diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index cc966faf0d3d5729016c2f05da657a7af227477c..e683af01813e7417fbab420a689234f11218eaee 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -44,7 +44,7 @@
"text": [
"IDLE 2020 - Practical Work Module\n",
" Version : 0.2\n",
- " Run time : Friday 24 January 2020, 11:42:30\n",
+ " Run time : Monday 27 January 2020, 14:51:37\n",
" Matplotlib style : fidle/talk.mplstyle\n",
" TensorFlow version : 2.0.0\n",
" Keras version : 2.2.4-tf\n"
@@ -94,103 +94,103 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7a\" ><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_a297dd9a_410c_11ea_9598_bf6724e350b9\" ><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_3a6ab490_3e96_11ea_bb15_dba25722cc7alevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " <th id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7alevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " <th id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7alevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " <th id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7alevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " <th id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7alevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_3a6ab490_3e96_11ea_bb15_dba25722cc7arow4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " <th id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_a297dd9a_410c_11ea_9598_bf6724e350b9row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fc61db614d0>"
+ "<pandas.io.formats.style.Styler at 0x7f51d48099d0>"
]
},
"metadata": {},
@@ -217,13 +217,13 @@
"source": [
"## 3/ Preparing the data\n",
"### 3.1/ Split data\n",
- "We will use 80% of the data for training and 20% for validation. \n",
+ "We will use 70% of the data for training and 30% for validation. \n",
"x will be input data and y the expected output"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -268,146 +268,146 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7a\" ><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_e374dea2_410d_11ea_9598_bf6724e350b9\" ><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_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col0\" class=\"data row1 col0\" >3.97</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col1\" class=\"data row1 col1\" >10.27</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col2\" class=\"data row1 col2\" >11.51</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col3\" class=\"data row1 col3\" >0.07</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col4\" class=\"data row1 col4\" >0.56</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col5\" class=\"data row1 col5\" >6.26</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col6\" class=\"data row1 col6\" >70.20</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col7\" class=\"data row1 col7\" >3.70</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col8\" class=\"data row1 col8\" >9.88</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col9\" class=\"data row1 col9\" >414.85</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col10\" class=\"data row1 col10\" >18.51</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col11\" class=\"data row1 col11\" >354.34</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow1_col12\" class=\"data row1 col12\" >13.01</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col0\" class=\"data row1 col0\" >3.67</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col1\" class=\"data row1 col1\" >11.34</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col2\" class=\"data row1 col2\" >11.13</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col6\" class=\"data row1 col6\" >69.39</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col7\" class=\"data row1 col7\" >3.82</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col8\" class=\"data row1 col8\" >9.46</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col9\" class=\"data row1 col9\" >405.71</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col10\" class=\"data row1 col10\" >18.44</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col11\" class=\"data row1 col11\" >357.31</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row1_col12\" class=\"data row1 col12\" >12.47</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col0\" class=\"data row2 col0\" >9.57</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col1\" class=\"data row2 col1\" >22.19</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col2\" class=\"data row2 col2\" >6.78</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col3\" class=\"data row2 col3\" >0.25</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col5\" class=\"data row2 col5\" >0.70</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col6\" class=\"data row2 col6\" >27.68</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col7\" class=\"data row2 col7\" >2.07</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col8\" class=\"data row2 col8\" >8.80</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col9\" class=\"data row2 col9\" >169.62</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col10\" class=\"data row2 col10\" >2.13</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col11\" class=\"data row2 col11\" >94.06</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow2_col12\" class=\"data row2 col12\" >7.27</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col0\" class=\"data row2 col0\" >8.87</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col1\" class=\"data row2 col1\" >23.30</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col2\" class=\"data row2 col2\" >6.87</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col5\" class=\"data row2 col5\" >0.72</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col6\" class=\"data row2 col6\" >27.58</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col7\" class=\"data row2 col7\" >2.11</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col8\" class=\"data row2 col8\" >8.61</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col9\" class=\"data row2 col9\" >168.03</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col10\" class=\"data row2 col10\" >2.27</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col11\" class=\"data row2 col11\" >90.18</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row2_col12\" class=\"data row2 col12\" >6.94</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col2\" class=\"data row3 col2\" >0.74</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col6\" class=\"data row3 col6\" >2.90</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col7\" class=\"data row3 col7\" >1.13</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col11\" class=\"data row3 col11\" >2.52</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col5\" class=\"data row3 col5\" >3.86</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col11\" class=\"data row3 col11\" >2.52</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col0\" class=\"data row4 col0\" >0.09</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col2\" class=\"data row4 col2\" >5.86</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col5\" class=\"data row4 col5\" >5.88</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col6\" class=\"data row4 col6\" >47.25</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col7\" class=\"data row4 col7\" >2.05</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col9\" class=\"data row4 col9\" >284.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col10\" class=\"data row4 col10\" >17.40</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col11\" class=\"data row4 col11\" >374.49</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow4_col12\" class=\"data row4 col12\" >7.40</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col2\" class=\"data row4 col2\" >5.15</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col6\" class=\"data row4 col6\" >45.73</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col7\" class=\"data row4 col7\" >2.08</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col10\" class=\"data row4 col10\" >16.92</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col11\" class=\"data row4 col11\" >374.83</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row4_col12\" class=\"data row4 col12\" >7.18</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col0\" class=\"data row5 col0\" >0.29</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col2\" class=\"data row5 col2\" >9.90</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col5\" class=\"data row5 col5\" >6.19</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col6\" class=\"data row5 col6\" >80.10</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col7\" class=\"data row5 col7\" >3.09</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col9\" class=\"data row5 col9\" >334.50</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col10\" class=\"data row5 col10\" >19.10</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col11\" class=\"data row5 col11\" >390.81</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow5_col12\" class=\"data row5 col12\" >11.73</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col2\" class=\"data row5 col2\" >9.12</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col5\" class=\"data row5 col5\" >6.21</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col6\" class=\"data row5 col6\" >79.05</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col7\" class=\"data row5 col7\" >3.29</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col9\" class=\"data row5 col9\" >330.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col11\" class=\"data row5 col11\" >391.34</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row5_col12\" class=\"data row5 col12\" >11.30</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col0\" class=\"data row6 col0\" >3.85</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col1\" class=\"data row6 col1\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col5\" class=\"data row6 col5\" >6.59</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col6\" class=\"data row6 col6\" >94.57</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col7\" class=\"data row6 col7\" >4.94</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col11\" class=\"data row6 col11\" >396.04</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow6_col12\" class=\"data row6 col12\" >17.11</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col0\" class=\"data row6 col0\" >3.40</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col1\" class=\"data row6 col1\" >16.25</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col5\" class=\"data row6 col5\" >6.65</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col6\" class=\"data row6 col6\" >94.10</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col7\" class=\"data row6 col7\" >5.29</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col11\" class=\"data row6 col11\" >395.98</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row6_col12\" class=\"data row6 col12\" >16.50</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7alevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col1\" class=\"data row7 col1\" >95.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col5\" class=\"data row7 col5\" >8.72</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_3dd46306_3e96_11ea_bb15_dba25722cc7arow7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " <th id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_e374dea2_410d_11ea_9598_bf6724e350b9row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fc62513f7d0>"
+ "<pandas.io.formats.style.Styler at 0x7f51d5ee6390>"
]
},
"metadata": {},
@@ -417,139 +417,139 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7a\" ><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_e3945b88_410d_11ea_9598_bf6724e350b9\" ><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_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col8\" class=\"data row1 col8\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col11\" class=\"data row1 col11\" >0.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow1_col12\" class=\"data row1 col12\" >0.00</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col1\" class=\"data row3 col1\" >-0.46</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col2\" class=\"data row3 col2\" >-1.59</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col3\" class=\"data row3 col3\" >-0.27</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col4\" class=\"data row3 col4\" >-1.43</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col5\" class=\"data row3 col5\" >-3.87</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col6\" class=\"data row3 col6\" >-2.43</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col7\" class=\"data row3 col7\" >-1.24</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col8\" class=\"data row3 col8\" >-1.01</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col9\" class=\"data row3 col9\" >-1.34</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col10\" class=\"data row3 col10\" >-2.78</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col11\" class=\"data row3 col11\" >-3.74</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow3_col12\" class=\"data row3 col12\" >-1.55</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col2\" class=\"data row3 col2\" >-1.55</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col4\" class=\"data row3 col4\" >-1.48</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col5\" class=\"data row3 col5\" >-3.39</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col6\" class=\"data row3 col6\" >-2.41</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col7\" class=\"data row3 col7\" >-1.27</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col10\" class=\"data row3 col10\" >-2.58</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col11\" class=\"data row3 col11\" >-3.93</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row3_col12\" class=\"data row3 col12\" >-1.55</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col0\" class=\"data row4 col0\" >-0.41</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col1\" class=\"data row4 col1\" >-0.46</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col2\" class=\"data row4 col2\" >-0.83</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col3\" class=\"data row4 col3\" >-0.27</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col5\" class=\"data row4 col5\" >-0.55</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col6\" class=\"data row4 col6\" >-0.83</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col7\" class=\"data row4 col7\" >-0.80</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col8\" class=\"data row4 col8\" >-0.67</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col10\" class=\"data row4 col10\" >-0.52</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col11\" class=\"data row4 col11\" >0.21</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow4_col12\" class=\"data row4 col12\" >-0.77</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col4\" class=\"data row4 col4\" >-0.88</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col5\" class=\"data row4 col5\" >-0.58</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col6\" class=\"data row4 col6\" >-0.86</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col8\" class=\"data row4 col8\" >-0.63</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col9\" class=\"data row4 col9\" >-0.75</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col10\" class=\"data row4 col10\" >-0.67</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col11\" class=\"data row4 col11\" >0.19</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row4_col12\" class=\"data row4 col12\" >-0.76</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col0\" class=\"data row5 col0\" >-0.38</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col1\" class=\"data row5 col1\" >-0.46</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col2\" class=\"data row5 col2\" >-0.24</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col3\" class=\"data row5 col3\" >-0.27</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col4\" class=\"data row5 col4\" >-0.18</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col5\" class=\"data row5 col5\" >-0.10</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col6\" class=\"data row5 col6\" >0.36</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col7\" class=\"data row5 col7\" >-0.29</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col8\" class=\"data row5 col8\" >-0.55</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col9\" class=\"data row5 col9\" >-0.47</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col10\" class=\"data row5 col10\" >0.28</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col11\" class=\"data row5 col11\" >0.39</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow5_col12\" class=\"data row5 col12\" >-0.18</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col0\" class=\"data row5 col0\" >-0.38</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col2\" class=\"data row5 col2\" >-0.29</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col5\" class=\"data row5 col5\" >-0.13</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col7\" class=\"data row5 col7\" >-0.25</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col8\" class=\"data row5 col8\" >-0.52</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col9\" class=\"data row5 col9\" >-0.45</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col10\" class=\"data row5 col10\" >0.29</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row5_col12\" class=\"data row5 col12\" >-0.17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col0\" class=\"data row6 col0\" >-0.01</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col1\" class=\"data row6 col1\" >-0.46</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col2\" class=\"data row6 col2\" >0.97</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col3\" class=\"data row6 col3\" >-0.27</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col4\" class=\"data row6 col4\" >0.55</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col5\" class=\"data row6 col5\" >0.49</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col6\" class=\"data row6 col6\" >0.88</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col7\" class=\"data row6 col7\" >0.60</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col8\" class=\"data row6 col8\" >1.60</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col9\" class=\"data row6 col9\" >1.48</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col10\" class=\"data row6 col10\" >0.79</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow6_col12\" class=\"data row6 col12\" >0.56</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col0\" class=\"data row6 col0\" >-0.03</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col1\" class=\"data row6 col1\" >0.21</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col2\" class=\"data row6 col2\" >1.01</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col4\" class=\"data row6 col4\" >0.60</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col5\" class=\"data row6 col5\" >0.49</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col7\" class=\"data row6 col7\" >0.69</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col8\" class=\"data row6 col8\" >1.69</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col9\" class=\"data row6 col9\" >1.55</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col10\" class=\"data row6 col10\" >0.78</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row6_col12\" class=\"data row6 col12\" >0.58</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7alevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col0\" class=\"data row7 col0\" >8.89</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col1\" class=\"data row7 col1\" >3.82</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col2\" class=\"data row7 col2\" >2.39</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col3\" class=\"data row7 col3\" >3.70</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col4\" class=\"data row7 col4\" >2.67</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col5\" class=\"data row7 col5\" >3.55</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col6\" class=\"data row7 col6\" >1.08</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col7\" class=\"data row7 col7\" >4.07</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col8\" class=\"data row7 col8\" >1.60</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col9\" class=\"data row7 col9\" >1.75</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col10\" class=\"data row7 col10\" >1.64</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col11\" class=\"data row7 col11\" >0.45</td>\n",
- " <td id=\"T_3dddca22_3e96_11ea_bb15_dba25722cc7arow7_col12\" class=\"data row7 col12\" >3.43</td>\n",
+ " <th id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col0\" class=\"data row7 col0\" >9.62</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col1\" class=\"data row7 col1\" >3.80</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col2\" class=\"data row7 col2\" >2.42</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col4\" class=\"data row7 col4\" >2.74</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col5\" class=\"data row7 col5\" >3.44</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col6\" class=\"data row7 col6\" >1.11</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col7\" class=\"data row7 col7\" >3.93</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col8\" class=\"data row7 col8\" >1.69</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col9\" class=\"data row7 col9\" >1.82</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col10\" class=\"data row7 col10\" >1.57</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
+ " <td id=\"T_e3945b88_410d_11ea_9598_bf6724e350b9row7_col12\" class=\"data row7 col12\" >3.68</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fc61da1b110>"
+ "<pandas.io.formats.style.Styler at 0x7f51d4821d10>"
]
},
"metadata": {},
@@ -584,7 +584,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -610,22 +610,22 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Model: \"sequential\"\n",
+ "Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
- "dense (Dense) (None, 64) 896 \n",
+ "dense_3 (Dense) (None, 64) 896 \n",
"_________________________________________________________________\n",
- "dense_1 (Dense) (None, 64) 4160 \n",
+ "dense_4 (Dense) (None, 64) 4160 \n",
"_________________________________________________________________\n",
- "dense_2 (Dense) (None, 1) 65 \n",
+ "dense_5 (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 5,121\n",
"Trainable params: 5,121\n",
@@ -649,15 +649,223 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "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: 487.0888 - mae: 19.9032 - mse: 487.0888 - val_loss: 308.1071 - val_mae: 15.7207 - val_mse: 308.1070\n",
+ "Epoch 2/100\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 247.0264 - mae: 13.2414 - mse: 247.0264 - val_loss: 97.8105 - val_mae: 8.1237 - val_mse: 97.8105\n",
+ "Epoch 3/100\n",
+ "354/354 [==============================] - 0s 193us/sample - loss: 86.8949 - mae: 6.9520 - mse: 86.8949 - val_loss: 42.4257 - val_mae: 5.0994 - val_mse: 42.4257\n",
+ "Epoch 4/100\n",
+ "354/354 [==============================] - 0s 200us/sample - loss: 45.7664 - mae: 4.7469 - mse: 45.7664 - val_loss: 29.7263 - val_mae: 4.1665 - val_mse: 29.7263\n",
+ "Epoch 5/100\n",
+ "354/354 [==============================] - 0s 199us/sample - loss: 33.3429 - mae: 4.0681 - mse: 33.3429 - val_loss: 21.7644 - val_mae: 3.6424 - val_mse: 21.7644\n",
+ "Epoch 6/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 27.5133 - mae: 3.6070 - mse: 27.5133 - val_loss: 19.8807 - val_mae: 3.5024 - val_mse: 19.8807\n",
+ "Epoch 7/100\n",
+ "354/354 [==============================] - 0s 186us/sample - loss: 24.1182 - mae: 3.3727 - mse: 24.1182 - val_loss: 17.9948 - val_mae: 3.2151 - val_mse: 17.9948\n",
+ "Epoch 8/100\n",
+ "354/354 [==============================] - 0s 182us/sample - loss: 21.8060 - mae: 3.1366 - mse: 21.8060 - val_loss: 16.4886 - val_mae: 3.0627 - val_mse: 16.4886\n",
+ "Epoch 9/100\n",
+ "354/354 [==============================] - 0s 185us/sample - loss: 20.1659 - mae: 3.0417 - mse: 20.1659 - val_loss: 16.3110 - val_mae: 3.1179 - val_mse: 16.3110\n",
+ "Epoch 10/100\n",
+ "354/354 [==============================] - 0s 178us/sample - loss: 18.6065 - mae: 2.9420 - mse: 18.6065 - val_loss: 15.5141 - val_mae: 3.0108 - val_mse: 15.5141\n",
+ "Epoch 11/100\n",
+ "354/354 [==============================] - 0s 193us/sample - loss: 17.8489 - mae: 2.8333 - mse: 17.8489 - val_loss: 14.7017 - val_mae: 2.8789 - val_mse: 14.7017\n",
+ "Epoch 12/100\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 16.5612 - mae: 2.7414 - mse: 16.5612 - val_loss: 13.9433 - val_mae: 2.7222 - val_mse: 13.9433\n",
+ "Epoch 13/100\n",
+ "354/354 [==============================] - 0s 201us/sample - loss: 15.6009 - mae: 2.6732 - mse: 15.6009 - val_loss: 13.3117 - val_mae: 2.6386 - val_mse: 13.3117\n",
+ "Epoch 14/100\n",
+ "354/354 [==============================] - 0s 207us/sample - loss: 15.0688 - mae: 2.6302 - mse: 15.0688 - val_loss: 13.5431 - val_mae: 2.6898 - val_mse: 13.5431\n",
+ "Epoch 15/100\n",
+ "354/354 [==============================] - 0s 206us/sample - loss: 14.4971 - mae: 2.5893 - mse: 14.4971 - val_loss: 13.5570 - val_mae: 2.7085 - val_mse: 13.5570\n",
+ "Epoch 16/100\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 13.8436 - mae: 2.5135 - mse: 13.8436 - val_loss: 12.8769 - val_mae: 2.5970 - val_mse: 12.8769\n",
+ "Epoch 17/100\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 13.2484 - mae: 2.4756 - mse: 13.2484 - val_loss: 13.8003 - val_mae: 2.8335 - val_mse: 13.8003\n",
+ "Epoch 18/100\n",
+ "354/354 [==============================] - 0s 205us/sample - loss: 13.1481 - mae: 2.4546 - mse: 13.1481 - val_loss: 13.0239 - val_mae: 2.6989 - val_mse: 13.0239\n",
+ "Epoch 19/100\n",
+ "354/354 [==============================] - 0s 200us/sample - loss: 12.5412 - mae: 2.4286 - mse: 12.5412 - val_loss: 12.4162 - val_mae: 2.5496 - val_mse: 12.4162\n",
+ "Epoch 20/100\n",
+ "354/354 [==============================] - 0s 210us/sample - loss: 12.4616 - mae: 2.4215 - mse: 12.4616 - val_loss: 12.1216 - val_mae: 2.5368 - val_mse: 12.1216\n",
+ "Epoch 21/100\n",
+ "354/354 [==============================] - 0s 219us/sample - loss: 12.0377 - mae: 2.3799 - mse: 12.0377 - val_loss: 12.4182 - val_mae: 2.6287 - val_mse: 12.4182\n",
+ "Epoch 22/100\n",
+ "354/354 [==============================] - 0s 201us/sample - loss: 11.5818 - mae: 2.3088 - mse: 11.5818 - val_loss: 11.7357 - val_mae: 2.4065 - val_mse: 11.7357\n",
+ "Epoch 23/100\n",
+ "354/354 [==============================] - 0s 198us/sample - loss: 11.6820 - mae: 2.2713 - mse: 11.6820 - val_loss: 12.2529 - val_mae: 2.5771 - val_mse: 12.2529\n",
+ "Epoch 24/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 11.2786 - mae: 2.2865 - mse: 11.2786 - val_loss: 12.6007 - val_mae: 2.6822 - val_mse: 12.6007\n",
+ "Epoch 25/100\n",
+ "354/354 [==============================] - 0s 188us/sample - loss: 10.7723 - mae: 2.2619 - mse: 10.7723 - val_loss: 12.6062 - val_mae: 2.5114 - val_mse: 12.6062\n",
+ "Epoch 26/100\n",
+ "354/354 [==============================] - 0s 198us/sample - loss: 10.9011 - mae: 2.2711 - mse: 10.9011 - val_loss: 11.4349 - val_mae: 2.4340 - val_mse: 11.4349\n",
+ "Epoch 27/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 10.6218 - mae: 2.2447 - mse: 10.6218 - val_loss: 12.2449 - val_mae: 2.5705 - val_mse: 12.2449\n",
+ "Epoch 28/100\n",
+ "354/354 [==============================] - 0s 195us/sample - loss: 10.5279 - mae: 2.2312 - mse: 10.5279 - val_loss: 11.5037 - val_mae: 2.4417 - val_mse: 11.5037\n",
+ "Epoch 29/100\n",
+ "354/354 [==============================] - 0s 206us/sample - loss: 10.1019 - mae: 2.1807 - mse: 10.1019 - val_loss: 11.2753 - val_mae: 2.4330 - val_mse: 11.2753\n",
+ "Epoch 30/100\n",
+ "354/354 [==============================] - 0s 205us/sample - loss: 9.7553 - mae: 2.1740 - mse: 9.7553 - val_loss: 11.1414 - val_mae: 2.4706 - val_mse: 11.1414\n",
+ "Epoch 31/100\n",
+ "354/354 [==============================] - 0s 199us/sample - loss: 9.8119 - mae: 2.1677 - mse: 9.8119 - val_loss: 11.2484 - val_mae: 2.4870 - val_mse: 11.2484\n",
+ "Epoch 32/100\n",
+ "354/354 [==============================] - 0s 199us/sample - loss: 9.6109 - mae: 2.1458 - mse: 9.6109 - val_loss: 11.6936 - val_mae: 2.5334 - val_mse: 11.6936\n",
+ "Epoch 33/100\n",
+ "354/354 [==============================] - 0s 185us/sample - loss: 9.6240 - mae: 2.1389 - mse: 9.6240 - val_loss: 11.9366 - val_mae: 2.5851 - val_mse: 11.9366\n",
+ "Epoch 34/100\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 9.0395 - mae: 2.0717 - mse: 9.0395 - val_loss: 11.4917 - val_mae: 2.5152 - val_mse: 11.4917\n",
+ "Epoch 35/100\n",
+ "354/354 [==============================] - 0s 184us/sample - loss: 9.0385 - mae: 2.1342 - mse: 9.0385 - val_loss: 11.1387 - val_mae: 2.4661 - val_mse: 11.1387\n",
+ "Epoch 36/100\n",
+ "354/354 [==============================] - 0s 189us/sample - loss: 9.1489 - mae: 2.1074 - mse: 9.1489 - val_loss: 11.3799 - val_mae: 2.4694 - val_mse: 11.3799\n",
+ "Epoch 37/100\n",
+ "354/354 [==============================] - 0s 183us/sample - loss: 8.8084 - mae: 2.0669 - mse: 8.8084 - val_loss: 11.3758 - val_mae: 2.4844 - val_mse: 11.3758\n",
+ "Epoch 38/100\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 8.4417 - mae: 2.0440 - mse: 8.4417 - val_loss: 11.9173 - val_mae: 2.6668 - val_mse: 11.9173\n",
+ "Epoch 39/100\n",
+ "354/354 [==============================] - 0s 187us/sample - loss: 8.5509 - mae: 2.0358 - mse: 8.5509 - val_loss: 10.9374 - val_mae: 2.4372 - val_mse: 10.9374\n",
+ "Epoch 40/100\n",
+ "354/354 [==============================] - 0s 195us/sample - loss: 8.1358 - mae: 2.0316 - mse: 8.1358 - val_loss: 10.9421 - val_mae: 2.4311 - val_mse: 10.9421\n",
+ "Epoch 41/100\n",
+ "354/354 [==============================] - 0s 185us/sample - loss: 8.0176 - mae: 1.9935 - mse: 8.0176 - val_loss: 11.4450 - val_mae: 2.5430 - val_mse: 11.4450\n",
+ "Epoch 42/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 8.1927 - mae: 2.0241 - mse: 8.1927 - val_loss: 11.6573 - val_mae: 2.5369 - val_mse: 11.6573\n",
+ "Epoch 43/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 7.8327 - mae: 1.9730 - mse: 7.8327 - val_loss: 12.8139 - val_mae: 2.7067 - val_mse: 12.8139\n",
+ "Epoch 44/100\n",
+ "354/354 [==============================] - 0s 203us/sample - loss: 7.6695 - mae: 1.9709 - mse: 7.6695 - val_loss: 11.4870 - val_mae: 2.5244 - val_mse: 11.4870\n",
+ "Epoch 45/100\n",
+ "354/354 [==============================] - 0s 198us/sample - loss: 7.5597 - mae: 1.9331 - mse: 7.5597 - val_loss: 11.3806 - val_mae: 2.4968 - val_mse: 11.3806\n",
+ "Epoch 46/100\n",
+ "354/354 [==============================] - 0s 204us/sample - loss: 7.7065 - mae: 1.9642 - mse: 7.7065 - val_loss: 12.6317 - val_mae: 2.6573 - val_mse: 12.6317\n",
+ "Epoch 47/100\n",
+ "354/354 [==============================] - 0s 206us/sample - loss: 7.3382 - mae: 1.9147 - mse: 7.3382 - val_loss: 12.5079 - val_mae: 2.6235 - val_mse: 12.5080\n",
+ "Epoch 48/100\n",
+ "354/354 [==============================] - 0s 203us/sample - loss: 7.3958 - mae: 1.9476 - mse: 7.3958 - val_loss: 13.0458 - val_mae: 2.6728 - val_mse: 13.0458\n",
+ "Epoch 49/100\n",
+ "354/354 [==============================] - 0s 207us/sample - loss: 7.1153 - mae: 1.9121 - mse: 7.1153 - val_loss: 12.0881 - val_mae: 2.6703 - val_mse: 12.0881\n",
+ "Epoch 50/100\n",
+ "354/354 [==============================] - 0s 211us/sample - loss: 7.0243 - mae: 1.8684 - mse: 7.0243 - val_loss: 12.6583 - val_mae: 2.5839 - val_mse: 12.6583\n",
+ "Epoch 51/100\n",
+ "354/354 [==============================] - 0s 196us/sample - loss: 7.0586 - mae: 1.9037 - mse: 7.0586 - val_loss: 11.6811 - val_mae: 2.5166 - val_mse: 11.6811\n",
+ "Epoch 52/100\n",
+ "354/354 [==============================] - 0s 193us/sample - loss: 6.9316 - mae: 1.8744 - mse: 6.9316 - val_loss: 11.6157 - val_mae: 2.5379 - val_mse: 11.6157\n",
+ "Epoch 53/100\n",
+ "354/354 [==============================] - 0s 197us/sample - loss: 6.6836 - mae: 1.8344 - mse: 6.6836 - val_loss: 11.9705 - val_mae: 2.4756 - val_mse: 11.9705\n",
+ "Epoch 54/100\n",
+ "354/354 [==============================] - 0s 195us/sample - loss: 6.8097 - mae: 1.8709 - mse: 6.8097 - val_loss: 11.9780 - val_mae: 2.5914 - val_mse: 11.9780\n",
+ "Epoch 55/100\n",
+ "354/354 [==============================] - 0s 184us/sample - loss: 6.5109 - mae: 1.8337 - mse: 6.5109 - val_loss: 11.4136 - val_mae: 2.4470 - val_mse: 11.4136\n",
+ "Epoch 56/100\n",
+ "354/354 [==============================] - 0s 194us/sample - loss: 6.2732 - mae: 1.7802 - mse: 6.2732 - val_loss: 12.0206 - val_mae: 2.5746 - val_mse: 12.0206\n",
+ "Epoch 57/100\n",
+ "354/354 [==============================] - 0s 195us/sample - loss: 6.5366 - mae: 1.8209 - mse: 6.5366 - val_loss: 12.6829 - val_mae: 2.5894 - val_mse: 12.6829\n",
+ "Epoch 58/100\n",
+ "354/354 [==============================] - 0s 201us/sample - loss: 6.3653 - mae: 1.8091 - mse: 6.3653 - val_loss: 12.2595 - val_mae: 2.5639 - val_mse: 12.2595\n",
+ "Epoch 59/100\n",
+ "354/354 [==============================] - 0s 203us/sample - loss: 6.2574 - mae: 1.7916 - mse: 6.2574 - val_loss: 12.1620 - val_mae: 2.6142 - val_mse: 12.1620\n",
+ "Epoch 60/100\n",
+ "354/354 [==============================] - 0s 205us/sample - loss: 6.1418 - mae: 1.7777 - mse: 6.1418 - val_loss: 11.1183 - val_mae: 2.4707 - val_mse: 11.1183\n",
+ "Epoch 61/100\n",
+ "354/354 [==============================] - 0s 184us/sample - loss: 5.9966 - mae: 1.7573 - mse: 5.9966 - val_loss: 12.8536 - val_mae: 2.6316 - val_mse: 12.8536\n",
+ "Epoch 62/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 6.0276 - mae: 1.7560 - mse: 6.0276 - val_loss: 12.3637 - val_mae: 2.5437 - val_mse: 12.3637\n",
+ "Epoch 63/100\n",
+ "354/354 [==============================] - 0s 170us/sample - loss: 5.7683 - mae: 1.7580 - mse: 5.7683 - val_loss: 12.9217 - val_mae: 2.6327 - val_mse: 12.9217\n",
+ "Epoch 64/100\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 5.9041 - mae: 1.7513 - mse: 5.9041 - val_loss: 12.3764 - val_mae: 2.5927 - val_mse: 12.3764\n",
+ "Epoch 65/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 5.7884 - mae: 1.7469 - mse: 5.7884 - val_loss: 11.6560 - val_mae: 2.4941 - val_mse: 11.6560\n",
+ "Epoch 66/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 5.6512 - mae: 1.7268 - mse: 5.6512 - val_loss: 11.5758 - val_mae: 2.4672 - val_mse: 11.5758\n",
+ "Epoch 67/100\n",
+ "354/354 [==============================] - 0s 185us/sample - loss: 5.7579 - mae: 1.7162 - mse: 5.7579 - val_loss: 11.2974 - val_mae: 2.4503 - val_mse: 11.2974\n",
+ "Epoch 68/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 5.5736 - mae: 1.6954 - mse: 5.5736 - val_loss: 11.2716 - val_mae: 2.4342 - val_mse: 11.2716\n",
+ "Epoch 69/100\n",
+ "354/354 [==============================] - 0s 166us/sample - loss: 5.5444 - mae: 1.7170 - mse: 5.5444 - val_loss: 11.8384 - val_mae: 2.5068 - val_mse: 11.8384\n",
+ "Epoch 70/100\n",
+ "354/354 [==============================] - 0s 179us/sample - loss: 5.5349 - mae: 1.6860 - mse: 5.5349 - val_loss: 12.7368 - val_mae: 2.6333 - val_mse: 12.7368\n",
+ "Epoch 71/100\n",
+ "354/354 [==============================] - 0s 174us/sample - loss: 5.2756 - mae: 1.6923 - mse: 5.2756 - val_loss: 11.3542 - val_mae: 2.4393 - val_mse: 11.3542\n",
+ "Epoch 72/100\n",
+ "354/354 [==============================] - 0s 201us/sample - loss: 5.4573 - mae: 1.6965 - mse: 5.4573 - val_loss: 12.2775 - val_mae: 2.5390 - val_mse: 12.2775\n",
+ "Epoch 73/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 5.2504 - mae: 1.6379 - mse: 5.2504 - val_loss: 14.2026 - val_mae: 2.8445 - val_mse: 14.2026\n",
+ "Epoch 74/100\n",
+ "354/354 [==============================] - 0s 193us/sample - loss: 5.1999 - mae: 1.6461 - mse: 5.1999 - val_loss: 12.8913 - val_mae: 2.5668 - val_mse: 12.8913\n",
+ "Epoch 75/100\n",
+ "354/354 [==============================] - 0s 197us/sample - loss: 5.2465 - mae: 1.6492 - mse: 5.2465 - val_loss: 13.1523 - val_mae: 2.6433 - val_mse: 13.1523\n",
+ "Epoch 76/100\n",
+ "354/354 [==============================] - 0s 192us/sample - loss: 4.7688 - mae: 1.6293 - mse: 4.7688 - val_loss: 11.4894 - val_mae: 2.4949 - val_mse: 11.4894\n",
+ "Epoch 77/100\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 4.8269 - mae: 1.5878 - mse: 4.8269 - val_loss: 12.2086 - val_mae: 2.4659 - val_mse: 12.2086\n",
+ "Epoch 78/100\n",
+ "354/354 [==============================] - 0s 198us/sample - loss: 5.1623 - mae: 1.6396 - mse: 5.1623 - val_loss: 12.0159 - val_mae: 2.5066 - val_mse: 12.0159\n",
+ "Epoch 79/100\n",
+ "354/354 [==============================] - 0s 180us/sample - loss: 4.7876 - mae: 1.5834 - mse: 4.7876 - val_loss: 12.2993 - val_mae: 2.4944 - val_mse: 12.2993\n",
+ "Epoch 80/100\n",
+ "354/354 [==============================] - 0s 201us/sample - loss: 4.7760 - mae: 1.6227 - mse: 4.7760 - val_loss: 12.7998 - val_mae: 2.5831 - val_mse: 12.7998\n",
+ "Epoch 81/100\n",
+ "354/354 [==============================] - 0s 200us/sample - loss: 4.7677 - mae: 1.6271 - mse: 4.7677 - val_loss: 14.0602 - val_mae: 2.7332 - val_mse: 14.0602\n",
+ "Epoch 82/100\n",
+ "354/354 [==============================] - 0s 200us/sample - loss: 4.6595 - mae: 1.5751 - mse: 4.6595 - val_loss: 13.3032 - val_mae: 2.6053 - val_mse: 13.3032\n",
+ "Epoch 83/100\n",
+ "354/354 [==============================] - 0s 177us/sample - loss: 4.6215 - mae: 1.5647 - mse: 4.6215 - val_loss: 11.8613 - val_mae: 2.4638 - val_mse: 11.8613\n",
+ "Epoch 84/100\n",
+ "354/354 [==============================] - 0s 198us/sample - loss: 4.6157 - mae: 1.5557 - mse: 4.6157 - val_loss: 12.8239 - val_mae: 2.5180 - val_mse: 12.8238\n",
+ "Epoch 85/100\n",
+ "354/354 [==============================] - 0s 237us/sample - loss: 4.5351 - mae: 1.5573 - mse: 4.5351 - val_loss: 13.5689 - val_mae: 2.8086 - val_mse: 13.5689\n",
+ "Epoch 86/100\n",
+ "354/354 [==============================] - 0s 193us/sample - loss: 4.5319 - mae: 1.5225 - mse: 4.5319 - val_loss: 13.0932 - val_mae: 2.5791 - val_mse: 13.0932\n",
+ "Epoch 87/100\n",
+ "354/354 [==============================] - 0s 206us/sample - loss: 4.4577 - mae: 1.5536 - mse: 4.4577 - val_loss: 12.6290 - val_mae: 2.5426 - val_mse: 12.6290\n",
+ "Epoch 88/100\n",
+ "354/354 [==============================] - 0s 203us/sample - loss: 4.3580 - mae: 1.4978 - mse: 4.3580 - val_loss: 14.6875 - val_mae: 2.7585 - val_mse: 14.6875\n",
+ "Epoch 89/100\n",
+ "354/354 [==============================] - 0s 188us/sample - loss: 4.3651 - mae: 1.5227 - mse: 4.3651 - val_loss: 12.4273 - val_mae: 2.5638 - val_mse: 12.4273\n",
+ "Epoch 90/100\n",
+ "354/354 [==============================] - 0s 200us/sample - loss: 4.2446 - mae: 1.4888 - mse: 4.2446 - val_loss: 12.3269 - val_mae: 2.5284 - val_mse: 12.3269\n",
+ "Epoch 91/100\n",
+ "354/354 [==============================] - 0s 193us/sample - loss: 4.3251 - mae: 1.5277 - mse: 4.3251 - val_loss: 12.7420 - val_mae: 2.6027 - val_mse: 12.7420\n",
+ "Epoch 92/100\n",
+ "354/354 [==============================] - 0s 192us/sample - loss: 4.1757 - mae: 1.4551 - mse: 4.1757 - val_loss: 12.1442 - val_mae: 2.4757 - val_mse: 12.1442\n",
+ "Epoch 93/100\n",
+ "354/354 [==============================] - 0s 190us/sample - loss: 4.0847 - mae: 1.4603 - mse: 4.0847 - val_loss: 11.6362 - val_mae: 2.4419 - val_mse: 11.6362\n",
+ "Epoch 94/100\n",
+ "354/354 [==============================] - 0s 187us/sample - loss: 4.2191 - mae: 1.4756 - mse: 4.2191 - val_loss: 13.8848 - val_mae: 2.7260 - val_mse: 13.8848\n",
+ "Epoch 95/100\n",
+ "354/354 [==============================] - 0s 184us/sample - loss: 4.0348 - mae: 1.4452 - mse: 4.0348 - val_loss: 14.4677 - val_mae: 2.7378 - val_mse: 14.4677\n",
+ "Epoch 96/100\n",
+ "354/354 [==============================] - 0s 188us/sample - loss: 4.0928 - mae: 1.4585 - mse: 4.0928 - val_loss: 13.5557 - val_mae: 2.7637 - val_mse: 13.5557\n",
+ "Epoch 97/100\n",
+ "354/354 [==============================] - 0s 188us/sample - loss: 4.0744 - mae: 1.4829 - mse: 4.0744 - val_loss: 13.5958 - val_mae: 2.7007 - val_mse: 13.5958\n",
+ "Epoch 98/100\n",
+ "354/354 [==============================] - 0s 191us/sample - loss: 3.9560 - mae: 1.4143 - mse: 3.9560 - val_loss: 11.9201 - val_mae: 2.5081 - val_mse: 11.9201\n",
+ "Epoch 99/100\n",
+ "354/354 [==============================] - 0s 169us/sample - loss: 3.9206 - mae: 1.4251 - mse: 3.9206 - val_loss: 12.2918 - val_mae: 2.5079 - val_mse: 12.2918\n",
+ "Epoch 100/100\n",
+ "354/354 [==============================] - 0s 197us/sample - loss: 3.9572 - mae: 1.4454 - mse: 3.9572 - val_loss: 12.0796 - val_mae: 2.4845 - val_mse: 12.0796\n"
+ ]
+ }
+ ],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs = 100,\n",
" batch_size = 10,\n",
- " verbose = 0,\n",
+ " verbose = 1,\n",
" validation_data = (x_test, y_test))"
]
},
@@ -673,16 +881,16 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "x_test / loss : 14.9185\n",
- "x_test / mae : 2.5638\n",
- "x_test / mse : 14.9185\n"
+ "x_test / loss : 12.0796\n",
+ "x_test / mae : 2.4845\n",
+ "x_test / mse : 12.0796\n"
]
}
],
@@ -704,14 +912,148 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>loss</th>\n",
+ " <th>mae</th>\n",
+ " <th>mse</th>\n",
+ " <th>val_loss</th>\n",
+ " <th>val_mae</th>\n",
+ " <th>val_mse</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>100.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>16.923365</td>\n",
+ " <td>2.358762</td>\n",
+ " <td>16.923366</td>\n",
+ " <td>16.978941</td>\n",
+ " <td>2.841583</td>\n",
+ " <td>16.978941</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>54.036160</td>\n",
+ " <td>2.224360</td>\n",
+ " <td>54.036166</td>\n",
+ " <td>30.835249</td>\n",
+ " <td>1.454863</td>\n",
+ " <td>30.835245</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>3.920552</td>\n",
+ " <td>1.414271</td>\n",
+ " <td>3.920552</td>\n",
+ " <td>10.937433</td>\n",
+ " <td>2.406456</td>\n",
+ " <td>10.937432</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>5.190493</td>\n",
+ " <td>1.639175</td>\n",
+ " <td>5.190493</td>\n",
+ " <td>11.675147</td>\n",
+ " <td>2.494811</td>\n",
+ " <td>11.675148</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>7.041478</td>\n",
+ " <td>1.889034</td>\n",
+ " <td>7.041478</td>\n",
+ " <td>12.345290</td>\n",
+ " <td>2.572589</td>\n",
+ " <td>12.345290</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>10.804498</td>\n",
+ " <td>2.264170</td>\n",
+ " <td>10.804498</td>\n",
+ " <td>13.190069</td>\n",
+ " <td>2.684103</td>\n",
+ " <td>13.190069</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>487.088752</td>\n",
+ " <td>19.903234</td>\n",
+ " <td>487.088806</td>\n",
+ " <td>308.107062</td>\n",
+ " <td>15.720711</td>\n",
+ " <td>308.107025</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "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 16.923365 2.358762 16.923366 16.978941 2.841583 16.978941\n",
+ "std 54.036160 2.224360 54.036166 30.835249 1.454863 30.835245\n",
+ "min 3.920552 1.414271 3.920552 10.937433 2.406456 10.937432\n",
+ "25% 5.190493 1.639175 5.190493 11.675147 2.494811 11.675148\n",
+ "50% 7.041478 1.889034 7.041478 12.345290 2.572589 12.345290\n",
+ "75% 10.804498 2.264170 10.804498 13.190069 2.684103 13.190069\n",
+ "max 487.088752 19.903234 487.088806 308.107062 15.720711 308.107025"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "df=pd.DataFrame(data=history.history)\n",
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "min( val_mae ) : 2.1862\n"
+ "min( val_mae ) : 2.4065\n"
]
}
],
@@ -721,12 +1063,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -738,7 +1080,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -750,7 +1092,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
@@ -762,12 +1104,53 @@
}
],
"source": [
- "reload(ooo)\n",
"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 - Make a prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "my_data = [ 1.26425925, -0.48522739, 1.0436489 , -0.23112788, 1.37120745,\n",
+ " -2.14308942, 1.13489104, -1.06802005, 1.71189006, 1.57042287,\n",
+ " 0.77859951, 0.14769795, 2.7585581 ]\n",
+ "real_price = 10.4\n",
+ "\n",
+ "my_data=np.array(my_data).reshape(1,13)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prédiction : 9.84 K$\n",
+ "Reality : 10.40 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "predictions = model.predict( my_data )\n",
+ "print(\"Prédiction : {:.2f} K$\".format(predictions[0][0]))\n",
+ "print(\"Reality : {:.2f} K$\".format(real_price))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/BHPD/02-DNN-Regression Premium.ipynb b/BHPD/02-DNN-Regression Premium.ipynb
index 0c444d504843c6a3ecf40fb1c33dade35490c42b..9f387f191fc27d23de20a3ede32e9a521635d4ca 100644
--- a/BHPD/02-DNN-Regression Premium.ipynb
+++ b/BHPD/02-DNN-Regression Premium.ipynb
@@ -37,9 +37,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IDLE 2020 - Practical Work Module\n",
+ " Version : 0.2\n",
+ " Run time : Monday 27 January 2020, 15:48:21\n",
+ " Matplotlib style : fidle/talk.mplstyle\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",
@@ -83,9 +96,116 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afb\" ><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_13ff0f06_4114_11ea_af1b_698ca2e84afblevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afblevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afblevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afblevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afblevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_13ff0f06_4114_11ea_af1b_698ca2e84afbrow4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f5fda4af110>"
+ ]
+ },
+ "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",
@@ -105,9 +225,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "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",
@@ -140,9 +270,294 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afb\" ><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_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col0\" class=\"data row1 col0\" >3.50</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col1\" class=\"data row1 col1\" >11.26</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col2\" class=\"data row1 col2\" >10.98</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col3\" class=\"data row1 col3\" >0.05</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col4\" class=\"data row1 col4\" >0.55</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col5\" class=\"data row1 col5\" >6.30</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col6\" class=\"data row1 col6\" >68.18</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col7\" class=\"data row1 col7\" >3.83</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col8\" class=\"data row1 col8\" >9.29</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col9\" class=\"data row1 col9\" >403.31</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col10\" class=\"data row1 col10\" >18.54</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col11\" class=\"data row1 col11\" >359.90</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow1_col12\" class=\"data row1 col12\" >12.38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col0\" class=\"data row2 col0\" >8.57</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col1\" class=\"data row2 col1\" >23.21</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col2\" class=\"data row2 col2\" >6.82</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col3\" class=\"data row2 col3\" >0.22</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col4\" class=\"data row2 col4\" >0.11</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col5\" class=\"data row2 col5\" >0.66</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col6\" class=\"data row2 col6\" >28.04</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col7\" class=\"data row2 col7\" >2.10</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col8\" class=\"data row2 col8\" >8.59</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col9\" class=\"data row2 col9\" >167.27</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col10\" class=\"data row2 col10\" >2.13</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col11\" class=\"data row2 col11\" >88.19</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow2_col12\" class=\"data row2 col12\" >6.61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col2\" class=\"data row3 col2\" >0.74</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col5\" class=\"data row3 col5\" >3.86</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col6\" class=\"data row3 col6\" >2.90</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col11\" class=\"data row3 col11\" >2.52</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow3_col12\" class=\"data row3 col12\" >1.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col5\" class=\"data row4 col5\" >5.89</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col6\" class=\"data row4 col6\" >45.18</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col7\" class=\"data row4 col7\" >2.12</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col9\" class=\"data row4 col9\" >279.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col11\" class=\"data row4 col11\" >377.59</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow4_col12\" class=\"data row4 col12\" >7.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col0\" class=\"data row5 col0\" >0.23</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col2\" class=\"data row5 col2\" >8.56</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col5\" class=\"data row5 col5\" >6.22</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col6\" class=\"data row5 col6\" >76.50</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col7\" class=\"data row5 col7\" >3.21</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col9\" class=\"data row5 col9\" >329.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col11\" class=\"data row5 col11\" >391.81</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow5_col12\" class=\"data row5 col12\" >11.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col0\" class=\"data row6 col0\" >2.89</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col1\" class=\"data row6 col1\" >12.50</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col5\" class=\"data row6 col5\" >6.61</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col6\" class=\"data row6 col6\" >93.57</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col7\" class=\"data row6 col7\" >5.12</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col8\" class=\"data row6 col8\" >8.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col11\" class=\"data row6 col11\" >396.21</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow6_col12\" class=\"data row6 col12\" >16.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afblevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col1\" class=\"data row7 col1\" >95.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col5\" class=\"data row7 col5\" >8.72</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_1878bc44_4114_11ea_af1b_698ca2e84afbrow7_col12\" class=\"data row7 col12\" >36.98</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f5fd31583d0>"
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+ "data": {
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+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_188108ae_4114_11ea_af1b_698ca2e84afb\" ><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_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col11\" class=\"data row1 col11\" >0.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col2\" class=\"data row3 col2\" >-1.50</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col3\" class=\"data row3 col3\" >-0.23</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col4\" class=\"data row3 col4\" >-1.47</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col5\" class=\"data row3 col5\" >-3.68</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col6\" class=\"data row3 col6\" >-2.33</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col7\" class=\"data row3 col7\" >-1.29</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col8\" class=\"data row3 col8\" >-0.96</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col9\" class=\"data row3 col9\" >-1.29</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col10\" class=\"data row3 col10\" >-2.79</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col11\" class=\"data row3 col11\" >-4.05</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow3_col12\" class=\"data row3 col12\" >-1.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col2\" class=\"data row4 col2\" >-0.85</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col3\" class=\"data row4 col3\" >-0.23</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col5\" class=\"data row4 col5\" >-0.61</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col6\" class=\"data row4 col6\" >-0.82</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col8\" class=\"data row4 col8\" >-0.62</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col9\" class=\"data row4 col9\" >-0.74</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col10\" class=\"data row4 col10\" >-0.54</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col11\" class=\"data row4 col11\" >0.20</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow4_col12\" class=\"data row4 col12\" >-0.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col0\" class=\"data row5 col0\" >-0.38</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col2\" class=\"data row5 col2\" >-0.35</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col3\" class=\"data row5 col3\" >-0.23</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col4\" class=\"data row5 col4\" >-0.11</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col6\" class=\"data row5 col6\" >0.30</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col7\" class=\"data row5 col7\" >-0.30</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col8\" class=\"data row5 col8\" >-0.50</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col9\" class=\"data row5 col9\" >-0.44</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col10\" class=\"data row5 col10\" >0.26</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col11\" class=\"data row5 col11\" >0.36</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow5_col12\" class=\"data row5 col12\" >-0.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col0\" class=\"data row6 col0\" >-0.07</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col1\" class=\"data row6 col1\" >0.05</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col2\" class=\"data row6 col2\" >1.04</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col3\" class=\"data row6 col3\" >-0.23</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col4\" class=\"data row6 col4\" >0.65</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col5\" class=\"data row6 col5\" >0.48</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col6\" class=\"data row6 col6\" >0.91</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col7\" class=\"data row6 col7\" >0.61</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col8\" class=\"data row6 col8\" >-0.15</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col9\" class=\"data row6 col9\" >1.57</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col10\" class=\"data row6 col10\" >0.78</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col11\" class=\"data row6 col11\" >0.41</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow6_col12\" class=\"data row6 col12\" >0.63</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_188108ae_4114_11ea_af1b_698ca2e84afblevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col0\" class=\"data row7 col0\" >9.98</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col1\" class=\"data row7 col1\" >3.61</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col2\" class=\"data row7 col2\" >2.46</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col3\" class=\"data row7 col3\" >4.31</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col4\" class=\"data row7 col4\" >2.93</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col5\" class=\"data row7 col5\" >3.67</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col6\" class=\"data row7 col6\" >1.13</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col7\" class=\"data row7 col7\" >3.95</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col8\" class=\"data row7 col8\" >1.71</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col9\" class=\"data row7 col9\" >1.84</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col10\" class=\"data row7 col10\" >1.62</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col11\" class=\"data row7 col11\" >0.42</td>\n",
+ " <td id=\"T_188108ae_4114_11ea_af1b_698ca2e84afbrow7_col12\" class=\"data row7 col12\" >3.72</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f5fd2d75f50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
"\n",
@@ -162,7 +577,7 @@
"metadata": {},
"source": [
"## 4/ Build a model\n",
- "About informations about : \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",
@@ -171,7 +586,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -198,9 +613,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
"source": [
"model=get_model_v1( (13,) )\n",
"\n",
@@ -216,7 +652,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -234,7 +670,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -259,9 +695,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 14.2504\n",
+ "x_test / mae : 2.4316\n",
+ "x_test / mse : 14.2504\n"
+ ]
+ }
+ ],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -280,18 +726,63 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "min( val_mae ) : 2.2208\n"
+ ]
+ }
+ ],
"source": [
"print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "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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gx8BMfEgoOhvoCJ4z7HtAEvjwYNa/EhoMKSIiUdYwoQG4HJgFXNLP44cAzcBjZR57PNjOATCzGHAE8Ixzrquk7BNAgb4BoyZ02aWIiERZQ4QGM9sH+CrwNefcK/0UmxJsV5V5rLhvarAdB7SWK+uc6wbWh8rWjBatEhGRKGuI0ADcCCwHrh2gTFuw7S7zWFdJmYHKFsu3lXvAzC4yswUD1GOnha+gyKilQUREIqbuocHMzgFOAj7hnMsOULQj2DaXeaylpMxAZYvlO8o94Jz7kXPuqAHqsdPUPSEiIlFW10suzawZ37pwD/C6me0fPFTsOhgT7FuHv0Ii/FhYcV+xO2Ij0FmubPA7JwAP7fILqJIGQoqISJTVu6WhFZgInAq8FLo9GDx+TvDzBcAifHfD0WWeZ26wXQDgnCsATwOHByEh7G341z0kXRADCS9apZYGERGJmnpP7tQOnFFm/0T8JEz3Aj8BnnPObTOz3wHvN7NDnXPPApjZSHyoeAl/ZUTRncCxwEX4yZ2KLsNPDjV/kF/LDmkgpIiIRFldQ0MwhuEXpfvNbEZw92XnXPjxLwDvAn5vZtcBW/AzQk4FTnV952a+GTgfuDZ4vsXAKcD7gKudc8sH87VUIhlX94SIiERXvVsaquKc+5uZHQt8E/hXoAnfDfHu8BTSQdmMmZ0IXA2chR/H8DJwKXBDTSseUGgQEZEoa8jQEMzVUHZRKefcYuD0Cp9nE36yqP4mjKqpPgMh1T0hIiIRU++BkLuV0pUuRUREokShoYY0T4OIiESZQkMNaZ4GERGJMoWGGuo7EFJLY4uISLQoNNSQ5mkQEZEoU2ioIQ2EFBGRKFNoqCENhBQRkShTaKghDYQUEZEoU2iooYRaGkREJMIUGmoo3D2R0UBIERGJGIWGGkqGlsZW94SIiESNQkMNaSCkiIhEmUJDDSUT8Z77mqdBRESiRqGhhhKh7gm1NIiISNQoNNRQUpM7iYhIhCk01JBCg4iIRJlCQy3ksnDXzbzlDz/j4g0PAQoNIiISPQoNtXLfL5nwxH2cuvV5QAMhRUQkehQaaiGe6LmbpIA5R05LY4uISMQoNNSCGSSSPT8myKt7QkREIkehoVZCoSHpFBpERCR6FBpqpSQ0aJ4GERGJGoWGWkmGQ0NBC1aJiEjkKDTUironREQk4hQaakXdEyIiEnEKDbWSCF92mSdfcBScLrsUEZHoUGiolZKWBtCiVSIiEi0KDbUSnqchCA2aFVJERKJEoaFWQqGhqRga1NIgIiIRotBQK8ntuycUGkREJEoUGmolvP6EuidERCSCFBpqJdzSgAZCiohI9Cg01EqfgZA+LKh7QkREokShoVbKXHKp0CAiIlGi0FArZUODJncSEZHoUGiolXKhQQMhRUQkQhQaakUzQoqISMQpNNRKmasnNKZBRESiRKGhVtQ9ISIiEafQUCvlJndSS4OIiESIQkOtaBppERGJOIWGWtFASBERiTiFhloJzwiJZoQUEZHoUWiolXLdExoIKSIiEaLQUCtxdU+IiEi0KTTUSpmWhoxCg4iIRIhCQ61owSoREYk4hYZaSWw/I6S6J0REJEoUGmpFM0KKiEjEKTTUSmL7GSFzWhpbREQipO6hwcwOMLPbzWyxmW02sw4zW2Jm15rZXv2U/42ZbTSzdjN7xMxO6Oe5x5jZ9Wa2ysy6zOwFM7vYzGzoX1kJjWkQEZGIS+y4yJCbBuwF/BpYCeSA2cBFwJlmdphz7k0AM9sPeDQo821gM3AhcJ+Zvcc5d3/xSc2sCfgDcDhwPbAYeA+QBiYBV9XixfUIXT2RcMHkTrl8TasgIiKyK+oeGpxz/wP8T+l+M3sYmA+chw8IANcAY4EjnXMLg3K3AS8AN5jZLOdcsc3/AmAO8Cnn3PXBvpvN7JfAFWZ2i3NuxRC9rO2Va2koqHtCRESio+7dEwMofqCPAzCzEcBpwIPFwADgnNsG/BiYiQ8JRWcDHcDNJc/7PSAJfHhoqt2PMldPaCCkiIhEScOEBjNrMbM9zGyamZ0E/DB46J5gewjQDDxW5vDHg+2c4LliwBHAM865rpKyTwAF+gaMoacFq0REJOIaJjTguxPWAq8B9+G7Ic5xzj0SPD4l2K4qc2xx39RgOw5oLVfWOdcNrA+VrQ0NhBQRkYhrpNDwG+AfgPcBXwM2ARNDj7cF2+4yx3aVlBmobLF8W7kHzOwiM1tQYZ0rF4+D+dMdxxFzBXVPiIhIpDRMaHDOrXTO3e+c+41z7ivAR4FvmdkXgiIdwba5zOEtJWUGKlss31HuAefcj5xzR1VX+wqVrD+hlgYREYmShgkNpZxzzwHPAKlg1+pgW65bobiv2B2xEegsV9bMmoEJlO/mGFoJhQYREYmuhg0NgVZgfHB/Eb674egy5eYG2wUAzrkC8DRweBASwt6Gf92D3wWxI/HQrJDkNRBSREQipe6hwcwm97P/eOBggisjgksrfwccZ2aHhsqNxA+ifAl/ZUTRnfhxCxeVPPVl+Mmh5g/SS6icuidERCTC6j65E3BjMF30H/FzM7QARwJnAluBz4TKfgF4F/B7M7sO2IKfEXIqcGpoYifw8zOcD1xrZjPwM0Kegh9oebVzbvkQvqbyEn1nhWzXQEgREYmQRggNd+IHPZ6Lv1rC4cPDD4HvOOdeLRZ0zv3NzI4Fvgn8K9CE74Z4d3gK6aBsxsxOBK4GzsKPY3gZuBS4YahfVFklYxrUPSEiIlFS99DgnJtPFV0FzrnFwOkVlt0EXBLc6q9kpUt1T4iISJTUfUzDbiXU0tDk8hQc5AsKDiIiEg0KDbVUbv2JvBatEhGRaFBoqKU+y2Nr0SoREYkWhYZa0qJVIiISYQoNtaRFq0REJMIUGmqpXGhQ94SIiESEQkMtqaVBREQiTKGhlvpcPeHDgkKDiIhEhUJDLSU1EFJERKJrUGaETKVSCfziUgY8n06ns4PxvMNOvO+MkKCWBhERiY6KWhpSqdQ+qVTqn1Op1Mwyj50CrASewi83vTqVSn1gcKs5TJRpadBASBERiYpKuyc+hl81MhPemUql9gHuAvYEXgOWAOOBO1Kp1EGDWM/hQQMhRUQkwioNDX8PLEqn06+U7P8U0IoPFPuk0+mDgA8DSfxqkhIWXhobhQYREYmWSkPDPsDzZfa/G8gBn0+n0w4gnU7/AngMeOeg1HA40YyQIiISYZWGhonAq+EdqVRqFDATWJBOpzeVlH8amLbr1Rtm1D0hIiIRVmlocMCYkn2H4a+WeLpM+c0M0pUZw4pmhBQRkQirNDS8Ahxbsu94fJj4S5nyE4E3dr5aw5TmaRARkQirtDXg98CnUqnUl4Dv47slLsaPZ7i3TPmjKOnOEMrOCJlRaBARkYiotKXh28Am4KvARnzrwiTglnQ6vTZcMJVKTQcOBx4axHoOD2UHQrp61UZERKQqFYWGdDq9BpgHPAB0AauA7+IvuSz1UfyYhv8epDoOH4kyM0JqTIOIiERExYMV0+n0C8CJFZT7GvC1XanUsKWrJ0REJMK0YFUtaZ4GERGJsMFasOpQ/NUUBjySTqcXDMbzDjuhqycSamkQEZGIqXTBqnmpVOq2VCo1t8xjV+Hnavgu8G/AX1Kp1LWDWsvhIrzKJRrTICIi0VJp98QZwIeAxeGdqVTqHcCVQAG4HbgJWA98Olj9UsISTT13NaZBRESiptLQcDTwl3Q6vblk/8fxEzx9Kp1O/1M6nf4k/iqLHHD+4FVzmCi3NLZCg4iIRESloWEK8FKZ/ScA7fhVLgFIp9NLgPuAObtcu+FGAyFFRCTCKg0NEyiZFjqVSk0GJgOPptPpXEn5l/CTP0lYn9Dgw4JaGkREJCoqDQ2dbB8Cjgi2z5Qp343vopAwTe4kIiIRVmloWAK8J5VKhS/RPBU/nuHRMuXfAqzZxboNP6GWhiby4JxaGkREJDIqnafhF/j1J36bSqVuwi9Y9TH8dNF/KFP+WEqutBAgFoN4HPK+lSFBQaFBREQio9LQcD1wFvBu4ORgnwH/O51Od4ULplKptwMzgmOkVCLZExqSLq8Fq0REJDIqXbCqG38p5ZX4pbBvB96bTqd/UKb4YcB/Ab8brEoOK4m+s0Jmc/k6VkZERKRy1SxY1Q5cXUG5HwI/3JVKDWslgyHVPSEiIlGhBatqrWSuBnVPiIhIVFS9YFUqlRoBfBA/2HEK/gqKNcCfgV8ELRLSn5IrKLaopUFERCKiqpaGVCp1DvAK8H+BC4BT8JdeXhDseyWVSn1kkOs4vJS0NKh7QkREoqLi0JBKpS4FfoqfHXIB8A3gYiAV3H8yeOy2VCp1yeBXdZjoszx2QZM7iYhIZFTUPZFKpWYC1wIbgY+k0+n7yhT7UiqV+gfgDuDaVCr1+3Q6vXTwqjpMxLdvaXDOYWZ1rJSIiMiOVdrS8Gn8vAyn9xMYAEin038ATgfiwKd2vXrDUJmVLvMFDYYUEZHGV2loeBfwYDqd/vOOCqbT6UeBB4ATd6Viw1Z4TANaHltERKKj0tAwDXiqiud9KjhGSpVM7gQKDSIiEg2VhgaH756olDro+xO+5DIIDZmsQoOIiDS+SkPDSuCoKp73SOC16quzGyizPHZXVquIi4hI46s0NPwRmJdKpY7dUcFUKnUMcFxwjJRKbD8Qsiuj9SdERKTxVRoavofvovh1KpXqd4BjKpV6F/AbIA/8+65Xbxgqc/VEV1ahQUREGl9F8zSk0+mXUqnUZ/Dh4b5UKvUEcD++C8IBe+OvlngbfjzDv2iOhn6EB0LixzJ0KzSIiEgEVLPK5fdTqdQmfHB4Oz4ghBmwCfhMOp2+ZfCqOMzE1dIgIiLRVNXaE+l0+jZ8q8I/A7cA9wL3AbcCHwOmKzDsQLnuiYwGQoqISOOrepXLdDq9DR8Sbu2vTCqVagGa0un0lp2u2XBVbiCkWhpERCQCqmppqMKNwIZKCprZTDP7mpk9bmZrzWyrmS00sy+a2Ygy5Q8ws9+Y2UYzazezR8zshH6ee4yZXW9mq8ysy8xeMLOLrZ4LPSg0iIhIRFXd0lCFSj+Y/xn4JPBb4HYgCxwPXA18yMzmOuc6AcxsP+BRIAd8G9gMXAjcZ2bvcc7d3/PLzZqAPwCHA9cDi4H3AGlgEnDVLr6+nZPcfhppXXIpIiJRMJShoVK/AK5xzm0O7bvJzF4CvogfK/GDYP81wFjgSOfcQgAzuw14AbjBzGY554qrP10AzAE+5Zy7Pth3s5n9ErjCzG5xzq0Y0ldWTrzc5E4KDSIi0viGqnuiYs65BSWBoejnwfZggKCr4jTgwWJgCI7fBvwYmIkPCUVnAx3AzSXP+z0gCXx4UF5Atcp2T2ggpIiINL66h4YBFBe8eiPYHgI0A4+VKft4sJ0DYGYx4AjgGedcV0nZJ4ACfQNG7ZS9ekItDSIi0vgaMjSYWRy4Ej924Y5g95Rgu6rMIcV9U4PtOKC1XFnnXDewPlS2tjQQUkREIqohQwO+C2EucKVz7sVgX1uw7S5TvqukzEBli+Xbyj1gZheZ2YLqqluFPktj+xkhFRpERCQKKhoImUqlavapZmZfBy4BfuScuyb0UEewbS5zWEtJmYHKFst3lHvAOfcj4EepVMqVe3yXhVe5DK6e0DTSIiISBZW2NNhO3KpmZlcBX8LPNvmJkodXB9ty3QrFfcXuiI1AZ7myZtYMTKB8N8fQSzT13NWYBhERiZJKF6wa8m4MM/sK8BXgNuCC0KWTRYvw3Q1Hlzl8brBdAOCcK5jZ08DhZtYcjGMoehs+LA1dF8RAdPWEiIhEVEOMaTCzK/GTLf0MON+5oLM/JLi08nfAcWZ2aOjYkfg5GV7CXxlRdCd+3MJFJU91GX6A5fxBfAmV09LYIiISUXWf3MnMPgl8FXgVv9z22SWzPL/hnPtDcP8LwLuA35vZdcAW/IyQU4FTS1onbgbOB641sxn4GSFPAd4HXO2cWz5Ur2lA5Voa1D0hIiIRUPfQQO98CXsDPy3z+EP46aBxzv3NzI4Fvgn8K9AEPA28OzyFdFA2Y2Yn4qejPgs/juFl4FLghiF4HZUJD4TU1RMiIhIhdQ8NzrnzgPOqKL8YOL3CspvwV2JcsjN1GxKJ7dee6M7mKThHrPGEQqsAACAASURBVI7raImIiOxIQ4xp2K2U6Z4AyKi1QUREGpxCQ62FBkI2hUKDuihERKTRKTTUWnhGSEKhQYMhRUSkwSk01FpoaeyEK2DBBR9qaRARkUan0FBrZmVbGzTBk4iINDqFhnrQSpciIhJBCg31oAmeREQkghQa6kETPImISAQpNNRDmZYGLY8tIiKNTqGhHsp2T2ggpIiINDaFhnpIbj+VtLonRESk0Sk01IMGQoqISAQpNNRDIjzBk1oaREQkGhQa6iGx/foTCg0iItLoFBrqQQMhRUQkghQa6kEzQoqISAQpNNSDrp4QEZEIUmioh3howapgRkhN7iQiIo1OoaEekrrkUkREokehoR40pkFERCJIoaEeyoYGXT0hIiKNTaGhHjQjpIiIRJBCQz2El8bW1RMiIhIRCg310GdGSH/1RL7gyOYL9aqRiIjIDik01EMoNLTEeoOCuihERKSRKTTUQ+iSyxYLhQYNhhQRkQam0FAPiX5Cg1oaRESkgSk01ENoIGSTuZ77GgwpIiKNTKGhHhJNPXeb6Q0KCg0iItLIFBrqIXz1BL3dE1p/QkREGplCQz0kw6Eh1NKQ0UBIERFpXAoN9RAPjWlw6p4QEZFoUGioh1D3RILwJZcKDSIi0rgUGuqhzNLYoEsuRUSksSk01EO4paHQO45BLQ0iItLIFBrqoU9o0EBIERGJBoWGeghN7hTXQEgREYkIhYZ6CLU0xPPqnhARkWhQaKiH0IyQsfCYBg2EFBGRBqbQUA+hqydioZaGbq1yKSIiDUyhoR4S4dCQ7bmv7gkREWlkCg31EIuBGQDmHDHnJ3hSaBARkUam0FAPZn1aG4oTPGlMg4iINDKFhnopExq0yqWIiDQyhYZ6aeq9gqLF+QGQ6p4QEZFGptBQL6PH9dwdl28HNCOkiIg0NoWGehkzoefuhCA0dOcKFJyrV41EREQGpNBQL2N7WxomuY6e+xrXICIijUqhoV7G9rY0TKSz576uoBARkUal0FAvoe6JiYXelgYNhhQRkUZV99BgZl8ws7vMbJmZOTN7ZQfl325m95vZVjPbYmb3mtlh/ZSdYma3mdlaM+s0swVmdsaQvJBqhVoaxufae+5rMKSIiDSquocG4BvACcDLwMaBCprZXOAhYB/gSuArwFuBR8xsdknZ8cCfgPcDNwKfBrYB883s/EF+DdUbO77n7rjctp77amkQEZFGlah3BYD9nHPLAMzseWDkAGW/D2SAec65VcEx84HFwHeBk0Jl/xUfLk5zzv0uKPsT4DHg38zsLufcNuplTG9oGJtVaBARkcZX95aGYmDYETPbH5gD3FUMDMHxq4C7gBPNbHLokLOBl4uBISibB64HxgOnDEL1d97ocT3rT4zIdhDXrJAiItLg6h4aqjAn2D5W5rHHAQOOBDCzvYCpwf5yZcPPVx+JBIwaC0AMx7i8v4JCV0+IiEijilJomBJsV5V5rLhv6k6U7cPMLjKzBTtVw2qFxjVMyPsuiq6sBkKKiEhjilJoaAu23WUe6yopU03ZPpxzP3LOHbVTNaxW+AqK4lTS6p4QEZEGFaXQUJzMoLnMYy0lZaopWz9jwi0NxfUnFBpERKQxRSk0rA625boVivtW7UTZ+unT0uAzjFoaRESkUUUpNDwZbI8u89hcwAFPATjn1uBDwdx+ygLUZtzCQMJjGnLqnhARkcYWmdDgnPsb/oP+DDMrDnQkuH8G8Efn3OuhQ+4E9jOz94bKxoFLgU3APTWp+ED6rHQZDITUjJAiItKg6j65k5mdC0wPfpwINJnZl4KfVzjnfhYq/mngAfwMkNcH+y7Fh5/PlDz1N/Fh4g4zuxbf8nAW/lLLC5xzWwf9xVRrbJkxDWppEBGRBlX30AB8DHhnyb6vB9uHgJ7Q4Jx71MyOA64Obg54FDjDOfds+Amcc+vN7Fh8ePgkfqbJvwJnOud+PgSvo3rlrp7QQEgREWlQdQ8Nzrnjqiz/GPCuCsuuAs7diWrVxqixYDFwBcYWuki4vFoaRESkYUVmTMOwFI/D6LE9P47Ld2hyJxERaVgKDfU2NjwYsp3ubKGOlREREemfQkO9jek7lbRaGkREpFEpNNRbyVwNGggpIiKNSqGh3kq6JzQQUkREGpVCQ72VTCXdlcnjnKtjhURERMpTaKi3UPfE+Hw7BefI5jUYUkREGo9CQ72VW+lSXRQiItKAFBrqrWRMA2hWSBERaUwKDfU2agzE/D/D6EIXSZdTS4OIiDQkhYZ6i8X7dFGMz3ewfmtXHSskIiJSnkJDIxgzrufuhFw7L67aVMfKiIiIlKfQ0AhKVrtcotAgIiINSKGhEYzpOxjyxdWbNFeDiIg0HIWGRjC27/oTG7Z1s3aLxjWIiEhjUWhoBCWzQgLqohARkYaj0NAIykzwtGTVxnrVRkREpCyFhkZQZoIntTSIiEijUWhoBCVXTwD8bc1mclqDQkREGohCQyMYORricQBGFbppKuTozhVY/ubWOldMRESkl0JDI4jF+oxrmJj3YUFdFCIi0kgUGhrFpKk9d+d1/A1AM0OKiEhDUWhoFMee1HP31K2LiLu8rqAQEZGGotDQKI78exg1FoCJ+XaO6VjGa+vb2daVrXPFREREPIWGRpFsgnee0vPjaVufA+DF1eqiEBGRxqDQ0EiOO7XnKopDulezT2adxjWIiEjDUGhoJGMnwOHH9vx4+tZndQWFiIg0DIWGRvOu03ruHt++lNdeXaMVL0VEpCEoNDSa/Q/CvWVfAFpcjmPefJbVGzvqXCkRERGFhsZjhr3r9J4f37t1EXc+tKSOFRIREfEUGhrR244j1zICgMn5LRz7xx/zzIur61wpERHZ3Sk0NKKmZhKnnNHz49Gdy2lLX0n3Jg2KFBGR+lFoaFTv+TCdJ7yv58cD2lfS/vXLYeO6OlZKRER2ZwoNjcqM1rM/zuJjelscxm9eQ+6rl8Dvbld4EBGRmlNoaHAHnP/P/MfM95EL/qkS2zbBf/0MPvdP8P2vwHNPgC7JFBGRGlBoaHAxM+adfzZfnfReNsZaex9wBXjuL/D9K+EHV6nlQUREhpxCQwTsPXEUs04+kXOnnc839jiZZ1qm9S3w7F/gyo/DI/eq1UFERIZMot4VkMqc9Y79aUrEuPWBBA+NmMmU7CY+uOVpTt32gi/Q2Q4//R48/kc45h/goCP8tNQiIiKDRKEhImJmnHHMfhy2zx5881fPsHIDfH/CCTww4gAuX/8/TM1t9gVffM7fAN6yLxx0FBx8JOx3oF9JU0REZCeZ1jUoL5VKOYB0Ol3vqmynK5Pjxt//lXufeQ2A5kKWf9r8F963ZSFx+vn3bGqGAw6BWYfBxMkwbg9/Gz0WYvEa1l5ERBqc9fuAQkN5jRwaihatWM/8x5bxxEtvAjAtu5FjOpZxVOcKDupeQ4LCjp8knoBJU2HKdJg63W/HTYSx42H0OEgEjVHOQTYD3V2QTEJTC8Q0JEZEZBjqNzSoeyLCZk+fwOzpE1ixdiu/fHwZf1wUY35yHPPHHElbIcOhXa9xZOdrHNH1am/3Ral8Dlav8LcFJY+ZwYjRvkx3JxRKQkhTM7S0QWtbsB3h7yeTYEGgiMV8S0YiAYmk3yabINHkt01NPoCMGAlto/w2m4XlL/be1r0OI0fDmHEwerwPNOMnwoQ9YcJkvx09tm/3Sy4HK5fDssXw2jJ//KzDYP+/g+aWQfs3EBHZnailoR9RaGkotam9m4f+uoYHnl/F4pV9p5yenN3MkV2vsl9mLXvk25ns2pmYb6ctO4xW0Gxpg1Fj/PaNlZDp3r5MIgn7zoLJ03ywiZnfxhM+wCSb/dYBXR1+gGlnBxTyvQGpdQSMGAV7TvG3sRN8OMpmYO3r8MYq2LjWh6HWNmgb4Y9NNvnfn0z60DRyNMQH6BrK52HVcnh5CaxY6sPXjJm+/lP29mVWLoelz8PSRbB1E4wa61uIxozzLUYzD4aJe/kAWEvbtvirenJZPyh3j8lD/zud87+3ucUHWhHZWeqeqFYUQ0PYmo0dPPjCap56eS1LVm0imy/fVdFWyLB3dgPTsxs4KLaFfdnChHw7I7u2kOzcioX/PhJJ/2acz/luCvGamn0A2Ljez59RqXjcf7DvMQkmTPIf7J3tPqy0b/OtP+WCD0Bzqw88nRWEvgl7woGH+7CRzcDWzf7W1eED0Kgxvv6tI2DzRtjwBqx/EzZt8MfueyDsNwumv9XPB/LS8/72ylIfsvY5ILjNhDWv+St4nnvC/50UTX8rHHks/N0RPkzFE5CI+w/6jm3+9XZsg67O3iAXi/ltIe8DVD7n71vQehWL+davNa/61qTXXvavy8yfz8nTYPJbfMtUcyu0tPhtsskfH4/7bWubL982ou95y2Vhw1r/t77HJH9+wo+98hIseda3hO29Hxw6158vaUyFgv/bqHWAjiaFhmpFPTSEdWfzLFm1iedWrOf51zawZOUmurL5HR4Xd3nGk2HkyDaaR45g1KhWxrQ1MXF0K5PHNDOlLc7kFmNsIkdTpjv4Vt7u39yd8x+ghWCbzfo32lwOchn/4ZXNQCbjuz46tkH7Vv/h4Qrwlv1g3+DDaMp0/7ybN/gPss0bYH3wwbb+Tdjwpv+wKO0+mbCnv2pk+kxffslCWP3qEJ1liby2kT48NDX5v6vNG/rOezJqrG9ZSjb5bq9ygW7aPnDI23xrT/gDqqsTOtqhc5v/W7YYtLT6ANXS6m+tI3q7+JqaQy1hcf8WXij4+hQK/udksy9XbCFLJoMuwKQvt2UjbFrvX8e2rf7x5uB3JZv8/6fF/wdzWf9craFuxrET/HOVk8sBrv/H+5PN9J6HfN7/P9rSVt1z9NQh64P1Ky/5ALtyuX99e+8Le+/vrx7btsVfTbZ0Eby82B836zB/RdlBR8GkKTv3u8O2bYEXnoLnF/jwOmVveMe74YBD+4772rwB/vZXf5733t+3BpbqaPdhtpIuVOf8637mMf+F4LC5u/5aeik0VGs4hYZS+UKB5W9s5a8rN7Jk1SZefn0Lr67bRmEX/hZaknHGtDUxuq2JcSOb2WNUCxNHt7DH6BZGNCf9X6CBYTQn44wb4cuNbmsiNhjJv1Dw/8Nt2+SDx4Q9y89TsWk9vPQCdGztDTSFgn8Dy3b7D4JMxpdtDXVHxGL+W32xu2LzBli7Bt5c7d80wH9AjJ8Ik6b5b6bZbG/LQWeHf8PMBeEp09173EDGT/Tf9Pc9APIF/2G1bIn//QBjxsMBs+Gts2HyVNi6xX9YbN4Aq17xb5j1ahXa5wDfjbN4Yd9Wh6HU1OzPezUtPlJeLOa7tiZP87dMt/97f3MNrHvDn+N4ojf0NLX4MUvxhA8oBee/EHQVbx3+b7/UqDE+jE2Y5MNMPGgFsqAlKZcNAk4W2rfAlk3+S8K2Ml8UqjVqbFD3Zv9B3dzi/2ZHjoGRo3pbl5zzt3yu7/vA+jdg+dLyf28T94Jj/8GXfeFpH2rCxk7w4SGR8K1V697wX57MYK+9gy9Ns2DqjFA4bPL/Bs88Bs882jsT8GFHwyVf2bVz0ZdCQ7WGc2gopzubZ/mbW1n2xhZWrN3Kq+u2sWLtVtZv7ad5fJDEzBjRkiBmRjxmmEEyHmN0axNjRjQxpq2JUa1NxGz742IxI25GPB6jrTnBqJYkI1oSjGptYlRLMjg2SSLu075zjlzB0Z3Nk4zHaE4O0qWm7Vv9m9j4idX1pWe6/RvFutd9M3gs1ndg6cTJ5YOPc/7NwhVg/J4DN7fmgkGlf33Gj7VoG+nfpItjPzrbgzfgLf4Na/RY/5zFwaVrXusNKqtf9cfvfxC89WA/qDTTDcuXwLIXYcVLvu5HvQPmnuC7BsA/77N/gaf/7OuQz/V2N4B/zhGj/LalNdRKFXyzLnYlxBN+WwgeK+T943tM8i1Tb9nPn7N8zr+xvr7Sj21p3wpdXf5Dq7vTf0su/v583j++/g0f6sLM/PlvavYtD6UfeHtM9pcxT57muylefK78h6LIUEs2wffmD+Ygb4WGau1uoaE/nZkcm9ozbGrvZnNHho3t3by5qZPXN3Xw+qZO3tjcwab2DPlC4/4dtTUnwEFXNt+nNSUZjzGqNcnIliRNiZhv+XWOgnPEYz6ItDUnaGtK0JKMEwtCTTHgJOMxkokYTYk4ibiRiMVIxI14LEa8NOXQe1wi7h+Px4xkIkYyHiMRj9GciNGSTNDSFKc5Gccs+Gx0jkLB9fxuMyMW8/VPxmNYrfpoc9nesQTDjXP+G+y61/3rHD/Rz2NSbH4v5H1Qe3O1b9Ga8Vb/zTisq8N/o3x5sX8O5wDnB9W2tPoxE60j/baQ90Em09X7Lbz47bWj3XfhFcORK/jniBXHeZh/7kwmaB0Ltrlsbzegmf+2PHaCb40aNdo/1t3pW54y3cGVTME32ETS7+sKfn/HNt8q19/ng5mvS37H3Zx9xOP+HLS2+ePLhbFq7DHJj5eZMdN/a+/q8GNbXn3Zf7NvavYBd+ZsH/By2d6uhMUL+x8zVA0z36o2e47fPveEH9fTsa1vuXjCd5fm876O5X53ssmH2kpbytpG+rE0Rxzjf3+13UX9U2iolkJD5ZxzdHTn2NyRYXNHhg3bulm3pZO1W7pYt7WLzoz/Rlj8S+vM5Ni4rZuN7Rm2demb2a6Ix4zWpjgtTQmS8Ri5fIFc3pErFHAORhSDT7MPPgXXG0IKzvkAYr0tN4mYEYvFSJSEm/D9nhae4v1YjJjRE5Z8cPL7nfMtO13ZPF0ZH9pakr6+LU1xWpJxWkP3mxN+ejLnHD6H+gBXrE8iHiObL5DN5enOFcjlC7Qk44xo8eFvZIu/ijybd2SDx4tBsZitYmY9Ac4HQQt+n8M5SMRjtAxWK1Q9FJvSdzXcdXf5kLTmNd9i09QMe06FPffyTe/JJv8h3NXhg0+m27fe5HK9QaClFZrbQl0YzX1bxgp5P4D4zdU+lBUHu+bzPjD1tDAlfMgZOTpoKRvr7+/KLLe5rA+K2UzQLRkEuG1bem9dHYD1HZxbvLy8bYT/0J4x09cpLNMNTz0Czz/l63nQETDzEH8Oiq/79VU+PIBvtdpjkh8L093lW+2Wvehb8Tas7e3azGb8az7oSDj8GB+GBroCa+cpNFRLoaE2Mrk8Hd25Pt/yM9kCmzszbG7PsLmjm21dOVx4pkvnu0vzhQIFB7l8gY7uHNu6smztyrKtM8uWzgxbO7Ns7cwQbgRJxIymZJxsrtDvFSUizYkYY0Y0M6atiRHNCfIFR77gw1ihTKuamWHFbSiExQxiMfPd4QVHvlAgX3AkYjGaEjGaknGaE7FQcKGnVSkes57nAcjl/fG54Pc3B61czcm4f65EcRvrCWyF0HP2hjF/fFtzwget5iRtLQkKBUdXNk93Nk8mFwpbA5yn4us1fGj0rWbFFrQ4zUkfwJoScZKJWE/DS8/xwX+K45p6zoFzuKDFrzeIxvrUpfh6iv824VbE4r9FsUWwKeFbBePDsZVsaOyekzuZWQz4NPBxYAawFpgPXOmca69j1STg3+i2T8pTGVGmdPUKztHelSNm0JyM9xnf0J0rsDUIF/mCwyB4ozey+QKdmRwd3f5W7Npwwbf0fMEF33gLZIIAUvxQyRcc+byj9B2u0PMGV+j5AMjmHbm8P774jbw7+FbucKEPH+t5PYWC/0acyeV7PkBkcHXnCry5uZM3N3fWuyoyiGLBmKlit6KZD2PZvG+VKnazFkMH0BOKwL8/tDb5Lsu2Zt9Cli8437oX/H9cKDjyzvU8X7GlrthaBuHWPv++NKatqecWixntXVnag/eeTC7f0yIIviWsNRmntTlBa5O/7TG6hdPnzKjJORzWoQG4DvgU8Gvgu8CBwc+Hm9mJzmmI9XAXM2NU6/b9fGbmm8mTrUwc3VqHmg2OYrjp7M6Ryzs/tiJoxgeC0JPtCT79NcsXx04U3+j8G2Ghz7fjXPEbXaE3/BTfIMP7w8HJDFqaEn68RpMf8NqVzdGV8QGpM+Pvd2ZzdGd8l0PMwt9gLQhXBfJBt0si3vutOhGP0ZXxrUzburK0d+V6BtMWz0N4fEm4Rav4Op1zPV0WGGqFGsYKzgfC7lwB2NEVPeUDeWcmzwaGdoB4tfbeY6RCw64ys4OAS4FfOec+ENq/HPg+cCZwR52qJzIokvEYydYmRreW79sd07Z7rWzqgnEau/ocHZlc0D2WoSOTCw1ytZ7BqKXHFMdgFLsDit8m886VjAExcnlHJlcgk/MtS47iIFe/LT5fPtQdEg99Wy22NHXn8nRn/fOEW71y+UJJN0nvcxfDYrFLb1vwrTYRj9GS8FcVNSXjfS6FLndenevt8ih2vxR/dzbv65IJWs+K9eq99Dp4DopDMFwwDCNcT3rCZzGIluoJwcHrNHq7LRz+3yF8boZru1xrU+0+yodtaADOwv9tfq9k/83AN4FzUGgQGVYG40oSM2NEc5IRzUmmjB+cbjKpv+L4h2K3YjYYJFu8CikRj/WMHymO9XPhMRjB8Z2ZvG+9y+TozOT7DNJNhgYMF0NiIWilKw5SBnrGupgZ3dm8H0Te3s3mziyFgmNES4IRzUnamn0rXU+4wneFdGZyPS2MHZk8Y0fU7svBcA4Nc4AC8ER4p3Ouy8wWBo+LiMhuwMx6uu/6aZiryIiWJLD7Lno3nIeSTgHWOefKdT6tAvYws+3+dMzsIjMrXe9RRERktzecQ0Mb9DtapStUpg/n3I+cc0cNWa1EREQiajiHhg6gvzl9W0JlREREpALDOTSsxndBlAsOU/FdF5kyj4mIiEgZwzk0PIl/fW8L7zSzFuAwQOMWREREqjCcQ8PP8VfLXFay/0L8WIbba14jERGRCBu2l1w65xaZ2Q3AJWb2K+AeemeEfAjN0SAiIlKVYRsaApcBrwAXAacC64Dr8WtPaJ5YERGRKgzr0OCcy+PXnPhuvesiIiISdcN5TIOIiIgMIoUGERERqciw7p4YDKlUqt5VEBERqSWXTqfLrv6mlgYRERGpiBWXAJWhZ2YLtK7FrtN5HBw6j4ND53Fw6DwOjqE+j2ppEBERkYooNIiIiEhFFBpq60f1rsAwofM4OHQeB4fO4+DQeRwcQ3oeNaZBREREKqKWBhEREamIQoOIiIhURKFhiJlZzMwuN7MlZtZlZq+Z2XfNbES969ZozGymmX3NzB43s7VmttXMFprZF8udLzM7wMx+Y2YbzazdzB4xsxPqUfdGZ2ZtZrbczJyZ/aDM4zqX/TCz8Wb2b2b2t+D/4bVm9oCZvaOk3NvN7P7g73aLmd1rZofVq96NxMxGmtkVZrYoOD/rzOxRMzvPzKyk7G5/Hs3sC2Z2l5ktC/6ffWUH5Ss+Z2Y2xcxuC/6OO81sgZmdUXHdNKZhaJnZv+OX4/418N/45bkvBR4BTtRqm73M7JvAJ4HfAo8DWeB44EPAc8Bc51xnUHY/4AkgB3wP2AxcCBwMvMc5d3/NX0ADM7N/Az4OjARucM5dEnpM57IfZjYdeBB/3n4CLAXGAIcA9znn/jMoNzcotwoohrJLgD2BY5xzi2pa8QZiZjHgIeAY4Kf4/7fbgLOAtwHfds59Piir8wiYmQM2AE8DRwJbnHMz+ilb8Tkzs/HAguCxa4GVwNnAO4F/ds7dssPKOed0G6IbcBBQAH5Zsv9SwAFn17uOjXQDjgLGlNl/dXC+Lgntmw/kgcNC+0YCK4AXCQKxbg7gCHwg+JfgPP6g5HGdy/7P3SPAa8BeOyj3BLAFmBraNzXY9/t6v446n8Ojg7+760r2NwHLgE06j9uds31D958HXhmgbMXnDPh28G/x3tC+ePAc64GRO6qbuieG1lmA4b+9hd0MdADn1LxGDcw5t8A5t7nMQz8PtgcDBF0VpwEPOucWho7fBvwYmAnMGeLqRoKZxfF/b/cCvyrzuM5lP8xsHvD3+G/Ca8wsaWZtZcrtjz9HdznnVhX3B/fvAk40s8m1qncDGh1sV4d3OucywDqgHXQew5xzyyoptxPn7GzgZefc70Jl88D1wHjglB39ToWGoTUH39LwRHinc64LWMhu+ma8E6YF2zeC7SFAM/BYmbKPB1udW+9yYBa+ubIcncv+Fd9AXzWz3wGdQLuZLTWzcOAvnp/+zqHhm5h3V08Am4DPmdkZZrZ3MIbmGvx5uSoop/NYvYrPmZnthW+BeLyfsuHn65dCw9CaAqxzznWXeWwVsIeZNdW4TpESfFO+Et+8fkewe0qwXVXmkOK+qUNctYZnZvsAXwW+5px7pZ9iOpf9OyDY3oz/FvZR4GNABviZmZ0fPK5zOADn3EZ8a9YGfFfYCmAJfvzSB5xzNwdFdR6rV805G5Tzq6Wxh1YbUC4wAHSFymRqU51I+h4wF7jCOfdisK/YRFzu3HaVlNmd3Qgsxw946o/OZf9GBdutwPFBczpm9mt8X/w3zOyn6BxWYhu+b/63wKP4EPZJ4A4zO9059wd0HndGNedsUM6vQsPQ6sCPUi2nJVRGyjCzr+Ob1X/knLsm9FDxnDWXOUznFQiaz08C5jnnsgMU1bnsX2ewvbMYGMB/czaz3wL/hG+N0DkcgJnNxgeFy51zN4X234kPEjcHV/DoPFavmnM2KOdX3RNDazW+C6LcP9JUfNeFWhnKMLOrgC8BtwCfKHm4OKCqXFNacV+5JrjdQvD3di1wD/C6me0fDJiaHhQZE+wbi87lQFYG29fLPLYm2I5D53BHLsd/KN0V3umc6wDuxv9dzkDncWdUc84G5fwqNAytJ/Hn+G3hnWbWAhyGv15WSpjZV4CvALcBF7jguqCQRfgmtqPLHD432O7O57YVmAicCrwUuj0YPH5O8PMF6FwOpDiAeVqZx4r73sT/fw79n0MHPDW4VYuU4gdSvMxjidBW57F6FZ8z59wafCiY209ZqOT/9Xpfjzqcb8BsBp6n4Zx617HRbvhBjw4f74oiZwAABbFJREFUGGIDlLsLP7fAoaF9xbkFlrJ7zy2QBD5Y5nZxcG7/O/h5ps7lgOdxHP5a95WErl8H9sL30S8N7XsyKDsltG9KsO/+er+WOp/H64K/u8+V7C+2dG0AEjqP/Z6/Hc3TUPE5A75D//M0bARG7ag+mhFyiJnZ9fh++V/jm4sPxM8Q+WfgBKcZIXuY2SfxM5q9CnwZH7jC3nB+wFTx+uQn8LNGXof/H+RCfFA71Tl3X63qHRVmNgM/MLJ0Rkidy36Y2UXAD4EXgP+Ln5DoYnxw+Efn3O+DcscAD+ADxvXB4ZcCk4BjnXPP1rjqDSOYVfNpfAi7Hf/eNx7/NzYD+KRzLh2U1XkEzOxcersTL8X/3X03+HmFc+5nobIVnzMzm4BveZiA78JchZ9P6Dh8q+5Pdli5eqeo4X7Dp7jP4GfW6w7+ka6lgpm3drcbcCs+Bfd3e7Ck/IHAf+GvAe8A/oSfmrvur6URb/g36O1mhNS53OF5ez/+OvZ2/JUUvw/ejEvLHQ38D74VYitwH3BEvevfCDdgP/wU0ivx4XQL8DDwfp3HsufrwUrfB6s9Z/juop/hJ9bqwge6D1daN7U0iIiISEU0EFJEREQqotAgIiIiFVFoEBERkYooNIiIiEhFFBpERESkIgoNIiIiUhGFBhEREamIVrkUkWEvlUpdhV/P5Ph0Ov1gfWsjEl0KDSKyQ6lUqpJZ4PSBLDLMKTSISDW+OsBjr9SqEiJSHwoNIlKxdDp9Vb3rICL1o9AgIoMuPIYAv1rfZcAs/GI6/w+4Ip1Ov17muLfiVzh9FzARv6jO/cDX0+n0S2XKx/GrJZ4LHIxfDXAVfsGfb/VzzAeBzwXlu/ALUH0mnU6v2pXXLLI70NUTIjKULgduAp4Fvodf7fV84NFUKjUxXDCVSs0BFgDnAE8C/4ZfXfIjwIJUKnVUSfkm4F7gRuAtwP9v7w5CtKqiAI7/hyBx0+RKTVyLLUKDEnJIFxK2EGshQ2K6aCNnH0QEztKdIHQQ3EiZphsLgxaJUCgiRA4UWK6EwKg2DQSpaNPi3k8ej/fG97TZ6P8HH5fvvPvu3Lv6ztx3732ngKOUV/++DWzt6E8AJymPUj4GfgJmgQsRseKxRys94ZxpkDRYnUHocjszD3fE3wS2ZOa1RhtHKDMPh4H3amwK+AR4DtiXmZ816s8CnwMnI+LFzPy3XpoDdgDngT2Zeadxz4raVttO4JXM/LFR9xTwDrAbONs7eEnONEga5VDP54Oe+p82E4ZqDlgA9jb+u3+N8vjiSjNhAMjMM8AlYAMwAw8eSwTwD3CwmTDUe+5k5p8d/TnaTBiq47V8tWcMkipnGiQNlplTI2/5tqONhYiYB7YBG4F54OV6+WJPOxcpCcNm4DtKgjENXM3MWyP6831H7NdarhrRjvRUcqZB0nL6vSc+WQQ53Sp/66k/iT/fKscuXvyrI3avls+MbEt66pg0SFpOq3via2q50CrXdNQFWNuqN/nxX/foXZM0lkmDpOW0rR2IiGlgE2W74/Uanqx72N7TziT+Qy1/piQOL0XEC/9HRyU9nEmDpOX0bkRsbsXmKI8jTjcWMF6mbMecqecoPFC/vw7coCyIJDPvAwmsBI61t0tGxLPtLZ2SHp8LISUNtsSWS4AvMnO+FfsauBwRZynrEmbq5yaNHReZuRgRB4BvgDMR8SVlNmED8BblUKj9je2WUI603gLsAm5ExFe13nrgDeB94MQjDVRSJ5MGSWMcWuLaTcpOiKYjwDnKuQyzwN+UH/IPM/OPZsXMvFoPePqIcv7CLsqJkKcpJ0L+0qp/NyJ2AgeB/cABYAq4Vf/mpfHDk7SUqcXFIS+vk6ThfBW19GRyTYMkSRrEpEGSJA1i0iBJkgZxTYMkSRrEmQZJkjSISYMkSRrEpEGSJA1i0iBJkgYxaZAkSYOYNEiSpEH+A6TmBdvwua7KAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"reload(ooo)\n",
"ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
@@ -315,9 +806,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (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",
@@ -333,9 +846,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 12.2278\n",
+ "x_test / mae : 2.2441\n",
+ "x_test / mse : 12.2278\n"
+ ]
+ }
+ ],
"source": [
"score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -353,11 +876,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
- "predictions = loaded_model.predict( x_train[13].reshape(1,13) )\n",
+ "mon_test=[ 1.26425925, -0.48522739, 1.0436489 , -0.23112788, 1.37120745,\n",
+ " -2.14308942, 1.13489104, -1.06802005, 1.71189006, 1.57042287,\n",
+ " 0.77859951, 0.14769795, 2.7585581 ]\n",
+ "\n",
+ "mon_test=np.array(mon_test).reshape(1,13)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prédiction : 8.66 K$ Reality : 10.20 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "predictions = loaded_model.predict( mon_test )\n",
"print(\"Prédiction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], y_train[13]))"
]
},
diff --git a/GTSRB/05.2-Full-convolutions-reports.ipynb b/GTSRB/05.2-Full-convolutions-reports.ipynb
index 52062df9d7bcc2b039296c4303bff706f3c5edd9..877b5b1631b3c872a51c6dc9d37226226e4327bf 100644
--- a/GTSRB/05.2-Full-convolutions-reports.ipynb
+++ b/GTSRB/05.2-Full-convolutions-reports.ipynb
@@ -90,7 +90,7 @@
"\n",
"\n",
"\n",
- "Report : report_2020_01_20_17h22m23s\n",
+ "Report : report_009557\n",
"Desc. : train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False \n",
"\n"
]
@@ -99,97 +99,97 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col4 {\n",
+ " #T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col2 {\n",
" background-color: yellow;\n",
- " } #T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col6 {\n",
+ " } #T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col4 {\n",
" background-color: yellow;\n",
- " } #T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col2 {\n",
+ " } #T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col6 {\n",
" background-color: yellow;\n",
- " }</style><table id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >95.39 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >58.2 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col4\" class=\"data row0 col4\" >97.32 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col5\" class=\"data row0 col5\" >52.6 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col6\" class=\"data row0 col6\" >95.16 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow0_col7\" class=\"data row0 col7\" >50.3 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >95.54 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >95.3 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col4\" class=\"data row0 col4\" >96.81 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col5\" class=\"data row0 col5\" >105.4 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col6\" class=\"data row0 col6\" >95.68 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow0_col7\" class=\"data row0 col7\" >115.4 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col2\" class=\"data row1 col2\" >96.11 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col3\" class=\"data row1 col3\" >47.2 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col4\" class=\"data row1 col4\" >97.77 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col5\" class=\"data row1 col5\" >55.2 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col6\" class=\"data row1 col6\" >96.52 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow1_col7\" class=\"data row1 col7\" >52.3 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col2\" class=\"data row1 col2\" >96.09 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col3\" class=\"data row1 col3\" >84.7 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col4\" class=\"data row1 col4\" >96.57 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col5\" class=\"data row1 col5\" >106.7 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col6\" class=\"data row1 col6\" >95.44 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow1_col7\" class=\"data row1 col7\" >119.0 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col2\" class=\"data row2 col2\" >95.98 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col3\" class=\"data row2 col3\" >135.2 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col4\" class=\"data row2 col4\" >97.88 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col5\" class=\"data row2 col5\" >118.3 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col6\" class=\"data row2 col6\" >97.38 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow2_col7\" class=\"data row2 col7\" >92.0 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col2\" class=\"data row2 col2\" >95.99 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col3\" class=\"data row2 col3\" >160.5 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col4\" class=\"data row2 col4\" >97.84 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col5\" class=\"data row2 col5\" >149.2 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col6\" class=\"data row2 col6\" >97.59 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow2_col7\" class=\"data row2 col7\" >139.7 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col2\" class=\"data row3 col2\" >96.29 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col3\" class=\"data row3 col3\" >138.7 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col4\" class=\"data row3 col4\" >97.95 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col5\" class=\"data row3 col5\" >124.7 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col6\" class=\"data row3 col6\" >97.53 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow3_col7\" class=\"data row3 col7\" >98.5 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col2\" class=\"data row3 col2\" >96.54 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col3\" class=\"data row3 col3\" >161.6 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col4\" class=\"data row3 col4\" >98.08 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col5\" class=\"data row3 col5\" >156.6 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col6\" class=\"data row3 col6\" >97.42 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow3_col7\" class=\"data row3 col7\" >144.2 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col2\" class=\"data row4 col2\" >95.79 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col3\" class=\"data row4 col3\" >44.3 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col4\" class=\"data row4 col4\" >96.41 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col5\" class=\"data row4 col5\" >53.1 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col6\" class=\"data row4 col6\" >95.72 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow4_col7\" class=\"data row4 col7\" >50.9 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col2\" class=\"data row4 col2\" >95.72 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col3\" class=\"data row4 col3\" >79.0 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col4\" class=\"data row4 col4\" >96.80 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col5\" class=\"data row4 col5\" >105.7 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col6\" class=\"data row4 col6\" >94.93 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow4_col7\" class=\"data row4 col7\" >115.0 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col2\" class=\"data row5 col2\" >95.35 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col3\" class=\"data row5 col3\" >46.0 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col4\" class=\"data row5 col4\" >96.80 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col5\" class=\"data row5 col5\" >54.5 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col6\" class=\"data row5 col6\" >94.29 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow5_col7\" class=\"data row5 col7\" >52.5 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col2\" class=\"data row5 col2\" >95.15 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col3\" class=\"data row5 col3\" >82.0 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col4\" class=\"data row5 col4\" >97.10 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col5\" class=\"data row5 col5\" >109.1 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col6\" class=\"data row5 col6\" >94.05 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow5_col7\" class=\"data row5 col7\" >121.6 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col2\" class=\"data row6 col2\" >96.72 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col3\" class=\"data row6 col3\" >131.8 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col4\" class=\"data row6 col4\" >97.80 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col5\" class=\"data row6 col5\" >117.9 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col6\" class=\"data row6 col6\" >97.16 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow6_col7\" class=\"data row6 col7\" >92.8 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col2\" class=\"data row6 col2\" >95.64 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col3\" class=\"data row6 col3\" >156.4 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col4\" class=\"data row6 col4\" >97.68 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col5\" class=\"data row6 col5\" >146.0 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col6\" class=\"data row6 col6\" >97.93 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow6_col7\" class=\"data row6 col7\" >136.4 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col2\" class=\"data row7 col2\" >94.58 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col3\" class=\"data row7 col3\" >140.0 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col4\" class=\"data row7 col4\" >97.77 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col5\" class=\"data row7 col5\" >124.6 s</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col6\" class=\"data row7 col6\" >97.11 %</td>\n",
- " <td id=\"T_fdf852f6_3c24_11ea_a423_bda8ba485a9erow7_col7\" class=\"data row7 col7\" >100.1 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col2\" class=\"data row7 col2\" >95.31 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col3\" class=\"data row7 col3\" >168.0 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col4\" class=\"data row7 col4\" >97.84 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col5\" class=\"data row7 col5\" >155.6 s</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col6\" class=\"data row7 col6\" >97.06 %</td>\n",
+ " <td id=\"T_041f3e10_411b_11ea_a8da_77f37aa3c79brow7_col7\" class=\"data row7 col7\" >144.5 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd191e7f9d0>"
+ "<pandas.io.formats.style.Styler at 0x7f23a5086510>"
]
},
"metadata": {},
@@ -211,97 +211,97 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col2 {\n",
+ " #T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col2 {\n",
" background-color: yellow;\n",
- " } #T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col4 {\n",
+ " } #T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col4 {\n",
" background-color: yellow;\n",
- " } #T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col6 {\n",
+ " } #T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col6 {\n",
" background-color: yellow;\n",
- " }</style><table id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >95.53 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >99.6 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col4\" class=\"data row0 col4\" >96.86 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col5\" class=\"data row0 col5\" >105.9 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col6\" class=\"data row0 col6\" >95.53 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow0_col7\" class=\"data row0 col7\" >115.2 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >95.53 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >99.6 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col4\" class=\"data row0 col4\" >96.86 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col5\" class=\"data row0 col5\" >105.9 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col6\" class=\"data row0 col6\" >95.53 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow0_col7\" class=\"data row0 col7\" >115.2 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col2\" class=\"data row1 col2\" >96.55 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col3\" class=\"data row1 col3\" >85.7 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col4\" class=\"data row1 col4\" >97.38 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col5\" class=\"data row1 col5\" >109.0 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col6\" class=\"data row1 col6\" >96.18 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow1_col7\" class=\"data row1 col7\" >116.0 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col2\" class=\"data row1 col2\" >96.55 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col3\" class=\"data row1 col3\" >85.7 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col4\" class=\"data row1 col4\" >97.38 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col5\" class=\"data row1 col5\" >109.0 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col6\" class=\"data row1 col6\" >96.18 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow1_col7\" class=\"data row1 col7\" >116.0 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col2\" class=\"data row2 col2\" >97.13 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col3\" class=\"data row2 col3\" >154.3 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col4\" class=\"data row2 col4\" >97.78 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col5\" class=\"data row2 col5\" >147.9 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col6\" class=\"data row2 col6\" >97.47 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow2_col7\" class=\"data row2 col7\" >136.1 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col2\" class=\"data row2 col2\" >97.13 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col3\" class=\"data row2 col3\" >154.3 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col4\" class=\"data row2 col4\" >97.78 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col5\" class=\"data row2 col5\" >147.9 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col6\" class=\"data row2 col6\" >97.47 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow2_col7\" class=\"data row2 col7\" >136.1 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col2\" class=\"data row3 col2\" >96.44 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col3\" class=\"data row3 col3\" >161.1 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col4\" class=\"data row3 col4\" >98.20 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col5\" class=\"data row3 col5\" >155.9 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col6\" class=\"data row3 col6\" >97.70 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow3_col7\" class=\"data row3 col7\" >144.3 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col2\" class=\"data row3 col2\" >96.44 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col3\" class=\"data row3 col3\" >161.1 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col4\" class=\"data row3 col4\" >98.20 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col5\" class=\"data row3 col5\" >155.9 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col6\" class=\"data row3 col6\" >97.70 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow3_col7\" class=\"data row3 col7\" >144.3 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col2\" class=\"data row4 col2\" >95.84 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col3\" class=\"data row4 col3\" >80.9 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col4\" class=\"data row4 col4\" >96.71 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col5\" class=\"data row4 col5\" >106.2 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col6\" class=\"data row4 col6\" >95.21 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow4_col7\" class=\"data row4 col7\" >113.0 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col2\" class=\"data row4 col2\" >95.84 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col3\" class=\"data row4 col3\" >80.9 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col4\" class=\"data row4 col4\" >96.71 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col5\" class=\"data row4 col5\" >106.2 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col6\" class=\"data row4 col6\" >95.21 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow4_col7\" class=\"data row4 col7\" >113.0 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col2\" class=\"data row5 col2\" >95.34 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col3\" class=\"data row5 col3\" >85.2 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col4\" class=\"data row5 col4\" >96.90 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col5\" class=\"data row5 col5\" >105.6 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col6\" class=\"data row5 col6\" >94.32 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow5_col7\" class=\"data row5 col7\" >115.9 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col2\" class=\"data row5 col2\" >95.34 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col3\" class=\"data row5 col3\" >85.2 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col4\" class=\"data row5 col4\" >96.90 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col5\" class=\"data row5 col5\" >105.6 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col6\" class=\"data row5 col6\" >94.32 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow5_col7\" class=\"data row5 col7\" >115.9 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col2\" class=\"data row6 col2\" >96.90 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col3\" class=\"data row6 col3\" >152.2 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col4\" class=\"data row6 col4\" >97.85 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col5\" class=\"data row6 col5\" >147.3 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col6\" class=\"data row6 col6\" >97.61 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow6_col7\" class=\"data row6 col7\" >137.5 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col2\" class=\"data row6 col2\" >96.90 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col3\" class=\"data row6 col3\" >152.2 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col4\" class=\"data row6 col4\" >97.85 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col5\" class=\"data row6 col5\" >147.3 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col6\" class=\"data row6 col6\" >97.61 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow6_col7\" class=\"data row6 col7\" >137.5 s</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col2\" class=\"data row7 col2\" >95.40 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col3\" class=\"data row7 col3\" >160.8 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col4\" class=\"data row7 col4\" >97.76 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col5\" class=\"data row7 col5\" >155.1 s</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col6\" class=\"data row7 col6\" >96.74 %</td>\n",
- " <td id=\"T_fdff89fe_3c24_11ea_a423_bda8ba485a9erow7_col7\" class=\"data row7 col7\" >143.6 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col2\" class=\"data row7 col2\" >95.40 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col3\" class=\"data row7 col3\" >160.8 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col4\" class=\"data row7 col4\" >97.76 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col5\" class=\"data row7 col5\" >155.1 s</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col6\" class=\"data row7 col6\" >96.74 %</td>\n",
+ " <td id=\"T_0421d0d0_411b_11ea_a8da_77f37aa3c79brow7_col7\" class=\"data row7 col7\" >143.6 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd1913b6b10>"
+ "<pandas.io.formats.style.Styler at 0x7f23a3a8cfd0>"
]
},
"metadata": {},
@@ -314,8 +314,8 @@
"\n",
"\n",
"\n",
- "Report : report_009557\n",
- "Desc. : train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False \n",
+ "Report : report_041040\n",
+ "Desc. : train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True \n",
"\n"
]
},
@@ -323,97 +323,19 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col2 {\n",
+ " #T_042379e4_411b_11ea_a8da_77f37aa3c79brow0_col2 {\n",
" background-color: yellow;\n",
- " } #T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col4 {\n",
- " background-color: yellow;\n",
- " } #T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col6 {\n",
- " background-color: yellow;\n",
- " }</style><table id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_042379e4_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >95.54 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >95.3 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col4\" class=\"data row0 col4\" >96.81 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col5\" class=\"data row0 col5\" >105.4 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col6\" class=\"data row0 col6\" >95.68 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow0_col7\" class=\"data row0 col7\" >115.4 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col2\" class=\"data row1 col2\" >96.09 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col3\" class=\"data row1 col3\" >84.7 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col4\" class=\"data row1 col4\" >96.57 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col5\" class=\"data row1 col5\" >106.7 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col6\" class=\"data row1 col6\" >95.44 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow1_col7\" class=\"data row1 col7\" >119.0 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col2\" class=\"data row2 col2\" >95.99 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col3\" class=\"data row2 col3\" >160.5 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col4\" class=\"data row2 col4\" >97.84 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col5\" class=\"data row2 col5\" >149.2 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col6\" class=\"data row2 col6\" >97.59 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow2_col7\" class=\"data row2 col7\" >139.7 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col2\" class=\"data row3 col2\" >96.54 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col3\" class=\"data row3 col3\" >161.6 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col4\" class=\"data row3 col4\" >98.08 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col5\" class=\"data row3 col5\" >156.6 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col6\" class=\"data row3 col6\" >97.42 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow3_col7\" class=\"data row3 col7\" >144.2 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col2\" class=\"data row4 col2\" >95.72 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col3\" class=\"data row4 col3\" >79.0 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col4\" class=\"data row4 col4\" >96.80 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col5\" class=\"data row4 col5\" >105.7 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col6\" class=\"data row4 col6\" >94.93 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow4_col7\" class=\"data row4 col7\" >115.0 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col2\" class=\"data row5 col2\" >95.15 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col3\" class=\"data row5 col3\" >82.0 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col4\" class=\"data row5 col4\" >97.10 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col5\" class=\"data row5 col5\" >109.1 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col6\" class=\"data row5 col6\" >94.05 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow5_col7\" class=\"data row5 col7\" >121.6 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col2\" class=\"data row6 col2\" >95.64 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col3\" class=\"data row6 col3\" >156.4 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col4\" class=\"data row6 col4\" >97.68 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col5\" class=\"data row6 col5\" >146.0 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col6\" class=\"data row6 col6\" >97.93 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow6_col7\" class=\"data row6 col7\" >136.4 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col2\" class=\"data row7 col2\" >95.31 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col3\" class=\"data row7 col3\" >168.0 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col4\" class=\"data row7 col4\" >97.84 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col5\" class=\"data row7 col5\" >155.6 s</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col6\" class=\"data row7 col6\" >97.06 %</td>\n",
- " <td id=\"T_fe033f90_3c24_11ea_a423_bda8ba485a9erow7_col7\" class=\"data row7 col7\" >144.5 s</td>\n",
+ " <td id=\"T_042379e4_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_042379e4_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_042379e4_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >98.76 %</td>\n",
+ " <td id=\"T_042379e4_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >662.1 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd19090d610>"
+ "<pandas.io.formats.style.Styler at 0x7f23d0aa4a10>"
]
},
"metadata": {},
@@ -426,8 +348,8 @@
"\n",
"\n",
"\n",
- "Report : report_093384\n",
- "Desc. : train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False \n",
+ "Report : report_088809\n",
+ "Desc. : train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True \n",
"\n"
]
},
@@ -435,97 +357,19 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col4 {\n",
- " background-color: yellow;\n",
- " } #T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col2 {\n",
+ " #T_0424b5e8_411b_11ea_a8da_77f37aa3c79brow0_col2 {\n",
" background-color: yellow;\n",
- " } #T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col6 {\n",
- " background-color: yellow;\n",
- " }</style><table id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_0424b5e8_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >95.90 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >87.6 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col4\" class=\"data row0 col4\" >97.09 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col5\" class=\"data row0 col5\" >105.0 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col6\" class=\"data row0 col6\" >95.72 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow0_col7\" class=\"data row0 col7\" >116.7 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col2\" class=\"data row1 col2\" >96.02 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col3\" class=\"data row1 col3\" >88.6 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col4\" class=\"data row1 col4\" >97.40 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col5\" class=\"data row1 col5\" >103.7 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col6\" class=\"data row1 col6\" >95.26 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow1_col7\" class=\"data row1 col7\" >116.9 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col2\" class=\"data row2 col2\" >95.42 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col3\" class=\"data row2 col3\" >155.1 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col4\" class=\"data row2 col4\" >98.04 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col5\" class=\"data row2 col5\" >147.8 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col6\" class=\"data row2 col6\" >97.42 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow2_col7\" class=\"data row2 col7\" >139.8 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col2\" class=\"data row3 col2\" >96.53 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col3\" class=\"data row3 col3\" >162.2 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col4\" class=\"data row3 col4\" >98.04 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col5\" class=\"data row3 col5\" >155.5 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col6\" class=\"data row3 col6\" >97.74 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow3_col7\" class=\"data row3 col7\" >146.7 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col2\" class=\"data row4 col2\" >95.82 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col3\" class=\"data row4 col3\" >86.6 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col4\" class=\"data row4 col4\" >96.58 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col5\" class=\"data row4 col5\" >105.7 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col6\" class=\"data row4 col6\" >94.86 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow4_col7\" class=\"data row4 col7\" >117.0 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col2\" class=\"data row5 col2\" >95.38 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col3\" class=\"data row5 col3\" >82.6 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col4\" class=\"data row5 col4\" >96.51 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col5\" class=\"data row5 col5\" >107.2 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col6\" class=\"data row5 col6\" >93.97 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow5_col7\" class=\"data row5 col7\" >109.3 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col2\" class=\"data row6 col2\" >96.93 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col3\" class=\"data row6 col3\" >155.6 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col4\" class=\"data row6 col4\" >97.85 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col5\" class=\"data row6 col5\" >148.0 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col6\" class=\"data row6 col6\" >97.90 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow6_col7\" class=\"data row6 col7\" >137.5 s</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col2\" class=\"data row7 col2\" >95.68 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col3\" class=\"data row7 col3\" >161.0 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col4\" class=\"data row7 col4\" >98.00 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col5\" class=\"data row7 col5\" >156.1 s</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col6\" class=\"data row7 col6\" >96.75 %</td>\n",
- " <td id=\"T_fe09ca54_3c24_11ea_a423_bda8ba485a9erow7_col7\" class=\"data row7 col7\" >146.4 s</td>\n",
+ " <td id=\"T_0424b5e8_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_0424b5e8_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_0424b5e8_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >98.85 %</td>\n",
+ " <td id=\"T_0424b5e8_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >657.2 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd190942750>"
+ "<pandas.io.formats.style.Styler at 0x7f23a3a32550>"
]
},
"metadata": {},
@@ -538,8 +382,8 @@
"\n",
"\n",
"\n",
- "Report : report_094801\n",
- "Desc. : train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True \n",
+ "Report : report_093384\n",
+ "Desc. : train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False \n",
"\n"
]
},
@@ -547,19 +391,97 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fe0d0700_3c24_11ea_a423_bda8ba485a9erow0_col2 {\n",
+ " #T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col4 {\n",
+ " background-color: yellow;\n",
+ " } #T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col2 {\n",
+ " background-color: yellow;\n",
+ " } #T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col6 {\n",
" background-color: yellow;\n",
- " }</style><table id=\"T_fe0d0700_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fe0d0700_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fe0d0700_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe0d0700_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >98.90 %</td>\n",
- " <td id=\"T_fe0d0700_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >652.0 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >95.90 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >87.6 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col4\" class=\"data row0 col4\" >97.09 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col5\" class=\"data row0 col5\" >105.0 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col6\" class=\"data row0 col6\" >95.72 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow0_col7\" class=\"data row0 col7\" >116.7 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col2\" class=\"data row1 col2\" >96.02 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col3\" class=\"data row1 col3\" >88.6 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col4\" class=\"data row1 col4\" >97.40 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col5\" class=\"data row1 col5\" >103.7 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col6\" class=\"data row1 col6\" >95.26 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow1_col7\" class=\"data row1 col7\" >116.9 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col2\" class=\"data row2 col2\" >95.42 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col3\" class=\"data row2 col3\" >155.1 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col4\" class=\"data row2 col4\" >98.04 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col5\" class=\"data row2 col5\" >147.8 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col6\" class=\"data row2 col6\" >97.42 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow2_col7\" class=\"data row2 col7\" >139.8 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col2\" class=\"data row3 col2\" >96.53 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col3\" class=\"data row3 col3\" >162.2 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col4\" class=\"data row3 col4\" >98.04 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col5\" class=\"data row3 col5\" >155.5 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col6\" class=\"data row3 col6\" >97.74 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow3_col7\" class=\"data row3 col7\" >146.7 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col2\" class=\"data row4 col2\" >95.82 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col3\" class=\"data row4 col3\" >86.6 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col4\" class=\"data row4 col4\" >96.58 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col5\" class=\"data row4 col5\" >105.7 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col6\" class=\"data row4 col6\" >94.86 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow4_col7\" class=\"data row4 col7\" >117.0 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col2\" class=\"data row5 col2\" >95.38 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col3\" class=\"data row5 col3\" >82.6 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col4\" class=\"data row5 col4\" >96.51 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col5\" class=\"data row5 col5\" >107.2 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col6\" class=\"data row5 col6\" >93.97 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow5_col7\" class=\"data row5 col7\" >109.3 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col2\" class=\"data row6 col2\" >96.93 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col3\" class=\"data row6 col3\" >155.6 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col4\" class=\"data row6 col4\" >97.85 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col5\" class=\"data row6 col5\" >148.0 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col6\" class=\"data row6 col6\" >97.90 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow6_col7\" class=\"data row6 col7\" >137.5 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col2\" class=\"data row7 col2\" >95.68 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col3\" class=\"data row7 col3\" >161.0 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col4\" class=\"data row7 col4\" >98.00 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col5\" class=\"data row7 col5\" >156.1 s</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col6\" class=\"data row7 col6\" >96.75 %</td>\n",
+ " <td id=\"T_0426fe66_411b_11ea_a8da_77f37aa3c79brow7_col7\" class=\"data row7 col7\" >146.4 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd1908fa590>"
+ "<pandas.io.formats.style.Styler at 0x7f23a3a32d90>"
]
},
"metadata": {},
@@ -572,7 +494,7 @@
"\n",
"\n",
"\n",
- "Report : report_041040\n",
+ "Report : report_094801\n",
"Desc. : train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True \n",
"\n"
]
@@ -581,19 +503,19 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fe10bbb6_3c24_11ea_a423_bda8ba485a9erow0_col2 {\n",
+ " #T_04292d6c_411b_11ea_a8da_77f37aa3c79brow0_col2 {\n",
" background-color: yellow;\n",
- " }</style><table id=\"T_fe10bbb6_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_04292d6c_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fe10bbb6_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fe10bbb6_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe10bbb6_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >98.76 %</td>\n",
- " <td id=\"T_fe10bbb6_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >662.1 s</td>\n",
+ " <td id=\"T_04292d6c_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_04292d6c_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_04292d6c_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >98.90 %</td>\n",
+ " <td id=\"T_04292d6c_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >652.0 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd190913350>"
+ "<pandas.io.formats.style.Styler at 0x7f23a3a32dd0>"
]
},
"metadata": {},
@@ -606,8 +528,8 @@
"\n",
"\n",
"\n",
- "Report : report_088809\n",
- "Desc. : train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True \n",
+ "Report : report_2020_01_20_17h22m23s\n",
+ "Desc. : train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False \n",
"\n"
]
},
@@ -615,19 +537,97 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_fe146810_3c24_11ea_a423_bda8ba485a9erow0_col2 {\n",
+ " #T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col4 {\n",
+ " background-color: yellow;\n",
+ " } #T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col6 {\n",
" background-color: yellow;\n",
- " }</style><table id=\"T_fe146810_3c24_11ea_a423_bda8ba485a9e\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v2_Accuracy</th> <th class=\"col_heading level0 col3\" >v2_Duration</th> </tr></thead><tbody>\n",
+ " } #T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col2 {\n",
+ " background-color: yellow;\n",
+ " }</style><table id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79b\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Dataset</th> <th class=\"col_heading level0 col1\" >Size</th> <th class=\"col_heading level0 col2\" >v1_Accuracy</th> <th class=\"col_heading level0 col3\" >v1_Duration</th> <th class=\"col_heading level0 col4\" >v2_Accuracy</th> <th class=\"col_heading level0 col5\" >v2_Duration</th> <th class=\"col_heading level0 col6\" >v3_Accuracy</th> <th class=\"col_heading level0 col7\" >v3_Duration</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_fe146810_3c24_11ea_a423_bda8ba485a9erow0_col0\" class=\"data row0 col0\" >set-48x48-RGB</td>\n",
- " <td id=\"T_fe146810_3c24_11ea_a423_bda8ba485a9erow0_col1\" class=\"data row0 col1\" >2736.36 Mo</td>\n",
- " <td id=\"T_fe146810_3c24_11ea_a423_bda8ba485a9erow0_col2\" class=\"data row0 col2\" >98.85 %</td>\n",
- " <td id=\"T_fe146810_3c24_11ea_a423_bda8ba485a9erow0_col3\" class=\"data row0 col3\" >657.2 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col2\" class=\"data row0 col2\" >95.39 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col3\" class=\"data row0 col3\" >58.2 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col4\" class=\"data row0 col4\" >97.32 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col5\" class=\"data row0 col5\" >52.6 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col6\" class=\"data row0 col6\" >95.16 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow0_col7\" class=\"data row0 col7\" >50.3 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col2\" class=\"data row1 col2\" >96.11 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col3\" class=\"data row1 col3\" >47.2 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col4\" class=\"data row1 col4\" >97.77 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col5\" class=\"data row1 col5\" >55.2 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col6\" class=\"data row1 col6\" >96.52 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow1_col7\" class=\"data row1 col7\" >52.3 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col0\" class=\"data row2 col0\" >set-48x48-L</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col1\" class=\"data row2 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col2\" class=\"data row2 col2\" >95.98 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col3\" class=\"data row2 col3\" >135.2 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col4\" class=\"data row2 col4\" >97.88 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col5\" class=\"data row2 col5\" >118.3 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col6\" class=\"data row2 col6\" >97.38 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow2_col7\" class=\"data row2 col7\" >92.0 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col0\" class=\"data row3 col0\" >set-48x48-RGB</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col1\" class=\"data row3 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col2\" class=\"data row3 col2\" >96.29 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col3\" class=\"data row3 col3\" >138.7 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col4\" class=\"data row3 col4\" >97.95 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col5\" class=\"data row3 col5\" >124.7 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col6\" class=\"data row3 col6\" >97.53 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow3_col7\" class=\"data row3 col7\" >98.5 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col0\" class=\"data row4 col0\" >set-24x24-L-LHE</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col1\" class=\"data row4 col1\" >228.77 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col2\" class=\"data row4 col2\" >95.79 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col3\" class=\"data row4 col3\" >44.3 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col4\" class=\"data row4 col4\" >96.41 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col5\" class=\"data row4 col5\" >53.1 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col6\" class=\"data row4 col6\" >95.72 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow4_col7\" class=\"data row4 col7\" >50.9 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col0\" class=\"data row5 col0\" >set-24x24-RGB-HE</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col1\" class=\"data row5 col1\" >684.39 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col2\" class=\"data row5 col2\" >95.35 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col3\" class=\"data row5 col3\" >46.0 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col4\" class=\"data row5 col4\" >96.80 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col5\" class=\"data row5 col5\" >54.5 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col6\" class=\"data row5 col6\" >94.29 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow5_col7\" class=\"data row5 col7\" >52.5 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col0\" class=\"data row6 col0\" >set-48x48-L-LHE</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col1\" class=\"data row6 col1\" >913.90 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col2\" class=\"data row6 col2\" >96.72 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col3\" class=\"data row6 col3\" >131.8 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col4\" class=\"data row6 col4\" >97.80 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col5\" class=\"data row6 col5\" >117.9 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col6\" class=\"data row6 col6\" >97.16 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow6_col7\" class=\"data row6 col7\" >92.8 s</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col0\" class=\"data row7 col0\" >set-48x48-RGB-HE</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col1\" class=\"data row7 col1\" >2736.36 Mo</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col2\" class=\"data row7 col2\" >94.58 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col3\" class=\"data row7 col3\" >140.0 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col4\" class=\"data row7 col4\" >97.77 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col5\" class=\"data row7 col5\" >124.6 s</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col6\" class=\"data row7 col6\" >97.11 %</td>\n",
+ " <td id=\"T_042c0d66_411b_11ea_a8da_77f37aa3c79brow7_col7\" class=\"data row7 col7\" >100.1 s</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fd1915413d0>"
+ "<pandas.io.formats.style.Styler at 0x7f23a3a4d590>"
]
},
"metadata": {},
diff --git a/IMDB/01-Embedding-Keras.ipynb b/IMDB/01-Embedding-Keras.ipynb
index 6b75b2d2b65b7eb32d70ff14f8dd563009d6a0fc..1d5df5e1007f686a715596646ca35c374e6686af 100644
--- a/IMDB/01-Embedding-Keras.ipynb
+++ b/IMDB/01-Embedding-Keras.ipynb
@@ -17,8 +17,6 @@
"Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
"For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
"\n",
- "https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks\n",
- "\n",
"What we're going to do:\n",
"\n",
" - Retrieve data\n",
@@ -32,12 +30,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1/ Init python stuff"
+ "## Step 1 - Init python stuff"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -45,8 +43,8 @@
"output_type": "stream",
"text": [
"IDLE 2020 - Practical Work Module\n",
- " Version : 0.2\n",
- " Run time : Friday 24 January 2020, 09:13:23\n",
+ " Version : 0.2.4\n",
+ " Run time : Monday 27 January 2020, 22:38:12\n",
" Matplotlib style : fidle/talk.mplstyle\n",
" TensorFlow version : 2.0.0\n",
" Keras version : 2.2.4-tf\n"
@@ -54,13 +52,20 @@
}
],
"source": [
- "import tensorflow as tf\n",
"import numpy as np\n",
- "from tensorflow import keras\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras as keras\n",
+ "import tensorflow.keras.datasets.imdb as imdb\n",
+ "\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
+ "import seaborn as sns\n",
+ "\n",
+ "import os,h5py,json\n",
"\n",
"import fidle.pwk as ooo\n",
+ "from importlib import reload\n",
"\n",
"ooo.init()"
]
@@ -69,14 +74,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2/ Retrieve data\n",
+ "## Step 2 - Retrieve data\n",
"\n",
"**From Keras :**\n",
"This IMDb dataset can bet get directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
"\n",
"Due to their nature, textual data can be somewhat complex.\n",
"\n",
- "#### Data structure : \n",
+ "### 2.1 - Data structure : \n",
"The dataset is composed of 2 parts: **reviews** and **opinions** (positive/negative), with a **dictionary**\n",
"\n",
" - dataset = (reviews, opinions)\n",
@@ -90,7 +95,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2.1/ Get dataset\n",
+ "### 2.2 - Get dataset\n",
"For simplicity, we will use a pre-formatted dataset. \n",
"See : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data \n",
"\n",
@@ -100,15 +105,15 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "imdb = keras.datasets.imdb\n",
+ "vocab_size = 10000\n",
"\n",
"# ----- Retrieve x,y\n",
"#\n",
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = 10000, \n",
+ "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,\n",
" skip_top = 0,\n",
" maxlen = None,\n",
" seed = 42,\n",
@@ -119,13 +124,14 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ " Max(x_train,x_test) : 9999\n",
" x_train : (25000,) y_train : (25000,)\n",
" x_test : (25000,) y_test : (25000,)\n",
"\n",
@@ -136,6 +142,7 @@
}
],
"source": [
+ "print(\" Max(x_train,x_test) : \", ooo.rmax([x_train,x_test]) )\n",
"print(\" x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
"print(\" x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))\n",
"\n",
@@ -146,18 +153,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2.2/ Have a look for humans (optional)\n",
+ "### 2.3 - Have a look for humans (optional)\n",
"When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
"So, we shifted the dataset by 3 with the parameter index_from=3"
]
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "# ---- Retrieve dictionary {word:index}\n",
+ "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
"\n",
"word_index = imdb.get_word_index()\n",
"\n",
@@ -171,7 +178,7 @@
"\n",
"# ---- Create a reverse dictionary : {index:word}\n",
"\n",
- "index_word = {(index):word for word,index in word_index.items()} \n",
+ "index_word = {index:word for word,index in word_index.items()} \n",
"\n",
"# ---- Add a nice function to transpose :\n",
"#\n",
@@ -181,7 +188,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -211,17 +218,17 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2.3/ Have a look for neurons"
+ "### 2.4 - Have a look for neurons"
]
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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fNQ/3b/ZndoWdRfk/er/m8cOG5EzwrRWU+2VgcsOtVqTHUJXmm4R/bR5+vde3OM2MOv/VPDyMcoX8PsoQmqFExHITXCgY6hyjrNB9EGVSgf+bmb8ZLz4zL2PsQ9InxluJNiJWaH0rJE0bk3tpFoqIVSJi64g4gbKwzArANYwlaIP4WER8PSJe24yZ7bT9hIg4ijIWP4Efduoy81bKmF94+GwZU2Eh5Yryf0bE45v+rRoRn2DsBr4DeiQgnQR1v4h4Tefr9ojYnDJL0HhJU2eWmx0iYqjhO00/Osna9s1S9qs3z71687p2horsN8TsLUvqRMoKmwCnRsRWnYQqIl5BeQ8tRzn3SX/zs4Q+yNjMMOdERHt4DRGxfkTsTRmP/YI+bXSG37yKsffnsENyOtNrdj48vi0iToqIh6YfjYi5EfGiiDiaPrM5MZas95risv24Xz3AHhFxekTsHBEPDZVp/g18iLLeApRvLYZ1G2XY3XujTMVJRPwfyjj1Z1CGw3x8nOM7w4Je2Oy/03wjMaxnAr+NiL0jYoPW+3K5iHg9Yx80JjzH5lux/6LkPgdm5ikD9mEvymq0zwLObv59dP5mLBMRz4yI/SiLbU3JdJ3SUIadGN/NzW1mbIwt0nQviy+hfhdji74kZRjJibRWr+xqZ0t6L7h0ZFc7tzG2emZn+1CP9g5s1Xeukl8F7N2KOZMBFpjpF9fuM+Vm3855/o2xFTsT+EyfdldjbNXLpCQqdzY/X00ZI9xvEaunM7bs/H2UmUOuosx5Pu5r2qo/pPXcD/To96F9jht3sSgGXESsz7HrN+fReY67ut5LV9NaHXSYfk3wvAfQY6GoPrGbMLZYUOf1v4mxlXk720v7HB+UFXjbr/1ak+0b5YPag12vWffv8so+x76m67jluurX7jqnZ/doY++umDsZW8W3s31+kr+PLwOnMPY3pt3u/cCOA7R1QeuYVw373mja2LjrfBZRhle1X+MLaFYybh13Jl1/O4BdW8fcwOJ/N7u3dbra24Yy1Khz/D3Ne+9eFu/fUyZznm5uo9y8ci898i3H2HRsa1D+07mCMjvIhynLvL85xx9X2ssRwHsoV+r+l5IYzaF8A3Ai8JLM/FiP4w4CPkC5EhyUmzSfwhQN08nMIymJUmcYwyLK1+i7ZOaefY65hTJ/9TGUbxqWoSQM8yk3513b67jm2MsoC9V8n/KB54mU8+s3fr9XG/tRbnD+H0qCsHLz/N8CtsrMfcc5fEpkmfp0I8rvr72Q1W8p0w8+JzP/d2n3qy3L/R5Pp7y/zqFMCboqZWjGhcAngE0ys9+Np8ni4+t/kpl/6RU7YH8Oobxmx9CshEyZEvEvlEWN9mDsZthuZzN2/8Y52XVDZ2Zex9gQrr+x+O+k42uU+2lOpHxjcR/lvfQXyntp+8x812TOjZKovpFyZfxSyrdZtwDfAf4hM/97gDY6V8Y7r8dkXEr5xvFzNFNgUqY8vZ2ycuxewAsz8/a+LfT2eMaf2nKxefQz83vABpQP5hdR/s6s2vTjHMrwqmdkawEsabpE+VsnSZI0OhHxQ8o6Ap/IzA9Od3+kRwuTe0mSNFLNbFWdb3o2yMVX1pU0hRyWI0mSRqaZrWg+ZZjSd0zspaXLK/eSJGmJNbMV7U25D2UOZVz68zPz99PaMelRxiv3kiRpFFal3Fz+AOUm01ea2EtLn1fuR6SqqgSo63pprMopSZIkPcxjprsDs5CfliRJkjSV+l5MdliOJEmSNEuY3EuSJEmzhMm9JEmSNEuY3EuSJEmzhMm9JEmSNEuY3EuSJEmzhMm9JEmSNEuY3EuSJEmzhMm9JEmSNEuY3EuSJEmzhMm9JEmSNEuY3EuSJEmzhMm9JEmSNEuY3EuSJEmzxGOmuwOaflsffNqkjjv9I9uNuCeSJElaEl65lyRJkmYJk3tJkiRpljC5lyRJkmYJk3tJkiRpljC5lyRJkmYJZ8vRpDnLjiRJ0swy467cR8SKEXFlRGREfKZH/YYRcWpE3BIRd0XE2RHx8j5trRIR8yNiQUQsiojfRcQeERE9YpeJiH0i4rIm9pqIOCwiVpqK85QkSZJGbcYl98BBwBq9KiJiPeAcYAvgk8D7gZWB0yNiq67Y5YEfAu8GTgT2Av4A1MBHezR/BHA48Psm9mTgPcC3I2Imvk6SJEnSYmbUsJyIeB6wN/D/gMN6hBwKrAo8PzMvbo75CvA74OiIeHpmZhO7G7AJ8J7MnN+UHRsR3wA+FBFfysyrmzaeSUnoT8nM17f6cyVwFLAj8LXRnq0kSZI0WjPminRELAscC3wfOKVH/UrAa4AzO4k9QGbeCXwB2ICSzHfsDCxs2mw7ElgOeHOrbCcgmrq2Y5s2dhn+jCRJkqSla8Yk98A+wNOBPfvUPweYA5zbo+68Zr8JlPHzwPOAX2Xmoq7Y84EHWfyDwCZN2fntwObYi7tiJUmSpBlpRiT3EfFU4EDgoMy8qk/Y2s1+QY+6Ttm8Zr8asEKv2My8B7i5Fdtp+6amrlfbazRj+Hv1ffeIuLBPnyVJkqSlZkYk98BngSspN7T2s2Kz75WAL+qKGS+2E79i6/GKE8S221xMZh6TmS/oc6wkSZK01Ez7DbURsQvwSuAlmXnfOKELm/2cHnVzu2LGi+3EL2w9Xgg8fpzYdpuSJEnSjDStV+4jYg7lav13gesjYv2IWB94ShOySlO2KnBdUzavR1Odss4wnFuAu3vFNs+5OosP2bmOMvSm14eBeZQhO/cOfmaSJEnS0jfdw3JWANYEtgMub21nNvW7NI93Ay6hDJ3Zokc7mzf7CwEy80HgIuC5PRL2TSnn3R4nf0FTtmk7MCLmAht3xUqSJEkz0nQn93cBb+yxVU3995vH32qmvPw2sGVEbNRpICJWpiT/l7P4bDcnUMbJ7971nHsD9wMntcpOBLKpa3tn08bxkzs9SZIkaemZ1jH3zRj7r3eXR8S6zY9/ysx2/b7AK4AfRMQRwO2UBHwesF1rASsoc9S/DTi8ae9SYFvgdcAhmXllqx+XRMTRwJ4RcQplmNAzKCvUnoULWEmSJOkRYNpvqB1GZv4xIl4IfBz4ILA8ZfjNP2XmGV2x90bEVsAhlEWqVgf+RFmJ9ugeze8NXEW50r8dcBMwH9i/GeYjSZIkzWgzMrlv5rqPPnWXAtsP2M6tlEWx+i2M1Y59ADis2SRJkqRHnOkecy9JkiRpREzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUzuJUmSpFnC5F6SJEmaJaY9uY+IDSPi+Ii4NCJui4iFEXFZRBweEWt1xR4QEdln+7cebS8TEfs07S2KiGsi4rCIWKlPX7aNiHMi4q6I+FtEnBwRT52qc5ckSZJG6THT3QHgScBawDeBa4H7gWcDuwM7RsTGmfnXrmP2AW7qKvtlj7aPAN7TtH0Y8Izm8XMjYqvMfLATGBE7AF8Hfg28H1gF2Bv4eUS8IDOvW6KzlCRJkqbYtCf3mfkj4Efd5RHxU+AkYFfgk13Vp2bmVeO1GxHPBPYCTsnM17fKrwSOAnYEvtaULQfMB64BXpyZdzbl36N8aDiA8mFDkiRJmrGmfVjOOK5u9qv1qoyIx0bEeB9OdgICOLKr/FhgIbBLq+ylwNrAFzqJPUBmXgycCby5+QAgSZIkzVgzJrmPiLkRsUZEPCkiXgl8vqn6bo/w3wC3AYuaMfLb9IjZBHgQOL9dmJmLgIub+nYswLk92jkPeCywwcAnI0mSJE2DGZPcA7sBN1KGxpwOrArskplnt2JuBY6hDLfZHtgXeApwWkTs2tXe2sBNmXlPj+daAKwREcu3YjvlvWIB5g11NpIkSdJSNpOS+1OBfwReBxxESeTXbAdk5pGZ+a7M/HJmfiszPwU8B7gBOCIiVm6Frwj0SuwBFrVi2vte8d2xi4mI3SPiwv6nJUmSJC0dMya5z8xrM/OMzDw1Mz8KvBX4RETsO8FxNwOfo1zp/4dW1UJgTp/D5rZi2vte8d2x3c9/TGa+YLw+SpIkSUvDjEnuu2Xmb4BfAdUA4Vc1+zVaZddRht70StjnUYbs3NuK7ZT3ioXeQ3YkSZKkGWPGJveNFYDHDRD3tGZ/Q6vsAsr5bdoOjIi5wMbAhV2xAFv0aHtz4HbgfwfohyRJkjRtpj25j4gn9il/GfAsymw1RMRjImKVHnHrAHsANwPntKpOBJKyEFXbOynj549vlZ0F/AXYrT1uPyI2ArYETs7M+4Y6MUmSJGkpm/ZFrIDPRsRawI8pc9vPBZ5PWWTqDuB9TdzKwJURcSpwKXALsCFllp2VgZ0y8+5Oo5l5SUQcDewZEadQptTsrFB7Fs0CVk3sfRHxXsoHgrMj4ljK9Jf7UGbw+egUnbskSZI0MjMhuT+BcvPsWyiz4yQlyf888KnM/HMTdzfwDWAz4LWUhP4m4Azgk5l5Pg+3N2U8/u7Adk38fGD/zHywHZiZJ0fE3cB+wKcpM+f8CPhAZjreXpIkSTPetCf3mXkScNIAcfdQrtIP0/YDwGHNNkj8d4DvDPMckiRJ0kwx7WPuJUmSJI2Gyb0kSZI0S5jcS5IkSbOEyb0kSZI0S5jcS5IkSbOEyb0kSZI0S5jcS5IkSbOEyb0kSZI0S5jcS5IkSbOEyb0kSZI0S5jcS5IkSbOEyb0kSZI0S5jcS5IkSbOEyb0kSZI0S5jcS5IkSbOEyb0kSZI0Swyc3FdV9cWqql4zQcyrqqr64pJ3S5IkSdKwhrlyvyuw8QQxGwFvnXRvJEmSJE3aqIflzAEeGHGbkiRJkgYwbHKf/SqqqpoDvAS4fol6JEmSJGlSHjNeZVVVV3QV7VNV1dt6hC4LrEm5cv+5EfVNkiRJ0hDGTe4pV/Y7V+sTiGbrdh9wCfAj4JCR9U6SJEnSwMZN7uu6Xrfzc1VVDwJH1HV90FR3SpIkSdLwJrpy3/Yy4Kop6ockSZKkJTTwDbV1XZ9V1/XVo+5ARGwYEcdHxKURcVtELIyIyyLi8IhYq0/8qRFxS0TcFRFnR8TL+7S9SkTMj4gFEbEoIn4XEXtExMOGFkXEMhGxT/PciyLimog4LCJWGvU5S5IkSVNhmCv3AFRV9QJgU2A1yo203bKu64OHaPJJwFrAN4FrgfuBZwO7AztGxMaZ+VeAiFgPOKeJ+SRwG/BO4PSI2CYzz+g0GhHLAz8EngvMBy4FtgFq4AnAAV39OAJ4T9OPw4BnNI+fGxFbZeaDQ5yTJEmStNQNnNxXVfVY4BTK8JxeN9V2JDBwcp+ZP6LciLuYiPgpcBJl8axPNsWHAqsCz8/Mi5u4rwC/A46OiKdnZucG4N2ATYD3ZOb8puzYiPgG8KGI+FJmXt208UxgL+CUzHx9qw9XAkcBOwJfG/ScJEmSpOkwzDz3nwJeDvwMeDvwj5REv3vrOURmEjpDgFYDaIbHvAY4s5PYA2TmncAXgA0oyXzHzsBC4Niudo8ElgPe3CrbifKB5ciu2GObNnZZkhORJEmSloZhhuVsD1wEvKyu65EPUYmIucDKwFzg74FPNFXfbfbPocyjf26Pw89r9psA50fEMsDzgIsyc1FX7PnAgyz+QWCTpuz8dmBmLoqIi7tiJUmSpBlpmCv3qwA/mYrEvrEbcCNwDXA6ZfjNLpl5dlO/drNf0OPYTtm8Zr8asEKv2My8B7i5Fdtp+6amrlfbazRj+B8mInaPiAv7nZQkSZK0tAyT3F9OuRF1qpxKGerzOuAg4FbKqrcdKzb7Xgn4oq6Y8WI78Su2Hq84QWy7zcVk5jGZ+YI+x0qSJElLzTDJ/dHAq6uqmjdh5CRk5rWZeUZmnpqZHwXeCnwiIvZtQhY2+zk9Dp/bFTNebCd+Yevxwgli221KkiRJM9IwY+6/R7lZ9udVVR0I/JJydf1h6rr+85J2LDN/ExG/AirKLDnXNVW9Plx0yjrDcG4B7u4VGxFzgNWBs1rF1wF/H3FdAkcAACAASURBVBFzegzNmUcZsnPvpE5EkiRJWkqGSe6vokxzGZTZafrJIdsdzwrA45qfL6EMndmiR9zmzf5CgMx8MCIuosxR352wb0r5xqI9Tv4C4JVNXWeMf+cm342Bny75qUiSJElTa5gk/CuUxH2kIuKJmXl9j/KXAc8CzoQy5WVEfBvYISI2ysxfN3ErU27GvZzFZ7s5AXghZTGs+a3yvSmLYJ3UKjsR+FBTd3ar/J2UsfbHL8EpSpIkSUvFwMl9Xde7TlEfPhsRawE/psxtPxd4PmXhqDuA97Vi9wVeAfwgIo4Abqck4POA7VoLWEGZo/5twOERsS5lhdptKTfsHpKZV3YCM/OSiDga2DMiTqFMv9lZofYsXMBKkiRJjwCjGj6zJE6g3Dz7FsrsOElJ8j8PfCozHxq/n5l/jIgXAh8HPggsT5l7/58y84x2o5l5b0RsBRxCWaRqdeBPlJVoj+7Rj70pQ492B7YDbqJc8d8/M6dq+k9JkiRpZKY9uc/Mk1h8iMxE8ZdSFtQaJPZWYM9mmyj2AeCwZpMkSZIecQZO7quq+uKAoVnX9Tsm2R9JkiRJkzTMlftdJ6jvzKSTgMm9JEmStJQNk9w/tU/5qsAmwEeAcyhj4SVJkiQtZcPMlnN1n6qrgV9XVXU68BvgDOA/R9A3SZIkSUNYZlQN1XV9DfBt4L2jalOSJEnS4EaW3DduAJ424jYlSZIkDWBkyX1VVcsCLwduG1WbkiRJkgY3zFSYLxmnjXUoq8FuDHxhBP3SLLb1wadN6rjTP7LdiHsiSZI0uwwzW86ZlGku+wngp8D7l6RDkiRJkiZnmOT+IHon9w8CtwDn13V9/kh6JUmSJGlow0yFecAU9kOSJEnSEhr1bDmSJEmSpskww3IAqKpqRWAH4LmU1WlvAy4CvlnX9V2j7Z4kSZKkQQ115b6qqm0pK9J+GdiHMkPO3s3jq6qqetXIeyhJkiRpIAMn91VVPQ84hXK1/njg7cA2zf74pvzrVVU9fwr6KUmSJGkCwwzL+TBltpwX13V9XlfdcVVVHU2ZLvNDwOtH0z1JkiRJgxpmWM6LgZN7JPYA1HX9C+DrTZwkSZKkpWyY5H4V4JoJYv4MPHby3ZEkSZI0WcMk99cBm04Q8wLgL5PvjiRJkqTJGmbM/XeBd1dV9UHgU3VdP9CpqKpqGcrsOVsBnxttFyVJkiQNYpjk/mDgtcC/A++qqupsylX6JwIvAtYFrgcOGXEfJUmSJA1g4GE5dV1fD7wQOAN4CrAL8H7gLcBTm/IX1XXtsBxJkiRpGgy1Qm1d11cBW1dVNY+yQu0qlBVqf1XX9YLRd0+SJEnSoIZK7juaRH4kyXxEbED5FuCVwHrAXOBPwMnAkZl5Vyv2AOCjfZp6f2Z+uqvtZYD3Au+iDBu6ETgJ2L/dbit+W2A/YCPgHuBHwP/LzCsnf4aSJEnS0jFwcl9V1ZrAMyhX6e/oUf9YYGPg93Vd3zREH94O/AvwLcpKt/cBL6OM3X9TRGyemXd3HbMP0P0cv+zR9hHAe4BvAoc1/X8P8NyI2CozH+wERsQOlHn6f00ZbrQKsDfw84h4QWZeN8Q5SZIkSUvdMFfu9wPeBqzVp/4B4NvAF4D3DdHu14FDM/O2VtnnIuJyyqq47wA+03XMqZl51XiNRsQzgb2AUzLz9a3yK4GjgB2BrzVlywHzKfP4vzgz72zKv0f50HAAsPsQ5yRJkiQtdcPMc/+PwA/qun7YcBaApvwHwNbDdCAzL+xK7DtObPbP6nVcRDw2Isb7cLITEMCRXeXHAgspQ4E6XgqsDXyhk9g3fbsYOBN4c/MBQJIkSZqxhknu16GMhR/PFU3cKDyp2d/Qo+43lBt5F0XEORGxTY+YTYAHgfPbhZm5CLi4qW/HApzbo53zKKvubjB41yVJkqSlb5jkPoHlJ4hZHlh28t0pImJZYH/gfpqhM41bgWMow222B/alTMt5WkTs2tXM2sBNmXlPj6dYAKwREcu3YjvlvWIB5vXp6+4RceG4JyRJkiQtBcMk939gnCE3VVVFU//HJe0UZSjN5pRZbf7QKczMIzPzXZn55cz8VmZ+CngO5er+ERGxcquNFSkz3vSyqBXT3veK745dTGYek5kvmPCMJEmSpCk2THL/deDpVVV9pqqqFdoVzePPABsyNlZ+UiLiYGBP4JjMPHSi+My8GfgcsCrwD62qhcCcPofNbcW0973iu2MlSZKkGWmY2XKOotykugfw2qqqfkoZsjIPeAllaMuvefgNrANr5rHfD/gS8O4hDr2q2a/RKrsO+PuImNNjaM48ypCde1uxnfJLe8TCiOb1lyRJkqbKwFfu67q+G9iScmX+iZSpJN/X7J9IGRv/siZuaBHxUcoCVV8BdsvMHOLwpzX79s23F1DOb9Ou55lLmY//wq5YgC16tL05cDvwv0P0R5IkSVrqhlqhtq7rW4Gdq6p6L2WGmVUpN7meP+TCVYuJiP0pc8l/FXhbe3GpVsxjgJW6p82MiHUo3ybcDJzTqjoR+BBlIaqzW+XvpIyfP75VdhbwF2C3iDiiNc/9RpQPNF/KzPsme36SJEnS0jBUct9R1/WNwHdH0YGI+BfgQODPwBnAzhHRDrkhM38IrAxcGRGnUobO3EIZ479bU7dTeyXbzLwkIo4G9oyIU5r+dlaoPYvWLDyZeV9EvJfygeDsiDiWMv3lPsCNlG8UJEmSpBltUsn9iHXmmH8y8OUe9WcBPwTuBr4BbAa8lpLQ30T5QPDJzDy/x7F7U8bj7w5s18TPp8zCs9i3A5l5ckTcTRnz/2nKzDk/Aj6QmY63lyRJ0ow37cl9Zu4K7DpA3D2Uq/TDtP0AcFizDRL/HeA7wzyHJEmSNFMMMxWmJEmSpBnM5F6SJEmaJUzuJUmSpFnC5F6SJEmaJUaS3FdVtVpVVSuNoi1JkiRJkzPwbDlVVb0C2Bo4tK7rW5qyxwMnAy8C7q+q6ui6rv91SnoqSZIkaVzDXLnfC9ihk9g3Pg28GPgjZYXY91ZV9aYR9k+SJEnSgIZJ7jcCftZ5UFXVCsAbgB/Wdb0hZbXYa4B3j7SHkiRJkgYyTHL/eOC61uPNgLnAcQB1Xd9BWQBqw1F1TpIkSdLghknu7wFWaD1+MZDAT1tltwOPG0G/JEmSJA1pmOT+SuDlrcevBy6v63pBq2wd4KZRdEySJEnScAaeLQf4MnBkVVW/AO4Fng0c2BXzPOAPI+qbJEmSpCEMk9x/FtgceDMQwLeBT3Qqq6raFHgGcMIoOyhJkiRpMAMn93Vd3wfsXFXVu4FsbqBtuwJ4LnDV6LonSZIkaVDDXLkHoK7r2/uU34Tj7SVJkqRpM8wNtZIkSZJmsHGv3FdVdUVXUdZ1vd4U9keSJEnSJE00LOfPlLnsJUmSJM1w4yb3dV1vuZT6IUmSJGkJOeZekiRJmiUGTu6rqtq1qqp1prIzkiRJkiZvmKkwvwhkc5Ptj4AfAz9upsCUJEmSNM2GSe73BF4OvBTYHXgnJdn/HWPJ/lk9FreSJEmStBQMPCynLt4APB54PvAB4IfA/wH2Bv4HuLmqqnOG6UBEbBARB0XEeRFxY0TcEREXR8SHI2KlHvEbRsSpEXFLRNwVEWdHxMv7tL1KRMyPiAURsSgifhcRe0RE9IhdJiL2iYjLmthrIuKwXn2QJEmSZqLJrFCbwK+a7dNVVS0P7AHsS0n8NxuyybcD/wJ8CzgeuA94GXAI8KaI2Dwz7waIiPWAc4D7gU8Ct1G+QTg9IrbJzDM6jUbE8pQPH88F5gOXAtsANfAE4ICufhwBvAf4JnAY8Izm8XMjYqvMfHDI85IkSZKWqqGTe4CqqtYHXtFsLwMeBwTwJ8oQnWF8HTg0M29rlX0uIi4HPgy8A/hMU34osCrw/My8GCAivgL8Djg6Ip6emZ15+XcDNgHek5nzm7JjI+IbwIci4kuZeXXTxjOBvYBTMvP1nU5ExJXAUcCOwNeGPC9JkiRpqRo4ua+q6p8ZS+ifREnmrwO+T0nof1TX9TXDdiAzL+xTdSIluX8WQDM85jXAmZ3Evjn+zoj4AnAQJZk/v6naGVgIHNvV7pHADsCbKVf/AXZqzufIrthjgY8Du2ByL0mSpBlumCv3X6WsVvsTSsL7k7quL5uSXhVPavY3NPvnAHOAc3vEntfsNwHOj4hlgOcBF2Xmoq7Y84EHm1haxz3I2AcDADJzUURc3BUrSZIkzUjDJPf3AssDLwFWAtauqupHwDl1Xd87yk5FxLLA/pSx9Z0r5ms3+wU9DumUzWv2qwEr9IrNzHsi4uZWbKftmzLznj5t/0NELJ+ZIz1PSZIkaZSGSe5XBV7I2NCcDwIfAu6pqurnNENzgAubm26XxJHA5sCHMvMPTdmKzb5XAr6oK2a82E78iq3HK04Q24l5WHIfEbsDu++xxx59DpckSZKWjoGT+7quFzGWwFNV1WMpN9O+vNn+vdluo9xgOykRcTBlTv1jMvPQVtXCZj+nx2Fzu2LGi+3EL2w9XkiZ6adfbLvNxWTmMcAxVVUt6QcaSZIkaYkMPM99t7qubwd+3mznAjdRbkpdZbJtRsQBwH7Al4B3d1Vf1+zn8XCdss4wnFuAu3vFRsQcYHUWH7JzHbBGU9er7ZsckiNJkqSZbqipMKuqWoky5r4zNOfZlIQ+gFspC1kNOxUmABHxUeCjwFeA3VpTWnZcQhk6s0WPwzdv9hcCZOaDEXERZY76OV1j6TelfKhpz9JzAfDKpu7sVp/mAhsDP53MOWm0tj74tEkdd/pHthtxTyRJkmamYabCPJuS/D6GkszfTUnkf9zsfznZsfYRsT9lUamvAm/rtWBUM+Xlt4EdImKjzPx1c+zKlDntL2fx2W5OoNwjsDtlEauOvSk36p7UKjuRcv/A3rSSe8oCWStSFteSJEmSZrRhrtxvBvyCsWT+3Lqu71vSDkTEvwAHAn8GzgB2joh2yA2Z+cPm530p3xj8ICKOAG6nJODzgO26rvYfC7wNODwi1qWsULst8DrgkMy8shOYmZdExNHAnhFxCvBdxlaoPQvnuJckSdIjwDDJ/Wp1Xd81BX3ozCH/ZODLPerPAn4IkJl/jIgXUubZ/yBlas6LgH/KzDPaB2XmvRGxFXAIZZGq1Skr6O4FHN3jefYGrqJc6d+Ocg/BfGD/Xt8kSJIkSTPNMLPlTEViT2buCuw6RPylwPYDxt5KmXlnzwFiHwAOazZJkiTpEWeoG2oBqqp6NfDPlGErK9V1vX5T/gzg1cDxdV33WmhKkiRJ0hQa5obaAI4DdmmK7qasAttxC/Axys22nxhR/yRJkiQNaJh57ivgLZQ56B8HfLpdWdf19ZQ57513UJIkSZoGwyT37wB+DbyzruvbgF7TXl4OPHUUHZMkSZI0nGGS+w2Bn0wwl/1fgTWXrEuSJEmSJmOY5P5+YO4EMfOAOyffHUmSJEmTNUxy/3tgy+bG2oepqmou8HLgV6PomCRJkqThDJPcfxV4OnBEVVWLHVdV1bLA4cDalBl1JEmSJC1lw8xz/3ngNcB7gDcCdwBUVfV1YHNKYv8/dV0fP+pOSpIkSZrYwFfu67p+AHgVcBCwPLABZU77HYAVgYMpSb8kSZKkaTDUCrV1Xd8PHFBV1YGU5H514Dbgsib5lyRJkjRNhkruO5rpMP8w4r5IkiRJWgLD3FArSZIkaQbre+W+qqofU1ahfWtd19c2jweRdV2/YiS9kyRJkjSw8YblbElJ7ldsPR7EeCvYSpIkSZoifZP7uq6XGe+xJEmSpJnFhF2SJEmaJQZO7quq2qZ7ZVpJkiRJM8cwyfppwDVVVX2yqqpnTVWHJEmSJE3OMPPcH0NZgfbfgPdVVXUR8GXghLqub56KzkmSJEka3MBX7uu6fjewFvBm4HvARsB/AAuqqjqlqqrXVFU1qUWxJEmSJC25ocbQ13V9b13XJ9d1/SrgScD7KSvVvhb4JnBdVVVHjr6bkiRJkiYy6SvtdV3/FTgcOLyqqo2AXYEK2AvYeyS9kyRJkjSwJZ79pqqqDYA3ATsAyw17fETsGxEnR8QVEZERcdU4scc1Mb22N/SInxMRB0XElRFxT0T8KSL2i4ie/YyI/xsRv4qIuyPihoj4QkSsOew5SZIkSdNhUlfuq6paFdgReCuwKRDA7cB/AscN2dzHgL8BFwGrDnjMW3qUnd+j7ERge+CLwLnAFsDBwPqUbxoeEhH7UL6JOAt4L2XY0b8CW0TEppl514B9kyRJkqbFwMl9M8f9NpSE/tXA8kACP6Ik9KfUdb1oEn1YLzOvAIiI3wIrT3RAZv7XRDERsS0lsT88M9/XFH8hIm4F/jUijsnMc5rYNYBDgAuAV2TmA035BcC3KMn+x4Y+M0mSJGkpGubK/XXAmpSr9P9LmQbzK3VdL1iSDnQS+2FERAB/B9yZmQ/2Cdu52Xff4Hsk5Yr8LsA5TdlrgRWB+Z3EvunbtyPiiibW5P4RauuDTxv6mNM/st0U9ESSJGlqDZPczwWOBY6r6/q8KerPoG6jJPf3RsRPgf0y8xddMZsACzLzmnZhZl4TEdc19e1YKEN3up0H7BQRK2fmnaPpviRJkjR6wyT3T6jr+p4p68lgrgeOAH4J3EWZa39v4OyI2DYzz2jFrg38vk87Cyhj6tuxnfJesdHE/O/kuy5JkiRNrWEWsVossa+qarWqqtYZfZf6y8wPZua/ZubxmXlqZh5IuaH3PuCzXeErAv0+jCxq6tux9Ilf1BWzmIjYPSIuHOgEJEmSpCk01Gw5VVWtDBwI/DNl/H122qiqajPgo8B+dV1fNOJ+9pWZl0fEScCuEbFBZnauri8E5vQ5bG5TTyuWJv7uHrHtmO7nPwY4pqqqHLrzkiRJ0ggNfOW+qqpVKGPS96HcXHspZbhKxyXAi4GdRtnBAV3V7NdolV0HzOsTP4/Fh+Bc1yrvFZutGEmSJGlGGubK/YeBZwK71nX9laqqPgrs36ms63phVVVnAa8YcR8H8bRmf0Or7ALgnyNinfZNtRGxDmX8/Le6YnenzIP/x662NwP+8Ei4mXYys8JIkiRp9hhmhdodgNPruv7KODFX0/9q+RKJiJUiYm6P8ucCbwQuzcw/tapOaPZ7dx3SeXx8q+x/KMNx9oyIZVttvxpYrytWkiRJmpGGuXL/JOAbE8TcCawyTAci4i3AU5qHawLLR8R+zeOrM/Orzc9PA74XEacClzM2W87bgQcoV94fkpmnRcR3KAtWdYYUbQG8A/ivzPxZK/bGiPgI8GngjIg4gfIh5X3AZTx8rnxJkiRpxhkmub8DePwEMU8FbhqyD+8AXtpVdnCzPwvoJPfXA2cAL6Pc0LsC8BfgRODQzLysR9tvBPajLEL1Fso4+/2Bj3cHZuZhEXEz5Z6Co4DbgZOADz4ShuRIkiRJwyT3FwCvqqrq7+q6vqO7sqqqtYBtge8M04HM3HLAuOspCfowbS+iJPf7TRTbxB8HHDfMc0iSJEkzxTBj7v8DWB34blVVz2hXNI9PpkwbedTouidJkiRpUANfua/r+vSqqg4ADgB+S1k4iqqqbgJWo0yL+YG6rs8ZfTcfXZz1RpIkSZMxzJV76ro+iDLV5beAWyg3sibwXWCruq4/NfIeSpIkSRrIUCvUAtR1/RPgJ1PQF0mSJElLYKgr94OoqmrNUbcpSZIkaWJDX7nvp6qqVYAPAHsCjx1Vu5IkSZIGM1ByX1XVU4DnU26iPb+u6xtadXMpc8P/G+XG2oVT0E9JkiRJE5hwWE5VVUcBf6JMdXkqcFVVVVVTtyXwB+AQyqJS/wH8n6nqrCRJkqT+xr1yX1XVWynDbB4ELqVMd7khcFRVVXcBnweWbfaH1HV93dR2V5IkSVI/Ew3L2RW4F3hZXdfnAlRV9RLgh8B/AtcCr67r+pKp7KQkSZKkiU00LOc5wDc7iT1AXdc/pQzPCeDtJvaSJEnSzDBRcr8K8Mce5Zc3+3N71EmSJEmaBhMl98tQZsjpdh9AXdd3j7xHkiRJkiZlkEWscsp7IUmSJGmJDTLP/QFVVR3Qq6Kqqgd6FGdd1yNbHEuSJEnSYAZJwmPINoeNlyRJkjQC4yb3dV0PMmxHkiRJ0gxg8i5JkiTNEo6Nl3rY+uDTJnXc6R/ZbsQ9kSRJGpxX7iVJkqRZwuRekiRJmiVM7iVJkqRZwuRekiRJmiVM7iVJkqRZYkYk9xGxb0ScHBFXRERGxFUTxG8WEWdExB0RcXtEfD8iNu4Tu3ZEfCUiboyIuyPiwoh4Y5/YORFxUERcGRH3RMSfImK/iFhuBKcpSZIkTamZMhXmx4C/ARcBq44XGBGbA2cCC4D9m+I9gbMj4h8y85JW7OOAnwGPBw4HrgV2Bk6KiLdn5pe6mj8R2B74InAusAVwMLA+sOvkT0+SJEmaejMluV8vM68AiIjfAiuPE3sUcC/wksxc0BxzEnApcBjwylbsB4GnAq/JzG83sf9JSdw/HREnZ+adTfm2lMT+8Mx8X3P8FyLiVuBfI+KYzDxnNKcrSZIkjd6MGJbTSewnEhHrA5sAJ3cS++b4BcDJwFYR8cTWITsDf+ok9k3sA8B84HHAtl2xAEd2PW3n8S6D9FGSJEmaLjMiuR/CJs3+3B515wEBPB8gItYC5jXlvWLb7XV+XpCZ17QDm8fXdcVKkiRJM84jLblfu9kv6FHXKZs3idhOfK/YTvy8XhURsXtEXNjnOEmSJGmpmSlj7ge1YrO/p0fdoq6YYWI7P/eK7cSv2KsiM48BjqmqKvscq0eRrQ8+bVLHnf6R7UbcE0mS9Gj0SLtyv7DZz+lRN7crZpjYzs+9YjvxC/vUSZIkSTPCIy25v67Z9xoi0ylbMInYTnzPoTdNeb8hO5IkSdKM8EhL7i9o9lv0qNscSOCXAJn5F0pCvnmfWID2WPkLgHkRsU47sHm8dlesJEmSNOM8opL7zPwjJcl+Y0R0bpil+fmNwI8z8/rWIScA60XEq1uxywJ7AbcC3+2KBdi762k7j48fyUlIkiRJU2RG3FAbEW8BntI8XBNYPiL2ax5fnZlfbYW/F/gJZUXa+U3ZXpQPKu9jcR+nJP1fi4jDKVfyd6JMa7lbZt7RCczM0yLiO5QFq1ZhbIXadwD/lZk/G83ZSpIkSVNjRiT3lAT6pV1lBzf7s4CHkvvMPCcitgQOabYEzgHemJm/bjeQmTdHxAspSf6/UFa+/T2wY2ae2KMfbwT2oyxY9RbKh4H9m+MlSZKkGW1GJPeZueWQ8ecCrxgwdgElUR8kdhElud9volhJkiRppnlEjbmXJEmS1J/JvSRJkjRLmNxLkiRJs4TJvSRJkjRLmNxLkiRJs4TJvSRJkjRLzIipMKVHu60PPm1Sx53+ke1G3BNJkvRI5pV7SZIkaZYwuZckSZJmCZN7SZIkaZYwuZckSZJmCZN7SZIkaZYwuZckSZJmCafClB7BnEJTkiS1eeVekiRJmiVM7iVJkqRZwuRekiRJmiVM7iVJkqRZwuRekiRJmiVM7iVJkqRZwuRekiRJmiVM7iVJkqRZwuRekiRJmiUeccl9RGSf7c4esRtGxKkRcUtE3BURZ0fEy/u0u0pEzI+IBRGxKCJ+FxF7RERM/VlJkiRJS+4x092BSTobOKar7L72g4hYDzgHuB/4JHAb8E7g9IjYJjPPaMUuD/wQeC4wH7gU2AaogScAB0zJWUiSJEn/v737DperKvc4/n0JkJAEKaFIApd44VLkIj00QZqCVEWRIk0pyqJILuqVamhiIyCBBRKRJvAIAiKCgpQg0kJocpHeSaRKSQ+Qdf9413h2JjNnzpmcOTNnzu/zPPPsnLXX3rP3uycz71577bV7UF9N7l9IKf2mRp0zgCWBDVJKjwKY2WXAE8B5ZrZGSinlugcDGwFHpZTG5bLxZnYtcJyZXZxSernnd0NEREREpOf01eS+1Nq+aEqpUnecIcCuwIRSYg+QUppmZr8CTsGT+Yl51j7ADGB82arOBnYH9sRb/0Xawvan3lTXcrecuFMPb4mIiIj0pD7X5z77Kp6MTzWzN3Nf+SUK8z8DDATuq7Ds/Xm6EYCZLQSsDzySUppVVnciMLdUV0RERESklfXFlvuJwDXAc8AngB2BI4DPmdlmuSV/eK47ucLypbIReboUsFiluiml2Wb2TqGuiIiIiEjL6nMt9ymljVNKP08p/T6ldFlKaS/geGBt4Du52uA8nV1hFbPK6nRWt1R/cJV5mNmhZjapyzsgIiIiItIgfS65r+JnwByg1CF4Rp4OrFB3UFmdzuqW6s+oMo+U0oUppQ27vqkiIiIiIo3RF7vlzCel9KGZTQGWyUVT8rRSd5pSWakbzrvAzEp1zWwgMAy4q+e2VqTv0o24IiIira0tWu7NbBCwIvBGLnoc72azaYXqm+TpJICU0lzgYWC9nMwXjcJjpG43IiIiItLy+lRyb2bDqsw6Fb8KcSP4kJf531uZ2TqF5YfiY9o/S8cwmABX4f3qDy1b79H4Q7Cu7ontFxERERFppL7WLecEM9sEuBN4BRiKj5azNfAA/nTZkmOBbYFbzews4AP8CbUjgJ0KD7ACH9/+G8BYMxuJP6F2R+DLwGkppRcbuE8iIiIiIj2iryX3E4BPAwfgfeE/xlvhjwfGFsepTyk9Z2abAz8GfgAsine/2SGldFtxpSmlOWa2HXAasHde9/PAkcB5Dd4nEREREZEe0aeS+5TSDcAN3aj/JLBbF+u+h4+Xf0R9Wyci1dRzI65uwhUREem+PtXnXkREREREqlNyLyIiIiLSJpTci4iIiIi0iT7V515E+g89MEtERKT71HIvIiIiItImlNyLiIiIiLQJJfciIiIiIm1Cyb2IiIiISJvQDbUi0lZ0I66IiPRnarkXEREREWkTSu5FRERERNqEuuWIiKDuPCIi0h6U3IuILACdFIiISCtReqbiOQAAFHpJREFUct9g9f7wi4iIiIh0l/rci4iIiIi0CSX3IiIiIiJtQt1yRESaQH31RUSkEdRyLyIiIiLSJtRyLyLSh6jFX0REOqOWexERERGRNqGWexGRfqA3h+XVVQIRkeZRci8iIj1KXYdERJpHyb2IiPRpOpkQEemg5L7AzBYCvgN8CxgJvAVcDZyUUprexE0TEWl7vf1Eb50UzK+3j0E7x1KkWZTcz+ss4CjgeuBMYM3893pmtl1KaW4zN05ERJpPJwUi0sqU3GdmthZwJHBdSukrhfIXgXOAvYArm7R5IiLSD/V2S7qI9H1K7jvsDRhwdln5eODHwL4ouRcRkTopUReR3qBx7jtsBMwFJhYLU0qzgEfzfBERERGRlmUppWZvQ0sws8eB5VJKy1eYdzWwBzAwpTSn0vIhBAVSRERERHpFjNEqlavlvsNgYHaVebMKdeZhZoea2aSGbZWIiIiISBepz32HGcByVeYNKtSZR0rpQuBCADOblFLasDGbJ6AY9wbFuHcozo2nGDeeYtx4inHvaKc4q+W+wxRgGTMbWGHeCODtal1yRERERERagZL7Dg/i8RhVLDSzQcC6gLreiIiIiEhLU3Lf4bdAAo4uKz8E72t/RRfWcWFPb5TMRzFuPMW4dyjOjacYN55i3HiKce9omzhrtJwCMxsHHIE/ofZmOp5Qew+wjZ5QKyIiIiKtTMl9gZkNwFvuDwVGAm/jLfonpZSmNXHTRERERERqUnIvIiIiItIm1Od+AZjZQmY22syeMrNZZvaqmZ1pZkOavW2tysxWM7NTzOx+M3vLzKaa2aNmdnyluJnZ6mb2ezN718ymm9ndZrZNlXUvYWbjzGxyPh5PmNlhZlbxIQ/9iZkNNrMXzSyZ2bkV5ivOdTKzpc3s52b2XI7HW2Z2p5ltUVZvYzO7LX/mPzCzP5vZulXWOdzMLsvrmmlmk8xsj97Zo9ZiZkPN7DgzezzH7m0zu9fMDiz/zCnGtZnZsWZ2jZm9kL8PXqpRvyExNbOB+bfgRTObbWbPm9kJZrZID+xmU3U1xmY2yMwOMbMbzOylHLMXzOwqM1uzyjLdipuZ7W9mj+R1v2FmvzKzZXtwd5uiu5/jsmV/mpep2COjLWKcUtKrzhfwC/wm3OvwG2/HAh8CdwALNXv7WvEF/BiYit+gfCTwbTpuZn4MWKxQdxXgHeAN4FggAI/kGG9Xtt5FgYl53th8PK7L6x3T7P1u9gv4eY57As4tm6c41x/XlYEXgbfyZ/ubwGjgYmCvQr1N8IfhPZ/nj87/ngqsXbbOpYEXgGnAKXg3wQk5xt9o9j73cnwXAu4GPgZ+nWNxNPBAjsdPFONuxzTl/+9/Af4FvNRJ3YbFFPh9nncRcHCeJuCSZseot2IMrJHr3g2cCBwEnJ6XmQ1svSBxy8cr5eNwaD4u04AngCHNjlNvfY7LllsX//2aCkyrUqfPx7jpB6ivvoC1gLnAtWXlR+YDvU+zt7EVX8CGwBIVyk/LcTuiUHY1/qO+bqFsKPAy8DS5W1kuD3n5I8vWey0wB1i52fvexJivD3wE/A+Vk3vFuf7Y3g28CqxQo95E4ANgRKFsRC67tazuT3OMdymUDcjreAcY2uz97sX4bppjcVZZ+aJ4IvmeYtztmP5n4d//R+fJfUNiCuyY655Zto4zc/lmzY5Tb8QYGFb83i2UfxpP7ieVlXc5bsAywPQc/wGF8l1y3eOaHafeiHHZMgPwYc//gCfj8yX37RLjph+gvvqiIxndoqx8UD7YNzd7G/vSC1g7x/OC/PcQvMXo9gp1T8x1RxXK/pbjPqis7ha57vebvY9NiusA4CHgj/hN4vMk94rzAsV2SwonOsAiwOAK9VbN9S6qMO8ivJHgk4Wy14DnKtTdL6/na83e916M8fZ5n79XYd5EYLJivEDx7SzxbFhMgd/kspXK6q6Uy2OzY9MbMa6x3EPArLKyLscNb3FOwH4V1v088I9mx6a3Y4y3sk/Hr7hOoHJy3xYxVp/7+m2Ef7lNLBamlGYBj+b50nUr5ukbefoZYCBwX4W69+fpRuD3PuCt04/k+BdNxI9Tfz0eo/FLv0dUma8412/HPH3FzG4EZgLTzewZM9u3UK8Uk2oxNmADADNbAW8Zvb9K3eL6+oOJwHvA981sDzP7D/P7Q87AYzYm11OMe14jY7oRfmL2arFi/nsK/Tz++bt2BTp+D0u6E7dax28NMxvaM1vc+sxsZeBU4OSU0sudVG2LGCu5r99w4O2U0uwK8yYDy5jZor28TX2S+RCkJ+FdR67MxcPzdHKFRUplI/J0KWCxSnXz8XmnULffMLNPAScDp6SUXqpSTXGu3+p5Oh7vb3wA3md2DnC5mX0jz+9OjLtTt+2llN4FdsX71F6NdxV7Cjgc+EpKaXyuqhj3vEbGdHiVuqX6/T3+h+HJ/aVl5d2JW61jYoU6/cH5+P1RY2vUa4sYL9yMN20Tg/E+cZXMKtSZ0zub06edjd+4dVxK6elcNjhPK8V4VlmdzuqW6g+uMq+ddeXLTHGu3+J5OhW/8W0OgJldj/cH/5GZXUrPxbi8bn8xDb/s/gfgXvxE6nDgSjPbLaX0FxTjRmhkTGv9fvbb+JvZZnj/7r8DPyqb3Z246XOemdnewA7AZ1NKH9Wo3hYxVnJfvxnAclXmDSrUkU6Y2al4l5ELU0pnFGaVYjewwmLl8e2sbql+vzoWuVvIF4AtU0ofdlJVca7fzDy9qpTYg7c2m9kfgP3x1v2einG/+14xs7XxhH50SumCQvlVeMI/3sxWQTFuhEbGdEaVuqX6/TL+ZrYBcBPe/WPHCt0fuxO34jGZWaFusU7bMrOl8QbEi1JK93ZhkbaIsbrl1G8K3vWm0odgBN5lR632nTCzMcAJ+LCB3y6bPSVPK12eLZWVLoW9i//Hmq9uPj7DqH6Zre3kfR4L3Ay8bmarmtmq+E1EAEvksiVRnBfEa3n6eoV5/8zTpehejLtTtz8Yjf9IXlMsTCnNwJOglfEbxRXjntfImE6pUrdUv9/F38zWx4d1fB+/ElgpBt2JW61jkgp12tkP8YEjxpd+C/Pv4WKA5b9XKtRvixgrua/fg3j8RhULzWwQPo7qpGZsVF9hZj/E/9NdBhyc8u3lBY/jl7o2rbD4Jnk6CSClNBd4GFivwsnWKPw49afjsRiwLLAT8GzhNSHP3zf/fTCK84Io3Uy/YoV5pbI38e8KqB7jhI+MQUrpn/iPxyZV6kL/inHpR3NAhXkLF6aKcc9rZEwfBEaUJVXkv4fTz+JvZuvhiX2pi1+1Gz67E7fOjt/GwNMppYoPcWozK+PJ/QPM+3s4Cu8y8yzwp0L99ohxs4cw6qsvfOjGzsa537fZ29iqL/zm2YQn9lUf9oW31n0MrFMoK42//gzzjr9+ONXHX/8Q+FSz97sX47sI8NUKr8NyjP6U/15NcV6gOC+Fj/f9GvOO4b0C3k/8mULZg7nu8ELZ8Fx2W9l6f0b18cLfBRZv9r73YozPosIQq0DpqtO/gIUV47rjW2uc+4bEFG94SFQfS/yzzY5NL8Z4PXwwglcojN1epW6X44Y38MzAk9pKY7Cf0OzY9EaM8cS70u/hE/iV6K8Cn2+3GFveEKmDmY3D+4tfj3eBWBM4CrgH2CZ5S6cUmNnhwLn4F9mJ+AlS0RvJb5AjXzorPQ31LPwH5RD8xGqnlNIthfUuivfNXQc4B3gSH6rwy8BpKaUTG7hbfYKZjcRvsD0vpXREoVxxrpOZHQr8Ev+h+DX+cKXSSBc7p5RuzfU2A+7ETwTG5cWPBJYHNk8pPVZY5zC8RXQY3r1qMrA3sBV+leuihu9Yi8jD1z2Mn0hdgX+3Lo1/PkcCh6eUYq6rGHeBme1HRxe9I/HP7Jn575dTSpcX6jYspnn42J3xMfPvw5Owg4DfpJT266HdbYquxjh/vh/CP9Mn42Ojl7s+pTS9sO4ux83MjsGfTj4BuAq/EnYM/uC9jVIfbrnvzue4yvITgA1TSvMNVdkWMW72GVdffuGtEsfgT/GcjX+ZjaWNn27YAzG7BD+jrfaaUFZ/TeAGfKzrGfhDlLarsu4l8ROHKfl4/AM/+bJG71dfeFHhIVaKc4/EdXd8TOPp+GX1W/HEp7zepsDteKv+VOAWYP0q6xwBXA68jY+68DCwZ7P3tUnxXQUfEvA1/AT0A+CvwO6KcV3xnNDV799GxhS/l+I04KX8PfIC3uCzSLNj1Fsxxk98Ovs9TMDIBYkbcCDwWD4eb+KNEMs1O0a9/Tmusvx8D7Fqlxir5V5EREREpE3ohloRERERkTah5F5EREREpE0ouRcRERERaRNK7kVERERE2oSSexERERGRNqHkXkRERESkTSi5FxERERFpEws3ewNERKT1hBAScFeMcatmb0szhRC2wp/SenKMcUxzt0ZEpDYl9yIiTZST6KK5wPvA3/EnOl8aY+y3TxsMIUwARsYYRzZ5U0RE+gQl9yIireHkPF0EWBX4MvA5YEPgiCZsz5rAjCa8b6uZiMfi7WZviIhIV1hK/bZBSESk6Uot9zFGKyvfHPgrYMAqMcYXm7B5TaeWexGR7lHLvYhIC4ox3hNCeAr4NLABMF9yH0LYHvgOMApYHHgNuA44Pcb4Xq4zCHgdmAMMjzF+VGE9FwDfAnaOMd6Uyyr2uQ8hLAwcCuyft21h4GngIt/sODfXGwr8C3gwxrh5YfnFgHeBgcD+McbLC/MCcB5wUIzx19ViE0JYFPg2cCDwqbyuN4HHgHExxtuqLVtYx/LA94BdgBWBD4E3gPuAU2KML+R6W1HW5z6EMAb4YWfrr3CyVvNYiYj0BI2WIyLSukoJ4oflM0IIJwF/BjYGbgLOAZ4DvgvcE0L4BECMcRbwW2BZ4IsV1jMQ+Bqe2N7S2caEEBYB/ogn4EsCVwIX4r8l44BLS3VjjNPwLi2jQgiLF1azOZ6MA2xb9hbb5OntnW0Hfi/CL/AuTJfh+/5XYG1ghxrLEkIYDNwDHAO8DJyPn5w8DuyGn7R0ZgLejar8VTohmVn2fl06ViIiPUEt9yIiLSiEsCWwOt7iPrFs3tZ4MnkfsGOx5TeEcCBwcZ4/Ohdfgre2HwDcWPZWuwJLAWMrteqXOR7YHjgXODrG+HF+zwF4kv/NEMLvYow35Pp34Mn8lnhSC57Qf4wn4/9O7kMICwFbAS/EGF8ulVe4crAEsBfwELBxaRsK84fV2IfSNqwCnB1jHF2cka8KDKy4VMc2TcAT/OJynwD+ht8QvW+hvLvHSkRkgajlXkSkBYQQxuTX6SGE3wK34S33340x/rOs+lF5ekh5l44Y4yXAo8DXC2X3Ac8Au4QQli5b1wF5eimdyMn3EXgXn9HFpDr/+xggFd+Xjhb4Ygv9tnhifi2wYghhtVy+LjCM2q32CY/LbDyRnkeM8Z0ayxfNLC+IMc6JMU7txjpKXZWuwa8cfC/GeF1hdreOlYjIglLLvYhIayjvw53wvucXV6i7Kd5VZ48Qwh4V5i8KLBtCGFZIdi8FTsdbvSP8u9/59sAjMca/19i+1fDk+1ngBO8eP5+Z+MgyJfflsm3z+y0BrA/8FG/VJ897ho4uOXfQiRjjByGEG/G+8o+GEK4F7gYeiDF2dXSfu4DJwA9CCOsDN+PddB4tvxLQRecDX/DNi2PL5tVzrERE6qbkXkSkBZRuwAwhDMETwouAC0IIL8cYyxPeYfj3d6c3dQJDgVLCeBlwKt5SH3PZ1/N6Om21L7wnwH/VeN+hpX/EGOeEEP4GbBdCWA7frwHA7THGJ0MIU/Dk/vw8TdRI7rM9gf8F9qFjCNFZIYTf4Vc63uhs4XyCsEledlf8BAfg7RBCBE6LMc53n0MlIYRjgYPxbkdHVahSz7ESEambknsRkRYSY5wO3BZC2AV4GLg0hLB6Wav0+8BCMcbyLjadrfe1EMIdeKK9RozxKTzR/xC/MbaW9/P0+hjj7l19XzxZ/zzeMr8Z3p3mnjzvTuCL+abeLYAnYoxvdmFfZgJjgDEhhJXwPv0H4n3dR+Z11VrHa8BBIQTDb6DdBjgcOAnvsnpirXWEEPbEr4Y8AuxVpdW/28dKRGRBqM+9iEgLyt1kxuPDNJbfbHk/sFQIYa1urvaSPD0ghLAu8BngTzHGt7qw7FPAe8AmedScrir2u98GuCeP4FOatzRwGDCE2v3t5xNjfDXGeAXe+v4s8Nku3lRbWj7FGJ+IMY7DT0IAvlRruRDCZng8J+NDiE6rUrXeYyUiUhcl9yIires0YBbw3RDCUoXys/J0fAhhePlCIYQhudtJueuAD/AW7gNz2SVd2ZA8ks44YAXgnDxeffn7rhBCKB9G8iH8pGA3YC3mTeBL/z42T2t2yQkhLBtC2LjCrCH4+PEf4SMMdbaO/w4hjKwwa/k87bTvfghhVeAG/KrHTjHGKZ1Ur/dYiYjURd1yRERaVIxxcgjhl/jDj75PToJjjLeHEH4AnAE8G0K4GX/I1VBgZeBz+LCMO5Stb2YI4RrgICDgfbxvoutOBdbBHyC1S+7mMxlYDu+Lvzk+XOY/Cu85N4RwF57cQyG5jzG+EkJ4Hh+W8mP8RtdaRgD3hxCexLstvQp8AtgZ+CRwThdGu9kOGBtCuBe/IvEmfoVkN3wEnp/VWP4cYBn8ZGT3EMJ83ZRKD7yq91iJiNRLLfciIq3tDLwl+ag8ug0AMcaf0DF+/ObA0cAeePJ7IXBClfVdkqeLAFfFGDtt5S7KN5l+CX867dN4Qn0MnpiW+qlfUWHRUkL/ATCpyryHYozvU9tL+M2prwNbA/8D7I4nzPvgcajlFuBsYBCe0B+Dx/IvwBYxxt/VWH5wnm6Tt6XS698W4FiJiHSbpZSavQ0iIiIiItID1HIvIiIiItImlNyLiIiIiLQJJfciIiIiIm1Cyb2IiIiISJtQci8iIiIi0iaU3IuIiIiItAkl9yIiIiIibULJvYiIiIhIm1ByLyIiIiLSJpTci4iIiIi0if8HlTZrQp3ricgAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
@@ -233,13 +240,12 @@
}
],
"source": [
- "fig, ax = plt.subplots(1,1)\n",
- "fig.set_size_inches(12,6)\n",
- "ax.hist([len(i) for i in x_train],80 )\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "ax=sns.distplot([len(i) for i in x_train],bins=60)\n",
"ax.set_title('Distribution of reviews by size')\n",
- "ax.set_xlim(left=0,right=1500)\n",
- "ax.set_xlabel(\"Review's size\")\n",
- "ax.set_ylabel(\"Review's count\")\n",
+ "plt.xlabel(\"Review's sizes\")\n",
+ "plt.ylabel('Density')\n",
+ "ax.set_xlim(0, 1500)\n",
"plt.show()"
]
},
@@ -249,13 +255,13 @@
"source": [
"## Step 3 - Preprocess the data\n",
"In order to be processed by an NN, all entries must have the same length. \n",
- "We chose a maxlength of **max_len** \n",
+ "We chose a review length of **review_len** \n",
"We will therefore complete them with a padding (of \\<pad\\>\\) "
]
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -292,17 +298,17 @@
}
],
"source": [
- "max_len = 256\n",
+ "review_len = 256\n",
"\n",
"x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
" value = 0,\n",
" padding = 'post',\n",
- " maxlen = max_len)\n",
+ " maxlen = review_len)\n",
"\n",
- "x_test = keras.preprocessing.sequence.pad_sequences(x_valid,\n",
+ "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
" value = 0 ,\n",
" padding = 'post',\n",
- " maxlen = max_len)\n",
+ " maxlen = review_len)\n",
"\n",
"print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
"print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
@@ -312,17 +318,92 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 4 - Building the model\n",
+ "### Save dataset and dictionary (can be usefull)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Saved.\n"
+ ]
+ }
+ ],
+ "source": [
+ "os.makedirs('./data', mode=0o750, exist_ok=True)\n",
+ "\n",
+ "with h5py.File('./data/dataset_imdb.h5', 'w') as f:\n",
+ " f.create_dataset(\"x_train\", data=x_train)\n",
+ " f.create_dataset(\"y_train\", data=y_train)\n",
+ " f.create_dataset(\"x_test\", data=x_test)\n",
+ " f.create_dataset(\"y_test\", data=y_test)\n",
+ "\n",
+ "with open('./data/word_index.json', 'w') as fp:\n",
+ " json.dump(word_index, fp)\n",
+ "\n",
+ "with open('./data/index_word.json', 'w') as fp:\n",
+ " json.dump(index_word, fp)\n",
+ "\n",
+ "print('Saved.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build the model\n",
"Few remarks :\n",
- "1. GlobalAveragePooling1D : Fait un pooling sur la seconde dimension (batch_size, steps, features) -> (batch_size, features) \n",
- "Autrement dit : on moyenne l'ensemble des mots d'une phrase\n",
- "2. L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer"
+ "1. We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
+ "2. **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
+ "In other words: we average the set of vectors/words of a sentence\n",
+ "3. L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
+ "Voir : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer\n",
+ "\n",
+ "A SUIVRE : https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks\n",
+ "### 4.1 - Build\n",
+ "More documentation about :\n",
+ " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
+ " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model(dense_vector_size=32):\n",
+ " \n",
+ " model = keras.Sequential()\n",
+ " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
+ " output_dim = dense_vector_size, \n",
+ " input_length = review_len))\n",
+ " model.add(keras.layers.GlobalAveragePooling1D())\n",
+ " model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
+ " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
+ "\n",
+ " model.compile(optimizer = 'adam',\n",
+ " loss = 'binary_crossentropy',\n",
+ " metrics = ['accuracy'])\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train the model\n",
+ "### 5.1 - Get it"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -349,39 +430,39 @@
}
],
"source": [
+ "model = get_model(32)\n",
"\n",
- "vocab_size = 10000\n",
- "n_neurons = 32 # 200 pour voir overfiting\n",
- "\n",
- "keras.backend.clear_session()\n",
- "\n",
- "model = keras.Sequential()\n",
- "model.add(keras.layers.Embedding(vocab_size, n_neurons, input_length=256))\n",
- "model.add(keras.layers.GlobalAveragePooling1D())\n",
- "model.add(keras.layers.Dense(n_neurons, activation=tf.nn.relu))\n",
- "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
- "\n",
- "model.summary()\n",
- "\n",
- "model.compile(optimizer='adam',\n",
- " loss='binary_crossentropy',\n",
- " metrics=['accuracy'])\n",
- "\n",
- "callbacks=[]\n",
- "callbacks.append( keras.callbacks.ModelCheckpoint(ooo.get_check_dir()+'/model{epoch:02d}-{val_acc:.3f}.h5') )\n",
- "callbacks.append( keras.callbacks.ModelCheckpoint(ooo.get_model_dir()+'/best_model.h5', save_best_only=True) )"
+ "model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Entrainement"
+ "### 5.2 - Add callback"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = \"./run/models/best_model.h5\"\n",
+ "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -390,67 +471,67 @@
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/30\n",
- "25000/25000 [==============================] - 1s 60us/sample - loss: 0.6882 - acc: 0.6171 - val_loss: 0.6787 - val_acc: 0.6901\n",
+ "25000/25000 [==============================] - 2s 62us/sample - loss: 0.6889 - accuracy: 0.5997 - val_loss: 0.6801 - val_accuracy: 0.7119\n",
"Epoch 2/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.6512 - acc: 0.7332 - val_loss: 0.6168 - val_acc: 0.7786\n",
+ "25000/25000 [==============================] - 1s 31us/sample - loss: 0.6539 - accuracy: 0.7605 - val_loss: 0.6206 - val_accuracy: 0.7505\n",
"Epoch 3/30\n",
- "25000/25000 [==============================] - 1s 51us/sample - loss: 0.5554 - acc: 0.8075 - val_loss: 0.5076 - val_acc: 0.8237\n",
+ "25000/25000 [==============================] - 1s 31us/sample - loss: 0.5606 - accuracy: 0.8069 - val_loss: 0.5124 - val_accuracy: 0.8144\n",
"Epoch 4/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.4378 - acc: 0.8525 - val_loss: 0.4123 - val_acc: 0.8485\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.4439 - accuracy: 0.8508 - val_loss: 0.4171 - val_accuracy: 0.8472\n",
"Epoch 5/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.3519 - acc: 0.8786 - val_loss: 0.3565 - val_acc: 0.8620\n",
+ "25000/25000 [==============================] - 1s 31us/sample - loss: 0.3577 - accuracy: 0.8750 - val_loss: 0.3616 - val_accuracy: 0.8614\n",
"Epoch 6/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.2993 - acc: 0.8922 - val_loss: 0.3269 - val_acc: 0.8690\n",
+ "25000/25000 [==============================] - 1s 31us/sample - loss: 0.3053 - accuracy: 0.8904 - val_loss: 0.3297 - val_accuracy: 0.8683\n",
"Epoch 7/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.2654 - acc: 0.9026 - val_loss: 0.3090 - val_acc: 0.8749\n",
+ "25000/25000 [==============================] - 1s 31us/sample - loss: 0.2697 - accuracy: 0.9019 - val_loss: 0.3115 - val_accuracy: 0.8736\n",
"Epoch 8/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.2401 - acc: 0.9123 - val_loss: 0.2997 - val_acc: 0.8769\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.2441 - accuracy: 0.9113 - val_loss: 0.2999 - val_accuracy: 0.8768\n",
"Epoch 9/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.2201 - acc: 0.9212 - val_loss: 0.2912 - val_acc: 0.8803\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.2235 - accuracy: 0.9193 - val_loss: 0.2926 - val_accuracy: 0.8797\n",
"Epoch 10/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.2027 - acc: 0.9272 - val_loss: 0.2880 - val_acc: 0.8826\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.2069 - accuracy: 0.9260 - val_loss: 0.2889 - val_accuracy: 0.8823\n",
"Epoch 11/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.1886 - acc: 0.9330 - val_loss: 0.2868 - val_acc: 0.8816\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1922 - accuracy: 0.9318 - val_loss: 0.2869 - val_accuracy: 0.8834\n",
"Epoch 12/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.1763 - acc: 0.9377 - val_loss: 0.2878 - val_acc: 0.8825\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1799 - accuracy: 0.9368 - val_loss: 0.2901 - val_accuracy: 0.8822\n",
"Epoch 13/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1656 - acc: 0.9428 - val_loss: 0.2903 - val_acc: 0.8816\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1693 - accuracy: 0.9406 - val_loss: 0.2905 - val_accuracy: 0.8809\n",
"Epoch 14/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1560 - acc: 0.9474 - val_loss: 0.2935 - val_acc: 0.8826\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1596 - accuracy: 0.9451 - val_loss: 0.2918 - val_accuracy: 0.8826\n",
"Epoch 15/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1469 - acc: 0.9507 - val_loss: 0.2976 - val_acc: 0.8808\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1502 - accuracy: 0.9492 - val_loss: 0.2994 - val_accuracy: 0.8790\n",
"Epoch 16/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1387 - acc: 0.9537 - val_loss: 0.3032 - val_acc: 0.8813\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1422 - accuracy: 0.9526 - val_loss: 0.3014 - val_accuracy: 0.8800\n",
"Epoch 17/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1316 - acc: 0.9575 - val_loss: 0.3092 - val_acc: 0.8790\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1342 - accuracy: 0.9551 - val_loss: 0.3079 - val_accuracy: 0.8788\n",
"Epoch 18/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1248 - acc: 0.9595 - val_loss: 0.3190 - val_acc: 0.8749\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1276 - accuracy: 0.9582 - val_loss: 0.3154 - val_accuracy: 0.8771\n",
"Epoch 19/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1186 - acc: 0.9623 - val_loss: 0.3224 - val_acc: 0.8767\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1207 - accuracy: 0.9611 - val_loss: 0.3205 - val_accuracy: 0.8779\n",
"Epoch 20/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.1120 - acc: 0.9651 - val_loss: 0.3299 - val_acc: 0.8758\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1146 - accuracy: 0.9636 - val_loss: 0.3294 - val_accuracy: 0.8745\n",
"Epoch 21/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.1075 - acc: 0.9663 - val_loss: 0.3381 - val_acc: 0.8756\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1093 - accuracy: 0.9658 - val_loss: 0.3361 - val_accuracy: 0.8749\n",
"Epoch 22/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.1016 - acc: 0.9690 - val_loss: 0.3473 - val_acc: 0.8744\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1036 - accuracy: 0.9683 - val_loss: 0.3463 - val_accuracy: 0.8710\n",
"Epoch 23/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.0966 - acc: 0.9718 - val_loss: 0.3602 - val_acc: 0.8706\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0993 - accuracy: 0.9702 - val_loss: 0.3546 - val_accuracy: 0.8722\n",
"Epoch 24/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.0923 - acc: 0.9736 - val_loss: 0.3655 - val_acc: 0.8712\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.0941 - accuracy: 0.9718 - val_loss: 0.3643 - val_accuracy: 0.8704\n",
"Epoch 25/30\n",
- "25000/25000 [==============================] - 1s 50us/sample - loss: 0.0872 - acc: 0.9756 - val_loss: 0.3773 - val_acc: 0.8683\n",
+ "25000/25000 [==============================] - 1s 31us/sample - loss: 0.0891 - accuracy: 0.9749 - val_loss: 0.3783 - val_accuracy: 0.8669\n",
"Epoch 26/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.0836 - acc: 0.9776 - val_loss: 0.3853 - val_acc: 0.8688\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0850 - accuracy: 0.9761 - val_loss: 0.3877 - val_accuracy: 0.8680\n",
"Epoch 27/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.0791 - acc: 0.9797 - val_loss: 0.3969 - val_acc: 0.8666\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.0807 - accuracy: 0.9772 - val_loss: 0.4038 - val_accuracy: 0.8640\n",
"Epoch 28/30\n",
- "25000/25000 [==============================] - 1s 49us/sample - loss: 0.0758 - acc: 0.9806 - val_loss: 0.4066 - val_acc: 0.8658\n",
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.0774 - accuracy: 0.9791 - val_loss: 0.4123 - val_accuracy: 0.8642\n",
"Epoch 29/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.0721 - acc: 0.9814 - val_loss: 0.4177 - val_acc: 0.8642\n",
+ "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0733 - accuracy: 0.9811 - val_loss: 0.4195 - val_accuracy: 0.8640\n",
"Epoch 30/30\n",
- "25000/25000 [==============================] - 1s 48us/sample - loss: 0.0685 - acc: 0.9834 - val_loss: 0.4281 - val_acc: 0.8635\n",
- "CPU times: user 51.5 s, sys: 2.2 s, total: 53.7 s\n",
- "Wall time: 37.1 s\n"
+ "25000/25000 [==============================] - 1s 29us/sample - loss: 0.0693 - accuracy: 0.9823 - val_loss: 0.4328 - val_accuracy: 0.8625\n",
+ "CPU times: user 1min 35s, sys: 4.22 s, total: 1min 39s\n",
+ "Wall time: 23.2 s\n"
]
}
],
@@ -462,56 +543,82 @@
"\n",
"history = model.fit(x_train,\n",
" y_train,\n",
- " epochs=n_epochs,\n",
- " batch_size=batch_size,\n",
- " validation_data=(x_valid, y_valid),\n",
- " verbose=1,\n",
- " callbacks=callbacks)\n"
+ " epochs = n_epochs,\n",
+ " batch_size = batch_size,\n",
+ " validation_data = (x_test, y_test),\n",
+ " verbose = 1,\n",
+ " callbacks = [savemodel_callback])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Evalulation du modèle"
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Training history"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 13,
+ "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"
+ }
+ ],
+ "source": [
+ "ooo.plot_history(history)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Reload and evaluate best model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Load best model... Done.\n",
- "\n",
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "embedding (Embedding) (None, 256, 32) 320000 \n",
- "_________________________________________________________________\n",
- "global_average_pooling1d (Gl (None, 32) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 32) 1056 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 1) 33 \n",
- "=================================================================\n",
- "Total params: 321,089\n",
- "Trainable params: 321,089\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "None\n",
- "25000/25000 [==============================] - 1s 20us/sample - loss: 0.2868 - acc: 0.8816\n"
+ "x_test / loss : 0.2869\n",
+ "x_test / accuracy : 0.8834\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "text/markdown": [
+ "#### Accuracy donut is :"
+ ],
"text/plain": [
- "<Figure size 576x576 with 1 Axes>"
+ "<IPython.core.display.Markdown object>"
]
},
"metadata": {},
@@ -519,74 +626,87 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
- "<Figure size 1728x576 with 2 Axes>"
+ "<Figure size 432x432 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "#### Confusion matrix is :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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+ " }</style><table id=\"T_65f76824_414d_11ea_86c3_492c802c4115\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >0</th> <th class=\"col_heading level0 col1\" >1</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_65f76824_414d_11ea_86c3_492c802c4115level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row0_col0\" class=\"data row0 col0\" >0.89</td>\n",
+ " <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row0_col1\" class=\"data row0 col1\" >0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_65f76824_414d_11ea_86c3_492c802c4115level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row1_col0\" class=\"data row1 col0\" >0.12</td>\n",
+ " <td id=\"T_65f76824_414d_11ea_86c3_492c802c4115row1_col1\" class=\"data row1 col1\" >0.88</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f9bc836a9d0>"
+ ]
},
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
- "print('Load best model... ', end='')\n",
- "model = keras.models.load_model(ooo.get_model_dir()+'/best_model.h5')\n",
- "print('Done.\\n')\n",
- "\n",
- "print(model.summary())\n",
- "# ------ Evaluation du résultat ------------------------------------\n",
- "\n",
- "results = model.evaluate(x_valid, y_valid)\n",
- "accuracy = results[1]\n",
- "\n",
- "fig, axs = plt.subplots()\n",
- "fig.set_size_inches(8,8)\n",
- "axs.pie([accuracy,1-accuracy], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
- " autopct='%1.1f%%', shadow=False, startangle=70, \n",
- " colors=[\"lightsteelblue\",\"coral\"], textprops={'fontsize': 18})\n",
- "ooo.save_fig('accuracy')\n",
- "plt.show()\n",
- "\n",
- "\n",
- "# ------ Statistiques d'apprentissage -------------------------------\n",
- "\n",
- "acc = history.history['acc']\n",
- "val_acc = history.history['val_acc']\n",
- "loss = history.history['loss']\n",
- "val_loss = history.history['val_loss']\n",
- "x_epochs = range(1, len(acc) + 1)\n",
- "\n",
- "fig, (ax1,ax2) = plt.subplots(1,2)\n",
- "fig.set_size_inches(24,8)\n",
- "ax1.plot(x_epochs, loss, 'o', fillstyle='full', markersize=6, color='steelblue', label='Training loss')\n",
- "ax1.plot(x_epochs, val_loss, '-', fillstyle='none', markersize=6, color='red', label='Validation loss')\n",
- "ax1.axhline(y=results[0], linestyle='--', linewidth=1, color='grey', label='Final test loss({:5.3})'.format(results[0]))\n",
- "ax1.set_title('Training and validation loss')\n",
- "ax1.set_xlabel('Epochs')\n",
- "ax1.set_ylabel('Loss')\n",
- "ax1.legend()\n",
- "\n",
- "ax2.plot(x_epochs, acc, 'o', fillstyle='full', markersize=6, color='steelblue', label='Training accuracy')\n",
- "ax2.plot(x_epochs, val_acc, '-', fillstyle='none', markersize=6, color='red', label='Validation accuracy')\n",
- "ax2.axhline(y=results[1], linestyle='--', linewidth=1, color='grey', label='Final test accuracy ({:5.3})'.format(results[1]))\n",
- "ax2.set_title('Training and validation accuracy')\n",
- "ax2.set_xlabel('Epochs')\n",
- "ax2.set_ylabel('Accuracy')\n",
- "ax2.legend()\n",
- "\n",
- "ooo.save_fig(\"Evaluations\")\n",
- "plt.show()\n"
+ "model = keras.models.load_model('./run/models/best_model.h5')\n",
+ "\n",
+ "# ---- Evaluate\n",
+ "reload(ooo)\n",
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
+ "\n",
+ "values=[score[1], 1-score[1]]\n",
+ "ooo.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\")\n",
+ "\n",
+ "# ---- Confusion matrix\n",
+ "\n",
+ "y_pred = model.predict_classes(x_test)\n",
+ "\n",
+ "ooo.display_confusion_matrix(y_test,y_pred,labels=range(2),color='orange',font_size='20pt')\n"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "code",
"execution_count": null,
diff --git a/IMDB/02-Prediction.ipynb b/IMDB/02-Prediction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a932667c3949b42e3c7cef5d5199920f8a4c9416
--- /dev/null
+++ b/IMDB/02-Prediction.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Text Embedding - IMDB dataset\n",
+ "=============================\n",
+ "---\n",
+ "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "\n",
+ "## Reviews analysis :\n",
+ "\n",
+ "The objective is to guess whether our new and personals films reviews are **positive or negative** . \n",
+ "For this, we will use our previously saved model.\n",
+ "\n",
+ "What we're going to do:\n",
+ "\n",
+ " - Preparing the data\n",
+ " - Retrieve our saved model\n",
+ " - Evaluate the result\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Init python stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IDLE 2020 - Practical Work Module\n",
+ " Version : 0.2.4\n",
+ " Run time : Monday 27 January 2020, 21:57:44\n",
+ " Matplotlib style : fidle/talk.mplstyle\n",
+ " TensorFlow version : 2.0.0\n",
+ " Keras version : 2.2.4-tf\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras as keras\n",
+ "import tensorflow.keras.datasets.imdb as imdb\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import seaborn as sns\n",
+ "import pandas as pd\n",
+ "\n",
+ "import os,h5py,json,re\n",
+ "\n",
+ "import fidle.pwk as ooo\n",
+ "from importlib import reload\n",
+ "\n",
+ "ooo.init()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 : Preparing the data\n",
+ "### 2.1 - Our reviews :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reviews = [ \"This film is particularly nice, a must see.\",\n",
+ " \"Some films are classics and cannot be ignored.\",\n",
+ " \"This movie is just abominable and doesn't deserve to be seen!\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Retrieve dictionaries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('./data/word_index.json', 'r') as fp:\n",
+ " word_index = json.load(fp)\n",
+ " index_word = {index:word for word,index in word_index.items()} "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3 - Clean, index and padd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "max_len = 256\n",
+ "vocab_size = 10000\n",
+ "\n",
+ "\n",
+ "nb_reviews = len(reviews)\n",
+ "x_data = []\n",
+ "\n",
+ "# ---- For all reviews\n",
+ "for review in reviews:\n",
+ " # ---- First index must be <start>\n",
+ " index_review=[1]\n",
+ " # ---- For all words\n",
+ " for w in review.split(' '):\n",
+ " # ---- Clean it\n",
+ " w_clean = re.sub(r\"[^a-zA-Z0-9]\", \"\", w)\n",
+ " # ---- Not empty ?\n",
+ " if len(w_clean)>0:\n",
+ " # ---- Get the index\n",
+ " w_index = word_index.get(w,2)\n",
+ " if w_index>vocab_size : w_index=2\n",
+ " # ---- Add the index if < vocab_size\n",
+ " index_review.append(w_index)\n",
+ " # ---- Add the indexed review\n",
+ " x_data.append(index_review) \n",
+ "\n",
+ "# ---- Padding\n",
+ "x_data = keras.preprocessing.sequence.pad_sequences(x_data, value = 0, padding = 'post', maxlen = max_len)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.4 - Have a look"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Text review : This film is particularly nice, a must see.\n",
+ "x_train[0] : [1, 2, 22, 9, 572, 2, 6, 215, 2, 0, 0, 0, 0, 0] (...)\n",
+ "Translation : <start> <unknown> film is particularly <unknown> a must <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
+ "\n",
+ "Text review : Some films are classics and cannot be ignored.\n",
+ "x_train[1] : [1, 2, 108, 26, 2239, 5, 566, 30, 2, 0, 0, 0, 0, 0] (...)\n",
+ "Translation : <start> <unknown> films are classics and cannot be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
+ "\n",
+ "Text review : This movie is just abominable and doesn't deserve to be seen!\n",
+ "x_train[2] : [1, 2, 20, 9, 43, 2, 5, 152, 1833, 8, 30, 2, 0, 0, 0, 0, 0] (...)\n",
+ "Translation : <start> <unknown> movie is just <unknown> and doesn't deserve to be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def translate(x):\n",
+ " return ' '.join( [index_word.get(i,'?') for i in x] )\n",
+ "\n",
+ "for i in range(nb_reviews):\n",
+ " imax=np.where(x_data[i]==0)[0][0]+5\n",
+ " print(f'\\nText review :', reviews[i])\n",
+ " print( f'x_train[{i:}] :', list(x_data[i][:imax]), '(...)')\n",
+ " print( 'Translation :', translate(x_data[i][:imax]), '(...)')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Bring back the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = keras.models.load_model('./run/models/best_model.h5')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Predict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_pred = model.predict(x_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### And the winner is :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "This film is particularly nice, a must see. => POSITIVE (0.62)\n",
+ "\n",
+ "Some films are classics and cannot be ignored. => POSITIVE (0.57)\n",
+ "\n",
+ "This movie is just abominable and doesn't deserve to be seen! => NEGATIVE (0.42)\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(nb_reviews):\n",
+ " print(f'\\n{reviews[i]:<70} =>',('NEGATIVE' if y_pred[i][0]<0.5 else 'POSITIVE'),f'({y_pred[i][0]:.2f})')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 0, 1, 2]"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a=[1]+[i for i in range(3)]\n",
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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/IMDB/03-LSTM-Keras.ipynb b/IMDB/03-LSTM-Keras.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2f9dadcc5dfcbe63d9eda93131344d35136789dc
--- /dev/null
+++ b/IMDB/03-LSTM-Keras.ipynb
@@ -0,0 +1,440 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Text Embedding - IMDB dataset\n",
+ "=============================\n",
+ "---\n",
+ "Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "\n",
+ "## Text classification using **Text embedding** :\n",
+ "\n",
+ "The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
+ "\n",
+ "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
+ "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
+ "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
+ "\n",
+ "What we're going to do:\n",
+ "\n",
+ " - Retrieve data\n",
+ " - Preparing the data\n",
+ " - Build a model\n",
+ " - Train the model\n",
+ " - Evaluate the result\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Init python stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras as keras\n",
+ "import tensorflow.keras.datasets.imdb as imdb\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import seaborn as sns\n",
+ "\n",
+ "import os,h5py,json\n",
+ "\n",
+ "import fidle.pwk as ooo\n",
+ "from importlib import reload\n",
+ "\n",
+ "ooo.init()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve data\n",
+ "\n",
+ "**From Keras :**\n",
+ "This IMDb dataset can bet get directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
+ "\n",
+ "Due to their nature, textual data can be somewhat complex.\n",
+ "\n",
+ "### 2.1 - Data structure : \n",
+ "The dataset is composed of 2 parts: **reviews** and **opinions** (positive/negative), with a **dictionary**\n",
+ "\n",
+ " - dataset = (reviews, opinions)\n",
+ " - reviews = \\[ review_0, review_1, ...\\]\n",
+ " - review_i = [ int1, int2, ...] where int_i is the index of the word in the dictionary.\n",
+ " - opinions = \\[ int0, int1, ...\\] where int_j == 0 if opinion is negative or 1 if opinion is positive.\n",
+ " - dictionary = \\[ mot1:int1, mot2:int2, ... ]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Get dataset\n",
+ "For simplicity, we will use a pre-formatted dataset. \n",
+ "See : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data \n",
+ "\n",
+ "However, Keras offers some usefull tools for formatting textual data. \n",
+ "See : https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab_size = 10000\n",
+ "\n",
+ "# ----- Retrieve x,y\n",
+ "#\n",
+ "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,\n",
+ " skip_top = 0,\n",
+ " maxlen = None,\n",
+ " seed = 42,\n",
+ " start_char = 1,\n",
+ " oov_char = 2,\n",
+ " index_from = 3, )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\" Max(x_train,x_test) : \", ooo.rmax([x_train,x_test]) )\n",
+ "print(\" x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
+ "print(\" x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))\n",
+ "\n",
+ "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3 - Have a look for humans (optional)\n",
+ "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
+ "So, we shifted the dataset by 3 with the parameter index_from=3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
+ "\n",
+ "word_index = imdb.get_word_index()\n",
+ "\n",
+ "# ---- Shift the dictionary from +3\n",
+ "\n",
+ "word_index = {w:(i+3) for w,i in word_index.items()}\n",
+ "\n",
+ "# ---- Add <pad>, <start> and unknown tags\n",
+ "\n",
+ "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
+ "\n",
+ "# ---- Create a reverse dictionary : {index:word}\n",
+ "\n",
+ "index_word = {index:word for word,index in word_index.items()} \n",
+ "\n",
+ "# ---- Add a nice function to transpose :\n",
+ "#\n",
+ "def dataset2text(review):\n",
+ " return ' '.join([index_word.get(i, '?') for i in review])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print('\\nDictionary size : ', len(word_index))\n",
+ "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
+ "print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.4 - Have a look for neurons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(12, 6))\n",
+ "ax=sns.distplot([len(i) for i in x_train],bins=60)\n",
+ "ax.set_title('Distribution of reviews by size')\n",
+ "plt.xlabel(\"Review's sizes\")\n",
+ "plt.ylabel('Density')\n",
+ "ax.set_xlim(0, 1500)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preprocess the data\n",
+ "In order to be processed by an NN, all entries must have the same length. \n",
+ "We chose a review length of **review_len** \n",
+ "We will therefore complete them with a padding (of \\<pad\\>\\) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "review_len = 256\n",
+ "\n",
+ "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
+ " value = 0,\n",
+ " padding = 'post',\n",
+ " maxlen = review_len)\n",
+ "\n",
+ "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
+ " value = 0 ,\n",
+ " padding = 'post',\n",
+ " maxlen = review_len)\n",
+ "\n",
+ "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
+ "print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Save dataset and dictionary (can be usefull)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.makedirs('./data', mode=0o750, exist_ok=True)\n",
+ "\n",
+ "with h5py.File('./data/dataset_imdb.h5', 'w') as f:\n",
+ " f.create_dataset(\"x_train\", data=x_train)\n",
+ " f.create_dataset(\"y_train\", data=y_train)\n",
+ " f.create_dataset(\"x_test\", data=x_test)\n",
+ " f.create_dataset(\"y_test\", data=y_test)\n",
+ "\n",
+ "with open('./data/word_index.json', 'w') as fp:\n",
+ " json.dump(word_index, fp)\n",
+ "\n",
+ "with open('./data/index_word.json', 'w') as fp:\n",
+ " json.dump(index_word, fp)\n",
+ "\n",
+ "print('Saved.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build the model\n",
+ "Few remarks :\n",
+ "1. We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
+ "2. **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
+ "In other words: we average the set of vectors/words of a sentence\n",
+ "3. L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
+ "Voir : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer\n",
+ "\n",
+ "A SUIVRE : https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks\n",
+ "### 4.1 - Build\n",
+ "More documentation about :\n",
+ " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
+ " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model(dense_vector_size=16):\n",
+ " \n",
+ " model = keras.Sequential()\n",
+ " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
+ " output_dim = dense_vector_size, \n",
+ " input_length = review_len))\n",
+ " model.add(keras.layers.LSTM(100))\n",
+ " model.add(keras.layers.Dense(16, activation='relu'))\n",
+ " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
+ "\n",
+ " model.compile(optimizer = 'adam',\n",
+ " loss = 'binary_crossentropy',\n",
+ " metrics = ['accuracy'])\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_model()\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Add callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = \"./run/models/best_model.h5\"\n",
+ "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "n_epochs = 5\n",
+ "batch_size = 512\n",
+ "\n",
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = n_epochs,\n",
+ " batch_size = batch_size,\n",
+ " validation_data = (x_test, y_test),\n",
+ " verbose = 1,\n",
+ " callbacks = [savemodel_callback])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Training history"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ooo.plot_history(history)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Reload and evaluate best model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = keras.models.load_model('./run/models/best_model.h5')\n",
+ "\n",
+ "# ---- Evaluate\n",
+ "reload(ooo)\n",
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
+ "\n",
+ "values=[score[1], 1-score[1]]\n",
+ "ooo.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\")\n",
+ "\n",
+ "# ---- Confusion matrix\n",
+ "\n",
+ "y_pred = model.predict_classes(x_test)\n",
+ "\n",
+ "ooo.display_confusion_matrix(y_test,y_pred,labels=range(2),color='orange',font_size='20pt')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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/IMDB/fidle/pwk.py b/IMDB/fidle/pwk.py
index fd7bc208cc25b092430d6b75439e341c253fd702..8cdba2c56fabc3d865fa816057aa9f5c06c5ed88 100644
--- a/IMDB/fidle/pwk.py
+++ b/IMDB/fidle/pwk.py
@@ -18,15 +18,20 @@ import datetime, time
import math
import numpy as np
+from collections.abc import Iterable
import tensorflow as tf
from tensorflow import keras
+from sklearn.metrics import confusion_matrix
+import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sn
-VERSION='0.2'
+from IPython.display import display, Markdown
+
+VERSION='0.2.4'
# -------------------------------------------------------------
@@ -75,12 +80,29 @@ def get_directory_size(path):
# -------------------------------------------------------------
#
def shuffle_np_dataset(x, y):
+ """
+ Shuffle a dataset (x,y)
+ args:
+ x,y : dataset
+ return:
+ x,y mixed
+ """
assert (len(x) == len(y)), "x and y must have same size"
p = np.random.permutation(len(x))
return x[p], y[p]
def update_progress(what,i,imax):
+ """
+ Display a text progress bar, as :
+ My progress bar : ############# 34%
+ args:
+ what : Progress bas name
+ i : Current progress
+ imax : Max value for i
+ return:
+ nothing
+ """
bar_length = min(40,imax)
if (i%int(imax/bar_length))!=0 and i<imax:
return
@@ -90,6 +112,44 @@ def update_progress(what,i,imax):
text = "{:16s} [{}] {:>5.1f}% of {}".format( what, "#"*block+"-"*(bar_length-block), progress*100, imax)
print(text, end=endofline)
+
+def rmax(l):
+ """
+ Recursive max() for a given iterable of iterables
+ Should be np.array of np.array or list of list, etc.
+ args:
+ l : Iterable of iterables
+ return:
+ max value
+ """
+ maxi = float('-inf')
+ for item in l:
+ if isinstance(item, Iterable):
+ t = rmax(item)
+ else:
+ t = item
+ if t > maxi:
+ maxi = t
+ return maxi
+
+def rmin(l):
+ """
+ Recursive min() for a given iterable of iterables
+ Should be np.array of np.array or list of list, etc.
+ args:
+ l : Iterable of iterables
+ return:
+ min value
+ """
+ mini = float('inf')
+ for item in l:
+ if isinstance(item, Iterable):
+ t = rmin(item)
+ else:
+ t = item
+ if t < mini:
+ mini = t
+ return mini
# -------------------------------------------------------------
# show_images
@@ -146,13 +206,29 @@ def plot_images(x,y, indices, columns=12, x_size=1, y_size=1, colorbar=False, y_
fig.colorbar(img,orientation="vertical", shrink=0.65)
plt.show()
+
def plot_image(x,cm='binary', figsize=(4,4)):
- (lx,ly,lz)=x.shape
+ """
+ Draw a single image.
+ Image shape can be (lx,ly), (lx,ly,1) or (lx,ly,n)
+ args:
+ x : image as np array
+ cm : color map ('binary')
+ figsize : fig size (4,4)
+ """
+ # ---- Shape is (lx,ly)
+ if len(x.shape)==2:
+ xx=x
+ # ---- Shape is (lx,ly,n)
+ if len(x.shape)==3:
+ (lx,ly,lz)=x.shape
+ if lz==1:
+ xx=x.reshape(lx,ly)
+ else:
+ xx=x
+ # ---- Draw it
plt.figure(figsize=figsize)
- if lz==1:
- plt.imshow(x.reshape(lx,ly), cmap = cm, interpolation='lanczos')
- else:
- plt.imshow(x.reshape(lx,ly,lz),cmap = cm, interpolation='lanczos')
+ plt.imshow(xx, cmap = cm, interpolation='lanczos')
plt.show()
@@ -184,6 +260,7 @@ def plot_history(history, figsize=(8,6),
# -------------------------------------------------------------
# plot_confusion_matrix
# -------------------------------------------------------------
+# Bug in Matplotlib 3.1.1
#
def plot_confusion_matrix(cm,
title='Confusion matrix',
@@ -194,6 +271,7 @@ def plot_confusion_matrix(cm,
xticks=5,yticks=5):
"""
given a sklearn confusion matrix (cm), make a nice plot
+ Note:bug in matplotlib 3.1.1
Args:
cm: confusion matrix from sklearn.metrics.confusion_matrix
@@ -210,8 +288,67 @@ def plot_confusion_matrix(cm,
plt.figure(figsize=figsize)
sn.heatmap(cm, linewidths=1, linecolor="#ffffff",square=True,
cmap=cmap, xticklabels=xticks, yticklabels=yticks,
- vmin=vmin,vmax=vmax)
+ vmin=vmin,vmax=vmax,annot=True)
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
plt.show()
+
+
+
+def display_confusion_matrix(y_true,y_pred,labels=None,color='green',
+ font_size='12pt', title="#### Confusion matrix is :"):
+ """
+ Show a confusion matrix for a predictions.
+ see : sklearn.metrics.confusion_matrix
+
+ Args:
+ y_true Real classes
+ y_pred Predicted classes
+ labels List of classes to show in the cm
+ color: Color for the palette (green)
+ font_size: Values font size
+ title: the text to display at the top of the matrix
+ """
+ assert (labels!=None),"Label must be set"
+
+ if title != None : display(Markdown(title))
+
+ cm = confusion_matrix( y_true,y_pred, normalize="true", labels=labels)
+ df=pd.DataFrame(cm)
+
+ cmap = sn.light_palette(color, as_cmap=True)
+ df.style.set_properties(**{'font-size': '20pt'})
+ display(df.style.format('{:.2f}') \
+ .background_gradient(cmap=cmap)
+ .set_properties(**{'font-size': font_size}))
+
+
+def plot_donut(values, labels, colors=["lightsteelblue","coral"], figsize=(6,6), title=None):
+ """
+ Draw a donut
+ args:
+ values : list of values
+ labels : list of labels
+ colors : list of color (["lightsteelblue","coral"])
+ figsize : size of figure ( (6,6) )
+ return:
+ nothing
+ """
+ # ---- Title or not
+ if title != None : display(Markdown(title))
+ # ---- Donut
+ plt.figure(figsize=figsize)
+ # ---- Draw a pie chart..
+ plt.pie(values, labels=labels,
+ colors = colors, autopct='%1.1f%%', startangle=70, pctdistance=0.85,
+ textprops={'fontsize': 18},
+ wedgeprops={"edgecolor":"w",'linewidth': 5, 'linestyle': 'solid', 'antialiased': True})
+ # ---- ..with a white circle
+ circle = plt.Circle((0,0),0.70,fc='white')
+ ax = plt.gca()
+ ax.add_artist(circle)
+ # Equal aspect ratio ensures that pie is drawn as a circle
+ plt.axis('equal')
+ plt.tight_layout()
+ plt.show()
\ No newline at end of file
diff --git a/MNIST/01-DNN-MNIST.ipynb b/MNIST/01-DNN-MNIST.ipynb
index 7f595c6c6122cc4ac3ade32b3dba135998df080c..c79ea48c985d0f8eb6df5a4b09f43e2ead53932e 100644
--- a/MNIST/01-DNN-MNIST.ipynb
+++ b/MNIST/01-DNN-MNIST.ipynb
@@ -28,9 +28,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IDLE 2020 - Practical Work Module\n",
+ " Version : 0.2.4\n",
+ " Run time : Monday 27 January 2020, 16:14:16\n",
+ " Matplotlib style : fidle/talk.mplstyle\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",
@@ -55,9 +68,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_train : (60000, 28, 28)\n",
+ "y_train : (60000,)\n",
+ "x_test : (10000, 28, 28)\n",
+ "y_test : (10000,)\n"
+ ]
+ }
+ ],
"source": [
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"\n",
@@ -76,14 +100,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Before normalization : Min=0, max=255\n",
+ "After normalization : Min=0.0, max=1.0\n"
+ ]
+ }
+ ],
"source": [
"print('Before normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))\n",
"\n",
- "x_train = x_train / 255.0\n",
- "x_test = x_test / 255.0\n",
+ "xmax=x_train.max()\n",
+ "x_train = x_train / xmax\n",
+ "x_test = x_test / xmax\n",
"\n",
"print('After normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))"
]
@@ -97,9 +131,32 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 4320x385.2 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x291.6 with 36 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"ooo.plot_images(x_train, y_train, [27], x_size=5,y_size=5, colorbar=True)\n",
"ooo.plot_images(x_train, y_train, range(5,41), columns=12)"
@@ -113,12 +170,13 @@
"About informations about : \n",
" - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
" - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
- " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)"
+ " - [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": null,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -146,9 +204,49 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 60000 samples, validate on 10000 samples\n",
+ "Epoch 1/16\n",
+ "60000/60000 [==============================] - 1s 20us/sample - loss: 0.5785 - accuracy: 0.8466 - val_loss: 0.2573 - val_accuracy: 0.9258\n",
+ "Epoch 2/16\n",
+ "60000/60000 [==============================] - 1s 12us/sample - loss: 0.2139 - accuracy: 0.9378 - val_loss: 0.1769 - val_accuracy: 0.9492\n",
+ "Epoch 3/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.1583 - accuracy: 0.9545 - val_loss: 0.1548 - val_accuracy: 0.9544\n",
+ "Epoch 4/16\n",
+ "60000/60000 [==============================] - 1s 9us/sample - loss: 0.1259 - accuracy: 0.9635 - val_loss: 0.1246 - val_accuracy: 0.9615\n",
+ "Epoch 5/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.1032 - accuracy: 0.9698 - val_loss: 0.1083 - val_accuracy: 0.9676\n",
+ "Epoch 6/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0861 - accuracy: 0.9751 - val_loss: 0.1021 - val_accuracy: 0.9688\n",
+ "Epoch 7/16\n",
+ "60000/60000 [==============================] - 1s 11us/sample - loss: 0.0745 - accuracy: 0.9782 - val_loss: 0.0924 - val_accuracy: 0.9718\n",
+ "Epoch 8/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0638 - accuracy: 0.9822 - val_loss: 0.0877 - val_accuracy: 0.9741\n",
+ "Epoch 9/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0571 - accuracy: 0.9832 - val_loss: 0.0815 - val_accuracy: 0.9744\n",
+ "Epoch 10/16\n",
+ "60000/60000 [==============================] - 1s 9us/sample - loss: 0.0485 - accuracy: 0.9864 - val_loss: 0.0782 - val_accuracy: 0.9763\n",
+ "Epoch 11/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0431 - accuracy: 0.9881 - val_loss: 0.0783 - val_accuracy: 0.9756\n",
+ "Epoch 12/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0379 - accuracy: 0.9894 - val_loss: 0.0781 - val_accuracy: 0.9761\n",
+ "Epoch 13/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0341 - accuracy: 0.9904 - val_loss: 0.0827 - val_accuracy: 0.9759\n",
+ "Epoch 14/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0287 - accuracy: 0.9922 - val_loss: 0.0790 - val_accuracy: 0.9770\n",
+ "Epoch 15/16\n",
+ "60000/60000 [==============================] - 1s 10us/sample - loss: 0.0256 - accuracy: 0.9934 - val_loss: 0.0755 - val_accuracy: 0.9779\n",
+ "Epoch 16/16\n",
+ "60000/60000 [==============================] - 1s 9us/sample - loss: 0.0230 - accuracy: 0.9940 - val_loss: 0.0824 - val_accuracy: 0.9762\n"
+ ]
+ }
+ ],
"source": [
"batch_size = 512\n",
"epochs = 16\n",
@@ -170,9 +268,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test loss : 0.08242619763978146\n",
+ "Test accuracy : 0.9762\n"
+ ]
+ }
+ ],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -189,9 +296,34 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"ooo.plot_history(history, figsize=(6,4))"
]
@@ -205,9 +337,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x1652.4 with 200 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"y_pred = model.predict_classes(x_test)\n",
"ooo.plot_images(x_test, y_test, range(0,200), columns=12, x_size=1, y_size=1, y_pred=y_pred)"
@@ -222,15 +365,592 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x507.6 with 15 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"errors=[ i for i in range(len(x_test)) if y_pred[i]!=y_test[i] ]\n",
"errors=errors[:min(24,len(errors))]\n",
"ooo.plot_images(x_test, y_test, errors[:15], columns=6, x_size=2, y_size=2, y_pred=y_pred)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "#### Confusion matrix is :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col2 {\n",
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+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col1 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col2 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col3 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col2 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col3 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col2 {\n",
+ " background-color: #e4fee4;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col3 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col2 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col3 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col4 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col2 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col3 {\n",
+ " background-color: #e4fee4;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col5 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col2 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col3 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col6 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col2 {\n",
+ " background-color: #e4fee4;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col3 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col7 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col2 {\n",
+ " background-color: #e4fee4;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col3 {\n",
+ " background-color: #e2fde2;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col4 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col8 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col9 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col0 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col1 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col2 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col3 {\n",
+ " background-color: #e4fee4;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col4 {\n",
+ " background-color: #e4fee4;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col5 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col6 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col7 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col8 {\n",
+ " background-color: #e5ffe5;\n",
+ " color: #000000;\n",
+ " font-size: 12pt;\n",
+ " } #T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col9 {\n",
+ " background-color: #008000;\n",
+ " color: #f1f1f1;\n",
+ " font-size: 12pt;\n",
+ " }</style><table id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >0</th> <th class=\"col_heading level0 col1\" >1</th> <th class=\"col_heading level0 col2\" >2</th> <th class=\"col_heading level0 col3\" >3</th> <th class=\"col_heading level0 col4\" >4</th> <th class=\"col_heading level0 col5\" >5</th> <th class=\"col_heading level0 col6\" >6</th> <th class=\"col_heading level0 col7\" >7</th> <th class=\"col_heading level0 col8\" >8</th> <th class=\"col_heading level0 col9\" >9</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col0\" class=\"data row0 col0\" >0.99</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col5\" class=\"data row0 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col8\" class=\"data row0 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row0_col9\" class=\"data row0 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col1\" class=\"data row1 col1\" >0.99</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col2\" class=\"data row2 col2\" >0.98</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col4\" class=\"data row2 col4\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col5\" class=\"data row2 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col6\" class=\"data row2 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col7\" class=\"data row2 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col8\" class=\"data row2 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col0\" class=\"data row3 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col3\" class=\"data row3 col3\" >0.98</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col4\" class=\"data row3 col4\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col5\" class=\"data row3 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col6\" class=\"data row3 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col8\" class=\"data row3 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row3_col9\" class=\"data row3 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col0\" class=\"data row4 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col2\" class=\"data row4 col2\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col4\" class=\"data row4 col4\" >0.98</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col5\" class=\"data row4 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col6\" class=\"data row4 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col7\" class=\"data row4 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col8\" class=\"data row4 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row4_col9\" class=\"data row4 col9\" >0.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col0\" class=\"data row5 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col3\" class=\"data row5 col3\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col4\" class=\"data row5 col4\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col5\" class=\"data row5 col5\" >0.97</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col6\" class=\"data row5 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col8\" class=\"data row5 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col0\" class=\"data row6 col0\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col2\" class=\"data row6 col2\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col4\" class=\"data row6 col4\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col5\" class=\"data row6 col5\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col6\" class=\"data row6 col6\" >0.97</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col7\" class=\"data row6 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col8\" class=\"data row6 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row6_col9\" class=\"data row6 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col0\" class=\"data row7 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col1\" class=\"data row7 col1\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col2\" class=\"data row7 col2\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col3\" class=\"data row7 col3\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col4\" class=\"data row7 col4\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col6\" class=\"data row7 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col7\" class=\"data row7 col7\" >0.98</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col8\" class=\"data row7 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row7_col9\" class=\"data row7 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col0\" class=\"data row8 col0\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col1\" class=\"data row8 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col2\" class=\"data row8 col2\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col3\" class=\"data row8 col3\" >0.02</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col4\" class=\"data row8 col4\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col5\" class=\"data row8 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col6\" class=\"data row8 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col7\" class=\"data row8 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col8\" class=\"data row8 col8\" >0.95</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row8_col9\" class=\"data row8 col9\" >0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col0\" class=\"data row9 col0\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col1\" class=\"data row9 col1\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col3\" class=\"data row9 col3\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col4\" class=\"data row9 col4\" >0.01</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col6\" class=\"data row9 col6\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col7\" class=\"data row9 col7\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col8\" class=\"data row9 col8\" >0.00</td>\n",
+ " <td id=\"T_d6b058f4_4117_11ea_aa86_11140e163fe6row9_col9\" class=\"data row9 col9\" >0.97</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f3ee0543090>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ooo.display_confusion_matrix(y_test,y_pred, range(10))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/MNIST/fidle/pwk.py b/MNIST/fidle/pwk.py
index fd7bc208cc25b092430d6b75439e341c253fd702..8cdba2c56fabc3d865fa816057aa9f5c06c5ed88 100644
--- a/MNIST/fidle/pwk.py
+++ b/MNIST/fidle/pwk.py
@@ -18,15 +18,20 @@ import datetime, time
import math
import numpy as np
+from collections.abc import Iterable
import tensorflow as tf
from tensorflow import keras
+from sklearn.metrics import confusion_matrix
+import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sn
-VERSION='0.2'
+from IPython.display import display, Markdown
+
+VERSION='0.2.4'
# -------------------------------------------------------------
@@ -75,12 +80,29 @@ def get_directory_size(path):
# -------------------------------------------------------------
#
def shuffle_np_dataset(x, y):
+ """
+ Shuffle a dataset (x,y)
+ args:
+ x,y : dataset
+ return:
+ x,y mixed
+ """
assert (len(x) == len(y)), "x and y must have same size"
p = np.random.permutation(len(x))
return x[p], y[p]
def update_progress(what,i,imax):
+ """
+ Display a text progress bar, as :
+ My progress bar : ############# 34%
+ args:
+ what : Progress bas name
+ i : Current progress
+ imax : Max value for i
+ return:
+ nothing
+ """
bar_length = min(40,imax)
if (i%int(imax/bar_length))!=0 and i<imax:
return
@@ -90,6 +112,44 @@ def update_progress(what,i,imax):
text = "{:16s} [{}] {:>5.1f}% of {}".format( what, "#"*block+"-"*(bar_length-block), progress*100, imax)
print(text, end=endofline)
+
+def rmax(l):
+ """
+ Recursive max() for a given iterable of iterables
+ Should be np.array of np.array or list of list, etc.
+ args:
+ l : Iterable of iterables
+ return:
+ max value
+ """
+ maxi = float('-inf')
+ for item in l:
+ if isinstance(item, Iterable):
+ t = rmax(item)
+ else:
+ t = item
+ if t > maxi:
+ maxi = t
+ return maxi
+
+def rmin(l):
+ """
+ Recursive min() for a given iterable of iterables
+ Should be np.array of np.array or list of list, etc.
+ args:
+ l : Iterable of iterables
+ return:
+ min value
+ """
+ mini = float('inf')
+ for item in l:
+ if isinstance(item, Iterable):
+ t = rmin(item)
+ else:
+ t = item
+ if t < mini:
+ mini = t
+ return mini
# -------------------------------------------------------------
# show_images
@@ -146,13 +206,29 @@ def plot_images(x,y, indices, columns=12, x_size=1, y_size=1, colorbar=False, y_
fig.colorbar(img,orientation="vertical", shrink=0.65)
plt.show()
+
def plot_image(x,cm='binary', figsize=(4,4)):
- (lx,ly,lz)=x.shape
+ """
+ Draw a single image.
+ Image shape can be (lx,ly), (lx,ly,1) or (lx,ly,n)
+ args:
+ x : image as np array
+ cm : color map ('binary')
+ figsize : fig size (4,4)
+ """
+ # ---- Shape is (lx,ly)
+ if len(x.shape)==2:
+ xx=x
+ # ---- Shape is (lx,ly,n)
+ if len(x.shape)==3:
+ (lx,ly,lz)=x.shape
+ if lz==1:
+ xx=x.reshape(lx,ly)
+ else:
+ xx=x
+ # ---- Draw it
plt.figure(figsize=figsize)
- if lz==1:
- plt.imshow(x.reshape(lx,ly), cmap = cm, interpolation='lanczos')
- else:
- plt.imshow(x.reshape(lx,ly,lz),cmap = cm, interpolation='lanczos')
+ plt.imshow(xx, cmap = cm, interpolation='lanczos')
plt.show()
@@ -184,6 +260,7 @@ def plot_history(history, figsize=(8,6),
# -------------------------------------------------------------
# plot_confusion_matrix
# -------------------------------------------------------------
+# Bug in Matplotlib 3.1.1
#
def plot_confusion_matrix(cm,
title='Confusion matrix',
@@ -194,6 +271,7 @@ def plot_confusion_matrix(cm,
xticks=5,yticks=5):
"""
given a sklearn confusion matrix (cm), make a nice plot
+ Note:bug in matplotlib 3.1.1
Args:
cm: confusion matrix from sklearn.metrics.confusion_matrix
@@ -210,8 +288,67 @@ def plot_confusion_matrix(cm,
plt.figure(figsize=figsize)
sn.heatmap(cm, linewidths=1, linecolor="#ffffff",square=True,
cmap=cmap, xticklabels=xticks, yticklabels=yticks,
- vmin=vmin,vmax=vmax)
+ vmin=vmin,vmax=vmax,annot=True)
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
plt.show()
+
+
+
+def display_confusion_matrix(y_true,y_pred,labels=None,color='green',
+ font_size='12pt', title="#### Confusion matrix is :"):
+ """
+ Show a confusion matrix for a predictions.
+ see : sklearn.metrics.confusion_matrix
+
+ Args:
+ y_true Real classes
+ y_pred Predicted classes
+ labels List of classes to show in the cm
+ color: Color for the palette (green)
+ font_size: Values font size
+ title: the text to display at the top of the matrix
+ """
+ assert (labels!=None),"Label must be set"
+
+ if title != None : display(Markdown(title))
+
+ cm = confusion_matrix( y_true,y_pred, normalize="true", labels=labels)
+ df=pd.DataFrame(cm)
+
+ cmap = sn.light_palette(color, as_cmap=True)
+ df.style.set_properties(**{'font-size': '20pt'})
+ display(df.style.format('{:.2f}') \
+ .background_gradient(cmap=cmap)
+ .set_properties(**{'font-size': font_size}))
+
+
+def plot_donut(values, labels, colors=["lightsteelblue","coral"], figsize=(6,6), title=None):
+ """
+ Draw a donut
+ args:
+ values : list of values
+ labels : list of labels
+ colors : list of color (["lightsteelblue","coral"])
+ figsize : size of figure ( (6,6) )
+ return:
+ nothing
+ """
+ # ---- Title or not
+ if title != None : display(Markdown(title))
+ # ---- Donut
+ plt.figure(figsize=figsize)
+ # ---- Draw a pie chart..
+ plt.pie(values, labels=labels,
+ colors = colors, autopct='%1.1f%%', startangle=70, pctdistance=0.85,
+ textprops={'fontsize': 18},
+ wedgeprops={"edgecolor":"w",'linewidth': 5, 'linestyle': 'solid', 'antialiased': True})
+ # ---- ..with a white circle
+ circle = plt.Circle((0,0),0.70,fc='white')
+ ax = plt.gca()
+ ax.add_artist(circle)
+ # Equal aspect ratio ensures that pie is drawn as a circle
+ plt.axis('equal')
+ plt.tight_layout()
+ plt.show()
\ No newline at end of file