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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/uja62cb/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_82c24dc4_f657_11ea_a7d3_0cc47af5c763\" ><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_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" <th id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
" <td id=\"T_82c24dc4_f657_11ea_a7d3_0cc47af5c763row4_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_82cbb4ae_f657_11ea_a7d3_0cc47af5c763\" ><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_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >3.74</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >10.51</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >11.22</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >6.29</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >69.82</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >3.72</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >9.62</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >407.45</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >18.46</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >353.89</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >12.75</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >8.87</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >22.27</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >6.75</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >0.72</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >27.50</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >2.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >8.71</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >167.90</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >2.19</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >95.77</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >7.23</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >1.17</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >5.90</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >47.25</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >2.09</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >374.83</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >6.86</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col2\" class=\"data row5 col2\" >9.79</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col5\" class=\"data row5 col5\" >6.20</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col6\" class=\"data row5 col6\" >79.50</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col7\" class=\"data row5 col7\" >3.16</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col10\" class=\"data row5 col10\" >19.05</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col11\" class=\"data row5 col11\" >391.96</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row5_col12\" class=\"data row5 col12\" >11.49</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col0\" class=\"data row6 col0\" >3.69</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col6\" class=\"data row6 col6\" >94.05</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col7\" class=\"data row6 col7\" >4.98</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col11\" class=\"data row6 col11\" >396.32</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row6_col12\" class=\"data row6 col12\" >17.10</td>\n",
" <th id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
" <td id=\"T_82cbb4ae_f657_11ea_a7d3_0cc47af5c763row7_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_82dc5bc4_f657_11ea_a7d3_0cc47af5c763\" ><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_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >-0.42</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >-1.48</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >-1.48</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >-3.80</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >-2.43</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >-1.28</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >-1.31</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >-2.68</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >-3.67</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >-0.41</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >-0.47</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >-0.54</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >-0.82</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >-0.48</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >0.22</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >-0.81</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col1\" class=\"data row5 col1\" >-0.47</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col7\" class=\"data row5 col7\" >-0.28</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col10\" class=\"data row5 col10\" >0.27</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row5_col12\" class=\"data row5 col12\" >-0.17</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col0\" class=\"data row6 col0\" >-0.01</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col1\" class=\"data row6 col1\" >0.09</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col5\" class=\"data row6 col5\" >0.48</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col6\" class=\"data row6 col6\" >0.88</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col7\" class=\"data row6 col7\" >0.63</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col8\" class=\"data row6 col8\" >1.65</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col9\" class=\"data row6 col9\" >1.54</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col10\" class=\"data row6 col10\" >0.80</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row6_col12\" class=\"data row6 col12\" >0.60</td>\n",
" <th id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col0\" class=\"data row7 col0\" >9.60</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col1\" class=\"data row7 col1\" >4.02</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col2\" class=\"data row7 col2\" >2.45</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col4\" class=\"data row7 col4\" >2.79</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col5\" class=\"data row7 col5\" >3.48</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col6\" class=\"data row7 col6\" >1.10</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col7\" class=\"data row7 col7\" >3.49</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col8\" class=\"data row7 col8\" >1.65</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col9\" class=\"data row7 col9\" >1.81</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col10\" class=\"data row7 col10\" >1.62</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col11\" class=\"data row7 col11\" >0.45</td>\n",
" <td id=\"T_82dc5bc4_f657_11ea_a7d3_0cc47af5c763row7_col12\" class=\"data row7 col12\" >3.35</td>\n",
" </tr>\n",
" </tbody></table>"
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"text/html": [
"<style type=\"text/css\" >\n",
"</style><table id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763\" ><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_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row0\" class=\"row_heading level0 row0\" >256</th>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col0\" class=\"data row0 col0\" >-0.42</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col1\" class=\"data row0 col1\" >3.57</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col2\" class=\"data row0 col2\" >-1.11</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col3\" class=\"data row0 col3\" >-0.26</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col4\" class=\"data row0 col4\" >-1.44</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col5\" class=\"data row0 col5\" >1.63</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col6\" class=\"data row0 col6\" >-1.30</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col7\" class=\"data row0 col7\" >1.31</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col8\" class=\"data row0 col8\" >-0.76</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col9\" class=\"data row0 col9\" >-0.97</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col10\" class=\"data row0 col10\" >-1.17</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col11\" class=\"data row0 col11\" >0.34</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row0_col12\" class=\"data row0 col12\" >-1.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row1\" class=\"row_heading level0 row1\" >124</th>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col0\" class=\"data row1 col0\" >-0.41</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col1\" class=\"data row1 col1\" >-0.47</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col2\" class=\"data row1 col2\" >2.14</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col3\" class=\"data row1 col3\" >-0.26</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col4\" class=\"data row1 col4\" >0.22</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col5\" class=\"data row1 col5\" >-0.57</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col6\" class=\"data row1 col6\" >0.94</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col7\" class=\"data row1 col7\" >-0.86</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col8\" class=\"data row1 col8\" >-0.88</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col9\" class=\"data row1 col9\" >-1.31</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col10\" class=\"data row1 col10\" >0.29</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col11\" class=\"data row1 col11\" >0.27</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row1_col12\" class=\"data row1 col12\" >0.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row2\" class=\"row_heading level0 row2\" >268</th>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col0\" class=\"data row2 col0\" >-0.36</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col1\" class=\"data row2 col1\" >0.43</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col2\" class=\"data row2 col2\" >-1.07</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col3\" class=\"data row2 col3\" >-0.26</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col4\" class=\"data row2 col4\" >0.17</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col5\" class=\"data row2 col5\" >1.65</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col6\" class=\"data row2 col6\" >-0.63</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col7\" class=\"data row2 col7\" >-0.43</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col8\" class=\"data row2 col8\" >-0.53</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col9\" class=\"data row2 col9\" >-0.85</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col10\" class=\"data row2 col10\" >-2.50</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col11\" class=\"data row2 col11\" >0.38</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row2_col12\" class=\"data row2 col12\" >-1.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row3\" class=\"row_heading level0 row3\" >489</th>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col2\" class=\"data row3 col2\" >2.45</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col4\" class=\"data row3 col4\" >0.47</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col5\" class=\"data row3 col5\" >-1.21</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col6\" class=\"data row3 col6\" >1.04</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col7\" class=\"data row3 col7\" >-0.98</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col8\" class=\"data row3 col8\" >-0.65</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col9\" class=\"data row3 col9\" >1.81</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col10\" class=\"data row3 col10\" >0.75</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col11\" class=\"data row3 col11\" >-0.10</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row3_col12\" class=\"data row3 col12\" >1.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763level0_row4\" class=\"row_heading level0 row4\" >332</th>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col1\" class=\"data row4 col1\" >1.10</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col2\" class=\"data row4 col2\" >-0.76</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col4\" class=\"data row4 col4\" >-1.05</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col5\" class=\"data row4 col5\" >-0.35</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col6\" class=\"data row4 col6\" >-1.69</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col7\" class=\"data row4 col7\" >1.46</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col8\" class=\"data row4 col8\" >-0.99</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col9\" class=\"data row4 col9\" >-0.62</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col10\" class=\"data row4 col10\" >-0.71</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col11\" class=\"data row4 col11\" >0.09</td>\n",
" <td id=\"T_82dd3b98_f657_11ea_a7d3_0cc47af5c763row4_col12\" class=\"data row4 col12\" >-0.68</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: 503.0777 - mae: 20.2737 - mse: 503.0777 - val_loss: 393.9827 - val_mae: 17.6556 - val_mse: 393.9827\n",
"36/36 [==============================] - 0s 3ms/step - loss: 279.2001 - mae: 14.1952 - mse: 279.2001 - val_loss: 149.7944 - val_mae: 9.8029 - val_mse: 149.7944\n",
"36/36 [==============================] - 0s 3ms/step - loss: 92.0400 - mae: 7.3086 - mse: 92.0400 - val_loss: 56.1463 - val_mae: 5.1862 - val_mse: 56.1463\n",
"36/36 [==============================] - 0s 3ms/step - loss: 40.7789 - mae: 4.5229 - mse: 40.7789 - val_loss: 39.5256 - val_mae: 4.1634 - val_mse: 39.5256\n",
"36/36 [==============================] - 0s 3ms/step - loss: 29.7879 - mae: 3.8496 - mse: 29.7879 - val_loss: 31.2629 - val_mae: 3.6489 - val_mse: 31.2629\n",
"36/36 [==============================] - 0s 3ms/step - loss: 24.4654 - mae: 3.5160 - mse: 24.4654 - val_loss: 26.0581 - val_mae: 3.4063 - val_mse: 26.0581\n",
"36/36 [==============================] - 0s 3ms/step - loss: 21.0785 - mae: 3.2459 - mse: 21.0785 - val_loss: 23.6567 - val_mae: 3.2537 - val_mse: 23.6567\n",
"36/36 [==============================] - 0s 3ms/step - loss: 19.4353 - mae: 3.1121 - mse: 19.4353 - val_loss: 21.8422 - val_mae: 3.0457 - val_mse: 21.8422\n",
"36/36 [==============================] - 0s 3ms/step - loss: 17.8308 - mae: 2.9690 - mse: 17.8308 - val_loss: 20.7882 - val_mae: 3.0116 - val_mse: 20.7882\n",
"36/36 [==============================] - 0s 3ms/step - loss: 16.8658 - mae: 2.8324 - mse: 16.8658 - val_loss: 19.7575 - val_mae: 2.8680 - val_mse: 19.7575\n",
"36/36 [==============================] - 0s 3ms/step - loss: 15.8717 - mae: 2.7445 - mse: 15.8717 - val_loss: 18.9251 - val_mae: 2.7688 - val_mse: 18.9251\n",
"36/36 [==============================] - 0s 3ms/step - loss: 15.0276 - mae: 2.6749 - mse: 15.0276 - val_loss: 18.8640 - val_mae: 2.9710 - val_mse: 18.8640\n",
"36/36 [==============================] - 0s 3ms/step - loss: 14.3917 - mae: 2.5725 - mse: 14.3917 - val_loss: 17.7454 - val_mae: 2.8248 - val_mse: 17.7454\n",
"36/36 [==============================] - 0s 3ms/step - loss: 13.5518 - mae: 2.5363 - mse: 13.5518 - val_loss: 17.7311 - val_mae: 2.7501 - val_mse: 17.7311\n",
"36/36 [==============================] - 0s 3ms/step - loss: 13.4551 - mae: 2.4748 - mse: 13.4551 - val_loss: 17.3188 - val_mae: 2.8649 - val_mse: 17.3188\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.9481 - mae: 2.4157 - mse: 12.9481 - val_loss: 16.7981 - val_mae: 2.7170 - val_mse: 16.7981\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.3689 - mae: 2.3805 - mse: 12.3689 - val_loss: 16.6066 - val_mae: 2.7056 - val_mse: 16.6066\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.2772 - mae: 2.3851 - mse: 12.2772 - val_loss: 16.6416 - val_mae: 2.7872 - val_mse: 16.6416\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.7965 - mae: 2.3402 - mse: 11.7965 - val_loss: 16.8247 - val_mae: 2.8627 - val_mse: 16.8247\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.6902 - mae: 2.3298 - mse: 11.6902 - val_loss: 16.3016 - val_mae: 2.7847 - val_mse: 16.3016\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.6052 - mae: 2.2741 - mse: 11.6052 - val_loss: 14.8726 - val_mae: 2.5843 - val_mse: 14.8726\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.0375 - mae: 2.2424 - mse: 11.0375 - val_loss: 14.7919 - val_mae: 2.5355 - val_mse: 14.7919\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.6963 - mae: 2.2055 - mse: 10.6963 - val_loss: 15.4898 - val_mae: 2.7335 - val_mse: 15.4898\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.6611 - mae: 2.2158 - mse: 10.6611 - val_loss: 16.7863 - val_mae: 2.7952 - val_mse: 16.7863\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.5480 - mae: 2.1719 - mse: 10.5480 - val_loss: 16.3515 - val_mae: 2.8322 - val_mse: 16.3515\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.3655 - mae: 2.1437 - mse: 10.3655 - val_loss: 14.6908 - val_mae: 2.5558 - val_mse: 14.6908\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.9280 - mae: 2.1000 - mse: 9.9280 - val_loss: 14.5626 - val_mae: 2.6410 - val_mse: 14.5626\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.9190 - mae: 2.1106 - mse: 9.9190 - val_loss: 14.9380 - val_mae: 2.6679 - val_mse: 14.9380\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.8377 - mae: 2.1032 - mse: 9.8377 - val_loss: 14.6915 - val_mae: 2.5614 - val_mse: 14.6915\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.6516 - mae: 2.0634 - mse: 9.6516 - val_loss: 14.0798 - val_mae: 2.4975 - val_mse: 14.0798\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.5721 - mae: 2.0818 - mse: 9.5721 - val_loss: 14.6150 - val_mae: 2.4934 - val_mse: 14.6150\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.2323 - mae: 2.0112 - mse: 9.2323 - val_loss: 13.9540 - val_mae: 2.4628 - val_mse: 13.9540\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.9889 - mae: 2.0254 - mse: 8.9889 - val_loss: 13.2860 - val_mae: 2.5377 - val_mse: 13.2860\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.1012 - mae: 2.0766 - mse: 9.1012 - val_loss: 12.8317 - val_mae: 2.4198 - val_mse: 12.8317\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.8631 - mae: 2.0241 - mse: 8.8631 - val_loss: 13.3025 - val_mae: 2.4192 - val_mse: 13.3025\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.8026 - mae: 1.9883 - mse: 8.8026 - val_loss: 12.9319 - val_mae: 2.4633 - val_mse: 12.9319\n",
"36/36 [==============================] - 0s 3ms/step - loss: 8.8867 - mae: 1.9741 - mse: 8.8867 - val_loss: 12.6364 - val_mae: 2.3791 - val_mse: 12.6364\n",
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"36/36 [==============================] - 0s 3ms/step - loss: 5.8058 - mae: 1.6580 - mse: 5.8058 - val_loss: 12.2608 - val_mae: 2.4840 - val_mse: 12.2608\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.0826 - mae: 1.7135 - mse: 6.0826 - val_loss: 10.9988 - val_mae: 2.3573 - val_mse: 10.9988\n",
"36/36 [==============================] - 0s 3ms/step - loss: 6.0445 - mae: 1.7116 - mse: 6.0445 - val_loss: 10.5661 - val_mae: 2.2273 - val_mse: 10.5661\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.8601 - mae: 1.6876 - mse: 5.8601 - val_loss: 11.8910 - val_mae: 2.3362 - val_mse: 11.8910\n",
"36/36 [==============================] - 0s 3ms/step - loss: 5.9526 - mae: 1.6862 - mse: 5.9526 - val_loss: 10.9216 - val_mae: 2.2957 - val_mse: 10.9216\n",
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