding:0px;
+ margin-left: 20px;
+ display: inline-block;
+ width: 60px;
+ }
+
+.fid_desc {
+ font-size:1.em;
+ padding:0px;
+ margin-left: 85px;
+ display: inline-block;
+ width: 600px;
+ }
+
+
+
+div.fid_section {
+ font-size:1.2em;
+ color:black;
+ margin-left: 0px;
+ margin-top: 12px;
+ margin-bottom:8px;
+ border-bottom: solid;
+ border-block-width: 1px;
+ border-block-color: #dadada;
+ width: 700px;
+ }
+
+</style>
+<div class="fid_section">Linear and logistic regression</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="LinearReg/01-Linear-Regression.ipynb">LINR1</a>
+ </span> <a href="LinearReg/01-Linear-Regression.ipynb">Linear regression with direct resolution</a><br>
+ <span class="fid_desc">Direct determination of linear regression </span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="LinearReg/02-Gradient-descent.ipynb">GRAD1</a>
+ </span> <a href="LinearReg/02-Gradient-descent.ipynb">Linear regression with gradient descent</a><br>
+ <span class="fid_desc">An example of gradient descent in the simple case of a linear regression.</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="LinearReg/03-Polynomial-Regression.ipynb">POLR1</a>
+ </span> <a href="LinearReg/03-Polynomial-Regression.ipynb">Complexity Syndrome</a><br>
+ <span class="fid_desc">Illustration of the problem of complexity with the polynomial regression</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="LinearReg/04-Logistic-Regression.ipynb">LOGR1</a>
+ </span> <a href="LinearReg/04-Logistic-Regression.ipynb">Logistic regression, with sklearn</a><br>
+ <span class="fid_desc">Logistic Regression using Sklearn</span>
+ </div>
+
+<div class="fid_section">Perceptron Model 1957</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="IRIS/01-Simple-Perceptron.ipynb">PER57</a>
+ </span> <a href="IRIS/01-Simple-Perceptron.ipynb">Perceptron Model 1957</a><br>
+ <span class="fid_desc">A simple perceptron, with the IRIS dataset.</span>
+ </div>
+
+<div class="fid_section">Basic regression using DNN</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="BHPD/01-DNN-Regression.ipynb">BHPD1</a>
+ </span> <a href="BHPD/01-DNN-Regression.ipynb">Regression with a Dense Network (DNN)</a><br>
+ <span class="fid_desc">A Simple regression with a Dense Neural Network (DNN) - BHPD dataset</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="BHPD/02-DNN-Regression-Premium.ipynb">BHPD2</a>
+ </span> <a href="BHPD/02-DNN-Regression-Premium.ipynb">Regression with a Dense Network (DNN) - Advanced code</a><br>
+ <span class="fid_desc">More advanced example of DNN network code - BHPD dataset</span>
+ </div>
+
+<div class="fid_section">Basic classification using a DNN</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="MNIST/01-DNN-MNIST.ipynb">MNIST1</a>
+ </span> <a href="MNIST/01-DNN-MNIST.ipynb">Simple classification with DNN</a><br>
+ <span class="fid_desc">Example of classification with a fully connected neural network</span>
+ </div>
+
+<div class="fid_section">Images classification with Convolutional Neural Networks (CNN)</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/01-Preparation-of-data.ipynb">GTSRB1</a>
+ </span> <a href="GTSRB/01-Preparation-of-data.ipynb">CNN with GTSRB dataset - Data analysis and preparation</a><br>
+ <span class="fid_desc">Episode 1 : Data analysis and creation of a usable dataset</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/02-First-convolutions.ipynb">GTSRB2</a>
+ </span> <a href="GTSRB/02-First-convolutions.ipynb">CNN with GTSRB dataset - First convolutions</a><br>
+ <span class="fid_desc">Episode 2 : First convolutions and first results</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/03-Tracking-and-visualizing.ipynb">GTSRB3</a>
+ </span> <a href="GTSRB/03-Tracking-and-visualizing.ipynb">CNN with GTSRB dataset - Monitoring </a><br>
+ <span class="fid_desc">Episode 3 : Monitoring and analysing training, managing checkpoints</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/04-Data-augmentation.ipynb">GTSRB4</a>
+ </span> <a href="GTSRB/04-Data-augmentation.ipynb">CNN with GTSRB dataset - Data augmentation </a><br>
+ <span class="fid_desc">Episode 4 : Improving the results with data augmentation</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/05-Full-convolutions.ipynb">GTSRB5</a>
+ </span> <a href="GTSRB/05-Full-convolutions.ipynb">CNN with GTSRB dataset - Full convolutions </a><br>
+ <span class="fid_desc">Episode 5 : A lot of models, a lot of datasets and a lot of results.</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/06-Notebook-as-a-batch.ipynb">GTSRB6</a>
+ </span> <a href="GTSRB/06-Notebook-as-a-batch.ipynb">Full convolutions as a batch</a><br>
+ <span class="fid_desc">Episode 6 : Run Full convolution notebook as a batch</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/07-Show-report.ipynb">GTSRB7</a>
+ </span> <a href="GTSRB/07-Show-report.ipynb">CNN with GTSRB dataset - Show reports</a><br>
+ <span class="fid_desc">Episode 7 : Displaying a jobs report</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/batch_oar.sh">GTSRB10</a>
+ </span> <a href="GTSRB/batch_oar.sh">OAR batch submission</a><br>
+ <span class="fid_desc">Bash script for OAR batch submission of GTSRB notebook </span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="GTSRB/batch_slurm.sh">GTSRB11</a>
+ </span> <a href="GTSRB/batch_slurm.sh">SLURM batch script</a><br>
+ <span class="fid_desc">Bash script for SLURM batch submission of GTSRB notebooks </span>
+ </div>
+
+<div class="fid_section">Sentiment analysis with word embedding</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="IMDB/01-Embedding-Keras.ipynb">IMDB1</a>
+ </span> <a href="IMDB/01-Embedding-Keras.ipynb">Text embedding with IMDB</a><br>
+ <span class="fid_desc">A very classical example of word embedding for text classification (sentiment analysis)</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="IMDB/02-Prediction.ipynb">IMDB2</a>
+ </span> <a href="IMDB/02-Prediction.ipynb">Text embedding with IMDB - Reloaded</a><br>
+ <span class="fid_desc">Example of reusing a previously saved model</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="IMDB/03-LSTM-Keras.ipynb">IMDB3</a>
+ </span> <a href="IMDB/03-LSTM-Keras.ipynb">Text embedding/LSTM model with IMDB</a><br>
+ <span class="fid_desc">Still the same problem, but with a network combining embedding and LSTM</span>
+ </div>
+
+<div class="fid_section">Time series with Recurrent Neural Network (RNN)</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="SYNOP/01-Preparation-of-data.ipynb">SYNOP1</a>
+ </span> <a href="SYNOP/01-Preparation-of-data.ipynb">Time series with RNN - Preparation of data</a><br>
+ <span class="fid_desc">Episode 1 : Data analysis and creation of a usable dataset</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="SYNOP/02-First-predictions.ipynb">SYNOP2</a>
+ </span> <a href="SYNOP/02-First-predictions.ipynb">Time series with RNN - Try a prediction</a><br>
+ <span class="fid_desc">Episode 2 : Training session and first predictions</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="SYNOP/03-12h-predictions.ipynb">SYNOP3</a>
+ </span> <a href="SYNOP/03-12h-predictions.ipynb">Time series with RNN - 12h predictions</a><br>
+ <span class="fid_desc">Episode 3: Attempt to predict in the longer term </span>
+ </div>
+
+<div class="fid_section">Unsupervised learning with an autoencoder neural network (AE)</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="AE/01-AE-with-MNIST.ipynb">AE1</a>
+ </span> <a href="AE/01-AE-with-MNIST.ipynb">AutoEncoder (AE) with MNIST</a><br>
+ <span class="fid_desc">Episode 1 : Model construction and Training</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="AE/02-AE-with-MNIST-post.ipynb">AE2</a>
+ </span> <a href="AE/02-AE-with-MNIST-post.ipynb">AutoEncoder (AE) with MNIST - Analysis</a><br>
+ <span class="fid_desc">Episode 2 : Exploring our denoiser</span>
+ </div>
+
+<div class="fid_section">Generative network with Variational Autoencoder (VAE)</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/01-VAE-with-MNIST.ipynb">VAE1</a>
+ </span> <a href="VAE/01-VAE-with-MNIST.ipynb">Variational AutoEncoder (VAE) with MNIST</a><br>
+ <span class="fid_desc">Building a simple model with the MNIST dataset</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/02-VAE-with-MNIST-post.ipynb">VAE2</a>
+ </span> <a href="VAE/02-VAE-with-MNIST-post.ipynb">Variational AutoEncoder (VAE) with MNIST - Analysis</a><br>
+ <span class="fid_desc">Visualization and analysis of latent space</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/05-About-CelebA.ipynb">VAE3</a>
+ </span> <a href="VAE/05-About-CelebA.ipynb">About the CelebA dataset</a><br>
+ <span class="fid_desc">Presentation of the CelebA dataset and problems related to its size</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/06-Prepare-CelebA-datasets.ipynb">VAE6</a>
+ </span> <a href="VAE/06-Prepare-CelebA-datasets.ipynb">Preparation of the CelebA dataset</a><br>
+ <span class="fid_desc">Preparation of a clustered dataset, batchable</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/07-Check-CelebA.ipynb">VAE7</a>
+ </span> <a href="VAE/07-Check-CelebA.ipynb">Checking the clustered CelebA dataset</a><br>
+ <span class="fid_desc">Check the clustered dataset</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/08-VAE-with-CelebA==1090048==.ipynb">VAE8</a>
+ </span> <a href="VAE/08-VAE-with-CelebA==1090048==.ipynb">Variational AutoEncoder (VAE) with CelebA (small)</a><br>
+ <span class="fid_desc">Variational AutoEncoder (VAE) with CelebA (small res. 128x128)</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/09-VAE-withCelebA-post.ipynb">VAE9</a>
+ </span> <a href="VAE/09-VAE-withCelebA-post.ipynb">Variational AutoEncoder (VAE) with CelebA - Analysis</a><br>
+ <span class="fid_desc">Exploring latent space of our trained models</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="VAE/batch_slurm.sh">VAE10</a>
+ </span> <a href="VAE/batch_slurm.sh">SLURM batch script</a><br>
+ <span class="fid_desc">Bash script for SLURM batch submission of VAE notebooks </span>
+ </div>
+
+<div class="fid_section">Miscellaneous</div>
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="Misc/Activation-Functions.ipynb">ACTF1</a>
+ </span> <a href="Misc/Activation-Functions.ipynb">Activation functions</a><br>
+ <span class="fid_desc">Some activation functions, with their derivatives.</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="Misc/Numpy.ipynb">NP1</a>
+ </span> <a href="Misc/Numpy.ipynb">A short introduction to Numpy</a><br>
+ <span class="fid_desc">Numpy is an essential tool for the Scientific Python.</span>
+ </div>
+
+<div class="fid_line">
+ <span class="fid_id">
+ <a href="Misc/Using-Tensorboard.ipynb">TSB1</a>
+ </span> <a href="Misc/Using-Tensorboard.ipynb">Tensorboard with/from Jupyter </a><br>
+ <span class="fid_desc">4 ways to use Tensorboard from the Jupyter environment</span>
+ </div>
+
<!-- INDEX_END -->
diff --git a/fidle/01 - Set and reset.ipynb b/fidle/01 - Set and reset.ipynb
index 35cc5c5b15994b8a152cffa648038496010cd06f..a2febdacac0c8dc96ec681d750cc6b4db746bd93 100644
--- a/fidle/01 - Set and reset.ipynb
+++ b/fidle/01 - Set and reset.ipynb
@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -52,11 +52,21 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "directories_to_index = ['LinearReg', 'IRIS', 'BHPD', 'MNIST', 'GTSRB', 'IMDB', 'SYNOP', 'VAE', 'Misc']"
+ "directories_to_index = {'LinearReg':'Linear and logistic regression', \n",
+ " 'IRIS':'Perceptron Model 1957', \n",
+ " 'BHPD':'Basic regression using DNN',\n",
+ " 'MNIST':'Basic classification using a DNN',\n",
+ " 'GTSRB':'Images classification with Convolutional Neural Networks (CNN)',\n",
+ " 'IMDB':'Sentiment analysis with word embedding',\n",
+ " 'SYNOP':'Time series with Recurrent Neural Network (RNN)',\n",
+ " 'AE':'Unsupervised learning with an autoencoder neural network (AE)',\n",
+ " 'VAE':'Generative network with Variational Autoencoder (VAE)',\n",
+ " 'Misc':'Miscellaneous'\n",
+ " }"
]
},
{
@@ -69,7 +79,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -77,14 +87,14 @@
"output_type": "stream",
"text": [
"Catalog saved as ../fidle/log/catalog.json\n",
- "Entries : 32\n"
+ "Entries : 36\n"
]
}
],
"source": [
"# ---- Get the notebook list\n",
"#\n",
- "files_list = cooker.get_files(directories_to_index)\n",
+ "files_list = cooker.get_files(directories_to_index.keys())\n",
"\n",
"# ---- Get a detailled catalog for this list\n",
"#\n",
@@ -105,7 +115,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -125,12 +135,16 @@
"text/html": [
"<style>\n",
"\n",
+ ".fid_line{\n",
+ " padding-top: 10px\n",
+ "}\n",
+ "\n",
".fid_id { \n",
" font-size:1.em;\n",
" color:black;\n",
" font-weight: bold; \n",
" padding:0px;\n",
- " padding-left: 0px;\n",
+ " margin-left: 20px;\n",
" display: inline-block;\n",
" width: 60px;\n",
" }\n",
@@ -138,48 +152,288 @@
".fid_desc { \n",
" font-size:1.em;\n",
" padding:0px;\n",
- " padding-left: 60px;\n",
+ " margin-left: 85px;\n",
" display: inline-block;\n",
- " width: 400px;\n",
+ " width: 600px;\n",
" }\n",
"\n",
"\n",
"\n",
"div.fid_section { \n",
- " font-size:1.4em;\n",
- " color:darkgray;\n",
- " padding: 1.em;!important;\n",
+ " font-size:1.2em;\n",
+ " color:black;\n",
+ " margin-left: 0px;\n",
+ " margin-top: 12px;\n",
+ " margin-bottom:8px;\n",
+ " border-bottom: solid;\n",
+ " border-block-width: 1px;\n",
+ " border-block-color: #dadada;\n",
+ " width: 700px;\n",
" }\n",
"\n",
"</style>\n",
"<div class=\"fid_section\">Linear and logistic regression</div>\n",
"<div class=\"fid_line\">\n",
- " <span class=\"fid_id\">LINR1</span> <a href=\"LinearReg/01-Linear-Regression.ipynb\">Linear regression with direct resolution</a><br>\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/01-Linear-Regression.ipynb\">LINR1</a>\n",
+ " </span> <a href=\"LinearReg/01-Linear-Regression.ipynb\">Linear regression with direct resolution</a><br>\n",
" <span class=\"fid_desc\">Direct determination of linear regression </span>\n",
" </div>\n",
" \n",
"<div class=\"fid_line\">\n",
- " <span class=\"fid_id\">GRAD1</span> <a href=\"LinearReg/02-Gradient-descent.ipynb\">Linear regression with gradient descent</a><br>\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/02-Gradient-descent.ipynb\">GRAD1</a>\n",
+ " </span> <a href=\"LinearReg/02-Gradient-descent.ipynb\">Linear regression with gradient descent</a><br>\n",
" <span class=\"fid_desc\">An example of gradient descent in the simple case of a linear regression.</span>\n",
" </div>\n",
" \n",
"<div class=\"fid_line\">\n",
- " <span class=\"fid_id\">POLR1</span> <a href=\"LinearReg/03-Polynomial-Regression.ipynb\">Complexity Syndrome</a><br>\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/03-Polynomial-Regression.ipynb\">POLR1</a>\n",
+ " </span> <a href=\"LinearReg/03-Polynomial-Regression.ipynb\">Complexity Syndrome</a><br>\n",
" <span class=\"fid_desc\">Illustration of the problem of complexity with the polynomial regression</span>\n",
" </div>\n",
" \n",
"<div class=\"fid_line\">\n",
- " <span class=\"fid_id\">LOGR1</span> <a href=\"LinearReg/04-Logistic-Regression.ipynb\">Logistic regression, with sklearn</a><br>\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"LinearReg/04-Logistic-Regression.ipynb\">LOGR1</a>\n",
+ " </span> <a href=\"LinearReg/04-Logistic-Regression.ipynb\">Logistic regression, with sklearn</a><br>\n",
" <span class=\"fid_desc\">Logistic Regression using Sklearn</span>\n",
" </div>\n",
" \n",
"<div class=\"fid_section\">Perceptron Model 1957</div>\n",
"<div class=\"fid_line\">\n",
- " <span class=\"fid_id\">PER57</span> <a href=\"IRIS/01-Simple-Perceptron.ipynb\">Perceptron Model 1957</a><br>\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IRIS/01-Simple-Perceptron.ipynb\">PER57</a>\n",
+ " </span> <a href=\"IRIS/01-Simple-Perceptron.ipynb\">Perceptron Model 1957</a><br>\n",
" <span class=\"fid_desc\">A simple perceptron, with the IRIS dataset.</span>\n",
" </div>\n",
" \n",
- "<div class=\"fid_section\">Basic regression using DNN</div>"
+ "<div class=\"fid_section\">Basic regression using DNN</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"BHPD/01-DNN-Regression.ipynb\">BHPD1</a>\n",
+ " </span> <a href=\"BHPD/01-DNN-Regression.ipynb\">Regression with a Dense Network (DNN)</a><br>\n",
+ " <span class=\"fid_desc\">A Simple regression with a Dense Neural Network (DNN) - BHPD dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"BHPD/02-DNN-Regression-Premium.ipynb\">BHPD2</a>\n",
+ " </span> <a href=\"BHPD/02-DNN-Regression-Premium.ipynb\">Regression with a Dense Network (DNN) - Advanced code</a><br>\n",
+ " <span class=\"fid_desc\">More advanced example of DNN network code - BHPD dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Basic classification using a DNN</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"MNIST/01-DNN-MNIST.ipynb\">MNIST1</a>\n",
+ " </span> <a href=\"MNIST/01-DNN-MNIST.ipynb\">Simple classification with DNN</a><br>\n",
+ " <span class=\"fid_desc\">Example of classification with a fully connected neural network</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Images classification with Convolutional Neural Networks (CNN)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/01-Preparation-of-data.ipynb\">GTSRB1</a>\n",
+ " </span> <a href=\"GTSRB/01-Preparation-of-data.ipynb\">CNN with GTSRB dataset - Data analysis and preparation</a><br>\n",
+ " <span class=\"fid_desc\">Episode 1 : Data analysis and creation of a usable dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/02-First-convolutions.ipynb\">GTSRB2</a>\n",
+ " </span> <a href=\"GTSRB/02-First-convolutions.ipynb\">CNN with GTSRB dataset - First convolutions</a><br>\n",
+ " <span class=\"fid_desc\">Episode 2 : First convolutions and first results</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/03-Tracking-and-visualizing.ipynb\">GTSRB3</a>\n",
+ " </span> <a href=\"GTSRB/03-Tracking-and-visualizing.ipynb\">CNN with GTSRB dataset - Monitoring </a><br>\n",
+ " <span class=\"fid_desc\">Episode 3 : Monitoring and analysing training, managing checkpoints</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/04-Data-augmentation.ipynb\">GTSRB4</a>\n",
+ " </span> <a href=\"GTSRB/04-Data-augmentation.ipynb\">CNN with GTSRB dataset - Data augmentation </a><br>\n",
+ " <span class=\"fid_desc\">Episode 4 : Improving the results with data augmentation</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/05-Full-convolutions.ipynb\">GTSRB5</a>\n",
+ " </span> <a href=\"GTSRB/05-Full-convolutions.ipynb\">CNN with GTSRB dataset - Full convolutions </a><br>\n",
+ " <span class=\"fid_desc\">Episode 5 : A lot of models, a lot of datasets and a lot of results.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/06-Notebook-as-a-batch.ipynb\">GTSRB6</a>\n",
+ " </span> <a href=\"GTSRB/06-Notebook-as-a-batch.ipynb\">Full convolutions as a batch</a><br>\n",
+ " <span class=\"fid_desc\">Episode 6 : Run Full convolution notebook as a batch</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/07-Show-report.ipynb\">GTSRB7</a>\n",
+ " </span> <a href=\"GTSRB/07-Show-report.ipynb\">CNN with GTSRB dataset - Show reports</a><br>\n",
+ " <span class=\"fid_desc\">Episode 7 : Displaying a jobs report</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/batch_oar.sh\">GTSRB10</a>\n",
+ " </span> <a href=\"GTSRB/batch_oar.sh\">OAR batch submission</a><br>\n",
+ " <span class=\"fid_desc\">Bash script for OAR batch submission of GTSRB notebook </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"GTSRB/batch_slurm.sh\">GTSRB11</a>\n",
+ " </span> <a href=\"GTSRB/batch_slurm.sh\">SLURM batch script</a><br>\n",
+ " <span class=\"fid_desc\">Bash script for SLURM batch submission of GTSRB notebooks </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Sentiment analysis with word embedding</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IMDB/01-Embedding-Keras.ipynb\">IMDB1</a>\n",
+ " </span> <a href=\"IMDB/01-Embedding-Keras.ipynb\">Text embedding with IMDB</a><br>\n",
+ " <span class=\"fid_desc\">A very classical example of word embedding for text classification (sentiment analysis)</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IMDB/02-Prediction.ipynb\">IMDB2</a>\n",
+ " </span> <a href=\"IMDB/02-Prediction.ipynb\">Text embedding with IMDB - Reloaded</a><br>\n",
+ " <span class=\"fid_desc\">Example of reusing a previously saved model</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"IMDB/03-LSTM-Keras.ipynb\">IMDB3</a>\n",
+ " </span> <a href=\"IMDB/03-LSTM-Keras.ipynb\">Text embedding/LSTM model with IMDB</a><br>\n",
+ " <span class=\"fid_desc\">Still the same problem, but with a network combining embedding and LSTM</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Time series with Recurrent Neural Network (RNN)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"SYNOP/01-Preparation-of-data.ipynb\">SYNOP1</a>\n",
+ " </span> <a href=\"SYNOP/01-Preparation-of-data.ipynb\">Time series with RNN - Preparation of data</a><br>\n",
+ " <span class=\"fid_desc\">Episode 1 : Data analysis and creation of a usable dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"SYNOP/02-First-predictions.ipynb\">SYNOP2</a>\n",
+ " </span> <a href=\"SYNOP/02-First-predictions.ipynb\">Time series with RNN - Try a prediction</a><br>\n",
+ " <span class=\"fid_desc\">Episode 2 : Training session and first predictions</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"SYNOP/03-12h-predictions.ipynb\">SYNOP3</a>\n",
+ " </span> <a href=\"SYNOP/03-12h-predictions.ipynb\">Time series with RNN - 12h predictions</a><br>\n",
+ " <span class=\"fid_desc\">Episode 3: Attempt to predict in the longer term </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Unsupervised learning with an autoencoder neural network (AE)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"AE/01-AE-with-MNIST.ipynb\">AE1</a>\n",
+ " </span> <a href=\"AE/01-AE-with-MNIST.ipynb\">AutoEncoder (AE) with MNIST</a><br>\n",
+ " <span class=\"fid_desc\">Episode 1 : Model construction and Training</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"AE/02-AE-with-MNIST-post.ipynb\">AE2</a>\n",
+ " </span> <a href=\"AE/02-AE-with-MNIST-post.ipynb\">AutoEncoder (AE) with MNIST - Analysis</a><br>\n",
+ " <span class=\"fid_desc\">Episode 2 : Exploring our denoiser</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Generative network with Variational Autoencoder (VAE)</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/01-VAE-with-MNIST.ipynb\">VAE1</a>\n",
+ " </span> <a href=\"VAE/01-VAE-with-MNIST.ipynb\">Variational AutoEncoder (VAE) with MNIST</a><br>\n",
+ " <span class=\"fid_desc\">Building a simple model with the MNIST dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/02-VAE-with-MNIST-post.ipynb\">VAE2</a>\n",
+ " </span> <a href=\"VAE/02-VAE-with-MNIST-post.ipynb\">Variational AutoEncoder (VAE) with MNIST - Analysis</a><br>\n",
+ " <span class=\"fid_desc\">Visualization and analysis of latent space</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/05-About-CelebA.ipynb\">VAE3</a>\n",
+ " </span> <a href=\"VAE/05-About-CelebA.ipynb\">About the CelebA dataset</a><br>\n",
+ " <span class=\"fid_desc\">Presentation of the CelebA dataset and problems related to its size</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/06-Prepare-CelebA-datasets.ipynb\">VAE6</a>\n",
+ " </span> <a href=\"VAE/06-Prepare-CelebA-datasets.ipynb\">Preparation of the CelebA dataset</a><br>\n",
+ " <span class=\"fid_desc\">Preparation of a clustered dataset, batchable</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/07-Check-CelebA.ipynb\">VAE7</a>\n",
+ " </span> <a href=\"VAE/07-Check-CelebA.ipynb\">Checking the clustered CelebA dataset</a><br>\n",
+ " <span class=\"fid_desc\">Check the clustered dataset</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/08-VAE-with-CelebA==1090048==.ipynb\">VAE8</a>\n",
+ " </span> <a href=\"VAE/08-VAE-with-CelebA==1090048==.ipynb\">Variational AutoEncoder (VAE) with CelebA (small)</a><br>\n",
+ " <span class=\"fid_desc\">Variational AutoEncoder (VAE) with CelebA (small res. 128x128)</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/09-VAE-withCelebA-post.ipynb\">VAE9</a>\n",
+ " </span> <a href=\"VAE/09-VAE-withCelebA-post.ipynb\">Variational AutoEncoder (VAE) with CelebA - Analysis</a><br>\n",
+ " <span class=\"fid_desc\">Exploring latent space of our trained models</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"VAE/batch_slurm.sh\">VAE10</a>\n",
+ " </span> <a href=\"VAE/batch_slurm.sh\">SLURM batch script</a><br>\n",
+ " <span class=\"fid_desc\">Bash script for SLURM batch submission of VAE notebooks </span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_section\">Miscellaneous</div>\n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"Misc/Activation-Functions.ipynb\">ACTF1</a>\n",
+ " </span> <a href=\"Misc/Activation-Functions.ipynb\">Activation functions</a><br>\n",
+ " <span class=\"fid_desc\">Some activation functions, with their derivatives.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"Misc/Numpy.ipynb\">NP1</a>\n",
+ " </span> <a href=\"Misc/Numpy.ipynb\">A short introduction to Numpy</a><br>\n",
+ " <span class=\"fid_desc\">Numpy is an essential tool for the Scientific Python.</span>\n",
+ " </div>\n",
+ " \n",
+ "<div class=\"fid_line\">\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"Misc/Using-Tensorboard.ipynb\">TSB1</a>\n",
+ " </span> <a href=\"Misc/Using-Tensorboard.ipynb\">Tensorboard with/from Jupyter </a><br>\n",
+ " <span class=\"fid_desc\">4 ways to use Tensorboard from the Jupyter environment</span>\n",
+ " </div>\n",
+ " "
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -190,16 +444,11 @@
}
],
"source": [
- "menu = {'LinearReg':'Linear and logistic regression', \n",
- " 'IRIS':'Perceptron Model 1957', \n",
- " 'BHPD':'Basic regression using DNN'}\n",
- "\n",
"styles = open('css/readme.css', \"r\").read()\n",
- "# lines=[styles,'| | |','|--|--|']\n",
"lines=[styles]\n",
"\n",
- "for directory,title in menu.items():\n",
- "# lines.append( f'|<div class=\"tagdir\"></div>| <div class=\"tagdesc\">{title}</div>|')\n",
+ "for directory,title in directories_to_index.items():\n",
+ " \n",
" lines.append( f'<div class=\"fid_section\">{title}</div>')\n",
" entries = { k:v for k,v in catalog.items() if v['dirname']==directory }\n",
"\n",
@@ -211,9 +460,10 @@
" description = about['description']\n",
"\n",
" link=f'{dirname}/{basename}'.replace(' ','%20')\n",
- "# lines.append( f'|{id}| [{title}]({link})<br>{description}|')\n",
" line=f\"\"\"<div class=\"fid_line\">\n",
- " <span class=\"fid_id\">{id}</span> <a href=\"{link}\">{title}</a><br>\n",
+ " <span class=\"fid_id\">\n",
+ " <a href=\"{link}\">{id}</a>\n",
+ " </span> <a href=\"{link}\">{title}</a><br>\n",
" <span class=\"fid_desc\">{description}</span>\n",
" </div>\n",
" \"\"\"\n",
@@ -225,85 +475,15 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "|||\n",
- "|-|-|\n",
- "|aaaa|ba dg dgf dfg d fgdfgr|\n",
- "|<div style=\"font-size:30px\">HJGJR</div>| kjh tretez lkjl kpo tr|"
- ]
- },
- {
- "cell_type": "markdown",
+ "cell_type": "raw",
"metadata": {},
"source": [
"### 3.2 build index"
]
},
{
- "cell_type": "code",
- "execution_count": 50,
+ "cell_type": "raw",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**Index is :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "| | |\n",
- "|--|--|\n",
- "|LINR1| [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br>Direct determination of linear regression |\n",
- "|GRAD1| [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br>An example of gradient descent in the simple case of a linear regression.|\n",
- "|POLR1| [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br>Illustration of the problem of complexity with the polynomial regression|\n",
- "|LOGR1| [Logistic regression, with sklearn](LinearReg/04-Logistic-Regression.ipynb)<br>Logistic Regression using Sklearn|\n",
- "|PER57| [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)<br>A simple perceptron, with the IRIS dataset.|\n",
- "|MNIST1| [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)<br>Example of classification with a fully connected neural network|\n",
- "|GTSRB1| [CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|\n",
- "|GTSRB2| [CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br>Episode 2 : First convolutions and first results|\n",
- "|GTSRB3| [CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br>Episode 3 : Monitoring and analysing training, managing checkpoints|\n",
- "|GTSRB4| [CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br>Episode 4 : Improving the results with data augmentation|\n",
- "|GTSRB5| [CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br>Episode 5 : A lot of models, a lot of datasets and a lot of results.|\n",
- "|GTSRB6| [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)<br>Episode 6 : Run Full convolution notebook as a batch|\n",
- "|GTSRB7| [CNN with GTSRB dataset - Show reports](GTSRB/07-Show-report.ipynb)<br>Episode 7 : Displaying a jobs report|\n",
- "|GTSRB10| [OAR batch submission](GTSRB/batch_oar.sh)<br>Bash script for OAR batch submission of GTSRB notebook |\n",
- "|GTSRB11| [SLURM batch script](GTSRB/batch_slurm.sh)<br>Bash script for SLURM batch submission of GTSRB notebooks |\n",
- "|IMDB1| [Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br>A very classical example of word embedding for text classification (sentiment analysis)|\n",
- "|IMDB2| [Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br>Example of reusing a previously saved model|\n",
- "|IMDB3| [Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br>Still the same problem, but with a network combining embedding and LSTM|\n",
- "|SYNOP1| [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|\n",
- "|SYNOP2| [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)<br>Episode 2 : Training session and first predictions|\n",
- "|SYNOP3| [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)<br>Episode 3: Attempt to predict in the longer term |\n",
- "|VAE1| [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)<br>Building a simple model with the MNIST dataset|\n",
- "|VAE2| [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)<br>Visualization and analysis of latent space|\n",
- "|VAE3| [About the CelebA dataset](VAE/05-About-CelebA.ipynb)<br>Presentation of the CelebA dataset and problems related to its size|\n",
- "|VAE6| [Preparation of the CelebA dataset](VAE/06-Prepare-CelebA-datasets.ipynb)<br>Preparation of a clustered dataset, batchable|\n",
- "|VAE7| [Checking the clustered CelebA dataset](VAE/07-Check-CelebA.ipynb)<br>Check the clustered dataset|\n",
- "|VAE8| [Variational AutoEncoder (VAE) with CelebA](VAE/08-VAE-with-CelebA.ipynb)<br>Building a VAE and train it, using a data generator|\n",
- "|VAE9| [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/09-VAE-withCelebA-post.ipynb)<br>Exploring latent space of our trained models|\n",
- "|VAE10| [SLURM batch script](VAE/batch_slurm.sh)<br>Bash script for SLURM batch submission of VAE notebooks |\n",
- "|ACTF1| [Activation functions](Misc/Activation-Functions.ipynb)<br>Some activation functions, with their derivatives.|\n",
- "|NP1| [A short introduction to Numpy](Misc/Numpy.ipynb)<br>Numpy is an essential tool for the Scientific Python.|\n",
- "|TSB1| [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)<br>4 ways to use Tensorboard from the Jupyter environment|"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
"source": [
"# ---- Create a markdown index\n",
"#\n",
@@ -333,7 +513,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -408,7 +588,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -456,7 +636,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
diff --git a/fidle/cooker.py b/fidle/cooker.py
index e1b39c40b68e13a3ea607d9415a597a0519ef29e..e3e5c36cf44641e40268f4ccc8acf1db9cb630db 100644
--- a/fidle/cooker.py
+++ b/fidle/cooker.py
@@ -44,7 +44,7 @@ def get_files(directories, top_dir='..'):
files = []
regex = re.compile('.*==\d+==.*')
- for d in directories:
+ for d in directories:
notebooks = glob.glob( f'{top_dir}/{d}/*.ipynb')
notebooks.sort()
scripts = glob.glob( f'{top_dir}/{d}/*.sh')
@@ -52,7 +52,7 @@ def get_files(directories, top_dir='..'):
files.extend(notebooks)
files.extend(scripts)
- files = [x for x in files if not regex.match(x)]
+# files = [x for x in files if not regex.match(x)]
files = [ x.replace(f'{top_dir}/','') for x in files]
return files
diff --git a/fidle/css/readme.css b/fidle/css/readme.css
index dc2d105ca10f62b717cfe553b3c31dfdd15b6e38..b38d9d0e7427311fb944f391a01e9ad96ef4a82a 100644
--- a/fidle/css/readme.css
+++ b/fidle/css/readme.css
@@ -1,11 +1,15 @@
<style>
+.fid_line{
+ padding-top: 10px
+}
+
.fid_id {
font-size:1.em;
color:black;
font-weight: bold;
padding:0px;
- padding-left: 0px;
+ margin-left: 20px;
display: inline-block;
width: 60px;
}
@@ -13,17 +17,23 @@
.fid_desc {
font-size:1.em;
padding:0px;
- padding-left: 60px;
+ margin-left: 85px;
display: inline-block;
- width: 400px;
+ width: 600px;
}
div.fid_section {
- font-size:1.4em;
- color:darkgray;
- padding: 1.em;!important;
+ font-size:1.2em;
+ color:black;
+ margin-left: 0px;
+ margin-top: 12px;
+ margin-bottom:8px;
+ border-bottom: solid;
+ border-block-width: 1px;
+ border-block-color: #dadada;
+ width: 700px;
}
</style>
\ No newline at end of file
diff --git a/fidle/log/catalog.json b/fidle/log/catalog.json
index bc9e0090bdda120aa59bfeb1623d6c4de4bdf91e..8e5d391ee25c901305a85b4c862d1277d7385676 100644
--- a/fidle/log/catalog.json
+++ b/fidle/log/catalog.json
@@ -34,6 +34,20 @@
"title": "Perceptron Model 1957",
"description": "A simple perceptron, with the IRIS dataset."
},
+ "BHPD1": {
+ "id": "BHPD1",
+ "dirname": "BHPD",
+ "basename": "01-DNN-Regression.ipynb",
+ "title": "Regression with a Dense Network (DNN)",
+ "description": "A Simple regression with a Dense Neural Network (DNN) - BHPD dataset"
+ },
+ "BHPD2": {
+ "id": "BHPD2",
+ "dirname": "BHPD",
+ "basename": "02-DNN-Regression-Premium.ipynb",
+ "title": "Regression with a Dense Network (DNN) - Advanced code",
+ "description": "More advanced example of DNN network code - BHPD dataset"
+ },
"MNIST1": {
"id": "MNIST1",
"dirname": "MNIST",
@@ -146,6 +160,20 @@
"title": "Time series with RNN - 12h predictions",
"description": "Episode 3: Attempt to predict in the longer term "
},
+ "AE1": {
+ "id": "AE1",
+ "dirname": "AE",
+ "basename": "01-AE-with-MNIST.ipynb",
+ "title": "AutoEncoder (AE) with MNIST",
+ "description": "Episode 1 : Model construction and Training"
+ },
+ "AE2": {
+ "id": "AE2",
+ "dirname": "AE",
+ "basename": "02-AE-with-MNIST-post.ipynb",
+ "title": "AutoEncoder (AE) with MNIST - Analysis",
+ "description": "Episode 2 : Exploring our denoiser"
+ },
"VAE1": {
"id": "VAE1",
"dirname": "VAE",
@@ -184,9 +212,9 @@
"VAE8": {
"id": "VAE8",
"dirname": "VAE",
- "basename": "08-VAE-with-CelebA.ipynb",
- "title": "Variational AutoEncoder (VAE) with CelebA",
- "description": "Building a VAE and train it, using a data generator"
+ "basename": "08-VAE-with-CelebA==1090048==.ipynb",
+ "title": "Variational AutoEncoder (VAE) with CelebA (small)",
+ "description": "Variational AutoEncoder (VAE) with CelebA (small res. 128x128)"
},
"VAE9": {
"id": "VAE9",