diff --git a/BHPD/00-Fidle-header-01.png b/BHPD/00-Fidle-header-01.png
deleted file mode 100644
index bfba6ea8d904880b5890e62f9ac3346f274973b4..0000000000000000000000000000000000000000
Binary files a/BHPD/00-Fidle-header-01.png and /dev/null differ
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 6406040b78d7157f398195cbc09eea9570df11e2..01de329b1f0aa1e8d7569cba252e4c3af216da47 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -4,13 +4,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n",
+ "\n",
"\n",
- "# Deep Neural Network (DNN) - BHPD dataset\n",
+ "# <!-- TITLE --> Regression with a Dense Network (DNN)\n",
"\n",
- "<!-- INDEX : Simple regression with a Dense Neural Network (DNN) - BHPD dataset -->\n",
- "\n",
- "A very simple and classic example of **regression** :\n",
+ "<!-- DESC --> A Simple regression with a Dense Neural Network (DNN) - BHPD dataset\n",
"\n",
"## Objectives :\n",
" - Predicts **housing prices** from a set of house features. \n",
@@ -83,7 +81,7 @@
"\n",
"FIDLE 2020 - Practical Work Module\n",
"Version : 0.2.9\n",
- "Run time : Tuesday 18 February 2020, 14:42:02\n",
+ "Run time : Wednesday 19 February 2020, 09:49:10\n",
"TensorFlow version : 2.0.0\n",
"Keras version : 2.2.4-tf\n"
]
@@ -98,14 +96,12 @@
"import pandas as pd\n",
"import os,sys\n",
"\n",
- "from IPython.display import display, Markdown\n",
"from importlib import reload\n",
"\n",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
- "ooo.init()\n",
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)"
+ "ooo.init()"
]
},
{
@@ -135,103 +131,103 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1\" ><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",
+ "</style><table id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " <th id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_c2c3b2a2_5254_11ea_af59_f73cd2c607e1row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_b21e4e08_52f4_11ea_be4c_af031994fcb0row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f580e75c6d0>"
+ "<pandas.io.formats.style.Styler at 0x7f1b92065150>"
]
},
"metadata": {},
@@ -264,7 +260,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -309,146 +305,146 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_df175364_5254_11ea_af59_f73cd2c607e1\" ><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",
+ "</style><table id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col0\" class=\"data row1 col0\" >3.97</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col1\" class=\"data row1 col1\" >10.60</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col2\" class=\"data row1 col2\" >11.23</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col5\" class=\"data row1 col5\" >6.25</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col6\" class=\"data row1 col6\" >68.57</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col7\" class=\"data row1 col7\" >3.76</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col8\" class=\"data row1 col8\" >9.42</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col9\" class=\"data row1 col9\" >405.81</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col10\" class=\"data row1 col10\" >18.46</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col11\" class=\"data row1 col11\" >356.90</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row1_col12\" class=\"data row1 col12\" >12.85</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col0\" class=\"data row1 col0\" >3.45</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col1\" class=\"data row1 col1\" >11.62</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col2\" class=\"data row1 col2\" >11.13</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col6\" class=\"data row1 col6\" >69.31</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col7\" class=\"data row1 col7\" >3.87</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col8\" class=\"data row1 col8\" >9.27</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col9\" class=\"data row1 col9\" >403.18</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col10\" class=\"data row1 col10\" >18.44</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col11\" class=\"data row1 col11\" >360.95</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row1_col12\" class=\"data row1 col12\" >12.53</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col0\" class=\"data row2 col0\" >9.77</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col1\" class=\"data row2 col1\" >22.74</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col2\" class=\"data row2 col2\" >6.81</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col5\" class=\"data row2 col5\" >0.71</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col6\" class=\"data row2 col6\" >28.45</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col7\" class=\"data row2 col7\" >2.12</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col8\" class=\"data row2 col8\" >8.58</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col9\" class=\"data row2 col9\" >165.60</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col10\" class=\"data row2 col10\" >2.16</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col11\" class=\"data row2 col11\" >92.34</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row2_col12\" class=\"data row2 col12\" >7.31</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col0\" class=\"data row2 col0\" >8.66</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col1\" class=\"data row2 col1\" >23.54</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col2\" class=\"data row2 col2\" >6.86</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col5\" class=\"data row2 col5\" >0.71</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col6\" class=\"data row2 col6\" >27.60</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col7\" class=\"data row2 col7\" >2.19</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col8\" class=\"data row2 col8\" >8.53</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col9\" class=\"data row2 col9\" >165.86</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col10\" class=\"data row2 col10\" >2.18</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col11\" class=\"data row2 col11\" >84.83</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row2_col12\" class=\"data row2 col12\" >7.04</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col5\" class=\"data row3 col5\" >3.86</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col5\" class=\"data row4 col5\" >5.87</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col6\" class=\"data row4 col6\" >44.03</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col7\" class=\"data row4 col7\" >2.10</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col9\" class=\"data row4 col9\" >281.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col10\" class=\"data row4 col10\" >17.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col11\" class=\"data row4 col11\" >375.61</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row4_col12\" class=\"data row4 col12\" >7.28</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col5\" class=\"data row4 col5\" >5.88</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col6\" class=\"data row4 col6\" >45.73</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col7\" class=\"data row4 col7\" >2.09</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col10\" class=\"data row4 col10\" >17.10</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col11\" class=\"data row4 col11\" >375.91</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row4_col12\" class=\"data row4 col12\" >6.78</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col0\" class=\"data row5 col0\" >0.25</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col5\" class=\"data row5 col5\" >6.16</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col6\" class=\"data row5 col6\" >76.95</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col7\" class=\"data row5 col7\" >3.10</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col11\" class=\"data row5 col11\" >391.42</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row5_col12\" class=\"data row5 col12\" >11.49</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col0\" class=\"data row5 col0\" >0.23</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col5\" class=\"data row5 col5\" >6.22</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col6\" class=\"data row5 col6\" >78.50</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col7\" class=\"data row5 col7\" >3.22</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col11\" class=\"data row5 col11\" >391.38</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row5_col12\" class=\"data row5 col12\" >11.35</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col0\" class=\"data row6 col0\" >3.44</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col5\" class=\"data row6 col5\" >6.59</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col6\" class=\"data row6 col6\" >94.10</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col7\" class=\"data row6 col7\" >5.11</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col8\" class=\"data row6 col8\" >20.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col11\" class=\"data row6 col11\" >396.90</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row6_col12\" class=\"data row6 col12\" >16.93</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col0\" class=\"data row6 col0\" >2.77</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col5\" class=\"data row6 col5\" >6.62</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col6\" class=\"data row6 col6\" >94.10</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col7\" class=\"data row6 col7\" >5.23</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col8\" class=\"data row6 col8\" >8.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col11\" class=\"data row6 col11\" >396.27</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row6_col12\" class=\"data row6 col12\" >16.93</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df175364_5254_11ea_af59_f73cd2c607e1level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col1\" class=\"data row7 col1\" >95.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col5\" class=\"data row7 col5\" >8.72</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_df175364_5254_11ea_af59_f73cd2c607e1row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " <th id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_b227e0e4_52f4_11ea_be4c_af031994fcb0row7_col12\" class=\"data row7 col12\" >36.98</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f5810b1d310>"
+ "<pandas.io.formats.style.Styler at 0x7f1b8fb9bfd0>"
]
},
"metadata": {},
@@ -458,139 +454,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1\" ><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",
+ "</style><table id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col1\" class=\"data row3 col1\" >-0.47</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col2\" class=\"data row3 col2\" >-1.47</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col4\" class=\"data row3 col4\" >-1.49</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col5\" class=\"data row3 col5\" >-3.35</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col6\" class=\"data row3 col6\" >-2.20</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col7\" class=\"data row3 col7\" >-1.24</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col9\" class=\"data row3 col9\" >-1.32</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col10\" class=\"data row3 col10\" >-2.71</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col11\" class=\"data row3 col11\" >-3.86</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col0\" class=\"data row3 col0\" >-0.40</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col2\" class=\"data row3 col2\" >-1.56</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col4\" class=\"data row3 col4\" >-1.49</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col5\" class=\"data row3 col5\" >-3.84</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col6\" class=\"data row3 col6\" >-2.41</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col7\" class=\"data row3 col7\" >-1.25</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col8\" class=\"data row3 col8\" >-0.97</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col10\" class=\"data row3 col10\" >-2.68</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col11\" class=\"data row3 col11\" >-4.23</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row3_col12\" class=\"data row3 col12\" >-1.53</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col1\" class=\"data row4 col1\" >-0.47</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col4\" class=\"data row4 col4\" >-0.88</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col5\" class=\"data row4 col5\" >-0.53</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col6\" class=\"data row4 col6\" >-0.86</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col7\" class=\"data row4 col7\" >-0.78</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col8\" class=\"data row4 col8\" >-0.63</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col9\" class=\"data row4 col9\" >-0.75</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col10\" class=\"data row4 col10\" >-0.68</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col11\" class=\"data row4 col11\" >0.20</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row4_col12\" class=\"data row4 col12\" >-0.76</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col0\" class=\"data row4 col0\" >-0.39</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col4\" class=\"data row4 col4\" >-0.90</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col5\" class=\"data row4 col5\" >-0.58</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col6\" class=\"data row4 col6\" >-0.85</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col8\" class=\"data row4 col8\" >-0.62</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col10\" class=\"data row4 col10\" >-0.62</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col11\" class=\"data row4 col11\" >0.18</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row4_col12\" class=\"data row4 col12\" >-0.82</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col0\" class=\"data row5 col0\" >-0.38</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col1\" class=\"data row5 col1\" >-0.47</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col2\" class=\"data row5 col2\" >-0.23</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col5\" class=\"data row5 col5\" >-0.13</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col6\" class=\"data row5 col6\" >0.29</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col7\" class=\"data row5 col7\" >-0.31</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col8\" class=\"data row5 col8\" >-0.51</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col10\" class=\"data row5 col10\" >0.30</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col11\" class=\"data row5 col11\" >0.37</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row5_col12\" class=\"data row5 col12\" >-0.19</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col0\" class=\"data row5 col0\" >-0.37</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col5\" class=\"data row5 col5\" >-0.11</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col6\" class=\"data row5 col6\" >0.33</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col7\" class=\"data row5 col7\" >-0.30</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col8\" class=\"data row5 col8\" >-0.50</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col9\" class=\"data row5 col9\" >-0.44</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col10\" class=\"data row5 col10\" >0.30</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col11\" class=\"data row5 col11\" >0.36</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row5_col12\" class=\"data row5 col12\" >-0.17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col0\" class=\"data row6 col0\" >-0.05</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col1\" class=\"data row6 col1\" >-0.47</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col2\" class=\"data row6 col2\" >1.01</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col4\" class=\"data row6 col4\" >0.61</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col5\" class=\"data row6 col5\" >0.49</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col7\" class=\"data row6 col7\" >0.64</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col8\" class=\"data row6 col8\" >1.23</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col9\" class=\"data row6 col9\" >1.57</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col10\" class=\"data row6 col10\" >0.81</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row6_col12\" class=\"data row6 col12\" >0.56</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col0\" class=\"data row6 col0\" >-0.08</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col1\" class=\"data row6 col1\" >0.04</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col5\" class=\"data row6 col5\" >0.46</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col7\" class=\"data row6 col7\" >0.62</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col8\" class=\"data row6 col8\" >-0.15</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col9\" class=\"data row6 col9\" >1.58</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col10\" class=\"data row6 col10\" >0.81</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col11\" class=\"data row6 col11\" >0.42</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row6_col12\" class=\"data row6 col12\" >0.63</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col0\" class=\"data row7 col0\" >8.70</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col1\" class=\"data row7 col1\" >3.71</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col2\" class=\"data row7 col2\" >2.43</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col4\" class=\"data row7 col4\" >2.77</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col5\" class=\"data row7 col5\" >3.48</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col6\" class=\"data row7 col6\" >1.11</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col7\" class=\"data row7 col7\" >3.95</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col8\" class=\"data row7 col8\" >1.70</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col9\" class=\"data row7 col9\" >1.84</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col10\" class=\"data row7 col10\" >1.64</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col11\" class=\"data row7 col11\" >0.43</td>\n",
- " <td id=\"T_df259c62_5254_11ea_af59_f73cd2c607e1row7_col12\" class=\"data row7 col12\" >3.44</td>\n",
+ " <th id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col0\" class=\"data row7 col0\" >9.87</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col1\" class=\"data row7 col1\" >3.75</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col2\" class=\"data row7 col2\" >2.42</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col4\" class=\"data row7 col4\" >2.75</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col5\" class=\"data row7 col5\" >3.49</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col6\" class=\"data row7 col6\" >1.11</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col7\" class=\"data row7 col7\" >3.76</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col8\" class=\"data row7 col8\" >1.73</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col9\" class=\"data row7 col9\" >1.86</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col10\" class=\"data row7 col10\" >1.63</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col11\" class=\"data row7 col11\" >0.42</td>\n",
+ " <td id=\"T_b236ee40_52f4_11ea_be4c_af031994fcb0row7_col12\" class=\"data row7 col12\" >3.47</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f580e946fd0>"
+ "<pandas.io.formats.style.Styler at 0x7f1b8fbb4c10>"
]
},
"metadata": {},
@@ -625,7 +621,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -653,7 +649,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -683,7 +679,7 @@
"<IPython.core.display.Image object>"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -704,7 +700,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -713,205 +709,205 @@
"text": [
"Train on 354 samples, validate on 152 samples\n",
"Epoch 1/100\n",
- "354/354 [==============================] - 1s 2ms/sample - loss: 414.5603 - mae: 18.2577 - mse: 414.5602 - val_loss: 266.3728 - val_mae: 13.9913 - val_mse: 266.3728\n",
+ "354/354 [==============================] - 1s 2ms/sample - loss: 536.0845 - mae: 21.3335 - mse: 536.0846 - val_loss: 439.6562 - val_mae: 19.3198 - val_mse: 439.6562\n",
"Epoch 2/100\n",
- "354/354 [==============================] - 0s 190us/sample - loss: 165.4507 - mae: 10.4618 - mse: 165.4507 - val_loss: 74.4125 - val_mae: 6.0372 - val_mse: 74.4125\n",
+ "354/354 [==============================] - 0s 216us/sample - loss: 354.0647 - mae: 16.8618 - mse: 354.0648 - val_loss: 231.3198 - val_mae: 13.5154 - val_mse: 231.3199\n",
"Epoch 3/100\n",
- "354/354 [==============================] - 0s 187us/sample - loss: 54.2313 - mae: 5.3763 - mse: 54.2313 - val_loss: 47.0203 - val_mae: 4.7399 - val_mse: 47.0203\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 155.7450 - mae: 9.9432 - mse: 155.7450 - val_loss: 69.8093 - val_mae: 6.2267 - val_mse: 69.8093\n",
"Epoch 4/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 32.3303 - mae: 4.2632 - mse: 32.3303 - val_loss: 38.0120 - val_mae: 4.2484 - val_mse: 38.0120\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 55.4497 - mae: 5.2375 - mse: 55.4497 - val_loss: 28.5090 - val_mae: 4.0794 - val_mse: 28.5090\n",
"Epoch 5/100\n",
- "354/354 [==============================] - 0s 153us/sample - loss: 25.3763 - mae: 3.7745 - mse: 25.3763 - val_loss: 32.4707 - val_mae: 3.8465 - val_mse: 32.4707\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 31.6844 - mae: 4.0017 - mse: 31.6844 - val_loss: 21.9792 - val_mae: 3.3949 - val_mse: 21.9792\n",
"Epoch 6/100\n",
- "354/354 [==============================] - 0s 153us/sample - loss: 22.2331 - mae: 3.4720 - mse: 22.2331 - val_loss: 29.6142 - val_mae: 3.4844 - val_mse: 29.6142\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 24.5126 - mae: 3.4343 - mse: 24.5126 - val_loss: 18.8066 - val_mae: 3.1393 - val_mse: 18.8066\n",
"Epoch 7/100\n",
- "354/354 [==============================] - 0s 154us/sample - loss: 19.7834 - mae: 3.2245 - mse: 19.7834 - val_loss: 27.1649 - val_mae: 3.5465 - val_mse: 27.1649\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 21.5744 - mae: 3.2008 - mse: 21.5744 - val_loss: 16.6019 - val_mae: 3.0136 - val_mse: 16.6019\n",
"Epoch 8/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 18.0991 - mae: 3.0669 - mse: 18.0991 - val_loss: 26.0093 - val_mae: 3.5617 - val_mse: 26.0093\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 19.6449 - mae: 3.0134 - mse: 19.6449 - val_loss: 15.8376 - val_mae: 2.9888 - val_mse: 15.8376\n",
"Epoch 9/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 16.9247 - mae: 2.9184 - mse: 16.9247 - val_loss: 23.2549 - val_mae: 3.3243 - val_mse: 23.2549\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 18.6252 - mae: 2.9144 - mse: 18.6252 - val_loss: 15.3001 - val_mae: 2.9692 - val_mse: 15.3001\n",
"Epoch 10/100\n",
- "354/354 [==============================] - 0s 150us/sample - loss: 16.0827 - mae: 2.8116 - mse: 16.0827 - val_loss: 21.1365 - val_mae: 3.0248 - val_mse: 21.1365\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 17.0981 - mae: 2.7810 - mse: 17.0981 - val_loss: 14.8818 - val_mae: 2.9166 - val_mse: 14.8818\n",
"Epoch 11/100\n",
- "354/354 [==============================] - 0s 170us/sample - loss: 15.0334 - mae: 2.7214 - mse: 15.0334 - val_loss: 20.0163 - val_mae: 2.9800 - val_mse: 20.0163\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 16.0782 - mae: 2.6914 - mse: 16.0782 - val_loss: 14.3696 - val_mae: 2.8419 - val_mse: 14.3696\n",
"Epoch 12/100\n",
- "354/354 [==============================] - 0s 180us/sample - loss: 14.4011 - mae: 2.6949 - mse: 14.4011 - val_loss: 19.8958 - val_mae: 2.9262 - val_mse: 19.8958\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 15.5677 - mae: 2.6683 - mse: 15.5677 - val_loss: 13.9912 - val_mae: 2.8576 - val_mse: 13.9912\n",
"Epoch 13/100\n",
- "354/354 [==============================] - 0s 184us/sample - loss: 13.9168 - mae: 2.5674 - mse: 13.9168 - val_loss: 18.5729 - val_mae: 2.7302 - val_mse: 18.5729\n",
+ "354/354 [==============================] - 0s 185us/sample - loss: 14.8428 - mae: 2.5991 - mse: 14.8428 - val_loss: 14.3104 - val_mae: 2.8784 - val_mse: 14.3104\n",
"Epoch 14/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 13.5575 - mae: 2.5442 - mse: 13.5575 - val_loss: 17.8812 - val_mae: 2.6748 - val_mse: 17.8812\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 14.3035 - mae: 2.5320 - mse: 14.3035 - val_loss: 13.7014 - val_mae: 2.7929 - val_mse: 13.7014\n",
"Epoch 15/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 12.8689 - mae: 2.4779 - mse: 12.8689 - val_loss: 18.9649 - val_mae: 2.7560 - val_mse: 18.9649\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 13.6874 - mae: 2.4875 - mse: 13.6874 - val_loss: 13.2517 - val_mae: 2.7346 - val_mse: 13.2517\n",
"Epoch 16/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 12.6470 - mae: 2.4670 - mse: 12.6470 - val_loss: 16.5834 - val_mae: 2.6016 - val_mse: 16.5834\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 13.3831 - mae: 2.4476 - mse: 13.3831 - val_loss: 13.0551 - val_mae: 2.7135 - val_mse: 13.0551\n",
"Epoch 17/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 12.3566 - mae: 2.4280 - mse: 12.3566 - val_loss: 16.7371 - val_mae: 2.6670 - val_mse: 16.7371\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 13.1403 - mae: 2.4844 - mse: 13.1403 - val_loss: 13.0990 - val_mae: 2.6770 - val_mse: 13.0990\n",
"Epoch 18/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 12.3328 - mae: 2.4060 - mse: 12.3328 - val_loss: 16.3754 - val_mae: 2.6027 - val_mse: 16.3754\n",
+ "354/354 [==============================] - 0s 167us/sample - loss: 12.7370 - mae: 2.3913 - mse: 12.7370 - val_loss: 12.6409 - val_mae: 2.6264 - val_mse: 12.6409\n",
"Epoch 19/100\n",
- "354/354 [==============================] - 0s 152us/sample - loss: 11.8357 - mae: 2.3106 - mse: 11.8357 - val_loss: 16.1015 - val_mae: 2.6255 - val_mse: 16.1015\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 12.3546 - mae: 2.3600 - mse: 12.3546 - val_loss: 12.5174 - val_mae: 2.7141 - val_mse: 12.5174\n",
"Epoch 20/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 11.6722 - mae: 2.3482 - mse: 11.6722 - val_loss: 16.1405 - val_mae: 2.6889 - val_mse: 16.1405\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 12.1547 - mae: 2.3828 - mse: 12.1547 - val_loss: 12.1408 - val_mae: 2.6063 - val_mse: 12.1408\n",
"Epoch 21/100\n",
- "354/354 [==============================] - 0s 175us/sample - loss: 11.2774 - mae: 2.3344 - mse: 11.2774 - val_loss: 15.2110 - val_mae: 2.5038 - val_mse: 15.2110\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 11.8888 - mae: 2.3270 - mse: 11.8888 - val_loss: 11.9719 - val_mae: 2.5967 - val_mse: 11.9719\n",
"Epoch 22/100\n",
- "354/354 [==============================] - 0s 180us/sample - loss: 11.2491 - mae: 2.3055 - mse: 11.2491 - val_loss: 15.4745 - val_mae: 2.4494 - val_mse: 15.4744\n",
+ "354/354 [==============================] - 0s 189us/sample - loss: 11.6794 - mae: 2.3303 - mse: 11.6794 - val_loss: 11.8047 - val_mae: 2.5511 - val_mse: 11.8047\n",
"Epoch 23/100\n",
- "354/354 [==============================] - 0s 187us/sample - loss: 10.9102 - mae: 2.2171 - mse: 10.9102 - val_loss: 15.1145 - val_mae: 2.4282 - val_mse: 15.1145\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 11.3378 - mae: 2.3021 - mse: 11.3378 - val_loss: 12.4017 - val_mae: 2.6941 - val_mse: 12.4017\n",
"Epoch 24/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 10.7952 - mae: 2.2533 - mse: 10.7952 - val_loss: 14.3789 - val_mae: 2.3683 - val_mse: 14.3789\n",
+ "354/354 [==============================] - 0s 186us/sample - loss: 10.9016 - mae: 2.3034 - mse: 10.9016 - val_loss: 12.3386 - val_mae: 2.5292 - val_mse: 12.3386\n",
"Epoch 25/100\n",
- "354/354 [==============================] - 0s 171us/sample - loss: 10.7250 - mae: 2.2489 - mse: 10.7250 - val_loss: 15.1102 - val_mae: 2.3422 - val_mse: 15.1102\n",
+ "354/354 [==============================] - 0s 202us/sample - loss: 10.7163 - mae: 2.3021 - mse: 10.7163 - val_loss: 12.2563 - val_mae: 2.5674 - val_mse: 12.2563\n",
"Epoch 26/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 10.4010 - mae: 2.1702 - mse: 10.4010 - val_loss: 14.3260 - val_mae: 2.3176 - val_mse: 14.3260\n",
+ "354/354 [==============================] - 0s 192us/sample - loss: 10.8481 - mae: 2.2104 - mse: 10.8481 - val_loss: 11.2348 - val_mae: 2.4873 - val_mse: 11.2348\n",
"Epoch 27/100\n",
- "354/354 [==============================] - 0s 149us/sample - loss: 10.1442 - mae: 2.1797 - mse: 10.1442 - val_loss: 13.6694 - val_mae: 2.3864 - val_mse: 13.6694\n",
+ "354/354 [==============================] - 0s 192us/sample - loss: 10.7446 - mae: 2.2232 - mse: 10.7446 - val_loss: 11.4269 - val_mae: 2.5686 - val_mse: 11.4269\n",
"Epoch 28/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 10.1391 - mae: 2.1809 - mse: 10.1391 - val_loss: 14.0177 - val_mae: 2.3467 - val_mse: 14.0177\n",
+ "354/354 [==============================] - 0s 187us/sample - loss: 10.1381 - mae: 2.1918 - mse: 10.1381 - val_loss: 13.4143 - val_mae: 2.6246 - val_mse: 13.4143\n",
"Epoch 29/100\n",
- "354/354 [==============================] - 0s 149us/sample - loss: 9.9119 - mae: 2.1267 - mse: 9.9119 - val_loss: 14.0739 - val_mae: 2.4617 - val_mse: 14.0739\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 10.5442 - mae: 2.1971 - mse: 10.5442 - val_loss: 11.4616 - val_mae: 2.4741 - val_mse: 11.4616\n",
"Epoch 30/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 10.0176 - mae: 2.1669 - mse: 10.0176 - val_loss: 13.5116 - val_mae: 2.3158 - val_mse: 13.5116\n",
+ "354/354 [==============================] - 0s 218us/sample - loss: 10.2099 - mae: 2.1867 - mse: 10.2099 - val_loss: 11.4631 - val_mae: 2.4684 - val_mse: 11.4631\n",
"Epoch 31/100\n",
- "354/354 [==============================] - 0s 189us/sample - loss: 9.8259 - mae: 2.1407 - mse: 9.8259 - val_loss: 13.7364 - val_mae: 2.3531 - val_mse: 13.7364\n",
+ "354/354 [==============================] - 0s 202us/sample - loss: 9.5920 - mae: 2.1342 - mse: 9.5920 - val_loss: 12.5109 - val_mae: 2.6033 - val_mse: 12.5109\n",
"Epoch 32/100\n",
- "354/354 [==============================] - 0s 178us/sample - loss: 9.4495 - mae: 2.0922 - mse: 9.4495 - val_loss: 14.1936 - val_mae: 2.3887 - val_mse: 14.1936\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 9.9940 - mae: 2.1424 - mse: 9.9940 - val_loss: 11.1528 - val_mae: 2.4392 - val_mse: 11.1528\n",
"Epoch 33/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 9.6721 - mae: 2.0870 - mse: 9.6721 - val_loss: 13.4267 - val_mae: 2.3508 - val_mse: 13.4267\n",
+ "354/354 [==============================] - 0s 197us/sample - loss: 9.5950 - mae: 2.1156 - mse: 9.5950 - val_loss: 12.0327 - val_mae: 2.6225 - val_mse: 12.0327\n",
"Epoch 34/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 9.1042 - mae: 2.0644 - mse: 9.1042 - val_loss: 13.3821 - val_mae: 2.4709 - val_mse: 13.3821\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 9.6256 - mae: 2.0962 - mse: 9.6256 - val_loss: 10.8296 - val_mae: 2.4168 - val_mse: 10.8296\n",
"Epoch 35/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 9.0129 - mae: 2.0482 - mse: 9.0129 - val_loss: 14.2184 - val_mae: 2.2754 - val_mse: 14.2184\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 9.3365 - mae: 2.1271 - mse: 9.3365 - val_loss: 10.7088 - val_mae: 2.5094 - val_mse: 10.7088\n",
"Epoch 36/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 9.2470 - mae: 2.0661 - mse: 9.2470 - val_loss: 14.3466 - val_mae: 2.5561 - val_mse: 14.3466\n",
+ "354/354 [==============================] - 0s 184us/sample - loss: 9.2796 - mae: 2.0914 - mse: 9.2796 - val_loss: 10.7439 - val_mae: 2.4282 - val_mse: 10.7439\n",
"Epoch 37/100\n",
- "354/354 [==============================] - 0s 169us/sample - loss: 9.1695 - mae: 2.0766 - mse: 9.1695 - val_loss: 13.3818 - val_mae: 2.2373 - val_mse: 13.3818\n",
+ "354/354 [==============================] - 0s 186us/sample - loss: 8.7178 - mae: 2.0390 - mse: 8.7178 - val_loss: 13.1923 - val_mae: 2.5942 - val_mse: 13.1923\n",
"Epoch 38/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 9.1663 - mae: 2.0617 - mse: 9.1663 - val_loss: 14.7461 - val_mae: 2.5061 - val_mse: 14.7461\n",
+ "354/354 [==============================] - 0s 202us/sample - loss: 8.8195 - mae: 2.0927 - mse: 8.8195 - val_loss: 10.9034 - val_mae: 2.5152 - val_mse: 10.9034\n",
"Epoch 39/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 8.7273 - mae: 2.0208 - mse: 8.7273 - val_loss: 12.5890 - val_mae: 2.3037 - val_mse: 12.5890\n",
+ "354/354 [==============================] - 0s 190us/sample - loss: 8.9152 - mae: 2.0784 - mse: 8.9152 - val_loss: 11.3023 - val_mae: 2.4404 - val_mse: 11.3023\n",
"Epoch 40/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 8.9038 - mae: 2.0352 - mse: 8.9038 - val_loss: 12.9754 - val_mae: 2.2079 - val_mse: 12.9754\n",
+ "354/354 [==============================] - 0s 196us/sample - loss: 8.8418 - mae: 2.0187 - mse: 8.8418 - val_loss: 10.7721 - val_mae: 2.5067 - val_mse: 10.7721\n",
"Epoch 41/100\n",
- "354/354 [==============================] - 0s 153us/sample - loss: 8.6155 - mae: 2.0267 - mse: 8.6155 - val_loss: 13.9239 - val_mae: 2.3525 - val_mse: 13.9239\n",
+ "354/354 [==============================] - 0s 181us/sample - loss: 8.6890 - mae: 2.0260 - mse: 8.6890 - val_loss: 11.0856 - val_mae: 2.5693 - val_mse: 11.0856\n",
"Epoch 42/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 8.5479 - mae: 2.0170 - mse: 8.5479 - val_loss: 13.6362 - val_mae: 2.2694 - val_mse: 13.6362\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 8.4768 - mae: 2.0517 - mse: 8.4768 - val_loss: 11.3269 - val_mae: 2.4414 - val_mse: 11.3269\n",
"Epoch 43/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 8.7087 - mae: 2.0062 - mse: 8.7087 - val_loss: 13.1138 - val_mae: 2.2386 - val_mse: 13.1138\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 8.5229 - mae: 1.9943 - mse: 8.5229 - val_loss: 10.4669 - val_mae: 2.4794 - val_mse: 10.4669\n",
"Epoch 44/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 8.3942 - mae: 1.9622 - mse: 8.3942 - val_loss: 12.3461 - val_mae: 2.2337 - val_mse: 12.3461\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 8.0707 - mae: 1.9900 - mse: 8.0707 - val_loss: 11.6943 - val_mae: 2.5034 - val_mse: 11.6943\n",
"Epoch 45/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 8.4101 - mae: 2.0098 - mse: 8.4101 - val_loss: 13.2116 - val_mae: 2.2682 - val_mse: 13.2116\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 8.1752 - mae: 1.9715 - mse: 8.1752 - val_loss: 10.6043 - val_mae: 2.3636 - val_mse: 10.6043\n",
"Epoch 46/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 8.3264 - mae: 1.9483 - mse: 8.3264 - val_loss: 12.5519 - val_mae: 2.4063 - val_mse: 12.5519\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 8.2037 - mae: 1.9739 - mse: 8.2037 - val_loss: 10.5447 - val_mae: 2.3784 - val_mse: 10.5447\n",
"Epoch 47/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 8.1445 - mae: 1.9549 - mse: 8.1445 - val_loss: 12.1838 - val_mae: 2.2591 - val_mse: 12.1838\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 7.9866 - mae: 1.9744 - mse: 7.9866 - val_loss: 10.6746 - val_mae: 2.4501 - val_mse: 10.6746\n",
"Epoch 48/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 8.0389 - mae: 1.9304 - mse: 8.0389 - val_loss: 12.6978 - val_mae: 2.1907 - val_mse: 12.6978\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 7.7703 - mae: 1.9705 - mse: 7.7703 - val_loss: 10.4041 - val_mae: 2.4620 - val_mse: 10.4041\n",
"Epoch 49/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 8.0705 - mae: 1.9493 - mse: 8.0705 - val_loss: 12.4833 - val_mae: 2.4720 - val_mse: 12.4833\n",
+ "354/354 [==============================] - 0s 182us/sample - loss: 7.8774 - mae: 1.9809 - mse: 7.8774 - val_loss: 10.6823 - val_mae: 2.4969 - val_mse: 10.6823\n",
"Epoch 50/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 8.1872 - mae: 1.9630 - mse: 8.1872 - val_loss: 12.0043 - val_mae: 2.2610 - val_mse: 12.0043\n",
+ "354/354 [==============================] - 0s 167us/sample - loss: 7.8654 - mae: 1.9666 - mse: 7.8654 - val_loss: 10.6351 - val_mae: 2.4191 - val_mse: 10.6351\n",
"Epoch 51/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 8.0357 - mae: 1.8946 - mse: 8.0357 - val_loss: 11.3982 - val_mae: 2.1770 - val_mse: 11.3982\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 7.6560 - mae: 1.9236 - mse: 7.6560 - val_loss: 10.3918 - val_mae: 2.3943 - val_mse: 10.3918\n",
"Epoch 52/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 7.6882 - mae: 1.8951 - mse: 7.6882 - val_loss: 13.0714 - val_mae: 2.4109 - val_mse: 13.0714\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 7.3560 - mae: 1.8763 - mse: 7.3560 - val_loss: 10.3560 - val_mae: 2.5009 - val_mse: 10.3560\n",
"Epoch 53/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 7.9639 - mae: 1.9103 - mse: 7.9639 - val_loss: 12.4297 - val_mae: 2.2996 - val_mse: 12.4297\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 7.5076 - mae: 1.8973 - mse: 7.5076 - val_loss: 10.5798 - val_mae: 2.4698 - val_mse: 10.5798\n",
"Epoch 54/100\n",
- "354/354 [==============================] - 0s 183us/sample - loss: 7.7929 - mae: 1.8971 - mse: 7.7929 - val_loss: 11.9751 - val_mae: 2.2491 - val_mse: 11.9751\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 7.4315 - mae: 1.8962 - mse: 7.4315 - val_loss: 10.0018 - val_mae: 2.3756 - val_mse: 10.0018\n",
"Epoch 55/100\n",
- "354/354 [==============================] - 0s 185us/sample - loss: 7.4411 - mae: 1.8631 - mse: 7.4411 - val_loss: 11.3761 - val_mae: 2.3416 - val_mse: 11.3761\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 7.2476 - mae: 1.9127 - mse: 7.2476 - val_loss: 10.0664 - val_mae: 2.4074 - val_mse: 10.0664\n",
"Epoch 56/100\n",
- "354/354 [==============================] - 0s 186us/sample - loss: 7.6105 - mae: 1.9111 - mse: 7.6105 - val_loss: 12.4939 - val_mae: 2.4095 - val_mse: 12.4939\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 7.1336 - mae: 1.8297 - mse: 7.1336 - val_loss: 10.5519 - val_mae: 2.4670 - val_mse: 10.5519\n",
"Epoch 57/100\n",
- "354/354 [==============================] - 0s 190us/sample - loss: 7.5013 - mae: 1.9146 - mse: 7.5013 - val_loss: 11.6668 - val_mae: 2.1468 - val_mse: 11.6668\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 7.0707 - mae: 1.8462 - mse: 7.0707 - val_loss: 11.4684 - val_mae: 2.7035 - val_mse: 11.4684\n",
"Epoch 58/100\n",
- "354/354 [==============================] - 0s 195us/sample - loss: 7.4096 - mae: 1.8515 - mse: 7.4096 - val_loss: 13.8000 - val_mae: 2.5222 - val_mse: 13.8000\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 6.9632 - mae: 1.8780 - mse: 6.9632 - val_loss: 10.6361 - val_mae: 2.4145 - val_mse: 10.6361\n",
"Epoch 59/100\n",
- "354/354 [==============================] - 0s 180us/sample - loss: 7.2263 - mae: 1.8241 - mse: 7.2263 - val_loss: 10.8964 - val_mae: 2.2130 - val_mse: 10.8964\n",
+ "354/354 [==============================] - 0s 208us/sample - loss: 7.1218 - mae: 1.8522 - mse: 7.1218 - val_loss: 10.3080 - val_mae: 2.3628 - val_mse: 10.3080\n",
"Epoch 60/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 7.1773 - mae: 1.8526 - mse: 7.1773 - val_loss: 10.7862 - val_mae: 2.1088 - val_mse: 10.7862\n",
+ "354/354 [==============================] - 0s 261us/sample - loss: 6.7623 - mae: 1.7823 - mse: 6.7623 - val_loss: 10.3923 - val_mae: 2.3174 - val_mse: 10.3923\n",
"Epoch 61/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 7.0812 - mae: 1.8308 - mse: 7.0812 - val_loss: 10.8147 - val_mae: 2.3209 - val_mse: 10.8147\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 6.9012 - mae: 1.8504 - mse: 6.9012 - val_loss: 10.1488 - val_mae: 2.3802 - val_mse: 10.1488\n",
"Epoch 62/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 7.2235 - mae: 1.8367 - mse: 7.2235 - val_loss: 11.0399 - val_mae: 2.2583 - val_mse: 11.0399\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 6.6419 - mae: 1.8210 - mse: 6.6419 - val_loss: 10.7578 - val_mae: 2.5222 - val_mse: 10.7578\n",
"Epoch 63/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 7.0341 - mae: 1.8172 - mse: 7.0341 - val_loss: 10.9894 - val_mae: 2.1429 - val_mse: 10.9894\n",
+ "354/354 [==============================] - 0s 181us/sample - loss: 6.5397 - mae: 1.8096 - mse: 6.5397 - val_loss: 10.5892 - val_mae: 2.5217 - val_mse: 10.5892\n",
"Epoch 64/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 6.8729 - mae: 1.7492 - mse: 6.8729 - val_loss: 10.5465 - val_mae: 2.1532 - val_mse: 10.5465\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 6.4273 - mae: 1.7990 - mse: 6.4273 - val_loss: 10.7066 - val_mae: 2.4491 - val_mse: 10.7066\n",
"Epoch 65/100\n",
- "354/354 [==============================] - 0s 164us/sample - loss: 6.9345 - mae: 1.7837 - mse: 6.9345 - val_loss: 11.5379 - val_mae: 2.1963 - val_mse: 11.5379\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 6.2635 - mae: 1.7888 - mse: 6.2635 - val_loss: 10.2444 - val_mae: 2.4960 - val_mse: 10.2444\n",
"Epoch 66/100\n",
- "354/354 [==============================] - 0s 166us/sample - loss: 6.8218 - mae: 1.7714 - mse: 6.8218 - val_loss: 10.1486 - val_mae: 2.1617 - val_mse: 10.1486\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 6.3313 - mae: 1.7769 - mse: 6.3313 - val_loss: 10.1284 - val_mae: 2.3855 - val_mse: 10.1284\n",
"Epoch 67/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 6.8711 - mae: 1.8045 - mse: 6.8711 - val_loss: 10.3196 - val_mae: 2.2297 - val_mse: 10.3196\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 6.2141 - mae: 1.7620 - mse: 6.2141 - val_loss: 10.3170 - val_mae: 2.4570 - val_mse: 10.3170\n",
"Epoch 68/100\n",
- "354/354 [==============================] - 0s 162us/sample - loss: 6.7281 - mae: 1.7762 - mse: 6.7281 - val_loss: 11.2361 - val_mae: 2.2046 - val_mse: 11.2361\n",
+ "354/354 [==============================] - 0s 183us/sample - loss: 6.1732 - mae: 1.7589 - mse: 6.1732 - val_loss: 9.7494 - val_mae: 2.3912 - val_mse: 9.7494\n",
"Epoch 69/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 6.5518 - mae: 1.7292 - mse: 6.5518 - val_loss: 10.2378 - val_mae: 2.1494 - val_mse: 10.2378\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 6.1812 - mae: 1.7704 - mse: 6.1812 - val_loss: 10.7702 - val_mae: 2.3626 - val_mse: 10.7702\n",
"Epoch 70/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 6.6489 - mae: 1.7383 - mse: 6.6489 - val_loss: 11.1613 - val_mae: 2.2212 - val_mse: 11.1613\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 6.1634 - mae: 1.8019 - mse: 6.1634 - val_loss: 9.6836 - val_mae: 2.3618 - val_mse: 9.6836\n",
"Epoch 71/100\n",
- "354/354 [==============================] - 0s 176us/sample - loss: 6.5827 - mae: 1.7564 - mse: 6.5827 - val_loss: 10.0177 - val_mae: 2.2440 - val_mse: 10.0177\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 6.0410 - mae: 1.7080 - mse: 6.0410 - val_loss: 9.8525 - val_mae: 2.3718 - val_mse: 9.8525\n",
"Epoch 72/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 6.3411 - mae: 1.7463 - mse: 6.3411 - val_loss: 10.7929 - val_mae: 2.1946 - val_mse: 10.7929\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 5.7556 - mae: 1.7068 - mse: 5.7556 - val_loss: 11.4228 - val_mae: 2.4962 - val_mse: 11.4228\n",
"Epoch 73/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 6.3621 - mae: 1.7466 - mse: 6.3621 - val_loss: 9.7344 - val_mae: 2.1441 - val_mse: 9.7344\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 5.8854 - mae: 1.7138 - mse: 5.8854 - val_loss: 9.8943 - val_mae: 2.4214 - val_mse: 9.8943\n",
"Epoch 74/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 6.2298 - mae: 1.7411 - mse: 6.2298 - val_loss: 11.2495 - val_mae: 2.1948 - val_mse: 11.2495\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 5.6033 - mae: 1.6994 - mse: 5.6033 - val_loss: 10.2695 - val_mae: 2.3981 - val_mse: 10.2695\n",
"Epoch 75/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 6.3037 - mae: 1.7169 - mse: 6.3037 - val_loss: 10.1339 - val_mae: 2.1716 - val_mse: 10.1339\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 5.7909 - mae: 1.6973 - mse: 5.7909 - val_loss: 10.0138 - val_mae: 2.3440 - val_mse: 10.0138\n",
"Epoch 76/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 6.0780 - mae: 1.6686 - mse: 6.0780 - val_loss: 11.9975 - val_mae: 2.3317 - val_mse: 11.9975\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 5.4470 - mae: 1.6519 - mse: 5.4470 - val_loss: 9.7148 - val_mae: 2.4004 - val_mse: 9.7148\n",
"Epoch 77/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 6.3311 - mae: 1.7082 - mse: 6.3311 - val_loss: 11.6433 - val_mae: 2.2756 - val_mse: 11.6433\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 5.6775 - mae: 1.6463 - mse: 5.6775 - val_loss: 10.6783 - val_mae: 2.3670 - val_mse: 10.6783\n",
"Epoch 78/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 6.0620 - mae: 1.6765 - mse: 6.0620 - val_loss: 13.0159 - val_mae: 2.5073 - val_mse: 13.0159\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 5.4289 - mae: 1.7021 - mse: 5.4289 - val_loss: 10.2150 - val_mae: 2.3861 - val_mse: 10.2150\n",
"Epoch 79/100\n",
- "354/354 [==============================] - 0s 167us/sample - loss: 6.1819 - mae: 1.7157 - mse: 6.1819 - val_loss: 10.1000 - val_mae: 2.1462 - val_mse: 10.1000\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 5.4991 - mae: 1.6477 - mse: 5.4991 - val_loss: 9.6550 - val_mae: 2.3681 - val_mse: 9.6550\n",
"Epoch 80/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 5.9085 - mae: 1.6720 - mse: 5.9085 - val_loss: 11.7867 - val_mae: 2.5045 - val_mse: 11.7866\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 5.3646 - mae: 1.6555 - mse: 5.3646 - val_loss: 11.0607 - val_mae: 2.4424 - val_mse: 11.0607\n",
"Epoch 81/100\n",
- "354/354 [==============================] - 0s 168us/sample - loss: 6.0201 - mae: 1.6678 - mse: 6.0201 - val_loss: 10.8789 - val_mae: 2.3031 - val_mse: 10.8789\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 5.3874 - mae: 1.6344 - mse: 5.3874 - val_loss: 11.2996 - val_mae: 2.6303 - val_mse: 11.2996\n",
"Epoch 82/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 6.1278 - mae: 1.6799 - mse: 6.1278 - val_loss: 9.8114 - val_mae: 2.1048 - val_mse: 9.8114\n",
+ "354/354 [==============================] - 0s 167us/sample - loss: 5.3116 - mae: 1.6345 - mse: 5.3116 - val_loss: 10.2543 - val_mae: 2.3943 - val_mse: 10.2543\n",
"Epoch 83/100\n",
- "354/354 [==============================] - 0s 150us/sample - loss: 5.6372 - mae: 1.6280 - mse: 5.6372 - val_loss: 10.0971 - val_mae: 2.1464 - val_mse: 10.0971\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 5.1442 - mae: 1.6227 - mse: 5.1442 - val_loss: 10.5314 - val_mae: 2.3998 - val_mse: 10.5314\n",
"Epoch 84/100\n",
- "354/354 [==============================] - 0s 153us/sample - loss: 5.9587 - mae: 1.6421 - mse: 5.9587 - val_loss: 9.4731 - val_mae: 2.1915 - val_mse: 9.4731\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 5.2872 - mae: 1.6288 - mse: 5.2872 - val_loss: 9.8682 - val_mae: 2.3268 - val_mse: 9.8682\n",
"Epoch 85/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 5.6189 - mae: 1.6223 - mse: 5.6189 - val_loss: 9.9788 - val_mae: 2.3332 - val_mse: 9.9788\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 5.1584 - mae: 1.6282 - mse: 5.1584 - val_loss: 10.2676 - val_mae: 2.4443 - val_mse: 10.2676\n",
"Epoch 86/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 5.8193 - mae: 1.6930 - mse: 5.8193 - val_loss: 10.4070 - val_mae: 2.1490 - val_mse: 10.4070\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 5.0609 - mae: 1.6078 - mse: 5.0609 - val_loss: 10.0901 - val_mae: 2.4020 - val_mse: 10.0901\n",
"Epoch 87/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 5.5919 - mae: 1.6152 - mse: 5.5919 - val_loss: 9.9985 - val_mae: 2.2546 - val_mse: 9.9985\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 5.1753 - mae: 1.6148 - mse: 5.1753 - val_loss: 10.7763 - val_mae: 2.3816 - val_mse: 10.7763\n",
"Epoch 88/100\n",
- "354/354 [==============================] - 0s 160us/sample - loss: 5.6652 - mae: 1.6246 - mse: 5.6652 - val_loss: 9.1506 - val_mae: 2.0642 - val_mse: 9.1506\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 5.0408 - mae: 1.6055 - mse: 5.0408 - val_loss: 10.1056 - val_mae: 2.3234 - val_mse: 10.1056\n",
"Epoch 89/100\n",
- "354/354 [==============================] - 0s 157us/sample - loss: 5.6349 - mae: 1.6108 - mse: 5.6349 - val_loss: 9.8522 - val_mae: 2.0813 - val_mse: 9.8522\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 5.0175 - mae: 1.6009 - mse: 5.0175 - val_loss: 9.6620 - val_mae: 2.3334 - val_mse: 9.6620\n",
"Epoch 90/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 5.6165 - mae: 1.6449 - mse: 5.6165 - val_loss: 9.1553 - val_mae: 2.0421 - val_mse: 9.1553\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 4.7522 - mae: 1.5615 - mse: 4.7522 - val_loss: 9.8084 - val_mae: 2.3036 - val_mse: 9.8084\n",
"Epoch 91/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.5416 - mae: 1.6153 - mse: 5.5416 - val_loss: 10.4231 - val_mae: 2.2880 - val_mse: 10.4231\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 4.8323 - mae: 1.5873 - mse: 4.8323 - val_loss: 10.7285 - val_mae: 2.4886 - val_mse: 10.7285\n",
"Epoch 92/100\n",
- "354/354 [==============================] - 0s 158us/sample - loss: 5.3909 - mae: 1.5863 - mse: 5.3909 - val_loss: 8.8087 - val_mae: 2.1022 - val_mse: 8.8087\n",
+ "354/354 [==============================] - 0s 165us/sample - loss: 4.8179 - mae: 1.5678 - mse: 4.8179 - val_loss: 10.1033 - val_mae: 2.3372 - val_mse: 10.1033\n",
"Epoch 93/100\n",
- "354/354 [==============================] - 0s 155us/sample - loss: 5.3540 - mae: 1.5986 - mse: 5.3540 - val_loss: 9.6963 - val_mae: 2.1931 - val_mse: 9.6963\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 4.7970 - mae: 1.5422 - mse: 4.7970 - val_loss: 9.8511 - val_mae: 2.3521 - val_mse: 9.8511\n",
"Epoch 94/100\n",
- "354/354 [==============================] - 0s 161us/sample - loss: 5.3198 - mae: 1.6074 - mse: 5.3198 - val_loss: 9.1875 - val_mae: 2.1917 - val_mse: 9.1875\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 4.7676 - mae: 1.5674 - mse: 4.7676 - val_loss: 10.1749 - val_mae: 2.4087 - val_mse: 10.1749\n",
"Epoch 95/100\n",
- "354/354 [==============================] - 0s 165us/sample - loss: 5.2299 - mae: 1.5638 - mse: 5.2299 - val_loss: 8.8746 - val_mae: 2.1273 - val_mse: 8.8746\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 4.7223 - mae: 1.5431 - mse: 4.7222 - val_loss: 10.2481 - val_mae: 2.3268 - val_mse: 10.2481\n",
"Epoch 96/100\n",
- "354/354 [==============================] - 0s 163us/sample - loss: 5.2789 - mae: 1.5651 - mse: 5.2789 - val_loss: 9.7351 - val_mae: 2.2359 - val_mse: 9.7351\n",
+ "354/354 [==============================] - 0s 164us/sample - loss: 4.6685 - mae: 1.5333 - mse: 4.6685 - val_loss: 10.7347 - val_mae: 2.5154 - val_mse: 10.7347\n",
"Epoch 97/100\n",
- "354/354 [==============================] - 0s 153us/sample - loss: 5.3399 - mae: 1.6002 - mse: 5.3399 - val_loss: 9.7185 - val_mae: 2.1080 - val_mse: 9.7185\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 4.5642 - mae: 1.5675 - mse: 4.5642 - val_loss: 11.3132 - val_mae: 2.4601 - val_mse: 11.3132\n",
"Epoch 98/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 5.0072 - mae: 1.5055 - mse: 5.0072 - val_loss: 8.3621 - val_mae: 2.0586 - val_mse: 8.3621\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 4.3886 - mae: 1.4906 - mse: 4.3886 - val_loss: 12.2466 - val_mae: 2.7436 - val_mse: 12.2466\n",
"Epoch 99/100\n",
- "354/354 [==============================] - 0s 156us/sample - loss: 5.2596 - mae: 1.5557 - mse: 5.2596 - val_loss: 8.6406 - val_mae: 2.0527 - val_mse: 8.6406\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 4.4689 - mae: 1.5368 - mse: 4.4689 - val_loss: 10.4188 - val_mae: 2.3596 - val_mse: 10.4188\n",
"Epoch 100/100\n",
- "354/354 [==============================] - 0s 159us/sample - loss: 5.0983 - mae: 1.5543 - mse: 5.0983 - val_loss: 8.4836 - val_mae: 2.0234 - val_mse: 8.4836\n"
+ "354/354 [==============================] - 0s 168us/sample - loss: 4.6496 - mae: 1.5348 - mse: 4.6496 - val_loss: 10.0829 - val_mae: 2.3822 - val_mse: 10.0829\n"
]
}
],
@@ -936,16 +932,16 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 8.4836\n",
- "x_test / mae : 2.0234\n",
- "x_test / mse : 8.4836\n"
+ "x_test / loss : 10.0829\n",
+ "x_test / mae : 2.3822\n",
+ "x_test / mse : 10.0829\n"
]
}
],
@@ -967,7 +963,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -1011,66 +1007,66 @@
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
- " <td>15.144930</td>\n",
- " <td>2.312168</td>\n",
- " <td>15.144930</td>\n",
- " <td>17.019036</td>\n",
- " <td>2.582618</td>\n",
- " <td>17.019036</td>\n",
+ " <td>19.466892</td>\n",
+ " <td>2.462477</td>\n",
+ " <td>19.466893</td>\n",
+ " <td>18.670107</td>\n",
+ " <td>2.852570</td>\n",
+ " <td>18.670107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
- " <td>43.707091</td>\n",
- " <td>1.906713</td>\n",
- " <td>43.707090</td>\n",
- " <td>26.587745</td>\n",
- " <td>1.288267</td>\n",
- " <td>26.587746</td>\n",
+ " <td>64.483863</td>\n",
+ " <td>2.592690</td>\n",
+ " <td>64.483872</td>\n",
+ " <td>48.257937</td>\n",
+ " <td>2.039701</td>\n",
+ " <td>48.257935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
- " <td>5.007155</td>\n",
- " <td>1.505515</td>\n",
- " <td>5.007155</td>\n",
- " <td>8.362053</td>\n",
- " <td>2.023406</td>\n",
- " <td>8.362053</td>\n",
+ " <td>4.388600</td>\n",
+ " <td>1.490624</td>\n",
+ " <td>4.388600</td>\n",
+ " <td>9.655048</td>\n",
+ " <td>2.303586</td>\n",
+ " <td>9.655047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
- " <td>6.285225</td>\n",
- " <td>1.716563</td>\n",
- " <td>6.285225</td>\n",
- " <td>10.419040</td>\n",
- " <td>2.192718</td>\n",
- " <td>10.419040</td>\n",
+ " <td>5.658976</td>\n",
+ " <td>1.698877</td>\n",
+ " <td>5.658976</td>\n",
+ " <td>10.269067</td>\n",
+ " <td>2.393491</td>\n",
+ " <td>10.269066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
- " <td>8.037316</td>\n",
- " <td>1.922454</td>\n",
- " <td>8.037317</td>\n",
- " <td>12.488579</td>\n",
- " <td>2.301342</td>\n",
- " <td>12.488580</td>\n",
+ " <td>7.713175</td>\n",
+ " <td>1.945081</td>\n",
+ " <td>7.713175</td>\n",
+ " <td>10.750849</td>\n",
+ " <td>2.469115</td>\n",
+ " <td>10.750849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
- " <td>10.482029</td>\n",
- " <td>2.189933</td>\n",
- " <td>10.482029</td>\n",
- " <td>14.470699</td>\n",
- " <td>2.503943</td>\n",
- " <td>14.470701</td>\n",
+ " <td>10.770471</td>\n",
+ " <td>2.242925</td>\n",
+ " <td>10.770470</td>\n",
+ " <td>12.249026</td>\n",
+ " <td>2.610316</td>\n",
+ " <td>12.249027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
- " <td>414.560260</td>\n",
- " <td>18.257650</td>\n",
- " <td>414.560242</td>\n",
- " <td>266.372801</td>\n",
- " <td>13.991282</td>\n",
- " <td>266.372803</td>\n",
+ " <td>536.084498</td>\n",
+ " <td>21.333506</td>\n",
+ " <td>536.084595</td>\n",
+ " <td>439.656211</td>\n",
+ " <td>19.319771</td>\n",
+ " <td>439.656189</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1079,16 +1075,16 @@
"text/plain": [
" loss mae mse val_loss val_mae val_mse\n",
"count 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000\n",
- "mean 15.144930 2.312168 15.144930 17.019036 2.582618 17.019036\n",
- "std 43.707091 1.906713 43.707090 26.587745 1.288267 26.587746\n",
- "min 5.007155 1.505515 5.007155 8.362053 2.023406 8.362053\n",
- "25% 6.285225 1.716563 6.285225 10.419040 2.192718 10.419040\n",
- "50% 8.037316 1.922454 8.037317 12.488579 2.301342 12.488580\n",
- "75% 10.482029 2.189933 10.482029 14.470699 2.503943 14.470701\n",
- "max 414.560260 18.257650 414.560242 266.372801 13.991282 266.372803"
+ "mean 19.466892 2.462477 19.466893 18.670107 2.852570 18.670107\n",
+ "std 64.483863 2.592690 64.483872 48.257937 2.039701 48.257935\n",
+ "min 4.388600 1.490624 4.388600 9.655048 2.303586 9.655047\n",
+ "25% 5.658976 1.698877 5.658976 10.269067 2.393491 10.269066\n",
+ "50% 7.713175 1.945081 7.713175 10.750849 2.469115 10.750849\n",
+ "75% 10.770471 2.242925 10.770470 12.249026 2.610316 12.249027\n",
+ "max 536.084498 21.333506 536.084595 439.656211 19.319771 439.656189"
]
},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -1101,14 +1097,14 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 2.0234\n"
+ "min( val_mae ) : 2.3036\n"
]
}
],
@@ -1118,12 +1114,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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NaRARkU6j0NBChezo6Vb3hIiIdBqFhhbS5E4iItLJFBpaaEz3hMY0iIhIh1FoaKGirp4QEZEOptDQQmPGNKh7QkREOoxCQwvp6gkREelkCg0tpHtPiIhIJ1NoaCFdPSEiIp1MoaGFdMMqERHpZAoNLaTuCRER6WQKDS2klgYREelkCg0tpFtji4hIJ1NoaCENhBQRkU6m0NBC+WyAxc8rtZBaGLa1PiIiItOh0NBCZjautUGhQUREOodCQyvUarD+1/A/9/Hy4cdHVpcq1TZWSkREZHqyUxeRPVatwP/5CwD+0rLcdsh5gFoaRESks6iloRVy+ZGnea+CO6DBkCIi0lkUGlohCCCbG/ky71FYUPeEiIh0EoWGVkm0NhQ8Cguaq0FERDqJQkOr5AujT+PQoO4JERHpJAoNrZIIDSMtDWWFBhER6RwKDa3SoHtiuKrQICIinUOhoVXy466gQN0TIiLSWRQaWiWn7gkREelsCg2topYGERHpcAoNrdKopUGhQUREOohCQ6skWxrC+uROCg0iItI5FBpapUFLg7onRESkkyg0tEqDMQ1qaRARkU6i0NAqamkQEZEOp9DQKrp6QkREOpxCQ6vo6gkREelwCg2tkku2NERhQS0NIiLSSVIXGsys28weNTM3s39osP2FZnadmW03swEzu93MTp1gXwvN7HIz22RmJTP7tZmdZ2Y290cyTqMbVik0iIhIB8m2uwINfAbYr9EGMzsCuAOoAp8HdgLvA35qZme4+02JsnngZ8BxwOXAOuAMoA/YH7h47g6hgZzGNIiISGdLVUuDmR0PfAS4aIIilwKLgNPd/VJ37wN+B9gMXDGuBeEc4ATgo+7+UXdf4+5nAT8APmFmh87ZgTSilgYREelwqQkNZpYB1gA/IfpgH799HvBm4BZ3X1tf7+67ga8DLyAKCXVvBwbjfSZdBuSAP5rN+k9JLQ0iItLhUhMagD8HjgLOn2D7sUABuLPBtp/HyxMAzCwAjgd+6e6lcWXvBkLGBoy5l7jkshAPhKyFTqUWtrQaIiIiM5WK0GBmhwOfBj7j7hsmKHZgvNzUYFt93cp4uRjoalTW3YeBbYmy4+tyrpnd21zNpyFxyWUXoy0Mam0QEZFOkYrQAHwVeBT44iRluuPlcINtpXFlJitbL9/daIO7X+nuL5ukHjOTbGmgOvJcoUFERDpF26+eMLN3Aq8DXunulUmKDsbLQoNtxXFlJitbLz84wba5MWZyp9GgUCorNIiISGdoa2gwswJR68KPgS1mdmS8qd51sDBe9wzRFRLJbUn1dfXuiO3AUKOy8XsuBW7d4wOYjjFjGkZbGnQFhYiIdIp2d090AcuAM4H1icct8fZ3xl+fAzxA1N1wcoP9nBQv7wVw9xC4DzguDglJLyc67tkftzCZREtDPkx0T1QVGkREpDO0u3tiAHhrg/XLiCZh+gnwDeBX7r7bzG4AzjKzl7j7/QBm1kMUKtYTXRlRdzVwCnAu0eROdR8hmhzqmlk+lsklWhpy4WgvjLonRESkU7Q1NMRjGP51/HozOyx++oi7J7d/HHgtcKOZfQnYRTQj5ErgTHf3RNk1wHuBL8b7Wwe8Afg94BJ3f3Q2j2VKiZaGXFgFdzDTQEgREekY7W5pmBZ3f9jMTgE+B1wA5Im6IV6fnEI6Lls2s9OAS4C3EY1jeAT4IHBFSysOkM1CEEAYEuBkCamSUWgQEZGOkcrQEM/V0PCmUu6+DnhLk/vZQTRZ1EQTRrVWrgDDQ0A0K2TVMpQq1SleJCIikg7tHgi5b2lwBYVaGkREpFMoNLRSgysoShVNIy0iIp1BoaGVGtx/Qt0TIiLSKRQaWilxe2zd6VJERDqNQkMrJbonivGM2QoNIiLSKRQaWinRPZEf6Z5QaBARkc6g0NBKY25ape4JERHpLAoNrTSmpUGhQUREOotCQys1aGlQ94SIiHQKhYZWatDSoNAgIiKdQqGhlTSmQUREOphCQyslWxpCXT0hIiKdRaGhlXK694SIiHQuhYZW0oyQIiLSwRQaWqlBS0OpXMPd21UjERGRpik0tFKipaFI1MLgQKWmO12KiEj6KTS0UqKloctGuyVKZXVRiIhI+ik0tFKDlgaA4apCg4iIpJ9CQys1GNMAGgwpIiKdQaGhlSZqaahoTIOIiKSfQkMrTdTSoO4JERHpAAoNrdRgngaAsronRESkAyg0tFIuOY30aGjQVNIiItIJFBpaKdHSkEuEhnJVYxpERCT9FBpaKdHSkAsrI8919YSIiHQChYZWSrQ0ZGsaCCkiIp1FoaGVsjkwAyDjNQKPuiXU0iAiIp1AoaGVzMYOhtSdLkVEpIMoNLTamLkaorCg0CAiIp1AoaHVGszVoKsnRESkEyg0tFqDWSE1T4OIiHQChYZWazCmoayrJ0REpAMoNLRaonuioIGQIiLSQRQaWq3BVNLDGtMgIiIdQKGh1fLFkadFtTSIiEgHUWhotXyDMQ0KDSIi0gEUGlpNV0+IiEiHUmhotTHzNERhQfM0iIhIJ1BoaLUGLQ0a0yAiIp1AoaHVGswIqbtciohIJ1BoaDW1NIiISIdSaGi1Ce494e7tqpGIiEhT2h4azOyFZvYdM1tnZjvNbNDMfmNmXzSzFROUv87MtpvZgJndbmanTrDvhWZ2uZltMrOSmf3azM4zM5v7I5tAoqWhi9EWBg2GFBGRtMu2uwLAQcAK4IfARqAKrALOBc42s9Xu/jSAmR0B3BGX+TywE3gf8FMzO8Pdb6rv1MzywM+A44DLgXXAGUAfsD9wcSsO7jkSLQ1dNhoahis1CrlMO2okIiLSlLaHBnf/f8D/G7/ezG4DrgHeQxQQAC4FFgEvdfe1cbmrgF8DV5jZUT7azn8OcALwIXe/PF63xsy+D3zCzL7p7o/N0WFNLNHSUEy0NGgwpIiIpF3buycmUf9AXwxgZvOANwO31AMDgLvvBr4OvIAoJNS9HRgE1ozb72VADvijuan2FBItDWNCgwZDiohIyqUmNJhZ0cz2M7ODzOx1wD/Gm34cL48FCsCdDV7+83h5QryvADge+KW7l8aVvRsIGRswWifZ0uDJ0KAxDSIikm5NhYbe3t4/7u3tPXbcunxvb++CCcq/qre398Jp1uUcYCvwBPBTom6Id7r77fH2A+Plpgavra9bGS8XA12Nyrr7MLAtUXYMMzvXzO6dZt2b1+DW2KDuCRERSb9mWxr+Cfjdces+DmyfoPyrgYumWZfrgP8N/B7wGWAHsCyxvTteDjd4bWlcmcnK1st3N9rg7le6+8uarPP0JW+NzWho0E2rREQk7do+ELLO3TcSXT0BcF08YPEeM+ty90uJxidA1EUxXv1+04Pjlo3K1ssPTrBtbiXnaQhHQ4NuWiUiImmXmjEN47n7r4BfAr3xqs3xslG3Qn1dvTtiOzDUqKyZFYClNO7mmHvJloZEaNA8DSIiknapDQ2xLmBJ/PwBou6GkxuUOyle3gvg7iFwH3BcHBKSXk503HM3bmEyiZaGXCI06OoJERFJu7aHBjM7YIL1rwFeTHxlRHxp5Q3Aq83sJYlyPUSDKNcTXRlRdzXRuIVzx+36I0STQ10zS4cwPYmWhlxYGXmugZAiIpJ2aRjT8NV4uuj/IJqboQi8FDgb6Af+IlH248BrgRvN7EvALqIZIVcCZ/rYGzisAd4LfNHMDiOaEfINRAMtL3H3R+fwmCaWaGnI1hKhQS0NIiKSctMJDYt6e3sPSX4N0NvbezAw/l4Oi6ax36uBdwPvIrpawonCwz8CX3D3x+sF3f1hMzsF+BxwAZAn6oZ4fXIK6bhs2cxOAy4B3kY0juER4IPAFdOo3+xKtDRkwyq4g5lCg4iIpN50QsOH48d4G/akAu5+DdPoKnD3dcBbmiy7Azg/fqRDEEA2B9WolSHvNcqWVWgQEZHUazY0PE7UAiCzIZcfCQ0Fr1Imq6snREQk9ZoKDX19fYfNcT32Lfk8DA1ET+NZITVPg4iIpF3br57YJ+WeO5V0WVdPiIhIyik0tEM+McFTHBo0pkFERNKuqe6J3t7eLmAF8ExfX9+ucdsOBb4EnEp0FcWtwP/X19f30CzXde/RoKVhWGMaREQk5ZptaTifaPKkFyVX9vb2zicKCW8BFgDzgTcCt/T29i6dxXruXca0NEQtDGppEBGRtGs2NPwO8ERfX9/Px60/DzgEuBM4EtgfuBw4gMaXZwo0HtOg0CAiIinX7CWXL6LxvRrOIroU80/6+vp+G6/7cG9v75nAGcCFe17FvVCDMQ26ekJERNKu2ZaGZcCYaZd7e3tzwHHAgw3GL/wHUcuDNJJsaQjrV09oTIOIiKRbs6GhAGTGrTsGyDH2JlF1TxPdLEoayTcYCKmWBhERSblmQ8MWojtOJr2CqGuiUbfFfODZPajX3q3RJZeap0FERFKu2dDwX8Cpvb29r4aRSzDfF2/7WYPyLwY27XHt9laNLrlUS4OIiKRcs6HhS/Hyxt7e3vuIxjccC9zS19f3YLJgb2/vAuAUYPyVFlLXoKWhXA0JXbf3EBGR9GoqNPT19d0LvAcYAlYDy4m6Jd7doPi7iW5ZfePsVHEvlGhp6GK0hUGDIUVEJM2avjV2X1/ft3t7e79P1PWwLXGJ5Xg3ALcB62ahfnun3GhLQ7eNBoVypUYxN368qYiISDo0HRoA+vr6hoB7piizYU8qtE9IXD3RZaMtDaVKjQXtqI+IiEgTdMOqdki0NBTHdE9oMKSIiKRXszes+uOZ7Lyvr++qmbxur5doaShSHXmuKyhERCTNmu2e+CeiORmaZXF5hYZGEi0NBR8NCrrTpYiIpNl0xjRUgf8L/M8c1WXfkU+GBrU0iIhIZ2g2NNwKvBL4XaLLLdcA1/T19ZXmqmJ7tcQll3mFBhER6RDNztPwGuCFwN8R3Yjqm8CTvb29l/f29h47h/XbOzWY3AkUGkREJN2mM0/Dw8DHent7Pwm8hWga6fOA3t7e3l8A/wj8S19f38Cc1HRvkmxpCEdDgyZ3EhGRNJv2JZd9fX3Vvr6+7/f19b0eOAL4LLACuBLY3Nvbe/Is13Hvk2hpyCVCQ0ktDSIikmJ7NE9DX1/fY319fX8NnEt0g6oeYNlsVGyvlmhpyIaVkeeap0FERNJsWjNCJvX29h4I/En8OBQoAd8G7pudqu3FEi0N2dpoaNCYBhERSbNphYbe3t4AeCNwDvD6+PUPAB8GvtXX17dz1mu4N0q0NGRqFXAHM4UGERFJtWZnhDwc+FPgvUTjFwaAfwbW9PX13T131dtLZbMQBBCGBO5kCamS0UBIERFJtWZbGh6Ol/cCFwFX6yqJPZQrwPAQEF12WbWMBkKKiEiqNRsaDKgQtTJcCFzY29s71Wu8r6/v0D2o294tPxoaimGVwaCggZAiIpJq0xnTkAMOmquK7HMKReiPnhY9GgypMQ0iIpJmTYWGvr4+3UJ7thWKI09HQoPGNIiISIopDLRLMjSEamkQEZH0U2hol0LXyNOuuKWhrNAgIiIpptDQLsVEaIhbGnT1hIiIpJlCQ7vkRyd4qo9p0DwNIiKSZgoN7ZJoadDVEyIi0gkUGtolMaahGN/pcljzNIiISIopNLRL4uqJLi8DamkQEZF0U2hol2RoCEfHNITu7aqRiIjIpBQa2iXRPdFt1ZHnGgwpIiJp1fbQYGYvMLPPmNnPzWyrmfWb2Voz+6SZzWtQ/oVmdp2ZbTezATO73cxOnWDfC83scjPbZGYlM/u1mZ1nZjb3RzaFREtDtydCg7ooREQkpaZz74m58ifAB4Drge8Q3RjrNcAlwB+a2UnuPgRgZkcAdwBV4PPATuB9wE/N7Ax3v6m+UzPLAz8DjgMuB9YBZwB9wP7Axa04uAklQsM8RkNDqVJjQTvqIyIiMoU0hIZ/BS51952JdV8zs/XAJ4E/Bf4hXn8psAh4qbuvBTCzq4BfA1eY2VHuI4MCzgFOAD7k7pfH69aY2feBT5jZN939sTk9sskkJ3eiMvJcd7oUEZG0anv3hLvfOy4w1H0vXr4YIO6qeDNwSz0wxK/fDXwdeAFRSKh7OzAIrBm338uI7tj5R7NyADOVTw6EHG1p0BUUIiKSVm0PDZOo34b7qXh5LE3JKjEAACAASURBVFAA7mxQ9ufx8gQAMwuA44FfuntpXNm7gZCxAaP1xkzuVB55rjtdiohIWqUyNJhZBriQaOzCd+PVB8bLTQ1eUl+3Ml4uBroalXX3YWBbomx7JMY0FMLR7gm1NIiISFqlMjQQdSGcBFzo7g/G67rj5XCD8qVxZSYrWy/f3WiDmZ1rZvdOr7ozkAwNNYUGERFJv9SFBjP7G+B84Ep3vzSxaTBeFp77KorjykxWtl5+sNEGd7/S3V/WfI1nKDFPQ6GW6J5QaBARkZRKVWgws4uBTwHfBP5s3ObN8bJRt0J9Xb07Yjsw1KismRWApTTu5midxF0uc2GFwKOxDJrcSURE0io1ocHMLgIuAq4CzklcOln3AFF3w8kNXn5SvLwXwN1D4D7guDgkJL2c6LjnvgtiMkEwJjgU4gmeSmppEBGRlEpFaDCzC4kmW/oW8N74Q3+M+NLKG4BXm9lLEq/tIZqTYT3RlRF1VxONWzh33K4+QjTA8ppZPISZSXRRjN5/QqFBRETSqe2TO5nZB4BPA48DNwFvHzfL81Pu/rP4+ceB1wI3mtmXgF1EM0KuBM4c1zqxBngv8EUzO4xoRsg3AL8HXOLuj87VMTWt2AX9O6KnHoUGjWkQEZG0antoYHS+hEOAf26w/Vai6aBx94fN7BTgc8AFQJ6oG+L1ySmk47JlMzuNaDrqtxGNY3gE+CBwxRwcx/QluicUGkREJO3aHhrc/T3Ae6ZRfh3wlibL7iC6EuP8mdRtzhWf2z2hyZ1ERCStUjGmYZ9VSM4KqZYGERFJN4WGdiok7z+h0CAiIumm0NBOidBQb2nQ1RMiIpJWCg3tpO4JERHpIAoN7dSoe0IDIUVEJKUUGtqpQfeEWhpERCStFBraqdEllwoNIiKSUgoN7ZRv0NKggZAiIpJSCg3tVEyGhuiGVeWKxjSIiEg6KTS005gbVpUBtTSIiEh6KTS0U/LqCQ2EFBGRlFNoaKdES0MhjLsnqiHhmJt1ioiIpINCQzslWhq6qYw8L2uuBhERSSGFhnZq0D0BUFYXhYiIpJBCQzslp5GOuycASgoNIiKSQgoN7ZScETK+egJ00yoREUknhYZ2yhdGnha8SuDRWIahskKDiIikj0JDOwXBmNaGQjzB0+5SZaJXiIiItI1CQ7vln3uny12D5YlKi4iItI1CQ7slblpVv/9Ev1oaREQkhRQa2q3B7bHV0iAiImmk0NBuhed2T/QPqaVBRETSR6Gh3QrP7Z7YNaSWBhERSR+FhnZr0NKwSy0NIiKSQgoN7dZgTEO/WhpERCSFFBrarUH3RP+gWhpERCR9FBrardioe0ItDSIikj4KDe2Wf+6dLneXKoTu7aqRiIhIQwoN7ZaY3KnHontOhA4DpepErxAREWkLhYZ2S4xpmB+M3qhKXRQiIpI2Cg3tVhi90+V8G21d0BUUIiKSNgoN7ZZoaZhniZYGXUEhIiIpo9DQbonQUB8ICWppEBGR9FFoaLfCc6+eAM0KKSIi6aPQ0G6J0FAIk6FBLQ0iIpIuCg3tlrjkMl8bDQq606WIiKSNQkO7JSZ3ylWHR57vGlRLg4iIpItCQ7slWhoylURLQ0ktDSIiki4KDe2Wy4MZAJlqmcBDQC0NIiKSPgoN7RYEkB+d4Kng0QRPGtMgIiJpo9CQBsm5GnSnSxERSSmFhjRIXHbZTRQahso1KrWwXTUSERF5jraHBjP7uJlda2a/NTM3sw1TlD/RzG4ys34z22VmPzGz1ROUPdDMrjKzrWY2ZGb3mtlb5+RA9kQiNCzJj67WrJAiIpImbQ8NwGeBU4FHgO2TFTSzk4BbgcOBC4GLgOcDt5vZqnFllwD/CZwFfBX4MLAbuMbM3jvLx7BnEt0TS3KjrQu6/4SIiKRJtt0VAI5w998CmNl/Az2TlP0KUAZe6e6b4tdcA6wD/h54XaLsBUTh4s3ufkNc9hvAncDfmdm17r57tg9mRoqJloasQzxdg1oaREQkTdre0lAPDFMxsyOBE4Br64Ehfv0m4FrgNDM7IPGStwOP1ANDXLYGXA4sAd4wC9WfHYkJnhZmRlsadAWFiIikSdtDwzScEC/vbLDt54ABLwUwsxXAynh9o7LJ/bVfYoKnZGjQFRQiIpImnRQaDoyXmxpsq69bOYOy7ZdoaZgfVEee606XIiKSJp0UGrrj5XCDbaVxZaZTdgwzO9fM7p1RDWcqMaahx2ojzzUrpIiIpEknhYbBeFlosK04rsx0yo7h7le6+8tmVMOZSlw90c1oS4PGNIiISJp0UmjYHC8bdSvU122aQdn2S07u5KNBQVdPiIhImnRSaLgnXp7cYNtJgAO/AHD3J4lCwUkTlAVobRfEZBItDcVEaNCYBhERSZOOCQ3u/jDRB/1bzaw+0JH4+VuB/3D3LYmXXA0cYWZvSpTNAB8EdgA/bknFm5FoaSjUkqFBLQ0iIpIebZ/cyczeBRwaf7kMyJvZp+KvH3P3byWKfxi4mWgGyMvjdR8kCj9/MW7XnyMKE981sy8StTy8jehSy3PcvX/WD2amEqEhXxsdu6kxDSIikiZtDw3AnwKvGrfub+LlrcBIaHD3O8zs1cAl8cOBO4C3uvv9yR24+zYzO4UoPHyAaKbJ/wHOdvfvzcFxzFyieyJbHW1d6B+q4O6YWTtqJSIiMkbbQ4O7v3qa5e8EXttk2U3Au2ZQrdZKXHKZqQyTzwaUqyGVWkipUqMr3/Zvk4iISOeMadirJSZ3ojTEgq7RW11qrgYREUkLhYY0SEwjTbnE/K7cyJe6gkJERNJCoSENEmMaKI0NDRoMKSIiaaHQkAaJqycol8Z2T+iySxERSQmFhjTI5aF+hUR5mIVdowMfNSukiIikhUJDGgTBmMGQi3M+8nzXoLonREQkHRQa0iLRRbEoG4487y8pNIiISDooNKRFMjRkRkODLrkUEZG0UGhIi67ukaeLwuRU0goNIiKSDgoNabF89C7eS/pH77uleRpERCQtFBrS4qDDR54ueGbjyHNdcikiImmh0JAWBx028rS49YmR55rcSURE0kKhIS0SLQ3ZJx8Djy673D1UoRb6RK8SERFpGYWGtFi6PxSjwZA20M/BmWgwpAMDuuxSRERSQKEhLcxg5WEjXx5tO0aea1yDiIikgUJDmiS6KI6obht5rnENIiKSBgoNaZIYDHlYeTQ0bNw20IbKiIiIjKXQkCaJloZDh58ZeX7X+qfaURsREZExFBrSZOVoaFi0awsZrwFw7yNbKVdr7aqViIgIoNCQLt3zYMlyAKxW4/juEgBD5RprH9022StFRETmnEJD2iS6KF6zaPQeFHc+pC4KERFpL4WGtEkMhlyV6R95/vOHniJ0TfIkIiLto9CQNomWhmU7n2Rhdx6AZ3cP89DmHRO9SkREZM4pNKRNIjTY5g28/PnLR76+40F1UYiISPsoNKTN8pWQzUXPn93K7xzSM7LpToUGERFpI4WGtMlmYcUhI1+uzvWTz0bfpsef2c2mZzXRk4iItIdCQxolBkMWnnqc45+3bORrtTaIiEi7KDSkUWJcAxsf5RUv3H/kyzse3NKGComIiCg0pFMyNGzawInPX47FX67buJ0dA8MNXyYiIjKXFBrSKHGLbDZuYFFXjhcdvBiA0OHSH/ySUrnanrqJiMg+S6EhjRYugZ6F0fPhIdjyBG986aEjm9du2Manrr6HwWEFBxERaR2FhjQyg4OfN/r1Vy7k1GUh73nNC0dWPfD4s3ziu3cxUKq0oYIiIrIvUmhIq9e/FYL42/PMU3DpR3nbijLvO+3okSLrNu7gY9++i18++oymmBYRkTmn0JBWxxwP518E+UL09UA//N0F/EF+M72nv2ik2Pond3LBt+/iT/tu4Zo7HtEgSRERmTPm+g+1od7eXgfo6+trb0UefRC+ciH07xxdd8TR3Hf4KVy4Pk+FzJjigcGRByxk9eH7sfrwpbz44CUUchlERESaZBNuUGhoLDWhAeCpzXDZJ2Hrk2NWVxcs5r6DTuDqgeX8jy+KxkKMExgsW9jFgYvnsWJxNwctnceRByzk+SsW0l3ItuoIRESkcyg0TFeqQgNA/w649utw1y1Qe+5VE0Pzl/CLniO4ubqMLZn5PJuZx45MF6E17oEy4OD9enj+ioUcvF8PKxZ3s3LJPA5Y1E1PMYs1CCAiIrJPUGiYrtSFhrqdz8KtP4ZbfgS7tk9atIaxI9PNrqDIrqBIf6ZIf1BgICgwYHkGgzwDQYH+oMDuoEB/EG0fzhbIdXczvyvP/K48i+flWTK/yNLuLPsVA3oWzGdBT4H5XXkWdOWY35UnEyhkiIjsJSb8g6726U6zcAm8+Z1wxh/CL++EX94B/30vDD33RlYZnKW1AZbWpn+TqxpGyXKUghw5r1EMK+SpARACu4IiOzPdPBZ0sTsoUM53US1048VuKBSxXA7L5QnyObKZDNnAyGaMrBmZfI5Mz3yyPfPJzV9AoWcexa4CxUKerq48mXwe8sXRq0faxR02Pwbr1kJpEI54ERz5Isjlp35dtTJ1uZkaLsG6X0bvcdRq6FkwN+8jIjKOQkOnyuXh5a+KHtUqPPxruP8uePJx2P5M1CKxe9eMd5/Bmedl5tXKz9kWAIvCEovCEiNTTg3N+K0mNBTkKAV5hjN5akGWMMgQBlk8kyEMMngmiwdZCAK6akN0lwfpGt5NfngAc8ctAAuigR0WRCHEDIIAn7eAcL8VVJceQGXp/oRd88jUalitQqZWJb/lMTK/WQs7to2tVC4Pz38xHHH02FBQHoanN8PTm+DpJ2FwNyxaGs23cfDzojuXDg3Atqdg29PR92f+IjjwkGjbikOiD/9MBoIMZOq/mh6FkFoNHvwV3HsbPHBP9H4QHdfzj4HjXhEFmkolCjilwejnorsn2m/PfCh2R68bLkG5FD0vl6EyDJVy9B7zF8GSZbB0OXTNi36GNj8WPZ7aDMUu2H8l7H8Q7H8gzJvf+JsXhhDWovplJhiIG9aiOprFj9Hvz5wqDcXfh6eiy5nDEBbvFx33kmXROZhpHcIaDA1Gx5LNRo8gE30P6+ekVoN8Plo/G6rVKEAWu6YuOzgAGx6MvpfLDoDDXjA7oTMMAW/umNzh2a3RGK1y/LNXrUTnZun+sPzA6J+j2e4iLQ1FdSx0zWzf7lF984XZqZt79Ls40A+7d0a/a/07o7/fzzwFz2yJfkYtiP7evPDY6LFk2dT7nkPqnphAarsnpqNShl07YGBX/IPZHy2HBqMPsNJg9OE2uDtaHz+8NIRVnhsWHKMaZMmFmlBqn5DLRz9Dk0mGMrPRgOPhaJkgiFqOcvnoefKDopFsDnI5yObj1xhgo3+oa9Xo9ZVK/GHZHX3wzV8QzaSaGfchPVyCgd0w2B/9rA8NTnFMFn0w5ItQiOsd1qL9uQMe1S1fr18m+h3r3xn9jiWPvb6/Rn9ni91RqOueF71fEMSBMRO9Vz0MVCvRup6FcQBcEG3fujkKqNvi4DN/YRTolq+MQp97tI9aJfpAevQh2PLEc+ux3wFw6JFRUB0eij5ch4eiuuTykCtEx5ovjJ6XfCE6l1ufjD/cnh79XnTPiwLnvPmj9e1ZENXl8UfgiUeivzWTyRdgv/3BiQNuGarlaL8LFsGCxVG4C8Po79hQHJQhDmu56HhKQ1E37s5nR4N2vjC6j56Fo/XtnhcdW60aP+IAuO3p0ZBZHo72u2BR9Nr5C0d/RuqPSnlsMK9VR3926qFycHcU4BqMT5vSwiUwryeqa7ErWi47AN523vT3NTGNaZiuvSI07IlaLfolHC5Ff8DrfyjMol/+3TvjP5I7qfX3M7RrJ8M7d1HZtYuwVKJWreCVCl4pE9ZCQoeaQy3+Q5YdHiRXHqJQGSJfK2NhSOAhGa9F3SGejimy+4MC9xcPYmfQxbGljRxc3dHuKgHwWG4JQ5kCLyg9qclWRPZ1Kw+DT39tNve4b45pMLMA+DDwfuAwYCtwDXChu0+/o39fkslE/yk0an7OZqOm90VLo6JAT/zYU+7OcDWkv1yhOjhAdWCA6uAQ1XKZSrlMdbhMdXiYWqVKWCkTVqvUKlUGM9FAzx2ZLrZTYHfFGSoNM1iqMlQaplqtEdZqVGs1vFJjSW03B1Z3saKygxXVnRTCCmUyVCxDmYBn6OIXhZWszy8fcwXKsmo/x5WeYGVlB8Zo4K5ahi3ZBWzOLmRzdhE7Ml2srO7geeVnOKK8lQOrO+kPijydnc9T8dUt+9V2c0jlWQ6pbOfgynaKXiHjIRmcjNfiPUfv4mZszfTwX91HcHv3kTyWj8794toAJw0+yklDj7K82s9QkGfQcgwGeWoE9ITDzA9LLAhLdIUVhoNsNFbFsgxbjuEgS9kylC1LiLGkNsDyaj/LarspepWSZXk8t4THckt4IreY7rDMyuoOVlZ2sLK6g8JIPZ+rQkCAk6HxPyYhUCVD1IbFpGVnU5mAp7MLeCo7n6eyC6gRsKzWz7LqbpbX+pkf7tkEaUOZAoaTCWtkvDYS6moW4Bbg2Ky31tWCDJlw4u9FXWgBA8sOorTfSorbn2Le048TzOS/3T0UFucRrjg4GgOVy+PZHLiT2fYUwdbNWIMxWnus3p04VevZZLK5iVvIZiKXj/7Gzq+3Ii2EhYuj1p+l+0etLUOD8NCv4MEH4JH/GW0xSSoUZ69OU9irWxrM7MvAh4AfAv8OHA18ELgdOM19fDviqH2+pWEf5+4MlqvsHCizc7BMuRrSXcjSXcgyr5AllwkoVWqUyjWGytWRsjsGy+wcGGb3cJViLjPymkI2Q6UWMjBcYXC4ylC5Rv13r355a7UWUqrUGK7UKFVq1ELH3QndCUOnVKmxa7DMrsEKgxPc5TQwIxMYQWBYfByhQy306U017k63lxmyPD5R/6070UegEwCBh4Rm1AgI690J7mQJyXuVYlglwBm2LKUgG01MNn7f7uTi8tGjFr8PIyGtYhnKlqFiWaoEdHuZhbUhFoZDLKiVCHBCDDcIMYYtF18hVGR3fKXQhMcUH0fRqxTDCkWvkPMaoRkhRkiAA3mvjdQxS0h/UGRn0MXOTJGaZZ6zvzDZvRKv6/IKPeEw88Jhcl4j6yEZQrIeUsOoWhRiqxaQ8xrzwxILa1EANJwnswvZnF3IluwCypZlv9puDqpsZ2V1J0tqA9QIqFhANQ6Fj+aW8nB+GcNBbqQeWa9xWHkbh1e2EWIMxqGzFOQIcPJhlUL8fSh4laJXomVYYSjIsSV+/y3ZBZQsR7eXR45pQVzXBWGJBbUhDGdDfikP55bxVHbBpOMC5tdKLKv1UyOgZFnCbJ4wk6W7VmJ+eTeLaoMsrA1Rs4BBy1PKFhjOFLDAyBFSICRPyLBl2Zbp5lnrpj/+H3leUGO/cJAlYfQz010r0x0O010bpkgVz2Qhk8Ey0ftuLyzgmcJithUWMpgpkKtV6KkMML8yxLzqAPmwSi6skgujn5VqkKWcyVPO5Khk8tH4Kwui3/MgoJLJU8p3M5zrIszmsPrvbLzMBEY2E5DPBmQzAQaE7lEPV61CcWAnVi4RlEsE5WGCcomeRfN54zt/d8LzOQP7XkuDmR1DFBB+4O6/n1j/KPAV4Gzgu22qnqScmTGvkGNeIceBS+Y1LDOvmGu4vhUqtZBytUYmCAiMkeVk82vUA0i15lTDkGrNqVTDkX1VqmE8JtFG/mIMVWoMlCoMlKrsHq7gzsgftsBszHuaReGkPG6fo+8RUgtDspmAIDAyZiPvVY8zUf2ispVqyHC1RjkOUcOVGsPVkG6DXCYgkwnIBlGUCEOn5s620KmG0T4q8aMWOrWaUwuj58U4SHn8h7gWRt1ndWH9gyhTIJuJjq1cnfD/iyk1misltIABiy5/ni1bs/PZmp3PL6fxmqpleLiwnIcLy2elDrN1TP1xy+EYIWyzHBQmGHw7kZFvXfRNHq4FPFtvGzUm/xSsxo8BiEZ7J0d8d8WPmRi/r5nIAN1AN0cWFvDGPdxbs/ba0AC8jehH4rJx69cAnwPeiUKDdKhcJiCXmd5oBrPogzoTQAFNLZ5UD1S1OD1kgmDM3CO1MGQoblUqV8IoOGWMbBBgFoWKcrXGcCWkUotaiWph1EJUb+Wpr6vGQWYkuNWiT7XAopaiegirh5z6I2ooGg08jQJivfXK49al+j6qoTNcqcV1jMJX8j/bwIxqLQppw5XoOAKDXPzfbjYwqqEzVK6OtK4B5LMZ8tmAXDb6WaxUw/h9ovcNAosuiBk5pqhO1Vp0TGZj/6WtH1u9da0ajn5PxssEFo0t3Itby5tVrbXuHOzNoeEEoox5d3Klu5fMbG28XURkTKBqJBME9BQDetrYurSvqrc+VWseN93bmHA1EshqITUfG9aSwageAquJ8DbS7E8UVuotZfXwVKmFIy1iQRC1rCW5j7aS4R6NP3Jwog0jX8fbwtBxfCTshHEYrPlonWthSCUOk9VaSBiHryCuQ8Ygl82QzRi5TBTq5rfw53JvDg0HAs+4e6NRTZuAV5hZ3t3HjIoxs3OBc887b1YvXxERkRkIzOIWjYm3B/EHqMy9vfksdwMTDYMuJcqM4e5XuvvL5qxWIiIiHWpvDg2DwEQjcoqJMiIiItKEvTk0bAb2M7NGwWElUdfFHlywKyIism/Zm0PDPUTH9/LkSjMrAquBe9tRKRERkU61N4eG7xENXv3IuPXvIxrL8J2W10hERKSD7bVXT7j7A2Z2BXC+mf0A+DHRjJAfAm5FczSIiIhMy14bGmIfATYA5wJnAs8AlxPde2LmU7yJiIjsg/bq0ODuNeDv44eIiIjsgb15TIOIiIjMIoUGERERaYpCg4iIiDRlrx7TMBt6e3vbXQUREZFW8r6+vufeRhW1NIiIiEiTzHUv8pYxs3t1M6w9p/M4O3QeZ4fO4+zQeZwdc30e1dIgIiIiTVFoEBERkaYoNLTWle2uwF5C53F26DzODp3H2aHzODvm9DxqTIOIiIg0RS0NIiIi0hSFBhEREWmKQsMcM7PAzP7czH5jZiUze8LM/t7M5rW7bmljZi8ws8+Y2c/NbKuZ9ZvZWjP7ZKPzZWYvNLPrzGy7mQ2Y2e1mdmo76p52ZtZtZo+amZvZPzTYrnM5ATNbYmZ/Z2YPx7/DW83sZjP7nXHlTjSzm+Kf211m9hMzW92ueqeJmfWY2SfM7IH4/DxjZneY2XvMzMaV3efPo5l93MyuNbPfxr+zG6Yo3/Q5M7MDzeyq+Od4yMzuNbO3Nl03jWmYW2b2ZeBDwA+BfweOBj4I3A6cplt0jzKzzwEfAK4Hfg5UgNcAfwj8CjjJ3YfiskcAdwNV4DJgJ/A+4MXAGe5+U8sPIMXM7O+A9wM9wBXufn5im87lBMzsUOAWovP2DeAhYCFwLPBTd/+XuNxJcblNQD2UnQ8sB17h7g+0tOIpYmYBcCvwCuCfiX63u4G3AS8HPu/uH4vL6jwCZubAs8B9wEuBXe5+2ARlmz5nZrYEuDfe9kVgI/B24FXAn7j7N6esnLvrMUcP4BggBL4/bv0HAQfe3u46pukBvAxY2GD9JfH5Oj+x7hqgBqxOrOsBHgMeJA7EejjA8USB4KPxefyHcdt1Lic+d7cDTwArpih3N7ALWJlYtzJed2O7j6PN5/Dk+OfuS+PW54HfAjt0Hp9zzp6XeP7fwIZJyjZ9zoDPx9+LNyXWZeJ9bAN6pqqbuifm1tsAI/rvLWkNMAi8s+U1SjF3v9fddzbY9L14+WKAuKvizcAt7r428frdwNeBFwAnzHF1O4KZZYh+3n4C/KDBdp3LCZjZK4H/RfSf8JNmljOz7gbljiQ6R9e6+6b6+vj5tcBpZnZAq+qdQgvi5ebkSncvA88AA6DzmOTuv22m3AzO2duBR9z9hkTZGnA5sAR4w1TvqdAwt04gamm4O7nS3UvAWvbRP8YzcFC8fCpeHgsUgDsblP15vNS5jfw5cBRRc2UjOpcTq/8BfdzMbgCGgAEze8jMkoG/fn4mOodG1MS8r7ob2AH8lZm91cwOicfQXEp0Xi6Oy+k8Tl/T58zMVhC1QPx8grLJ/U1IoWFuHQg84+7DDbZtAvYzs3yL69RR4v+ULyRqXv9uvPrAeLmpwUvq61bOcdVSz8wOBz4NfMbdN0xQTOdyYi+Ml2uI/gt7N/CnQBn4lpm9N96uczgJd99O1Jr1LFFX2GPAb4jGL/2+u6+Ji+o8Tt90ztmsnF/dGntudQONAgNAKVGm3JrqdKTLgJOAT7j7g/G6ehNxo3NbGldmX/ZV4FGiAU8T0bmc2Px42Q+8Jm5Ox8x+SNQX/1kz+2d0Dpuxm6hv/nrgDqIQ9gHgu2b2Fnf/GTqPMzGdczYr51ehYW4NEo1SbaSYKCMNmNnfEDWrX+nulyY21c9ZocHLdF6BuPn8dcAr3b0ySVGdy4kNxcur64EBov+czex64I+JWiN0DidhZquIgsKfu/vXEuuvJgoSa+IreHQep28652xWzq+6J+bWZqIuiEbfpJVEXRdqZWjAzC4GPgV8E/izcZvrA6oaNaXV1zVqgtsnxD9vXwR+DGwxsyPjAVOHxkUWxusWoXM5mY3xckuDbU/Gy8XoHE7lz4k+lK5NrnT3QeBHRD+Xh6HzOBPTOWezcn4VGubWPUTn+OXJlWZWBFYTXS8r45jZRcBFwFXAOR5fF5TwAFET28kNXn5SvNyXz20XsAw4E1ifeNwSb39n/PU56FxOpj6A+aAG2+rrnib6PYeJz6EDv5jdqnWU+gdSpsG2bGKp8zh9TZ8zd3+SKBScNEFZaOZ3vd3Xo+7ND2AVk8/T8M521zFtD6JBj04UGIJJyl1LNLfASxLr6nMLPMS+PbdADviDBo/z4nP77/HXL9C5nPQ8Lia61n0jievXgRVEffQPJdbdE5c9MLHuwHjdTe0+ljafxy/FP3d/NW59vaXrWSCr8zjh+ZtqnoamiFUOKQAABVNJREFUzxnwBSaep2H7/9/e3YZYUYUBHP8PgdKHspLIikCIMCMiI5NSeieMil4olkqTiMKOCIUUIUUbfTGoDKWTEUVkavWlIqE0KxMtLDGjorcv9qJlfcleNarpwzNrw+1ed262ru7+fzAMe+6Z2TOzu/c+e+ac5wAH9dceM0IOsKIoFhLP5Z8nuovHExki1wHnlmaE3KUoillERrMvgbuIgKtuWxkDpvrmJ79DZI2cT/yB3EgEaheVZblib7V7f1EUxVhiYGRrRkjvZQdFUdwEPAp8BDxBJCS6mQgcLi7LcmVV7wzgDSLAWFgdPhs4AphcluX7e7np+4wqq+ZGIghbQrz3HUb8jo0FZpVlmau63kegKIrp/PM4cTbxe/dA9fUXZVkurtVtfM+KohhN9DyMJh5hbiHyCZ1N9Oo+3m/jBjuKGuobEcXNITLr7ax+SA/SIPPWcNuAJ4kouNO2uqX+eOBFYg74r8BaIjX3oF/LvrgRb9D/ygjpvez3vl1BzGP/hZhJsbJ6M26tdzrwGtEL8ROwAjhlsNu/L2zAsUQK6a+J4PRHYA1whfex7f1a3fR9sNt7RjwuWkwk1tpBBHQ9TdtmT4MkSWrEgZCSJKkRgwZJktSIQYMkSWrEoEGSJDVi0CBJkhoxaJAkSY0YNEiSpEZc5VLSkJdS6iXWMzkn57x6cFsj7b8MGiT1K6XUJAucH8jSEGfQIKkb9+zmtc17qxGSBodBg6TGcs69g90GSYPHoEHS/64+hoBYre8W4HhiMZ3lwNyc87dtjjuOWOH0POBwYlGdVcC9OefP29Q/gFgtcTpwIrEa4BZiwZ/7OhxzJXB7VX8HsQDVnJzzlj25Zmk4cPaEpIF0K7AIeB94iFjt9XrgrZTS4fWKKaWJwAZgGvAucD+xuuS1wIaU0qkt9UcArwCPAMcAS4EFxNK/lwOT27QnAU8Tj1IeBj4EeoBVKaWRe3y10hBnT4OkxqoehHZ25JzntSm/EJiUc36vdo75RM/DPOCGqqwAngIOBqblnJfU6vcAzwBPp5ROyDn/Vb3UC5wPvARclXPeWTtmZHWuVlOBiTnnD2p1lwJXA5cCz3W8eEn2NEjqyt0dtjs61F9cDxgqvcB24Jraf/dnEI8v3q4HDAA552eBtcA4YArseiyRgN+AmfWAoTpmZ875+zbtWVAPGCqPVfvTOlyDpIo9DZIayzkXXR7yZptzbE8pbQLOAsYDm4BTqpdf73Ce14mAYQKwhggwRgHrc85bu2jPhjZlX1X7Q7s4jzQs2dMgaSBt61DeNwhyVMv+mw71+8oPadl3O3jxhzZlf1T7A7o8lzTsGDRIGkhHdCgfU+23t+zHtKkLcGRLvb4P/6P/e9MkdcugQdJAOqu1IKU0CjiZmO74cVXcN+7h7A7n6SvfWO0/IQKHk1JKR/0fDZXUP4MGSQNpekppQktZL/E4YlltAOM6YjrmlCqPwi7V12cCnxEDIsk5/wlk4EBgUet0yZTSiNYpnZL2nAMhJTW2mymXAC/knDe1lL0MrEspPUeMS5hSbZupzbjIOZcppRnAq8CzKaUXid6EccBlRFKo62rTLSFSWk8CLgE+Syktr+odA1wA3AY8+Z8uVFJbBg2SunH3bl7bTMyEqJsPPE/kZegBfiY+yOfmnL+rV8w5r68SPN1J5F+4hMgIuYzICPlpS/3fU0pTgZnAdcAMoAC2Vt9zbfeXJ2l3irJssnidJDXnUtTS0OSYBkmS1IhBgyRJasSgQZIkNeKYBkmS1Ig9DZIkqRGDBkmS1IhBgyRJasSgQZIkNWLQIEmSGjFokCRJjfwNbvdOUqRC6msAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1135,7 +1131,7 @@
},
{
"data": {
- "image/png": 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nMeZia+1v8mxDwWgFTBER8TMvhInZ1tqVAMaYV4Cq/g6w1v5+EM/3FWAW8H5r7QPp5/0V8DTwfWPMXdba5kGcf8C6hQkVYIqIiM8UfZgjEyQGwjijjDG70/6PAW9ngkS6DUngJmAscNpunHNQVIApIiJ+VvQwsZsa07c2Y8zDxpgj8jnIGFMLTAWeyfFwZtvCwjQxf1pnQkRE/MxvYWIz8CPgcuCDuHqLw4AnjDEn5XH8lPT9hhyPZbZNzXWgMeYyY8xzA2tufrTOhIiI+JmvwoS19ivW2i9Ya2+31v7FWvtN4HAgDtycxykq0vftOR6L9din53PfYq09bMCNzkN2z4SW0xYREb/xVZjIxVq7ArgT2McYs28/u7em70tzPFbWY59howt9iYiIn/k+TKStTt+P72e/jen7XEMZmW25hkCGlNaZEBERPxspYWJO+r6ur52stZtwYeHIHA9ntg1JXURftM6EiIj4mW/ChDGm0hhTlmP7wcA5wHJr7dtZ2yuMMXPTMziy/QGYbYx5X9a+QeAKYAfw9yF5AX0o6bHOhLV2uJsgIiKy24q+aJUx5nxgRvrbCUCJMebq9PdrrLW3pb+eAzxojPkLsAJoAQ4CLgaSwGU9Tn048CjwW+DCrO034sLHHcaYH+J6Kj6KmxJ6qbW2qXCvLj/BQICAgZR1t2TKEgqa4W6GiIjIbil6mAAuAY7rse269P1jQCZMbAYeAU4AzgPKgU3An4DvWGtfz+fJrLXbjDFH40LFp3Erbr4GnGut/dMgXseghENB2uNuWmg8mSIU9E2nkYiI7OGKHiastcfnud9mcl+To7f9FwM5P95bazcM5FzDoSQU6AwTHYkU5SVFbpCIiEie9PHXI7QKpoiI+JXChEdoFUwREfErhQmP6LYKpnomRETERxQmPKLbKphaa0JERHxEYcIjNMwhIiJ+pTDhEVoFU0RE/EphwiPCutiXiIj4VNHXmdjj/fNuaGnmjLfW8aqdRyxQogJMERHxFYWJYnvoz9DYwFFAxdR9iQVK1DMhIiK+omGOYguHu7606RUwkyrAFBER/1CYKLZQV5goIX1tDvVMiIiIjyhMFFu46yIcnT0TChMiIuIjChPFlt0zoTAhIiI+pDBRbDl6JrTOhIiI+InCRLGFchRgagVMERHxEYWJYgtnD3MkAPVMiIiIvyhMFFuOngnN5hARET9RmCi2rJqJEg1ziIiIDylMFJt6JkRExOcUJootR82EpoaKiIifKEwUWyhraiiZ5bQVJkRExD8UJootvOuiVRrmEBERP1GYKLacy2mrAFNERPxDYaLYchVgaphDRER8RGGi2HItp61hDhER8RGFiWLThb5ERMTnFCaKLZw9zKHltEVExH8UJopNF/oSERGfU5gothzDHKqZEBERP1GYKLacU0MVJkRExD+KHiaMMV81xtxljFlpjLHGmNW97FdmjPmkMeY+Y8xqY0xb+pg/GGP2H8DzXZh+nly3nxXsheUr16JVyRTW2mFvioiIyO4IFbsBwA1AA/ACMLqP/WYCtwBPAr8CNgJ7A5cDZxljTrXWPjrA513eY9sbAzi+MLKW0y6hq1YinkxREgoOe3NEREQGygthYra1diWAMeYVoKqX/eqBg621L2VvNMbcDrwIfA84bADP+7C1dvHAm1tgWT0TpdlhIqEwISIi/lD0MJEJEnnstw3YlmP7a+kQMm+gz22MqQbarbUdAz22YMK7XugLND1URET8o+g1E4NljAkAtUDdAA+9H9gJxIwxS40xHy944/KRYzYHqAhTRET8w/dhAlczUQv8Ns/9W4E7gM8D7wc+B5QBtxljru3tIGPMZcaY5wbZ1l3lmM0BWmtCRET8w9dhwhhzFPAD4GVcQWW/rLV3WmvPs9b+ylr7gLX2p8AC4BXgamPMzF6Ou8VaO5CajPzkWLQKtNaEiIj4h2/DhDHmUOBvuFkdp1lrY7t7LmttO/B9XA3JyYVpYZ5yLKcN0KGaCRER8QlfhgljzCHAw0AjcIK1dkMBTrs6fT++AOfKX3bPREo9EyIi4j++CxPGmINxQaIJFyTWFOjUc9L3Ay3kHJysMBGySUx6sSoVYIqIiF/4Kkykg8QjQAsuSKzqY98KY8xcY0xtj+3jcuxbA3wZ6AD+WdhW98OY7oECXexLRET8pejrTBhjzgdmpL+dAJQYY65Of7/GWntber8ZuB6JMcBPgaPSBZjZ7rXWtqS/Phx4FDfL48KsfZYZYx4DlgFbcCtrXoybEXKltXZ94V5dnsJhSMQBNz00bkJaZ0JERHyj6GECuAQ4rse269L3jwG3pb+eBWR6FRb1cq5ZuF6LvvwBOB5XaDkKV3exBLjIWju8vRIZoRLcjNWuGR2qmRAREb8oepiw1h6f536LATOA8+bc31p7Zb7nGDbdLvblZnSoZ0JERPzCVzUTI1bOy5CrZkJERPxBYcILciyprdkcIiLiFwoTXhDedRVM1UyIiIhfKEx4QY4ltdUzISIifqEw4QWhrpqJTAGmaiZERMQvFCa8INcwh2ZziIiITyhMeIEKMEVExMcUJrwge2oo6pkQERF/UZjwghwFmJrNISIifqEw4QXh7AJMLVolIiL+ojDhBd0KMLWctoiI+IvChBeEci2nrTAhIiL+oDDhBeFdZ3OoZkJERPxCYcILcqwzoZoJERHxC4UJL9By2iIi4mMKE16QY9EqFWCKiIhfKEx4QXjXAkzVTIiIiF8oTHhBt54JTQ0VERF/UZjwghzLaasAU0RE/EJhwgt0oS8REfGxvMJEJBJ5VyQS2Svfk0YikQWRSOQTu9+sPUyOmolkypJM2WK1SEREJG/59kw8ClyYvSESiXw5Eols62X/DwK/GUS79ixZ60yU0jW8oboJERHxg3zDhMmxrQwYXcC27LlCu17oCzSjQ0RE/EE1E16QvZx2Vs+EijBFRMQPFCa8IEcBJqhnQkRE/EFhwguyr82R3TOhmgkREfEBhQkvyL42Ryq7Z0LDHCIi4n0DCROapzhUckwNBWjXMIeIiPhAaAD7LopEIot6boxEIvr4PFjdrhqa6Pw61qEfrYiIeN9AwkSu6aF9UU9GvrJqJoKpJFgLxhCLJ/o4SERExBvyChPRaHRIayuMMV8FDgEOBWYBa6y1M/vY/wjg28ARuNDyFPAVa+1LA3jOQZ+jYAJBCAYhmSSAJUSKBEH1TIiIiC8MpGdiKN0ANAAv0M9CWMaYI4HFwAbgG+nNnwGeMMYcZa1d1t+TFeIcBRcqgWQb4OomEiZIW4d6JkRExPuGJExEIpH3ApdFo9EP5nnIbGvtSgBjzCtAVR/7/hToAN5lrd2QPuZOYDnwA+DkPJ6vEOcorHAY2l2YKLFJ2oBYXD0TIiLifQULE5FIZCpwMXAJMH0gx2aCRH+MMfsAC4FfZ0JA+vgNxpi7gIuMMZOttZuH8hxDIseMjjYNc4iIiA8MKkxEIhEDnA5cBpwKBNMPPQb8YnBNy2lh+v7pHI89gwszhwJ/G+JzFF63VTDd8EZMwxwiIuIDuxUmIpHIdOBS3BvvFLpmejwJXBSNRt8uTPN2MSV9vyHHY5ltU4fhHIXXbXqo65HQMIeIiPhB3mEiEokEgPcDn8TVFARxdQf34i43/gDw+hAGCYCK9H17jsdiPfYp6DmMMZcBl11++eX9tXH3hHcNEyrAFBERP8hrymckErkeWAfcA7wXWAr8NzAlGo2eHY1Gh2tIoDV9X5rjsbIe+xT0HNbaW6y1h/Xbwt2V42JfqpkQERE/yLdn4mtACrgZuDkajb46dE3q08b0fa5hiMy2XMMXhT5H4WUXYKJhDhER8Y98F6Oy6X3PAz4TiUSOGLom9enZ9P07czx2JK6dzw/DOQovx5LaKsAUERE/yDdMzACuA5qATwFPRSKR5ZFI5EuRSGTykLWuB2vtW8BzwDnGmEwhJemvzwH+lT2l0xgz3hgz1xhTs7vnGDY5poZqBUwREfGDvMJENBpdH41GrwVm4oow/wbsA9wIrItEIn8fTCOMMecbY642xlwNTABqMt8bY87vsftncfUOTxhjPmeM+RzwRPq1XNlj38/gFqLquXjWQM4xPHLVTOjaHCIi4gMDmhoajUZTwF+Bv0YikSl0TQ89Nb3LOZFIpB34VTQaHcg1Li4Bjuux7br0/WPAbZmN1tqnjDHHA9enb5nrapxjrV2az5MV4hwFp54JERHxqd2+gFc0Gt0YjUa/hbsw1+nAfUAl8Gng+Ugk8mxfx2ez1h5vrTW93I7Psf/T1tp3W2urrLXV1tpTrLUv5NhvUfoct+7uOYZNeNeeCYUJERHxg0Evpx2NRi3wIPBgJBKZhOtluAR3FVDJV45Fq9o6ElhrMWagV38XEREZPgW90Fc0Gq3DXQH0hkgkclIhzz3iZfVMlBsXJizQnkhRFg72cpCIiEjx7fYwR3+i0egjQ3XuESmrZ6I8YDu/1vRQERHxurx6JiKRyCd25+TRaPR3u3PcHimrALMikOr8OtaRdJUoIiIiHpXvMMetuF73fJn0/goT+crqmSg1XWFC1+cQERGvG0jNRAI3LfS1IWrLni2rZ6LMZA1zaEltERHxuHzDxGPAu4AzgYnAL4A7o9ForM+jJH/ZNRN0BQhd7EtERLwu3xUwTwD2A76PW/nyN8CmSCRyUyQSWTCE7dtzZPVMlGSFCRVgioiI1+U9zBGNRt8CvhyJRL4OfAD4JHA5EIlEIs8DPwf+GI1GW4akpSNdds0EWQWYGuYQERGPG/DU0Gg0mohGo/dEo9FTgdm4dSVqgVuAjZFIJNfVOKU/vfRMqABTRES8blDrTESj0TXRaPQa4DJgA1CFu1CXDFSO5bRBNRMiIuJ9u70CZvpCXxenbzOAGPB7oHjXt/CzHMtpg4Y5RETE+wYUJiKRSAA4A3e10FPTxy/DXdL7tmg02ljwFu4pwtlhomtoQwWYIiLidfmugDkLd/Gui3D1ES3Ab4FfRKPRJUPXvD1IVs9EMKWaCRER8Y98eybeSt8/B1wL/EGzNgosqwAzlIp3fq1hDhER8bp8w4QB4rheiW8A34hEIv0dY6PR6IxBtG3P0mvPhMKEiIh420BqJsLAtKFqyB4vq2cimFTNhIiI+EdeYSIajQ7ZpcolLatnIpDUMIeIiPiHQoJXZPVMZIcJDXOIiIjXKUx4RVbPhElkhwkNc4iIiLcpTHhFMAjGAGBSKQLWXZ+jXcMcIiLicQoTXmFMt6GOzCqY6pkQERGvU5jwktCu1+eIdSSx1harRSIiIv1SmPCSrCW1K4NumMMC7YlULweIiIgUn8KEl2T1TFQFuzZrrQkREfEyhQkvyaqZqM5aAUTTQ0VExMsUJrwkq2eiItRVJ6GeCRER8TKFCS/J6pmozPrNtGl6qIiIeJjChJeEdi3ABE0PFRERb1OY8JKsnomKYNcwR7tqJkRExMN8FSaMMYuMMbaPWzyPcyzu4/jDhuN19CqrZ6LcqGdCRET8YSCXIPeCPwNv5di+ALgKeCDP82wFPp9j+8rdbFdhZK0zURHo6plQzYSIiHiZr8KEtfZl4OWe240xP09/+as8T9Virf19wRpWKL30TMQ0zCEiIh7mq2GOXIwxFcC5wAbgHwM4LmCMGWVM+upaXpDVM1HWLUxomENERLzL92EC+DAwCviNtTbfj/BTgWagEWg2xvzZGDN3qBqYt6wCzFK6XoqGOURExMt8NczRi0twl7D4dZ77rwL+jRsuSQJHAJ8B3m2MOcZau2xIWpmPkHomRETEf3zdM2GM2Q84BviXtXZVPsdYay+y1n7dWvsna+3d1tqrgJOBKuCHfTzXZcaY5wrS8N5k9UyUZPdMqGZCREQ8zNdhAtcrAfDLwZzEWvsE8DhwgjGmvJd9brHWDu3U0ayeiVKrngkREfEH34YJY0wI+ATQANxbgFOuBoLAmAKca/dk9UyEs3omYqqZEBERD/NtmADeB0wCbrPWthfgfHOABC6cFEdWz0TYdvVGaJhDRES8zM9hIjPEkXNtCWNMrTFmbnrqaGZbjTEmmGPf04GjgYettbEhaW0+uoWJ7JoJDXOIiIh3+XI2hzFmCnAqsKSP2RffAS4ATgAWp7edAPzQGPMAbrXLBLhYv70AACAASURBVHA48HHcqpifG8Jm9y9rmCOU6goQGuYQEREv82WYAC7E1TcMtPDyDeB54AzcEEkYWA/8L3CDtXZDAds4cFmLVoVSWTUTGuYQEREP82WYsNbeANzQzz4X4kJH9rblwDlD1rDByhrmCKa6rlmmYQ4REfEyP9dMjDxZPRPBZFeAaI8nSVmb6wgREZGiU5jwkqyaCZOIUxJyvx4LdKhuQkREPEphwkuyhjmIxykv6RqFUhGmiIh4lcKEl2T1TBDvoKykaxar1poQERGvUpjwkuyeiUScsnB2mFARpoiIeJPChJdk90wkNMwhIiL+oDDhJd1qJnoOc6hnQkREvElhwkt2GebI6plQzYSIiHiUwoSXdCvAjFOe1TOhy5CLiIhXKUx4Sbh7z0R2zUSbaiZERMSjFCa8pOcwR8h0fquaCRER8SqFCS8xplugqMi6ckq7aiZERMSjFCa8JmuooyrQdT0ODXOIiIhXKUx4TXbPRHaY0DCHiIh4lMKE12TN6MgOE5oaKiIiXqUw4TVZPRPlgVTn15oaKiIiXqUw4TVZYaLMdPVGqGZCRES8SmHCa7KGOcrpChAa5hAREa9SmPCasRM6v6xqrOv8WgWYIiLiVQoTXjNtVueXFVvWd36tq4aKiIhXKUx4TVaYKK1b0/m1hjlERMSrFCa8JitMhDd1hQkNc4iIiFcpTHjNhMlQUgqAadpBTbIVcMMcKWv7OlJERKQoFCa8JhCEKTM6v9032dD5dYfqJkRExIMUJrwoa6hjTnJ759dtqpsQEREPUpjwoqwwMSu+rfNr1U2IiIgXKUx4UVaYmNFe3/m1poeKiIgXKUx40dSZnV/WxrYSsO4aHeqZEBERL1KY8KLqGhg9DoCSVIKpiR2A1poQERFvUpjwqqzeiZkdrm5ie0t7kRojIiLSO4UJr8pRhLliU2OxWiMiItIr34UJY4zt5dY8gHOcZox5yhjTYoxpMMbcZYyZ1f+RwygrTOzdsRWANzcqTIiIiPeEit2A3fQEcEuPbfF8DjTGnAXcDSwFrgJqgM8B/zbGHGat3VjIhu62rDAxM90z8dbmRhLJFKGg7zKgiIiMYH4NEyuttb8f6EHGmDBwE7AOONZa25ze/iDwPLAIuKyA7dx9k6dBMAjJJLWJnVSkOmhNlLCmvonZk2uK3ToREZFOvv2Ia4wpMcZUDfCw44ApwC8zQQLAWvsSsBj4SDpwFF+4BCZP7/w2U4T5hoY6RETEY/waJs4GWoEmY8wWY8xNxph8Pq4vTN8/neOxZ4BRwL4FauPgZc3omBXP1E3sKFJjREREcvNjmFiCG444G7gA+BfwGeCJPHoqpqTvN+R4LLNtaq4DjTGXGWOeG3BrByO7biLdM6EiTBER8RrfhQlr7RHW2u9ba/9irf2dtfZc4OvAfOCz/Rxekb7PtWBDrMc+PZ/3FmvtYbvV6N2VPaMj3TOxakuTltUWERFP8V2Y6MX3gA7g9H72a03fl+Z4rKzHPsWXHSYSDWAtKWt5e7N6J0RExDtGRJiw1saBjcD4fnbNTPvMNZSR2ZZrCKQ4xoyHCjdyU5FsZ0LS1YxqqENERLxkRIQJY0wZMA2o62fXZ9P378zx2JHATuDNAjZtcIzp1jtxcGwdoCJMERHxFl+FCWPMuF4eug63ZsYDWfvWGmPmGmOyayAeAzYBl2YXaxpjDgKOB+5K93J4x7xDO788e+eLGGs1PVRERDzFV2ECuNoY87Qx5gZjzH8ZY75ojPkX8EXgP7gFqTK+AywHDs9sSAeFzwLTcbM/IsaYrwAPAfXAtcP1QvJ23OlQWg7AjHgDR7atYkNDC80xb2UeERHZc/ktTCzGDUVcAPwY+CYwFjeb43hrbVt/J7DW3gW8Hzej4/vAl3HLcx9trfVOvURGZTUcf1rntx9pfA6sVd2EiIh4hq+W07bW3gfcl+e+FwIX9vLYX4G/FqxhQ+09Z8H/3Q+JOPt31LGgfQNvbtzBIXv3V28qIiIy9PzWM7FnGj0Ojjqp89uPND6vIkwREfEMhQm/OOUcrDEAHBZbS/vb3pl0IiIiezaFCb+YNAV76LGd35686Sm2NcX6OEBERGR4KEz4SOC0j3R+fWzrW6x6eXkRWyMiIuIoTPjJXrNZVzsXcL+48gd+W9z2iIiIoDDhO+XnXNT59YFbX2fdE08UsTUiIiIKE74zfsFBLJt2SOf3Jff8EpK6iqiIiBSPwoQP1Zx3GW0mDMCk5jq2/e3eIrdIRET2ZAoTPrTXnJn8e+/jOr8vf/AOaG4qYotERGRPpjDhU1PPPZ9NoVEAVMRbab371uI2SERE9lgKEz61/6xJPLTvqZ3fl/37Qdi4pogtEhGRPZXChI/Ne/8ZLC2dCkDApojf+hNIpYrcKhER2dMoTPjYIbMn8Nd9TyOR/jWGV76GfezvRW6ViIjsaRQmfMwYw7vfewx3juqaKpq485ewfWsRWyUiInsahQmfO3LfSWw/4SzWhUYDEI7HaPrlj8HaIrdMRET2FKFiN0AG75PvPYjoivfxudduA6D6jedoefoxKhceBStfh+UvwqZ10NIELc3Q2gQmAB+6GBa+q8itFxERv1OYGAFKQkHOveiDPHT9S5zcuAyA4G9/hP39jzAd7b0feMuNrgfj8ON630dERKQfGuYYISaPqWDMBZezLVgJQFmyve8gAWBT8Mv/geefHIYWiojISKWeiRFk4YK9eeSoj3DiE7/uTIlN1eOpPuRwmDMPRo2GimoIBuEXN8LGtW4q6S3fgcuvgXccWdT2i4iIPylMjDAnnH82t8aC1K9czaultdSFa/jM/gfyvsNmdt/xyhvhu1dB3QZ3obD//TZ85lqYd1hR2i0iIv6lYY4RJhgI8PFLzqRh/tHUhWsA+NmDr/KvZRu671gzFr74PzCh1n2fiMP/3gCb1w9zi0VExO8UJkagklCQRR8+jLlTR3du+959S/n7C2ux2VNGx4x3gWLsRPd9rBVuvh7aY8PcYhER8TOFiRGqvCTEdR9dyMwJ1QCkrOUnf1vGN/74LNuassLCuIkQuQZC7pLmbFgNt/1U61SIiEjeFCZGsFHlJdxw3uHsNb6qc9uSt+q57H8f59FXNnT1UsycAx+LdB34zL9Ay3KLiEieFCZGuHHVZdx06TF88IhZnduaY3FuvPclvnXX8129FMeeCkef3HXgH//XTRltaRrmFouIiN8Yq+7sAYlEIhYgGo0WuykDtnT1Nn7wwFLqdrR1bqsoDfHJk/bn1IOnE4h3wHc+D+tWdj9w/GTYazbsPRf2ORBm7APhkmFuvYiIFJnp9QGFiYHxc5gAaG1P8ItHlvP3F9Z22z5/r7FcfsoBzA60wHVXQFtL7ycJhWHWvnDUe+CYU8D0+u9LRERGDoWJQvF7mMhYunobP/nbMjY0dA8Nh+49no/PrWT/V/8Ps3qFK8hMJno/0dEnw/lXdBVwiojISKUwUSgjJUwAtMeT3P7ECu56aiWpHv8O9pk8ijMPn8Wx+46nrH4DrH4T3noN3noVtmzsfqJ957sZIVWjhrH1IiIyzBQmCmUkhYmMVXU7uePJt3hy+SZSPf45VJSGOGHeFE59x3Tm1NZgjIHG7XDPr+Gph7t2nFALVyyCKTO6n8BaWLkcnv83hEJwytlQWT3kr0lERApuZIQJY8y+wMeBk4HZQBnwNnAX8GNrbR8D/Z3nWAz0dpnMhdba5/o6fiSGiYyNDS38+T+r+OdL6+hIpHZ5vDQcZMKoMibWlDOxuowzG55l1pN3d99p8jTXUzH7ALdU95JHYWtd1+M1Y+H8/971OiCplLuF8ljhPd4Bby+HjWtgweGuQFRERIbaiAkTNwKfBu4HngHiwAnAh4GXgSOttW29n6EzTBwIfD7Hw3+31jb0dfxIDhMZO1raeWjpev7x4rpdaip6OqdkIxeveoBAomNgT/LOd8N7PwwrX4dXnoPXXoS2VlfYeeCh7jZrP1cI2tgAOxpg01p49Xl442XIXBG1rAIuvQre8c7dfLUiIpKnERMmDgNWWGsbe2y/Hvg6cIW19mf9nGMxMNNaO3N32rAnhIkMay2vrG3gwRfX8cybdbS05y7E3Kd9C1e2L2Fm41oCqeSuO1RUwUFHuiCwc3v+DTAm/5U43/9xOONjENjDlk5JxN3PSFN1RWTo9RomfHXV0D6GIP6ECxPz8j2XMSYAVAFN1k+JahgZY5g/YxzzZ4zDWktLe4ItjW3U72xj2ZoG7nt2NR2JFG+VTuTy0jMoqUqwX0cd82IbOSBeBxWVNB1wJLXHHst+MyYQbG2GP9wM/3k0vwb09WuZOMUNd2zf6r6///ew9i246Mo9pyZj3Ur46TegtRkuvzr3FV8Tcdi5A8ZOGP72jSRvvwZr3nKzl0rLit0a/2ptge31rrYq15TyRAIatrgaLE059xVf9Uz0xhjzXuDvwLestdf2s+9i4GjcEEk50Ar8E/iatfb1/p5rT+qZ6E/9zjZ+u/hNHlm6nv7+FVWVhdl3Sg0VJSHmNbzBSa/cR0V7Mzun7QcHHkr1wncSGjsBXn8RXnne9WJs3+qGMUaPdbUWo8fBvvPggEPcH5umRrjlO7D8pa4nCgTcAltz5rkFtiZPg1FjXMAY6l6L9avcrJcFR8Co0f3v359tW+DJf7r2H/deCAS7Htu5A67/b/eHF9zrW3Szu3hbRv1m+MFXYOtmOPx4uOgL/ujBeOU5d3vXe3ct6C2GlW/A/3wBkknY5wD44nfzq+2R7tasgB9dDc2NrhfxzE90fzyRgB9+Fd5cBnMPgv+6Gqr2kA8G/jEyhjlyMcYEgSeBw4B51to3+tn/N8BGXI1FEjgC+AzQARxjrV3Wy3GXAZddfvnlh4LCRLa3N+/k/mdXs6GhhW3NMbY1tdMezzHckSVgXYFnyrg3+FDAUDumgnHVZYypKmVsZQmTq8O888DpTBhV3vuJkkk3s+She/puZDAI1aPdm9Pcg2D/d7iVPLPfoPuTSEBHzA3bZFuzAh64A1562n1fUQUfutgtUd5XgEkkYMdWN6W2rKJre2MD/P1P7vooibjbtuBwuOwrbr9E3IWEFa92P99+C+DK77jX1NIE3/kCbF7X9fg+B8BnFg3vFN5UCpY9665Ie+gx/a9HsuxZ+Om1YFPutX76G+531ZfG7fDUI1A73f2cBhoa6zfBqy+4fyPvPKl7UEgm4dv/DWvf7tp26ofh7IsH9hx7ujUr4Adfdb1oACYAX/0R7L1f1z5//yP8+dau7ydPg89e5z44iFeM6DBxEy4MfM1a+53dPMexwGLgX9ba9/S1r3om+metpaktzstrtvHsW/U8+/YWtjW179a5AgaO3HcSZxw2g4Nnjcda2LyjlXVbm6lrbKM0FKCyNMyUVc8z9an7Kd2yDpPvv+nyShcoxkyAseO77kePhzHjoKrG1XgsexaW/gdee8Fdnr2qBqbOgKkzYVudeyyXOQfC+Z9159q0zhWQblrn3uA3r3dvYsl06Bo7AWr3cr0Qzz/RVWCabepM+O9vuuDy5D/dNmMA4958Ac68AE49G358Nby+dNdzTJrq/kBPnJK7zVs2umm802a6HqDgAMJWT2tWwB1RN/Mm0/5PfBZm7597/03r4IbPukLcjFAYLv0SHHZs7mO21cF3r3K9OABT9oL3fsT1xPTWdmtdEFv6NCxd0j1wHX4cXPrlrkDyyF/cdWp6+ux1MH9hb698zxVrcyE5u0ehZ5DImDoTrrnJ/Y43r4NFka7wnFFdA1d80y3lv7vaWlx4KevjQ4nka2SGCWPMdcDVwC3W2k8N8lyPAscC1X3NCFGYGDhrLWvqm6nf2UZbR5K2jgRtHQnqd8ZYU9/E6i1N1O+M9XueMZWlNMfixJO7TlvNqErGmJ+oY6Hdwtz2OiakWqjqaCYQa+31mF6Fwrv+cetPdY0bfskwga43+t0xeZoLHhmlZS7QZJx9ifv+gdvd94EAzH2HCz4ZR57o6lQy/9era+DjV7gZMJk33HgH/OMu+Nsfu15z9Wj35nrkiTBz3/zHsJub4C+/hcf+tmvdizFwwvvgrAu798a0NMENn3PTiXsyBs77DBx/evft27bA967qPvU4Y0Kt6xna5wCYMcf93Jp2uB6Mxx/M/TwZp57jfq7bt8LVn4T29J+D7N9t1Sj4xv/LXYtirfs5Pnq/C20HHAIHHAwz9xtcOPMya+Hxv8Odv3Q/r8wU8Wmz4C+/6woSFVXu31cmLJ95AZz2Efd7zPS0Tah1P/vMv8NwCXzyy3DI0QNrU6wV7rsN/u8+CJfCxz/tep4KLd7hZpjtvT9UVBb+/N4y8sKEMWYRcC3wG+CSwRZRpoc/LgSmWms39rafwsTQaGmPs3l7G9tb2tne3E5Dc4wXVm7lpdXbBn1uAxw+o4bT966kdttqAm8sZfTa16iK7Rz4yXIFDGNcF/4ZH3NvHn+9A/55d1evQ19qxrg3355Llk+fDR/8BMw/3L0B/u4nu+7zznfDxV90Qwnf/9Kuwx4AH7wQTj8XnnsCfvnd7m0fM97VJUyfDXf/qvsn9J5Ky9x6HhNqYcJk96YQDKVvQTc0U7eh65b9PJl9sntbRo+DI05wwxJ7z4WbFnUFoJJS9+Zxz6+7B6kjToBjTnbDOTsaXI/E1s3usVDYPU97js8BgQBMnu7alWtp+HCJ+71tWN217WMR9wbx/JPu+9q94As3wLc/CzvS/yb3OQCu+l73gJBKwu9/5gJLT+WVrofltI8Mvus+lXKf+Hc0uHqiseNdnU5fw3bxDtiwxg2tZaZbtzS51z5rP1drtDs1NTu2wa0/hlee7Xu/iir44o3w+stw5y1uWygMJ5wBD9/rvg8G4eqbXED+2SJoTv8fNQbOvhROPqv/UGut+7396eddBdoZJ53pzpMZylr7Njz6gAs7Hzh/4DU6jdtdENq83oXN//q6+/c5GIkELFvi/q2+4yiv1eeMrDBhjLkWWAT8DrjI2sF89Os855O4+olqa22vH5MVJobX2vom/vbCWh5eur5zaurYqlKmj69iypgKEilLayxOS3uCprY4W3a20dSWR2+CtUxJNDIl0ciERBMTks1MSDQzLtnM+GQL45MtVKbasSbAjqlz2LzXfNZPPZDtVeOpaNrGqO2bGLV9IyU2SejwdzHjHfMpDWf9IV+/isTvbiK08jVsMASTpmKm7OXelCZPc7dJ01zXayLhhjw2rnFvjrXTYd7C7mP/by6D//etrkvC7z0Xrvpu1x//bVvgm5HuXcnHnAwXfL7rj+9br3X/A92bqTPd8+wYfJDjwEPho5e7P4i//5krrOypZ0D7r6/BYe9yvQA/ucYVtWYbM969pob6ruMj17jhk3/d74YmMj+n3pRXwMLj3OJp+x3kzhH9VteQVc9pyV/6nvuk/eYr8L0vdfU2HXgonP5RN6SViMMv/gde+Hffzx0IuE/Ip5/bfbgplXK//3Ur3W39Krd90lR3mzzNvXm9vMS9cTc17nresRPcJ+Q581yx8uhx7mf+4tPuvq8eumAIps9yx+89F2bNhYm1LgSuftOtCZP5XdSMdYXRwbCrdejv550JEnvt4wLXDZ/f9fcK7mfywQvd13Ub3O8/e/n+406Dj0Z2fYNta4F1q9yMrqX/geUv9t6Wfee7UPHoX7vvV1bhhtR6LqjXm5Ym+P6Xu19lORCAj3wKTny/+zeUiLv2vPBv97M3AbfdGBfKDzjU/Z7CJS5A/fsh90EkM2w3eRp86BLXJm/Mbhk5YcIY8w3gm8BtwIW9BQljTC1QA6y11ramt9UAzdbaZI99Twf+CjxorT2tr+dXmCiOWDzJpoYWxo8qp7q87yK+lvY4W3a0sbJuJ48s28CLK7f2O9skl7JUBykCdAT6/2QQChjmTKlhxoRqNm9vZdWWJhpbO6hItRMzYVImwJjKUibUlDFrYjX7TRnNvlNGM2tiNaFgngWDWzbCH3/uPr19/ArXq5Ht+Sfh5uvd1/u/Az57/a5/dHdsc5/EHv+H6/bv9oIr4IMXuE+K4D6ZP/0vV1jac7y7P5Onw4cuckMpmT+C1sKSxe419HzujPed5z4hZsTa4JYb4eVe6lKCIRckDjoi65hW1xPz1qvuDXDTuq5gsPdceNdpsPBdu07xbI+5N4dVPWq4j3oPXHxl1/c9CwXBDQOFQi6wZRx5oqureO1F1+vS81NyIOB6KDra3XO3t+XXmzWcKqpcDUu+n9dO+iC872Oul+fNZbDiFReSPnwZTN+7a791K+H6K7q/3snT4Npo996RpkYX8rJ73Q48BI473T3H+lXuXD2vF5RRPdoVQy99Bl58qv/2G+PCzHs/3Pebd6wNfvS1rnqgnt55kuspeuqR3v+tZ5SUuplna992M11y2Xe++/+09/7FDhUjI0wYYz4N/AxYC1wD9PwXXmetfTi9763ABcAJ1trF6W1nAj8EHgBWAgngcNwS3Q3A0dbaHHG5i8KE/2xpbOPhpet5+s06DDB9fJW7jaukuryERCpFMmnpSCRZumYbTy7fTEPz7hWMDlQ4GGBURZhQMEAoECAUNFSXlzCmspRx1aWMrSpldGUp1WVhqsvDVJeXkLKWna0d7GyL09jaQUssTizualEmbnidmqZ6Vs85kvKqSqrKQoyqKGGv8dVMH19JSSjde5KIu09Lj/7VveEe/E73iSp7ammGte5TWP0mN9106+auN75EwnXHVla5npZJ02Dy1F1nvGTraIfXX3LFjy//p+tN9rBj4bKv7jobw1r3Kfbp/4Mlj3X9wQ2G3Poa/X2SbG1xbzqjalzI6cvOHfCdz7vXCm7K7fW/cG9KGakU/P6m3EMZGe/5IJzzya7XYq17c33g9tyFsbujerQbmti5w/0Me3sjyjZ+EkzeC0aPcb0WZRXuZ7Pqjb7rSPozdoIbcpt7UP7H/PlWF8zAvUF+6fuuh6eneAfc+qP816fJnO/4M1w4rqhyv7MH73S1PNnveSYAhx7tXn+mNwBcfUbtdDcE2bLT/X+ZONUVXtfOgHtv7d6rcc6l8OzjuXtbdkdVDSTj3YuRwb2WWfu6XqNps1wQCYddD1FpKYyd6Op5hi5wjJgwcSsuIPTmMWvt8T32zQ4T+wPfAg4BJgFhYD3wD+AGa22//5sUJka+ZMqt/PnE8k3U74xRFg5SFg5SGg4SDgUwuAW9DLCjtZ1X121n/bZdlx0vDQeZMqaCprY4Dc2xXS6iNtyCAcO0cZVMHVtJeyJFSyxOcyxOe3uCYChASci9zpL0a81+3eUlQcpLQpSl7yfVlDN78ijGVnV9uk+mUqze0szbdY2EAgEWzBjH+FH9LPBkrXsza2lyn776m9aZSLg1SN561fV69DYzZDDqNrjZMA31bjruocfk3m/jGjfW//T/dR+m+dDFroiztz/oby6D+293gaqnqhr3CX6vvWHa3q4Xqm6DG5Ov2+AC1AEHu1qTGXO6/7wyNRErXum6NTXCzDnuZ3XwUb0vFgXujXP1G+6N9e3X3QX6Wpvd/lNmuJ/1rP1cj86ObV11F5OnwbvPHHjxYbwDfvR19/M48xOu5qg31rqF6TKFxj0FAq6Ne+3jZmgdcIgLAz0te9YFk1irW4DsPWe54YamHXDzt11bBuqjl8O7P+Bez203db8AIriAfvTJrl3Wulsi7no1Xn2+e4gbO9HNxjr6ZDcN/YE7XCHzQHqsSstdaBw3yd1/6OJCLrQ2MsKEFyhMSC47Wtp5bd12Nu9oZfKYCmZNHMWk0eUE0n+4E8kUDc3tbNzewopNjbyxoZE3N+2gbkefl5LxvLFVpew9aRSt7Qne3txIe48LxO01voqDZ41n70nVJFKWjkSKeCJFIADjqsoYP6qMcdVlVJWF2dnawY7WDhpb2tnZFqelPU5LLEFLe5xE0rLflBoW7jORcdXDsAJlIuG69vMpSGzcDov/6t4cjjnFzYDJx7YtbnijtMy9AZSWFnZRMWvdm9DuFvBZ6wJVRaUrHh0KqSR0dOQ/bfM/j7pZR5Wj3CfzzG3qjPx/dsmke0vsWayaiLsVeh/7e/7tP/MCOOOjXd9b645/5C8uzBx7Chx4WN+zeOo3u+BXUeVWse35+6rb4ILUq8/3X+/UUzAEN983sPV0+qYwUSgKE1JIzbE4sY4kiWSKePq2s7WDhuZ2tjXHaGhuZ2drB81tcZpi8c7i0pqKEkaVhxlVUUJVWZjykhDlJUHKSkIY487bEkvQHItTvzPG6i072ezz4JIxp7aGhftMYExlaWctjLWW1nb3et0tQTgYoKI0RGVpiIrSEOOqy5g6tpJp4yqpqSghnkyxaksTb27cwZsbG9ne0k4yZTtvFSVBFswYxyF7j2fWpFGdwVBGuJeXuKGosnI3ZFBZ7YZDNq11vVEb1riejPec5QpGh+vfhbVu+G1Vuvdo62YXehNxd2trddOks2c0TZwCN/y6kK1QmCgUhQnxq9b2BKvrm6hvbHNvsmVhKktDlJeESCRTtMeTtCfS9/EksXiSWDxBrCPZWZMR60jS0h5n7dZmVtY17bLS6YRRZcypraG1PcGr67b3uSZIMVWUhognUnm3b3RlCQfNGMeUsZVMrClnwqgyqstL2NYUo25HK3WNbexo6aC6PMzYqlLGVJVSU+E+KccTKRLJFImUpaI0xOiKEmoqSxldWUJ1WRijkCKFkqlv2lrnwoa1rti4cEbGhb5EZPdVlIY4YNoYmDam/53zkExZNja0sHpLEyXhAPvWjmZMVWnn4+3xJK+u285Lq7bS0NxOOBQgHAxQEgoQT6bY1hRja5Nbfr21PU51eYl7o60oobrCvdFWloaoKAsTT6R4fmU9r6xtIFmA4pPWXq6A25sdLR089tqmQT9vT+NHlXH4PhM5fJ+JHDxrHKXhIA3N7azf1sKGhhbiiSTlpSEqSkJUlIaJdSTY0OAe27i9lVhHkok1ZUyszvXIpgAAEV9JREFUKWfS6ApqR1cwe/Ko4RkKEu8xxvWmVI1ytTLD+dTqmRgY9UyIFE9zLM7zb9fz2vrtJJKpbp/qK0pCVJWHqUqHkGTKXem2tT1BSyxOXWObeyPe1kJrhwsTtWMq0tN0a5g6tpJwMEAwYAgEDJu2t/LCyq28uGorja0dQ/7aMs8d6+e6NvkYV13KnNrRzJhQRSplaU+43qa2jiQ72zrY2Rp3w2exOKFggLKSroLbnsM5wYBxs42CAcJBQ1VZmHHVXfUuU8ZUMmNCVc4pzq63K5mzByZlLVvSPTrTxlVSVdbPdVvECzTMUSgKEyL+Zq2lsbWDYCDQ75ol4N703t68k7c3N7KlMcaWnW1saXSLo42vLmXS6Aom1pR3Lve+vbmdhpZ2GlvaCRjT2SMTDARojrnpvDta2/O6IJ5flIQC7DO5hn2n1FBeEmJNfRNr6pvZtL2FlHVBr3ZMBbVjKigrCbJ2azNr65u7BafJo8uZPWkUMyeOorwkSDAYIBQwGGOIxRO0tSdp7UjQHk9SO6aCA6aNYU5tTffF4vqwsaGFZ96s47m360lZOHzORI7df3LfFxKUnhQmCkVhQkQKIZlK8dr6HSxZsYUlK7awut6tIllVFmLauCqmjq2kojREa7u7lk1Le4JQMMCUMRVMHVvJlLEVVJSGqW9so66xjbodrazd2sxbm3eOmJDSn2DAMHvyKMrCwc4C3Jb2BCWhAKMrSqmpLGFUeQkr63aydmvuhdcOnD6Gg2aOI5l0PTixjiQpa9PFu2Eqy0IYY9iYHl7a0NBCQ1M7Y6tLmVRTzuTRFUweXcGk0W6oaVJNeecwUyz9e2vrSBCLJ+lI1yJ1JFKMrixh1sRRVJT2XW2Qspb6xjaCgUD/U62HnsJEoShMiMhQ2NHiFkqrqSgZVFFmMmVZt7WZFZsa2bS9lZKQG8YoDQcpDQXdTKB0bUplWYhk0na+2cXiyc41nTIriqes7ZptlEjR2NrRWe+ydWeMVVua2NKYe6ZQwEA4FOw13NRUlDC6soQN21pIFHshlgIKGPJeV2bK2ApmT3J1LtaCxWKt+/eQqZ3pSE+5PnD6GE4/ZC+OPaC2cwG65licNzbuYGNDa+dzG2Mwxg2dnbRgWiFfmgowRUS8bHRlaf875SEYMMycWM3MidX975w2qmJw61tsb27njY07WLGpkXgyxV7jq5g5oZrp46soCQVobO1g4/ZWNjW00NqRZPq4SmZOrO58zfFkirX1zays28n6bc3Ek6lu03TLwm6xtIrSEMGAYWXdTpav3866HIvF9aY0FODg/9/evUdZVZZxHP8+DMzAMDCCIjc1EsUsNTUvSKRprlZqdjeXhto922orcmVlF+myyszEMl8tumppyipLKy9RkXgLSTEzlUpB7mIwXGcYgbc/3vdM29M+Z87MHmYPzO+z1l6befdzzrzn4czMc9797nfvP4rjJu3NDg/znlzJwmf/w46d8IG6K3XRirVbOgqBzjyxdB1PLF3H9ff8g1dP2IslazZWHHEBGNowsKeLiYpUTIiISC4jmhqYPGk0kyeNzjy+x9CwLPwrK1xJNKhuABPHDGfimOFd+r4bWtv596qwkFPT4EEd64q0xxGUls1badncTnNjPYdN2JPBqfkVpx65Hy2bt/LgotWsbmkNIzcdk1CJp01Ki6btYMyIRvYZ2cT4kY3sOXwwazduZVXLFla1hFNMq1paWb1+C6tbWjsm7DbWD2RIQ11cB2ZgHB0Kc2hWr29lyZpNNRUzzY31bGp7seNKpg2tLzLvyc6vLurNq45VTIiIyC5p+JB6jnh5xv1kgL2bO59YucfQBk45Yr9ufe+hDYPYd6/se9C0b9vOwLoBnS501r5tO0viiMzmrdvCKQoAM4Y2DIzL3zcxbMgg1m5q4+6Fy/jdI8+95LTSADP2Hz2MiWOGM7BuQMepKTzUD6rxJoI9QMWEiIhID+q4oV4NcQeObebAsc2dxo5sGsxZUw/g3VMm8uizL7CqZQsTRg3jgLHNLxlxKYqKCRERkV1E3QDjqImjiu7G/+m9MRARERHZLamYEBERkVxUTIiIiEguKiZEREQkFxUTIiIikouKCREREclFxYSIiIjkomJCREREclExISIiIrmomBAREZFcVEyIiIhILro3RzclSVJ0F0RERHqTd85l3gpVIxMiIiKSi3nvi+6DAGa2wHt/VNH92NUpjz1DeewZymPPUB57xs7Mo0YmREREJBcVEyIiIpKLiom+43tFd2A3oTz2DOWxZyiPPUN57Bk7LY+aMyEiIiK5aGRCREREclExISIiIrmomCiImQ0ws+lm9pSZtZnZUjP7ppkNLbpvfZGZTTKzL5nZQ2a2xsw2mtlCM/tsVs7M7CAz+5WZrTOzzWY2z8xOKqLvfZmZNZrZs2bmzew7GceVxyrMbKSZXWlm/4o/x2vM7E9m9rqyuGPNbE58324ws7vM7PCi+t1XmFmTmV1qZo/H3LxgZg+Y2XvNzMpilUPAzD5jZrPN7Jn4c7u4k/ia82Zm48zshvg+bjWzBWZ2Rk390pyJYpjZt4CPAbcBdwIHAxcB84CTvfc7Cuxen2NmlwMXALcDDwEvAicC7wb+Bkz23rfG2InAfGAbcDWwHvgQcAhwivd+Tq+/gD7KzK4EPgI0Add67y9MHVMeqzCzlwFzCbn7AbAIaAYOA+723v88xk2OccuBUsF2IbA3MMV7/3ivdryPMLMBwJ+BKcBPCD/XjcBZwDHAFd77T8VY5TAyMw+sBR4BXgNs8N5PqBBbc97MbCSwIB67ClgGnA2cALzfe/+jqh3z3mvr5Q14FbAD+EVZ+0WAB84uuo99bQOOApoz2r8Sc3Zhqu1WYDtweKqtCVgCPE0sovv7BhxJKBQ+EXP4nbLjymP1/M0DlgJjO4mbD2wAxqfaxse2e4p+HQXm77j4vptZ1l4PPAO0KIeZeds/9e+/A4urxNacN+CK+P9xeqqtLj7Hf4Cmav3SaY5inAUY4dNe2ixgCzCt13vUx3nvF3jv12ccuiXuDwGIpzzeAsz13i9MPX4T8H1gEnD0Tu5un2dmdYT3213ALzOOK49VmNnxwFTCp+eVZjbIzBoz4g4g5Gm29355qT3+ezZwspmN6a1+9zHD435FutF73w68AGwG5bCc9/6ZWuK6kbezgX977+9IxW4HrgFGAqdW+34qJopxNGFkYn660XvfBiykH/+S7oZ94n513B8GNAAPZsQ+FPfKL0wHXkEY8syiPFZX+sX6nJndAbQCm81skZmlPwyUclQpj0YYqu6P5gMtwCVmdoaZ7Rfn6HyNkJMZMU457J6a82ZmYwkjFg9ViE0/XyYVE8UYB7zgvd+acWw5sJeZ1fdyn3Y58dP1FwhD9TfF5nFxvzzjIaW28Tu5a32amb0c+CLwJe/94gphymN1B8X9LMKntvOADwDtwI1m9r54XHmswHu/jjD6tZZwSm0J8BRhbtQ7vfezYqhy2D1dyVvuHOsW5MVoBLIKCYC2VEx773Rnl3U1MBm41Hv/dGwrDTVn5betLKa/ug54ljDJqhLlsbphcb8RODEOzWNmtxHO93/VzH6C8tiZTYTz/rcDDxAKswuAm8zsrd7736McdldX8pY7xyomirGFMGM2y+BUjFRgZl8mDNF/z3v/tdShUt4aMh7W73Mbh+DfCBzvvX+xSqjyWF1r3N9cKiQgfNo2s9uBcwmjF8pjBWZ2KKGAmO69vz7VfjOhwJgVryhSDrunK3nLnWOd5ijGCsKpjKz/uPGEUyAalajAzGYAnwN+BJxfdrg0mStrSK7UljWUt9uL77ergN8Bq8zsgDhJ62UxpDm27YHy2Jllcb8q49jKuB+B8ljNdMIfqtnpRu/9FuC3hPflBJTD7upK3nLnWMVEMR4m5P6YdKOZDQYOJ1zrKxnM7DLgMuAG4IM+Xr+U8jhhqO64jIdPjvv+mt8hwCjgNOCfqW1uPD4tfv1BlMfOlCZP75NxrNT2POFnHSrn0QN/7dmu7TJKf6TqMo4NTO2Vw+6pOW/e+5WEYmFyhVjo7Oe96Gtm++MGHEr1dSamFd3HvrgRJlt6QiExoErcbML6CK9OtZXWR1hEP10fARgEvCtj+2jM653x60nKY6e5HEG4Vn8ZqevvgbGEeQCLUm0Px9hxqbZxsW1O0a+lwBzOjO+7S8raSyNja4GBymHVHHa2zkTNeQO+QeV1JtYBw6r1RStgFsTMriGc87+NMOx8MGFFzPuBk7xWwHwJM7uAsILbc8DnCcVY2mofJmuVrq+eT1glcybhB+dDhCLuNO/93b3V712BmU0gTMgsXwFTeazCzD4MfBd4AvghYbGljxIKijd77++JcVOAPxEKj2viwy8CRgOv9d4/1std7xPiCqKPEAqznxF+940kvMcmABd4712MVQ4jMzuH/52avIjwvvtm/HqJ9/7GVGzNeTOzPQkjFXsSTocuJ6yJ9HrCKPAPqnas6Mqqv26Eiu9iwkqCW+N/3FV0sspYf92AHxOq5krb3LL4g4FfE65j3wLcR1imvPDX0tc2wi/u/1sBU3msKXfvIFyHv5lwZcc98Zd0edxxwB8IoxYbgbuBI4vuf9EbMJGwlPYyQtG6AbgXeIdyWDFnc2v9PdjVvBFOPd1IWDSsjVDsnVlLvzQyISIiIrloAqaIiIjkomJCREREclExISIiIrmomBAREZFcVEyIiIhILiomREREJBcVEyIiIpKL7hoqIv1WkiQzCPd6OdE5N7fY3ojsulRMiEi3JUlSy6p3+kMtsptTMSEiPeGLVY4t7q1OiEgxVEyISG7OuRlF90FEiqNiQkR6TXqOAuHOhx8HXkG4AdFvgEudc6syHncg4W6xbwBGEW5ENAf4snPunxnxdYS7T54DHEK4s+Jywk2Svl7hMe8CLonxbYSbdl3snFue5zWL9Ae6mkNEijAduB54DLiacPfc9wEPJEkyKh2YJMnRwAJgGvAwcCXhTp3vARYkSXJUWXw9cBdwHbAvcBPwbcLtld8OvDajPwnwU8IpmWuBvwNnAnOSJGnI/WpFdnMamRCR3OKIQ5Y259zlGe2nAMc65x5NPcdMwkjF5cAHYpsBNwDDgWnOuZ+l4s8Efg78NEmSVzrndsRDM4CTgTuAM5xzW1OPaYjPVe5NwNHOucdTsTcBZwFvBW6t+OJFRCMTItIjLquwfbpC/I3pQiKaAawHzk6NBkwhnAZ5MF1IADjnbgHuAw4CpkLH6Y0EaAXOTxcS8TFbnXNrMvrz7XQhEc2K+2MqvAYRiTQyISK5Oeesiw/5c8ZzrE+SZCFwAnAwsBA4Mh7+Y4Xn+SOhkDgCuJdQeDQDf3HOrehCfxZktC2N+xFdeB6RfkkjEyJShNUV2kuTL5vL9isrxJfa9yjbd3XSZEtG27a4r+vic4n0OyomRKQIoyu0j4n79WX7MRmxAGPL4kpFwfjud01EukrFhIgU4YTyhiRJmoHDCZdlPhmbS/MqXl/heUrtj8T9U4SC4rAkScb1REdFpHMqJkSkCOckSXJEWdsMwmmNm1MTJ+8nXDY6Na4D0SF+fTywiDARE+fcdsABQ4Dryy/rTJKkvvzSUxHJTxMwRSS3KpeGAvzKObewrO1O4P4kSW4lzHuYGrfFpK4Acc75JEnOA34P3JIkya8Jow8HAW8jLHZ1buqyUAhLex8LnA4sSpLkNzFuX+CNwCeBH3frhYpIJhUTItITLqtybDHhyoy0mcBthHUlzgQ2Ef7AX+qcez4d6Jz7S1y46nOE9SNOJ6yAeTNhBcyny+LbkyR5E3A+cC5wHmDAivg97+v6yxORasz7Wm76JyKSn275LbJ70pwJERERyUXFhIiIiOSiYkJERERy0ZwJERERyUUjEyIiIpKLigkRERHJRcWEiIiI5KJiQkRERHJRMSEiIiK5qJgQERGRXP4LMPa8H5O05CkAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1147,7 +1143,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -1173,7 +1169,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -1187,14 +1183,14 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Prédiction : 11.59 K$\n",
+ "Prédiction : 9.70 K$\n",
"Reality : 10.40 K$\n"
]
}
diff --git a/BHPD/02-DNN-Regression Premium.ipynb b/BHPD/02-DNN-Regression Premium.ipynb
deleted file mode 100644
index 5c172679767bc488735d3c6d2cc35348ba0f744b..0000000000000000000000000000000000000000
--- a/BHPD/02-DNN-Regression Premium.ipynb
+++ /dev/null
@@ -1,1196 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Deep Neural Network (DNN) - BHPD dataset\n",
- "========================================\n",
- "---\n",
- "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
- "\n",
- "## A very simple example of **regression** (Premium edition):\n",
- "\n",
- "Objective is to predicts **housing prices** from a set of house features. \n",
- "\n",
- "The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston. \n",
- "Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town, \n",
- "age of people who own the house and many other attributes...\n",
- "\n",
- "What we're going to do:\n",
- "\n",
- " - (Retrieve data)\n",
- " - (Preparing the data)\n",
- " - (Build a model)\n",
- " - Train and save the model\n",
- " - Restore saved model\n",
- " - Evaluate the model\n",
- " - Make some predictions\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Import and init"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "FIDLE 2020 - Practical Work Module\n",
- "Version : 0.2.8\n",
- "Run time : Saturday 15 February 2020, 12:32:05\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import os,sys\n",
- "\n",
- "from IPython.display import display, Markdown\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
- "\n",
- "ooo.init()\n",
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "\n",
- "### 2.1 - Option 1 : From Keras\n",
- "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) "
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Option 2 : From a csv file\n",
- "More fun !"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_cb315e50_4fe6_11ea_b235_859a5544c0e9row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f869c8eef10>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Données manquantes : 0 Shape is : (506, 14)\n"
- ]
- }
- ],
- "source": [
- "data = pd.read_csv('./data/BostonHousing.csv', header=0)\n",
- "\n",
- "display(data.head(5).style.format(\"{0:.2f}\"))\n",
- "print('Données manquantes : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preparing the data\n",
- "### 3.1 - Split data\n",
- "We will use 80% of the data for training and 20% for validation. \n",
- "x will be input data and y the expected output"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Original data shape was : (506, 14)\n",
- "x_train : (354, 13) y_train : (354,)\n",
- "x_test : (152, 13) y_test : (152,)\n"
- ]
- }
- ],
- "source": [
- "# ---- Split => train, test\n",
- "#\n",
- "data_train = data.sample(frac=0.7, axis=0)\n",
- "data_test = data.drop(data_train.index)\n",
- "\n",
- "# ---- Split => x,y (medv is price)\n",
- "#\n",
- "x_train = data_train.drop('medv', axis=1)\n",
- "y_train = data_train['medv']\n",
- "x_test = data_test.drop('medv', axis=1)\n",
- "y_test = data_test['medv']\n",
- "\n",
- "print('Original data shape was : ',data.shape)\n",
- "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
- "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3.2 - Data normalization\n",
- "**Note :** \n",
- " - All input data must be normalized, train and test. \n",
- " - To do this we will subtract the mean and divide by the standard deviation. \n",
- " - But test data should not be used in any way, even for normalization. \n",
- " - The mean and the standard deviation will therefore only be calculated with the train data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col0\" class=\"data row1 col0\" >3.76</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col1\" class=\"data row1 col1\" >11.31</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col2\" class=\"data row1 col2\" >11.18</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col5\" class=\"data row1 col5\" >6.25</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col6\" class=\"data row1 col6\" >69.29</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col7\" class=\"data row1 col7\" >3.82</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col8\" class=\"data row1 col8\" >9.95</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col9\" class=\"data row1 col9\" >413.48</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col10\" class=\"data row1 col10\" >18.49</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col11\" class=\"data row1 col11\" >354.37</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row1_col12\" class=\"data row1 col12\" >12.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col0\" class=\"data row2 col0\" >7.96</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col1\" class=\"data row2 col1\" >23.25</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col2\" class=\"data row2 col2\" >6.80</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col5\" class=\"data row2 col5\" >0.70</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col6\" class=\"data row2 col6\" >27.93</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col7\" class=\"data row2 col7\" >2.17</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col8\" class=\"data row2 col8\" >8.87</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col9\" class=\"data row2 col9\" >170.11</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col10\" class=\"data row2 col10\" >2.15</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col11\" class=\"data row2 col11\" >93.94</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row2_col12\" class=\"data row2 col12\" >7.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col6\" class=\"data row3 col6\" >6.20</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col2\" class=\"data row4 col2\" >5.13</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col5\" class=\"data row4 col5\" >5.88</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col6\" class=\"data row4 col6\" >45.18</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col7\" class=\"data row4 col7\" >2.08</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col9\" class=\"data row4 col9\" >280.25</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col11\" class=\"data row4 col11\" >374.49</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row4_col12\" class=\"data row4 col12\" >7.26</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col4\" class=\"data row5 col4\" >0.53</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col5\" class=\"data row5 col5\" >6.17</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col6\" class=\"data row5 col6\" >77.75</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col7\" class=\"data row5 col7\" >3.20</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col9\" class=\"data row5 col9\" >335.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col11\" class=\"data row5 col11\" >391.39</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row5_col12\" class=\"data row5 col12\" >11.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col0\" class=\"data row6 col0\" >4.52</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col5\" class=\"data row6 col5\" >6.60</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col6\" class=\"data row6 col6\" >94.45</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col7\" class=\"data row6 col7\" >5.19</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col11\" class=\"data row6 col11\" >396.12</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row6_col12\" class=\"data row6 col12\" >16.96</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col1\" class=\"data row7 col1\" >95.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col5\" class=\"data row7 col5\" >8.72</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_cb38331a_4fe6_11ea_b235_859a5544c0e9row7_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_cb3f14aa_4fe6_11ea_b235_859a5544c0e9\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col0\" class=\"data row3 col0\" >-0.47</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col2\" class=\"data row3 col2\" >-1.58</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col3\" class=\"data row3 col3\" >-0.27</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col4\" class=\"data row3 col4\" >-1.45</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col5\" class=\"data row3 col5\" >-3.86</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col6\" class=\"data row3 col6\" >-2.26</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col7\" class=\"data row3 col7\" >-1.24</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col8\" class=\"data row3 col8\" >-1.01</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col9\" class=\"data row3 col9\" >-1.33</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col10\" class=\"data row3 col10\" >-2.74</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col11\" class=\"data row3 col11\" >-3.77</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row3_col12\" class=\"data row3 col12\" >-1.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col0\" class=\"data row4 col0\" >-0.46</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col3\" class=\"data row4 col3\" >-0.27</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col5\" class=\"data row4 col5\" >-0.54</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col6\" class=\"data row4 col6\" >-0.86</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col7\" class=\"data row4 col7\" >-0.80</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col8\" class=\"data row4 col8\" >-0.67</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col10\" class=\"data row4 col10\" >-0.50</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row4_col12\" class=\"data row4 col12\" >-0.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col0\" class=\"data row5 col0\" >-0.44</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col2\" class=\"data row5 col2\" >-0.22</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col3\" class=\"data row5 col3\" >-0.27</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col4\" class=\"data row5 col4\" >-0.23</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col6\" class=\"data row5 col6\" >0.30</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col7\" class=\"data row5 col7\" >-0.28</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col8\" class=\"data row5 col8\" >-0.56</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col9\" class=\"data row5 col9\" >-0.46</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col10\" class=\"data row5 col10\" >0.29</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row5_col12\" class=\"data row5 col12\" >-0.14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col0\" class=\"data row6 col0\" >0.10</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col1\" class=\"data row6 col1\" >0.05</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col3\" class=\"data row6 col3\" >-0.27</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col5\" class=\"data row6 col5\" >0.50</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col7\" class=\"data row6 col7\" >0.63</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col8\" class=\"data row6 col8\" >1.58</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col9\" class=\"data row6 col9\" >1.48</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col10\" class=\"data row6 col10\" >0.80</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row6_col12\" class=\"data row6 col12\" >0.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col0\" class=\"data row7 col0\" >8.76</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col1\" class=\"data row7 col1\" >3.60</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col2\" class=\"data row7 col2\" >2.44</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col3\" class=\"data row7 col3\" >3.70</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col4\" class=\"data row7 col4\" >2.68</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col5\" class=\"data row7 col5\" >3.55</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col6\" class=\"data row7 col6\" >1.10</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col7\" class=\"data row7 col7\" >3.83</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col8\" class=\"data row7 col8\" >1.58</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col9\" class=\"data row7 col9\" >1.75</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col10\" class=\"data row7 col10\" >1.64</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col11\" class=\"data row7 col11\" >0.45</td>\n",
- " <td id=\"T_cb3f14aa_4fe6_11ea_b235_859a5544c0e9row7_col12\" class=\"data row7 col12\" >3.38</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f8699cc4310>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
- "\n",
- "mean = x_train.mean()\n",
- "std = x_train.std()\n",
- "x_train = (x_train - mean) / std\n",
- "x_test = (x_test - mean) / std\n",
- "\n",
- "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
- "\n",
- "x_train, y_train = np.array(x_train), np.array(y_train)\n",
- "x_test, y_test = np.array(x_test), np.array(y_test)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build a model\n",
- "More informations about : \n",
- " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
- " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- " def get_model_v1(shape):\n",
- " \n",
- " model = keras.models.Sequential()\n",
- " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
- " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
- " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
- " model.add(keras.layers.Dense(1, name='Output'))\n",
- " \n",
- " model.compile(optimizer = 'rmsprop',\n",
- " loss = 'mse',\n",
- " metrics = ['mae', 'mse'] )\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model=get_model_v1( (13,) )\n",
- "\n",
- "model.summary()\n",
- "keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
- "\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.3 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 354 samples, validate on 152 samples\n",
- "Epoch 1/100\n",
- "354/354 [==============================] - 1s 3ms/sample - loss: 446.5069 - mae: 19.1690 - mse: 446.5069 - val_loss: 328.7387 - val_mae: 16.4455 - val_mse: 328.7387\n",
- "Epoch 2/100\n",
- "354/354 [==============================] - 0s 301us/sample - loss: 206.7491 - mae: 12.2281 - mse: 206.7491 - val_loss: 102.8150 - val_mae: 8.6449 - val_mse: 102.8150\n",
- "Epoch 3/100\n",
- "354/354 [==============================] - 0s 302us/sample - loss: 65.8724 - mae: 6.2331 - mse: 65.8724 - val_loss: 33.7508 - val_mae: 4.5848 - val_mse: 33.7508\n",
- "Epoch 4/100\n",
- "354/354 [==============================] - 0s 318us/sample - loss: 33.4179 - mae: 4.2331 - mse: 33.4179 - val_loss: 27.0058 - val_mae: 3.9154 - val_mse: 27.0058\n",
- "Epoch 5/100\n",
- "354/354 [==============================] - 0s 312us/sample - loss: 24.9602 - mae: 3.5624 - mse: 24.9602 - val_loss: 23.2470 - val_mae: 3.5429 - val_mse: 23.2470\n",
- "Epoch 6/100\n",
- "354/354 [==============================] - 0s 316us/sample - loss: 21.4080 - mae: 3.2530 - mse: 21.4080 - val_loss: 22.1707 - val_mae: 3.4498 - val_mse: 22.1707\n",
- "Epoch 7/100\n",
- "354/354 [==============================] - 0s 262us/sample - loss: 18.3586 - mae: 3.0399 - mse: 18.3586 - val_loss: 24.4102 - val_mae: 3.4754 - val_mse: 24.4102\n",
- "Epoch 8/100\n",
- "354/354 [==============================] - 0s 307us/sample - loss: 16.9126 - mae: 2.8925 - mse: 16.9126 - val_loss: 20.1919 - val_mae: 3.2138 - val_mse: 20.1919\n",
- "Epoch 9/100\n",
- "354/354 [==============================] - 0s 312us/sample - loss: 15.5047 - mae: 2.7532 - mse: 15.5047 - val_loss: 19.0378 - val_mae: 3.0763 - val_mse: 19.0378\n",
- "Epoch 10/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 14.5763 - mae: 2.6404 - mse: 14.5763 - val_loss: 19.9752 - val_mae: 3.0986 - val_mse: 19.9752\n",
- "Epoch 11/100\n",
- "354/354 [==============================] - 0s 310us/sample - loss: 13.5901 - mae: 2.5801 - mse: 13.5901 - val_loss: 18.9675 - val_mae: 3.0192 - val_mse: 18.9675\n",
- "Epoch 12/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 12.9341 - mae: 2.5158 - mse: 12.9341 - val_loss: 20.6757 - val_mae: 3.1029 - val_mse: 20.6757\n",
- "Epoch 13/100\n",
- "354/354 [==============================] - 0s 311us/sample - loss: 12.4520 - mae: 2.5061 - mse: 12.4520 - val_loss: 17.6596 - val_mae: 2.8839 - val_mse: 17.6596\n",
- "Epoch 14/100\n",
- "354/354 [==============================] - 0s 311us/sample - loss: 11.9484 - mae: 2.4710 - mse: 11.9484 - val_loss: 16.7645 - val_mae: 2.8083 - val_mse: 16.7645\n",
- "Epoch 15/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 11.6260 - mae: 2.3959 - mse: 11.6260 - val_loss: 17.5048 - val_mae: 2.8007 - val_mse: 17.5048\n",
- "Epoch 16/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 11.2504 - mae: 2.3567 - mse: 11.2504 - val_loss: 18.6748 - val_mae: 2.8771 - val_mse: 18.6748\n",
- "Epoch 17/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 10.8352 - mae: 2.3051 - mse: 10.8352 - val_loss: 19.4796 - val_mae: 3.0041 - val_mse: 19.4796\n",
- "Epoch 18/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 10.6488 - mae: 2.3377 - mse: 10.6488 - val_loss: 17.0329 - val_mae: 2.7640 - val_mse: 17.0329\n",
- "Epoch 19/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 10.2134 - mae: 2.2439 - mse: 10.2134 - val_loss: 18.0589 - val_mae: 2.8565 - val_mse: 18.0589\n",
- "Epoch 20/100\n",
- "354/354 [==============================] - 0s 315us/sample - loss: 10.1024 - mae: 2.2432 - mse: 10.1024 - val_loss: 16.5968 - val_mae: 2.7402 - val_mse: 16.5968\n",
- "Epoch 21/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 10.0576 - mae: 2.2401 - mse: 10.0576 - val_loss: 18.4496 - val_mae: 2.8156 - val_mse: 18.4496\n",
- "Epoch 22/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 9.6590 - mae: 2.1500 - mse: 9.6590 - val_loss: 18.7084 - val_mae: 2.8309 - val_mse: 18.7084\n",
- "Epoch 23/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 9.4596 - mae: 2.1967 - mse: 9.4596 - val_loss: 18.0308 - val_mae: 2.7595 - val_mse: 18.0308\n",
- "Epoch 24/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 9.2778 - mae: 2.1680 - mse: 9.2778 - val_loss: 18.9343 - val_mae: 2.9152 - val_mse: 18.9343\n",
- "Epoch 25/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 9.1075 - mae: 2.1451 - mse: 9.1076 - val_loss: 18.0646 - val_mae: 2.8202 - val_mse: 18.0646\n",
- "Epoch 26/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 9.2196 - mae: 2.1282 - mse: 9.2196 - val_loss: 18.7244 - val_mae: 2.8288 - val_mse: 18.7244\n",
- "Epoch 27/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 8.5733 - mae: 2.0703 - mse: 8.5733 - val_loss: 16.9568 - val_mae: 2.8123 - val_mse: 16.9568\n",
- "Epoch 28/100\n",
- "354/354 [==============================] - 0s 309us/sample - loss: 8.6252 - mae: 2.0821 - mse: 8.6252 - val_loss: 16.4984 - val_mae: 2.7069 - val_mse: 16.4984\n",
- "Epoch 29/100\n",
- "354/354 [==============================] - 0s 307us/sample - loss: 8.6336 - mae: 2.0822 - mse: 8.6336 - val_loss: 16.0498 - val_mae: 2.6532 - val_mse: 16.0498\n",
- "Epoch 30/100\n",
- "354/354 [==============================] - 0s 321us/sample - loss: 8.5071 - mae: 2.0379 - mse: 8.5071 - val_loss: 15.1042 - val_mae: 2.6004 - val_mse: 15.1042\n",
- "Epoch 31/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 8.2888 - mae: 2.0627 - mse: 8.2888 - val_loss: 16.2730 - val_mae: 2.7019 - val_mse: 16.2730\n",
- "Epoch 32/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 8.2021 - mae: 2.0000 - mse: 8.2021 - val_loss: 17.2852 - val_mae: 2.7962 - val_mse: 17.2852\n",
- "Epoch 33/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 8.2973 - mae: 2.0336 - mse: 8.2973 - val_loss: 16.8973 - val_mae: 2.7318 - val_mse: 16.8973\n",
- "Epoch 34/100\n",
- "354/354 [==============================] - 0s 257us/sample - loss: 8.1033 - mae: 2.0105 - mse: 8.1033 - val_loss: 16.6509 - val_mae: 2.8218 - val_mse: 16.6509\n",
- "Epoch 35/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 8.0724 - mae: 2.0170 - mse: 8.0724 - val_loss: 16.0802 - val_mae: 2.6733 - val_mse: 16.0802\n",
- "Epoch 36/100\n",
- "354/354 [==============================] - 0s 257us/sample - loss: 7.7939 - mae: 1.9606 - mse: 7.7939 - val_loss: 17.1008 - val_mae: 2.7384 - val_mse: 17.1008\n",
- "Epoch 37/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 7.7812 - mae: 1.9719 - mse: 7.7812 - val_loss: 16.3472 - val_mae: 2.6939 - val_mse: 16.3472\n",
- "Epoch 38/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 7.4494 - mae: 1.9224 - mse: 7.4494 - val_loss: 19.3916 - val_mae: 2.9414 - val_mse: 19.3916\n",
- "Epoch 39/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 7.8023 - mae: 1.9978 - mse: 7.8023 - val_loss: 16.3499 - val_mae: 2.7018 - val_mse: 16.3499\n",
- "Epoch 40/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 7.3681 - mae: 1.9293 - mse: 7.3681 - val_loss: 16.0445 - val_mae: 2.6872 - val_mse: 16.0445\n",
- "Epoch 41/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 7.3013 - mae: 1.8820 - mse: 7.3013 - val_loss: 16.5657 - val_mae: 2.7222 - val_mse: 16.5657\n",
- "Epoch 42/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 7.3978 - mae: 1.9154 - mse: 7.3978 - val_loss: 15.9821 - val_mae: 2.6576 - val_mse: 15.9821\n",
- "Epoch 43/100\n",
- "354/354 [==============================] - 0s 319us/sample - loss: 6.9832 - mae: 1.9037 - mse: 6.9832 - val_loss: 14.4977 - val_mae: 2.5418 - val_mse: 14.4977\n",
- "Epoch 44/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 7.2307 - mae: 1.8968 - mse: 7.2307 - val_loss: 15.0962 - val_mae: 2.6188 - val_mse: 15.0962\n",
- "Epoch 45/100\n",
- "354/354 [==============================] - 0s 256us/sample - loss: 7.0289 - mae: 1.8685 - mse: 7.0289 - val_loss: 17.0531 - val_mae: 2.8123 - val_mse: 17.0531\n",
- "Epoch 46/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 6.9010 - mae: 1.8537 - mse: 6.9010 - val_loss: 16.7469 - val_mae: 2.7081 - val_mse: 16.7469\n",
- "Epoch 47/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 6.9256 - mae: 1.8664 - mse: 6.9256 - val_loss: 16.1227 - val_mae: 2.7760 - val_mse: 16.1227\n",
- "Epoch 48/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 6.8333 - mae: 1.8552 - mse: 6.8333 - val_loss: 14.9262 - val_mae: 2.6213 - val_mse: 14.9262\n",
- "Epoch 49/100\n",
- "354/354 [==============================] - 0s 313us/sample - loss: 6.7351 - mae: 1.8375 - mse: 6.7351 - val_loss: 14.2252 - val_mae: 2.5309 - val_mse: 14.2252\n",
- "Epoch 50/100\n",
- "354/354 [==============================] - 0s 276us/sample - loss: 6.6672 - mae: 1.7913 - mse: 6.6672 - val_loss: 16.5652 - val_mae: 2.7693 - val_mse: 16.5652\n",
- "Epoch 51/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 6.6222 - mae: 1.8325 - mse: 6.6222 - val_loss: 14.8928 - val_mae: 2.5921 - val_mse: 14.8928\n",
- "Epoch 52/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 6.5606 - mae: 1.8150 - mse: 6.5606 - val_loss: 14.7382 - val_mae: 2.6124 - val_mse: 14.7382\n",
- "Epoch 53/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 6.5737 - mae: 1.7757 - mse: 6.5737 - val_loss: 14.8866 - val_mae: 2.6357 - val_mse: 14.8866\n",
- "Epoch 54/100\n",
- "354/354 [==============================] - 0s 264us/sample - loss: 6.3009 - mae: 1.7569 - mse: 6.3009 - val_loss: 14.6100 - val_mae: 2.6115 - val_mse: 14.6100\n",
- "Epoch 55/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 6.2524 - mae: 1.7679 - mse: 6.2524 - val_loss: 17.4939 - val_mae: 2.8652 - val_mse: 17.4939\n",
- "Epoch 56/100\n",
- "354/354 [==============================] - 0s 319us/sample - loss: 6.2461 - mae: 1.7830 - mse: 6.2461 - val_loss: 14.0397 - val_mae: 2.5829 - val_mse: 14.0397\n",
- "Epoch 57/100\n",
- "354/354 [==============================] - 0s 267us/sample - loss: 6.3124 - mae: 1.7788 - mse: 6.3124 - val_loss: 15.4946 - val_mae: 2.7133 - val_mse: 15.4946\n",
- "Epoch 58/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 6.1133 - mae: 1.7282 - mse: 6.1133 - val_loss: 14.5244 - val_mae: 2.5982 - val_mse: 14.5244\n",
- "Epoch 59/100\n",
- "354/354 [==============================] - 0s 259us/sample - loss: 6.2866 - mae: 1.7860 - mse: 6.2866 - val_loss: 15.8915 - val_mae: 2.7331 - val_mse: 15.8915\n",
- "Epoch 60/100\n",
- "354/354 [==============================] - 0s 311us/sample - loss: 5.9945 - mae: 1.7178 - mse: 5.9945 - val_loss: 13.2656 - val_mae: 2.5189 - val_mse: 13.2656\n",
- "Epoch 61/100\n",
- "354/354 [==============================] - 0s 263us/sample - loss: 6.0649 - mae: 1.7064 - mse: 6.0649 - val_loss: 15.4134 - val_mae: 2.7351 - val_mse: 15.4134\n",
- "Epoch 62/100\n",
- "354/354 [==============================] - 0s 268us/sample - loss: 5.9954 - mae: 1.6767 - mse: 5.9954 - val_loss: 13.8741 - val_mae: 2.5721 - val_mse: 13.8741\n",
- "Epoch 63/100\n",
- "354/354 [==============================] - 0s 254us/sample - loss: 5.9648 - mae: 1.7023 - mse: 5.9648 - val_loss: 15.1974 - val_mae: 2.6602 - val_mse: 15.1974\n",
- "Epoch 64/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.7276 - mae: 1.7202 - mse: 5.7276 - val_loss: 14.5766 - val_mae: 2.6508 - val_mse: 14.5766\n",
- "Epoch 65/100\n",
- "354/354 [==============================] - 0s 266us/sample - loss: 5.8443 - mae: 1.6907 - mse: 5.8443 - val_loss: 15.5797 - val_mae: 2.6848 - val_mse: 15.5797\n",
- "Epoch 66/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 5.8195 - mae: 1.7295 - mse: 5.8195 - val_loss: 14.5484 - val_mae: 2.6527 - val_mse: 14.5484\n",
- "Epoch 67/100\n",
- "354/354 [==============================] - 0s 266us/sample - loss: 5.8216 - mae: 1.6966 - mse: 5.8216 - val_loss: 14.3616 - val_mae: 2.5733 - val_mse: 14.3616\n",
- "Epoch 68/100\n",
- "354/354 [==============================] - 0s 271us/sample - loss: 5.6572 - mae: 1.6543 - mse: 5.6572 - val_loss: 16.1438 - val_mae: 2.8151 - val_mse: 16.1438\n",
- "Epoch 69/100\n",
- "354/354 [==============================] - 0s 259us/sample - loss: 5.5142 - mae: 1.6657 - mse: 5.5142 - val_loss: 14.2295 - val_mae: 2.5796 - val_mse: 14.2295\n",
- "Epoch 70/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 5.4965 - mae: 1.6313 - mse: 5.4965 - val_loss: 15.2662 - val_mae: 2.6980 - val_mse: 15.2662\n",
- "Epoch 71/100\n",
- "354/354 [==============================] - 0s 270us/sample - loss: 5.4534 - mae: 1.6717 - mse: 5.4534 - val_loss: 14.5025 - val_mae: 2.6441 - val_mse: 14.5025\n",
- "Epoch 72/100\n",
- "354/354 [==============================] - 0s 253us/sample - loss: 5.5146 - mae: 1.6526 - mse: 5.5146 - val_loss: 13.7906 - val_mae: 2.5753 - val_mse: 13.7906\n",
- "Epoch 73/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.4499 - mae: 1.6130 - mse: 5.4499 - val_loss: 15.1649 - val_mae: 2.7624 - val_mse: 15.1649\n",
- "Epoch 74/100\n",
- "354/354 [==============================] - 0s 309us/sample - loss: 5.3808 - mae: 1.6297 - mse: 5.3808 - val_loss: 12.9326 - val_mae: 2.5007 - val_mse: 12.9326\n",
- "Epoch 75/100\n",
- "354/354 [==============================] - 0s 258us/sample - loss: 5.3546 - mae: 1.6313 - mse: 5.3546 - val_loss: 13.6397 - val_mae: 2.5810 - val_mse: 13.6397\n",
- "Epoch 76/100\n",
- "354/354 [==============================] - 0s 265us/sample - loss: 5.1666 - mae: 1.5998 - mse: 5.1666 - val_loss: 15.6069 - val_mae: 2.7630 - val_mse: 15.6069\n",
- "Epoch 77/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.2465 - mae: 1.6192 - mse: 5.2465 - val_loss: 14.8084 - val_mae: 2.6388 - val_mse: 14.8084\n",
- "Epoch 78/100\n",
- "354/354 [==============================] - 0s 265us/sample - loss: 5.1107 - mae: 1.5772 - mse: 5.1107 - val_loss: 13.6319 - val_mae: 2.5756 - val_mse: 13.6319\n",
- "Epoch 79/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.2677 - mae: 1.5989 - mse: 5.2677 - val_loss: 15.0306 - val_mae: 2.7715 - val_mse: 15.0306\n",
- "Epoch 80/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 5.0534 - mae: 1.5504 - mse: 5.0534 - val_loss: 13.3917 - val_mae: 2.5352 - val_mse: 13.3917\n",
- "Epoch 81/100\n",
- "354/354 [==============================] - 0s 272us/sample - loss: 5.1013 - mae: 1.5826 - mse: 5.1013 - val_loss: 14.6761 - val_mae: 2.7158 - val_mse: 14.6761\n",
- "Epoch 82/100\n",
- "354/354 [==============================] - 0s 258us/sample - loss: 5.1137 - mae: 1.5984 - mse: 5.1137 - val_loss: 14.7063 - val_mae: 2.6576 - val_mse: 14.7063\n",
- "Epoch 83/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 4.9343 - mae: 1.5545 - mse: 4.9343 - val_loss: 13.6205 - val_mae: 2.5494 - val_mse: 13.6205\n",
- "Epoch 84/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 4.9839 - mae: 1.5815 - mse: 4.9839 - val_loss: 13.3857 - val_mae: 2.6047 - val_mse: 13.3857\n",
- "Epoch 85/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 4.9946 - mae: 1.5818 - mse: 4.9946 - val_loss: 14.1012 - val_mae: 2.6176 - val_mse: 14.1012\n",
- "Epoch 86/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 4.7884 - mae: 1.5321 - mse: 4.7884 - val_loss: 14.5182 - val_mae: 2.6687 - val_mse: 14.5182\n",
- "Epoch 87/100\n",
- "354/354 [==============================] - 0s 311us/sample - loss: 4.8134 - mae: 1.5660 - mse: 4.8134 - val_loss: 12.7966 - val_mae: 2.5734 - val_mse: 12.7966\n",
- "Epoch 88/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 4.7923 - mae: 1.5483 - mse: 4.7923 - val_loss: 14.4001 - val_mae: 2.6707 - val_mse: 14.4001\n",
- "Epoch 89/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 4.6705 - mae: 1.5086 - mse: 4.6705 - val_loss: 15.3677 - val_mae: 2.7359 - val_mse: 15.3677\n",
- "Epoch 90/100\n",
- "354/354 [==============================] - 0s 280us/sample - loss: 4.8776 - mae: 1.5806 - mse: 4.8776 - val_loss: 14.4442 - val_mae: 2.6343 - val_mse: 14.4442\n",
- "Epoch 91/100\n",
- "354/354 [==============================] - 0s 260us/sample - loss: 4.6349 - mae: 1.5300 - mse: 4.6349 - val_loss: 14.2969 - val_mae: 2.7718 - val_mse: 14.2969\n",
- "Epoch 92/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 4.7835 - mae: 1.5637 - mse: 4.7835 - val_loss: 13.1123 - val_mae: 2.5578 - val_mse: 13.1123\n",
- "Epoch 93/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 4.6759 - mae: 1.5259 - mse: 4.6759 - val_loss: 14.3508 - val_mae: 2.6888 - val_mse: 14.3507\n",
- "Epoch 94/100\n",
- "354/354 [==============================] - 0s 273us/sample - loss: 4.7856 - mae: 1.5560 - mse: 4.7856 - val_loss: 14.5237 - val_mae: 2.6956 - val_mse: 14.5237\n",
- "Epoch 95/100\n",
- "354/354 [==============================] - 0s 313us/sample - loss: 4.7038 - mae: 1.5331 - mse: 4.7038 - val_loss: 12.7707 - val_mae: 2.5393 - val_mse: 12.7707\n",
- "Epoch 96/100\n",
- "354/354 [==============================] - 0s 277us/sample - loss: 4.6006 - mae: 1.5331 - mse: 4.6006 - val_loss: 13.8540 - val_mae: 2.6720 - val_mse: 13.8540\n",
- "Epoch 97/100\n",
- "354/354 [==============================] - 0s 269us/sample - loss: 4.4720 - mae: 1.4912 - mse: 4.4720 - val_loss: 13.1524 - val_mae: 2.6311 - val_mse: 13.1524\n",
- "Epoch 98/100\n",
- "354/354 [==============================] - 0s 309us/sample - loss: 4.4242 - mae: 1.4854 - mse: 4.4242 - val_loss: 11.7020 - val_mae: 2.4886 - val_mse: 11.7020\n",
- "Epoch 99/100\n",
- "354/354 [==============================] - 0s 280us/sample - loss: 4.5642 - mae: 1.4920 - mse: 4.5642 - val_loss: 12.6523 - val_mae: 2.5232 - val_mse: 12.6523\n",
- "Epoch 100/100\n",
- "354/354 [==============================] - 0s 274us/sample - loss: 4.1971 - mae: 1.4564 - mse: 4.1971 - val_loss: 18.7164 - val_mae: 3.0774 - val_mse: 18.7164\n"
- ]
- }
- ],
- "source": [
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = 100,\n",
- " batch_size = 10,\n",
- " verbose = 1,\n",
- " validation_data = (x_test, y_test),\n",
- " callbacks = [savemodel_callback])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Model evaluation\n",
- "MAE = Mean Absolute Error (between the labels and predictions) \n",
- "A mae equal to 3 represents an average error in prediction of $3k."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 18.7164\n",
- "x_test / mae : 3.0774\n",
- "x_test / mse : 18.7164\n"
- ]
- }
- ],
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
- "print('x_test / mse : {:5.4f}'.format(score[2]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Training history\n",
- "What was the best result during our training ?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min( val_mae ) : 2.4886\n"
- ]
- }
- ],
- "source": [
- "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
- " 'MAE' :['mae', 'val_mae'],\n",
- " 'LOSS':['loss','val_loss']})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 7 - Restore a model :"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.1 - Reload model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "Loaded.\n"
- ]
- }
- ],
- "source": [
- "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
- "loaded_model.summary()\n",
- "print(\"Loaded.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.2 - Evaluate it :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 11.7020\n",
- "x_test / mae : 2.4886\n",
- "x_test / mse : 11.7020\n"
- ]
- }
- ],
- "source": [
- "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
- "print('x_test / mse : {:5.4f}'.format(score[2]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 7.3 - Make a prediction"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "mon_test=[-0.20113196, -0.48631663, 1.23572348, -0.26929877, 2.67879106,\n",
- " -0.89623587, 1.09961251, -1.05826704, -0.55823117, -0.06159088,\n",
- " -1.76085159, -1.97039608, 0.52775666]\n",
- "\n",
- "mon_test=np.array(mon_test).reshape(1,13)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prédiction : 16.20 K$ Reality : 21.70 K$\n"
- ]
- }
- ],
- "source": [
- "predictions = loaded_model.predict( mon_test )\n",
- "print(\"Prédiction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], y_train[13]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "-----\n",
- "That's all folks !"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/BHPD/02-DNN-Regression-Premium.ipynb b/BHPD/02-DNN-Regression-Premium.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..364a6d5c4a5a03ac80d501baddebf9bd9df3bb1c
--- /dev/null
+++ b/BHPD/02-DNN-Regression-Premium.ipynb
@@ -0,0 +1,1197 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "# <!-- TITLE --> Regression with a Dense Network (DNN) - Advanced code\n",
+ "\n",
+ " <!-- DESC --> More advanced example of DNN network code - BHPD dataset\n",
+ "\n",
+ "## Objectives :\n",
+ " - Predicts **housing prices** from a set of house features. \n",
+ " - Understanding the principle and the architecture of a regression with a dense neural network with backup and restore of the trained model. \n",
+ "\n",
+ "The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston. \n",
+ "Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town, \n",
+ "age of people who own the house and many other attributes...\n",
+ "\n",
+ "What we're going to do:\n",
+ "\n",
+ " - (Retrieve data)\n",
+ " - (Preparing the data)\n",
+ " - (Build a model)\n",
+ " - Train and save the model\n",
+ " - Restore saved model\n",
+ " - Evaluate the model\n",
+ " - Make some predictions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "FIDLE 2020 - Practical Work Module\n",
+ "Version : 0.2.9\n",
+ "Run time : Wednesday 19 February 2020, 10:13:01\n",
+ "TensorFlow version : 2.0.0\n",
+ "Keras version : 2.2.4-tf\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import os,sys\n",
+ "\n",
+ "from IPython.display import display, Markdown\n",
+ "from importlib import reload\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as ooo\n",
+ "\n",
+ "ooo.init()\n",
+ "os.makedirs('./run/models', mode=0o750, exist_ok=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve data\n",
+ "\n",
+ "### 2.1 - Option 1 : From Keras\n",
+ "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) "
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Option 2 : From a csv file\n",
+ "More fun !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_07301338_52f8_11ea_a38b_eb599e736fda\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_07301338_52f8_11ea_a38b_eb599e736fdalevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07301338_52f8_11ea_a38b_eb599e736fdalevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07301338_52f8_11ea_a38b_eb599e736fdalevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07301338_52f8_11ea_a38b_eb599e736fdalevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07301338_52f8_11ea_a38b_eb599e736fdalevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_07301338_52f8_11ea_a38b_eb599e736fdarow4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7faa97ce3ad0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Données manquantes : 0 Shape is : (506, 14)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = pd.read_csv('./data/BostonHousing.csv', header=0)\n",
+ "\n",
+ "display(data.head(5).style.format(\"{0:.2f}\"))\n",
+ "print('Données manquantes : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preparing the data\n",
+ "### 3.1 - Split data\n",
+ "We will use 80% of the data for training and 20% for validation. \n",
+ "x will be input data and y the expected output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data shape was : (506, 14)\n",
+ "x_train : (354, 13) y_train : (354,)\n",
+ "x_test : (152, 13) y_test : (152,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Split => train, test\n",
+ "#\n",
+ "data_train = data.sample(frac=0.7, axis=0)\n",
+ "data_test = data.drop(data_train.index)\n",
+ "\n",
+ "# ---- Split => x,y (medv is price)\n",
+ "#\n",
+ "x_train = data_train.drop('medv', axis=1)\n",
+ "y_train = data_train['medv']\n",
+ "x_test = data_test.drop('medv', axis=1)\n",
+ "y_test = data_test['medv']\n",
+ "\n",
+ "print('Original data shape was : ',data.shape)\n",
+ "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
+ "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 - Data normalization\n",
+ "**Note :** \n",
+ " - All input data must be normalized, train and test. \n",
+ " - To do this we will subtract the mean and divide by the standard deviation. \n",
+ " - But test data should not be used in any way, even for normalization. \n",
+ " - The mean and the standard deviation will therefore only be calculated with the train data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fda\" ><caption>Before normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col0\" class=\"data row1 col0\" >3.91</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col1\" class=\"data row1 col1\" >11.73</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col2\" class=\"data row1 col2\" >11.21</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col3\" class=\"data row1 col3\" >0.07</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col5\" class=\"data row1 col5\" >6.28</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col6\" class=\"data row1 col6\" >68.51</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col7\" class=\"data row1 col7\" >3.84</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col8\" class=\"data row1 col8\" >9.96</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col9\" class=\"data row1 col9\" >414.16</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col10\" class=\"data row1 col10\" >18.52</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col11\" class=\"data row1 col11\" >351.61</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow1_col12\" class=\"data row1 col12\" >12.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col0\" class=\"data row2 col0\" >9.14</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col1\" class=\"data row2 col1\" >23.48</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col2\" class=\"data row2 col2\" >6.75</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col3\" class=\"data row2 col3\" >0.26</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col5\" class=\"data row2 col5\" >0.69</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col6\" class=\"data row2 col6\" >28.19</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col7\" class=\"data row2 col7\" >2.17</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col8\" class=\"data row2 col8\" >8.87</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col9\" class=\"data row2 col9\" >168.41</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col10\" class=\"data row2 col10\" >2.20</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col11\" class=\"data row2 col11\" >96.84</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow2_col12\" class=\"data row2 col12\" >7.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col6\" class=\"data row4 col6\" >45.02</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col7\" class=\"data row4 col7\" >2.09</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col9\" class=\"data row4 col9\" >285.50</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col11\" class=\"data row4 col11\" >370.98</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow4_col12\" class=\"data row4 col12\" >7.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col0\" class=\"data row5 col0\" >0.32</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col2\" class=\"data row5 col2\" >9.79</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col5\" class=\"data row5 col5\" >6.21</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col6\" class=\"data row5 col6\" >76.95</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col7\" class=\"data row5 col7\" >3.21</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col9\" class=\"data row5 col9\" >332.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col11\" class=\"data row5 col11\" >390.69</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow5_col12\" class=\"data row5 col12\" >11.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col0\" class=\"data row6 col0\" >4.08</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col1\" class=\"data row6 col1\" >19.50</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col4\" class=\"data row6 col4\" >0.63</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col5\" class=\"data row6 col5\" >6.60</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col6\" class=\"data row6 col6\" >94.47</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col7\" class=\"data row6 col7\" >5.21</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col11\" class=\"data row6 col11\" >395.98</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow6_col12\" class=\"data row6 col12\" >16.96</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdalevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_07389f4e_52f8_11ea_a38b_eb599e736fdarow7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7faa96773f90>"
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+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_073f797c_52f8_11ea_a38b_eb599e736fda\" ><caption>After normalization :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col0\" class=\"data row3 col0\" >-0.43</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col2\" class=\"data row3 col2\" >-1.59</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col4\" class=\"data row3 col4\" >-1.48</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col5\" class=\"data row3 col5\" >-3.93</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col6\" class=\"data row3 col6\" >-2.33</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col7\" class=\"data row3 col7\" >-1.25</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col8\" class=\"data row3 col8\" >-1.01</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col9\" class=\"data row3 col9\" >-1.35</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col10\" class=\"data row3 col10\" >-2.69</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col11\" class=\"data row3 col11\" >-3.63</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col0\" class=\"data row4 col0\" >-0.42</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col2\" class=\"data row4 col2\" >-0.89</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col5\" class=\"data row4 col5\" >-0.56</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col6\" class=\"data row4 col6\" >-0.83</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col8\" class=\"data row4 col8\" >-0.67</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col10\" class=\"data row4 col10\" >-0.51</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col11\" class=\"data row4 col11\" >0.20</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col1\" class=\"data row5 col1\" >-0.50</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col2\" class=\"data row5 col2\" >-0.21</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col3\" class=\"data row5 col3\" >-0.28</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col4\" class=\"data row5 col4\" >-0.17</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col5\" class=\"data row5 col5\" >-0.10</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col6\" class=\"data row5 col6\" >0.30</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col7\" class=\"data row5 col7\" >-0.29</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col8\" class=\"data row5 col8\" >-0.56</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col9\" class=\"data row5 col9\" >-0.49</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col10\" class=\"data row5 col10\" >0.26</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow5_col12\" class=\"data row5 col12\" >-0.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col0\" class=\"data row6 col0\" >0.02</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col1\" class=\"data row6 col1\" >0.33</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col2\" class=\"data row6 col2\" >1.02</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col3\" class=\"data row6 col3\" >-0.28</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col4\" class=\"data row6 col4\" >0.63</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col5\" class=\"data row6 col5\" >0.46</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col6\" class=\"data row6 col6\" >0.92</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col7\" class=\"data row6 col7\" >0.63</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col8\" class=\"data row6 col8\" >1.58</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col9\" class=\"data row6 col9\" >1.50</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col10\" class=\"data row6 col10\" >0.76</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow6_col12\" class=\"data row6 col12\" >0.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdalevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col0\" class=\"data row7 col0\" >9.30</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col1\" class=\"data row7 col1\" >3.76</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col2\" class=\"data row7 col2\" >2.45</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col3\" class=\"data row7 col3\" >3.62</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col4\" class=\"data row7 col4\" >2.68</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col5\" class=\"data row7 col5\" >3.61</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col6\" class=\"data row7 col6\" >1.12</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col7\" class=\"data row7 col7\" >3.82</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col8\" class=\"data row7 col8\" >1.58</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col9\" class=\"data row7 col9\" >1.76</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col10\" class=\"data row7 col10\" >1.58</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col11\" class=\"data row7 col11\" >0.47</td>\n",
+ " <td id=\"T_073f797c_52f8_11ea_a38b_eb599e736fdarow7_col12\" class=\"data row7 col12\" >3.49</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7faa9625a3d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
+ "\n",
+ "mean = x_train.mean()\n",
+ "std = x_train.std()\n",
+ "x_train = (x_train - mean) / std\n",
+ "x_test = (x_test - mean) / std\n",
+ "\n",
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
+ "\n",
+ "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+ "x_test, y_test = np.array(x_test), np.array(y_test)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build a model\n",
+ "More informations about : \n",
+ " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+ " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+ " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " def get_model_v1(shape):\n",
+ " \n",
+ " model = keras.models.Sequential()\n",
+ " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
+ " model.add(keras.layers.Dense(1, name='Output'))\n",
+ " \n",
+ " model.compile(optimizer = 'rmsprop',\n",
+ " loss = 'mse',\n",
+ " metrics = ['mae', 'mse'] )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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zV7oz/gjfk9He3u7g4EB/T0ZgYGBGRgb1saenRyQSGRoaNjc3qx/VokWLduzY0dfXh9dV7vDbb78ZGRm1tbXRN2RtbW1iYtLd3U014vdkHD9+XHmEYb8nA94vBJ46kDcVjDBvfvTRR0ZGRvX19SrWfe211xBC//73v9WP6tGjR/gfg+XNAeFn5GQyGb0xLCzMwcGhr69PoTO8XwgAwAKSJNPT0z09Pe3t7VV0q62tRQiNGzdO/ZEV3kmpDplMduPGDTc3N4VnzEJCQurq6k6dOsV0wMFA3gRgUHgW1JQpUzgcjpWV1RtvvEE9cT+SWny6U+tv5CorK5ubm3EdmcFkZmbW1NQ4OztPnTp1lMLo6OgoLi4ODg4WCoVHjhxRWIoPQnEdGY2AvAnAwJqammbOnPnVV1+lpqbev3//p59+MjMz8/Pzw6/w3rBhA/nnqayvv/46SZLUHFj0x0sy6OfR+DEn3C4Wi2UyWWxs7Pbt25uamv7973+3tbX5+vr++OOPIxwf08KzFQihK1euIITotQLorl27Fhsbu2zZMisrK3w7fjRi2L59u0Ag8PLyMjQ0zM3NnT59ukIHkUhEhaoRkDcBGFhiYuLNmzdTUlLmzZvH5/OdnZ2/+uorOzu7mJiYwe7wMtXV1bV//36JRMLj8Tw8PLKysnp7exUeNh22/v5+nEw1MtpgcHmtAWuvIIRcXV2zs7NXrlx55coVegUvzdqwYUNPT89vv/3m4uLi5uam/IZhPp9PEIQGK4FB3gRgYHjKYWBgINViYmLi5+f3+PFjTZ3xjWqtv5HXKlRHd3c3Qogq/afs3Llzqampqq9+jhyHw3Fxcfn888+Dg4M3bdr0r3/9S6GDkZGRcnmEYYO8CcAAcBU7U1NTCwsLerutrS1CqKmpSSNbGbDWH/qjcpVeMDU1RQj19fWxHch/BQUFIYTy8/MV2uVy+TBuNA0G8iYAAzAxMREIBN3d3Q8fPqS34zN0oVCIP46wFh+u9Udv0XKtv5HD5bVwpVRdYGJighBqa2ujN3Z0dJAkqcGys5A3ARhYSEgIQog+eaWnp6egoIDL5QYEBOCWEdbiY6vWnwbhmzAK5fcpcrncxcVllDYdHx8fERGh0Hj69Gn057chIITwPlS+XzRskDcBGNiOHTsmTZoUFxeXn5//8OHD6urqd955p7GxMTU1FZ+toxHX4hvVWn/auZ8uFottbGwqKyuVF6WlpfF4vLVr1yovioiIIAji5s2bI9z6V199tXXr1lu3bvX09Ny6dWvdunVZWVnu7u6RkZH0bvgdqP7+/iPc3P+hT4KH54XA00D9v/P79+/HxcVNmjTJ2NhYIBAEBAQUFBTQOwy7Fh85yrX+SDVqFVJG+LzQ+vXrB3xe6MCBA1wul/4GU4qvr6+5ublcLlexOfw+VAVpaWlUh/b29vT09ICAAPyaaHNzc3d39x07dlAPGlHCwsLo7/ujwHOWAKhLR/7OdafW3wjzpkwmE4lE9OfTVXvw4AGXy42MjGQc6LDg59OPHTumvAieswQAsEMgEOTl5eXk5Ozbt2/IziRJxsTE8Pl85VmWo6G2tlYqlSYmJi5cuFCDw0LeBAAw8MEHHyjX33RzcysvLz99+jSuv6lCc3NzbW1tQUEBNSdhVB08eDApKSkpKYneSNXffPLkyfCGhbwJgLbh58orKyvr6+sJgtiwYQPbEaklIiKCOlFVqL+JEHJycsrPz8f1N1UQCoVFRUXTpk0btTD/ZOfOncpHmlT9TZIkh3ffDF5uBYC2xcfHx8fHsx0FGD443gQAAGYgbwIAADOQNwEAgBnImwAAwMwA94VOnDih/TgA0JqSkhIEf+c0+Oly2CGDqaurUyzMTJ8Er/yiYQAAAArPCxHkKJeDBmB48Ltb4b3qQAfB9U0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZiBvAgAAM5A3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzkDcBAIAZyJsAAMAM5E0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYIYgSZLtGABACKHo6Ojr169TH3/++edJkyZZWVnhj4aGhpmZmQ4ODixFB8D/MWI7AAD+y9bW9tChQ/SWy5cvU/+ePHkyJE2gI+A8HeiKd955Z7BFHA7nvffe02IsAKgC5+lAh0yfPv3atWsD/k1ev37d2dlZ+yEBoAyON4EOWbp0qaGhoUIjQRBisRiSJtAdkDeBDlm0aNGTJ08UGg0NDd99911W4gFgQHCeDnTLrFmzfvrpp/7+fqqFIIi7d++KRCIWowKADo43gW5ZsmQJQRDURwMDAy8vL0iaQKdA3gS6JSwsjP6RIIilS5eyFQwAA4K8CXTLM8884+fnR90dIggiJCSE3ZAAUAB5E+iciIgIfNnd0NAwICDA2tqa7YgA+BPIm0DnSKVSDoeDECJJMiIigu1wAFAEeRPoHB6PN2/ePIQQh8MJCgpiOxwAFEHeBLpo8eLFCKGQkBAej8d2LAAoIVnF9rcHAOil48ePs5i42K+HFBcXJ5FI2I4C6JysrKyFCxcaGf3fn2hJSUlKSsrx48dZjEqn7NmzByH04Ycfsh2ItoWHh7MbAPt5UyKRLFiwgO0ogM4JDg42NTVVaExJSYG/Fkp2djZC6CncIaznTbi+CXSUctIEQEdA3gQAAGYgbwIAADOQNwEAgBnImwA8LW7fvh0cHNzR0YEQamxsTEpK8vDw4PP5QqHQx8cnNzd3eMN+9913zs7O9JkPFJIki4uLV6xY4ezsbGJiYmNj4+XllZWVRZ+DmJCQoHdzJCBvgrGvs7Pzueeew88gPbUqKio8PDz8/f35fD5CKCoqKiUlZcuWLY2NjaWlpQ4ODlKpNCEhgdGYNTU1wcHBiYmJzc3NA3a4fv26l5dXdXV1Tk5Oe3t7aWnphAkTlixZsmbNGqpPVFRUYmLixo0bR/LttAzyJhj7SJLs7++n10LWMnNzcy8vL7a2jhDq6OgICgp6++23V65cSTUmJycHBgbyeDwnJ6cvvvhCJBIlJye3tLSoP+zGjRtnzZp16dIlCwuLwfoYGRmdOHHC1dXV1NR08uTJGRkZ1tbWe/fu7enpwR2mTJmSm5ublJR04sSJYX9BLWN//iYAo83CwqKmpobtKNi0a9eupqamTZs2US35+fn0DhwOZ+rUqfX19devX7exsVFz2H/+859cLldFBxcXl76+PoUNOTo6VlRUdHd3m5iY4EaxWBwaGrp69WqpVDrg+b6ugeNNAMY4kiTT09M9PT3t7e1VdKutrUUIjRs3Tv2RVSfNAclkshs3bri5uQkEAnp7SEhIXV3dqVOnmA7ICsibYIw7efIk8Yfu7m6Fllu3boWHh1taWlpbW8+bN486LE1OTsYdHBwcysrK/Pz8LCwszMzM5syZU1xcjPts374d96HOwc+cOYNbnnnmGfo4XV1dxcXFeJH2j6cqKyubm5vFYrGKPpmZmTU1Nc7OzlOnTh2lMDo6OoqLi4ODg4VC4ZEjRxSWvvjiiwih77//fpS2rmEsPhuP76mx+3w+0CP4ruvw1p0/fz5C6PHjxwot8+fPv3DhQmdn59mzZ7lc7syZM+lricViHo8nkUhwn7KyMldXVw6HU1hYSPXh8XivvPIKfS13d3dra2t6i3IfbM6cOePGjSspKRnelwoNDQ0NDR2y29GjRxFCH3/88YBLr169GhMTY2BgYGVldfHixeFFIhKJDA0NVXTYtm0bTjizZ8++fPmycof29naEkLe3tzqbYz1vwPEmeKpFRkZKJBIejzd37tzAwMCysrL79+/TO3R1de3fvx/38fDwyMrK6u3tjY2N1cjW+/v7qUQwehobGxFCCufFFFdX1+zs7JUrV165cmXmzJmjFMOGDRt6enp+++03FxcXNzc3Ko1S+Hw+QRA4VN2nB5dgARg99Ezh6OiIEGpoaKDOshFCPB4Pn0JiM2bMsLe3r6ysbGxstLOzG+HWCwsLRziCOvDVCWNj48E6nDt3zsXFZbTD4HA4Li4un3/+eXNz86ZNmyQSydy5c+kdjIyMHj9+PNphaAQcb4KnGv0oDL+cQ2G6kqWlpcIq+HYzo/k67MIVUhTua7MI1/BXuKGPEJLL5cO40cQKyJsAqNLa2qpwHo0zJjVZx8DAoLe3l95BJpMpDEJ/I7z24eNifAFRF+DpR21tbfTGjo4OkiRHfgivHZA3AVClu7u7rKyM+vjrr782NDSIxWLqF25nZ1dfX091aGpqunPnjsIgZmZmVG59/vnnDx06NMpR/8n06dMRQnV1dQMulcvlo3eSHh8fr/xmvdOnT6M/XyFBCOF9iEPVfZA3AVBFIBCsX7++pKSkq6urvLw8IiKCw+GkpqZSHfz9/RsaGvbu3dvZ2VlTUxMbG6s8b/yll16qrq6+e/duSUlJbW2tt7c3bvf19bW2ti4tLR3VryAWi21sbCorK5UXpaWl8Xi8tWvXKi+KiIggCOLmzZsj3PpXX321devWW7du9fT03Lp1a926dVlZWe7u7pGRkfRuFRUVCCF/f/8Rbk5LWLyXT+rAfAKgR4Y3D0mhXMXixYtLSkroLR999BH55zPxwMBAvK5YLBaJRNeuXQsICLCwsOByuT4+PkVFRfTxZTJZZGSknZ0dl8v18vIqKytzd3fH46xbtw73qaqq8vb25vF4jo6O+/bto9b19va2srK6cOHC8HaImvOQSJJcv369kZFRfX29QvuBAwe4XG58fLzyKr6+vubm5nK5XMWweXl5yiklLS2N6tDe3p6enh4QEODk5MThcMzNzd3d3Xfs2PHo0SOFocLCwkQiUW9vrzpfh/W8AXkT6I2RzN8cHpw3tblFRtTPmzKZTCQSRUdHqznygwcPuFxuZGTkCKJjoKKigiCIY8eOqdmf9byhB+fp5ubmBA2eoCsWi5cvX37p0iW2oxtdKip0qeNp3nWATiAQ5OXl5eTk7Nu3b8jOJEnGxMTw+XzlWZajoba2ViqVJiYmLly4UAub0wg9yJudnZ2//PILQmj+/PkkSfb19VVVVW3durWqqsrDw+Mvf/nLo0eP2I5R84as0KWOp3PXgQG5ubmVl5efPn0a199Uobm5uba2tqCgQCgUaiGwgwcPJiUlJSUlaWFbmqIHeVOBoaGhra3t/Pnzz507t3bt2oyMjEWLFpFj7lXs6lToYuop2XUagZ8rr6ysrK+vJwhiw4YNbEekAU5OTvn5+bj+pgpCobCoqGjatGnaiWrnzp16dKSJ6V/epPvkk088PT2//fbbr7/+mu1YNOyf//xnQkLC6NWAGMO7TiMUbpVs376d7YiADtHvvEkQBK7Dun//frZj0bDRfnBiDO86AEabfudNhBAu4VVaWko9Rnbv3r2YmBg872H8+PFSqRRPDUPqFRBDCPX09GzatMnFxcXMzGzcuHFBQUHffvvtkydPqA4qNqGamgFoh37tOgB0CAv38GmQevMJ6Dc3FFCFABoaGkiSbGhomDhxoq2t7alTpx4+fHjlyhUfHx9TU1P6FLkhC4hFRkYKBIIffvjh0aNHTU1N8fHxCKHz58/jpepsQjV1KphRVFToUqcK2Vjaddqfh6Tj1J+HNMaomTdGMQAWt01qIm9Sd4Txj//dd99FCH355ZdUh8bGRhMTE3d3d6oF//jz8vKoltDQUITQvXv38MdJkybNmjWLvhVnZ2fqx6/OJlQbMgA6FXnTx8dnyFnTY2nXQd5UAHmTLXpfRw4X7DM2Nsa1v06ePGlgYEB/c6FQKJw2bdqlS5fq6uocHByodhUFxF5//fXPP//8/fffX7Zs2cyZMw0NDa9fv051Vn8Tqg1ZwWxII6xCpqe7To/e3jXa8CPnsEO0T+/zZlFREUJIIjcOHrUAACAASURBVJEYGxv39PTgoi8Dlmi9ceMG/ZepooDYvn37JBJJZmamn58fQsjb2zs6OjokJAQhxGgTqg1ZwWy06emuCw8PV/cbPh1gh2ifft8X6u/vx88/rFixAiFkYmJiaWlpZGTU19enfGg9Z84cNYclCGLJkiX/+te/ZDLZyZMnSZKUSqWfffaZBjfBOv3ddSM9xRpDnubzdHbpd95MTEy8ePFiSEhIWFgYbpFKpXK5nHpzFrZz584JEybI5XI1h7W0tKyqqkIIGRsbv/baa/hWMvWmPY1sgnWw6wAYNv3Lm/39/S0tLd98842fn9+uXbuWLVv25ZdfUnVhd+zYMWXKlGXLlp0+fbq9vb2tre3gwYNbt25NTk5mNIf8f/7nfy5fvtzT09PS0rJr1y6SJH19fTW7iZFjWoUMdh0AmsH68faQ98V4PB49YIIgBALBjBkzPvjgg0uXLin3b21tXbVq1eTJk42NjcePH+/v73/27Fm8SM0CYhUVFdHR0S+88AKehPjyyy+npaVRr9BSvQnV1K9gNmSFLlKNKmRjadeRcD9dydN8ns7u/XSCZPViAUEQx48fX7BgAYsxAH1x4sSJ8PBwdv9idQq+xpKdnc12INrGet7Qv/N0AABgF+RNAJ4Wt2/fDg4OxnXkGhsbk5KSPDw8+Hy+UCj08fFRKIyvpr6+vj179ri7u1tYWNjY2Lzxxhv4sYgBOwcHBxMEoVAkJSEhAV+B0SOQNzWMGNzmzZvZjg48vSoqKjw8PPz9/XEduaioqJSUlC1btjQ2NpaWljo4OEil0oSEBEZjdnV1+fr6ZmRk7Nmzp6Wlpby83NzcPDg4+OrVq8qdjxw5MuBV+6ioqMTExI0bNw7ve7GDxWurpA5c3wV6RMv3hXg83iuvvKLL46t/X6i9vd3BwYH+nozAwMCMjAzqY09PD36it7m5Wf0APvjgAz6f39TURLV0dnaamJj8+uuvCj3r6+utrKyWLFmCENq2bZvCUvyeDPVTAet5A443ARj7du3a1dTUtGnTJqolPz8flwvAOBzO1KlTnzx5Qn8uVrXm5uZDhw4tXrzY1taWauTxeN3d3cqv842KigoLCxvsdZVisTg0NHT16tX6Mo0X8iYAYxxJkunp6Z6envb29iq61dbWIoTGjRun5rC4QiCuRqja4cOHr169mpycrKJPSEhIXV0d9YiEjoO8CcYgPEt0ypQpHA7HysrqjTfeOH/+PF60fft2fLmZ+sGfOXMGt1BFVfBLMrq6uoqLi/EiPC0ftxME4eDgUFZW5ufnZ2FhYWZmNmfOHOohqJGMP0oqKyubm5vFYrGKPpmZmTU1Nc7OzlOnTlVz2J9//hkhZGVltXr1akdHRw6HM3HixJiYmLa2Nnq3urq61atXHz58WPUbX1588UWE0Pfff6/m1lnG4jUCUgeuUwA9oub1zcbGxkmTJtna2ubl5bW3t1+/fl0qlRIEQX9qQPnaoru7u7W1Nb1lsOuPYrGYx+NJJBJchLSsrMzV1ZXD4RQWFmpkfHXKqmJqXt88evQoQujjjz8ecOnVq1djYmLwu04vXrw45GgUXFRQKBQuXry4pqbmwYMHmZmZPB7P2dlZJpNR3QICApYvX06PRPn6JkmSuOaLt7e3OptmPW/A8SYYaxITE2/evJmSkjJv3jw+n+/s7PzVV1/Z2dnFxMSM5OWgdF1dXfv375dIJDwez8PDIysrq7e3NzY2ViODU49XaWQ09EfBwAHLUCGEXF1ds7OzV65ceeXKFXqFwCF1d3cjhLhcbkZGxuTJky0tLZcuXZqYmFhdXb17927cJy0t7caNG7t27RpyND6fTxAEDlX3Qd4EYw2ehxgYGEi1mJiY+Pn5PX78WFOngTweD59XYjNmzLC3t6+srNTIz76wsLCtrU0ikYx8KAwnOGNj48E6nDt3LjU1VfXVT2X4Kd65c+fSLzIEBQWhP06379y5s2bNmsOHDys87zsYIyMj6h0EOg7yJhhTcJVPU1NThatp+J5vU1OTRrZiaWmp0GJjY4MQamlp0cj4mmVqaooQot4ipSlOTk4IIWtra3oj3g/37t1DCOHrJLNnz6amMON5SBs3bsQff//9d/q6crl8tF9HqCmQN8GYYmJiIhAIuru7Hz58SG/HZ+hCoRB/NDAw6O3tpXeQyWQKQ1GVopS1trYqnEfjjImzxsjH1yw7OzuEEL6AqEH4xpfCITbeD/j/UitWrFC4LKhwffPZZ5+lVuzo6CBJEoeq+yBvgrEGl5enz2jp6ekpKCjgcrkBAQG4xc7Orr6+nurQ1NR0584dhXHMzMyo3Pf8888fOnSIWtTd3V1WVkZ9/PXXXxsaGsRiMfWzH+H4moVnU+KXaiiTy+UuLi7DGPbNN98UiURnzpzB1wEw/ETQW2+9xXQ0vLuUJ37qJsibYKzZsWPHpEmT4uLi8vPzHz58WF1d/c477zQ2NqamplIztP39/RsaGvbu3dvZ2VlTUxMbG0sdKlJeeuml6urqu3fvlpSU1NbWent7U4sEAsH69etLSkq6urrKy8sjIiI4HE5qairVYSTjMy2rOiSxWGxjY1NZWam8KC0tjcfjrV27VnlRREQEQRA3b94cbFgTE5P09PTW1taFCxfeuHFDJpMdPXp0x44dnp6eMTExTIPEr4MebGK8ztHivfsBILbnEwA9ov5zlvfv34+Li5s0aZKxsbFAIAgICCgoKKB3kMlkkZGRdnZ2XC7Xy8urrKzM3d0d/yLWrVuH+1RVVXl7e/N4PEdHx3379lHrisVikUh07dq1gIAACwsLLpfr4+NTVFSkqfGHLKtKUf85y/Xr1xsZGdXX1yu0HzhwgMvlxsfHK6/i6+trbm4ul8tVj3zhwoWAgACBQMDhcFxcXDZv3vzo0SPlbtHR0QqZJyAggN4hLCxMJBL19vaq83VYzxuQN4He0JG6xThvsh0FSTLJmzKZTCQS0Z9PV+3BgwdcLjcyMnIE0TGAn08/duyYmv1Zzxtwng7A2CcQCPLy8nJycvDL+FQjSTImJobP52/btk0LsdXW1kql0sTExIULF2phcxoBeROAp4Kbm1t5efnp06dx/U0Vmpuba2trCwoKqOkHo+rgwYNJSUlJSUla2JamQN4EQF34ufLKysr6+nqCIDZs2MB2RMw4OTnl5+fj+psqCIXCoqKiadOmaSeqnTt36tGRJgYvEQRAXfHx8fHx8WxHAdgHx5sAAMAM5E0AAGAG8iYAADADeRMAAJhh/77Qnj17srOz2Y4C6AH8hHVYWBjbgegK/Cwm7BDtI0jNlUcdBvhPDgZz+vRpNzc37UwhBHpn1apVGixRyhTLeROAweAXwy5YsIDtQABQBNc3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzkDcBAIAZyJsAAMAM5E0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZiBvAgAAM5A3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzRmwHAMB/yWQykiTpLV1dXQ8ePKA+mpubGxsbaz0uABQRCn+pALDF19f3/Pnzgy01NDSsr6+3tbXVZkgADAjO04GuWLRoEUEQAy4yMDB49dVXIWkCHQF5E+iK0NBQI6OBLxwRBLF06VItxwPAYCBvAl1hZWXl7+9vaGiovMjAwCAkJET7IQEwIMibQIdERET09/crNBoZGQUGBgoEAlZCAkAZ5E2gQ4KDg01MTBQanzx5EhERwUo8AAwI8ibQIWZmZiEhIQqTjbhc7ptvvslWSAAog7wJdMs777zT19dHfTQ2Ng4NDeVyuSyGBIACyJtAtwQEBNAvZfb19b3zzjssxgOAMsibQLcYGxsvXLiQw+Hgj5aWln5+fuyGBIACyJtA5yxatKi3txchZGxsHBERMdikTgDYAs9ZAp3T399vb2/f3NyMECoqKnrllVfYjgiAP4HjTaBzDAwMlixZghCys7ObNWsW2+EAoIjlM6ATJ06wGwDQTc888wxCyNPTMzs7m+1YgC6aNWuWg4MDa5snWcXa1wYA6LPjx4+zmLjYv+J+/PjxBQsWsB0F0Dk5OTmhoaH0lhMnToSHh8P/bilhYWEIoafwkHywullaA9c3gY5SSJoA6A7ImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CcDT4vbt28HBwR0dHQihxsbGpKQkDw8PPp8vFAp9fHxyc3OHMWZfX9+ePXvc3d0tLCxsbGzeeOONvLy8weY8BAcHEwSxfft2emNCQsLx48eHsWkWQd4EY19nZ+dzzz03b948tgNhU0VFhYeHh7+/P5/PRwhFRUWlpKRs2bKlsbGxtLTUwcFBKpUmJCQwGrOrq8vX1zcjI2PPnj0tLS3l5eXm5ubBwcFXr15V7nzkyJG8vDzl9qioqMTExI0bNw7ve7EC8iYY+0iS7O/vV34Dh9aYm5t7eXmxtXWEUEdHR1BQ0Ntvv71y5UqqMTk5OTAwkMfjOTk5ffHFFyKRKDk5uaWlRf1h16xZc/ny5R9++OHVV1/lcrkTJkzIyMhQrtiPEGpoaIiLi8OPzyqYMmVKbm5uUlKSHj09CHkTjH0WFhY1NTXfffcd24GwZteuXU1NTZs2baJa8vPz3333Xeojh8OZOnXqkydPrl+/ruaYzc3Nhw4dWrx4Mf39zDwer7u7e/r06Qqdo6KiwsLC/P39BxxKLBaHhoauXr1aLper+5VYBXkTgDGOJMn09HRPT097e3sV3WpraxFC48aNU3PYb7/99smTJ+ocRx8+fPjq1avJyckq+oSEhNTV1Z06dUrNrbML8iYY406ePEn8obu7W6Hl1q1b4eHhlpaW1tbW8+bNq6mpwWslJyfjDg4ODmVlZX5+fhYWFmZmZnPmzCkuLsZ9tm/fjvtQuePMmTO4Bdclocbp6uoqLi7Gi7RfTrSysrK5uVksFqvok5mZWVNT4+zsPHXqVDWH/fnnnxFCVlZWq1evdnR05HA4EydOjImJaWtro3erq6tbvXr14cOHLSwsVIz24osvIoS+//57NbfOMhafjcc33dh9Ph/oEXzXdXjrzp8/HyH0+PFjhZb58+dfuHChs7Pz7NmzXC535syZ9LXEYjGPx5NIJLhPWVmZq6srh8MpLCyk+vB4vFdeeYW+lru7u7W1Nb1FuQ82Z86ccePGlZSUDO9LhYaGhoaGDtnt6NGjCKGPP/54wKVXr16NiYkxMDCwsrK6ePGi+lvHO1AoFC5evLimpubBgweZmZk8Hs/Z2Vkmk1HdAgICli9fTo9k27ZtyqO1t7cjhLy9vdXZNOt5A443wVMtMjJSIpHweLy5c+cGBgaWlZXdv3+f3qGrq2v//v24j4eHR1ZWVm9vb2xsrEa23t/fTyWC0dPY2IgQGuwF9K6urtnZ2StXrrxy5crMmTPVHxYfvHO53IyMjMmTJ1taWi5dujQxMbG6unr37t24T1pa2o0bN3bt2jXkaHw+nyAIHKrug7wJnmr0TOHo6IgQamhooHfg8Xj4FBKbMWOGvb19ZWWlRn7hhYWFbW1tEolk5EOpgBOcwtuV6c6dO5eamqr66qcyHo+HEJo7dy79ykNQUBD643T7zp07a9asOXz4MO45JCMjo8ePHzOKgS2QN8FTjX4Uhl8GpzBdydLSUmEVGxsbhBCj+TrsMjU1RQjR366sEU5OTggha2treiPeOffu3UMI5eXltbe3z549m7qajOchbdy4EX/8/fff6evK5XJ9eeEz5E0AVGltbVU4j8YZEycIhJCBgQF+ixxFJpMpDMJuvUg7OzuEEL6AqEH4bpjCcTfeOXhm0ooVKxQuCypc33z22WepFTs6OkiSxKHqPsibAKjS3d1dVlZGffz1118bGhrEYjH1C7ezs6uvr6c6NDU13blzR2EQMzMzKrc+//zzhw4dGuWo/wTPpqyrqxtwqVwud3FxGcawb775pkgkOnPmDL4OgOEngt566y2mo+F9qDzxUzdB3gRAFYFAsH79+pKSkq6urvLy8oiICA6Hk5qaSnXw9/dvaGjYu3dvZ2dnTU1NbGwsdShKeemll6qrq+/evVtSUlJbW+vt7Y3bfX19ra2tS0tLR/UriMViGxubyspK5UVpaWk8Hm/t2rXKiyIiIgiCuHnz5mDDmpiYpKent7a2Lly48MaNGzKZ7OjRozt27PD09IyJiWEaZEVFBUJosInxOkeL9+4HgNieTwD0yPDmISmUq1i8eHFJSQm95aOPPiL/fCYeGBiI1xWLxSKR6Nq1awEBARYWFlwu18fHp6ioiD6+TCaLjIy0s7PjcrleXl5lZWXu7u54nHXr1uE+VVVV3t7ePB7P0dFx37591Lre3t5WVlYXLlwY3g5Rcx4SSZLr1683MjKqr69XaD9w4ACXy42Pj1dexdfX19zcXC6Xqx75woULAQEBAoGAw+G4uLhs3rz50aNHyt2io6MVMk9AQAC9Q1hYmEgk6u3tVefrsJ43IG8CvTGS+ZvDg/OmNrfIiPp5UyaTiUSi6OhoNUd+8OABl8uNjIwcQXQMVFRUEARx7NgxNfuznjf04Dzd3NycoMETdMVi8fLlyy9dusR2dKPiwYMHBw4c8PX1HTduHJfLfe655xYvXjzgeZZqT+GuAwMSCAR5eXk5OTn79u0bsjNJkjExMXw+f9u2bVqIrba2ViqVJiYmLly4UAub0wg9yJudnZ2//PILQmj+/PkkSfb19VVVVW3durWqqsrDw+Mvf/nLo0eP2I5Rw9asWfO3v/1t/vz5165da21tPXz4cEVFhbu7+8mTJxmN8xTuOjAYNze38vLy06dP4/qbKjQ3N9fW1hYUFAiFQi0EdvDgwaSkpKSkJC1sS2NYPNYl1T7epv/46fD17ODgYOq5i7Hhr3/96/vvv09vwVfNn3vuOaZDjaVdp83z9E8//ZT+M8HXQHWN+ufpY4yaeWP06MHxpgqffPKJp6fnt99++/XXX7Mdiyalp6cfPHiQ3iIWi7lcbk1NDamhZ/LG6q7TFIVbJQolysFTTr/zJkEQuA7r/v372Y5ldHV1dT1+/Hj69OmamkH99Ow6ADROv/Mm+uOhhdLSUuoxsnv37sXExDg5OXE4nPHjx0ulUnySi9QrIIYQ6unp2bRpk4uLi5mZ2bhx44KCgnCpQaqDik2opmYAyrKzsxFCH330EcPdo4p+7ToAdAhL1wf+C43s+iZJklQhgIaGBpIkGxoaJk6caGtre+rUqYcPH165csXHx8fU1JQ+RW7IAmKRkZECgeCHH3549OhRU1NTfHw8Quj8+fN4qTqbUE2dCmZ0TU1Ntra2ypNC1KlCNpZ2nfbnIek4uL7JWgAsbpvURN6k7gjjHz8u/f/ll19SHRobG01MTNzd3akW/OPHb93DQkNDEUL37t3DHydNmjRr1iz6VpydnakfvzqbUG3IAOju37//4osvhoeHK89A9vHxGXLW9FjadZA3FUDeZIu2S09rHC4rYGxsjCtsnzx50sDAgP7mQqFQOG3atEuXLtXV1Tk4OFDtAxYQw4O8/vrrn3/++fvvv79s2bKZM2caGhrSX7qi/iZUUxEApaurKyAgYOrUqUeOHDE0NFQYobCwUM1tDUhPd11YWNiwv/IYgx/QhB2ifXp/fbOoqAghJJFIjI2Ne3p62tvb+/v7BQIBfb43Luh/48YN+ooqCojt27fvyJEjtbW1fn5+fD7/9ddfp57VY7QJ1YasYCaXy/HDZ5mZmcpJc+T0d9cBwC79Pt7s7+/Hzz+sWLECIWRiYmJpadnZ2fn48eORvMUFFwpcsmRJX19fYWFhcnKyVCrdvXv3qlWrNLUJdURHR/f09OTm5lIbevbZZ7Oysl5++eWRD66/uw7fIgPojyPNp3CHsFuXD+n78WZiYuLFixdDQkKoUxWpVCqXy6k3Z2E7d+6cMGGC+q8YtbS0rKqqQggZGxu/9tpr+FYy9aY9jWxiSJs3b7569eo333wz4NuoR24M7zoARpv+5c3+/v6WlpZvvvnGz89v165dy5Yt+/LLL6n//+zYsWPKlCnLli07ffp0e3t7W1vbwYMHt27dmpyczOgY53/+538uX77c09PT0tKya9cukiR9fX01uwkVMjIytmzZ8tNPP1lYWNBPaRXmKjGtQvY07DoAtIHFe1KkevfFFF5OQhCEQCCYMWPGBx98cOnSJeX+ra2tq1atmjx5srGx8fjx4/39/c+ePYsXqVlArKKiIjo6+oUXXsCTEF9++eW0tDT684gqNqGamgEEBgYO9t+LPutoyCpkY2nXkXA/XQncT2cLQY7yu/RUIwji+PHjCxYsYDEGoC9OnDgRHh7O7l+sTnmar2+ymzf07zwdADA8t2/fDg4OxvWQGhsbk5KSPDw8+Hy+UCj08fFRKPCspr6+vj179ri7u1tYWNjY2Lzxxht4eu+AnYODgwmCUHjYPyEhAZ9J6BHImwA8FSoqKjw8PPz9/fl8PkIoKioqJSVly5YtjY2NpaWlDg4OUqk0ISGB0ZhdXV2+vr4ZGRl79uxpaWkpLy83NzcPDg6+evWqcucjR47gtw8piIqKSkxM3Lhx4/C+Fysgb2oYMbjNmzezHR1gwNzcHD/Cr6fj03V0dAQFBb399tu4mAuWnJwcGBjI4/GcnJy++OILkUiUnJzM6P3Ga9asuXz58g8//PDqq69yudwJEyZkZGQMOAOkoaEhLi4OvwdYwZQpU3Jzc5OSkk6cODGMr8YKuImpYXD1DeigXbt2NTU1bdq0iWrJz8+nd+BwOFOnTq2vr79+/brye+UG1NzcfOjQoffffx+/9Rfj8Xj011tSoqKiwsLCvL298auAFYjF4tDQ0NWrV0ulUr2YWQHHmwCMcSRJpqene3p62tvbq+hWW1uLEBo3bpyaw+JKV+ocMh8+fPjq1avJyckq+oSEhNTV1VFTfXUc5E0wBuHZTlOmTOFwOFZWVm+88cb58+fxou3bt+PLJtQP/syZM7iFKg6QnJxMEERXV1dxcTFehA+CcDtBEA4ODmVlZX5+fhYWFmZmZnPmzKEm849k/FFSWVnZ3NwsFotV9MnMzKypqXF2dp46daqaw+IHZK2srFavXu3o6MjhcCZOnBgTE9PW1kbvVldXt3r16sOHD1tYWKgY7cUXX0QIff/992punWUszoEidWAeFtAjas7fbGxsnDRpkq2tbV5eXnt7+/Xr16VSKUEQaWlpVB8ej/fKK6/Q13J3d7e2tqa3KPfBxGIxj8eTSCS4mF5ZWZmrqyuHwyksLNTI+OqUB8TUnL+JT40//vjjAZdevXo1JiYGv7Pv4sWLQ45GwcWxhELh4sWLa2pqHjx4kJmZyePxnJ2dZTIZ1S0gIGD58uX0SLZt26Y8Wnt7O0LI29tbnU2znjfgeBOMNYmJiTdv3kxJSZk3bx6fz3d2dv7qq6/s7OxiYmKam5s1somurq79+/dLJBIej+fh4ZGVldXb2xsbG6uRwanHBDQyGvqj8BW9Ggudq6trdnb2ypUrr1y5Qq90NSR8HZPL5WZkZEyePNnS0nLp0qWJiYnV1dW7d+/GfdLS0m7cuLFr164hR+Pz+QRB4FB1H+RNMNbgeYj0Z65MTEz8/PweP36sqdNAHo+HzyuxGTNm2NvbV1ZWauRnX1hY2NbWJpFIRj4UhhOcsbHxYB3OnTuXmpqq+uqnMvw02ty5c+kXGYKCgtAfp9t37txZs2bN4cOHFZ5bG4yRkRFVS1vHQd4EYwquVmdqaqpwNQ3f821qatLIViwtLRVa8D1oRpN4tMbU1BQhRL0NRVOcnJwQQtbW1vRGvB/u3buHEMLXSWbPnk1NxcPzkDZu3Ig//v777/R15XI5l8vVbJCjBPImGFNMTEwEAkF3d/fDhw/p7fgMnXohuIGBQW9vL72DTCZTGEpFsbLW1laF82icMakZPCMcX7Ps7OwQQvgCogbhG18Kh9h4P+D/S61YsULhsqDC9c1nn32WWrGjo4MkSRyq7oO8CcaakJAQhBB9RktPT09BQQGXyw0ICMAtdnZ29fX1VIempqY7d+4ojGNmZkblvueff/7QoUPUou7u7rKyMurjr7/+2tDQIBaLqZ/9CMfXrOnTpyOE6urqBlwql8tdXFyGMeybb74pEonOnDlDn7CJnwh66623mI6GdxcOVfdB3gRjzY4dOyZNmhQXF5efn//w4cPq6up33nmnsbExNTWVmqHt7+/f0NCwd+/ezs7Ompqa2NhY5cneL730UnV19d27d0tKSmpra729valFAoFg/fr1JSUlXV1d5eXlERERHA4nNTWV6jCS8ZmWBxySWCy2sbGprKxUXpSWlsbj8dauXau8KCIigiCImzdvDjasiYlJenp6a2vrwoULb9y4IZPJjh49umPHDk9Pz5iYGKZB4tea+vv7M12RHVq8dz8AxPZ8AqBH1K8jd//+/bi4uEmTJhkbGwsEgoCAgIKCAnoHmUwWGRlpZ2fH5XK9vLzKysrc3d3xL2LdunW4T1VVlbe3N4/Hc3R03LdvH7WuWCwWiUTXrl0LCAiwsLDgcrk+Pj5FRUWaGn/I8oAU9evIrV+/3sjIqL6+XqH9wIEDXC43Pj5eeRVfX19zc3PltwEquHDhQkBAgEAg4HA4Li4umzdvfvTokXK36OhohcwTEBBA74BfCdPb26vO12E9b0DeBHpDR+pv4rzJdhQkySRvymQykUgUHR2t5sgPHjzgcrnKr54eJRUVFQRBHDt2TM3+rOcNOE8HYOwTCAR5eXk5OTn4pVKqkSQZExPD5/O3bdumhdhqa2ulUmliYuLChQu1sDmNgLwJwFPBzc2tvLz89OnTuP6mCs3NzbW1tQUFBdT0g4kfVAAAFiBJREFUg1F18ODBpKSkpKQkLWxLUyBvAqAu/Fx5ZWVlfX09QRAbNmxgOyJmnJyc8vPzcf1NFYRCYVFR0bRp07QT1c6dO/XoSBPTg5JNAOiI+Pj4+Ph4tqMA7IPjTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAhlicc0/CK8wAAMPC7vNCLM9D0rv3zQOtCQ8Pj4uL02D5XjCWzJo1i8WtE3DQB3QTQRDHjx9fsGAB24EAoAiubwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZiBvAgAAM5A3AQCAGcibAADADORNAABgBvImAAAwA3kTAACYgbwJAADMQN4EAABmIG8CAAAzkDcBAIAZyJsAAMAM5E0AAGAG8iYAADADeRMAAJiBvAkAAMxA3gQAAGYgbwIAADOQNwEAgBnImwAAwAzkTQAAYAbyJgAAMAN5EwAAmIG8CQAAzEDeBAAAZozYDgCA/zp27NjDhw/pLf/6179kMhn1MSQkZPz48VqPCwBFBEmSbMcAAEIIvffee5mZmcbGxvgj/sskCAIh9OTJE3Nz85aWFhMTEzZDBAAhBOfpQHcsWrQIIdT3B7lcLpfL8b8NDQ3DwsIgaQIdAcebQFfI5XJbW9u2trYBlxYUFPj6+mo5JAAGBMebQFcYGRktWrSIOk+ne+aZZ3x8fLQfEgADgrwJdMiiRYv6+voUGo2NjZcsWWJoaMhKSAAog/N0oENIkpwwYUJdXZ1C+8WLF2fOnMlKSAAog+NNoEMIgoiIiFA4VXd0dPTw8GArJACUQd4EukXhVN3Y2Pi9997Ds5EA0BFwng50jouLy/Xr16mPV65cmTZtGovxAKAAjjeBzlmyZAl1qj516lRImkDXQN4EOiciIkIulyOEjI2N3333XbbDAUARnKcDXeTh4XHp0iWCIG7dujVhwgS2wwHgT+B4E+iipUuXIoQ8PT0haQIdpAf1kEpKSj777DO2owBa1d3dTRBET09PWFgY27EArZJIJKtWrWI7iiHowfHm3bt3c3Jy2I4CjK6cnBz6dHdTU1NbW1sHBwcWQ2JRXV3d0/k3X1paWlJSwnYUQ9OD400sOzub7RDAKCII4sMPP1ywYAHV8vvvvz/77LMshsSiEydOhIeHP4V/8/pyeqEHx5vg6fTUJk2g+yBvAgAAM5A3AQCAGcibAADADORNAPTe7du3g4ODOzo6EEKNjY1JSUkeHh58Pl8oFPr4+OTm5g5jzL6+vj179ri7u1tYWNjY2Lzxxht5eXmDPSYTHBxMEMT27dvpjQkJCcePHx/GpnUf5E2gxzo7O5977rl58+axHQibKioqPDw8/P39+Xw+QigqKiolJWXLli2NjY2lpaUODg5SqTQhIYHRmF1dXb6+vhkZGXv27GlpaSkvLzc3Nw8ODr569apy5yNHjuTl5Sm3R0VFJSYmbty4cXjfS5dB3gR6jCTJ/v7+/v5+tgIwNzf38vJia+sIoY6OjqCgoLfffnvlypVUY3JycmBgII/Hc3Jy+uKLL0QiUXJycktLi/rDrlmz5vLlyz/88MOrr77K5XInTJiQkZEx4HvxGhoa4uLilixZorxoypQpubm5SUlJJ06cGMZX02WQN4Ees7CwqKmp+e6779gOhDW7du1qamratGkT1ZKfn08vhsLhcKZOnfrkyRN6aT7VmpubDx06tHjxYltbW6qRx+N1d3dPnz5doXNUVFRYWJi/v/+AQ4nF4tDQ0NWrV+NCLWMG5E0A9BVJkunp6Z6envb29iq61dbWIoTGjRun5rDffvvtkydP1DmOPnz48NWrV5OTk1X0CQkJqaurO3XqlJpb1wuQN4G+OnnyJPGH7u5uhZZbt26Fh4dbWlpaW1vPmzevpqYGr5WcnIw7ODg4lJWV+fn5WVhYmJmZzZkzp7i4GPfZvn077kPljjNnzuCWZ555hj5OV1dXcXExXmRkpO2n7yorK5ubm8VisYo+mZmZNTU1zs7OU6dOVXPYn3/+GSFkZWW1evVqR0dHDoczceLEmJgYhVc019XVrV69+vDhwxYWFipGe/HFFxFC33//vZpb1w+kzsO35NiOAowuhNDx48eHseL8+fMRQo8fP1ZomT9//oULFzo7O8+ePcvlcmfOnElfSywW83g8iUSC+5SVlbm6unI4nMLCQqoPj8d75ZVX6Gu5u7tbW1vTW5T7YHPmzBk3blxJSckwvhGp9t/80aNHEUIff/zxgEuvXr0aExNjYGBgZWV18eJF9beOd6BQKFy8eHFNTc2DBw8yMzN5PJ6zs7NMJqO6BQQELF++nB7Jtm3blEdrb29HCHl7e6uz6dDQ0NDQUPVDZQscb4KxKTIyUiKR8Hi8uXPnBgYGlpWV3b9/n96hq6tr//79uI+Hh0dWVlZvb29sbKxGtt7f349/YBoZbTCNjY0IIYFAMOBSV1fX7OzslStXXrlyhdHbQPHBO5fLzcjImDx5sqWl5dKlSxMTE6urq3fv3o37pKWl3bhxY9euXUOOxufzCYLAoY4ZkDfB2ETPFI6OjgihhoYGegcej4dPIbEZM2bY29tXVlZq5BdeWFjY1tYmkUhGPpQKOMEpvP6T7ty5c6mpqaqvfirj8XgIoblz59KvPAQFBaE/Trfv3LmzZs2aw4cP455DMjIyevz4MaMYdBzkTTA20Y/COBwOQkhhupKlpaXCKjY2NgghRvN12GVqaooQor/+UyOcnJwQQtbW1vRGvHPu3buHEMrLy2tvb589ezZ1NRnPQ9q4cSP++Pvvv9PXlcvlXC5Xs0GyC/ImeEq1trYqnEfjjIkTBELIwMCgt7eX3kEmkykMwu4Liu3s7BBC+AKiBuG7YQrH3Xjn4JlJK1asULjep3B9k17LqqOjgyRJHOqYAXkTPKW6u7vLysqoj7/++mtDQ4NYLKZ+4XZ2dvX19VSHpqamO3fuKAxiZmZG5dbnn3/+0KFDoxz1n+DZlPR6z3RyudzFxWUYw7755psikejMmTP4OgCGnwh66623mI6G96HyxE+9BnkTPKUEAsH69etLSkq6urrKy8sjIiI4HE5qairVwd/fv6GhYe/evZ2dnTU1NbGxsdShKOWll16qrq6+e/duSUlJbW2tt7c3bvf19bW2ti4tLR3VryAWi21sbCorK5UXpaWl8Xi8tWvXKi+KiIggCOLmzZuDDWtiYpKent7a2rpw4cIbN27IZLKjR4/u2LHD09MzJiaGaZAVFRUIocEmxusrLd67HyaYh/Q0QMznISmUq1i8eLHCKxY++ugj8s9n4oGBgXhdsVgsEomuXbsWEBBgYWHB5XJ9fHyKioro48tkssjISDs7Oy6X6+XlVVZW5u7ujsdZt24d7lNVVeXt7c3j8RwdHfft20et6+3tbWVldeHCheHtDfX/5tevX29kZFRfX6/QfuDAAS6XGx8fr7yKr6+vubm5XC5XPfKFCxcCAgIEAgGHw3Fxcdm8efOjR4+Uu0VHRyuklICAAHqHsLAwkUjU29urztfRl3lIepCPIG8+DYaRN0cC502tbY4p9f/mZTKZSCSKjo5Wc+QHDx5wudzIyMgRRMdARUUFQRDHjh1Ts7++5E04TwdAjwkEgry8vJycnH379g3ZmSTJmJgYPp+/bds2LcRWW1srlUoTExMXLlyohc1p0xjMm+Xl5e+9956Tk5OpqamlpeXMmTO3bt2qfCdU35mbmxM0+LEQsVi8fPnyS5cusR0d0B43N7fy8vLTp0/j+psqNDc319bWFhQUCIVCLQR28ODBpKSkpKQkLWxLy8Za3kxMTHz55ZetrKzy8/NlMtnNmzf//ve/5+bmOjs7U08fjw2dnZ2//PILQmj+/PkkSfb19VVVVW3durWqqsrDw+Mvf/nLo0eP2I5RF+HnyisrK+vr6wmC2LBhA9sRaYCTk1N+fj6uv6mCUCgsKiqaNm2adqLauXPn2DvS/C+2LxQMTf1rPfjs48CBAwrtXV1duGz1b7/9xnTrgz2ArCkjGZ+eN+nwXdTg4GDqaT/dh7R7fVPHPbXX9OH6prb9/vvvW7Zseemll5Rv8JmZme3Zs+fhw4fDmEWhjz755BNPT89vv/3266+/ZjsWAMagsZM3Dxw4IJfLB3tvvbe3t729/dmzZ3EtwrGNIAhc/Xv//v1sxwLAGDR28uaPP/6IEFJRixAv+s9//oNGVmBRLwo44u2WlpZSDy/fu3cvJibGycmJw+GMHz9eKpXiCclIvbKVCKGenp5Nmza5uLiYmZmNGzcuKCgIF7ilOqjYBABjCtsXCoam5rUe/HjcTz/9NFiHiIgI9OdihSMpsKgLBRwHu75JkiRVfqahoYEkyYaGhokTJ9ra2p46derhw4dXrlzx8fExNTWlT8wesmxlZGSkQCD44YcfHj161NTUFB8fjxA6f/48XqrOJlRAcH2TBq5v6rixc7yJDVlnQYOFGHS5gKPCiomJibdv3/7ss8/efPNNc3PzadOmff311yRJ/u1vf1NYUUXZyoKCgmnTpr322mtcLtfW1vbTTz91dnYexiYA0Hfaruw/euzt7RsbG1tbWwfrgBcxrUWogooCjiOv/lJYWDiS1XExG2NjY3xZ4OTJkwYGBvT35QqFwmnTpl26dKmurs7BwYFqH7BsJR7k9ddf//zzz99///1ly5bNnDnT0NCQ/qov9TcxmPDw8PDw8JF86zGG3WJLbAkNDWU7hKGNnbzp4+Nz6dKlioqK119/fcAOuPzB7NmzNbXFAQs4NjQ0tLS0sF41q6ioCCEkkUiMjY17enpwqbEBC4PfuHGDntRUlK3ct2+fRCLJzMz08/NDCHl7e0dHR4eEhCCEGG1iMHFxcaNd6FdflJSUpKSk4LP1p8qePXvYDkEtYydvRkdH/+Mf/8jOzk5ISFBeWlRU1NDQEBQUNGHCBKpxhAUWcQFHegcdKeDY39+Pn7pbsWIFQsjExMTS0rKzs/Px48cjufWEy9MuWbKkr6+vsLAwOTlZKpXu3r171apVGtmERCJZsGDBsMMbY1JSUp7CvZGdnc12CGoZO9c3nZ2d//73v//8888HDx5UWPTo0aO4uDhra+uUlBR6+wgLLOpsAcfExMSLFy+GhIRQs7KkUqlcLld4Ymrnzp0TJkxQ/8XWlpaWVVVVCCFjY+PXXnsN34Wn3u+qkU0AoB/YuyWlLkb3FhMTEw0NDT/88MMrV650d3c/ePAgLy/Pzc1NJBKVl5crdMaTHP/3f/9/e/cf0sQbxwH8OdqmS91RUS5OyDkYZT9WMigCESbMwhJavyQEIRIJYklJNcEockThH45vfxgrIoIiCwo2jIhVf0STVJpggdImlTYPtMwVLVmsPx467uvmdbeb7e72ef3n3eNzj/P4sN3z7P38F41G3717d+DAAYqi5s1379ixgyTJDx8+vHz5UqVSvX37Fh83m80kSVZXV3PMp4vpX+h8+q9fv2iafvjwodVqRQgdPnyYnfpF07TRaCwrK+vt7Z2ZmZmenu7u7l66dCl7Cjt5Y8jTp08jhF6/fo1/JEmyqqpqaGgoFovRNH3u3DmEUEdHB/9LcEAwn84C8+kSJ4P/jdB7qL+/v7Gxcc2aNRqNpqioyGKxdHR0sPcvZYgJWMx6gOO8LbEIgiBJcuPGjUePHh0cHExuPz09feLEibKyMrVavXLlSpvN9uTJE3yKZ2xlMBhsbm5et24dXr+5bds2j8fD/ionxyX+CuomG9RNiSMSi7xVqXg9PT0HDx6U2jg3b948NTW10BYFQCiCIO7evZuDT/RSkuY9/w/gJ0vSf8qpnOebAADG+/fv6+rqcLJcJBJxuVwWi0Wn0+n1+qqqqnlR+fz19vaaTKaUU39nzpzJnQUAUDcBUJpgMGixWGw2G06Wa2pq6urqOn/+fCQS6evrKykpsdvtKZedcAiFQnV1dU6nk6bplA2ampqcTmd7e3sG/gDJg7opmCIDHHNKYWEhkxsgx/65zc7O7t69e+/evXhaEuvs7KytrS0oKCgtLb1x4wZFUZ2dnYJ2im9vb9++ffvg4GBRUVHKBkaj8cGDBy6Xq6enR+zfIHnKWb/5z7S2tuKvZgMgQZcvX56cnDx79ixzxOfzsRtoNJry8vKJiYmRkZHkHToXcv36da1Wy93GbDbv27fv5MmTdrs9gyE1EgTvNwFQjkQice3ata1bt3J/nxinKS5fvpx/z38tmtiePXvGx8eZVb1KBXUTyAle6mQ0GjUazbJly3bu3Pns2TN8SvHZgHwMDQ3RNM2RpogQunnzZigUMplM5eXlGR8ATmx4/PhxxnuWluwug+IjZ9ey5RTEY/1mJBIxGAzFxcVer/fr168jIyN2u50gCI/Hw7SRezYglvY9f+vWLfT/sES2N2/eOBwOvIXfq1ev0ug/kUhQFLVkyZKFzuKYgsrKyvQ6l8v6TXi/CWTD6XSOjY11dXXt2rVLp9OZTKbbt2+vXr3a4XAsNMkrlJSzAfnAOVgp01UQQps2bbp3796xY8eGh4fZwVcZpNPpCILAw1AwqJtANvCqw9raWuZIXl5edXX1jx8/MvXBkCMbUHznz58///z586JmPsViMYSQWq1eqMHTp0/dbncG0xSTqVQqJjZbqaBuAnnAUXX5+fnz1sEUFxcjhCYnJzNylZTZgOhP0pX05efnI4SYzVGyIh6P85xEki+om0Ae8vLySJKMxWLRaJR9HH9C1+v1+MeMZAOyj0gkG5AnnMWFHzJmxezsbOLPpjUKBnUTyAbOSGavcfn586ff79dqtTU1NfiIUrMBedqwYQNCaKHYhHg8vnbt2kUdAH5x8DAUDOomkI2LFy8aDIaWlhafzxeNRkdHRw8dOhSJRNxuN/60jhCy2WyfPn26cuXKt2/fQqHQ8ePHk5d2V1RUjI6Ofvz4MRAIhMPhyspK5hRJkm1tbYFA4Pv37wMDAw0NDRqNxu12Mw3E9G+1WlesWNHX15f5l+YPs9m8atUqvLXBPB6Pp6Cg4NSpU8mnGhoaCIIYGxsTPwC8g6nNZhPflaRlczKfH1iHlAsQvxy5qamplpYWg8GgVqtJkqypqfH7/ewGss4GZIi559va2lQq1cTExLzj3d3dWq22tbU1+VesVmthYWE8Hufo1uv1JlcP9gowbP/+/RRFzc3NpTd4uaxDkkE9grqZC3jWzUWF62Z2x4CJuednZmYoimpububZ/suXL1qt9siRI+ldji0YDBIEcefOnbR7kEvdhM/pACgKSZJer/f+/ft4jyluiUTC4XDodLoLFy6IvG44HLbb7U6ns76+XmRX0gd1EwCl2bJly8DAwKNHj3D+JgeapsPhsN/vZxYkpO3q1asul8vlconsRxagbgKgwGzA0tJSn8+H8zc56PX6Fy9erF+/XvwVL126lAvvNDElZz0BwBNkAwJB4P0mAAAIA3UTAACEgboJAADCQN0EAABhZDMvlAubPeW4QCCQ7SFIBX4pcvCeHx8fLykpyfYoeMj2wvu/y51NmQEAsvi+EJFYzPRpAABQHni+CQAAwkDdBAAAYaBuAgCAMFA3AQBAmN/I1Q74MRFjcwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model=get_model_v1( (13,) )\n",
+ "\n",
+ "model.summary()\n",
+ "keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Add callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = \"./run/models/best_model.h5\"\n",
+ "\n",
+ "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.3 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 354 samples, validate on 152 samples\n",
+ "Epoch 1/100\n",
+ "354/354 [==============================] - 1s 2ms/sample - loss: 483.2389 - mae: 20.0008 - mse: 483.2388 - val_loss: 421.2562 - val_mae: 18.2848 - val_mse: 421.2561\n",
+ "Epoch 2/100\n",
+ "354/354 [==============================] - 0s 232us/sample - loss: 270.7346 - mae: 13.9655 - mse: 270.7346 - val_loss: 187.7437 - val_mae: 10.9696 - val_mse: 187.7437\n",
+ "Epoch 3/100\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 108.5703 - mae: 7.5648 - mse: 108.5703 - val_loss: 70.6387 - val_mae: 6.1047 - val_mse: 70.6387\n",
+ "Epoch 4/100\n",
+ "354/354 [==============================] - 0s 234us/sample - loss: 53.8803 - mae: 5.1135 - mse: 53.8803 - val_loss: 40.0765 - val_mae: 4.6628 - val_mse: 40.0765\n",
+ "Epoch 5/100\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 34.2413 - mae: 4.1321 - mse: 34.2413 - val_loss: 29.1298 - val_mae: 3.8690 - val_mse: 29.1298\n",
+ "Epoch 6/100\n",
+ "354/354 [==============================] - 0s 226us/sample - loss: 24.9834 - mae: 3.4851 - mse: 24.9834 - val_loss: 22.8731 - val_mae: 3.3820 - val_mse: 22.8731\n",
+ "Epoch 7/100\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 21.2207 - mae: 3.2139 - mse: 21.2207 - val_loss: 20.9766 - val_mae: 3.2391 - val_mse: 20.9766\n",
+ "Epoch 8/100\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 19.2641 - mae: 3.0025 - mse: 19.2641 - val_loss: 19.5046 - val_mae: 3.0795 - val_mse: 19.5046\n",
+ "Epoch 9/100\n",
+ "354/354 [==============================] - 0s 220us/sample - loss: 17.8432 - mae: 2.8878 - mse: 17.8432 - val_loss: 18.3068 - val_mae: 3.0834 - val_mse: 18.3068\n",
+ "Epoch 10/100\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 16.7673 - mae: 2.7365 - mse: 16.7673 - val_loss: 17.4260 - val_mae: 3.0035 - val_mse: 17.4260\n",
+ "Epoch 11/100\n",
+ "354/354 [==============================] - 0s 225us/sample - loss: 15.6927 - mae: 2.6873 - mse: 15.6927 - val_loss: 17.3096 - val_mae: 3.0492 - val_mse: 17.3096\n",
+ "Epoch 12/100\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 15.4113 - mae: 2.6274 - mse: 15.4113 - val_loss: 15.7095 - val_mae: 2.8104 - val_mse: 15.7095\n",
+ "Epoch 13/100\n",
+ "354/354 [==============================] - 0s 220us/sample - loss: 14.7243 - mae: 2.5393 - mse: 14.7243 - val_loss: 15.6497 - val_mae: 2.9052 - val_mse: 15.6497\n",
+ "Epoch 14/100\n",
+ "354/354 [==============================] - 0s 228us/sample - loss: 14.2611 - mae: 2.5371 - mse: 14.2611 - val_loss: 14.9650 - val_mae: 2.8165 - val_mse: 14.9650\n",
+ "Epoch 15/100\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 14.0530 - mae: 2.5289 - mse: 14.0530 - val_loss: 14.8840 - val_mae: 2.8196 - val_mse: 14.8840\n",
+ "Epoch 16/100\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 13.3820 - mae: 2.4568 - mse: 13.3820 - val_loss: 13.7568 - val_mae: 2.6754 - val_mse: 13.7568\n",
+ "Epoch 17/100\n",
+ "354/354 [==============================] - 0s 218us/sample - loss: 13.2232 - mae: 2.4318 - mse: 13.2232 - val_loss: 13.6934 - val_mae: 2.6355 - val_mse: 13.6934\n",
+ "Epoch 18/100\n",
+ "354/354 [==============================] - 0s 183us/sample - loss: 12.8038 - mae: 2.3743 - mse: 12.8038 - val_loss: 13.7276 - val_mae: 2.6466 - val_mse: 13.7276\n",
+ "Epoch 19/100\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 12.4826 - mae: 2.3804 - mse: 12.4826 - val_loss: 13.0037 - val_mae: 2.5279 - val_mse: 13.0037\n",
+ "Epoch 20/100\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 12.2345 - mae: 2.3264 - mse: 12.2345 - val_loss: 12.8911 - val_mae: 2.5583 - val_mse: 12.8911\n",
+ "Epoch 21/100\n",
+ "354/354 [==============================] - 0s 231us/sample - loss: 12.0720 - mae: 2.3410 - mse: 12.0720 - val_loss: 12.5983 - val_mae: 2.5747 - val_mse: 12.5983\n",
+ "Epoch 22/100\n",
+ "354/354 [==============================] - 0s 224us/sample - loss: 11.7805 - mae: 2.2897 - mse: 11.7805 - val_loss: 12.1645 - val_mae: 2.5094 - val_mse: 12.1645\n",
+ "Epoch 23/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 11.4012 - mae: 2.2581 - mse: 11.4012 - val_loss: 13.6673 - val_mae: 2.7201 - val_mse: 13.6673\n",
+ "Epoch 24/100\n",
+ "354/354 [==============================] - 0s 227us/sample - loss: 11.2741 - mae: 2.2712 - mse: 11.2741 - val_loss: 11.6918 - val_mae: 2.4039 - val_mse: 11.6918\n",
+ "Epoch 25/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 11.2056 - mae: 2.2226 - mse: 11.2056 - val_loss: 12.3935 - val_mae: 2.6021 - val_mse: 12.3935\n",
+ "Epoch 26/100\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 10.8629 - mae: 2.2289 - mse: 10.8629 - val_loss: 11.9155 - val_mae: 2.3744 - val_mse: 11.9155\n",
+ "Epoch 27/100\n",
+ "354/354 [==============================] - 0s 218us/sample - loss: 11.0500 - mae: 2.2151 - mse: 11.0500 - val_loss: 11.2193 - val_mae: 2.3695 - val_mse: 11.2193\n",
+ "Epoch 28/100\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 10.4915 - mae: 2.1578 - mse: 10.4915 - val_loss: 11.9919 - val_mae: 2.5344 - val_mse: 11.9919\n",
+ "Epoch 29/100\n",
+ "354/354 [==============================] - 0s 182us/sample - loss: 10.5519 - mae: 2.1307 - mse: 10.5519 - val_loss: 11.3573 - val_mae: 2.4664 - val_mse: 11.3573\n",
+ "Epoch 30/100\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 10.0504 - mae: 2.1281 - mse: 10.0504 - val_loss: 11.7304 - val_mae: 2.5102 - val_mse: 11.7304\n",
+ "Epoch 31/100\n",
+ "354/354 [==============================] - 0s 216us/sample - loss: 9.8992 - mae: 2.1397 - mse: 9.8992 - val_loss: 10.9137 - val_mae: 2.3602 - val_mse: 10.9137\n",
+ "Epoch 32/100\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 9.9473 - mae: 2.0665 - mse: 9.9473 - val_loss: 11.1929 - val_mae: 2.4503 - val_mse: 11.1929\n",
+ "Epoch 33/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 9.6057 - mae: 2.0609 - mse: 9.6057 - val_loss: 11.5105 - val_mae: 2.4419 - val_mse: 11.5105\n",
+ "Epoch 34/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 9.6783 - mae: 2.0484 - mse: 9.6783 - val_loss: 11.0130 - val_mae: 2.4072 - val_mse: 11.0130\n",
+ "Epoch 35/100\n",
+ "354/354 [==============================] - 0s 211us/sample - loss: 9.3834 - mae: 2.0337 - mse: 9.3834 - val_loss: 10.8769 - val_mae: 2.3960 - val_mse: 10.8769\n",
+ "Epoch 36/100\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 9.4563 - mae: 2.0349 - mse: 9.4563 - val_loss: 10.7918 - val_mae: 2.4397 - val_mse: 10.7918\n",
+ "Epoch 37/100\n",
+ "354/354 [==============================] - 0s 223us/sample - loss: 9.4023 - mae: 2.0246 - mse: 9.4023 - val_loss: 10.4927 - val_mae: 2.3926 - val_mse: 10.4927\n",
+ "Epoch 38/100\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 8.9702 - mae: 2.0006 - mse: 8.9702 - val_loss: 10.9715 - val_mae: 2.4245 - val_mse: 10.9715\n",
+ "Epoch 39/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 9.0225 - mae: 2.0207 - mse: 9.0225 - val_loss: 10.9499 - val_mae: 2.4785 - val_mse: 10.9499\n",
+ "Epoch 40/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 8.8586 - mae: 1.9994 - mse: 8.8586 - val_loss: 10.5540 - val_mae: 2.3401 - val_mse: 10.5540\n",
+ "Epoch 41/100\n",
+ "354/354 [==============================] - 0s 214us/sample - loss: 8.7666 - mae: 1.9705 - mse: 8.7666 - val_loss: 10.3300 - val_mae: 2.3298 - val_mse: 10.3300\n",
+ "Epoch 42/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 8.4090 - mae: 1.9556 - mse: 8.4090 - val_loss: 11.9413 - val_mae: 2.5568 - val_mse: 11.9413\n",
+ "Epoch 43/100\n",
+ "354/354 [==============================] - 0s 216us/sample - loss: 8.4974 - mae: 1.9809 - mse: 8.4974 - val_loss: 10.2694 - val_mae: 2.2804 - val_mse: 10.2694\n",
+ "Epoch 44/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 8.4512 - mae: 1.9371 - mse: 8.4512 - val_loss: 10.6134 - val_mae: 2.3782 - val_mse: 10.6134\n",
+ "Epoch 45/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 8.3356 - mae: 1.9116 - mse: 8.3356 - val_loss: 10.5007 - val_mae: 2.3672 - val_mse: 10.5007\n",
+ "Epoch 46/100\n",
+ "354/354 [==============================] - 0s 220us/sample - loss: 8.0746 - mae: 1.9163 - mse: 8.0746 - val_loss: 9.9081 - val_mae: 2.1968 - val_mse: 9.9081\n",
+ "Epoch 47/100\n",
+ "354/354 [==============================] - 0s 183us/sample - loss: 8.2374 - mae: 1.9080 - mse: 8.2374 - val_loss: 10.2771 - val_mae: 2.3529 - val_mse: 10.2771\n",
+ "Epoch 48/100\n",
+ "354/354 [==============================] - 0s 216us/sample - loss: 8.0765 - mae: 1.9000 - mse: 8.0765 - val_loss: 9.7120 - val_mae: 2.1879 - val_mse: 9.7120\n",
+ "Epoch 49/100\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 7.7848 - mae: 1.8825 - mse: 7.7848 - val_loss: 10.2084 - val_mae: 2.2360 - val_mse: 10.2084\n",
+ "Epoch 50/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 7.5973 - mae: 1.8669 - mse: 7.5973 - val_loss: 10.1582 - val_mae: 2.2808 - val_mse: 10.1582\n",
+ "Epoch 51/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 7.8596 - mae: 1.9102 - mse: 7.8596 - val_loss: 9.9785 - val_mae: 2.3041 - val_mse: 9.9785\n",
+ "Epoch 52/100\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 7.5027 - mae: 1.8527 - mse: 7.5027 - val_loss: 10.2315 - val_mae: 2.3614 - val_mse: 10.2315\n",
+ "Epoch 53/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 7.3160 - mae: 1.8556 - mse: 7.3160 - val_loss: 10.7149 - val_mae: 2.4225 - val_mse: 10.7149\n",
+ "Epoch 54/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 7.4478 - mae: 1.8692 - mse: 7.4478 - val_loss: 13.1244 - val_mae: 2.7923 - val_mse: 13.1244\n",
+ "Epoch 55/100\n",
+ "354/354 [==============================] - 0s 222us/sample - loss: 7.2579 - mae: 1.8375 - mse: 7.2579 - val_loss: 9.4053 - val_mae: 2.1927 - val_mse: 9.4053\n",
+ "Epoch 56/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 7.3045 - mae: 1.8785 - mse: 7.3045 - val_loss: 10.3231 - val_mae: 2.4311 - val_mse: 10.3231\n",
+ "Epoch 57/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 6.8708 - mae: 1.8047 - mse: 6.8708 - val_loss: 11.3678 - val_mae: 2.6010 - val_mse: 11.3678\n",
+ "Epoch 58/100\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 6.9471 - mae: 1.8179 - mse: 6.9471 - val_loss: 10.2855 - val_mae: 2.3937 - val_mse: 10.2855\n",
+ "Epoch 59/100\n",
+ "354/354 [==============================] - 0s 217us/sample - loss: 6.8858 - mae: 1.7987 - mse: 6.8858 - val_loss: 9.1795 - val_mae: 2.1552 - val_mse: 9.1795\n",
+ "Epoch 60/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 6.8982 - mae: 1.7783 - mse: 6.8982 - val_loss: 10.0291 - val_mae: 2.3000 - val_mse: 10.0291\n",
+ "Epoch 61/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 6.8502 - mae: 1.7688 - mse: 6.8502 - val_loss: 9.5141 - val_mae: 2.2370 - val_mse: 9.5141\n",
+ "Epoch 62/100\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 6.6801 - mae: 1.7737 - mse: 6.6801 - val_loss: 9.6853 - val_mae: 2.2719 - val_mse: 9.6853\n",
+ "Epoch 63/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 6.5468 - mae: 1.7479 - mse: 6.5468 - val_loss: 9.5858 - val_mae: 2.2346 - val_mse: 9.5858\n",
+ "Epoch 64/100\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 6.3406 - mae: 1.6985 - mse: 6.3406 - val_loss: 9.8893 - val_mae: 2.2439 - val_mse: 9.8893\n",
+ "Epoch 65/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 6.4070 - mae: 1.7780 - mse: 6.4071 - val_loss: 10.4085 - val_mae: 2.3908 - val_mse: 10.4085\n",
+ "Epoch 66/100\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 6.4227 - mae: 1.7042 - mse: 6.4227 - val_loss: 9.5313 - val_mae: 2.1998 - val_mse: 9.5313\n",
+ "Epoch 67/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 6.3353 - mae: 1.7095 - mse: 6.3353 - val_loss: 9.9436 - val_mae: 2.2965 - val_mse: 9.9436\n",
+ "Epoch 68/100\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 5.8545 - mae: 1.6760 - mse: 5.8545 - val_loss: 9.9311 - val_mae: 2.2837 - val_mse: 9.9311\n",
+ "Epoch 69/100\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 6.1148 - mae: 1.7286 - mse: 6.1148 - val_loss: 9.6456 - val_mae: 2.1932 - val_mse: 9.6456\n",
+ "Epoch 70/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 6.0462 - mae: 1.7194 - mse: 6.0462 - val_loss: 10.7485 - val_mae: 2.3224 - val_mse: 10.7485\n",
+ "Epoch 71/100\n",
+ "354/354 [==============================] - 0s 171us/sample - loss: 5.8132 - mae: 1.7049 - mse: 5.8132 - val_loss: 9.8704 - val_mae: 2.1916 - val_mse: 9.8704\n",
+ "Epoch 72/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 5.7957 - mae: 1.6492 - mse: 5.7957 - val_loss: 10.0593 - val_mae: 2.3159 - val_mse: 10.0593\n",
+ "Epoch 73/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 5.9002 - mae: 1.6952 - mse: 5.9002 - val_loss: 10.1425 - val_mae: 2.3594 - val_mse: 10.1425\n",
+ "Epoch 74/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 5.5721 - mae: 1.6277 - mse: 5.5721 - val_loss: 9.9564 - val_mae: 2.2284 - val_mse: 9.9564\n",
+ "Epoch 75/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 5.6730 - mae: 1.6669 - mse: 5.6730 - val_loss: 10.0358 - val_mae: 2.2259 - val_mse: 10.0358\n",
+ "Epoch 76/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 5.5947 - mae: 1.6216 - mse: 5.5947 - val_loss: 9.7815 - val_mae: 2.2282 - val_mse: 9.7815\n",
+ "Epoch 77/100\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 5.2870 - mae: 1.6492 - mse: 5.2870 - val_loss: 9.3813 - val_mae: 2.1987 - val_mse: 9.3813\n",
+ "Epoch 78/100\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 5.6015 - mae: 1.6183 - mse: 5.6015 - val_loss: 9.5577 - val_mae: 2.2139 - val_mse: 9.5577\n",
+ "Epoch 79/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 5.3793 - mae: 1.6202 - mse: 5.3793 - val_loss: 9.4099 - val_mae: 2.1957 - val_mse: 9.4099\n",
+ "Epoch 80/100\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 5.4258 - mae: 1.5943 - mse: 5.4258 - val_loss: 9.7489 - val_mae: 2.2233 - val_mse: 9.7489\n",
+ "Epoch 81/100\n",
+ "354/354 [==============================] - 0s 181us/sample - loss: 5.3006 - mae: 1.5934 - mse: 5.3006 - val_loss: 10.0298 - val_mae: 2.2258 - val_mse: 10.0298\n",
+ "Epoch 82/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 5.2590 - mae: 1.5854 - mse: 5.2590 - val_loss: 9.9642 - val_mae: 2.2718 - val_mse: 9.9642\n",
+ "Epoch 83/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 5.1325 - mae: 1.5765 - mse: 5.1325 - val_loss: 10.0795 - val_mae: 2.2524 - val_mse: 10.0795\n",
+ "Epoch 84/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 5.0736 - mae: 1.5846 - mse: 5.0736 - val_loss: 10.1607 - val_mae: 2.3146 - val_mse: 10.1607\n",
+ "Epoch 85/100\n",
+ "354/354 [==============================] - 0s 168us/sample - loss: 5.0863 - mae: 1.5598 - mse: 5.0863 - val_loss: 10.0663 - val_mae: 2.2961 - val_mse: 10.0663\n",
+ "Epoch 86/100\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 5.0422 - mae: 1.5758 - mse: 5.0422 - val_loss: 9.3842 - val_mae: 2.2033 - val_mse: 9.3842\n",
+ "Epoch 87/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 4.8308 - mae: 1.5587 - mse: 4.8308 - val_loss: 9.4605 - val_mae: 2.1797 - val_mse: 9.4605\n",
+ "Epoch 88/100\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 4.7424 - mae: 1.5468 - mse: 4.7424 - val_loss: 12.0587 - val_mae: 2.6306 - val_mse: 12.0587\n",
+ "Epoch 89/100\n",
+ "354/354 [==============================] - 0s 172us/sample - loss: 4.9329 - mae: 1.5937 - mse: 4.9329 - val_loss: 9.9514 - val_mae: 2.2366 - val_mse: 9.9514\n",
+ "Epoch 90/100\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 4.7181 - mae: 1.5625 - mse: 4.7181 - val_loss: 9.6245 - val_mae: 2.1626 - val_mse: 9.6245\n",
+ "Epoch 91/100\n",
+ "354/354 [==============================] - 0s 182us/sample - loss: 4.6726 - mae: 1.5040 - mse: 4.6726 - val_loss: 9.9543 - val_mae: 2.2394 - val_mse: 9.9543\n",
+ "Epoch 92/100\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 4.7058 - mae: 1.5416 - mse: 4.7058 - val_loss: 10.6368 - val_mae: 2.3900 - val_mse: 10.6368\n",
+ "Epoch 93/100\n",
+ "354/354 [==============================] - 0s 176us/sample - loss: 4.6515 - mae: 1.5235 - mse: 4.6515 - val_loss: 10.0118 - val_mae: 2.2661 - val_mse: 10.0118\n",
+ "Epoch 94/100\n",
+ "354/354 [==============================] - 0s 163us/sample - loss: 4.6973 - mae: 1.5262 - mse: 4.6973 - val_loss: 9.4214 - val_mae: 2.1961 - val_mse: 9.4214\n",
+ "Epoch 95/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 4.7056 - mae: 1.5392 - mse: 4.7056 - val_loss: 9.6110 - val_mae: 2.1998 - val_mse: 9.6110\n",
+ "Epoch 96/100\n",
+ "354/354 [==============================] - 0s 167us/sample - loss: 4.4156 - mae: 1.4496 - mse: 4.4156 - val_loss: 10.1083 - val_mae: 2.3143 - val_mse: 10.1083\n",
+ "Epoch 97/100\n",
+ "354/354 [==============================] - 0s 173us/sample - loss: 4.5201 - mae: 1.5019 - mse: 4.5201 - val_loss: 9.7179 - val_mae: 2.2635 - val_mse: 9.7179\n",
+ "Epoch 98/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 4.3824 - mae: 1.4403 - mse: 4.3824 - val_loss: 10.2802 - val_mae: 2.2846 - val_mse: 10.2802\n",
+ "Epoch 99/100\n",
+ "354/354 [==============================] - 0s 175us/sample - loss: 4.3252 - mae: 1.4806 - mse: 4.3252 - val_loss: 9.5943 - val_mae: 2.1745 - val_mse: 9.5943\n",
+ "Epoch 100/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 4.4134 - mae: 1.4451 - mse: 4.4134 - val_loss: 12.2396 - val_mae: 2.6152 - val_mse: 12.2396\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = 100,\n",
+ " batch_size = 10,\n",
+ " verbose = 1,\n",
+ " validation_data = (x_test, y_test),\n",
+ " callbacks = [savemodel_callback])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Model evaluation\n",
+ "MAE = Mean Absolute Error (between the labels and predictions) \n",
+ "A mae equal to 3 represents an average error in prediction of $3k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 12.2396\n",
+ "x_test / mae : 2.6152\n",
+ "x_test / mse : 12.2396\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Training history\n",
+ "What was the best result during our training ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "min( val_mae ) : 2.1552\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ " 'MAE' :['mae', 'val_mae'],\n",
+ " 'LOSS':['loss','val_loss']})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 7 - Restore a model :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.1 - Reload model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "Loaded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
+ "loaded_model.summary()\n",
+ "print(\"Loaded.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.2 - Evaluate it :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 9.1795\n",
+ "x_test / mae : 2.1552\n",
+ "x_test / mse : 9.1795\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.3 - Make a prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mon_test=[-0.20113196, -0.48631663, 1.23572348, -0.26929877, 2.67879106,\n",
+ " -0.89623587, 1.09961251, -1.05826704, -0.55823117, -0.06159088,\n",
+ " -1.76085159, -1.97039608, 0.52775666]\n",
+ "\n",
+ "mon_test=np.array(mon_test).reshape(1,13)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prédiction : 16.51 K$ Reality : 20.20 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "predictions = loaded_model.predict( mon_test )\n",
+ "print(\"Prédiction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], y_train[13]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "-----\n",
+ "That's all folks !"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 4c3043c5dada2e8919f150cfa31e84fa297ab59a..353d301e4ed82f6f8b6afcab67ad708dcfc95955 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,14 @@ You will find here :
- sheets and practical information :
- **[Configuration SSH](../-/wikis/howto-ssh)**
+- [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)<br>
+ A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
+
+- [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)<br>
+ More advanced example of DNN network code - BHPD dataset
+
+
+
## Installation