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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",
"Version : 0.56 DEV\n",
"Run time : Wednesday 9 September 2020, 10:45:12\n",
"TensorFlow version : 2.2.0\n",
"Keras version : 2.3.0-tf\n",
"Current place : Fidle at IDRIS\n",
"Dataset 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",
"place, dataset_dir = ooo.init()"
]
},
{
"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_c6701b60_f278_11ea_97b3_0cc47af5c7c7\" ><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_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" <th id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
" <td id=\"T_c6701b60_f278_11ea_97b3_0cc47af5c7c7row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x154ab65c8990>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"data = pd.read_csv(f'{dataset_dir}/BHPD/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_c67974bc_f278_11ea_97b3_0cc47af5c7c7\" ><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_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >3.53</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >12.31</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >11.13</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.08</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >68.27</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >3.82</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >9.25</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >404.34</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >18.29</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >356.99</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >12.78</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >8.82</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >24.61</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >6.90</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >0.27</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >0.71</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >28.39</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >2.12</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >8.60</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >166.70</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >2.25</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >91.85</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >7.53</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >4.14</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >5.15</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >42.32</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >2.10</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >16.60</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >376.25</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >6.88</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col0\" class=\"data row5 col0\" >0.26</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col5\" class=\"data row5 col5\" >6.19</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col6\" class=\"data row5 col6\" >78.20</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col7\" class=\"data row5 col7\" >3.35</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col9\" class=\"data row5 col9\" >329.50</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col10\" class=\"data row5 col10\" >18.60</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col11\" class=\"data row5 col11\" >391.88</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row5_col12\" class=\"data row5 col12\" >11.23</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col0\" class=\"data row6 col0\" >3.10</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col1\" class=\"data row6 col1\" >16.25</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col5\" class=\"data row6 col5\" >6.61</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col6\" class=\"data row6 col6\" >93.80</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col7\" class=\"data row6 col7\" >5.12</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col8\" class=\"data row6 col8\" >8.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col11\" class=\"data row6 col11\" >396.12</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row6_col12\" class=\"data row6 col12\" >16.72</td>\n",
" <th id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
" <td id=\"T_c67974bc_f278_11ea_97b3_0cc47af5c7c7row7_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_c6811690_f278_11ea_97b3_0cc47af5c7c7\" ><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_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >-1.55</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >-0.30</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >-1.40</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >-3.03</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >-2.30</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >-1.27</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >-0.96</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >-2.52</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >-3.88</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >-1.47</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >-0.39</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >-0.30</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >-0.90</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >-0.91</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >-0.61</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >-0.75</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col0\" class=\"data row5 col0\" >-0.37</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col1\" class=\"data row5 col1\" >-0.50</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col3\" class=\"data row5 col3\" >-0.30</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col4\" class=\"data row5 col4\" >-0.13</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col5\" class=\"data row5 col5\" >-0.15</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col7\" class=\"data row5 col7\" >-0.22</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col8\" class=\"data row5 col8\" >-0.49</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col9\" class=\"data row5 col9\" >-0.45</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col10\" class=\"data row5 col10\" >0.14</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row5_col12\" class=\"data row5 col12\" >-0.21</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col0\" class=\"data row6 col0\" >-0.05</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col1\" class=\"data row6 col1\" >0.16</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col2\" class=\"data row6 col2\" >1.01</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col3\" class=\"data row6 col3\" >-0.30</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col5\" class=\"data row6 col5\" >0.44</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col7\" class=\"data row6 col7\" >0.61</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col8\" class=\"data row6 col8\" >-0.15</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col9\" class=\"data row6 col9\" >1.57</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col10\" class=\"data row6 col10\" >0.85</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row6_col12\" class=\"data row6 col12\" >0.52</td>\n",
" <th id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col0\" class=\"data row7 col0\" >9.68</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col1\" class=\"data row7 col1\" >3.56</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col2\" class=\"data row7 col2\" >2.41</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col3\" class=\"data row7 col3\" >3.34</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col4\" class=\"data row7 col4\" >2.71</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col5\" class=\"data row7 col5\" >3.49</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col6\" class=\"data row7 col6\" >1.12</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col7\" class=\"data row7 col7\" >3.92</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col8\" class=\"data row7 col8\" >1.72</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col9\" class=\"data row7 col9\" >1.84</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col10\" class=\"data row7 col10\" >1.65</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col11\" class=\"data row7 col11\" >0.43</td>\n",
" <td id=\"T_c6811690_f278_11ea_97b3_0cc47af5c7c7row7_col12\" class=\"data row7 col12\" >3.35</td>\n",
" </tr>\n",
" </tbody></table>"
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"<style type=\"text/css\" >\n",
"</style><table id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7\" ><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_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row0\" class=\"row_heading level0 row0\" >191</th>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col0\" class=\"data row0 col0\" >-0.39</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col1\" class=\"data row0 col1\" >1.33</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col2\" class=\"data row0 col2\" >-1.11</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col3\" class=\"data row0 col3\" >-0.30</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col4\" class=\"data row0 col4\" >-0.99</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col5\" class=\"data row0 col5\" >0.62</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col6\" class=\"data row0 col6\" >-1.32</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col7\" class=\"data row0 col7\" >1.26</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col8\" class=\"data row0 col8\" >-0.49</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col9\" class=\"data row0 col9\" >-0.04</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col10\" class=\"data row0 col10\" >-1.37</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col11\" class=\"data row0 col11\" >0.36</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row0_col12\" class=\"data row0 col12\" >-1.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row1\" class=\"row_heading level0 row1\" >300</th>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col0\" class=\"data row1 col0\" >-0.39</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col1\" class=\"data row1 col1\" >2.34</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col2\" class=\"data row1 col2\" >-1.29</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col3\" class=\"data row1 col3\" >-0.30</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col4\" class=\"data row1 col4\" >-1.31</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col5\" class=\"data row1 col5\" >0.81</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col6\" class=\"data row1 col6\" >-0.74</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col7\" class=\"data row1 col7\" >1.89</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col8\" class=\"data row1 col8\" >-0.49</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col9\" class=\"data row1 col9\" >-0.28</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col10\" class=\"data row1 col10\" >-1.55</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col11\" class=\"data row1 col11\" >0.37</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row1_col12\" class=\"data row1 col12\" >-0.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row2\" class=\"row_heading level0 row2\" >65</th>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col0\" class=\"data row2 col0\" >-0.40</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col1\" class=\"data row2 col1\" >2.75</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col2\" class=\"data row2 col2\" >-1.12</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col3\" class=\"data row2 col3\" >-0.30</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col4\" class=\"data row2 col4\" >-1.33</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col5\" class=\"data row2 col5\" >-0.01</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col6\" class=\"data row2 col6\" >-1.78</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col7\" class=\"data row2 col7\" >1.32</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col8\" class=\"data row2 col8\" >-0.61</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col9\" class=\"data row2 col9\" >-0.40</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col10\" class=\"data row2 col10\" >-0.97</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col11\" class=\"data row2 col11\" >0.43</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row2_col12\" class=\"data row2 col12\" >-1.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row3\" class=\"row_heading level0 row3\" >119</th>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col0\" class=\"data row3 col0\" >-0.38</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col2\" class=\"data row3 col2\" >-0.16</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col3\" class=\"data row3 col3\" >-0.30</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col4\" class=\"data row3 col4\" >-0.05</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col5\" class=\"data row3 col5\" >-0.79</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col6\" class=\"data row3 col6\" >-0.11</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col7\" class=\"data row3 col7\" >-0.50</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col8\" class=\"data row3 col8\" >-0.38</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col9\" class=\"data row3 col9\" >0.17</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col10\" class=\"data row3 col10\" >-0.22</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col11\" class=\"data row3 col11\" >0.38</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row3_col12\" class=\"data row3 col12\" >0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7level0_row4\" class=\"row_heading level0 row4\" >128</th>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col0\" class=\"data row4 col0\" >-0.36</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col2\" class=\"data row4 col2\" >1.56</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col3\" class=\"data row4 col3\" >-0.30</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col4\" class=\"data row4 col4\" >0.60</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col5\" class=\"data row4 col5\" >0.19</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col6\" class=\"data row4 col6\" >1.08</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col7\" class=\"data row4 col7\" >-0.95</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col8\" class=\"data row4 col8\" >-0.61</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col9\" class=\"data row4 col9\" >0.20</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col10\" class=\"data row4 col10\" >1.29</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col11\" class=\"data row4 col11\" >0.43</td>\n",
" <td id=\"T_c681f33a_f278_11ea_97b3_0cc47af5c7c7row4_col12\" class=\"data row4 col12\" >0.35</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x154b37589ad0>"
]
},
"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: 547.8102 - mae: 21.3789 - mse: 547.8102 - val_loss: 426.4636 - val_mae: 18.9591 - val_mse: 426.4636\n",
"36/36 [==============================] - 0s 3ms/step - loss: 359.6963 - mae: 16.8183 - mse: 359.6963 - val_loss: 226.9481 - val_mae: 13.1573 - val_mse: 226.9481\n",
"36/36 [==============================] - 0s 3ms/step - loss: 160.5077 - mae: 10.3363 - mse: 160.5077 - val_loss: 84.0127 - val_mae: 7.2714 - val_mse: 84.0127\n",
"36/36 [==============================] - 0s 3ms/step - loss: 68.7662 - mae: 6.2226 - mse: 68.7662 - val_loss: 42.7031 - val_mae: 5.0207 - val_mse: 42.7031\n",
"36/36 [==============================] - 0s 3ms/step - loss: 39.3656 - mae: 4.5027 - mse: 39.3656 - val_loss: 28.1898 - val_mae: 3.9144 - val_mse: 28.1898\n",
"36/36 [==============================] - 0s 3ms/step - loss: 27.7994 - mae: 3.7284 - mse: 27.7994 - val_loss: 22.5556 - val_mae: 3.4554 - val_mse: 22.5556\n",
"36/36 [==============================] - 0s 3ms/step - loss: 23.3525 - mae: 3.4556 - mse: 23.3525 - val_loss: 21.7096 - val_mae: 3.2439 - val_mse: 21.7096\n",
"36/36 [==============================] - 0s 3ms/step - loss: 21.6431 - mae: 3.1815 - mse: 21.6431 - val_loss: 19.3019 - val_mae: 3.0922 - val_mse: 19.3019\n",
"36/36 [==============================] - 0s 3ms/step - loss: 19.4970 - mae: 3.0120 - mse: 19.4970 - val_loss: 18.5831 - val_mae: 2.9942 - val_mse: 18.5831\n",
"36/36 [==============================] - 0s 3ms/step - loss: 18.0860 - mae: 2.8919 - mse: 18.0860 - val_loss: 17.3483 - val_mae: 2.9057 - val_mse: 17.3483\n",
"36/36 [==============================] - 0s 3ms/step - loss: 16.9134 - mae: 2.7540 - mse: 16.9134 - val_loss: 16.5643 - val_mae: 2.8748 - val_mse: 16.5643\n",
"36/36 [==============================] - 0s 3ms/step - loss: 15.9337 - mae: 2.6675 - mse: 15.9337 - val_loss: 16.0137 - val_mae: 2.8492 - val_mse: 16.0137\n",
"36/36 [==============================] - 0s 3ms/step - loss: 15.3994 - mae: 2.6116 - mse: 15.3994 - val_loss: 15.4497 - val_mae: 2.7655 - val_mse: 15.4497\n",
"36/36 [==============================] - 0s 3ms/step - loss: 14.7345 - mae: 2.5935 - mse: 14.7345 - val_loss: 15.5044 - val_mae: 2.7343 - val_mse: 15.5044\n",
"36/36 [==============================] - 0s 3ms/step - loss: 13.9952 - mae: 2.4952 - mse: 13.9952 - val_loss: 15.4225 - val_mae: 2.7673 - val_mse: 15.4225\n",
"36/36 [==============================] - 0s 3ms/step - loss: 13.6492 - mae: 2.4715 - mse: 13.6492 - val_loss: 15.9129 - val_mae: 2.7843 - val_mse: 15.9129\n",
"36/36 [==============================] - 0s 3ms/step - loss: 13.1960 - mae: 2.4395 - mse: 13.1960 - val_loss: 13.8508 - val_mae: 2.6311 - val_mse: 13.8508\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.8516 - mae: 2.4187 - mse: 12.8516 - val_loss: 14.0965 - val_mae: 2.6200 - val_mse: 14.0965\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.5172 - mae: 2.3631 - mse: 12.5172 - val_loss: 14.6048 - val_mae: 2.6703 - val_mse: 14.6048\n",
"36/36 [==============================] - 0s 3ms/step - loss: 12.0961 - mae: 2.3411 - mse: 12.0961 - val_loss: 13.6234 - val_mae: 2.6658 - val_mse: 13.6234\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.9956 - mae: 2.3587 - mse: 11.9956 - val_loss: 14.0870 - val_mae: 2.6412 - val_mse: 14.0870\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.7839 - mae: 2.3047 - mse: 11.7839 - val_loss: 13.5144 - val_mae: 2.5665 - val_mse: 13.5144\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.5465 - mae: 2.2461 - mse: 11.5465 - val_loss: 13.4045 - val_mae: 2.6249 - val_mse: 13.4045\n",
"36/36 [==============================] - 0s 3ms/step - loss: 11.3203 - mae: 2.2785 - mse: 11.3203 - val_loss: 13.4534 - val_mae: 2.5680 - val_mse: 13.4534\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.9813 - mae: 2.2193 - mse: 10.9813 - val_loss: 13.4274 - val_mae: 2.6316 - val_mse: 13.4274\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.8622 - mae: 2.2494 - mse: 10.8622 - val_loss: 13.2414 - val_mae: 2.5662 - val_mse: 13.2414\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.5865 - mae: 2.2073 - mse: 10.5865 - val_loss: 13.4347 - val_mae: 2.6453 - val_mse: 13.4347\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.4764 - mae: 2.1905 - mse: 10.4764 - val_loss: 13.6223 - val_mae: 2.6343 - val_mse: 13.6223\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.1884 - mae: 2.1639 - mse: 10.1884 - val_loss: 13.2782 - val_mae: 2.5879 - val_mse: 13.2782\n",
"36/36 [==============================] - 0s 3ms/step - loss: 10.2006 - mae: 2.1927 - mse: 10.2006 - val_loss: 13.2758 - val_mae: 2.5862 - val_mse: 13.2758\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.9884 - mae: 2.1413 - mse: 9.9884 - val_loss: 14.8800 - val_mae: 2.7099 - val_mse: 14.8800\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.8956 - mae: 2.1278 - mse: 9.8956 - val_loss: 13.9123 - val_mae: 2.6520 - val_mse: 13.9123\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.5863 - mae: 2.1298 - mse: 9.5863 - val_loss: 13.7808 - val_mae: 2.6443 - val_mse: 13.7808\n",
"36/36 [==============================] - 0s 3ms/step - loss: 9.7643 - mae: 2.1279 - mse: 9.7643 - val_loss: 13.5272 - val_mae: 2.6014 - val_mse: 13.5272\n",
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