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"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\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",
" - 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",
"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",
" - 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",
" - Retrieve data\n",
" - Preparing the data\n",
" - Build a model\n",
" - Train the model\n",
" - Evaluate the result\n"
]
},
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"\n",
"FIDLE 2020 - Practical Work Module\n",
"TensorFlow version : 2.2.0\n",
"Keras version : 2.3.0-tf\n",
"Current place : Fidle at IDRIS\n",
"Datasets dir : /gpfswork/rech/mlh/commun/datasets\n",
"Update keras cache : Done\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",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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,
"# (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,
"outputs": [
{
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"<style type=\"text/css\" >\n",
"</style><table id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63b\" ><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",
" <th id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >0.01</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >18.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >2.31</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >0.54</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >6.58</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >65.20</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >4.09</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >1.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >296.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >15.30</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >396.90</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >4.98</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" <th id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >7.07</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >0.47</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >6.42</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >78.90</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >4.97</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >2.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >242.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >17.80</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >396.90</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >9.14</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" <th id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >0.03</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >7.07</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >0.47</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >7.18</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >61.10</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >4.97</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >2.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >242.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >17.80</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >392.83</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >4.03</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" <th id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >0.03</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >2.18</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >0.46</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >7.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >45.80</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >6.06</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >3.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >222.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >18.70</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >394.63</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >2.94</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" <th id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >0.07</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >2.18</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >0.46</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >7.15</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >54.20</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >6.06</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >3.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >222.00</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >18.70</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >396.90</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >5.33</td>\n",
" <td id=\"T_0738d7b2_f3fb_11ea_bd64_0cc47af5c63brow4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\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,
"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"
]
}
],
"# ---- 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": [
"**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,
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
"</style><table id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63b\" ><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",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >3.46</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >11.04</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >11.44</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.08</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >0.56</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >6.27</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >68.25</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >3.81</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >9.74</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >413.56</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >18.49</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >358.39</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >12.71</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >7.75</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >22.76</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >6.87</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >0.27</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >0.12</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >0.68</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >28.72</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >2.08</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >8.75</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >167.90</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >2.09</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >87.89</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >6.95</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >0.01</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >0.46</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >0.39</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >3.56</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >2.90</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >1.13</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >1.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >187.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >12.60</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >0.32</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >0.08</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >5.22</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >0.45</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >5.88</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >42.23</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >2.11</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >4.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >284.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >17.07</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >376.25</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >7.04</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col0\" class=\"data row5 col0\" >0.25</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col1\" class=\"data row5 col1\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col2\" class=\"data row5 col2\" >9.90</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col3\" class=\"data row5 col3\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col4\" class=\"data row5 col4\" >0.54</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col5\" class=\"data row5 col5\" >6.21</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col6\" class=\"data row5 col6\" >77.15</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col7\" class=\"data row5 col7\" >3.21</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col8\" class=\"data row5 col8\" >5.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col9\" class=\"data row5 col9\" >351.50</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col10\" class=\"data row5 col10\" >19.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col11\" class=\"data row5 col11\" >391.06</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow5_col12\" class=\"data row5 col12\" >11.70</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col0\" class=\"data row6 col0\" >3.82</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col1\" class=\"data row6 col1\" >12.50</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col2\" class=\"data row6 col2\" >18.10</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col3\" class=\"data row6 col3\" >0.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col4\" class=\"data row6 col4\" >0.62</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col5\" class=\"data row6 col5\" >6.63</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col6\" class=\"data row6 col6\" >93.97</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col7\" class=\"data row6 col7\" >5.29</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col8\" class=\"data row6 col8\" >24.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col9\" class=\"data row6 col9\" >666.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col10\" class=\"data row6 col10\" >20.20</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col11\" class=\"data row6 col11\" >395.76</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow6_col12\" class=\"data row6 col12\" >17.14</td>\n",
" <th id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63blevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col0\" class=\"data row7 col0\" >73.53</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col1\" class=\"data row7 col1\" >100.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col2\" class=\"data row7 col2\" >27.74</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col3\" class=\"data row7 col3\" >1.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col4\" class=\"data row7 col4\" >0.87</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col5\" class=\"data row7 col5\" >8.78</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col6\" class=\"data row7 col6\" >100.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col7\" class=\"data row7 col7\" >12.13</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col8\" class=\"data row7 col8\" >24.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col9\" class=\"data row7 col9\" >711.00</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col10\" class=\"data row7 col10\" >21.20</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_col11\" class=\"data row7 col11\" >396.90</td>\n",
" <td id=\"T_0744ba78_f3fb_11ea_bd64_0cc47af5c63brow7_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_074d8df6_f3fb_11ea_bd64_0cc47af5c63b\" ><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",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >0.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >-0.45</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >-1.60</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >-0.29</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >-1.46</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >-4.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >-2.28</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >-1.29</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >-1.00</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >-1.35</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >-2.82</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >-4.07</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >-1.58</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >-0.91</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >-0.29</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >-0.58</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >-0.91</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >-0.66</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >-0.68</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >0.20</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >-0.82</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col0\" class=\"data row5 col0\" >-0.41</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col2\" class=\"data row5 col2\" >-0.22</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col3\" class=\"data row5 col3\" >-0.29</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col5\" class=\"data row5 col5\" >-0.09</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col6\" class=\"data row5 col6\" >0.31</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col7\" class=\"data row5 col7\" >-0.29</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col8\" class=\"data row5 col8\" >-0.54</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col9\" class=\"data row5 col9\" >-0.37</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col10\" class=\"data row5 col10\" >0.25</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col11\" class=\"data row5 col11\" >0.37</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow5_col12\" class=\"data row5 col12\" >-0.15</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col0\" class=\"data row6 col0\" >0.05</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col1\" class=\"data row6 col1\" >0.06</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col2\" class=\"data row6 col2\" >0.97</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col3\" class=\"data row6 col3\" >-0.29</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col4\" class=\"data row6 col4\" >0.57</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col5\" class=\"data row6 col5\" >0.52</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col6\" class=\"data row6 col6\" >0.90</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col7\" class=\"data row6 col7\" >0.71</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col8\" class=\"data row6 col8\" >1.63</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col9\" class=\"data row6 col9\" >1.50</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col10\" class=\"data row6 col10\" >0.82</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col11\" class=\"data row6 col11\" >0.43</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow6_col12\" class=\"data row6 col12\" >0.64</td>\n",
" <th id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63blevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col0\" class=\"data row7 col0\" >9.04</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col1\" class=\"data row7 col1\" >3.91</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col2\" class=\"data row7 col2\" >2.37</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col3\" class=\"data row7 col3\" >3.48</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col4\" class=\"data row7 col4\" >2.67</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col5\" class=\"data row7 col5\" >3.69</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col6\" class=\"data row7 col6\" >1.11</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col7\" class=\"data row7 col7\" >3.99</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col8\" class=\"data row7 col8\" >1.63</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col9\" class=\"data row7 col9\" >1.77</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col10\" class=\"data row7 col10\" >1.30</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col11\" class=\"data row7 col11\" >0.44</td>\n",
" <td id=\"T_074d8df6_f3fb_11ea_bd64_0cc47af5c63brow7_col12\" class=\"data row7 col12\" >3.63</td>\n",
" </tr>\n",
" </tbody></table>"
],
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"text/html": [
"<style type=\"text/css\" >\n",
"</style><table id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63b\" ><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",
" <th id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63blevel0_row0\" class=\"row_heading level0 row0\" >433</th>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col0\" class=\"data row0 col0\" >0.27</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col1\" class=\"data row0 col1\" >-0.49</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col2\" class=\"data row0 col2\" >0.97</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col3\" class=\"data row0 col3\" >-0.29</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col4\" class=\"data row0 col4\" >1.33</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col5\" class=\"data row0 col5\" >0.24</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col6\" class=\"data row0 col6\" >0.68</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col7\" class=\"data row0 col7\" >-0.72</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col8\" class=\"data row0 col8\" >1.63</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col9\" class=\"data row0 col9\" >1.50</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col10\" class=\"data row0 col10\" >0.82</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col11\" class=\"data row0 col11\" >-2.94</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow0_col12\" class=\"data row0 col12\" >0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63blevel0_row1\" class=\"row_heading level0 row1\" >200</th>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col0\" class=\"data row1 col0\" >-0.44</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col1\" class=\"data row1 col1\" >3.69</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col2\" class=\"data row1 col2\" >-1.45</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col3\" class=\"data row1 col3\" >-0.29</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col4\" class=\"data row1 col4\" >-1.31</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col5\" class=\"data row1 col5\" >1.27</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col6\" class=\"data row1 col6\" >-1.89</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col7\" class=\"data row1 col7\" >1.84</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col8\" class=\"data row1 col8\" >-0.77</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col9\" class=\"data row1 col9\" >-0.07</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col10\" class=\"data row1 col10\" >-0.71</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col11\" class=\"data row1 col11\" >0.29</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow1_col12\" class=\"data row1 col12\" >-1.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63blevel0_row2\" class=\"row_heading level0 row2\" >471</th>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col0\" class=\"data row2 col0\" >0.07</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col1\" class=\"data row2 col1\" >-0.49</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col2\" class=\"data row2 col2\" >0.97</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col3\" class=\"data row2 col3\" >-0.29</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col4\" class=\"data row2 col4\" >-0.21</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col5\" class=\"data row2 col5\" >-0.07</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col6\" class=\"data row2 col6\" >0.78</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col7\" class=\"data row2 col7\" >-0.34</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col8\" class=\"data row2 col8\" >1.63</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col9\" class=\"data row2 col9\" >1.50</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col10\" class=\"data row2 col10\" >0.82</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col11\" class=\"data row2 col11\" >0.42</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow2_col12\" class=\"data row2 col12\" >0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63blevel0_row3\" class=\"row_heading level0 row3\" >344</th>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col0\" class=\"data row3 col0\" >-0.44</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col1\" class=\"data row3 col1\" >1.93</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col2\" class=\"data row3 col2\" >-1.11</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col3\" class=\"data row3 col3\" >-0.29</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col4\" class=\"data row3 col4\" >-0.62</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col5\" class=\"data row3 col5\" >0.88</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col6\" class=\"data row3 col6\" >-1.40</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col7\" class=\"data row3 col7\" >1.27</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col8\" class=\"data row3 col8\" >-0.54</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col9\" class=\"data row3 col9\" >-0.26</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col10\" class=\"data row3 col10\" >-0.43</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col11\" class=\"data row3 col11\" >0.34</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow3_col12\" class=\"data row3 col12\" >-1.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63blevel0_row4\" class=\"row_heading level0 row4\" >374</th>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col0\" class=\"data row4 col0\" >1.94</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col2\" class=\"data row4 col2\" >0.97</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col3\" class=\"data row4 col3\" >-0.29</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col4\" class=\"data row4 col4\" >0.95</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col5\" class=\"data row4 col5\" >-3.15</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col6\" class=\"data row4 col6\" >1.11</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col7\" class=\"data row4 col7\" >-1.28</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col8\" class=\"data row4 col8\" >1.63</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col9\" class=\"data row4 col9\" >1.50</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col10\" class=\"data row4 col10\" >0.82</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col11\" class=\"data row4 col11\" >0.44</td>\n",
" <td id=\"T_074e709a_f3fb_11ea_bd64_0cc47af5c63brow4_col12\" class=\"data row4 col12\" >3.63</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
]
},
"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": [
"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,
"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",
"Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
]
},
{
"data": {
"text/plain": [
"source": [
"model=get_model_v1( (13,) )\n",
"\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": [
]
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"36/36 [==============================] - 0s 9ms/step - loss: 446.6037 - mae: 19.2578 - mse: 446.6037 - val_loss: 386.7883 - val_mae: 17.0661 - val_mse: 386.7883\n",
"36/36 [==============================] - 0s 3ms/step - loss: 218.0804 - mae: 12.4885 - mse: 218.0804 - val_loss: 154.0005 - val_mae: 9.9977 - val_mse: 154.0005\n",
"36/36 [==============================] - 0s 3ms/step - loss: 77.5444 - mae: 6.8722 - mse: 77.5444 - val_loss: 67.8402 - val_mae: 6.1398 - val_mse: 67.8402\n",
"36/36 [==============================] - 0s 3ms/step - loss: 39.4528 - mae: 4.8223 - mse: 39.4528 - val_loss: 42.8524 - val_mae: 4.5159 - val_mse: 42.8524\n",
"36/36 [==============================] - 0s 3ms/step - loss: 24.7109 - mae: 3.6920 - mse: 24.7109 - val_loss: 33.6560 - val_mae: 3.9751 - val_mse: 33.6560\n",
"36/36 [==============================] - 0s 3ms/step - loss: 20.0575 - mae: 3.2955 - mse: 20.0575 - val_loss: 28.1081 - val_mae: 3.4396 - val_mse: 28.1081\n",
"36/36 [==============================] - 0s 3ms/step - loss: 17.5427 - mae: 3.0599 - mse: 17.5427 - val_loss: 25.3431 - val_mae: 3.3389 - val_mse: 25.3431\n",
"36/36 [==============================] - 0s 3ms/step - loss: 15.4941 - mae: 2.8394 - mse: 15.4941 - val_loss: 22.9326 - val_mae: 3.1904 - val_mse: 22.9326\n",
"36/36 [==============================] - 0s 3ms/step - loss: 14.3105 - mae: 2.6853 - mse: 14.3105 - val_loss: 22.8496 - val_mae: 3.2345 - val_mse: 22.8496\n",
"36/36 [==============================] - 0s 3ms/step - loss: 13.1828 - mae: 2.5744 - mse: 13.1828 - val_loss: 20.5914 - val_mae: 3.0849 - val_mse: 20.5914\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.5782 - mae: 2.4991 - mse: 12.5782 - val_loss: 19.7353 - val_mae: 3.0247 - val_mse: 19.7353\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.1698 - mae: 2.4564 - mse: 12.1698 - val_loss: 18.8873 - val_mae: 2.9056 - val_mse: 18.8873\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.7293 - mae: 2.3903 - mse: 11.7293 - val_loss: 19.1095 - val_mae: 2.8887 - val_mse: 19.1095\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.3441 - mae: 2.3263 - mse: 11.3441 - val_loss: 18.5024 - val_mae: 2.8737 - val_mse: 18.5024\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.8598 - mae: 2.2824 - mse: 10.8598 - val_loss: 19.2043 - val_mae: 2.9070 - val_mse: 19.2043\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.5605 - mae: 2.2494 - mse: 10.5605 - val_loss: 17.8935 - val_mae: 2.8521 - val_mse: 17.8935\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.4829 - mae: 2.2239 - mse: 10.4829 - val_loss: 18.2009 - val_mae: 2.9156 - val_mse: 18.2009\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.0201 - mae: 2.1768 - mse: 10.0201 - val_loss: 18.8540 - val_mae: 2.9471 - val_mse: 18.8540\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.8773 - mae: 2.1541 - mse: 9.8773 - val_loss: 17.9174 - val_mae: 2.9169 - val_mse: 17.9174\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.7499 - mae: 2.1822 - mse: 9.7499 - val_loss: 17.6539 - val_mae: 2.8095 - val_mse: 17.6539\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.4263 - mae: 2.1179 - mse: 9.4263 - val_loss: 17.3389 - val_mae: 2.8069 - val_mse: 17.3389\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.2461 - mae: 2.1105 - mse: 9.2461 - val_loss: 17.3363 - val_mae: 2.7985 - val_mse: 17.3363\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.2967 - mae: 2.0983 - mse: 9.2967 - val_loss: 17.7068 - val_mae: 2.8551 - val_mse: 17.7068\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.0171 - mae: 2.0590 - mse: 9.0171 - val_loss: 17.6013 - val_mae: 2.8819 - val_mse: 17.6013\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.0039 - mae: 2.0880 - mse: 9.0039 - val_loss: 18.1039 - val_mae: 2.8558 - val_mse: 18.1039\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.6806 - mae: 2.0975 - mse: 8.6806 - val_loss: 17.1357 - val_mae: 2.7136 - val_mse: 17.1357\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.5762 - mae: 2.0289 - mse: 8.5762 - val_loss: 17.1106 - val_mae: 2.7783 - val_mse: 17.1106\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.6417 - mae: 2.0260 - mse: 8.6417 - val_loss: 17.0406 - val_mae: 2.7918 - val_mse: 17.0406\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.5833 - mae: 2.0244 - mse: 8.5833 - val_loss: 18.0475 - val_mae: 2.8360 - val_mse: 18.0475\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.2054 - mae: 2.0043 - mse: 8.2054 - val_loss: 17.0133 - val_mae: 2.7095 - val_mse: 17.0133\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.2301 - mae: 1.9699 - mse: 8.2301 - val_loss: 18.3437 - val_mae: 2.9549 - val_mse: 18.3437\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.9471 - mae: 1.9438 - mse: 7.9471 - val_loss: 19.1730 - val_mae: 3.0434 - val_mse: 19.1730\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.0745 - mae: 1.9784 - mse: 8.0745 - val_loss: 16.7722 - val_mae: 2.7913 - val_mse: 16.7722\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.7404 - mae: 1.9509 - mse: 7.7404 - val_loss: 16.2314 - val_mae: 2.7512 - val_mse: 16.2314\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.7764 - mae: 1.9542 - mse: 7.7764 - val_loss: 17.5090 - val_mae: 2.8030 - val_mse: 17.5090\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.6741 - mae: 1.8898 - mse: 7.6741 - val_loss: 16.4816 - val_mae: 2.7928 - val_mse: 16.4816\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.6694 - mae: 1.9532 - mse: 7.6694 - val_loss: 16.6889 - val_mae: 2.7137 - val_mse: 16.6889\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.5041 - mae: 1.9462 - mse: 7.5041 - val_loss: 16.7302 - val_mae: 2.6825 - val_mse: 16.7302\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.2888 - mae: 1.8822 - mse: 7.2888 - val_loss: 16.4285 - val_mae: 2.7840 - val_mse: 16.4285\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.2930 - mae: 1.8663 - mse: 7.2930 - val_loss: 16.8343 - val_mae: 2.7821 - val_mse: 16.8343\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.2549 - mae: 1.8683 - mse: 7.2549 - val_loss: 15.7884 - val_mae: 2.7318 - val_mse: 15.7884\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.0984 - mae: 1.8449 - mse: 7.0984 - val_loss: 17.2381 - val_mae: 2.8447 - val_mse: 17.2381\n",
"36/36 [==============================] - 0s 3ms/step - loss: 7.2413 - mae: 1.8850 - mse: 7.2413 - val_loss: 16.0552 - val_mae: 2.6502 - val_mse: 16.0552\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.9367 - mae: 1.8398 - mse: 6.9367 - val_loss: 17.2112 - val_mae: 2.8687 - val_mse: 17.2112\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.9827 - mae: 1.8430 - mse: 6.9827 - val_loss: 17.0917 - val_mae: 2.8490 - val_mse: 17.0917\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.9057 - mae: 1.8588 - mse: 6.9057 - val_loss: 15.8673 - val_mae: 2.6752 - val_mse: 15.8673\n",
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"36/36 [==============================] - 0s 3ms/step - loss: 6.6772 - mae: 1.8257 - mse: 6.6772 - val_loss: 15.8700 - val_mae: 2.6894 - val_mse: 15.8700\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.7987 - mae: 1.8268 - mse: 6.7987 - val_loss: 15.9252 - val_mae: 2.6604 - val_mse: 15.9252\n",
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"36/36 [==============================] - 0s 3ms/step - loss: 6.4992 - mae: 1.7764 - mse: 6.4992 - val_loss: 17.6725 - val_mae: 2.9236 - val_mse: 17.6725\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.4255 - mae: 1.7977 - mse: 6.4255 - val_loss: 15.9662 - val_mae: 2.6543 - val_mse: 15.9662\n",
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"36/36 [==============================] - 0s 3ms/step - loss: 6.0313 - mae: 1.7406 - mse: 6.0313 - val_loss: 16.0014 - val_mae: 2.6532 - val_mse: 16.0014\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.0825 - mae: 1.7771 - mse: 6.0825 - val_loss: 17.4272 - val_mae: 2.8013 - val_mse: 17.4272\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.9886 - mae: 1.7806 - mse: 5.9886 - val_loss: 15.6423 - val_mae: 2.6311 - val_mse: 15.6423\n",
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"36/36 [==============================] - 0s 3ms/step - loss: 6.0186 - mae: 1.7272 - mse: 6.0186 - val_loss: 16.2887 - val_mae: 2.7315 - val_mse: 16.2887\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.6992 - mae: 1.6959 - mse: 5.6992 - val_loss: 16.2439 - val_mae: 2.6728 - val_mse: 16.2439\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.7267 - mae: 1.6931 - mse: 5.7267 - val_loss: 17.3420 - val_mae: 2.9330 - val_mse: 17.3420\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.5663 - mae: 1.6936 - mse: 5.5663 - val_loss: 15.8615 - val_mae: 2.6978 - val_mse: 15.8615\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.6906 - mae: 1.7106 - mse: 5.6906 - val_loss: 16.3725 - val_mae: 2.7047 - val_mse: 16.3725\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.5431 - mae: 1.6698 - mse: 5.5431 - val_loss: 16.4046 - val_mae: 2.7959 - val_mse: 16.4046\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.5527 - mae: 1.6936 - mse: 5.5527 - val_loss: 15.5068 - val_mae: 2.6383 - val_mse: 15.5068\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.4648 - mae: 1.6626 - mse: 5.4648 - val_loss: 16.5591 - val_mae: 2.7705 - val_mse: 16.5591\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.4885 - mae: 1.6831 - mse: 5.4885 - val_loss: 16.0854 - val_mae: 2.7222 - val_mse: 16.0854\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.4027 - mae: 1.6897 - mse: 5.4027 - val_loss: 16.1947 - val_mae: 2.7365 - val_mse: 16.1947\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.4546 - mae: 1.6858 - mse: 5.4546 - val_loss: 15.5937 - val_mae: 2.6997 - val_mse: 15.5937\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.4391 - mae: 1.6800 - mse: 5.4391 - val_loss: 15.3495 - val_mae: 2.6369 - val_mse: 15.3495\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.2675 - mae: 1.6067 - mse: 5.2675 - val_loss: 15.3150 - val_mae: 2.6281 - val_mse: 15.3150\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.0789 - mae: 1.6050 - mse: 5.0789 - val_loss: 15.6628 - val_mae: 2.7041 - val_mse: 15.6628\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.1433 - mae: 1.6192 - mse: 5.1433 - val_loss: 15.6630 - val_mae: 2.7125 - val_mse: 15.6630\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.2613 - mae: 1.6272 - mse: 5.2613 - val_loss: 15.5012 - val_mae: 2.6684 - val_mse: 15.5012\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.0384 - mae: 1.5870 - mse: 5.0384 - val_loss: 15.4386 - val_mae: 2.5987 - val_mse: 15.4386\n",
"36/36 [==============================] - 0s 3ms/step - loss: 4.8677 - mae: 1.6115 - mse: 4.8677 - val_loss: 14.8730 - val_mae: 2.5933 - val_mse: 14.8730\n",