From e7e3d3f5c12cbe2c5ad57c6f051213c2a5e28e59 Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@grenoble-inp.fr>
Date: Thu, 14 Jan 2021 08:25:55 +0100
Subject: [PATCH] Add done notebooks
---
.gitignore | 2 +
BHPD/01-DNN-Regression.ipynb | 1131 +-----
BHPD/01-DNN-Regression==done==.ipynb | 3068 +++++++++++++++++
BHPD/02-DNN-Regression-Premium.ipynb | 919 +----
BHPD/02-DNN-Regression-Premium==done==.ipynb | 2923 ++++++++++++++++
GTSRB/05-Full-convolutions.ipynb | 6 -
IRIS/01-Simple-Perceptron==done==.ipynb | 743 ++++
LinearReg/01-Linear-Regression.ipynb | 154 +-
LinearReg/01-Linear-Regression==done==.ipynb | 410 +++
LinearReg/02-Gradient-descent.ipynb | 467 +--
LinearReg/02-Gradient-descent==done==.ipynb | 711 ++++
LinearReg/03-Polynomial-Regression.ipynb | 316 +-
.../03-Polynomial-Regression==done==.ipynb | 586 ++++
LinearReg/04-Logistic-Regression.ipynb | 272 +-
.../04-Logistic-Regression==done==.ipynb | 823 +++++
MNIST/01-DNN-MNIST.ipynb | 379 +-
MNIST/01-DNN-MNIST==done==.ipynb | 1957 +++++++++++
README.ipynb | 12 +-
README.md | 2 +-
fidle/01-update-index.ipynb | 41 +-
fidle/02-running-ci-tests.ipynb | 89 +-
fidle/03-ci-report.ipynb | 223 +-
fidle/ci/full_jdev.yml | 37 +
fidle/config.py | 2 +-
fidle/cookindex.py | 2 +-
fidle/logs/ci_report.html | 219 +-
fidle/logs/ci_report.json | 141 +-
27 files changed, 11714 insertions(+), 3921 deletions(-)
create mode 100644 BHPD/01-DNN-Regression==done==.ipynb
create mode 100644 BHPD/02-DNN-Regression-Premium==done==.ipynb
create mode 100644 IRIS/01-Simple-Perceptron==done==.ipynb
create mode 100644 LinearReg/01-Linear-Regression==done==.ipynb
create mode 100644 LinearReg/02-Gradient-descent==done==.ipynb
create mode 100644 LinearReg/03-Polynomial-Regression==done==.ipynb
create mode 100644 LinearReg/04-Logistic-Regression==done==.ipynb
create mode 100644 MNIST/01-DNN-MNIST==done==.ipynb
create mode 100644 fidle/ci/full_jdev.yml
diff --git a/.gitignore b/.gitignore
index e76966f..504a663 100755
--- a/.gitignore
+++ b/.gitignore
@@ -3,8 +3,10 @@
__pycache__
*/__pycache__/*
*==*==.ipynb
+!*==done==.ipynb
stderr.txt
stdout.txt
+debug.log
run/
figs/
data/
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 26819da..9c5332f 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -51,97 +51,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { content:url(data:image/svg+xml;base64,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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 1.2b1 DEV\n",
- "Notebook id : BHPD1\n",
- "Run time : Friday 8 January 2021, 01:09:13\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@@ -169,7 +81,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -186,116 +98,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_41981_\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_41981_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_41981_row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
- " <td id=\"T_41981_row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
- " <td id=\"T_41981_row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
- " <td id=\"T_41981_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
- " <td id=\"T_41981_row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
- " <td id=\"T_41981_row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
- " <td id=\"T_41981_row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
- " <td id=\"T_41981_row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
- " <td id=\"T_41981_row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
- " <td id=\"T_41981_row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
- " <td id=\"T_41981_row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
- " <td id=\"T_41981_row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
- " <td id=\"T_41981_row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
- " <td id=\"T_41981_row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_41981_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_41981_row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
- " <td id=\"T_41981_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_41981_row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
- " <td id=\"T_41981_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_41981_row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
- " <td id=\"T_41981_row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
- " <td id=\"T_41981_row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
- " <td id=\"T_41981_row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
- " <td id=\"T_41981_row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
- " <td id=\"T_41981_row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
- " <td id=\"T_41981_row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
- " <td id=\"T_41981_row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
- " <td id=\"T_41981_row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
- " <td id=\"T_41981_row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_41981_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_41981_row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
- " <td id=\"T_41981_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
- " <td id=\"T_41981_row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
- " <td id=\"T_41981_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
- " <td id=\"T_41981_row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
- " <td id=\"T_41981_row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
- " <td id=\"T_41981_row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
- " <td id=\"T_41981_row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
- " <td id=\"T_41981_row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
- " <td id=\"T_41981_row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
- " <td id=\"T_41981_row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
- " <td id=\"T_41981_row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
- " <td id=\"T_41981_row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
- " <td id=\"T_41981_row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_41981_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_41981_row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
- " <td id=\"T_41981_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_41981_row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
- " <td id=\"T_41981_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_41981_row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
- " <td id=\"T_41981_row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
- " <td id=\"T_41981_row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
- " <td id=\"T_41981_row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
- " <td id=\"T_41981_row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
- " <td id=\"T_41981_row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
- " <td id=\"T_41981_row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
- " <td id=\"T_41981_row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
- " <td id=\"T_41981_row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_41981_row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_41981_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_41981_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_41981_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_41981_row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_41981_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_41981_row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_41981_row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_41981_row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_41981_row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_41981_row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_41981_row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_41981_row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_41981_row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_41981_row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_41981_row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f0105877a10>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Missing Data : 0 Shape is : (506, 14)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
"\n",
@@ -316,19 +121,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Suffle and Split => train, test\n",
"#\n",
@@ -361,388 +156,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_b4ec0_\" ><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_b4ec0_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_b4ec0_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_b4ec0_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_b4ec0_row1_col0\" class=\"data row1 col0\" >3.37</td>\n",
- " <td id=\"T_b4ec0_row1_col1\" class=\"data row1 col1\" >11.25</td>\n",
- " <td id=\"T_b4ec0_row1_col2\" class=\"data row1 col2\" >11.05</td>\n",
- " <td id=\"T_b4ec0_row1_col3\" class=\"data row1 col3\" >0.07</td>\n",
- " <td id=\"T_b4ec0_row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
- " <td id=\"T_b4ec0_row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
- " <td id=\"T_b4ec0_row1_col6\" class=\"data row1 col6\" >68.01</td>\n",
- " <td id=\"T_b4ec0_row1_col7\" class=\"data row1 col7\" >3.77</td>\n",
- " <td id=\"T_b4ec0_row1_col8\" class=\"data row1 col8\" >9.68</td>\n",
- " <td id=\"T_b4ec0_row1_col9\" class=\"data row1 col9\" >409.00</td>\n",
- " <td id=\"T_b4ec0_row1_col10\" class=\"data row1 col10\" >18.39</td>\n",
- " <td id=\"T_b4ec0_row1_col11\" class=\"data row1 col11\" >355.75</td>\n",
- " <td id=\"T_b4ec0_row1_col12\" class=\"data row1 col12\" >12.62</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_b4ec0_row2_col0\" class=\"data row2 col0\" >7.50</td>\n",
- " <td id=\"T_b4ec0_row2_col1\" class=\"data row2 col1\" >23.19</td>\n",
- " <td id=\"T_b4ec0_row2_col2\" class=\"data row2 col2\" >6.73</td>\n",
- " <td id=\"T_b4ec0_row2_col3\" class=\"data row2 col3\" >0.26</td>\n",
- " <td id=\"T_b4ec0_row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
- " <td id=\"T_b4ec0_row2_col5\" class=\"data row2 col5\" >0.74</td>\n",
- " <td id=\"T_b4ec0_row2_col6\" class=\"data row2 col6\" >28.85</td>\n",
- " <td id=\"T_b4ec0_row2_col7\" class=\"data row2 col7\" >2.03</td>\n",
- " <td id=\"T_b4ec0_row2_col8\" class=\"data row2 col8\" >8.80</td>\n",
- " <td id=\"T_b4ec0_row2_col9\" class=\"data row2 col9\" >169.89</td>\n",
- " <td id=\"T_b4ec0_row2_col10\" class=\"data row2 col10\" >2.22</td>\n",
- " <td id=\"T_b4ec0_row2_col11\" class=\"data row2 col11\" >90.11</td>\n",
- " <td id=\"T_b4ec0_row2_col12\" class=\"data row2 col12\" >7.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_b4ec0_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_b4ec0_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
- " <td id=\"T_b4ec0_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_b4ec0_row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_b4ec0_row3_col6\" class=\"data row3 col6\" >2.90</td>\n",
- " <td id=\"T_b4ec0_row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
- " <td id=\"T_b4ec0_row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_b4ec0_row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
- " <td id=\"T_b4ec0_row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_b4ec0_row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_b4ec0_row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_b4ec0_row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
- " <td id=\"T_b4ec0_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
- " <td id=\"T_b4ec0_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_b4ec0_row4_col5\" class=\"data row4 col5\" >5.90</td>\n",
- " <td id=\"T_b4ec0_row4_col6\" class=\"data row4 col6\" >42.32</td>\n",
- " <td id=\"T_b4ec0_row4_col7\" class=\"data row4 col7\" >2.12</td>\n",
- " <td id=\"T_b4ec0_row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_b4ec0_row4_col9\" class=\"data row4 col9\" >279.00</td>\n",
- " <td id=\"T_b4ec0_row4_col10\" class=\"data row4 col10\" >16.90</td>\n",
- " <td id=\"T_b4ec0_row4_col11\" class=\"data row4 col11\" >374.46</td>\n",
- " <td id=\"T_b4ec0_row4_col12\" class=\"data row4 col12\" >6.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_b4ec0_row5_col0\" class=\"data row5 col0\" >0.29</td>\n",
- " <td id=\"T_b4ec0_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
- " <td id=\"T_b4ec0_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_b4ec0_row5_col5\" class=\"data row5 col5\" >6.20</td>\n",
- " <td id=\"T_b4ec0_row5_col6\" class=\"data row5 col6\" >77.70</td>\n",
- " <td id=\"T_b4ec0_row5_col7\" class=\"data row5 col7\" >3.21</td>\n",
- " <td id=\"T_b4ec0_row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_b4ec0_row5_col9\" class=\"data row5 col9\" >329.50</td>\n",
- " <td id=\"T_b4ec0_row5_col10\" class=\"data row5 col10\" >18.90</td>\n",
- " <td id=\"T_b4ec0_row5_col11\" class=\"data row5 col11\" >390.88</td>\n",
- " <td id=\"T_b4ec0_row5_col12\" class=\"data row5 col12\" >11.43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_b4ec0_row6_col0\" class=\"data row6 col0\" >3.76</td>\n",
- " <td id=\"T_b4ec0_row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_b4ec0_row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_b4ec0_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_b4ec0_row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
- " <td id=\"T_b4ec0_row6_col5\" class=\"data row6 col5\" >6.60</td>\n",
- " <td id=\"T_b4ec0_row6_col6\" class=\"data row6 col6\" >93.90</td>\n",
- " <td id=\"T_b4ec0_row6_col7\" class=\"data row6 col7\" >5.19</td>\n",
- " <td id=\"T_b4ec0_row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_b4ec0_row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_b4ec0_row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_b4ec0_row6_col11\" class=\"data row6 col11\" >395.56</td>\n",
- " <td id=\"T_b4ec0_row6_col12\" class=\"data row6 col12\" >16.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_b4ec0_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_b4ec0_row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
- " <td id=\"T_b4ec0_row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_b4ec0_row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_b4ec0_row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_b4ec0_row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_b4ec0_row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_b4ec0_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_b4ec0_row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
- " <td id=\"T_b4ec0_row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_b4ec0_row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_b4ec0_row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_b4ec0_row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_b4ec0_row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f010572c1d0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_be30a_\" ><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_be30a_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_be30a_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_be30a_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_be30a_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_be30a_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
- " <td id=\"T_be30a_row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_be30a_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_be30a_row1_col10\" class=\"data row1 col10\" >-0.00</td>\n",
- " <td id=\"T_be30a_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " <td id=\"T_be30a_row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_be30a_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_be30a_row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_be30a_row3_col0\" class=\"data row3 col0\" >-0.45</td>\n",
- " <td id=\"T_be30a_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_be30a_row3_col2\" class=\"data row3 col2\" >-1.46</td>\n",
- " <td id=\"T_be30a_row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
- " <td id=\"T_be30a_row3_col4\" class=\"data row3 col4\" >-1.49</td>\n",
- " <td id=\"T_be30a_row3_col5\" class=\"data row3 col5\" >-3.72</td>\n",
- " <td id=\"T_be30a_row3_col6\" class=\"data row3 col6\" >-2.26</td>\n",
- " <td id=\"T_be30a_row3_col7\" class=\"data row3 col7\" >-1.29</td>\n",
- " <td id=\"T_be30a_row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
- " <td id=\"T_be30a_row3_col9\" class=\"data row3 col9\" >-1.30</td>\n",
- " <td id=\"T_be30a_row3_col10\" class=\"data row3 col10\" >-2.61</td>\n",
- " <td id=\"T_be30a_row3_col11\" class=\"data row3 col11\" >-3.94</td>\n",
- " <td id=\"T_be30a_row3_col12\" class=\"data row3 col12\" >-1.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_be30a_row4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
- " <td id=\"T_be30a_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
- " <td id=\"T_be30a_row4_col2\" class=\"data row4 col2\" >-0.87</td>\n",
- " <td id=\"T_be30a_row4_col3\" class=\"data row4 col3\" >-0.28</td>\n",
- " <td id=\"T_be30a_row4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
- " <td id=\"T_be30a_row4_col5\" class=\"data row4 col5\" >-0.55</td>\n",
- " <td id=\"T_be30a_row4_col6\" class=\"data row4 col6\" >-0.89</td>\n",
- " <td id=\"T_be30a_row4_col7\" class=\"data row4 col7\" >-0.81</td>\n",
- " <td id=\"T_be30a_row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
- " <td id=\"T_be30a_row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
- " <td id=\"T_be30a_row4_col10\" class=\"data row4 col10\" >-0.67</td>\n",
- " <td id=\"T_be30a_row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
- " <td id=\"T_be30a_row4_col12\" class=\"data row4 col12\" >-0.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_be30a_row5_col0\" class=\"data row5 col0\" >-0.41</td>\n",
- " <td id=\"T_be30a_row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
- " <td id=\"T_be30a_row5_col2\" class=\"data row5 col2\" >-0.20</td>\n",
- " <td id=\"T_be30a_row5_col3\" class=\"data row5 col3\" >-0.28</td>\n",
- " <td id=\"T_be30a_row5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
- " <td id=\"T_be30a_row5_col5\" class=\"data row5 col5\" >-0.14</td>\n",
- " <td id=\"T_be30a_row5_col6\" class=\"data row5 col6\" >0.34</td>\n",
- " <td id=\"T_be30a_row5_col7\" class=\"data row5 col7\" >-0.28</td>\n",
- " <td id=\"T_be30a_row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
- " <td id=\"T_be30a_row5_col9\" class=\"data row5 col9\" >-0.47</td>\n",
- " <td id=\"T_be30a_row5_col10\" class=\"data row5 col10\" >0.23</td>\n",
- " <td id=\"T_be30a_row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
- " <td id=\"T_be30a_row5_col12\" class=\"data row5 col12\" >-0.16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_be30a_row6_col0\" class=\"data row6 col0\" >0.05</td>\n",
- " <td id=\"T_be30a_row6_col1\" class=\"data row6 col1\" >0.05</td>\n",
- " <td id=\"T_be30a_row6_col2\" class=\"data row6 col2\" >1.05</td>\n",
- " <td id=\"T_be30a_row6_col3\" class=\"data row6 col3\" >-0.28</td>\n",
- " <td id=\"T_be30a_row6_col4\" class=\"data row6 col4\" >0.61</td>\n",
- " <td id=\"T_be30a_row6_col5\" class=\"data row6 col5\" >0.40</td>\n",
- " <td id=\"T_be30a_row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
- " <td id=\"T_be30a_row6_col7\" class=\"data row6 col7\" >0.70</td>\n",
- " <td id=\"T_be30a_row6_col8\" class=\"data row6 col8\" >1.63</td>\n",
- " <td id=\"T_be30a_row6_col9\" class=\"data row6 col9\" >1.51</td>\n",
- " <td id=\"T_be30a_row6_col10\" class=\"data row6 col10\" >0.82</td>\n",
- " <td id=\"T_be30a_row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
- " <td id=\"T_be30a_row6_col12\" class=\"data row6 col12\" >0.58</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_be30a_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_be30a_row7_col0\" class=\"data row7 col0\" >9.35</td>\n",
- " <td id=\"T_be30a_row7_col1\" class=\"data row7 col1\" >3.83</td>\n",
- " <td id=\"T_be30a_row7_col2\" class=\"data row7 col2\" >2.48</td>\n",
- " <td id=\"T_be30a_row7_col3\" class=\"data row7 col3\" >3.55</td>\n",
- " <td id=\"T_be30a_row7_col4\" class=\"data row7 col4\" >2.78</td>\n",
- " <td id=\"T_be30a_row7_col5\" class=\"data row7 col5\" >3.36</td>\n",
- " <td id=\"T_be30a_row7_col6\" class=\"data row7 col6\" >1.11</td>\n",
- " <td id=\"T_be30a_row7_col7\" class=\"data row7 col7\" >3.41</td>\n",
- " <td id=\"T_be30a_row7_col8\" class=\"data row7 col8\" >1.63</td>\n",
- " <td id=\"T_be30a_row7_col9\" class=\"data row7 col9\" >1.78</td>\n",
- " <td id=\"T_be30a_row7_col10\" class=\"data row7 col10\" >1.63</td>\n",
- " <td id=\"T_be30a_row7_col11\" class=\"data row7 col11\" >0.46</td>\n",
- " <td id=\"T_be30a_row7_col12\" class=\"data row7 col12\" >3.49</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f01bb4b2a10>"
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- "data": {
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- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_38be7_\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_38be7_level0_row0\" class=\"row_heading level0 row0\" >357</th>\n",
- " <td id=\"T_38be7_row0_col0\" class=\"data row0 col0\" >0.06</td>\n",
- " <td id=\"T_38be7_row0_col1\" class=\"data row0 col1\" >-0.49</td>\n",
- " <td id=\"T_38be7_row0_col2\" class=\"data row0 col2\" >1.05</td>\n",
- " <td id=\"T_38be7_row0_col3\" class=\"data row0 col3\" >3.55</td>\n",
- " <td id=\"T_38be7_row0_col4\" class=\"data row0 col4\" >1.89</td>\n",
- " <td id=\"T_38be7_row0_col5\" class=\"data row0 col5\" >0.12</td>\n",
- " <td id=\"T_38be7_row0_col6\" class=\"data row0 col6\" >0.80</td>\n",
- " <td id=\"T_38be7_row0_col7\" class=\"data row0 col7\" >-0.62</td>\n",
- " <td id=\"T_38be7_row0_col8\" class=\"data row0 col8\" >1.63</td>\n",
- " <td id=\"T_38be7_row0_col9\" class=\"data row0 col9\" >1.51</td>\n",
- " <td id=\"T_38be7_row0_col10\" class=\"data row0 col10\" >0.82</td>\n",
- " <td id=\"T_38be7_row0_col11\" class=\"data row0 col11\" >0.39</td>\n",
- " <td id=\"T_38be7_row0_col12\" class=\"data row0 col12\" >0.09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_38be7_level0_row1\" class=\"row_heading level0 row1\" >274</th>\n",
- " <td id=\"T_38be7_row1_col0\" class=\"data row1 col0\" >-0.44</td>\n",
- " <td id=\"T_38be7_row1_col1\" class=\"data row1 col1\" >1.24</td>\n",
- " <td id=\"T_38be7_row1_col2\" class=\"data row1 col2\" >-0.69</td>\n",
- " <td id=\"T_38be7_row1_col3\" class=\"data row1 col3\" >3.55</td>\n",
- " <td id=\"T_38be7_row1_col4\" class=\"data row1 col4\" >-0.95</td>\n",
- " <td id=\"T_38be7_row1_col5\" class=\"data row1 col5\" >0.61</td>\n",
- " <td id=\"T_38be7_row1_col6\" class=\"data row1 col6\" >-1.22</td>\n",
- " <td id=\"T_38be7_row1_col7\" class=\"data row1 col7\" >0.15</td>\n",
- " <td id=\"T_38be7_row1_col8\" class=\"data row1 col8\" >-0.65</td>\n",
- " <td id=\"T_38be7_row1_col9\" class=\"data row1 col9\" >-0.91</td>\n",
- " <td id=\"T_38be7_row1_col10\" class=\"data row1 col10\" >-0.35</td>\n",
- " <td id=\"T_38be7_row1_col11\" class=\"data row1 col11\" >0.46</td>\n",
- " <td id=\"T_38be7_row1_col12\" class=\"data row1 col12\" >-1.25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_38be7_level0_row2\" class=\"row_heading level0 row2\" >412</th>\n",
- " <td id=\"T_38be7_row2_col0\" class=\"data row2 col0\" >2.06</td>\n",
- " <td id=\"T_38be7_row2_col1\" class=\"data row2 col1\" >-0.49</td>\n",
- " <td id=\"T_38be7_row2_col2\" class=\"data row2 col2\" >1.05</td>\n",
- " <td id=\"T_38be7_row2_col3\" class=\"data row2 col3\" >-0.28</td>\n",
- " <td id=\"T_38be7_row2_col4\" class=\"data row2 col4\" >0.37</td>\n",
- " <td id=\"T_38be7_row2_col5\" class=\"data row2 col5\" >-2.27</td>\n",
- " <td id=\"T_38be7_row2_col6\" class=\"data row2 col6\" >1.11</td>\n",
- " <td id=\"T_38be7_row2_col7\" class=\"data row2 col7\" >-1.09</td>\n",
- " <td id=\"T_38be7_row2_col8\" class=\"data row2 col8\" >1.63</td>\n",
- " <td id=\"T_38be7_row2_col9\" class=\"data row2 col9\" >1.51</td>\n",
- " <td id=\"T_38be7_row2_col10\" class=\"data row2 col10\" >0.82</td>\n",
- " <td id=\"T_38be7_row2_col11\" class=\"data row2 col11\" >-3.63</td>\n",
- " <td id=\"T_38be7_row2_col12\" class=\"data row2 col12\" >2.99</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_38be7_level0_row3\" class=\"row_heading level0 row3\" >474</th>\n",
- " <td id=\"T_38be7_row3_col0\" class=\"data row3 col0\" >0.62</td>\n",
- " <td id=\"T_38be7_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_38be7_row3_col2\" class=\"data row3 col2\" >1.05</td>\n",
- " <td id=\"T_38be7_row3_col3\" class=\"data row3 col3\" >-0.28</td>\n",
- " <td id=\"T_38be7_row3_col4\" class=\"data row3 col4\" >0.26</td>\n",
- " <td id=\"T_38be7_row3_col5\" class=\"data row3 col5\" >-1.19</td>\n",
- " <td id=\"T_38be7_row3_col6\" class=\"data row3 col6\" >0.95</td>\n",
- " <td id=\"T_38be7_row3_col7\" class=\"data row3 col7\" >-0.66</td>\n",
- " <td id=\"T_38be7_row3_col8\" class=\"data row3 col8\" >1.63</td>\n",
- " <td id=\"T_38be7_row3_col9\" class=\"data row3 col9\" >1.51</td>\n",
- " <td id=\"T_38be7_row3_col10\" class=\"data row3 col10\" >0.82</td>\n",
- " <td id=\"T_38be7_row3_col11\" class=\"data row3 col11\" >-0.04</td>\n",
- " <td id=\"T_38be7_row3_col12\" class=\"data row3 col12\" >0.76</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_38be7_level0_row4\" class=\"row_heading level0 row4\" >216</th>\n",
- " <td id=\"T_38be7_row4_col0\" class=\"data row4 col0\" >-0.44</td>\n",
- " <td id=\"T_38be7_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
- " <td id=\"T_38be7_row4_col2\" class=\"data row4 col2\" >0.42</td>\n",
- " <td id=\"T_38be7_row4_col3\" class=\"data row4 col3\" >3.55</td>\n",
- " <td id=\"T_38be7_row4_col4\" class=\"data row4 col4\" >-0.04</td>\n",
- " <td id=\"T_38be7_row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
- " <td id=\"T_38be7_row4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
- " <td id=\"T_38be7_row4_col7\" class=\"data row4 col7\" >-0.32</td>\n",
- " <td id=\"T_38be7_row4_col8\" class=\"data row4 col8\" >-0.53</td>\n",
- " <td id=\"T_38be7_row4_col9\" class=\"data row4 col9\" >-0.78</td>\n",
- " <td id=\"T_38be7_row4_col10\" class=\"data row4 col10\" >-0.90</td>\n",
- " <td id=\"T_38be7_row4_col11\" class=\"data row4 col11\" >0.41</td>\n",
- " <td id=\"T_38be7_row4_col12\" class=\"data row4 col12\" >0.12</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f010572c090>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
"\n",
@@ -772,7 +188,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -800,30 +216,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"model=get_model_v1( (13,) )\n",
"\n",
@@ -842,216 +237,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/100\n",
- "36/36 [==============================] - 0s 5ms/step - loss: 528.6602 - mae: 20.8095 - mse: 528.6602 - val_loss: 382.0872 - val_mae: 17.4028 - val_mse: 382.0872\n",
- "Epoch 2/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 324.0841 - mae: 15.5158 - mse: 324.0841 - val_loss: 186.1084 - val_mae: 11.6776 - val_mse: 186.1084\n",
- "Epoch 3/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 137.9440 - mae: 9.1263 - mse: 137.9440 - val_loss: 73.6228 - val_mae: 6.5124 - val_mse: 73.6228\n",
- "Epoch 4/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 58.4952 - mae: 5.7129 - mse: 58.4952 - val_loss: 46.9296 - val_mae: 4.9677 - val_mse: 46.9296\n",
- "Epoch 5/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 34.6571 - mae: 4.3862 - mse: 34.6571 - val_loss: 36.0974 - val_mae: 4.0957 - val_mse: 36.0974\n",
- "Epoch 6/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 25.0004 - mae: 3.6589 - mse: 25.0004 - val_loss: 31.2137 - val_mae: 3.7278 - val_mse: 31.2137\n",
- "Epoch 7/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 21.0519 - mae: 3.3354 - mse: 21.0519 - val_loss: 28.1914 - val_mae: 3.4788 - val_mse: 28.1914\n",
- "Epoch 8/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 18.5185 - mae: 3.1083 - mse: 18.5185 - val_loss: 25.0752 - val_mae: 3.1716 - val_mse: 25.0752\n",
- "Epoch 9/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 16.6720 - mae: 2.8630 - mse: 16.6720 - val_loss: 23.5365 - val_mae: 3.0271 - val_mse: 23.5365\n",
- "Epoch 10/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 15.6488 - mae: 2.7623 - mse: 15.6488 - val_loss: 21.7859 - val_mae: 2.9200 - val_mse: 21.7859\n",
- "Epoch 11/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 14.5972 - mae: 2.6339 - mse: 14.5972 - val_loss: 20.7650 - val_mae: 2.8556 - val_mse: 20.7650\n",
- "Epoch 12/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 13.8285 - mae: 2.5940 - mse: 13.8285 - val_loss: 20.2548 - val_mae: 2.7733 - val_mse: 20.2548\n",
- "Epoch 13/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 13.3525 - mae: 2.5089 - mse: 13.3525 - val_loss: 19.2741 - val_mae: 2.7488 - val_mse: 19.2741\n",
- "Epoch 14/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 12.9928 - mae: 2.4619 - mse: 12.9928 - val_loss: 19.1163 - val_mae: 2.8952 - val_mse: 19.1163\n",
- "Epoch 15/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 12.6822 - mae: 2.4667 - mse: 12.6822 - val_loss: 18.0593 - val_mae: 2.7012 - val_mse: 18.0593\n",
- "Epoch 16/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 12.0337 - mae: 2.4042 - mse: 12.0337 - val_loss: 18.4064 - val_mae: 2.7333 - val_mse: 18.4064\n",
- "Epoch 17/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 11.9141 - mae: 2.3476 - mse: 11.9141 - val_loss: 17.7628 - val_mae: 2.7539 - val_mse: 17.7628\n",
- "Epoch 18/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 11.4745 - mae: 2.3208 - mse: 11.4745 - val_loss: 17.0482 - val_mae: 2.6804 - val_mse: 17.0482\n",
- "Epoch 19/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 11.3928 - mae: 2.3049 - mse: 11.3928 - val_loss: 16.8050 - val_mae: 2.6693 - val_mse: 16.8050\n",
- "Epoch 20/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 11.3859 - mae: 2.3019 - mse: 11.3859 - val_loss: 16.5509 - val_mae: 2.6351 - val_mse: 16.5509\n",
- "Epoch 21/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.7268 - mae: 2.2695 - mse: 10.7268 - val_loss: 16.8328 - val_mae: 2.5932 - val_mse: 16.8328\n",
- "Epoch 22/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.8960 - mae: 2.2326 - mse: 10.8960 - val_loss: 16.8741 - val_mae: 2.7190 - val_mse: 16.8741\n",
- "Epoch 23/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.6260 - mae: 2.2117 - mse: 10.6260 - val_loss: 16.9705 - val_mae: 2.8508 - val_mse: 16.9705\n",
- "Epoch 24/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.3208 - mae: 2.2318 - mse: 10.3208 - val_loss: 15.7215 - val_mae: 2.5971 - val_mse: 15.7215\n",
- "Epoch 25/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.1904 - mae: 2.1686 - mse: 10.1904 - val_loss: 15.5120 - val_mae: 2.5987 - val_mse: 15.5120\n",
- "Epoch 26/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.9797 - mae: 2.1702 - mse: 9.9797 - val_loss: 15.5233 - val_mae: 2.6006 - val_mse: 15.5233\n",
- "Epoch 27/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.0867 - mae: 2.1720 - mse: 10.0867 - val_loss: 15.4992 - val_mae: 2.6143 - val_mse: 15.4992\n",
- "Epoch 28/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.8478 - mae: 2.1402 - mse: 9.8478 - val_loss: 15.3322 - val_mae: 2.5853 - val_mse: 15.3322\n",
- "Epoch 29/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.7488 - mae: 2.1326 - mse: 9.7488 - val_loss: 15.3041 - val_mae: 2.6232 - val_mse: 15.3041\n",
- "Epoch 30/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.4161 - mae: 2.0990 - mse: 9.4161 - val_loss: 15.3295 - val_mae: 2.5953 - val_mse: 15.3295\n",
- "Epoch 31/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.3843 - mae: 2.0823 - mse: 9.3843 - val_loss: 15.5947 - val_mae: 2.6750 - val_mse: 15.5947\n",
- "Epoch 32/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.2416 - mae: 2.0897 - mse: 9.2416 - val_loss: 15.2306 - val_mae: 2.5839 - val_mse: 15.2306\n",
- "Epoch 33/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.1938 - mae: 2.0604 - mse: 9.1938 - val_loss: 14.7121 - val_mae: 2.5371 - val_mse: 14.7121\n",
- "Epoch 34/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.9643 - mae: 2.0460 - mse: 8.9643 - val_loss: 14.8987 - val_mae: 2.5898 - val_mse: 14.8987\n",
- "Epoch 35/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.7968 - mae: 2.0337 - mse: 8.7968 - val_loss: 14.7443 - val_mae: 2.5886 - val_mse: 14.7443\n",
- "Epoch 36/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.6585 - mae: 1.9865 - mse: 8.6585 - val_loss: 15.1119 - val_mae: 2.6940 - val_mse: 15.1119\n",
- "Epoch 37/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.5932 - mae: 2.0139 - mse: 8.5932 - val_loss: 14.4391 - val_mae: 2.5568 - val_mse: 14.4391\n",
- "Epoch 38/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.6503 - mae: 1.9818 - mse: 8.6503 - val_loss: 14.6089 - val_mae: 2.5932 - val_mse: 14.6089\n",
- "Epoch 39/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.4741 - mae: 1.9945 - mse: 8.4741 - val_loss: 14.4245 - val_mae: 2.5198 - val_mse: 14.4245\n",
- "Epoch 40/100\n",
- "36/36 [==============================] - 0s 4ms/step - loss: 8.3792 - mae: 1.9386 - mse: 8.3792 - val_loss: 14.8579 - val_mae: 2.5530 - val_mse: 14.8579\n",
- "Epoch 41/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.2416 - mae: 1.9460 - mse: 8.2416 - val_loss: 14.1700 - val_mae: 2.4763 - val_mse: 14.1700\n",
- "Epoch 42/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.1441 - mae: 1.9615 - mse: 8.1441 - val_loss: 14.7666 - val_mae: 2.5614 - val_mse: 14.7666\n",
- "Epoch 43/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.9628 - mae: 1.9341 - mse: 7.9628 - val_loss: 14.9515 - val_mae: 2.5567 - val_mse: 14.9515\n",
- "Epoch 44/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.8643 - mae: 1.9443 - mse: 7.8643 - val_loss: 14.1425 - val_mae: 2.4835 - val_mse: 14.1425\n",
- "Epoch 45/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.8650 - mae: 1.9313 - mse: 7.8650 - val_loss: 14.2290 - val_mae: 2.4958 - val_mse: 14.2290\n",
- "Epoch 46/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.7619 - mae: 1.8989 - mse: 7.7619 - val_loss: 14.1142 - val_mae: 2.5040 - val_mse: 14.1142\n",
- "Epoch 47/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.6118 - mae: 1.8890 - mse: 7.6118 - val_loss: 13.9982 - val_mae: 2.5404 - val_mse: 13.9982\n",
- "Epoch 48/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.4609 - mae: 1.8513 - mse: 7.4609 - val_loss: 13.7267 - val_mae: 2.4774 - val_mse: 13.7267\n",
- "Epoch 49/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.4310 - mae: 1.8845 - mse: 7.4310 - val_loss: 13.6127 - val_mae: 2.5150 - val_mse: 13.6127\n",
- "Epoch 50/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.6582 - mae: 1.8754 - mse: 7.6582 - val_loss: 13.6260 - val_mae: 2.5100 - val_mse: 13.6260\n",
- "Epoch 51/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.3610 - mae: 1.8299 - mse: 7.3610 - val_loss: 13.9227 - val_mae: 2.4919 - val_mse: 13.9227\n",
- "Epoch 52/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.3116 - mae: 1.8142 - mse: 7.3116 - val_loss: 13.3471 - val_mae: 2.4560 - val_mse: 13.3471\n",
- "Epoch 53/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.1248 - mae: 1.8402 - mse: 7.1248 - val_loss: 13.7073 - val_mae: 2.4522 - val_mse: 13.7073\n",
- "Epoch 54/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.1053 - mae: 1.8392 - mse: 7.1053 - val_loss: 13.7225 - val_mae: 2.5354 - val_mse: 13.7225\n",
- "Epoch 55/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.0583 - mae: 1.7580 - mse: 7.0583 - val_loss: 13.2049 - val_mae: 2.4446 - val_mse: 13.2049\n",
- "Epoch 56/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7665 - mae: 1.7663 - mse: 6.7665 - val_loss: 14.4253 - val_mae: 2.5627 - val_mse: 14.4253\n",
- "Epoch 57/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.8827 - mae: 1.7752 - mse: 6.8827 - val_loss: 13.2658 - val_mae: 2.5167 - val_mse: 13.2658\n",
- "Epoch 58/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.8156 - mae: 1.7930 - mse: 6.8156 - val_loss: 13.7665 - val_mae: 2.5500 - val_mse: 13.7665\n",
- "Epoch 59/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.8988 - mae: 1.7702 - mse: 6.8988 - val_loss: 13.4498 - val_mae: 2.4387 - val_mse: 13.4498\n",
- "Epoch 60/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7312 - mae: 1.7800 - mse: 6.7312 - val_loss: 13.7434 - val_mae: 2.4560 - val_mse: 13.7434\n",
- "Epoch 61/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7225 - mae: 1.7679 - mse: 6.7225 - val_loss: 13.0107 - val_mae: 2.4511 - val_mse: 13.0107\n",
- "Epoch 62/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.5523 - mae: 1.7499 - mse: 6.5523 - val_loss: 13.6302 - val_mae: 2.5602 - val_mse: 13.6302\n",
- "Epoch 63/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.6796 - mae: 1.7420 - mse: 6.6796 - val_loss: 12.8770 - val_mae: 2.4025 - val_mse: 12.8770\n",
- "Epoch 64/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.4967 - mae: 1.7323 - mse: 6.4967 - val_loss: 12.8915 - val_mae: 2.4153 - val_mse: 12.8915\n",
- "Epoch 65/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.1840 - mae: 1.7119 - mse: 6.1840 - val_loss: 15.0107 - val_mae: 2.5778 - val_mse: 15.0107\n",
- "Epoch 66/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.3673 - mae: 1.7264 - mse: 6.3673 - val_loss: 12.7434 - val_mae: 2.4700 - val_mse: 12.7434\n",
- "Epoch 67/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.0817 - mae: 1.6988 - mse: 6.0817 - val_loss: 13.4032 - val_mae: 2.4337 - val_mse: 13.4032\n",
- "Epoch 68/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.2663 - mae: 1.6922 - mse: 6.2663 - val_loss: 12.6261 - val_mae: 2.4417 - val_mse: 12.6261\n",
- "Epoch 69/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.1028 - mae: 1.6903 - mse: 6.1028 - val_loss: 12.7853 - val_mae: 2.5243 - val_mse: 12.7853\n",
- "Epoch 70/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.1497 - mae: 1.7011 - mse: 6.1497 - val_loss: 12.2234 - val_mae: 2.3462 - val_mse: 12.2234\n",
- "Epoch 71/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.1940 - mae: 1.6558 - mse: 6.1940 - val_loss: 12.8567 - val_mae: 2.4579 - val_mse: 12.8567\n",
- "Epoch 72/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.9576 - mae: 1.6938 - mse: 5.9576 - val_loss: 12.7185 - val_mae: 2.4336 - val_mse: 12.7185\n",
- "Epoch 73/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.9010 - mae: 1.6688 - mse: 5.9010 - val_loss: 12.8730 - val_mae: 2.4404 - val_mse: 12.8730\n",
- "Epoch 74/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.7729 - mae: 1.6628 - mse: 5.7729 - val_loss: 12.9853 - val_mae: 2.5059 - val_mse: 12.9853\n",
- "Epoch 75/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.7743 - mae: 1.6401 - mse: 5.7743 - val_loss: 12.4382 - val_mae: 2.3861 - val_mse: 12.4382\n",
- "Epoch 76/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.5220 - mae: 1.6460 - mse: 5.5220 - val_loss: 12.8593 - val_mae: 2.3853 - val_mse: 12.8593\n",
- "Epoch 77/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.6033 - mae: 1.6650 - mse: 5.6033 - val_loss: 12.7523 - val_mae: 2.4240 - val_mse: 12.7523\n",
- "Epoch 78/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.5523 - mae: 1.6175 - mse: 5.5523 - val_loss: 12.2824 - val_mae: 2.4211 - val_mse: 12.2824\n",
- "Epoch 79/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.5198 - mae: 1.5948 - mse: 5.5198 - val_loss: 13.4955 - val_mae: 2.4499 - val_mse: 13.4955\n",
- "Epoch 80/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.6490 - mae: 1.6285 - mse: 5.6490 - val_loss: 12.1719 - val_mae: 2.4192 - val_mse: 12.1719\n",
- "Epoch 81/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.6101 - mae: 1.6227 - mse: 5.6101 - val_loss: 12.1732 - val_mae: 2.3812 - val_mse: 12.1732\n",
- "Epoch 82/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.5147 - mae: 1.5905 - mse: 5.5147 - val_loss: 12.5534 - val_mae: 2.5524 - val_mse: 12.5534\n",
- "Epoch 83/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.3745 - mae: 1.5853 - mse: 5.3745 - val_loss: 11.7885 - val_mae: 2.3590 - val_mse: 11.7885\n",
- "Epoch 84/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.2953 - mae: 1.5975 - mse: 5.2953 - val_loss: 12.1622 - val_mae: 2.3873 - val_mse: 12.1622\n",
- "Epoch 85/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.4370 - mae: 1.5739 - mse: 5.4370 - val_loss: 12.4707 - val_mae: 2.5029 - val_mse: 12.4707\n",
- "Epoch 86/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.3153 - mae: 1.5867 - mse: 5.3153 - val_loss: 12.1893 - val_mae: 2.3688 - val_mse: 12.1893\n",
- "Epoch 87/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.2006 - mae: 1.5596 - mse: 5.2006 - val_loss: 11.8706 - val_mae: 2.4313 - val_mse: 11.8706\n",
- "Epoch 88/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.3182 - mae: 1.5731 - mse: 5.3182 - val_loss: 12.0165 - val_mae: 2.4454 - val_mse: 12.0165\n",
- "Epoch 89/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.1297 - mae: 1.5592 - mse: 5.1297 - val_loss: 11.9012 - val_mae: 2.3429 - val_mse: 11.9012\n",
- "Epoch 90/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.1805 - mae: 1.5531 - mse: 5.1805 - val_loss: 12.1738 - val_mae: 2.3710 - val_mse: 12.1738\n",
- "Epoch 91/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.1514 - mae: 1.5627 - mse: 5.1514 - val_loss: 11.3544 - val_mae: 2.2900 - val_mse: 11.3544\n",
- "Epoch 92/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.1515 - mae: 1.5425 - mse: 5.1515 - val_loss: 11.1602 - val_mae: 2.2967 - val_mse: 11.1602\n",
- "Epoch 93/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 4.9348 - mae: 1.5161 - mse: 4.9348 - val_loss: 11.2760 - val_mae: 2.2890 - val_mse: 11.2760\n",
- "Epoch 94/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 4.8446 - mae: 1.4886 - mse: 4.8446 - val_loss: 11.6856 - val_mae: 2.3227 - val_mse: 11.6856\n",
- "Epoch 95/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.0610 - mae: 1.5577 - mse: 5.0610 - val_loss: 11.3523 - val_mae: 2.3237 - val_mse: 11.3523\n",
- "Epoch 96/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 4.9049 - mae: 1.4806 - mse: 4.9049 - val_loss: 11.9970 - val_mae: 2.5045 - val_mse: 11.9970\n",
- "Epoch 97/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 4.9395 - mae: 1.5305 - mse: 4.9395 - val_loss: 11.6265 - val_mae: 2.3327 - val_mse: 11.6265\n",
- "Epoch 98/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 4.9233 - mae: 1.5680 - mse: 4.9233 - val_loss: 11.5942 - val_mae: 2.3879 - val_mse: 11.5942\n",
- "Epoch 99/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 4.6708 - mae: 1.5022 - mse: 4.6708 - val_loss: 12.3912 - val_mae: 2.5328 - val_mse: 12.3912\n",
- "Epoch 100/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 4.5990 - mae: 1.4997 - mse: 4.5990 - val_loss: 11.2350 - val_mae: 2.3192 - val_mse: 11.2350\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
@@ -1073,19 +261,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 11.2350\n",
- "x_test / mae : 2.3192\n",
- "x_test / mse : 11.2350\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -1104,164 +282,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
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- " <td>1.480641</td>\n",
- " <td>4.904853</td>\n",
- " <td>11.996970</td>\n",
- " <td>2.504455</td>\n",
- " <td>11.996970</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>96</th>\n",
- " <td>4.939476</td>\n",
- " <td>1.530525</td>\n",
- " <td>4.939476</td>\n",
- " <td>11.626506</td>\n",
- " <td>2.332702</td>\n",
- " <td>11.626506</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>97</th>\n",
- " <td>4.923304</td>\n",
- " <td>1.568031</td>\n",
- " <td>4.923304</td>\n",
- " <td>11.594161</td>\n",
- " <td>2.387874</td>\n",
- " <td>11.594161</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>98</th>\n",
- " <td>4.670831</td>\n",
- " <td>1.502215</td>\n",
- " <td>4.670831</td>\n",
- " <td>12.391187</td>\n",
- " <td>2.532793</td>\n",
- " <td>12.391187</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>99</th>\n",
- " <td>4.598972</td>\n",
- " <td>1.499657</td>\n",
- " <td>4.598972</td>\n",
- " <td>11.235036</td>\n",
- " <td>2.319229</td>\n",
- " <td>11.235036</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>100 rows × 6 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " loss mae mse val_loss val_mae val_mse\n",
- "0 528.660156 20.809525 528.660156 382.087250 17.402838 382.087250\n",
- "1 324.084076 15.515764 324.084076 186.108353 11.677550 186.108353\n",
- "2 137.944000 9.126263 137.944000 73.622841 6.512352 73.622841\n",
- "3 58.495167 5.712880 58.495167 46.929630 4.967716 46.929630\n",
- "4 34.657124 4.386191 34.657124 36.097355 4.095750 36.097355\n",
- ".. ... ... ... ... ... ...\n",
- "95 4.904853 1.480641 4.904853 11.996970 2.504455 11.996970\n",
- "96 4.939476 1.530525 4.939476 11.626506 2.332702 11.626506\n",
- "97 4.923304 1.568031 4.923304 11.594161 2.387874 11.594161\n",
- "98 4.670831 1.502215 4.670831 12.391187 2.532793 12.391187\n",
- "99 4.598972 1.499657 4.598972 11.235036 2.319229 11.235036\n",
- "\n",
- "[100 rows x 6 columns]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"df=pd.DataFrame(data=history.history)\n",
"display(df)"
@@ -1269,99 +292,18 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min( val_mae ) : 2.2890\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/BHPD1-01-history_0</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
" 'MAE' :['mae', 'val_mae'],\n",
@@ -1378,7 +320,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1392,18 +334,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prediction : 12.02 K$\n",
- "Reality : 10.40 K$\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"\n",
"predictions = model.predict( my_data )\n",
@@ -1413,19 +346,9 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Friday 8 January 2021, 01:09:24\n",
- "Duration is : 00:00:11 984ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/BHPD/01-DNN-Regression==done==.ipynb b/BHPD/01-DNN-Regression==done==.ipynb
new file mode 100644
index 0000000..44dc270
--- /dev/null
+++ b/BHPD/01-DNN-Regression==done==.ipynb
@@ -0,0 +1,3068 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "\n",
+ "# <!-- TITLE --> [BHPD1] - Regression with a Dense Network (DNN)\n",
+ "<!-- DESC --> Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Predicts **housing prices** from a set of house features. \n",
+ " - Understanding the **principle** and the **architecture** of a regression with a **dense neural network** \n",
+ "\n",
+ "\n",
+ "The **[Boston Housing Prices Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston. \n",
+ "Alongside with price, the dataset also provide theses informations : \n",
+ "\n",
+ " - CRIM: This is the per capita crime rate by town\n",
+ " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
+ " - INDUS: This is the proportion of non-retail business acres per town\n",
+ " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
+ " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
+ " - RM: This is the average number of rooms per dwelling\n",
+ " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
+ " - DIS: This is the weighted distances to five Boston employment centers\n",
+ " - RAD: This is the index of accessibility to radial highways\n",
+ " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
+ " - PTRATIO: This is the pupil-teacher ratio by town\n",
+ " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
+ " - LSTAT: This is the percentage lower status of the population\n",
+ " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\n",
+ "## 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 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:17.995164Z",
+ "iopub.status.busy": "2021-01-14T07:11:17.994838Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.332433Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.332801Z"
+ }
+ },
+ "outputs": [
+ {
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+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
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+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : BHPD1\n",
+ "Run time : Thursday 14 January 2021, 08:11:19\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import os,sys\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('BHPD1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve data\n",
+ "\n",
+ "### 2.1 - Option 1 : From Keras\n",
+ "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.335678Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.335359Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.338208Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.337910Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# (x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Option 2 : From a csv file\n",
+ "More fun !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.341659Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.341245Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.382604Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.382262Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_a658c_\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> <th class=\"col_heading level0 col13\" >medv</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_a658c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_a658c_row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
+ " <td id=\"T_a658c_row0_col1\" class=\"data row0 col1\" >18.00</td>\n",
+ " <td id=\"T_a658c_row0_col2\" class=\"data row0 col2\" >2.31</td>\n",
+ " <td id=\"T_a658c_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
+ " <td id=\"T_a658c_row0_col4\" class=\"data row0 col4\" >0.54</td>\n",
+ " <td id=\"T_a658c_row0_col5\" class=\"data row0 col5\" >6.58</td>\n",
+ " <td id=\"T_a658c_row0_col6\" class=\"data row0 col6\" >65.20</td>\n",
+ " <td id=\"T_a658c_row0_col7\" class=\"data row0 col7\" >4.09</td>\n",
+ " <td id=\"T_a658c_row0_col8\" class=\"data row0 col8\" >1.00</td>\n",
+ " <td id=\"T_a658c_row0_col9\" class=\"data row0 col9\" >296.00</td>\n",
+ " <td id=\"T_a658c_row0_col10\" class=\"data row0 col10\" >15.30</td>\n",
+ " <td id=\"T_a658c_row0_col11\" class=\"data row0 col11\" >396.90</td>\n",
+ " <td id=\"T_a658c_row0_col12\" class=\"data row0 col12\" >4.98</td>\n",
+ " <td id=\"T_a658c_row0_col13\" class=\"data row0 col13\" >24.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_a658c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_a658c_row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
+ " <td id=\"T_a658c_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_a658c_row1_col2\" class=\"data row1 col2\" >7.07</td>\n",
+ " <td id=\"T_a658c_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_a658c_row1_col4\" class=\"data row1 col4\" >0.47</td>\n",
+ " <td id=\"T_a658c_row1_col5\" class=\"data row1 col5\" >6.42</td>\n",
+ " <td id=\"T_a658c_row1_col6\" class=\"data row1 col6\" >78.90</td>\n",
+ " <td id=\"T_a658c_row1_col7\" class=\"data row1 col7\" >4.97</td>\n",
+ " <td id=\"T_a658c_row1_col8\" class=\"data row1 col8\" >2.00</td>\n",
+ " <td id=\"T_a658c_row1_col9\" class=\"data row1 col9\" >242.00</td>\n",
+ " <td id=\"T_a658c_row1_col10\" class=\"data row1 col10\" >17.80</td>\n",
+ " <td id=\"T_a658c_row1_col11\" class=\"data row1 col11\" >396.90</td>\n",
+ " <td id=\"T_a658c_row1_col12\" class=\"data row1 col12\" >9.14</td>\n",
+ " <td id=\"T_a658c_row1_col13\" class=\"data row1 col13\" >21.60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_a658c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_a658c_row2_col0\" class=\"data row2 col0\" >0.03</td>\n",
+ " <td id=\"T_a658c_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
+ " <td id=\"T_a658c_row2_col2\" class=\"data row2 col2\" >7.07</td>\n",
+ " <td id=\"T_a658c_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
+ " <td id=\"T_a658c_row2_col4\" class=\"data row2 col4\" >0.47</td>\n",
+ " <td id=\"T_a658c_row2_col5\" class=\"data row2 col5\" >7.18</td>\n",
+ " <td id=\"T_a658c_row2_col6\" class=\"data row2 col6\" >61.10</td>\n",
+ " <td id=\"T_a658c_row2_col7\" class=\"data row2 col7\" >4.97</td>\n",
+ " <td id=\"T_a658c_row2_col8\" class=\"data row2 col8\" >2.00</td>\n",
+ " <td id=\"T_a658c_row2_col9\" class=\"data row2 col9\" >242.00</td>\n",
+ " <td id=\"T_a658c_row2_col10\" class=\"data row2 col10\" >17.80</td>\n",
+ " <td id=\"T_a658c_row2_col11\" class=\"data row2 col11\" >392.83</td>\n",
+ " <td id=\"T_a658c_row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_a658c_row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_a658c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_a658c_row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_a658c_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_a658c_row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_a658c_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_a658c_row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_a658c_row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_a658c_row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_a658c_row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_a658c_row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_a658c_row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_a658c_row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_a658c_row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_a658c_row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_a658c_row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_a658c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_a658c_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_a658c_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_a658c_row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_a658c_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_a658c_row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_a658c_row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_a658c_row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_a658c_row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_a658c_row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_a658c_row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_a658c_row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_a658c_row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_a658c_row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_a658c_row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7fca4da69490>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Missing Data : 0 Shape is : (506, 14)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
+ "\n",
+ "display(data.head(5).style.format(\"{0:.2f}\").set_caption(\"Few lines of the dataset :\"))\n",
+ "print('Missing Data : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preparing the data\n",
+ "### 3.1 - Split data\n",
+ "We will use 70% of the data for training and 30% for validation. \n",
+ "The dataset is **shuffled** and shared between **learning** and **testing**. \n",
+ "x will be input data and y the expected output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.390142Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.389749Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.392198Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.391841Z"
+ }
+ },
+ "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": [
+ "# ---- Suffle and Split => train, test\n",
+ "#\n",
+ "data_train = data.sample(frac=0.7, axis=0)\n",
+ "data_test = data.drop(data_train.index)\n",
+ "\n",
+ "# ---- Split => x,y (medv is price)\n",
+ "#\n",
+ "x_train = data_train.drop('medv', axis=1)\n",
+ "y_train = data_train['medv']\n",
+ "x_test = data_test.drop('medv', axis=1)\n",
+ "y_test = data_test['medv']\n",
+ "\n",
+ "print('Original data shape was : ',data.shape)\n",
+ "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
+ "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 - Data normalization\n",
+ "**Note :** \n",
+ " - All input data must be normalized, train and test. \n",
+ " - To do this we will **subtract the mean** and **divide by the standard deviation**. \n",
+ " - But test data should not be used in any way, even for normalization. \n",
+ " - The mean and the standard deviation will therefore only be calculated with the train data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.398787Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.398337Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.458125Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.457784Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_91f6c_\" ><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_91f6c_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_91f6c_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_91f6c_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_91f6c_row1_col0\" class=\"data row1 col0\" >3.83</td>\n",
+ " <td id=\"T_91f6c_row1_col1\" class=\"data row1 col1\" >11.23</td>\n",
+ " <td id=\"T_91f6c_row1_col2\" class=\"data row1 col2\" >11.09</td>\n",
+ " <td id=\"T_91f6c_row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_91f6c_row1_col4\" class=\"data row1 col4\" >0.55</td>\n",
+ " <td id=\"T_91f6c_row1_col5\" class=\"data row1 col5\" >6.26</td>\n",
+ " <td id=\"T_91f6c_row1_col6\" class=\"data row1 col6\" >68.56</td>\n",
+ " <td id=\"T_91f6c_row1_col7\" class=\"data row1 col7\" >3.75</td>\n",
+ " <td id=\"T_91f6c_row1_col8\" class=\"data row1 col8\" >9.52</td>\n",
+ " <td id=\"T_91f6c_row1_col9\" class=\"data row1 col9\" >405.68</td>\n",
+ " <td id=\"T_91f6c_row1_col10\" class=\"data row1 col10\" >18.44</td>\n",
+ " <td id=\"T_91f6c_row1_col11\" class=\"data row1 col11\" >356.78</td>\n",
+ " <td id=\"T_91f6c_row1_col12\" class=\"data row1 col12\" >12.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_91f6c_row2_col0\" class=\"data row2 col0\" >8.75</td>\n",
+ " <td id=\"T_91f6c_row2_col1\" class=\"data row2 col1\" >22.79</td>\n",
+ " <td id=\"T_91f6c_row2_col2\" class=\"data row2 col2\" >6.89</td>\n",
+ " <td id=\"T_91f6c_row2_col3\" class=\"data row2 col3\" >0.25</td>\n",
+ " <td id=\"T_91f6c_row2_col4\" class=\"data row2 col4\" >0.11</td>\n",
+ " <td id=\"T_91f6c_row2_col5\" class=\"data row2 col5\" >0.73</td>\n",
+ " <td id=\"T_91f6c_row2_col6\" class=\"data row2 col6\" >28.80</td>\n",
+ " <td id=\"T_91f6c_row2_col7\" class=\"data row2 col7\" >2.06</td>\n",
+ " <td id=\"T_91f6c_row2_col8\" class=\"data row2 col8\" >8.70</td>\n",
+ " <td id=\"T_91f6c_row2_col9\" class=\"data row2 col9\" >170.12</td>\n",
+ " <td id=\"T_91f6c_row2_col10\" class=\"data row2 col10\" >2.21</td>\n",
+ " <td id=\"T_91f6c_row2_col11\" class=\"data row2 col11\" >89.35</td>\n",
+ " <td id=\"T_91f6c_row2_col12\" class=\"data row2 col12\" >7.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_91f6c_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_91f6c_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row3_col2\" class=\"data row3 col2\" >1.21</td>\n",
+ " <td id=\"T_91f6c_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_91f6c_row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
+ " <td id=\"T_91f6c_row3_col6\" class=\"data row3 col6\" >6.20</td>\n",
+ " <td id=\"T_91f6c_row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_91f6c_row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_91f6c_row3_col9\" class=\"data row3 col9\" >188.00</td>\n",
+ " <td id=\"T_91f6c_row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_91f6c_row3_col11\" class=\"data row3 col11\" >2.60</td>\n",
+ " <td id=\"T_91f6c_row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_91f6c_row4_col0\" class=\"data row4 col0\" >0.08</td>\n",
+ " <td id=\"T_91f6c_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
+ " <td id=\"T_91f6c_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_91f6c_row4_col5\" class=\"data row4 col5\" >5.86</td>\n",
+ " <td id=\"T_91f6c_row4_col6\" class=\"data row4 col6\" >43.47</td>\n",
+ " <td id=\"T_91f6c_row4_col7\" class=\"data row4 col7\" >2.07</td>\n",
+ " <td id=\"T_91f6c_row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_91f6c_row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
+ " <td id=\"T_91f6c_row4_col10\" class=\"data row4 col10\" >17.10</td>\n",
+ " <td id=\"T_91f6c_row4_col11\" class=\"data row4 col11\" >375.61</td>\n",
+ " <td id=\"T_91f6c_row4_col12\" class=\"data row4 col12\" >7.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_91f6c_row5_col0\" class=\"data row5 col0\" >0.27</td>\n",
+ " <td id=\"T_91f6c_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row5_col2\" class=\"data row5 col2\" >8.56</td>\n",
+ " <td id=\"T_91f6c_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_91f6c_row5_col5\" class=\"data row5 col5\" >6.17</td>\n",
+ " <td id=\"T_91f6c_row5_col6\" class=\"data row5 col6\" >78.20</td>\n",
+ " <td id=\"T_91f6c_row5_col7\" class=\"data row5 col7\" >3.17</td>\n",
+ " <td id=\"T_91f6c_row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_91f6c_row5_col9\" class=\"data row5 col9\" >329.00</td>\n",
+ " <td id=\"T_91f6c_row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_91f6c_row5_col11\" class=\"data row5 col11\" >391.60</td>\n",
+ " <td id=\"T_91f6c_row5_col12\" class=\"data row5 col12\" >11.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_91f6c_row6_col0\" class=\"data row6 col0\" >3.83</td>\n",
+ " <td id=\"T_91f6c_row6_col1\" class=\"data row6 col1\" >20.00</td>\n",
+ " <td id=\"T_91f6c_row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_91f6c_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_91f6c_row6_col4\" class=\"data row6 col4\" >0.62</td>\n",
+ " <td id=\"T_91f6c_row6_col5\" class=\"data row6 col5\" >6.61</td>\n",
+ " <td id=\"T_91f6c_row6_col6\" class=\"data row6 col6\" >94.57</td>\n",
+ " <td id=\"T_91f6c_row6_col7\" class=\"data row6 col7\" >5.19</td>\n",
+ " <td id=\"T_91f6c_row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_91f6c_row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_91f6c_row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_91f6c_row6_col11\" class=\"data row6 col11\" >396.32</td>\n",
+ " <td id=\"T_91f6c_row6_col12\" class=\"data row6 col12\" >17.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_91f6c_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_91f6c_row7_col0\" class=\"data row7 col0\" >73.53</td>\n",
+ " <td id=\"T_91f6c_row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_91f6c_row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_91f6c_row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_91f6c_row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_91f6c_row7_col5\" class=\"data row7 col5\" >8.72</td>\n",
+ " <td id=\"T_91f6c_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_91f6c_row7_col7\" class=\"data row7 col7\" >10.71</td>\n",
+ " <td id=\"T_91f6c_row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_91f6c_row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_91f6c_row7_col10\" class=\"data row7 col10\" >21.20</td>\n",
+ " <td id=\"T_91f6c_row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_91f6c_row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7fca4da69490>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_7f7f2_\" ><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_7f7f2_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_7f7f2_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_7f7f2_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_7f7f2_row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+ " <td id=\"T_7f7f2_row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_7f7f2_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_7f7f2_row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_7f7f2_row3_col0\" class=\"data row3 col0\" >-0.44</td>\n",
+ " <td id=\"T_7f7f2_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_7f7f2_row3_col2\" class=\"data row3 col2\" >-1.43</td>\n",
+ " <td id=\"T_7f7f2_row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
+ " <td id=\"T_7f7f2_row3_col4\" class=\"data row3 col4\" >-1.45</td>\n",
+ " <td id=\"T_7f7f2_row3_col5\" class=\"data row3 col5\" >-3.69</td>\n",
+ " <td id=\"T_7f7f2_row3_col6\" class=\"data row3 col6\" >-2.16</td>\n",
+ " <td id=\"T_7f7f2_row3_col7\" class=\"data row3 col7\" >-1.27</td>\n",
+ " <td id=\"T_7f7f2_row3_col8\" class=\"data row3 col8\" >-0.98</td>\n",
+ " <td id=\"T_7f7f2_row3_col9\" class=\"data row3 col9\" >-1.28</td>\n",
+ " <td id=\"T_7f7f2_row3_col10\" class=\"data row3 col10\" >-2.64</td>\n",
+ " <td id=\"T_7f7f2_row3_col11\" class=\"data row3 col11\" >-3.96</td>\n",
+ " <td id=\"T_7f7f2_row3_col12\" class=\"data row3 col12\" >-1.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_7f7f2_row4_col0\" class=\"data row4 col0\" >-0.43</td>\n",
+ " <td id=\"T_7f7f2_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
+ " <td id=\"T_7f7f2_row4_col2\" class=\"data row4 col2\" >-0.86</td>\n",
+ " <td id=\"T_7f7f2_row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
+ " <td id=\"T_7f7f2_row4_col4\" class=\"data row4 col4\" >-0.92</td>\n",
+ " <td id=\"T_7f7f2_row4_col5\" class=\"data row4 col5\" >-0.55</td>\n",
+ " <td id=\"T_7f7f2_row4_col6\" class=\"data row4 col6\" >-0.87</td>\n",
+ " <td id=\"T_7f7f2_row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
+ " <td id=\"T_7f7f2_row4_col8\" class=\"data row4 col8\" >-0.63</td>\n",
+ " <td id=\"T_7f7f2_row4_col9\" class=\"data row4 col9\" >-0.76</td>\n",
+ " <td id=\"T_7f7f2_row4_col10\" class=\"data row4 col10\" >-0.61</td>\n",
+ " <td id=\"T_7f7f2_row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
+ " <td id=\"T_7f7f2_row4_col12\" class=\"data row4 col12\" >-0.77</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_7f7f2_row5_col0\" class=\"data row5 col0\" >-0.41</td>\n",
+ " <td id=\"T_7f7f2_row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
+ " <td id=\"T_7f7f2_row5_col2\" class=\"data row5 col2\" >-0.37</td>\n",
+ " <td id=\"T_7f7f2_row5_col3\" class=\"data row5 col3\" >-0.26</td>\n",
+ " <td id=\"T_7f7f2_row5_col4\" class=\"data row5 col4\" >-0.14</td>\n",
+ " <td id=\"T_7f7f2_row5_col5\" class=\"data row5 col5\" >-0.13</td>\n",
+ " <td id=\"T_7f7f2_row5_col6\" class=\"data row5 col6\" >0.33</td>\n",
+ " <td id=\"T_7f7f2_row5_col7\" class=\"data row5 col7\" >-0.28</td>\n",
+ " <td id=\"T_7f7f2_row5_col8\" class=\"data row5 col8\" >-0.52</td>\n",
+ " <td id=\"T_7f7f2_row5_col9\" class=\"data row5 col9\" >-0.45</td>\n",
+ " <td id=\"T_7f7f2_row5_col10\" class=\"data row5 col10\" >0.30</td>\n",
+ " <td id=\"T_7f7f2_row5_col11\" class=\"data row5 col11\" >0.39</td>\n",
+ " <td id=\"T_7f7f2_row5_col12\" class=\"data row5 col12\" >-0.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_7f7f2_row6_col0\" class=\"data row6 col0\" >-0.00</td>\n",
+ " <td id=\"T_7f7f2_row6_col1\" class=\"data row6 col1\" >0.38</td>\n",
+ " <td id=\"T_7f7f2_row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
+ " <td id=\"T_7f7f2_row6_col3\" class=\"data row6 col3\" >-0.26</td>\n",
+ " <td id=\"T_7f7f2_row6_col4\" class=\"data row6 col4\" >0.61</td>\n",
+ " <td id=\"T_7f7f2_row6_col5\" class=\"data row6 col5\" >0.48</td>\n",
+ " <td id=\"T_7f7f2_row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
+ " <td id=\"T_7f7f2_row6_col7\" class=\"data row6 col7\" >0.70</td>\n",
+ " <td id=\"T_7f7f2_row6_col8\" class=\"data row6 col8\" >1.66</td>\n",
+ " <td id=\"T_7f7f2_row6_col9\" class=\"data row6 col9\" >1.53</td>\n",
+ " <td id=\"T_7f7f2_row6_col10\" class=\"data row6 col10\" >0.80</td>\n",
+ " <td id=\"T_7f7f2_row6_col11\" class=\"data row6 col11\" >0.44</td>\n",
+ " <td id=\"T_7f7f2_row6_col12\" class=\"data row6 col12\" >0.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_7f7f2_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_7f7f2_row7_col0\" class=\"data row7 col0\" >7.96</td>\n",
+ " <td id=\"T_7f7f2_row7_col1\" class=\"data row7 col1\" >3.90</td>\n",
+ " <td id=\"T_7f7f2_row7_col2\" class=\"data row7 col2\" >2.42</td>\n",
+ " <td id=\"T_7f7f2_row7_col3\" class=\"data row7 col3\" >3.79</td>\n",
+ " <td id=\"T_7f7f2_row7_col4\" class=\"data row7 col4\" >2.78</td>\n",
+ " <td id=\"T_7f7f2_row7_col5\" class=\"data row7 col5\" >3.36</td>\n",
+ " <td id=\"T_7f7f2_row7_col6\" class=\"data row7 col6\" >1.09</td>\n",
+ " <td id=\"T_7f7f2_row7_col7\" class=\"data row7 col7\" >3.39</td>\n",
+ " <td id=\"T_7f7f2_row7_col8\" class=\"data row7 col8\" >1.66</td>\n",
+ " <td id=\"T_7f7f2_row7_col9\" class=\"data row7 col9\" >1.79</td>\n",
+ " <td id=\"T_7f7f2_row7_col10\" class=\"data row7 col10\" >1.25</td>\n",
+ " <td id=\"T_7f7f2_row7_col11\" class=\"data row7 col11\" >0.45</td>\n",
+ " <td id=\"T_7f7f2_row7_col12\" class=\"data row7 col12\" >3.33</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7fca4d81e910>"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_58565_\" ><caption>Few lines of the dataset :</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >crim</th> <th class=\"col_heading level0 col1\" >zn</th> <th class=\"col_heading level0 col2\" >indus</th> <th class=\"col_heading level0 col3\" >chas</th> <th class=\"col_heading level0 col4\" >nox</th> <th class=\"col_heading level0 col5\" >rm</th> <th class=\"col_heading level0 col6\" >age</th> <th class=\"col_heading level0 col7\" >dis</th> <th class=\"col_heading level0 col8\" >rad</th> <th class=\"col_heading level0 col9\" >tax</th> <th class=\"col_heading level0 col10\" >ptratio</th> <th class=\"col_heading level0 col11\" >b</th> <th class=\"col_heading level0 col12\" >lstat</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_58565_level0_row0\" class=\"row_heading level0 row0\" >503</th>\n",
+ " <td id=\"T_58565_row0_col0\" class=\"data row0 col0\" >-0.43</td>\n",
+ " <td id=\"T_58565_row0_col1\" class=\"data row0 col1\" >-0.49</td>\n",
+ " <td id=\"T_58565_row0_col2\" class=\"data row0 col2\" >0.12</td>\n",
+ " <td id=\"T_58565_row0_col3\" class=\"data row0 col3\" >-0.26</td>\n",
+ " <td id=\"T_58565_row0_col4\" class=\"data row0 col4\" >0.17</td>\n",
+ " <td id=\"T_58565_row0_col5\" class=\"data row0 col5\" >0.97</td>\n",
+ " <td id=\"T_58565_row0_col6\" class=\"data row0 col6\" >0.78</td>\n",
+ " <td id=\"T_58565_row0_col7\" class=\"data row0 col7\" >-0.77</td>\n",
+ " <td id=\"T_58565_row0_col8\" class=\"data row0 col8\" >-0.98</td>\n",
+ " <td id=\"T_58565_row0_col9\" class=\"data row0 col9\" >-0.78</td>\n",
+ " <td id=\"T_58565_row0_col10\" class=\"data row0 col10\" >1.16</td>\n",
+ " <td id=\"T_58565_row0_col11\" class=\"data row0 col11\" >0.45</td>\n",
+ " <td id=\"T_58565_row0_col12\" class=\"data row0 col12\" >-0.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_58565_level0_row1\" class=\"row_heading level0 row1\" >121</th>\n",
+ " <td id=\"T_58565_row1_col0\" class=\"data row1 col0\" >-0.43</td>\n",
+ " <td id=\"T_58565_row1_col1\" class=\"data row1 col1\" >-0.49</td>\n",
+ " <td id=\"T_58565_row1_col2\" class=\"data row1 col2\" >2.11</td>\n",
+ " <td id=\"T_58565_row1_col3\" class=\"data row1 col3\" >-0.26</td>\n",
+ " <td id=\"T_58565_row1_col4\" class=\"data row1 col4\" >0.24</td>\n",
+ " <td id=\"T_58565_row1_col5\" class=\"data row1 col5\" >-0.35</td>\n",
+ " <td id=\"T_58565_row1_col6\" class=\"data row1 col6\" >0.54</td>\n",
+ " <td id=\"T_58565_row1_col7\" class=\"data row1 col7\" >-0.75</td>\n",
+ " <td id=\"T_58565_row1_col8\" class=\"data row1 col8\" >-0.86</td>\n",
+ " <td id=\"T_58565_row1_col9\" class=\"data row1 col9\" >-1.28</td>\n",
+ " <td id=\"T_58565_row1_col10\" class=\"data row1 col10\" >0.30</td>\n",
+ " <td id=\"T_58565_row1_col11\" class=\"data row1 col11\" >0.23</td>\n",
+ " <td id=\"T_58565_row1_col12\" class=\"data row1 col12\" >0.17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_58565_level0_row2\" class=\"row_heading level0 row2\" >228</th>\n",
+ " <td id=\"T_58565_row2_col0\" class=\"data row2 col0\" >-0.40</td>\n",
+ " <td id=\"T_58565_row2_col1\" class=\"data row2 col1\" >-0.49</td>\n",
+ " <td id=\"T_58565_row2_col2\" class=\"data row2 col2\" >-0.71</td>\n",
+ " <td id=\"T_58565_row2_col3\" class=\"data row2 col3\" >-0.26</td>\n",
+ " <td id=\"T_58565_row2_col4\" class=\"data row2 col4\" >-0.44</td>\n",
+ " <td id=\"T_58565_row2_col5\" class=\"data row2 col5\" >1.94</td>\n",
+ " <td id=\"T_58565_row2_col6\" class=\"data row2 col6\" >-1.79</td>\n",
+ " <td id=\"T_58565_row2_col7\" class=\"data row2 col7\" >-0.18</td>\n",
+ " <td id=\"T_58565_row2_col8\" class=\"data row2 col8\" >-0.18</td>\n",
+ " <td id=\"T_58565_row2_col9\" class=\"data row2 col9\" >-0.58</td>\n",
+ " <td id=\"T_58565_row2_col10\" class=\"data row2 col10\" >-0.47</td>\n",
+ " <td id=\"T_58565_row2_col11\" class=\"data row2 col11\" >0.23</td>\n",
+ " <td id=\"T_58565_row2_col12\" class=\"data row2 col12\" >-1.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_58565_level0_row3\" class=\"row_heading level0 row3\" >478</th>\n",
+ " <td id=\"T_58565_row3_col0\" class=\"data row3 col0\" >0.73</td>\n",
+ " <td id=\"T_58565_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
+ " <td id=\"T_58565_row3_col2\" class=\"data row3 col2\" >1.02</td>\n",
+ " <td id=\"T_58565_row3_col3\" class=\"data row3 col3\" >-0.26</td>\n",
+ " <td id=\"T_58565_row3_col4\" class=\"data row3 col4\" >0.53</td>\n",
+ " <td id=\"T_58565_row3_col5\" class=\"data row3 col5\" >-0.11</td>\n",
+ " <td id=\"T_58565_row3_col6\" class=\"data row3 col6\" >0.98</td>\n",
+ " <td id=\"T_58565_row3_col7\" class=\"data row3 col7\" >-0.77</td>\n",
+ " <td id=\"T_58565_row3_col8\" class=\"data row3 col8\" >1.66</td>\n",
+ " <td id=\"T_58565_row3_col9\" class=\"data row3 col9\" >1.53</td>\n",
+ " <td id=\"T_58565_row3_col10\" class=\"data row3 col10\" >0.80</td>\n",
+ " <td id=\"T_58565_row3_col11\" class=\"data row3 col11\" >0.26</td>\n",
+ " <td id=\"T_58565_row3_col12\" class=\"data row3 col12\" >0.67</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_58565_level0_row4\" class=\"row_heading level0 row4\" >196</th>\n",
+ " <td id=\"T_58565_row4_col0\" class=\"data row4 col0\" >-0.43</td>\n",
+ " <td id=\"T_58565_row4_col1\" class=\"data row4 col1\" >3.02</td>\n",
+ " <td id=\"T_58565_row4_col2\" class=\"data row4 col2\" >-1.39</td>\n",
+ " <td id=\"T_58565_row4_col3\" class=\"data row4 col3\" >-0.26</td>\n",
+ " <td id=\"T_58565_row4_col4\" class=\"data row4 col4\" >-1.32</td>\n",
+ " <td id=\"T_58565_row4_col5\" class=\"data row4 col5\" >1.40</td>\n",
+ " <td id=\"T_58565_row4_col6\" class=\"data row4 col6\" >-1.20</td>\n",
+ " <td id=\"T_58565_row4_col7\" class=\"data row4 col7\" >1.73</td>\n",
+ " <td id=\"T_58565_row4_col8\" class=\"data row4 col8\" >-0.86</td>\n",
+ " <td id=\"T_58565_row4_col9\" class=\"data row4 col9\" >-0.45</td>\n",
+ " <td id=\"T_58565_row4_col10\" class=\"data row4 col10\" >-2.64</td>\n",
+ " <td id=\"T_58565_row4_col11\" class=\"data row4 col11\" >0.45</td>\n",
+ " <td id=\"T_58565_row4_col12\" class=\"data row4 col12\" >-1.19</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7fca4f616ed0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
+ "\n",
+ "mean = x_train.mean()\n",
+ "std = x_train.std()\n",
+ "x_train = (x_train - mean) / std\n",
+ "x_test = (x_test - mean) / std\n",
+ "\n",
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
+ "display(x_train.head(5).style.format(\"{0:.2f}\").set_caption(\"Few lines of the dataset :\"))\n",
+ "\n",
+ "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+ "x_test, y_test = np.array(x_test), np.array(y_test)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build a model\n",
+ "About informations about : \n",
+ " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+ " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+ " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.461934Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.461612Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.464012Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.463636Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ " def get_model_v1(shape):\n",
+ " \n",
+ " model = keras.models.Sequential()\n",
+ " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
+ " model.add(keras.layers.Dense(1, name='Output'))\n",
+ " \n",
+ " model.compile(optimizer = 'rmsprop',\n",
+ " loss = 'mse',\n",
+ " metrics = ['mae', 'mse'] )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.467070Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.466743Z",
+ "iopub.status.idle": "2021-01-14T07:11:19.524053Z",
+ "shell.execute_reply": "2021-01-14T07:11:19.523600Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model=get_model_v1( (13,) )\n",
+ "\n",
+ "model.summary()\n",
+ "\n",
+ "# img=keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)\n",
+ "# display(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:19.527677Z",
+ "iopub.status.busy": "2021-01-14T07:11:19.527319Z",
+ "iopub.status.idle": "2021-01-14T07:11:27.244568Z",
+ "shell.execute_reply": "2021-01-14T07:11:27.244221Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 407.3206 - mae: 18.8522 - mse: 407.3206"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 5ms/step - loss: 512.6953 - mae: 20.4214 - mse: 512.6953 - val_loss: 371.6987 - val_mae: 17.6408 - val_mse: 371.6987\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 2/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 416.8376 - mae: 19.2768 - mse: 416.8376"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 288.0607 - mae: 14.3196 - mse: 288.0607 - val_loss: 148.8738 - val_mae: 10.0385 - val_mse: 148.8738\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 3/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 127.6482 - mae: 9.7656 - mse: 127.6482"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 113.5676 - mae: 7.8054 - mse: 113.5676 - val_loss: 63.2754 - val_mae: 6.2580 - val_mse: 63.2754\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 4/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 45.3920 - mae: 4.4821 - mse: 45.3920"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 59.1808 - mae: 5.4870 - mse: 59.1808 - val_loss: 33.1852 - val_mae: 4.4691 - val_mse: 33.1852\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 5/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 57.9958 - mae: 5.6935 - mse: 57.9958"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 38.4790 - mae: 4.3153 - mse: 38.4790 - val_loss: 22.4361 - val_mae: 3.5836 - val_mse: 22.4361\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 6/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 69.7640 - mae: 5.7963 - mse: 69.7640"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 28.4822 - mae: 3.8363 - mse: 28.4822 - val_loss: 17.4567 - val_mae: 3.0233 - val_mse: 17.4567\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 7/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 11.3394 - mae: 2.6114 - mse: 11.3394"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 23.7231 - mae: 3.4658 - mse: 23.7231 - val_loss: 16.7736 - val_mae: 2.9227 - val_mse: 16.7736\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 8/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 24.4488 - mae: 3.8661 - mse: 24.4488"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 20.6117 - mae: 3.1927 - mse: 20.6117 - val_loss: 16.6082 - val_mae: 2.7662 - val_mse: 16.6082\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 9/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 14.4122 - mae: 3.1264 - mse: 14.4122"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 18.6191 - mae: 3.0232 - mse: 18.6191 - val_loss: 17.2049 - val_mae: 2.7424 - val_mse: 17.2049\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 10/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.4165 - mae: 1.9410 - mse: 5.4165"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 17.0503 - mae: 2.8785 - mse: 17.0503 - val_loss: 16.6766 - val_mae: 2.6310 - val_mse: 16.6766\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 11/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.4784 - mae: 2.1575 - mse: 8.4784"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 15.7881 - mae: 2.8371 - mse: 15.7881 - val_loss: 17.9815 - val_mae: 2.7781 - val_mse: 17.9815\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 12/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 13.8739 - mae: 2.6540 - mse: 13.8739"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 14.9784 - mae: 2.6886 - mse: 14.9784 - val_loss: 17.6274 - val_mae: 2.6635 - val_mse: 17.6274\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 13/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.3338 - mae: 2.6182 - mse: 9.3338"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 14.2906 - mae: 2.6540 - mse: 14.2906 - val_loss: 16.9495 - val_mae: 2.6102 - val_mse: 16.9495\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 14/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 10.4900 - mae: 2.5056 - mse: 10.4900"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 13.5513 - mae: 2.5605 - mse: 13.5513 - val_loss: 17.6257 - val_mae: 2.6396 - val_mse: 17.6257\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 15/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 10.3618 - mae: 2.4442 - mse: 10.3618"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.7591 - mae: 2.5359 - mse: 12.7591 - val_loss: 16.8116 - val_mae: 2.6863 - val_mse: 16.8116\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 16/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 12.4388 - mae: 2.4520 - mse: 12.4388"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.4915 - mae: 2.4515 - mse: 12.4915 - val_loss: 16.9179 - val_mae: 2.5154 - val_mse: 16.9179\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 17/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.0083 - mae: 2.2781 - mse: 9.0083"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.1468 - mae: 2.4788 - mse: 12.1468 - val_loss: 16.8544 - val_mae: 2.5007 - val_mse: 16.8544\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 18/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.3079 - mae: 2.0934 - mse: 7.3079"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.8557 - mae: 2.3900 - mse: 11.8557 - val_loss: 18.2582 - val_mae: 2.6944 - val_mse: 18.2582\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 19/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.2752 - mae: 2.0977 - mse: 7.2752"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.5226 - mae: 2.3760 - mse: 11.5226 - val_loss: 17.4870 - val_mae: 2.5315 - val_mse: 17.4870\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 20/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 12.8938 - mae: 3.0963 - mse: 12.8938"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.0516 - mae: 2.3540 - mse: 11.0516 - val_loss: 15.7856 - val_mae: 2.4997 - val_mse: 15.7856\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 21/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.5917 - mae: 1.7147 - mse: 8.5917"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.0540 - mae: 2.3189 - mse: 11.0540 - val_loss: 15.8711 - val_mae: 2.4711 - val_mse: 15.8711\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 22/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.7946 - mae: 1.9941 - mse: 5.7946"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.9296 - mae: 2.2981 - mse: 10.9296 - val_loss: 15.5044 - val_mae: 2.4868 - val_mse: 15.5044\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 23/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 27.7916 - mae: 3.7462 - mse: 27.7916"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.7570 - mae: 2.2583 - mse: 10.7570 - val_loss: 16.8619 - val_mae: 2.4752 - val_mse: 16.8619\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 24/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.5477 - mae: 1.8999 - mse: 5.5477"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.5309 - mae: 2.1914 - mse: 10.5309 - val_loss: 18.0421 - val_mae: 2.5139 - val_mse: 18.0421\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 25/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.8592 - mae: 1.6594 - mse: 3.8592"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.2207 - mae: 2.2299 - mse: 10.2207 - val_loss: 15.7059 - val_mae: 2.4589 - val_mse: 15.7059\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 26/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 24.1743 - mae: 3.4121 - mse: 24.1743"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.2164 - mae: 2.2412 - mse: 10.2164 - val_loss: 17.6199 - val_mae: 2.5253 - val_mse: 17.6199\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 27/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.3696 - mae: 2.0420 - mse: 7.3696"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.7297 - mae: 2.1741 - mse: 9.7297 - val_loss: 16.4737 - val_mae: 2.4798 - val_mse: 16.4737\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 28/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.2318 - mae: 1.1947 - mse: 2.2318"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.8063 - mae: 2.1716 - mse: 9.8063 - val_loss: 15.2677 - val_mae: 2.4739 - val_mse: 15.2677\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 29/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 22.2630 - mae: 3.0200 - mse: 22.2630"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.6612 - mae: 2.1797 - mse: 9.6612 - val_loss: 14.1823 - val_mae: 2.4188 - val_mse: 14.1823\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 30/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 22.5960 - mae: 3.0913 - mse: 22.5960"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.6477 - mae: 2.1720 - mse: 9.6477 - val_loss: 15.4119 - val_mae: 2.4218 - val_mse: 15.4119\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 31/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.5775 - mae: 1.8271 - mse: 4.5775"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.3244 - mae: 2.1315 - mse: 9.3244 - val_loss: 15.6595 - val_mae: 2.4155 - val_mse: 15.6595\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 32/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.1524 - mae: 1.8350 - mse: 5.1524"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.2568 - mae: 2.1106 - mse: 9.2568 - val_loss: 14.5170 - val_mae: 2.5860 - val_mse: 14.5170\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 33/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 15.8990 - mae: 2.7259 - mse: 15.8990"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.3815 - mae: 2.1274 - mse: 9.3815 - val_loss: 15.3208 - val_mae: 2.4046 - val_mse: 15.3208\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 34/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 25.0873 - mae: 3.0151 - mse: 25.0873"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.8580 - mae: 2.1207 - mse: 8.8580 - val_loss: 14.9003 - val_mae: 2.4407 - val_mse: 14.9003\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 35/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 47.2871 - mae: 3.2998 - mse: 47.2871"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.9432 - mae: 2.0632 - mse: 8.9432 - val_loss: 16.1420 - val_mae: 2.4350 - val_mse: 16.1420\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 36/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.4445 - mae: 2.1548 - mse: 7.4445"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6326 - mae: 2.0683 - mse: 8.6326 - val_loss: 14.2267 - val_mae: 2.4698 - val_mse: 14.2267\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 37/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.3137 - mae: 2.2289 - mse: 7.3137"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.7341 - mae: 2.0698 - mse: 8.7341 - val_loss: 14.6117 - val_mae: 2.4487 - val_mse: 14.6117\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 38/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.0914 - mae: 2.0325 - mse: 7.0914"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6943 - mae: 2.0261 - mse: 8.6943 - val_loss: 15.2316 - val_mae: 2.5800 - val_mse: 15.2316\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 39/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.2764 - mae: 1.5102 - mse: 3.2764"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.5876 - mae: 2.0295 - mse: 8.5876 - val_loss: 18.0986 - val_mae: 2.6038 - val_mse: 18.0986\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 40/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.7968 - mae: 2.1062 - mse: 5.7968"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 3ms/step - loss: 8.5341 - mae: 2.0621 - mse: 8.5341 - val_loss: 13.5799 - val_mae: 2.4364 - val_mse: 13.5799\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 41/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 20.1664 - mae: 3.0291 - mse: 20.1664"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.3663 - mae: 1.9890 - mse: 8.3663 - val_loss: 16.7243 - val_mae: 2.6952 - val_mse: 16.7243\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 42/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.1991 - mae: 2.2748 - mse: 7.1991"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.2688 - mae: 2.0357 - mse: 8.2688 - val_loss: 14.7858 - val_mae: 2.3847 - val_mse: 14.7858\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 43/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.8338 - mae: 1.0660 - mse: 1.8338"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.1713 - mae: 1.9501 - mse: 8.1713 - val_loss: 13.9294 - val_mae: 2.3886 - val_mse: 13.9294\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 44/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.2799 - mae: 1.3167 - mse: 3.2799"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.0837 - mae: 1.9946 - mse: 8.0837 - val_loss: 15.0571 - val_mae: 2.3772 - val_mse: 15.0571\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 45/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 12.0710 - mae: 2.5895 - mse: 12.0710"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8297 - mae: 1.9675 - mse: 7.8297 - val_loss: 14.4700 - val_mae: 2.4497 - val_mse: 14.4700\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 46/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.6554 - mae: 1.7192 - mse: 8.6554"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8721 - mae: 1.9562 - mse: 7.8721 - val_loss: 15.6275 - val_mae: 2.5021 - val_mse: 15.6275\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 47/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.6549 - mae: 1.7788 - mse: 6.6549"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8012 - mae: 1.9234 - mse: 7.8012 - val_loss: 15.4175 - val_mae: 2.4128 - val_mse: 15.4175\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 48/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.4386 - mae: 1.6254 - mse: 4.4386"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6501 - mae: 1.9264 - mse: 7.6501 - val_loss: 13.8509 - val_mae: 2.4258 - val_mse: 13.8509\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 49/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.6743 - mae: 1.6323 - mse: 3.6743"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6558 - mae: 1.9328 - mse: 7.6558 - val_loss: 14.5430 - val_mae: 2.4151 - val_mse: 14.5430\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 50/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.6029 - mae: 1.5487 - mse: 3.6029"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6111 - mae: 1.9027 - mse: 7.6111 - val_loss: 14.9128 - val_mae: 2.3905 - val_mse: 14.9128\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 51/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.6815 - mae: 1.6452 - mse: 3.6815"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.5492 - mae: 1.9115 - mse: 7.5492 - val_loss: 13.8725 - val_mae: 2.3764 - val_mse: 13.8725\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 52/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.0226 - mae: 2.2680 - mse: 9.0226"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3721 - mae: 1.9034 - mse: 7.3721 - val_loss: 14.9186 - val_mae: 2.4914 - val_mse: 14.9186\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 53/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.9170 - mae: 2.0245 - mse: 7.9170"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.2422 - mae: 1.8615 - mse: 7.2422 - val_loss: 15.5612 - val_mae: 2.4342 - val_mse: 15.5612\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 54/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 12.3707 - mae: 2.1982 - mse: 12.3707"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3332 - mae: 1.9150 - mse: 7.3332 - val_loss: 15.2095 - val_mae: 2.4613 - val_mse: 15.2095\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 55/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.1180 - mae: 2.1155 - mse: 7.1180"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.2277 - mae: 1.8782 - mse: 7.2277 - val_loss: 15.1990 - val_mae: 2.5274 - val_mse: 15.1990\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 56/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.8213 - mae: 2.1155 - mse: 7.8213"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0867 - mae: 1.8471 - mse: 7.0867 - val_loss: 15.9479 - val_mae: 2.5210 - val_mse: 15.9479\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 57/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.0981 - mae: 2.2654 - mse: 7.0981"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0518 - mae: 1.8468 - mse: 7.0518 - val_loss: 13.9539 - val_mae: 2.4011 - val_mse: 13.9539\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 58/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.4035 - mae: 1.4639 - mse: 3.4035"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0687 - mae: 1.8449 - mse: 7.0687 - val_loss: 13.0074 - val_mae: 2.4371 - val_mse: 13.0074\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 59/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0958 - mae: 1.5204 - mse: 4.0958"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.8857 - mae: 1.8453 - mse: 6.8857 - val_loss: 13.9144 - val_mae: 2.4056 - val_mse: 13.9144\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 60/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.9802 - mae: 1.8256 - mse: 5.9802"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.9196 - mae: 1.8199 - mse: 6.9196 - val_loss: 14.6482 - val_mae: 2.4412 - val_mse: 14.6482\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 61/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.4707 - mae: 1.7703 - mse: 4.4707"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7712 - mae: 1.8107 - mse: 6.7712 - val_loss: 13.8300 - val_mae: 2.4093 - val_mse: 13.8300\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 62/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.0282 - mae: 2.2276 - mse: 7.0282"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.6533 - mae: 1.8254 - mse: 6.6533 - val_loss: 13.9860 - val_mae: 2.4244 - val_mse: 13.9860\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 63/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.3382 - mae: 1.0856 - mse: 3.3382"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.6135 - mae: 1.8061 - mse: 6.6135 - val_loss: 13.0682 - val_mae: 2.5226 - val_mse: 13.0682\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 64/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.2947 - mae: 1.1612 - mse: 2.2947"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.6893 - mae: 1.7760 - mse: 6.6893 - val_loss: 13.0032 - val_mae: 2.4997 - val_mse: 13.0032\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 65/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.3190 - mae: 2.0487 - mse: 9.3190"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.5807 - mae: 1.7694 - mse: 6.5807 - val_loss: 12.8187 - val_mae: 2.4031 - val_mse: 12.8187\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 66/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.7041 - mae: 2.1078 - mse: 7.7041"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.6294 - mae: 1.7897 - mse: 6.6294 - val_loss: 13.4268 - val_mae: 2.3852 - val_mse: 13.4268\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 67/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.1215 - mae: 2.1463 - mse: 7.1215"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4963 - mae: 1.7647 - mse: 6.4963 - val_loss: 14.4899 - val_mae: 2.4291 - val_mse: 14.4899\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 68/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.8348 - mae: 2.0396 - mse: 6.8348"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4055 - mae: 1.7644 - mse: 6.4055 - val_loss: 13.8988 - val_mae: 2.4481 - val_mse: 13.8988\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 69/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.1401 - mae: 1.6643 - mse: 4.1401"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3309 - mae: 1.7587 - mse: 6.3309 - val_loss: 12.7296 - val_mae: 2.4945 - val_mse: 12.7296\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 70/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.3014 - mae: 1.5148 - mse: 4.3014"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3321 - mae: 1.7287 - mse: 6.3321 - val_loss: 12.9661 - val_mae: 2.4811 - val_mse: 12.9661\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 71/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.4335 - mae: 1.0470 - mse: 2.4335"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3025 - mae: 1.7535 - mse: 6.3025 - val_loss: 12.6502 - val_mae: 2.4611 - val_mse: 12.6502\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 72/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0539 - mae: 1.4353 - mse: 4.0539"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2513 - mae: 1.7225 - mse: 6.2513 - val_loss: 12.7984 - val_mae: 2.4493 - val_mse: 12.7984\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 73/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.2204 - mae: 1.3434 - mse: 3.2204"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.0639 - mae: 1.7208 - mse: 6.0639 - val_loss: 13.8270 - val_mae: 2.4772 - val_mse: 13.8270\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 74/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 10.4668 - mae: 2.1620 - mse: 10.4668"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.1868 - mae: 1.7145 - mse: 6.1868 - val_loss: 14.1506 - val_mae: 2.5845 - val_mse: 14.1506\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 75/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.4450 - mae: 1.7198 - mse: 3.4450"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.0078 - mae: 1.7327 - mse: 6.0078 - val_loss: 12.5845 - val_mae: 2.4593 - val_mse: 12.5845\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 76/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.9283 - mae: 1.8763 - mse: 8.9283"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.9769 - mae: 1.6597 - mse: 5.9769 - val_loss: 13.7416 - val_mae: 2.4264 - val_mse: 13.7416\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 77/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.7732 - mae: 1.1096 - mse: 1.7732"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.7378 - mae: 1.6430 - mse: 5.7378 - val_loss: 14.9285 - val_mae: 2.4672 - val_mse: 14.9285\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 78/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.6906 - mae: 1.5438 - mse: 4.6906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.0702 - mae: 1.7073 - mse: 6.0702 - val_loss: 13.5002 - val_mae: 2.4574 - val_mse: 13.5002\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 79/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.5350 - mae: 2.0934 - mse: 9.5350"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6094 - mae: 1.6573 - mse: 5.6094 - val_loss: 14.1111 - val_mae: 2.5431 - val_mse: 14.1111\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 80/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.2028 - mae: 2.3519 - mse: 9.2028"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6957 - mae: 1.6749 - mse: 5.6957 - val_loss: 13.2552 - val_mae: 2.5393 - val_mse: 13.2552\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 81/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.7361 - mae: 1.4276 - mse: 2.7361"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.7200 - mae: 1.6645 - mse: 5.7200 - val_loss: 13.0018 - val_mae: 2.4606 - val_mse: 13.0018\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 82/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.6291 - mae: 2.1848 - mse: 7.6291"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6837 - mae: 1.6614 - mse: 5.6837 - val_loss: 12.9487 - val_mae: 2.4460 - val_mse: 12.9487\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 83/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 11.1980 - mae: 2.2394 - mse: 11.1980"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6187 - mae: 1.6395 - mse: 5.6187 - val_loss: 13.6152 - val_mae: 2.4410 - val_mse: 13.6152\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 84/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.2645 - mae: 1.0280 - mse: 1.2645"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5375 - mae: 1.6625 - mse: 5.5375 - val_loss: 13.7843 - val_mae: 2.4875 - val_mse: 13.7843\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 85/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.9352 - mae: 1.8240 - mse: 6.9352"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5747 - mae: 1.6138 - mse: 5.5747 - val_loss: 12.9473 - val_mae: 2.4488 - val_mse: 12.9473\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 86/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.5318 - mae: 1.0513 - mse: 1.5318"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.4318 - mae: 1.5857 - mse: 5.4318 - val_loss: 13.7020 - val_mae: 2.5508 - val_mse: 13.7020\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 87/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.8362 - mae: 0.9933 - mse: 1.8362"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.4703 - mae: 1.6559 - mse: 5.4703 - val_loss: 13.3855 - val_mae: 2.4788 - val_mse: 13.3855\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 88/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.7715 - mae: 1.9173 - mse: 8.7715"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3036 - mae: 1.6029 - mse: 5.3036 - val_loss: 14.0921 - val_mae: 2.4609 - val_mse: 14.0921\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 89/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.0246 - mae: 1.4390 - mse: 3.0246"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3773 - mae: 1.5968 - mse: 5.3773 - val_loss: 13.9248 - val_mae: 2.4907 - val_mse: 13.9248\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 90/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.4601 - mae: 1.3539 - mse: 2.4601"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2268 - mae: 1.5829 - mse: 5.2268 - val_loss: 14.6749 - val_mae: 2.5361 - val_mse: 14.6749\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 91/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.8107 - mae: 1.6357 - mse: 3.8107"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.1736 - mae: 1.5926 - mse: 5.1736 - val_loss: 13.4514 - val_mae: 2.5856 - val_mse: 13.4514\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 92/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 19.5630 - mae: 3.1410 - mse: 19.5630"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2418 - mae: 1.5954 - mse: 5.2418 - val_loss: 12.6804 - val_mae: 2.4248 - val_mse: 12.6804\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 93/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.0834 - mae: 1.4256 - mse: 3.0834"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2488 - mae: 1.5989 - mse: 5.2488 - val_loss: 12.4736 - val_mae: 2.4226 - val_mse: 12.4736\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 94/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.8809 - mae: 2.1107 - mse: 5.8809"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0619 - mae: 1.5615 - mse: 5.0619 - val_loss: 13.4973 - val_mae: 2.4437 - val_mse: 13.4973\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 95/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.7440 - mae: 1.3863 - mse: 2.7440"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9695 - mae: 1.5607 - mse: 4.9695 - val_loss: 12.2446 - val_mae: 2.3704 - val_mse: 12.2446\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 96/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.5596 - mae: 1.2999 - mse: 2.5596"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0109 - mae: 1.5504 - mse: 5.0109 - val_loss: 12.8840 - val_mae: 2.5403 - val_mse: 12.8840\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 97/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.7687 - mae: 1.5506 - mse: 3.7687"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.8156 - mae: 1.5307 - mse: 4.8156 - val_loss: 13.2364 - val_mae: 2.5249 - val_mse: 13.2364\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 98/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.0708 - mae: 1.5772 - mse: 7.0708"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9354 - mae: 1.5551 - mse: 4.9354 - val_loss: 12.9653 - val_mae: 2.3904 - val_mse: 12.9653\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 99/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.7436 - mae: 1.3471 - mse: 2.7436"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0056 - mae: 1.5565 - mse: 5.0056 - val_loss: 12.4294 - val_mae: 2.4691 - val_mse: 12.4294\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 100/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.6867 - mae: 1.9347 - mse: 5.6867"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.7599 - mae: 1.5225 - mse: 4.7599 - val_loss: 14.2900 - val_mae: 2.5245 - val_mse: 14.2900\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = 100,\n",
+ " batch_size = 10,\n",
+ " verbose = 1,\n",
+ " validation_data = (x_test, y_test))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Model evaluation\n",
+ "MAE = Mean Absolute Error (between the labels and predictions) \n",
+ "A mae equal to 3 represents an average error in prediction of $3k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:27.248032Z",
+ "iopub.status.busy": "2021-01-14T07:11:27.247704Z",
+ "iopub.status.idle": "2021-01-14T07:11:27.271323Z",
+ "shell.execute_reply": "2021-01-14T07:11:27.271664Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 14.2900\n",
+ "x_test / mae : 2.5245\n",
+ "x_test / mse : 14.2900\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Training history\n",
+ "What was the best result during our training ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:27.282826Z",
+ "iopub.status.busy": "2021-01-14T07:11:27.282357Z",
+ "iopub.status.idle": "2021-01-14T07:11:27.285322Z",
+ "shell.execute_reply": "2021-01-14T07:11:27.285657Z"
+ }
+ },
+ "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>0</th>\n",
+ " <td>512.695251</td>\n",
+ " <td>20.421410</td>\n",
+ " <td>512.695251</td>\n",
+ " <td>371.698700</td>\n",
+ " <td>17.640818</td>\n",
+ " <td>371.698700</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>288.060730</td>\n",
+ " <td>14.319571</td>\n",
+ " <td>288.060730</td>\n",
+ " <td>148.873795</td>\n",
+ " <td>10.038457</td>\n",
+ " <td>148.873795</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>113.567566</td>\n",
+ " <td>7.805418</td>\n",
+ " <td>113.567551</td>\n",
+ " <td>63.275372</td>\n",
+ " <td>6.258007</td>\n",
+ " <td>63.275372</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>59.180847</td>\n",
+ " <td>5.487036</td>\n",
+ " <td>59.180840</td>\n",
+ " <td>33.185242</td>\n",
+ " <td>4.469059</td>\n",
+ " <td>33.185242</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>38.479008</td>\n",
+ " <td>4.315325</td>\n",
+ " <td>38.479008</td>\n",
+ " <td>22.436140</td>\n",
+ " <td>3.583613</td>\n",
+ " <td>22.436140</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>95</th>\n",
+ " <td>5.010880</td>\n",
+ " <td>1.550388</td>\n",
+ " <td>5.010880</td>\n",
+ " <td>12.883963</td>\n",
+ " <td>2.540313</td>\n",
+ " <td>12.883963</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96</th>\n",
+ " <td>4.815630</td>\n",
+ " <td>1.530723</td>\n",
+ " <td>4.815630</td>\n",
+ " <td>13.236412</td>\n",
+ " <td>2.524947</td>\n",
+ " <td>13.236412</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>97</th>\n",
+ " <td>4.935360</td>\n",
+ " <td>1.555126</td>\n",
+ " <td>4.935360</td>\n",
+ " <td>12.965294</td>\n",
+ " <td>2.390428</td>\n",
+ " <td>12.965294</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>98</th>\n",
+ " <td>5.005613</td>\n",
+ " <td>1.556515</td>\n",
+ " <td>5.005613</td>\n",
+ " <td>12.429406</td>\n",
+ " <td>2.469126</td>\n",
+ " <td>12.429406</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>99</th>\n",
+ " <td>4.759908</td>\n",
+ " <td>1.522508</td>\n",
+ " <td>4.759908</td>\n",
+ " <td>14.290004</td>\n",
+ " <td>2.524544</td>\n",
+ " <td>14.290004</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>100 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " loss mae mse val_loss val_mae val_mse\n",
+ "0 512.695251 20.421410 512.695251 371.698700 17.640818 371.698700\n",
+ "1 288.060730 14.319571 288.060730 148.873795 10.038457 148.873795\n",
+ "2 113.567566 7.805418 113.567551 63.275372 6.258007 63.275372\n",
+ "3 59.180847 5.487036 59.180840 33.185242 4.469059 33.185242\n",
+ "4 38.479008 4.315325 38.479008 22.436140 3.583613 22.436140\n",
+ ".. ... ... ... ... ... ...\n",
+ "95 5.010880 1.550388 5.010880 12.883963 2.540313 12.883963\n",
+ "96 4.815630 1.530723 4.815630 13.236412 2.524947 13.236412\n",
+ "97 4.935360 1.555126 4.935360 12.965294 2.390428 12.965294\n",
+ "98 5.005613 1.556515 5.005613 12.429406 2.469126 12.429406\n",
+ "99 4.759908 1.522508 4.759908 14.290004 2.524544 14.290004\n",
+ "\n",
+ "[100 rows x 6 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df=pd.DataFrame(data=history.history)\n",
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:27.288802Z",
+ "iopub.status.busy": "2021-01-14T07:11:27.288358Z",
+ "iopub.status.idle": "2021-01-14T07:11:27.291066Z",
+ "shell.execute_reply": "2021-01-14T07:11:27.290733Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "min( val_mae ) : 2.3704\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:27.296795Z",
+ "iopub.status.busy": "2021-01-14T07:11:27.296413Z",
+ "iopub.status.idle": "2021-01-14T07:11:28.313345Z",
+ "shell.execute_reply": "2021-01-14T07:11:28.312924Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/BHPD1-01-history_0</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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\n",
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+ },
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KzWRzfkzm7lPGkojX/GrYITfQcyUiIjXT58fa4f/OVUd63yGzdvUQEREZKHVVVFEtJ4FasGABp59+eq91jY2N/PnPf65qPUREJNoUHKooRu2mnT7ggAOYP39+db+piIgMO+qqGKCBjA3p3VUx/PsqRsJrFBEZaRQcBqCpqYl169b1+40xPG9Dbpi/pzrnWLduHU1NTdsvLCIikaGuigGYMWMGS5cuZc2aNf3ab8PWTroy/qqKbesaaEjEh6J6daOpqYkZM2bUuhoiIjKIFBwGIJlMsttuu/V7v0v/31P8edFqAL728cM4aO8pg101ERGRIaWuiipqSBROd77lQUREJEoUHKoo3DXRlcnWsCYiIiIDo+BQRUm1OIiISMQpOFRRuKuiWy0OIiISQQoOVdS7q0ItDiIiEj0KDlXUEFdXhYiIRJuCQxX1HuOgrgoREYkeBYcq6tVVkVWLg4iIRI+CQxX1Hhyp4CAiItGj4FBFDeqqEBGRiFNwqCJdVSEiIlGn4FBFmgBKRESiTsGhijQBlIiIRJ2CQxWpq0JERKJOwaGKdHdMERGJOgWHKtLdMUVEJOoUHKooqSmnRUQk4hQcqkjzOIiISNQpOFSRBkeKiEjUKThUkQZHiohI1Ck4VJHmcRARkahTcKii4pkjnXM1rI2IiEj/KThUUTwWIx4zAByQySk4iIhItCg4VJmurBARkShTcKiy8JUV3RogKSIiEaPgUGW6Q6aIiESZgkOVqatCRESiTMGhyhrimgRKRESiS8GhyjQJlIiIRJmCQ5UlNQmUiIhEmIJDlel+FSIiEmUKDlWmrgoREYkyBYcq01UVIiISZQoOVaauChERibK6Cw5mNsrMXjMzZ2bfKbF9HzO708w2mFmbmT1qZseUOVbMzC4ys5fMrMPM3jSzq8xs9NC/ktJ6DY7MKjiIiEi01F1wAC4HJpfaYGZ7AI8DRwBXAhcDLcB9ZnZsiV2uAa4GXgTOB24HLgDuMrOavHZ1VYiISJQlal2BMDM7FLgQ+AJwVYkiVwDjgbc75+YH+9wEvADMM7NZLrhXtZnthw8LdzjnTg59j9eA64BTgFuG6rWUo64KERGJsrppcTCzOHAjcC9wR4nto4GTgIfyoQHAObcV+AGwNzAntMupgAHXFh3qRqAdOG3wal+5hriuqhARkeiqm+AAXATMAs4rs/1AoBF4osS2PwXLcHCYA+SAJ8MFnXMdwPyislWTVFeFiIhEWF0EBzPbDfgacLlzbkmZYtOD5bIS2/LrWovKr3XOdZYpP9nMGsrU51wze3q7FR8A3VZbRESirC6CA/Bd4DX8QMZyRgXLUkGgo6hM/nmpsuXK93DOfd85d1gfdRkwDY4UEZEoq/ngSDM7DTgOmOuc6+6jaHuwbCyxramoTP75zmWOVap8VWjmSBERibKaBgcza8S3MtwDrDSzPYNN+S6HccG6tcDyom1h+XXhbozlwL5m1liiu6IV343RtaOvob90VYWIiERZrbsqmoGdgBOBRaHHQ8H204KvzwYW4LsejihxnMODZXhcwlP41/eOcEEzawIOLipbNbo7poiIRFmtuyragH8ssX4nII2/NPOHwF+dc1vN7C7gI2Z2kHPuOQAza8EHi0X0voLiVuDf8fNCPBpafw5+bMPNg/tSKtOrq0IzR4qISMTUNDgEYxp+UbzezGYGT191zoW3XwK8D7jfzK4BNuODQCtwYn7yp+DYC8xsHnCemd2B7w6ZjZ858mFqMPkTqKtCRESirdYtDv3inHvFzN4NfBP4EtAAPAuc4Jx7oMQuFwJLgHPx3SFrgeuBS51zNXnX1lUVIiISZXUZHIK5HKzMtoXAhys8ThY/dXWp6atrQvM4iIhIlNV6cOSIk9SU0yIiEmEKDlWmrgoREYkyBYcq0+BIERGJMgWHKtPMkSIiEmUKDlXWoAmgREQkwhQcqixZ1OIQmnpCRESk7tXl5ZjD0mP3w9OPEO/u4n3t0/i/UXvjgEzOkYyXvPJURESk7ig4VMvq5fC8vz3G9EktPau7Mtlel2iKiIjUM71jVUsy2fO0mcKgSE0CJSIiUaLgUC3Jhp6njVYYFKkrK0REJEoUHKolFByaCAcHXVkhIiLRoeBQLeEWh1BXhVocREQkShQcqiU0xqGRTM9zBQcREYkSBYdqCbU4NLhC94QmgRIRkShRcKiWRLirQoMjRUQkmhQcqqWhdIuDgoOIiESJgkO1hLoqki48xkFdFSIiEh0KDtUSHuOQ0+BIERGJJgWHagkFh0QoOHRnFRxERCQ6FByqJTQ4MplTV4WIiESTgkO1hOZxiKurQkREIkrBoVrKdFUoOIiISJQoOFRLKDjEs909z9VVISIiUaLgUC29gkNocKRaHEREJEIUHKolHoeYP90xlyPmfGBQi4OIiESJgkM19bpfhW910BgHERGJEgWHaipxoysFBxERiRIFh2oqERx0d0wREYkSBYdqShTmcujpqtDMkSIiEiEKDtXU0NjzNKmuChERiSAFh2oKzR7Z2DM4Ul0VIiISHQoO1VTiqgrN4yAiIlGi4FBNCV1VISIi0abgUE2hFofCGAd1VYiISHQoOFRTssRVFWpxEBGRCFFwqCZNACUiIhGn4FBNJQdHqqtCRESiQ8GhmkqOccjhnKtVjURERPpFwaGaQmMcmvDBwQGZnIKDiIhEg4JDNYVaHJqtMLZBV1aIiEhUKDhUU2geh6ZQcNAkUCIiEhUKDtWUDAeHQiuDrqwQEZGoUHCopvC9KggHB3VViIhINCg4VFOoxaGR8BgHtTiIiEg01Dw4mNk+ZnazmS00s01m1m5mL5nZ1WY2rUz5O81sg5m1mdmjZnZMmWPHzOyi4HgdZvammV1lZqOH/pWVELqtdlMwjwMoOIiISHQkal0BYAYwDfgVsBTIAAcA5wKnmNnBzrnVAGa2B/B4UOZKYBNwDnCfmX3AOfdA0bGvAS4Ijn0VMDv4+hAzO9Y5V9137ERoyulQV4UmgRIRkaioeXBwzv0f8H/F683sEeA24Ex8SAC4AhgPvN05Nz8odxPwAjDPzGa5YDYlM9sPOB+4wzl3cui4rwHXAacAtwzJiyqnxJTToBYHERGJjpp3VfTh9WA5ASDoXjgJeCgfGgCcc1uBHwB7A3NC+58KGHBt0XFvBNqB04ai0n0qMeU0KDiIiEh01E1wMLMmM5tsZjPM7Djge8Gme4LlgUAj8ESJ3f8ULMPBYQ6QA54MF3TOdQDzi8pWR4kpp0FXVYiISHTUTXAAzgbWAG8C9+G7JE5zzj0abJ8eLJeV2De/rjW0bjqw1jnXWab8ZDNrKLFt6IRvq51Ti4OIiERPPQWHO4H3A/8AXA5sBHYKbR8VLEsFgY6iMvnnpcqWK9/DzM41s6e3W+P+CrU4JEJdFd1ZBQcREYmGugkOzrmlzrkHnHN3Oue+CpwBfMvMLgmKtAfLxhK7NxWVyT8vVbZc+XBdvu+cO6zy2lcoHBx6tTioq0JERKJhUK6qSKVSBwFH4wcjPppOp3f407pz7q9m9hcghb+aYnmwqbVE8fy6cDfGcmBfM2ss0V3Riu/G6NrRevZLODhk1VUhIiLRU1GLQyqVmptKpW5KpVKHl9h2GfAsfp6E/wb+nEqlrh6k+jUDE4PnC/BdD0eUKJevVziwPIV/fe8IFzSzJuDgorLVkSjX4qDgICIi0VBpV8U/Ah8DFoZXplKp9wKX4q9euBm4AVgHfD6VSn2wkgOb2dQy648G9ie4YiK47PIu4CgzOyhUrgU/sHIRva+guBVwwIVFhz4HP7bh5krqN6hCgyPj2e6e5+qqEBGRqKi0q+II4M/pdHpT0frP4N+cL0in098FSKVS1wPPAWdRuJSyL98Nppb+A37uhibg7fgJmrYA/xoqewnwPuB+M7sG2IwPAq3AifnJnwCccwvMbB5wnpndEdQlP3Pkw1R78icoCg4ZcA7MdFttERGJjEpbHKbjP9EXOwZow0+qBEA6nX4JfzllpfMk/BzfSnE68D/AN/HdC98DDiya7OkV4N34Vogv4btG2oATnHP3lTj2hcC/AfsB8/Bh5Hrg76o+3TRALA5xn9UMRyK40VWnWhxERCQiKm1xmASsCq9IpVJTganA/el0OlNUfhH+0srtcs7dhp9auiLOuYXAhyssm8WPvbiq0uMPuWQDBAMjG1yGjMXp7FZwEBGRaKi0xWEbMKVo3aHB8i8lynfib0QlxUrcr0LBQUREoqLS4PAS8IFUKhVuoTgRP77h8RLl3was2MG6DU8l7leh4CAiIlFRaVfFL/B3qPxNKpW6AX9DqU/jb2v9+xLl303RFRgSCE87HbQ4dCg4iIhIRFQaHK7H323yBOD4YJ0B/5ZOpzvCBVOp1DuBmcE+UqxEi4OCg4iIREVFXRXpdLoTmIufs+Fe/BwIH0qn098pUfxg4Nf4ORekWIk7ZKqrQkREoqLiKafT6XQb8I0Kyn2Pwi2xpZgGR4qISITVzU2uRoxeYxzUVSEiItGywze5SqVSSeBz+MmgDD8r47yge0OKqcVBREQirNKbXH0qlUq9kUql3le0Pgb8FrgGOAn4EPBt4A9Fl25KXqL05Zih2bJFRETqVqVdFe8HxgAPFa0/Ndi2Cn+jqY8Df8bfrfLTg1PFYSbU4tAUTDmdc9Cd1f0qRESk/lUaHA4FHk+n08Vt6qfhJ4H6VDqd/t90On07cBx+foePDV41h5HQGIdRsUJY0DgHERGJgkqDwxRgcYn17wJWpdPpB/Ir0un0VuBu/C2xpVioxSEcHDTOQUREoqDS4DAWfxfKHqlUak9898UfS5RfCozfoZoNV6Hg0KzgICIiEVNpcNgA7Fa0Ln/b7FI3uUoAWwdaqWEtHBwohIWOLgUHERGpf5UGh78AJ6ZSqWmhdafgxzc8XKL8XugmV6WFxjg0WajFIaPgICIi9a/SSyZ/iB/0+EQqlboDf5OrDwKvpNPpXl0VwWWY78VPTS3Fko09T5vCLQ7qqhARkQio9F4VtwM/AHYBLsSHhk3AOSWKfwiYQOm7Zkqoq6KRUIuDuipERCQCKp5yOp1Onwu8B/gifs6G/dLpdKluinbgIuA3g1LD4SbUVdGoFgcREYmYfs3umE6nHwce306Z+4D7dqRSw1p4ymkyPc81xkFERKJAN7mqthL3qgBdjikiItHQ7/tJpFKpI4BzgXcD0/FXVqwAHgNuTKfTTwxqDYebUHBI5nQ5poiIREvFwSG4C2Ya+Cf8XTDD9gweZ6RSqf8FUul0unvQajmcJN56W21Qi4OIiERDf1ocfoi/N8UG/BUWvwfexIeIGfibXX0aHywagDMGtabDRajFIZErBIcOjXEQEZEIqCg4pFKpY/Gh4RngQ+l0emVRkZeAB1Kp1NX422yflkqlbkqn0/83qLUdDsoFB3VViIhIBFQ6OPIc/L0q/r5EaOiRTqdXAX8PbKP0HA8SDg5ZdVWIiEi0VBocjgB+l06nl22vYFDmbvydM6VYaB6HeK4wDETBQUREoqDS4LAzsKgfx30F2Kn/1RkBQi0O8UwoOGiMg4iIREClwaEdfwvtSo0BOvpfnREgFBxi2UJw0BgHERGJgkqDwyLgmH4c92j610IxcoSCg2XUVSEiItFSaXC4G5idSqVS2yuYSqU+C+yLv7pCioXmcYhlM5hzgO5VISIi0VDpPA7XAecD/5NKpaYD306n05vCBVKp1FjgYvxNsNYD1w9mRYcNM9/q0N0FQNJl6bKEWhxERCQSKgoO6XR6YyqVOhnf8nAJ8C+pVOoZ/ARQDn+77bcDjfhLMT+aTqc3DE2Vh4FQcGhwGbpIqMVBREQioT+31X4EeCfwINCEv1fFKcCpwfMm4CHg8DK325a8ZHjaaR8Y1OIgIiJR0N/bar8IHJtKpWYC7wGm4aecXgE8lk6nXwNIpVJNQEM6nd48uNUdJsI3ugruV9HZncU5h1nxbUBERETqR7/vjgmQTqeXAEv6KPJd4PSBHn/YSxSCw6iYHxzpgK5MjsZkvEaVEhER2b6KuyoGQB+dywm1OLTEXc9zdVeIiEi9G8rgIOWExjiMjud6nmuApIiI1DsFh1oItTiMjhVaHBQcRESk3ik41EIyPMah0OKgrgoREal3Cg61kHzr4EhQcBARkfqn4FALoTEOzVYIC+qqEBGRelfR5ZKpVErvaIMpfDmmqatCRESio9J5FgZyaaXbfpERKtRV0RRqcVBwEBGRelfpvSrUpTGYwsEBXY4pIiLRoUBQCw2lWxwUHEREpN7VPDiY2d5mdrmZ/cnM1pjZFjObb2ZfNrPRJcrvY2Z3mtkGM2szs0fN7Jgyx46Z2UVm9pKZdZjZm2Z2VanjVlWoxSF/kytQV4WIiNS/mgcH4J+Ai4BXgcuBi4G/Ad8AHjez5nxBM9sDeBw4ArgyKNsC3Gdmx5Y49jXA1cCLwPnA7cAFwF1mVrvXHhoc2YiCg4iIREc93ITqF8AVzrlNoXU3mNki4MvAp4HvBOuvAMYDb3fOzQcws5uAF4B5ZjbLOeeC9fvhw8IdzrmT8wc2s9eA6/C3BL9lCF9XeSVuqw3qqhARkfpX8xYH59zTRaEh79ZguT9A0L1wEvBQPjQE+28FfgDsDcwJ7X8q/mqQa4uOeyPQDpw2CNUfmBK31Qa1OIiISP2reXDow4xguSpYHgg0Ak+UKPunYBkODnOAHPBkuKBzrgOYX1S2usLBIaeuChERiY66DA5mFgcuBTIUuhOmB8tlJXbJr2sNrZsOrHXOdZYpP9nMGkpsw8zONbOn+13xSpVpcejoypQqLSIiUjfqMjjguxcOBy51zv0tWDcqWJYKAh1FZfLPS5UtV76Hc+77zrnDKq5tf4XGOCRyoeCQyZUqLSIiUjfqLjiY2deB84DvO+euCG1qD5aNJXZrKiqTf16qbLny1RNqcUjkunueq6tCRETqXV0FBzO7DPgP4EfAZ4s2Lw+WrbxVfl24G2M5vjuiVHhoxXdjdA28tjsgFBziGQUHERGJjroJDmb2VeCrwE3A2fnLKkMW4Lsejiix++HBMjwu4Sn863tH0fdpAg4uKltdoXkc4lmNcRARkeioi+BgZpcClwE/Bc5yzr2lsz+47PIu4CgzOyi0bwtwNrCI3ldQ3Iq/0daFRYc6Bz+24ebBewX9FBrjEMuGWhw0xkFEROpczSeAMrN/Br4GvAE8AHzCrNfNOFc5534fPL8EeB9wv5ldA2zGB4FW4MRwK4VzboGZzQPOM7M7gHuA2fiZIx+mVpM/Qa+uilimu2ckhroqRESk3tU8OFCYT2EX4Ccltj8M/B7AOfeKmb0b+CbwJaABeBY4wTn3QIl9LwSWAOcCJwJrgevxV2vU7uN9cXAIqKtCRETqXc2Dg3PuTODMfpRfCHy4wrJZ4KrgUT9Cd8ckUxif2ZnJkXOOWO8WFxERkbpRF2McRpxQi4N1d9OQKPwYujTOQURE6piCQy0kerc4NCbjPV+qu0JEROqZgkMtxOOQv6t3NsuoRKFrQgMkRUSknik41IJZr0syR8cLU1YoOIiISD1TcKiV0DiHMeHgoDEOIiJSxxQcaiUUHMItDhrjICIi9UzBoVbKBQd1VYiISB1TcKiV0BiHUbFC94TGOIiISD1TcKiV0CWZo60QHNTiICIi9UzBoVZCXRXNanEQEZGIUHColVBwaDIFBxERiQYFh1oJjXFoVleFiIhEhIJDrYRbHCiEBQUHERGpZwoOtVImOKirQkRE6pmCQ62EgkMjGuMgIiLRoOBQK72Cg7oqREQkGhQcaiUUHBpcYZpptTiIiEg9U3ColdBVFUmnMQ4iIhINCg610qvFQV0VIiISDQoOtRIKDkl1VYiISEQoONRKKDgkst09z9XiICIi9UzBoVaaR/U8TXS29zxXi4OIiNQzBYdaGTO+52mifXPPc7U4iIhIPVNwqJWWcT1P422F4KAWBxERqWcKDrUyphAcbGshOHRlcuScq0WNREREtkvBoVbCwWHLJhoThR+FWh1ERKReKTjUSkMjNDb559kME+KaBEpEROqfgkMthVodJltnz3MNkBQRkXql4FBLoQGSk+noed7RpeAgIiL1ScGhlkItDhNdocWhM6PgICIi9UnBoZZCwWG829bzXGMcRESkXik41FIoOIzLKjiIiEj9U3CopZbxPU/DwUFjHEREpF4pONRSqMVhTKZwvwpdVSEiIvVKwaGWQsGhJRQcNDhSRETqlYJDLYWCw+iutp7n6qoQEZF6peBQS6Hg0NxZCA4aHCkiIvVKwaGWQsGhqXNrz3MFBxERqVcKDrXU2AyJJACJbDeNuW5AYxxERKR+KTjUklnvSaBy/pJMjXEQEZF6peBQayUmgdLlmCIiUq8UHGotHByCFoe2zu5a1UZERKRPCg611vLWFodVG7eVKy0iIlJTCg61NmZ8z9PxPcGhHedcjSokIiJSXs2Dg5ldYma3m9liM3NmtmQ75fcxszvNbIOZtZnZo2Z2TJmyMTO7yMxeMrMOM3vTzK4ys9FD8mIGItRVMdn8rbU7Mzk2tHWW20NERKRmah4cgP8CjgFeBTb0VdDM9gAeB44ArgQuBlqA+8zs2BK7XANcDbwInA/cDlwA3GVm9fDaewWHKfGunucr1V0hIiJ1KFHrCgB7OOcWA5jZ8/ggUM4VwHjg7c65+cE+NwEvAPPMbJYL2vjNbD98WLjDOXdy/gBm9hpwHXAKcMugv5r+KtHiALByQzv7zphQixqJiIiUVfNP3fnQsD1B98JJwEP50BDsvxX4AbA3MCe0y6mAAdcWHepGoB04bcCVHkwlBkcCrNzYXqq0iIhITdU8OPTDgUAj8ESJbX8KluHgMAfIAU+GCzrnOoD5RWVrp8wdMnVlhYiI1KMoBYfpwXJZiW35da1F5dc650qNMlwGTDazhlLfyMzONbOnB1zT/gjfr6KjcL8KtTiIiEg9ilJwGBUsSwWBjqIy+eflLk0oVb6Hc+77zrnD+l3DgRjVAvE4APGuDpLOzxqp4CAiIvUoSsEh/07aWGJbU1GZ/PNSZcuVrw0zaBnb82V+nMPqTR1kc7la1UpERKSkKAWH5cGytcS2/LpwN8ZyfHdEqfDQiu/G6CqxrfpCAyR3acwAkHOONZs7yu0hIiJSE1EKDgvwXQ9HlNh2eLAMj0t4Cv/63hEuaGZNwMFFZWsrNHvkLk2FG1ypu0JEROpNZIJDcNnlXcBRZnZQfr2ZtQBnA4vofQXFrYADLiw61Dn4sQ03D2V9+yU0QLK1IdPzXFdWiIhIvan5BFBmdjqwa/DlTkCDmf1H8PXrzrmfhopfArwPuN/MrgE244NAK3CiC93gwTm3wMzmAeeZ2R3APcBs/MyRD1MPkz/lhYLD1HjhzpgrNqjFQURE6kvNgwPwaeDIonVfD5YPAz3BwTn3ipm9G/gm8CWgAXgWOME590CJY18ILAHOBU4E1gLXA5c65+pn5GEoOEyiMK5BXRUiIlJvah4cnHNH9bP8QuDDFZbNAlcFj/rVa/ZIBQcREalfkRnjMKyFZ4/sbut5rjEOIiJSbxQc6kEoODR2bCVmBsD6rZ10dmfL7SUiIlJ1Cg71IBQcbOsmdhrX1PP1KnVXiIhIHVFwqAeh4MCWTUwbX5gJe6W6K0REpI4oONSD0WP81NMA7VuZPrYw2aUGSIqISD1RcKgHsTiMLtyv4m3NhStFFRxERKSeKDjUi/DskcnC7JHqqhARkXqi4FAvQsFhSrxw7y0NjhQRkXqi4FAvQsFhMp09z9VVISIi9UTBoV6EZo8c3d1GY8L/aLZ2ZNiyrbvcXiIiIlWl4FAves3lsJkpvS7JVKuDiIjUBwWHelE0l8PU8c09Xyo4iIhIvVBwqBfh4LBpvVocRESkLik41IsprYXnS15m6rhCi4NudiUiIvVCwaFezNgdGoOwsGEtu8YLrQwrNqjFQURE6oOCQ72Ix2H3WT1f7rbpzZ7nL765ga6M7pIpIiK1p+BQT/bar+fppFWvMG2CH+fQ3pXh6VfW1KpWIiIiPRQc6kkoONgrL3LkvtN6vn74xRW1qJGIiEgvCg71ZLdZEAt+JMtf56jdCje++tPLq+joVneFiIjUloJDPWlqhl328M+dY+aWN5kxaTQAHd1Znlq0uoaVExERUXCoP3uGuysWcuS+03u+fvjF5bWokYiISA8Fh3oTCg688gJH7lcY5/DkotVs68qU2ElERKQ6FBzqzZ77Fp6/9jd2Hd/IzJ3GANCZyfGnl1fVqGIiIiIKDvVn/CTYKWhl6O6CN17p1erwiK6uEBGRGlJwqEe9uite7DXO4alX1tDWqdtsi4hIbSg41KPQfA4seoHWSaPZc6q/NLM7m+OJv6m7QkREakPBoR4VtTjgHHNDk0E98NdlOOdqUDERERnpFBzq0bS3QUsw+dPWTbBqaa/uir+8tpb/W7CsRpUTEZGRTMGhHpn1vrpi0QtMnTCKDx66S8+qeb97gZUbdddMERGpLgWHerVHqLvi5QUAfOb9s2md6GeSbO/KcOWd88nm1GUhIiLVo+BQr/bev/D8yYfgzcU0NST4wt8fTMwMgBfe3MDtj79am/qJiMiIpOBQr3afBXsE3RXZLPz4GshmmdU6nk/O3aun2E0Pv8yiFZtqVEkRERlpFBzqlRmceREkkv7r1xfB/b8E4NT37MHs1vEAZHOOL9/yJI8u1MRQIiIy9BQc6tm0t8FJpxW+/vVPYeWbxGMxvvD3B9OUjAOwqb2Lb/ziWb75q7+weVtXjSorIiIjgYJDvTv+o7DLnv55ptt3WeRyTJ84mm+cOofJY5t6ij74/HI+c8MjPPj8MrK5XI0qLCIiw5mCQ72Lx+Gsf/FL8BNC3f1zcI4Ddp3E9z4zl/cfOKOn+PqtnXzzV/M56zsP8cs/LaatQ9NTi4jI4DHNQFheKpVyAOl0utZVgTtvgt/eUvj6kHfBpz4PY8YB8MTfVvE/dy9gQ1tnr91GNSSYu+80DttzJw7dbTKjm5LVrLWIiESP9blRwaG8ugoO3V1w5cXw2t8K68ZNhH/6V9jv7QBsbu/iV0++xm+ffp3N297a0hAzY9+3TeCQmZOYPWMCs1rH9w4SmW5Y8SZ0dUDTKGhs8svm0YUWj2LOQS5Xfvv2XtOyJbD8Df+9Jkz2j3ETIDaA45WS6YZVy6CzA3bZozDYtJhzfkDqcJHJwOKF/k6rEybXujYiEi0KDgNVV8EBoKsTfvFD+MNveq+f0upDxPiJMHYimUSSxeu28fyyzaxrz9CS62R8rp3x2W2MzXXQTZzN8Sa2xJqwljHskuxiRtsqxm5cQSyXfev3NYOWcf4NfdxESDbA5g2waT1s2uDfnMdOgImTYeLOvgxBoMhm/TJmYDF/rEwGlr7mQ0M289bvF4vBtF38XBZ7HeCX4ycVtudy/jjhN3rnYOM6eONVeOOV4Pivw+plvjzAqBY4+Ag47L1+Zs7FL8ELz8KLz8Ly1/33mL4LTNvVD0xtnQnTd4VRo3fwBzcAHe2wdbM/n7F+9CiuWw2P/g4evdf/bBJJ+NAn/ViZRKJQrr0N/vK4/9nNOgh2nj68glM5zvnfuXIBUkRAwWHg6i445P31SfjR1bBlY61rUj2JJOSyhRBgMWhqhuZRfrll89Cdj4k7+QAxbReYOsOHip2nw5ZNPvwsXeKDR9sW6O6Eri6/bBrl95m+i182NcP6NbBhjV92Bi07o0b7Vp1spnC8tSv99x4zHvY9BPY9FGYd6F/3tjYfLNrbfB02b4DNG33LzQvPgCsxMHbGbv7y3pax8MCvfbDo3FbYPnkq7Hco7H2Afz5pZx8GzXxdV7zhW6O2boLRY2HseN9NFk/AqqU+pK14wweXWBySSR8wGxr9udtjNuw+GyZP8cfs7ioEz21tsK3dv6bODv8znTLDB+L8PVs2rYfVy2HlUl+f/GvevMEH6sZmf36bmqGhqfe/vUw3bFwPG9b6cNnd5UPhAXNg/8P8TeXCoaoU53w9N6z1dUk2+FA3ftLAWtv6o22Lf81Nzf73obGpOiGvkla4XBbWrPS//+tW+xaumXsFHx4iyjkf2jeu849Ro/3vS9OoWtestK1bYPGLcOA7B/OoCg4DVbfBAfw/zZ9e5z81DqIVibFsijXT7LoYleumOddFixvCSzx3mua7ELKZ4E11rX8zHEyTp/jAsX7N4B633iWS/k0zz4KWi1LBotz+8bh/Mx8sY8b5n0XblsrKj2rx5TuG8L4sTaN8uNtpmn9M3MnXb+3K4LHK/152db51X4vBhEn+TT2T8Y9stw8vk6bATlN9EBs30bccxYJWt3gc4kkfsPKtH/kQuGUjbFjnA9nKZT6shSUbfIDYeVoh0E7fxYe07q7CIxb3IaOxuRA2shnfCpjJwOb1sGq5/z6rl/tw1dUBHdv8zzwWg133LIS+qTNg9QofEpa/7oPqyjf99yo2YTK8bQ//s8u/pq2b/ZvwpCk+dE3a2QfDfMBsaPQBLt8yaTH/c9+8wQfMTeuhfWtwjjP+dzsW8z+viTv75egxvtyGdf5n1rYZko3BeWjy53rLRv9aN63z/0fNfABOJP3xtm7u/XeTN2lnaN3Nn4d8t+qEyb41Nh+Uk0kfglct8+d01TJ/Tqft4sN760xfx0zG/1w3rfc/945twaPd/56Fz0lDo9+nZax/NDb5LusX58NLf4HXX/Fh51s/8ed2cCg4DFRdB4e89q2FT0Gb1vs/hO6u0D+xjP9jHTPe/9NuGevXt22me9NG1q1cw6quOC/FJvBM5xgWbuimK9P7jSXusozPbmNito2J2XYaXIYN8VGsj49mfXwUXZZgUraNnTJb2Dm7lXHZbTggazGyxMiZEXOOxkSMhoTREI+xYfRkVo1vhebRNCXjNDUkaG6I09yQoMWyzNj0JtPWLGbyykWMWfEqsVJ/yEVcYxPZ1t1hlz2wXfYgNmMmNn1X/0/dOXjtZXjmEXj6Uf/paPqu/pP8fof6WTo3r/f/DJe/UfjnuOLN0t0pQy0W8//wt7X1f999D4UjPwgHvgMevMsPrC31pjd9F5g8Df72196tD8Odmf99EKmF5lE+XAy2My+C9xw/WEdTcBioSASHQZbNOTZs7WT15m2s2bSN1Zu3sX5LJ+u2dLB+ayfrt/rnHd0lxkIMkZjLESdHDiNHDGdG3GV9i4jrYlSuiy5LsCIxDhdqWo0ZJBPxILDEg4Dil81xcPFEr7Kjm5KMaU4ypqmBlia/LZfNMGrjGsZtWMa4zasZu3k1YzeuZPSWtXQ3NLN1UitbJs1g66RWMmMnkWhuoqG5mWRTI40dW2hcu5zGNUtpWL0MMt1kxk6ka+wkusZOxDWPZlSui+ZsBw1dHZhzvhtkRvCpJp6AN18NxmH8xT9vaAwGrI7yyzHjgm6D8b5rYa/9Ycr03idwzQq46X9g4Xz/9eyD4biT/aDaWMx/unp1of8+K97wn7DXrfKhFHzYnPY2/6lp3ET/aXzLJv/o6vDdNtN39UFk5+mAQSb41Nu2xQe2VxfCay8V/mHGYr6+4yb6T1NNzf71NDX78Ltqmf8knA884e6LnaYW9h073n8C6+jwn9Y6tvluol6/QDFfdsJOvnUAg5eeg+efhuef8q+3Eg2NweDdif61rV/tPwkPtWSD/yTZ1ek/LZf6hF9L4ybA9Jn+E/nKN/04o1JBNUqaR8H44Ge9eYP/XcxW739ev1gMdtsbjvsIHDZ30I7a50YFh/JGYnCohHOO9s4M67Z0sHZLJ2u3bGPNpg7WbN7Gms0dbNjaSWd3lo5M1i+7snRnNSFVX+IxY1RjglzOkXOOXHDX08agNaYp6QNPzjmyOf/IOYcBZuZbdjFiMSMePGLmv46Z/y+w65aldDWMYuPYKSTjRiIeIxGLEYtBPBYjZn4Zj/n9mjKdJMjR3dTS67iNiRiNyTgNiTjJRAwcOCDn/BMzgu/rH4n89zJo3rwW19hIdtTYXoM+zazntcQsWALJrRuIJZPEx44nkYiRjMdJxPN18XXNy58b51zP67Ht9dE751vsVi/33RKrV/gxKKPH+C6GyVP8cuLO/s2k+HjdXX7/7i7f1J1I+MDX3lbo6lizwvdDu5xvunfOjw3IdPtHd9Ca1jI2CIDjfTDaeTpMbfWBJ3+unPPdCBvX+TfpfOvYyqV+W0ODb5rPjwnq7PCtSZ0dfns8EdQxDqPG+JA5ZYb/XpN2Dq6maobGRh8cF7/kQ9+rC/152WlaISRO28UHhpYxvc9JNuvrtmyJ77IZG7ymlrE+SK5b7YPputU+SHZ1FsYGZTO+ns7585Vs9G/e4yb4czJ6jA9SieB1dHf7Lsh1q/yyfasvl+9GGDPOn+PODv/o7vL1GD/JP8aO92+8+S6mTAZGt7x1PEOm25/jZUv899qw1j/Wr/GvId89lOn253hKqz+nU1p94Fz+uh+wvfwNX8bM12PsBP8zz38QaGr25bMZf17y9W7b4rtdtmz2rZCTp/qxT7MPgX0O8F16g2vkBgcziwGfBz4DzATWALcBlzrnttsGrOAweLK5HNu6smzryrCtM0NnJkdHV4aOIFh0dAfbgjJbO7oLj23dbwkezvnWkUwu55fZHJmsozub849MFt1xfPjLh41cmf9j+bCTiMWIxwtLw1+eTBCqevYOnsTjRjIe63nkCzn8714sRs+xkvFYT4jx38sHL6NwfIL98uEmX7dYKOQ554LjO/8eH/OBK7+MWaFsPObDYjgglvxP3xPICqEMLAiJwWtMxEnGfYDMZh2ZnCOby+EcJOMxEvEYyUSMRMxCIdUfryFozWtI+DJd3TnauzI9f9tNyTgtTUlampO0NCWJBT+rXM6RDV5nqfegXkEyRk8IDb/m7YbCepTNQvsWH9qGelDtjunz5G5nKHHkXQNcAPwKuAqYHXx9iJkd61ylo8RkR8VjMVqaYrRUcQKqbC5HVyZHZ3eWrlBQ2daVpSuTLSrr2NrRzZZthcAC/pNzPPgnlXP5gOJDisP1dJU7oDv4HtuCEJTJup4WhPw/x/wbQTwWI5PNsaWjm83tXVXt+hlO8m+05eRbZ7rQn/pwkw8W4Hp9SMiHt3jch51wGHNAIghiPgzFcOT/Tn2wywfNRD5oBoEp34Lly9Pzd138ASVm/u88mf8e8RjxntY/H/oK+xX+L+TLJ+Oxnt/bbPDBKFw/5xyJeIzGRJyGpF8mEzGO3Hc6M3cuav0ZIsM2OJjZfsD5wB3OuZND618DrgNOAW4ps7sMA/FYjOaGGM0N9f9r3pXxgSYWfMKKB5+mwi0ynZlszyeveCz/6ZGef1z5T3Lh7oz8Pyjn/Ce8XM63ymSyju5MtmddNte7CyT/zyr/df55piiMdWeyEPqnCvR8z3xdMrnegauYCz7K50L/3Hu6bIK6dPfsn6M768hmc2RKNCnFg0/euRL/0GV4KRca87/HZEZWWNx9ylgFh0FwKj6UXlu0/kbgm8BpKDhInfDNvW9tumxqSEAN5p+KAudcT0DIN1+H5QNPJlcIGplsridQ5bsdnHM9TeN5mVyO7kyup+srL1/Otz75IJU/ZjYb+pToQq1RwZN890V+XEa58Sr5QJgtqnuuONiFgmKpkJT/vj6MFboFeoJkEMjyrzM/NiTfNQIEQS3Xs/T1Lpz77kwQJDNZujM5GpP+yqjmhjiNyTgdXdle3Y6+m8eCT+C9uyTC9Q7/bJwjOJ99v96RrrHE/4+hMpyDwxwgBzwZXumc6zCz+cF2EYkoM/8GVO7fZcyMWNxIxIFkXfcnSz+FA1B43IUjaHEIBcX8WJZ8sMy3fOXDUH7sRL4rId8qlw9W4S6JXM75sqGuh/wYkryc8y2I+TFXmWyu0KoXHCs//iU/tiVfl+6gRS0/XqbXQOfQOI9MNkdnJkdXJktnt1/OmFS9TxjDOThMB9Y650pdF7QMeJeZNTg3lLMbiYjIYPOhEYrH8BkQCwasytAZzmd3FFDuYuKOUJm3MLNzzezpIamViIhIhA3n4NAONJbZ1hQq8xbOue875w4bklqJiIhE2HAODsuByWZWKjy04rsx1E0hIiLSD8M5ODyFf33vCK80sybgYEBdESIiIv00nIPDrfhBthcWrT8HP7bh5mpXSEREJOqG7VUVzrkFZjYPOM/M7gDuoTBz5MNoDgcREZF+G7bBIXAhsAQ4FzgRWAtcj79XxciaVkxERGQQDOvg4JzL4u9RcVWt6yIiIjIcDOcxDiIiIjLIFBxERESkYgoOIiIiUrFhPcZhsKRSqVpXQUREpFpcOp22chvV4iAiIiIVs/w926U6zOxp3Qdjx+k8Dg6dx8Gh8zg4dB4Hx1CfR7U4iIiISMUUHERERKRiCg7V9/1aV2CY0HkcHDqPg0PncXDoPA6OIT2PGuMgIiIiFVOLg4iIiFRMwUFEREQqpuAwxMwsZmYXmdlLZtZhZm+a2VVmNrrWdatHZra3mV1uZn8yszVmtsXM5pvZl0udMzPbx8zuNLMNZtZmZo+a2TG1qHs9M7NRZvaamTkz+06J7TqPfTCziWb232b2SvB3vMbMHjSz9xaV03ksw8xazOzfzWxB8He91sweN7MzzcyKyo7482hml5jZ7Wa2OPi7XbKd8hWfsx19X9LMkUPvGuAC4Ff4u3TODr4+xMyO1e293+KfgH8GfgPcDHQDRwPfAD5mZoc757YBmNkewONABrgS2AScA9xnZh9wzj1Qg/rXq8uByaU26Dz2zcx2BR4CWoAfAi8D44ADgdZQOZ3HMswsBvwOeBfwE+B6YBRwKvAj/P/FLwZldR69/wLWA88C4/sqOIBztmPvS845PYboAewH5IBfFq0/H3DAJ2pdx3p7AIcB40qs/0Zwzs4LrbsNyAIHh9a1AK8DfyMY/DvSH8ChwT+UfwnO4XeKtus89n3+HgXeBKZtp5zOY/lzc0Twu3dN0foGYDGwUefxLeds99Dz54ElfZSt+JwNxvuSuiqG1qmAAdcWrb8RaAdOq3aF6p1z7mnn3KYSm24NlvsDBE1qJwEPOefmh/bfCvwA2BuYM7S1rX9mFsf/vt0L3FFiu85jH8xsLvAe4Ern3AozS5rZqBLldB77NjZYLg+vdM51AWuBNtB5DHPOLa6k3ADO2Q6/Lyk4DK05+GT3ZHilc64DmM8I+QMYJDOC5apgeSDQCDxRouyfgqXOL1wEzALOK7Nd57FvHwyWb5jZXcA2oM3MXjaz8D9Ynce+PQlsBL5gZv9oZrsEffJXAG8HLgvK6Tz2X3/P2Q6/Lyk4DK3pwFrnXGeJbcuAyWbWUOU6RU7wqflSfHP7LcHq6cFyWYld8utaS2wbMcxsN+BrwOXOuSVliuk89m2fYHkjMBE4A/g00AX81MzOCrbrPPbBObcB/6l4Pb5Z/XXgJfx4ppOdczcGRXUe+6+/52yH35c0OHJojQJK/XAAOkJluqpTnci6Fjgc+Hfn3N+Cdfnm4lLnt6OozEj1XeA14Oo+yug89m1MsNwCHB00rWNmv8L3zf+Xmf0EncdKbMX31f8GP5BvIj443GJmH3bO/R6dx4Ho7znb4fclBYeh1Q7sXGZbU6iMlGFmX8c3s3/fOXdFaFP+vDWW2G3En9ugGf04YK5zrruPojqPfdsWLH+eDw3gP0Gb2W+AT+FbJXQe+2BmB+DDwkXOuRtC63+ODxM3BlcG6Dz2X3/P2Q6/L6mrYmgtxzf7lPqBtuKbi9TaUIaZXQb8B/5yrc8Wbc4PsirVbJlfV6rpbtgLft+uBu4BVprZnma2J7BrUGRcsG48Oo/bszRYriyxbUWwnIDO4/ZchH9Tuj280jnXDtyN/92cic7jQPT3nO3w+5KCw9B6Cn+O3xFeaWZNwMHA0zWoUySY2VeBrwI3AWe74HqhkAX45rYjSux+eLAcqee3GdgJOBFYFHo8FGw/Lfj6bHQetyc/gGxGiW35davRedye/BtYvMS2RGip89h//T1nO/6+VOtrVYfzAziAvq+XPa3WdazHB34gpMOHhlgf5W7HX7t8UGhd/trllxkh13uXOC9J4KMlHp8Lzuvvgq/31nnc7rmcAGzGtzy0hNZPw/fZvxxap/NY/jxeE/zufaFo/Xj8J+D1QELnsez52948DhWfs8F4X9LdMYeYmV2P76P/Fb7pOD9D1x+BY5xmjuzFzP4Z+A7wBvAV/C942CrnB1ERNL8/iZ9d8hr8P/hz8H8YJzrn7qtWvaPAzGbiB0vOc86dF1qv89gHMzsX+B7wAvC/+EmLPocPD3/nnLs/KKfzWEYw++az+CB2M/7/30T8+ZkJ/LNzLh2U1XkEzOx0Ct2L5+N/764Kvn7dOffTUNl+nbMdfl+qdZIa7g9809y/4mfv6sT3NV1N6NOLHr3O14/xqbfc46Gi8rOBX+OvEW8HHgOOrfXrqMcH/h/0W2aO1Hms6Nx9BH9NfBv+Cov7gXfrPPbrHO6Bn256afAGtxl4BPiIzmPJ8/VQpf8H+3vOdvR9SS0OIiIiUjENjhQREZGKKTiIiIhIxRQcREREpGIKDiIiIlIxBQcRERGpmIKDiIiIVEzBQURERCqmu2OKyLCXSqUuw9/75Oh0Ov1QbWsjEm0KDiKyXalUqpKZ4vSmLDICKDiISH98rY9tS6pVCRGpHQUHEalYOp2+rNZ1EJHaUnAQkUEXHlOAv8PfhcAs/A2ifgv8ezqdXlliv73wd0V9H7ATsBZ4APh6Op1eVKJ8HH8XwNOB/fF3EFyGv0HQt8rs81HgC0H5DvwNq/41nU4v24GXLDJi6KoKERlKFwE3AM8B1+LvxncW8HgqldopXDCVSs0BngZOA54C/ht/R8pPAk+nUqnDiso3APcC3wXeBtwCXAc8A/wD8O4S9UkBP8N3q8wDngc+DjyQSqUad/TFiowEanEQkYoFLQmldKTT6W+WWP8B4J3pdPovoWNcg2+B+Cbw6WCdATcBY4HT0un0zaHyHwf+H/CzVCq1bzqdzgWbLgOOBe4C/jGdTneG9mkMjlXsBGBOOp1eECp7C3Aq8GHgtnKvXUQ8tTiISH98tczjS2XK/zQcGgKXAZuAT4Q+5b8L35XxRDg0AKTT6VuBx4B9gPdATxdFCtgGfDYcGoJ9OtPp9JoS9bkuHBoCNwbLd5R5DSISohYHEalYOp22fu7ycIljbEqlUvOBI4HZwHzg0GDzH8oc5w/40HAI8Ag+ZIwD/pxOp5f3oz5Pl1j3ZrCc0I/jiIxYanEQkaG0qsz6/MDIcUXLFWXK59ePL1r2d0DjxhLrMsEy3s9jiYxICg4iMpSmlFk/NVhuKlpOLVEWYFpRuY3BsnXANRORAVFwEJGhdGTxilQqNQ44GH8p5MJgdX4cxFFljpNf/2ywfAkfHg5MpVLTd7yaIlIpBQcRGUqnp1KpQ4rWXYbvmvh5aFDjH/GXar4nmGehR/D1XOBl/CBJ0ul0FkgDzcANxZdSplKphuLLPUVkcGhwpIhUrI/LMQHuTKfT84vW/Q74YyqVug0/TuE9wWMJoSsx0um0S6VSZwC/B25NpVK/xrcq7AP8PX7iqE+FLsUEP/31O4EPAS+nUqnfBuXeBhwHXAz8eAAvU0T6oOAgIv3x1T62LcFfIRF2DfAr/LwNHwe24t/M/z2dTq8OF0yn038OJoH6D/z8DB/Czxz5c/zMkX8rKt+VSqVOAD4LfAo4AzBgefA9H+vvixOR7TPnKrnpnYhI5XQba5HhS2McREREpGIKDiIiIlIxBQcRERGpmMY4iIiISMXU4iAiIiIVU3AQERGRiik4iIiISMUUHERERKRiCg4iIiJSMQUHERERqdj/B/pZnz8PPeZAAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ " 'MAE' :['mae', 'val_mae'],\n",
+ " 'LOSS':['loss','val_loss']}, save_as='01-history')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 7 - Make a prediction\n",
+ "The data must be normalized with the parameters (mean, std) previously used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:28.316757Z",
+ "iopub.status.busy": "2021-01-14T07:11:28.316418Z",
+ "iopub.status.idle": "2021-01-14T07:11:28.319009Z",
+ "shell.execute_reply": "2021-01-14T07:11:28.318619Z"
+ }
+ },
+ "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": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:28.322754Z",
+ "iopub.status.busy": "2021-01-14T07:11:28.321869Z",
+ "iopub.status.idle": "2021-01-14T07:11:28.409681Z",
+ "shell.execute_reply": "2021-01-14T07:11:28.409332Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction : 10.24 K$\n",
+ "Reality : 10.40 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "predictions = model.predict( my_data )\n",
+ "print(\"Prediction : {:.2f} K$\".format(predictions[0][0]))\n",
+ "print(\"Reality : {:.2f} K$\".format(real_price))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:28.412496Z",
+ "iopub.status.busy": "2021-01-14T07:11:28.412150Z",
+ "iopub.status.idle": "2021-01-14T07:11:28.415355Z",
+ "shell.execute_reply": "2021-01-14T07:11:28.415951Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:28\n",
+ "Duration is : 00:00:09 085ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/BHPD/02-DNN-Regression-Premium.ipynb b/BHPD/02-DNN-Regression-Premium.ipynb
index 1f0f5f3..53f75c0 100644
--- a/BHPD/02-DNN-Regression-Premium.ipynb
+++ b/BHPD/02-DNN-Regression-Premium.ipynb
@@ -52,97 +52,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
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- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { content:url(data:image/svg+xml;base64,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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 1.2b1 DEV\n",
- "Notebook id : BHPD2\n",
- "Run time : Friday 8 January 2021, 01:10:28\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@@ -173,7 +85,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -190,116 +102,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
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- " <td id=\"T_f13fa_row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
- " <td id=\"T_f13fa_row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_f13fa_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_f13fa_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
- " <td id=\"T_f13fa_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_f13fa_row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
- " <td id=\"T_f13fa_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_f13fa_row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
- " <td id=\"T_f13fa_row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
- " <td id=\"T_f13fa_row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
- " <td id=\"T_f13fa_row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
- " <td id=\"T_f13fa_row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
- " <td id=\"T_f13fa_row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
- " <td id=\"T_f13fa_row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
- " <td id=\"T_f13fa_row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
- " <td id=\"T_f13fa_row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
- " <td id=\"T_f13fa_row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f07873c2c10>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Missing Data : 0 Shape is : (506, 14)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
"\n",
@@ -319,19 +124,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Split => train, test\n",
"#\n",
@@ -364,294 +159,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_fc6dd_\" ><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_fc6dd_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_fc6dd_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_fc6dd_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_fc6dd_row1_col0\" class=\"data row1 col0\" >3.71</td>\n",
- " <td id=\"T_fc6dd_row1_col1\" class=\"data row1 col1\" >11.71</td>\n",
- " <td id=\"T_fc6dd_row1_col2\" class=\"data row1 col2\" >11.18</td>\n",
- " <td id=\"T_fc6dd_row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
- " <td id=\"T_fc6dd_row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
- " <td id=\"T_fc6dd_row1_col5\" class=\"data row1 col5\" >6.30</td>\n",
- " <td id=\"T_fc6dd_row1_col6\" class=\"data row1 col6\" >68.24</td>\n",
- " <td id=\"T_fc6dd_row1_col7\" class=\"data row1 col7\" >3.82</td>\n",
- " <td id=\"T_fc6dd_row1_col8\" class=\"data row1 col8\" >9.72</td>\n",
- " <td id=\"T_fc6dd_row1_col9\" class=\"data row1 col9\" >413.70</td>\n",
- " <td id=\"T_fc6dd_row1_col10\" class=\"data row1 col10\" >18.46</td>\n",
- " <td id=\"T_fc6dd_row1_col11\" class=\"data row1 col11\" >355.85</td>\n",
- " <td id=\"T_fc6dd_row1_col12\" class=\"data row1 col12\" >12.66</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_fc6dd_row2_col0\" class=\"data row2 col0\" >9.08</td>\n",
- " <td id=\"T_fc6dd_row2_col1\" class=\"data row2 col1\" >24.05</td>\n",
- " <td id=\"T_fc6dd_row2_col2\" class=\"data row2 col2\" >6.79</td>\n",
- " <td id=\"T_fc6dd_row2_col3\" class=\"data row2 col3\" >0.24</td>\n",
- " <td id=\"T_fc6dd_row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
- " <td id=\"T_fc6dd_row2_col5\" class=\"data row2 col5\" >0.73</td>\n",
- " <td id=\"T_fc6dd_row2_col6\" class=\"data row2 col6\" >28.03</td>\n",
- " <td id=\"T_fc6dd_row2_col7\" class=\"data row2 col7\" >2.13</td>\n",
- " <td id=\"T_fc6dd_row2_col8\" class=\"data row2 col8\" >8.77</td>\n",
- " <td id=\"T_fc6dd_row2_col9\" class=\"data row2 col9\" >168.30</td>\n",
- " <td id=\"T_fc6dd_row2_col10\" class=\"data row2 col10\" >2.22</td>\n",
- " <td id=\"T_fc6dd_row2_col11\" class=\"data row2 col11\" >93.00</td>\n",
- " <td id=\"T_fc6dd_row2_col12\" class=\"data row2 col12\" >7.29</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_fc6dd_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
- " <td id=\"T_fc6dd_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
- " <td id=\"T_fc6dd_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
- " <td id=\"T_fc6dd_row3_col5\" class=\"data row3 col5\" >3.56</td>\n",
- " <td id=\"T_fc6dd_row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
- " <td id=\"T_fc6dd_row3_col7\" class=\"data row3 col7\" >1.14</td>\n",
- " <td id=\"T_fc6dd_row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
- " <td id=\"T_fc6dd_row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
- " <td id=\"T_fc6dd_row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
- " <td id=\"T_fc6dd_row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
- " <td id=\"T_fc6dd_row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_fc6dd_row4_col0\" class=\"data row4 col0\" >0.09</td>\n",
- " <td id=\"T_fc6dd_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row4_col2\" class=\"data row4 col2\" >5.19</td>\n",
- " <td id=\"T_fc6dd_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
- " <td id=\"T_fc6dd_row4_col5\" class=\"data row4 col5\" >5.89</td>\n",
- " <td id=\"T_fc6dd_row4_col6\" class=\"data row4 col6\" >43.47</td>\n",
- " <td id=\"T_fc6dd_row4_col7\" class=\"data row4 col7\" >2.07</td>\n",
- " <td id=\"T_fc6dd_row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
- " <td id=\"T_fc6dd_row4_col9\" class=\"data row4 col9\" >284.00</td>\n",
- " <td id=\"T_fc6dd_row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
- " <td id=\"T_fc6dd_row4_col11\" class=\"data row4 col11\" >375.24</td>\n",
- " <td id=\"T_fc6dd_row4_col12\" class=\"data row4 col12\" >6.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_fc6dd_row5_col0\" class=\"data row5 col0\" >0.28</td>\n",
- " <td id=\"T_fc6dd_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
- " <td id=\"T_fc6dd_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
- " <td id=\"T_fc6dd_row5_col5\" class=\"data row5 col5\" >6.21</td>\n",
- " <td id=\"T_fc6dd_row5_col6\" class=\"data row5 col6\" >77.15</td>\n",
- " <td id=\"T_fc6dd_row5_col7\" class=\"data row5 col7\" >3.32</td>\n",
- " <td id=\"T_fc6dd_row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
- " <td id=\"T_fc6dd_row5_col9\" class=\"data row5 col9\" >341.00</td>\n",
- " <td id=\"T_fc6dd_row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
- " <td id=\"T_fc6dd_row5_col11\" class=\"data row5 col11\" >391.29</td>\n",
- " <td id=\"T_fc6dd_row5_col12\" class=\"data row5 col12\" >11.30</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_fc6dd_row6_col0\" class=\"data row6 col0\" >3.69</td>\n",
- " <td id=\"T_fc6dd_row6_col1\" class=\"data row6 col1\" >12.50</td>\n",
- " <td id=\"T_fc6dd_row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
- " <td id=\"T_fc6dd_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
- " <td id=\"T_fc6dd_row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
- " <td id=\"T_fc6dd_row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
- " <td id=\"T_fc6dd_row6_col6\" class=\"data row6 col6\" >93.55</td>\n",
- " <td id=\"T_fc6dd_row6_col7\" class=\"data row6 col7\" >5.21</td>\n",
- " <td id=\"T_fc6dd_row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
- " <td id=\"T_fc6dd_row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
- " <td id=\"T_fc6dd_row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
- " <td id=\"T_fc6dd_row6_col11\" class=\"data row6 col11\" >396.30</td>\n",
- " <td id=\"T_fc6dd_row6_col12\" class=\"data row6 col12\" >16.72</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_fc6dd_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_fc6dd_row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
- " <td id=\"T_fc6dd_row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
- " <td id=\"T_fc6dd_row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
- " <td id=\"T_fc6dd_row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
- " <td id=\"T_fc6dd_row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
- " <td id=\"T_fc6dd_row7_col5\" class=\"data row7 col5\" >8.78</td>\n",
- " <td id=\"T_fc6dd_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
- " <td id=\"T_fc6dd_row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
- " <td id=\"T_fc6dd_row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
- " <td id=\"T_fc6dd_row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
- " <td id=\"T_fc6dd_row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
- " <td id=\"T_fc6dd_row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
- " <td id=\"T_fc6dd_row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f06d332fcd0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_30f2b_\" ><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_30f2b_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_30f2b_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
- " <td id=\"T_30f2b_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_30f2b_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
- " <td id=\"T_30f2b_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
- " <td id=\"T_30f2b_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
- " <td id=\"T_30f2b_row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_30f2b_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_30f2b_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " <td id=\"T_30f2b_row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_30f2b_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " <td id=\"T_30f2b_row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_30f2b_row3_col0\" class=\"data row3 col0\" >-0.41</td>\n",
- " <td id=\"T_30f2b_row3_col1\" class=\"data row3 col1\" >-0.49</td>\n",
- " <td id=\"T_30f2b_row3_col2\" class=\"data row3 col2\" >-1.58</td>\n",
- " <td id=\"T_30f2b_row3_col3\" class=\"data row3 col3\" >-0.25</td>\n",
- " <td id=\"T_30f2b_row3_col4\" class=\"data row3 col4\" >-1.46</td>\n",
- " <td id=\"T_30f2b_row3_col5\" class=\"data row3 col5\" >-3.77</td>\n",
- " <td id=\"T_30f2b_row3_col6\" class=\"data row3 col6\" >-2.22</td>\n",
- " <td id=\"T_30f2b_row3_col7\" class=\"data row3 col7\" >-1.26</td>\n",
- " <td id=\"T_30f2b_row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
- " <td id=\"T_30f2b_row3_col9\" class=\"data row3 col9\" >-1.35</td>\n",
- " <td id=\"T_30f2b_row3_col10\" class=\"data row3 col10\" >-2.64</td>\n",
- " <td id=\"T_30f2b_row3_col11\" class=\"data row3 col11\" >-3.82</td>\n",
- " <td id=\"T_30f2b_row3_col12\" class=\"data row3 col12\" >-1.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_30f2b_row4_col0\" class=\"data row4 col0\" >-0.40</td>\n",
- " <td id=\"T_30f2b_row4_col1\" class=\"data row4 col1\" >-0.49</td>\n",
- " <td id=\"T_30f2b_row4_col2\" class=\"data row4 col2\" >-0.88</td>\n",
- " <td id=\"T_30f2b_row4_col3\" class=\"data row4 col3\" >-0.25</td>\n",
- " <td id=\"T_30f2b_row4_col4\" class=\"data row4 col4\" >-0.91</td>\n",
- " <td id=\"T_30f2b_row4_col5\" class=\"data row4 col5\" >-0.57</td>\n",
- " <td id=\"T_30f2b_row4_col6\" class=\"data row4 col6\" >-0.88</td>\n",
- " <td id=\"T_30f2b_row4_col7\" class=\"data row4 col7\" >-0.82</td>\n",
- " <td id=\"T_30f2b_row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
- " <td id=\"T_30f2b_row4_col9\" class=\"data row4 col9\" >-0.77</td>\n",
- " <td id=\"T_30f2b_row4_col10\" class=\"data row4 col10\" >-0.48</td>\n",
- " <td id=\"T_30f2b_row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
- " <td id=\"T_30f2b_row4_col12\" class=\"data row4 col12\" >-0.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_30f2b_row5_col0\" class=\"data row5 col0\" >-0.38</td>\n",
- " <td id=\"T_30f2b_row5_col1\" class=\"data row5 col1\" >-0.49</td>\n",
- " <td id=\"T_30f2b_row5_col2\" class=\"data row5 col2\" >-0.22</td>\n",
- " <td id=\"T_30f2b_row5_col3\" class=\"data row5 col3\" >-0.25</td>\n",
- " <td id=\"T_30f2b_row5_col4\" class=\"data row5 col4\" >-0.16</td>\n",
- " <td id=\"T_30f2b_row5_col5\" class=\"data row5 col5\" >-0.12</td>\n",
- " <td id=\"T_30f2b_row5_col6\" class=\"data row5 col6\" >0.32</td>\n",
- " <td id=\"T_30f2b_row5_col7\" class=\"data row5 col7\" >-0.23</td>\n",
- " <td id=\"T_30f2b_row5_col8\" class=\"data row5 col8\" >-0.54</td>\n",
- " <td id=\"T_30f2b_row5_col9\" class=\"data row5 col9\" >-0.43</td>\n",
- " <td id=\"T_30f2b_row5_col10\" class=\"data row5 col10\" >0.29</td>\n",
- " <td id=\"T_30f2b_row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
- " <td id=\"T_30f2b_row5_col12\" class=\"data row5 col12\" >-0.19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_30f2b_row6_col0\" class=\"data row6 col0\" >-0.00</td>\n",
- " <td id=\"T_30f2b_row6_col1\" class=\"data row6 col1\" >0.03</td>\n",
- " <td id=\"T_30f2b_row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
- " <td id=\"T_30f2b_row6_col3\" class=\"data row6 col3\" >-0.25</td>\n",
- " <td id=\"T_30f2b_row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
- " <td id=\"T_30f2b_row6_col5\" class=\"data row6 col5\" >0.45</td>\n",
- " <td id=\"T_30f2b_row6_col6\" class=\"data row6 col6\" >0.90</td>\n",
- " <td id=\"T_30f2b_row6_col7\" class=\"data row6 col7\" >0.65</td>\n",
- " <td id=\"T_30f2b_row6_col8\" class=\"data row6 col8\" >1.63</td>\n",
- " <td id=\"T_30f2b_row6_col9\" class=\"data row6 col9\" >1.50</td>\n",
- " <td id=\"T_30f2b_row6_col10\" class=\"data row6 col10\" >0.79</td>\n",
- " <td id=\"T_30f2b_row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
- " <td id=\"T_30f2b_row6_col12\" class=\"data row6 col12\" >0.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_30f2b_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_30f2b_row7_col0\" class=\"data row7 col0\" >9.39</td>\n",
- " <td id=\"T_30f2b_row7_col1\" class=\"data row7 col1\" >3.67</td>\n",
- " <td id=\"T_30f2b_row7_col2\" class=\"data row7 col2\" >2.44</td>\n",
- " <td id=\"T_30f2b_row7_col3\" class=\"data row7 col3\" >3.98</td>\n",
- " <td id=\"T_30f2b_row7_col4\" class=\"data row7 col4\" >2.67</td>\n",
- " <td id=\"T_30f2b_row7_col5\" class=\"data row7 col5\" >3.42</td>\n",
- " <td id=\"T_30f2b_row7_col6\" class=\"data row7 col6\" >1.13</td>\n",
- " <td id=\"T_30f2b_row7_col7\" class=\"data row7 col7\" >3.90</td>\n",
- " <td id=\"T_30f2b_row7_col8\" class=\"data row7 col8\" >1.63</td>\n",
- " <td id=\"T_30f2b_row7_col9\" class=\"data row7 col9\" >1.77</td>\n",
- " <td id=\"T_30f2b_row7_col10\" class=\"data row7 col10\" >1.60</td>\n",
- " <td id=\"T_30f2b_row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
- " <td id=\"T_30f2b_row7_col12\" class=\"data row7 col12\" >3.47</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f06d14fcb90>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
"\n",
@@ -680,7 +190,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -708,30 +218,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"model=get_model_v1( (13,) )\n",
"\n",
@@ -749,7 +238,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -768,216 +257,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/100\n",
- "36/36 [==============================] - 0s 7ms/step - loss: 475.3738 - mae: 19.9912 - mse: 475.3738 - val_loss: 355.5190 - val_mae: 17.1932 - val_mse: 355.5190\n",
- "Epoch 2/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 226.4147 - mae: 13.0131 - mse: 226.4147 - val_loss: 129.1039 - val_mae: 9.0031 - val_mse: 129.1039\n",
- "Epoch 3/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 75.2953 - mae: 6.7032 - mse: 75.2953 - val_loss: 54.9836 - val_mae: 5.2806 - val_mse: 54.9836\n",
- "Epoch 4/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 37.4691 - mae: 4.6232 - mse: 37.4691 - val_loss: 38.6827 - val_mae: 4.1977 - val_mse: 38.6827\n",
- "Epoch 5/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 26.8464 - mae: 3.8138 - mse: 26.8464 - val_loss: 33.8882 - val_mae: 3.7787 - val_mse: 33.8882\n",
- "Epoch 6/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 21.8734 - mae: 3.4106 - mse: 21.8734 - val_loss: 29.8489 - val_mae: 3.6882 - val_mse: 29.8489\n",
- "Epoch 7/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 19.1155 - mae: 3.1659 - mse: 19.1155 - val_loss: 27.4722 - val_mae: 3.4667 - val_mse: 27.4722\n",
- "Epoch 8/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 17.0937 - mae: 2.9284 - mse: 17.0937 - val_loss: 26.4015 - val_mae: 3.3250 - val_mse: 26.4015\n",
- "Epoch 9/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 15.7820 - mae: 2.7847 - mse: 15.7820 - val_loss: 25.4634 - val_mae: 3.1832 - val_mse: 25.4634\n",
- "Epoch 10/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 14.7705 - mae: 2.6322 - mse: 14.7705 - val_loss: 24.3008 - val_mae: 3.1048 - val_mse: 24.3008\n",
- "Epoch 11/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 14.0468 - mae: 2.5779 - mse: 14.0468 - val_loss: 23.5067 - val_mae: 3.1615 - val_mse: 23.5067\n",
- "Epoch 12/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 13.2634 - mae: 2.4898 - mse: 13.2634 - val_loss: 22.6733 - val_mae: 3.0316 - val_mse: 22.6733\n",
- "Epoch 13/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 12.8004 - mae: 2.4238 - mse: 12.8004 - val_loss: 22.1356 - val_mae: 2.9750 - val_mse: 22.1356\n",
- "Epoch 14/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 12.2295 - mae: 2.3461 - mse: 12.2295 - val_loss: 21.9092 - val_mae: 3.1515 - val_mse: 21.9092\n",
- "Epoch 15/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 12.0662 - mae: 2.3527 - mse: 12.0662 - val_loss: 20.9063 - val_mae: 2.9685 - val_mse: 20.9063\n",
- "Epoch 16/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 11.6152 - mae: 2.2743 - mse: 11.6152 - val_loss: 20.2121 - val_mae: 2.8589 - val_mse: 20.2121\n",
- "Epoch 17/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 11.1531 - mae: 2.2281 - mse: 11.1531 - val_loss: 19.7149 - val_mae: 2.8922 - val_mse: 19.7149\n",
- "Epoch 18/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.8126 - mae: 2.2231 - mse: 10.8126 - val_loss: 19.5988 - val_mae: 2.8813 - val_mse: 19.5988\n",
- "Epoch 19/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.9167 - mae: 2.2329 - mse: 10.9167 - val_loss: 19.9177 - val_mae: 2.8580 - val_mse: 19.9177\n",
- "Epoch 20/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.6797 - mae: 2.1405 - mse: 10.6797 - val_loss: 19.1656 - val_mae: 2.8241 - val_mse: 19.1656\n",
- "Epoch 21/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.3777 - mae: 2.1111 - mse: 10.3777 - val_loss: 19.2481 - val_mae: 2.9912 - val_mse: 19.2481\n",
- "Epoch 22/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 10.2719 - mae: 2.1766 - mse: 10.2719 - val_loss: 18.8485 - val_mae: 2.7967 - val_mse: 18.8485\n",
- "Epoch 23/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.1286 - mae: 2.1058 - mse: 10.1286 - val_loss: 18.0448 - val_mae: 2.9200 - val_mse: 18.0448\n",
- "Epoch 24/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 10.2453 - mae: 2.1320 - mse: 10.2453 - val_loss: 18.4625 - val_mae: 2.7887 - val_mse: 18.4625\n",
- "Epoch 25/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.9561 - mae: 2.1024 - mse: 9.9561 - val_loss: 17.8922 - val_mae: 2.7371 - val_mse: 17.8922\n",
- "Epoch 26/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.8956 - mae: 2.0484 - mse: 9.8956 - val_loss: 18.6694 - val_mae: 2.8163 - val_mse: 18.6694\n",
- "Epoch 27/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.7229 - mae: 2.0378 - mse: 9.7229 - val_loss: 17.7471 - val_mae: 2.7123 - val_mse: 17.7471\n",
- "Epoch 28/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.8073 - mae: 2.0552 - mse: 9.8073 - val_loss: 17.5750 - val_mae: 2.7652 - val_mse: 17.5750\n",
- "Epoch 29/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.2723 - mae: 2.0088 - mse: 9.2723 - val_loss: 17.8873 - val_mae: 2.8168 - val_mse: 17.8873\n",
- "Epoch 30/100\n",
- "36/36 [==============================] - 0s 4ms/step - loss: 9.4324 - mae: 1.9978 - mse: 9.4324 - val_loss: 17.4225 - val_mae: 2.7191 - val_mse: 17.4225\n",
- "Epoch 31/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.4192 - mae: 2.0191 - mse: 9.4192 - val_loss: 16.5583 - val_mae: 2.7164 - val_mse: 16.5583\n",
- "Epoch 32/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 9.0246 - mae: 2.0058 - mse: 9.0246 - val_loss: 16.5136 - val_mae: 2.7170 - val_mse: 16.5136\n",
- "Epoch 33/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.2024 - mae: 2.0063 - mse: 9.2024 - val_loss: 16.3303 - val_mae: 2.8191 - val_mse: 16.3303\n",
- "Epoch 34/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.1927 - mae: 2.0216 - mse: 9.1927 - val_loss: 16.3692 - val_mae: 2.6946 - val_mse: 16.3692\n",
- "Epoch 35/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.9978 - mae: 1.9756 - mse: 8.9978 - val_loss: 16.4107 - val_mae: 2.7557 - val_mse: 16.4107\n",
- "Epoch 36/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.7153 - mae: 1.9554 - mse: 8.7153 - val_loss: 16.7948 - val_mae: 2.7352 - val_mse: 16.7948\n",
- "Epoch 37/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 9.0232 - mae: 1.9594 - mse: 9.0232 - val_loss: 16.4719 - val_mae: 2.6820 - val_mse: 16.4719\n",
- "Epoch 38/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.5107 - mae: 1.9278 - mse: 8.5107 - val_loss: 15.8009 - val_mae: 2.6720 - val_mse: 15.8009\n",
- "Epoch 39/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.8051 - mae: 1.9010 - mse: 8.8051 - val_loss: 16.0620 - val_mae: 2.6799 - val_mse: 16.0620\n",
- "Epoch 40/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.2687 - mae: 1.9202 - mse: 8.2687 - val_loss: 19.2407 - val_mae: 2.9441 - val_mse: 19.2407\n",
- "Epoch 41/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.6178 - mae: 1.9433 - mse: 8.6178 - val_loss: 16.4289 - val_mae: 2.6687 - val_mse: 16.4289\n",
- "Epoch 42/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.2694 - mae: 1.9159 - mse: 8.2694 - val_loss: 15.6580 - val_mae: 2.7538 - val_mse: 15.6580\n",
- "Epoch 43/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 8.3998 - mae: 1.8863 - mse: 8.3998 - val_loss: 15.3973 - val_mae: 2.7006 - val_mse: 15.3973\n",
- "Epoch 44/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.4504 - mae: 1.8948 - mse: 8.4504 - val_loss: 15.3550 - val_mae: 2.6351 - val_mse: 15.3550\n",
- "Epoch 45/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.1592 - mae: 1.8847 - mse: 8.1592 - val_loss: 16.1119 - val_mae: 2.6115 - val_mse: 16.1119\n",
- "Epoch 46/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.0892 - mae: 1.8970 - mse: 8.0892 - val_loss: 15.5314 - val_mae: 2.6214 - val_mse: 15.5314\n",
- "Epoch 47/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 8.0762 - mae: 1.8906 - mse: 8.0762 - val_loss: 15.6544 - val_mae: 2.6558 - val_mse: 15.6544\n",
- "Epoch 48/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.9513 - mae: 1.8809 - mse: 7.9513 - val_loss: 16.9534 - val_mae: 2.6809 - val_mse: 16.9534\n",
- "Epoch 49/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.8678 - mae: 1.8491 - mse: 7.8678 - val_loss: 16.0355 - val_mae: 2.6036 - val_mse: 16.0355\n",
- "Epoch 50/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.9786 - mae: 1.8659 - mse: 7.9786 - val_loss: 15.8552 - val_mae: 2.6041 - val_mse: 15.8552\n",
- "Epoch 51/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.5612 - mae: 1.8174 - mse: 7.5612 - val_loss: 15.5496 - val_mae: 2.6545 - val_mse: 15.5496\n",
- "Epoch 52/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.4924 - mae: 1.8578 - mse: 7.4924 - val_loss: 16.1388 - val_mae: 2.6079 - val_mse: 16.1388\n",
- "Epoch 53/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.7586 - mae: 1.8298 - mse: 7.7586 - val_loss: 17.2166 - val_mae: 2.6738 - val_mse: 17.2166\n",
- "Epoch 54/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.7121 - mae: 1.8394 - mse: 7.7121 - val_loss: 16.0234 - val_mae: 2.5735 - val_mse: 16.0234\n",
- "Epoch 55/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.5455 - mae: 1.8023 - mse: 7.5455 - val_loss: 15.8189 - val_mae: 2.7010 - val_mse: 15.8189\n",
- "Epoch 56/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.5965 - mae: 1.8203 - mse: 7.5965 - val_loss: 15.8920 - val_mae: 2.6231 - val_mse: 15.8920\n",
- "Epoch 57/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.4273 - mae: 1.7775 - mse: 7.4273 - val_loss: 16.4608 - val_mae: 2.8454 - val_mse: 16.4608\n",
- "Epoch 58/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.4376 - mae: 1.8135 - mse: 7.4376 - val_loss: 15.9365 - val_mae: 2.7168 - val_mse: 15.9365\n",
- "Epoch 59/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.4878 - mae: 1.7901 - mse: 7.4878 - val_loss: 14.4604 - val_mae: 2.6034 - val_mse: 14.4604\n",
- "Epoch 60/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.1747 - mae: 1.7831 - mse: 7.1747 - val_loss: 15.3341 - val_mae: 2.5616 - val_mse: 15.3341\n",
- "Epoch 61/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.0954 - mae: 1.7754 - mse: 7.0954 - val_loss: 14.5495 - val_mae: 2.7065 - val_mse: 14.5495\n",
- "Epoch 62/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.0930 - mae: 1.7892 - mse: 7.0930 - val_loss: 14.7401 - val_mae: 2.6647 - val_mse: 14.7401\n",
- "Epoch 63/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.3064 - mae: 1.7758 - mse: 7.3064 - val_loss: 15.2798 - val_mae: 2.6231 - val_mse: 15.2798\n",
- "Epoch 64/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.1116 - mae: 1.7659 - mse: 7.1116 - val_loss: 14.2042 - val_mae: 2.5114 - val_mse: 14.2042\n",
- "Epoch 65/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.0160 - mae: 1.7421 - mse: 7.0160 - val_loss: 15.3414 - val_mae: 2.5520 - val_mse: 15.3414\n",
- "Epoch 66/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.9254 - mae: 1.7089 - mse: 6.9254 - val_loss: 15.8074 - val_mae: 2.6835 - val_mse: 15.8074\n",
- "Epoch 67/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 7.0619 - mae: 1.7703 - mse: 7.0619 - val_loss: 14.7753 - val_mae: 2.5483 - val_mse: 14.7753\n",
- "Epoch 68/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7564 - mae: 1.7313 - mse: 6.7564 - val_loss: 14.5842 - val_mae: 2.6013 - val_mse: 14.5842\n",
- "Epoch 69/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7918 - mae: 1.7587 - mse: 6.7918 - val_loss: 14.8742 - val_mae: 2.5747 - val_mse: 14.8742\n",
- "Epoch 70/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7486 - mae: 1.7229 - mse: 6.7486 - val_loss: 14.0923 - val_mae: 2.6278 - val_mse: 14.0923\n",
- "Epoch 71/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.8134 - mae: 1.7418 - mse: 6.8134 - val_loss: 15.0033 - val_mae: 2.5960 - val_mse: 15.0033\n",
- "Epoch 72/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.7563 - mae: 1.7649 - mse: 6.7563 - val_loss: 14.5488 - val_mae: 2.5460 - val_mse: 14.5488\n",
- "Epoch 73/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.6271 - mae: 1.7036 - mse: 6.6271 - val_loss: 13.5843 - val_mae: 2.5701 - val_mse: 13.5843\n",
- "Epoch 74/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.6084 - mae: 1.7073 - mse: 6.6084 - val_loss: 13.7134 - val_mae: 2.5716 - val_mse: 13.7134\n",
- "Epoch 75/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.4696 - mae: 1.7316 - mse: 6.4696 - val_loss: 14.4993 - val_mae: 2.5646 - val_mse: 14.4993\n",
- "Epoch 76/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.3220 - mae: 1.6540 - mse: 6.3220 - val_loss: 14.6173 - val_mae: 2.4900 - val_mse: 14.6173\n",
- "Epoch 77/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.3876 - mae: 1.6780 - mse: 6.3876 - val_loss: 14.2370 - val_mae: 2.5747 - val_mse: 14.2370\n",
- "Epoch 78/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.3408 - mae: 1.6560 - mse: 6.3408 - val_loss: 13.6508 - val_mae: 2.6113 - val_mse: 13.6508\n",
- "Epoch 79/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.9661 - mae: 1.6637 - mse: 5.9661 - val_loss: 16.3465 - val_mae: 2.6276 - val_mse: 16.3465\n",
- "Epoch 80/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.2598 - mae: 1.6500 - mse: 6.2598 - val_loss: 13.5719 - val_mae: 2.5735 - val_mse: 13.5719\n",
- "Epoch 81/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.1077 - mae: 1.6426 - mse: 6.1077 - val_loss: 13.8778 - val_mae: 2.5090 - val_mse: 13.8778\n",
- "Epoch 82/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.1184 - mae: 1.6258 - mse: 6.1184 - val_loss: 14.0033 - val_mae: 2.5179 - val_mse: 14.0033\n",
- "Epoch 83/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.8503 - mae: 1.6440 - mse: 5.8503 - val_loss: 13.9289 - val_mae: 2.5396 - val_mse: 13.9289\n",
- "Epoch 84/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 6.1745 - mae: 1.6489 - mse: 6.1745 - val_loss: 13.1696 - val_mae: 2.4742 - val_mse: 13.1696\n",
- "Epoch 85/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 6.0069 - mae: 1.6452 - mse: 6.0069 - val_loss: 12.9157 - val_mae: 2.5818 - val_mse: 12.9157\n",
- "Epoch 86/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.7613 - mae: 1.6045 - mse: 5.7613 - val_loss: 13.3368 - val_mae: 2.5157 - val_mse: 13.3368\n",
- "Epoch 87/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.9914 - mae: 1.6307 - mse: 5.9914 - val_loss: 13.1113 - val_mae: 2.5445 - val_mse: 13.1113\n",
- "Epoch 88/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.6568 - mae: 1.6085 - mse: 5.6568 - val_loss: 13.8029 - val_mae: 2.5412 - val_mse: 13.8029\n",
- "Epoch 89/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.8280 - mae: 1.5581 - mse: 5.8280 - val_loss: 13.3723 - val_mae: 2.5053 - val_mse: 13.3723\n",
- "Epoch 90/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.7126 - mae: 1.5804 - mse: 5.7126 - val_loss: 12.9745 - val_mae: 2.5436 - val_mse: 12.9745\n",
- "Epoch 91/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.6580 - mae: 1.5735 - mse: 5.6580 - val_loss: 12.8074 - val_mae: 2.5277 - val_mse: 12.8074\n",
- "Epoch 92/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.7264 - mae: 1.5897 - mse: 5.7264 - val_loss: 14.1068 - val_mae: 2.4713 - val_mse: 14.1068\n",
- "Epoch 93/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.6612 - mae: 1.5688 - mse: 5.6612 - val_loss: 13.0306 - val_mae: 2.5313 - val_mse: 13.0306\n",
- "Epoch 94/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.5369 - mae: 1.5979 - mse: 5.5369 - val_loss: 13.2463 - val_mae: 2.5441 - val_mse: 13.2463\n",
- "Epoch 95/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.3318 - mae: 1.5423 - mse: 5.3318 - val_loss: 14.2846 - val_mae: 2.4916 - val_mse: 14.2846\n",
- "Epoch 96/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.5833 - mae: 1.5621 - mse: 5.5833 - val_loss: 12.9900 - val_mae: 2.5152 - val_mse: 12.9900\n",
- "Epoch 97/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.3842 - mae: 1.5510 - mse: 5.3842 - val_loss: 12.7831 - val_mae: 2.4384 - val_mse: 12.7831\n",
- "Epoch 98/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.3732 - mae: 1.5228 - mse: 5.3732 - val_loss: 13.5497 - val_mae: 2.4686 - val_mse: 13.5497\n",
- "Epoch 99/100\n",
- "36/36 [==============================] - 0s 2ms/step - loss: 5.2501 - mae: 1.5620 - mse: 5.2501 - val_loss: 13.1683 - val_mae: 2.5256 - val_mse: 13.1683\n",
- "Epoch 100/100\n",
- "36/36 [==============================] - 0s 3ms/step - loss: 5.4235 - mae: 1.5454 - mse: 5.4235 - val_loss: 12.4521 - val_mae: 2.5606 - val_mse: 12.4521\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
@@ -1000,19 +282,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 12.4521\n",
- "x_test / mae : 2.5606\n",
- "x_test / mse : 12.4521\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -1031,99 +303,18 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min( val_mae ) : 2.4384\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/BHPD2-01-history_0</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
" 'MAE' :['mae', 'val_mae'],\n",
@@ -1146,31 +337,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "Dense_n1 (Dense) (None, 64) 896 \n",
- "_________________________________________________________________\n",
- "Dense_n2 (Dense) (None, 64) 4160 \n",
- "_________________________________________________________________\n",
- "Output (Dense) (None, 1) 65 \n",
- "=================================================================\n",
- "Total params: 5,121\n",
- "Trainable params: 5,121\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n",
- "Loaded.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
"loaded_model.summary()\n",
@@ -1186,19 +355,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 12.4521\n",
- "x_test / mae : 2.5606\n",
- "x_test / mse : 12.4521\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -1216,7 +375,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1230,17 +389,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prediction : 10.59 K$ Reality : 10.40 K$\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"predictions = loaded_model.predict( my_data )\n",
"print(\"Prediction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], real_price))"
@@ -1248,19 +399,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Friday 8 January 2021, 01:10:39\n",
- "Duration is : 00:00:12 582ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/BHPD/02-DNN-Regression-Premium==done==.ipynb b/BHPD/02-DNN-Regression-Premium==done==.ipynb
new file mode 100644
index 0000000..83a95b9
--- /dev/null
+++ b/BHPD/02-DNN-Regression-Premium==done==.ipynb
@@ -0,0 +1,2923 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [BHPD2] - Regression with a Dense Network (DNN) - Advanced code\n",
+ " <!-- DESC --> A more advanced implementation of the precedent example\n",
+ " <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Predicts **housing prices** from a set of house features. \n",
+ " - Understanding the principle and the architecture of a regression with a dense neural network with backup and restore of the trained model. \n",
+ "\n",
+ "The **[Boston Housing Prices Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston. \n",
+ "Alongside with price, the dataset also provide these information :\n",
+ "\n",
+ " - CRIM: This is the per capita crime rate by town\n",
+ " - ZN: This is the proportion of residential land zoned for lots larger than 25,000 sq.ft\n",
+ " - INDUS: This is the proportion of non-retail business acres per town\n",
+ " - CHAS: This is the Charles River dummy variable (this is equal to 1 if tract bounds river; 0 otherwise)\n",
+ " - NOX: This is the nitric oxides concentration (parts per 10 million)\n",
+ " - RM: This is the average number of rooms per dwelling\n",
+ " - AGE: This is the proportion of owner-occupied units built prior to 1940\n",
+ " - DIS: This is the weighted distances to five Boston employment centers\n",
+ " - RAD: This is the index of accessibility to radial highways\n",
+ " - TAX: This is the full-value property-tax rate per 10,000 dollars\n",
+ " - PTRATIO: This is the pupil-teacher ratio by town\n",
+ " - B: This is calculated as 1000(Bk — 0.63)^2, where Bk is the proportion of people of African American descent by town\n",
+ " - LSTAT: This is the percentage lower status of the population\n",
+ " - MEDV: This is the median value of owner-occupied homes in 1000 dollars\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ " - (Retrieve data)\n",
+ " - (Preparing the data)\n",
+ " - (Build a model)\n",
+ " - Train and save the model\n",
+ " - Restore saved model\n",
+ " - Evaluate the model\n",
+ " - Make some predictions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:29.428040Z",
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+ "shell.execute_reply": "2021-01-14T07:11:30.750181Z"
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+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : BHPD2\n",
+ "Run time : Thursday 14 January 2021, 08:11:30\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import os,sys\n",
+ "\n",
+ "from IPython.display import Markdown\n",
+ "from importlib import reload\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('BHPD2')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve data\n",
+ "\n",
+ "### 2.1 - Option 1 : From Keras\n",
+ "Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.753185Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.752868Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.755046Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.754768Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# (x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Option 2 : From a csv file\n",
+ "More fun !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.758214Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.757894Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.790895Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.791236Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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+ " <tr>\n",
+ " <th id=\"T_ee48b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_ee48b_row0_col0\" class=\"data row0 col0\" >0.01</td>\n",
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+ " <th id=\"T_ee48b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
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+ " <td id=\"T_ee48b_row2_col12\" class=\"data row2 col12\" >4.03</td>\n",
+ " <td id=\"T_ee48b_row2_col13\" class=\"data row2 col13\" >34.70</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ee48b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_ee48b_row3_col0\" class=\"data row3 col0\" >0.03</td>\n",
+ " <td id=\"T_ee48b_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_ee48b_row3_col2\" class=\"data row3 col2\" >2.18</td>\n",
+ " <td id=\"T_ee48b_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_ee48b_row3_col4\" class=\"data row3 col4\" >0.46</td>\n",
+ " <td id=\"T_ee48b_row3_col5\" class=\"data row3 col5\" >7.00</td>\n",
+ " <td id=\"T_ee48b_row3_col6\" class=\"data row3 col6\" >45.80</td>\n",
+ " <td id=\"T_ee48b_row3_col7\" class=\"data row3 col7\" >6.06</td>\n",
+ " <td id=\"T_ee48b_row3_col8\" class=\"data row3 col8\" >3.00</td>\n",
+ " <td id=\"T_ee48b_row3_col9\" class=\"data row3 col9\" >222.00</td>\n",
+ " <td id=\"T_ee48b_row3_col10\" class=\"data row3 col10\" >18.70</td>\n",
+ " <td id=\"T_ee48b_row3_col11\" class=\"data row3 col11\" >394.63</td>\n",
+ " <td id=\"T_ee48b_row3_col12\" class=\"data row3 col12\" >2.94</td>\n",
+ " <td id=\"T_ee48b_row3_col13\" class=\"data row3 col13\" >33.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_ee48b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_ee48b_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_ee48b_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_ee48b_row4_col2\" class=\"data row4 col2\" >2.18</td>\n",
+ " <td id=\"T_ee48b_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_ee48b_row4_col4\" class=\"data row4 col4\" >0.46</td>\n",
+ " <td id=\"T_ee48b_row4_col5\" class=\"data row4 col5\" >7.15</td>\n",
+ " <td id=\"T_ee48b_row4_col6\" class=\"data row4 col6\" >54.20</td>\n",
+ " <td id=\"T_ee48b_row4_col7\" class=\"data row4 col7\" >6.06</td>\n",
+ " <td id=\"T_ee48b_row4_col8\" class=\"data row4 col8\" >3.00</td>\n",
+ " <td id=\"T_ee48b_row4_col9\" class=\"data row4 col9\" >222.00</td>\n",
+ " <td id=\"T_ee48b_row4_col10\" class=\"data row4 col10\" >18.70</td>\n",
+ " <td id=\"T_ee48b_row4_col11\" class=\"data row4 col11\" >396.90</td>\n",
+ " <td id=\"T_ee48b_row4_col12\" class=\"data row4 col12\" >5.33</td>\n",
+ " <td id=\"T_ee48b_row4_col13\" class=\"data row4 col13\" >36.20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f63ac2b3a50>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Missing Data : 0 Shape is : (506, 14)\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = pd.read_csv(f'{datasets_dir}/BHPD/origine/BostonHousing.csv', header=0)\n",
+ "\n",
+ "display(data.head(5).style.format(\"{0:.2f}\"))\n",
+ "print('Missing Data : ',data.isna().sum().sum(), ' Shape is : ', data.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preparing the data\n",
+ "### 3.1 - Split data\n",
+ "We will use 80% of the data for training and 20% for validation. \n",
+ "x will be input data and y the expected output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.797963Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.797461Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.800339Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.800007Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data shape was : (506, 14)\n",
+ "x_train : (354, 13) y_train : (354,)\n",
+ "x_test : (152, 13) y_test : (152,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Split => train, test\n",
+ "#\n",
+ "data_train = data.sample(frac=0.7, axis=0)\n",
+ "data_test = data.drop(data_train.index)\n",
+ "\n",
+ "# ---- Split => x,y (medv is price)\n",
+ "#\n",
+ "x_train = data_train.drop('medv', axis=1)\n",
+ "y_train = data_train['medv']\n",
+ "x_test = data_test.drop('medv', axis=1)\n",
+ "y_test = data_test['medv']\n",
+ "\n",
+ "print('Original data shape was : ',data.shape)\n",
+ "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
+ "print('x_test : ',x_test.shape, 'y_test : ',y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 - Data normalization\n",
+ "**Note :** \n",
+ " - All input data must be normalized, train and test. \n",
+ " - To do this we will subtract the mean and divide by the standard deviation. \n",
+ " - But test data should not be used in any way, even for normalization. \n",
+ " - The mean and the standard deviation will therefore only be calculated with the train data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.806114Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.805722Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.860612Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.860272Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_43fc3_\" ><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_43fc3_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_43fc3_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_43fc3_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_43fc3_row1_col0\" class=\"data row1 col0\" >3.62</td>\n",
+ " <td id=\"T_43fc3_row1_col1\" class=\"data row1 col1\" >12.34</td>\n",
+ " <td id=\"T_43fc3_row1_col2\" class=\"data row1 col2\" >11.04</td>\n",
+ " <td id=\"T_43fc3_row1_col3\" class=\"data row1 col3\" >0.06</td>\n",
+ " <td id=\"T_43fc3_row1_col4\" class=\"data row1 col4\" >0.56</td>\n",
+ " <td id=\"T_43fc3_row1_col5\" class=\"data row1 col5\" >6.32</td>\n",
+ " <td id=\"T_43fc3_row1_col6\" class=\"data row1 col6\" >69.45</td>\n",
+ " <td id=\"T_43fc3_row1_col7\" class=\"data row1 col7\" >3.77</td>\n",
+ " <td id=\"T_43fc3_row1_col8\" class=\"data row1 col8\" >9.72</td>\n",
+ " <td id=\"T_43fc3_row1_col9\" class=\"data row1 col9\" >409.79</td>\n",
+ " <td id=\"T_43fc3_row1_col10\" class=\"data row1 col10\" >18.47</td>\n",
+ " <td id=\"T_43fc3_row1_col11\" class=\"data row1 col11\" >355.96</td>\n",
+ " <td id=\"T_43fc3_row1_col12\" class=\"data row1 col12\" >12.61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_43fc3_row2_col0\" class=\"data row2 col0\" >8.57</td>\n",
+ " <td id=\"T_43fc3_row2_col1\" class=\"data row2 col1\" >24.67</td>\n",
+ " <td id=\"T_43fc3_row2_col2\" class=\"data row2 col2\" >6.91</td>\n",
+ " <td id=\"T_43fc3_row2_col3\" class=\"data row2 col3\" >0.24</td>\n",
+ " <td id=\"T_43fc3_row2_col4\" class=\"data row2 col4\" >0.12</td>\n",
+ " <td id=\"T_43fc3_row2_col5\" class=\"data row2 col5\" >0.71</td>\n",
+ " <td id=\"T_43fc3_row2_col6\" class=\"data row2 col6\" >27.79</td>\n",
+ " <td id=\"T_43fc3_row2_col7\" class=\"data row2 col7\" >2.14</td>\n",
+ " <td id=\"T_43fc3_row2_col8\" class=\"data row2 col8\" >8.83</td>\n",
+ " <td id=\"T_43fc3_row2_col9\" class=\"data row2 col9\" >169.10</td>\n",
+ " <td id=\"T_43fc3_row2_col10\" class=\"data row2 col10\" >2.21</td>\n",
+ " <td id=\"T_43fc3_row2_col11\" class=\"data row2 col11\" >93.17</td>\n",
+ " <td id=\"T_43fc3_row2_col12\" class=\"data row2 col12\" >7.14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_43fc3_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+ " <td id=\"T_43fc3_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row3_col2\" class=\"data row3 col2\" >0.46</td>\n",
+ " <td id=\"T_43fc3_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row3_col4\" class=\"data row3 col4\" >0.39</td>\n",
+ " <td id=\"T_43fc3_row3_col5\" class=\"data row3 col5\" >3.86</td>\n",
+ " <td id=\"T_43fc3_row3_col6\" class=\"data row3 col6\" >6.00</td>\n",
+ " <td id=\"T_43fc3_row3_col7\" class=\"data row3 col7\" >1.13</td>\n",
+ " <td id=\"T_43fc3_row3_col8\" class=\"data row3 col8\" >1.00</td>\n",
+ " <td id=\"T_43fc3_row3_col9\" class=\"data row3 col9\" >187.00</td>\n",
+ " <td id=\"T_43fc3_row3_col10\" class=\"data row3 col10\" >12.60</td>\n",
+ " <td id=\"T_43fc3_row3_col11\" class=\"data row3 col11\" >0.32</td>\n",
+ " <td id=\"T_43fc3_row3_col12\" class=\"data row3 col12\" >1.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_43fc3_row4_col0\" class=\"data row4 col0\" >0.07</td>\n",
+ " <td id=\"T_43fc3_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row4_col2\" class=\"data row4 col2\" >4.93</td>\n",
+ " <td id=\"T_43fc3_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row4_col4\" class=\"data row4 col4\" >0.45</td>\n",
+ " <td id=\"T_43fc3_row4_col5\" class=\"data row4 col5\" >5.93</td>\n",
+ " <td id=\"T_43fc3_row4_col6\" class=\"data row4 col6\" >45.18</td>\n",
+ " <td id=\"T_43fc3_row4_col7\" class=\"data row4 col7\" >2.10</td>\n",
+ " <td id=\"T_43fc3_row4_col8\" class=\"data row4 col8\" >4.00</td>\n",
+ " <td id=\"T_43fc3_row4_col9\" class=\"data row4 col9\" >277.00</td>\n",
+ " <td id=\"T_43fc3_row4_col10\" class=\"data row4 col10\" >17.40</td>\n",
+ " <td id=\"T_43fc3_row4_col11\" class=\"data row4 col11\" >375.29</td>\n",
+ " <td id=\"T_43fc3_row4_col12\" class=\"data row4 col12\" >6.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_43fc3_row5_col0\" class=\"data row5 col0\" >0.25</td>\n",
+ " <td id=\"T_43fc3_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row5_col2\" class=\"data row5 col2\" >9.69</td>\n",
+ " <td id=\"T_43fc3_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row5_col4\" class=\"data row5 col4\" >0.54</td>\n",
+ " <td id=\"T_43fc3_row5_col5\" class=\"data row5 col5\" >6.24</td>\n",
+ " <td id=\"T_43fc3_row5_col6\" class=\"data row5 col6\" >79.20</td>\n",
+ " <td id=\"T_43fc3_row5_col7\" class=\"data row5 col7\" >3.10</td>\n",
+ " <td id=\"T_43fc3_row5_col8\" class=\"data row5 col8\" >5.00</td>\n",
+ " <td id=\"T_43fc3_row5_col9\" class=\"data row5 col9\" >336.00</td>\n",
+ " <td id=\"T_43fc3_row5_col10\" class=\"data row5 col10\" >19.10</td>\n",
+ " <td id=\"T_43fc3_row5_col11\" class=\"data row5 col11\" >391.47</td>\n",
+ " <td id=\"T_43fc3_row5_col12\" class=\"data row5 col12\" >11.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_43fc3_row6_col0\" class=\"data row6 col0\" >3.82</td>\n",
+ " <td id=\"T_43fc3_row6_col1\" class=\"data row6 col1\" >19.50</td>\n",
+ " <td id=\"T_43fc3_row6_col2\" class=\"data row6 col2\" >18.10</td>\n",
+ " <td id=\"T_43fc3_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
+ " <td id=\"T_43fc3_row6_col4\" class=\"data row6 col4\" >0.63</td>\n",
+ " <td id=\"T_43fc3_row6_col5\" class=\"data row6 col5\" >6.63</td>\n",
+ " <td id=\"T_43fc3_row6_col6\" class=\"data row6 col6\" >94.10</td>\n",
+ " <td id=\"T_43fc3_row6_col7\" class=\"data row6 col7\" >5.12</td>\n",
+ " <td id=\"T_43fc3_row6_col8\" class=\"data row6 col8\" >24.00</td>\n",
+ " <td id=\"T_43fc3_row6_col9\" class=\"data row6 col9\" >666.00</td>\n",
+ " <td id=\"T_43fc3_row6_col10\" class=\"data row6 col10\" >20.20</td>\n",
+ " <td id=\"T_43fc3_row6_col11\" class=\"data row6 col11\" >396.29</td>\n",
+ " <td id=\"T_43fc3_row6_col12\" class=\"data row6 col12\" >16.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_43fc3_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_43fc3_row7_col0\" class=\"data row7 col0\" >88.98</td>\n",
+ " <td id=\"T_43fc3_row7_col1\" class=\"data row7 col1\" >100.00</td>\n",
+ " <td id=\"T_43fc3_row7_col2\" class=\"data row7 col2\" >27.74</td>\n",
+ " <td id=\"T_43fc3_row7_col3\" class=\"data row7 col3\" >1.00</td>\n",
+ " <td id=\"T_43fc3_row7_col4\" class=\"data row7 col4\" >0.87</td>\n",
+ " <td id=\"T_43fc3_row7_col5\" class=\"data row7 col5\" >8.72</td>\n",
+ " <td id=\"T_43fc3_row7_col6\" class=\"data row7 col6\" >100.00</td>\n",
+ " <td id=\"T_43fc3_row7_col7\" class=\"data row7 col7\" >12.13</td>\n",
+ " <td id=\"T_43fc3_row7_col8\" class=\"data row7 col8\" >24.00</td>\n",
+ " <td id=\"T_43fc3_row7_col9\" class=\"data row7 col9\" >711.00</td>\n",
+ " <td id=\"T_43fc3_row7_col10\" class=\"data row7 col10\" >22.00</td>\n",
+ " <td id=\"T_43fc3_row7_col11\" class=\"data row7 col11\" >396.90</td>\n",
+ " <td id=\"T_43fc3_row7_col12\" class=\"data row7 col12\" >37.97</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f645c735150>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_c60d8_\" ><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_c60d8_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+ " <td id=\"T_c60d8_row0_col0\" class=\"data row0 col0\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col1\" class=\"data row0 col1\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col2\" class=\"data row0 col2\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col3\" class=\"data row0 col3\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col4\" class=\"data row0 col4\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col5\" class=\"data row0 col5\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col6\" class=\"data row0 col6\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col7\" class=\"data row0 col7\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col8\" class=\"data row0 col8\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col9\" class=\"data row0 col9\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col10\" class=\"data row0 col10\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col11\" class=\"data row0 col11\" >354.00</td>\n",
+ " <td id=\"T_c60d8_row0_col12\" class=\"data row0 col12\" >354.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+ " <td id=\"T_c60d8_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col3\" class=\"data row1 col3\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col5\" class=\"data row1 col5\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col6\" class=\"data row1 col6\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col10\" class=\"data row1 col10\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+ " <td id=\"T_c60d8_row1_col12\" class=\"data row1 col12\" >-0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+ " <td id=\"T_c60d8_row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+ " <td id=\"T_c60d8_row2_col12\" class=\"data row2 col12\" >1.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+ " <td id=\"T_c60d8_row3_col0\" class=\"data row3 col0\" >-0.42</td>\n",
+ " <td id=\"T_c60d8_row3_col1\" class=\"data row3 col1\" >-0.50</td>\n",
+ " <td id=\"T_c60d8_row3_col2\" class=\"data row3 col2\" >-1.53</td>\n",
+ " <td id=\"T_c60d8_row3_col3\" class=\"data row3 col3\" >-0.25</td>\n",
+ " <td id=\"T_c60d8_row3_col4\" class=\"data row3 col4\" >-1.46</td>\n",
+ " <td id=\"T_c60d8_row3_col5\" class=\"data row3 col5\" >-3.47</td>\n",
+ " <td id=\"T_c60d8_row3_col6\" class=\"data row3 col6\" >-2.28</td>\n",
+ " <td id=\"T_c60d8_row3_col7\" class=\"data row3 col7\" >-1.24</td>\n",
+ " <td id=\"T_c60d8_row3_col8\" class=\"data row3 col8\" >-0.99</td>\n",
+ " <td id=\"T_c60d8_row3_col9\" class=\"data row3 col9\" >-1.32</td>\n",
+ " <td id=\"T_c60d8_row3_col10\" class=\"data row3 col10\" >-2.65</td>\n",
+ " <td id=\"T_c60d8_row3_col11\" class=\"data row3 col11\" >-3.82</td>\n",
+ " <td id=\"T_c60d8_row3_col12\" class=\"data row3 col12\" >-1.52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+ " <td id=\"T_c60d8_row4_col0\" class=\"data row4 col0\" >-0.41</td>\n",
+ " <td id=\"T_c60d8_row4_col1\" class=\"data row4 col1\" >-0.50</td>\n",
+ " <td id=\"T_c60d8_row4_col2\" class=\"data row4 col2\" >-0.88</td>\n",
+ " <td id=\"T_c60d8_row4_col3\" class=\"data row4 col3\" >-0.25</td>\n",
+ " <td id=\"T_c60d8_row4_col4\" class=\"data row4 col4\" >-0.88</td>\n",
+ " <td id=\"T_c60d8_row4_col5\" class=\"data row4 col5\" >-0.54</td>\n",
+ " <td id=\"T_c60d8_row4_col6\" class=\"data row4 col6\" >-0.87</td>\n",
+ " <td id=\"T_c60d8_row4_col7\" class=\"data row4 col7\" >-0.78</td>\n",
+ " <td id=\"T_c60d8_row4_col8\" class=\"data row4 col8\" >-0.65</td>\n",
+ " <td id=\"T_c60d8_row4_col9\" class=\"data row4 col9\" >-0.79</td>\n",
+ " <td id=\"T_c60d8_row4_col10\" class=\"data row4 col10\" >-0.48</td>\n",
+ " <td id=\"T_c60d8_row4_col11\" class=\"data row4 col11\" >0.21</td>\n",
+ " <td id=\"T_c60d8_row4_col12\" class=\"data row4 col12\" >-0.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+ " <td id=\"T_c60d8_row5_col0\" class=\"data row5 col0\" >-0.39</td>\n",
+ " <td id=\"T_c60d8_row5_col1\" class=\"data row5 col1\" >-0.50</td>\n",
+ " <td id=\"T_c60d8_row5_col2\" class=\"data row5 col2\" >-0.20</td>\n",
+ " <td id=\"T_c60d8_row5_col3\" class=\"data row5 col3\" >-0.25</td>\n",
+ " <td id=\"T_c60d8_row5_col4\" class=\"data row5 col4\" >-0.18</td>\n",
+ " <td id=\"T_c60d8_row5_col5\" class=\"data row5 col5\" >-0.10</td>\n",
+ " <td id=\"T_c60d8_row5_col6\" class=\"data row5 col6\" >0.35</td>\n",
+ " <td id=\"T_c60d8_row5_col7\" class=\"data row5 col7\" >-0.32</td>\n",
+ " <td id=\"T_c60d8_row5_col8\" class=\"data row5 col8\" >-0.53</td>\n",
+ " <td id=\"T_c60d8_row5_col9\" class=\"data row5 col9\" >-0.44</td>\n",
+ " <td id=\"T_c60d8_row5_col10\" class=\"data row5 col10\" >0.28</td>\n",
+ " <td id=\"T_c60d8_row5_col11\" class=\"data row5 col11\" >0.38</td>\n",
+ " <td id=\"T_c60d8_row5_col12\" class=\"data row5 col12\" >-0.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+ " <td id=\"T_c60d8_row6_col0\" class=\"data row6 col0\" >0.02</td>\n",
+ " <td id=\"T_c60d8_row6_col1\" class=\"data row6 col1\" >0.29</td>\n",
+ " <td id=\"T_c60d8_row6_col2\" class=\"data row6 col2\" >1.02</td>\n",
+ " <td id=\"T_c60d8_row6_col3\" class=\"data row6 col3\" >-0.25</td>\n",
+ " <td id=\"T_c60d8_row6_col4\" class=\"data row6 col4\" >0.65</td>\n",
+ " <td id=\"T_c60d8_row6_col5\" class=\"data row6 col5\" >0.44</td>\n",
+ " <td id=\"T_c60d8_row6_col6\" class=\"data row6 col6\" >0.89</td>\n",
+ " <td id=\"T_c60d8_row6_col7\" class=\"data row6 col7\" >0.63</td>\n",
+ " <td id=\"T_c60d8_row6_col8\" class=\"data row6 col8\" >1.62</td>\n",
+ " <td id=\"T_c60d8_row6_col9\" class=\"data row6 col9\" >1.52</td>\n",
+ " <td id=\"T_c60d8_row6_col10\" class=\"data row6 col10\" >0.78</td>\n",
+ " <td id=\"T_c60d8_row6_col11\" class=\"data row6 col11\" >0.43</td>\n",
+ " <td id=\"T_c60d8_row6_col12\" class=\"data row6 col12\" >0.55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_c60d8_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+ " <td id=\"T_c60d8_row7_col0\" class=\"data row7 col0\" >9.96</td>\n",
+ " <td id=\"T_c60d8_row7_col1\" class=\"data row7 col1\" >3.55</td>\n",
+ " <td id=\"T_c60d8_row7_col2\" class=\"data row7 col2\" >2.42</td>\n",
+ " <td id=\"T_c60d8_row7_col3\" class=\"data row7 col3\" >3.98</td>\n",
+ " <td id=\"T_c60d8_row7_col4\" class=\"data row7 col4\" >2.71</td>\n",
+ " <td id=\"T_c60d8_row7_col5\" class=\"data row7 col5\" >3.40</td>\n",
+ " <td id=\"T_c60d8_row7_col6\" class=\"data row7 col6\" >1.10</td>\n",
+ " <td id=\"T_c60d8_row7_col7\" class=\"data row7 col7\" >3.90</td>\n",
+ " <td id=\"T_c60d8_row7_col8\" class=\"data row7 col8\" >1.62</td>\n",
+ " <td id=\"T_c60d8_row7_col9\" class=\"data row7 col9\" >1.78</td>\n",
+ " <td id=\"T_c60d8_row7_col10\" class=\"data row7 col10\" >1.59</td>\n",
+ " <td id=\"T_c60d8_row7_col11\" class=\"data row7 col11\" >0.44</td>\n",
+ " <td id=\"T_c60d8_row7_col12\" class=\"data row7 col12\" >3.55</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f63ac27aad0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
+ "\n",
+ "mean = x_train.mean()\n",
+ "std = x_train.std()\n",
+ "x_train = (x_train - mean) / std\n",
+ "x_test = (x_test - mean) / std\n",
+ "\n",
+ "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
+ "\n",
+ "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+ "x_test, y_test = np.array(x_test), np.array(y_test)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build a model\n",
+ "More informations about : \n",
+ " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+ " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+ " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.864460Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.864135Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.866230Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.865900Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ " def get_model_v1(shape):\n",
+ " \n",
+ " model = keras.models.Sequential()\n",
+ " model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
+ " model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
+ " model.add(keras.layers.Dense(1, name='Output'))\n",
+ " \n",
+ " model.compile(optimizer = 'rmsprop',\n",
+ " loss = 'mse',\n",
+ " metrics = ['mae', 'mse'] )\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.869131Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.868788Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.908515Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.908171Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model=get_model_v1( (13,) )\n",
+ "\n",
+ "model.summary()\n",
+ "# img=keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)\n",
+ "# display(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Add callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.912234Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.911911Z",
+ "iopub.status.idle": "2021-01-14T07:11:30.914352Z",
+ "shell.execute_reply": "2021-01-14T07:11:30.913990Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = \"./run/models/best_model.h5\"\n",
+ "\n",
+ "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.3 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:30.920720Z",
+ "iopub.status.busy": "2021-01-14T07:11:30.918911Z",
+ "iopub.status.idle": "2021-01-14T07:11:38.814802Z",
+ "shell.execute_reply": "2021-01-14T07:11:38.814494Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 389.7831 - mae: 18.9112 - mse: 389.7831"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 5ms/step - loss: 479.8606 - mae: 19.7624 - mse: 479.8606 - val_loss: 313.9207 - val_mae: 15.7939 - val_mse: 313.9207\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 2/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 433.2456 - mae: 17.4410 - mse: 433.2456"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 251.0171 - mae: 13.3376 - mse: 251.0171 - val_loss: 115.0622 - val_mae: 8.7007 - val_mse: 115.0622\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 3/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 74.5882 - mae: 7.4639 - mse: 74.5882"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 97.0846 - mae: 7.5000 - mse: 97.0846 - val_loss: 48.7695 - val_mae: 5.3898 - val_mse: 48.7695\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 4/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 155.7348 - mae: 9.1847 - mse: 155.7348"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 54.7528 - mae: 5.4036 - mse: 54.7528 - val_loss: 29.9846 - val_mae: 4.1754 - val_mse: 29.9846\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 5/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 40.2903 - mae: 5.0659 - mse: 40.2903"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 35.3013 - mae: 4.2298 - mse: 35.3013 - val_loss: 21.5246 - val_mae: 3.5020 - val_mse: 21.5246\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 6/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 20.8753 - mae: 3.7356 - mse: 20.8753"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 26.6657 - mae: 3.6815 - mse: 26.6657 - val_loss: 18.1473 - val_mae: 3.1323 - val_mse: 18.1473\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 7/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.2121 - mae: 2.0190 - mse: 5.2121"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 22.8042 - mae: 3.3342 - mse: 22.8042 - val_loss: 16.4765 - val_mae: 2.9579 - val_mse: 16.4765\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 8/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.9479 - mae: 2.4739 - mse: 8.9479"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 19.7496 - mae: 3.1010 - mse: 19.7496 - val_loss: 15.8385 - val_mae: 2.8592 - val_mse: 15.8385\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 9/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.2032 - mae: 2.8548 - mse: 9.2032"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 17.5905 - mae: 2.9337 - mse: 17.5905 - val_loss: 16.9321 - val_mae: 2.9213 - val_mse: 16.9321\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 10/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 18.1565 - mae: 3.8637 - mse: 18.1565"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 16.4101 - mae: 2.8449 - mse: 16.4101 - val_loss: 15.1185 - val_mae: 2.7206 - val_mse: 15.1185\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 11/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.8267 - mae: 1.9830 - mse: 5.8267"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 15.2091 - mae: 2.7126 - mse: 15.2091 - val_loss: 14.4734 - val_mae: 2.5941 - val_mse: 14.4734\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 12/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.2744 - mae: 2.2298 - mse: 6.2744"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 14.4579 - mae: 2.6564 - mse: 14.4579 - val_loss: 14.6208 - val_mae: 2.5923 - val_mse: 14.6208\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 13/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.3490 - mae: 1.9336 - mse: 7.3490"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 13.7957 - mae: 2.6243 - mse: 13.7957 - val_loss: 14.1156 - val_mae: 2.5347 - val_mse: 14.1156\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 14/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.1917 - mae: 2.2493 - mse: 7.1917"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.9634 - mae: 2.5102 - mse: 12.9634 - val_loss: 14.7697 - val_mae: 2.6119 - val_mse: 14.7697\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 15/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.7120 - mae: 2.1875 - mse: 7.7120"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.4422 - mae: 2.4581 - mse: 12.4422 - val_loss: 14.6517 - val_mae: 2.5601 - val_mse: 14.6517\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 16/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 10.8231 - mae: 2.9008 - mse: 10.8231"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 12.2225 - mae: 2.4426 - mse: 12.2225 - val_loss: 14.7885 - val_mae: 2.5623 - val_mse: 14.7885\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 17/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 27.0570 - mae: 3.0176 - mse: 27.0570"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.8573 - mae: 2.3869 - mse: 11.8573 - val_loss: 14.4856 - val_mae: 2.4999 - val_mse: 14.4856\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 18/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.2214 - mae: 1.5709 - mse: 3.2214"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 11.2766 - mae: 2.3107 - mse: 11.2766 - val_loss: 16.2192 - val_mae: 2.6771 - val_mse: 16.2192\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 19/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 53.4781 - mae: 4.6768 - mse: 53.4781"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.8904 - mae: 2.3221 - mse: 10.8904 - val_loss: 14.7529 - val_mae: 2.5405 - val_mse: 14.7529\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 20/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.6295 - mae: 2.1028 - mse: 5.6295"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.7788 - mae: 2.2706 - mse: 10.7788 - val_loss: 14.5959 - val_mae: 2.4926 - val_mse: 14.5959\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 21/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 11.0794 - mae: 2.7306 - mse: 11.0794"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.4161 - mae: 2.2696 - mse: 10.4161 - val_loss: 14.7912 - val_mae: 2.5766 - val_mse: 14.7912\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 22/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.3141 - mae: 2.6085 - mse: 9.3141"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.3055 - mae: 2.2356 - mse: 10.3055 - val_loss: 14.8611 - val_mae: 2.5488 - val_mse: 14.8611\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 23/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.9928 - mae: 1.6222 - mse: 3.9928"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.2244 - mae: 2.2373 - mse: 10.2244 - val_loss: 14.9809 - val_mae: 2.5411 - val_mse: 14.9809\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 24/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 18.7919 - mae: 3.8043 - mse: 18.7919"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 10.2347 - mae: 2.1896 - mse: 10.2347 - val_loss: 14.1357 - val_mae: 2.4829 - val_mse: 14.1357\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 25/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.7434 - mae: 2.4112 - mse: 9.7434"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.9127 - mae: 2.2199 - mse: 9.9127 - val_loss: 14.9639 - val_mae: 2.5534 - val_mse: 14.9639\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 26/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.2105 - mae: 1.5460 - mse: 4.2105"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.7643 - mae: 2.1699 - mse: 9.7643 - val_loss: 15.1165 - val_mae: 2.6070 - val_mse: 15.1165\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 27/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 13.3942 - mae: 2.9616 - mse: 13.3942"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.7912 - mae: 2.1629 - mse: 9.7912 - val_loss: 14.3873 - val_mae: 2.4969 - val_mse: 14.3873\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 28/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.3006 - mae: 1.8108 - mse: 5.3006"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.3235 - mae: 2.0974 - mse: 9.3235 - val_loss: 14.1615 - val_mae: 2.4547 - val_mse: 14.1615\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 29/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.5976 - mae: 2.0674 - mse: 6.5976"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.3655 - mae: 2.1701 - mse: 9.3655 - val_loss: 13.7438 - val_mae: 2.4387 - val_mse: 13.7438\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 30/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.5997 - mae: 2.5383 - mse: 9.5997"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 9.2931 - mae: 2.1114 - mse: 9.2931 - val_loss: 14.9816 - val_mae: 2.4900 - val_mse: 14.9816\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 31/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.8495 - mae: 1.8740 - mse: 6.8495"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 3ms/step - loss: 9.2603 - mae: 2.1166 - mse: 9.2603 - val_loss: 14.0484 - val_mae: 2.4778 - val_mse: 14.0484\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 32/100\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.3201 - mae: 1.8011 - mse: 5.3201"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.9376 - mae: 2.0842 - mse: 8.9376 - val_loss: 14.9942 - val_mae: 2.5042 - val_mse: 14.9942\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 33/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.2969 - mae: 2.1881 - mse: 8.2969"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.9194 - mae: 2.1055 - mse: 8.9194 - val_loss: 14.8121 - val_mae: 2.5208 - val_mse: 14.8121\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 34/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.3883 - mae: 1.6524 - mse: 5.3883"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.8447 - mae: 2.0719 - mse: 8.8447 - val_loss: 14.3170 - val_mae: 2.4864 - val_mse: 14.3170\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 35/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 43.8930 - mae: 3.2587 - mse: 43.8930"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6136 - mae: 2.0538 - mse: 8.6136 - val_loss: 14.6798 - val_mae: 2.4802 - val_mse: 14.6798\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 36/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.5058 - mae: 1.1864 - mse: 2.5058"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.7849 - mae: 2.0455 - mse: 8.7849 - val_loss: 13.8648 - val_mae: 2.4560 - val_mse: 13.8648\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 37/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.9070 - mae: 2.1361 - mse: 7.9070"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.6908 - mae: 2.0650 - mse: 8.6908 - val_loss: 14.2627 - val_mae: 2.4784 - val_mse: 14.2627\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 38/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.9295 - mae: 2.1939 - mse: 6.9295"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.3519 - mae: 2.0229 - mse: 8.3519 - val_loss: 14.1649 - val_mae: 2.4617 - val_mse: 14.1649\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 39/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.1359 - mae: 1.4777 - mse: 3.1359"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.3464 - mae: 2.0540 - mse: 8.3464 - val_loss: 13.2801 - val_mae: 2.4016 - val_mse: 13.2801\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 40/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0241 - mae: 1.5442 - mse: 4.0241"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.1010 - mae: 1.9930 - mse: 8.1010 - val_loss: 13.5650 - val_mae: 2.4058 - val_mse: 13.5650\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 41/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 11.5849 - mae: 2.2403 - mse: 11.5849"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.0642 - mae: 1.9751 - mse: 8.0642 - val_loss: 15.4452 - val_mae: 2.5221 - val_mse: 15.4452\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 42/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.4993 - mae: 2.3373 - mse: 7.4993"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.0004 - mae: 1.9871 - mse: 8.0004 - val_loss: 13.8006 - val_mae: 2.4717 - val_mse: 13.8006\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 43/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.1996 - mae: 1.9518 - mse: 9.1996"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 8.0023 - mae: 1.9510 - mse: 8.0023 - val_loss: 13.7937 - val_mae: 2.4129 - val_mse: 13.7937\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 44/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 12.7615 - mae: 2.7751 - mse: 12.7615"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.8025 - mae: 1.9665 - mse: 7.8025 - val_loss: 14.2702 - val_mae: 2.5141 - val_mse: 14.2702\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 45/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.2198 - mae: 2.2451 - mse: 9.2198"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.7570 - mae: 1.9942 - mse: 7.7570 - val_loss: 13.6718 - val_mae: 2.4283 - val_mse: 13.6718\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 46/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 12.3138 - mae: 2.8130 - mse: 12.3138"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6489 - mae: 1.9492 - mse: 7.6489 - val_loss: 13.4847 - val_mae: 2.4089 - val_mse: 13.4847\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 47/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0593 - mae: 1.6419 - mse: 4.0593"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6494 - mae: 1.9502 - mse: 7.6494 - val_loss: 14.3631 - val_mae: 2.4726 - val_mse: 14.3631\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 48/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.2665 - mae: 0.8139 - mse: 1.2665"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6113 - mae: 1.9259 - mse: 7.6113 - val_loss: 14.7889 - val_mae: 2.4596 - val_mse: 14.7889\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 49/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0618 - mae: 1.7699 - mse: 4.0618"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.6278 - mae: 1.9507 - mse: 7.6278 - val_loss: 15.5162 - val_mae: 2.5617 - val_mse: 15.5162\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 50/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.1634 - mae: 1.8709 - mse: 5.1634"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3041 - mae: 1.9047 - mse: 7.3041 - val_loss: 13.3008 - val_mae: 2.4135 - val_mse: 13.3008\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 51/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.6307 - mae: 1.4810 - mse: 3.6307"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.2821 - mae: 1.9214 - mse: 7.2821 - val_loss: 13.3205 - val_mae: 2.4189 - val_mse: 13.3205\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 52/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 13.3746 - mae: 2.3897 - mse: 13.3746"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3565 - mae: 1.8909 - mse: 7.3565 - val_loss: 13.3063 - val_mae: 2.3615 - val_mse: 13.3063\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 53/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 16.4461 - mae: 2.9820 - mse: 16.4461"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.2399 - mae: 1.8732 - mse: 7.2399 - val_loss: 13.0869 - val_mae: 2.3626 - val_mse: 13.0869\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 54/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.4357 - mae: 1.9095 - mse: 5.4357"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.3204 - mae: 1.8823 - mse: 7.3204 - val_loss: 13.6383 - val_mae: 2.3804 - val_mse: 13.6383\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 55/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.5260 - mae: 2.6376 - mse: 9.5260"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.9565 - mae: 1.8496 - mse: 6.9565 - val_loss: 14.2109 - val_mae: 2.4282 - val_mse: 14.2109\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 56/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.3659 - mae: 1.8715 - mse: 5.3659"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0620 - mae: 1.8954 - mse: 7.0620 - val_loss: 13.3942 - val_mae: 2.3656 - val_mse: 13.3942\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 57/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.4456 - mae: 1.0507 - mse: 1.4456"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 7.0092 - mae: 1.8819 - mse: 7.0092 - val_loss: 13.4036 - val_mae: 2.3867 - val_mse: 13.4036\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 58/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.4874 - mae: 2.1595 - mse: 6.4874"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.9920 - mae: 1.8659 - mse: 6.9920 - val_loss: 13.8693 - val_mae: 2.4016 - val_mse: 13.8693\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 59/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 2.6567 - mae: 1.3746 - mse: 2.6567"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.9226 - mae: 1.8695 - mse: 6.9226 - val_loss: 13.8495 - val_mae: 2.4325 - val_mse: 13.8495\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 60/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 11.7619 - mae: 2.2614 - mse: 11.7619"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.8162 - mae: 1.8100 - mse: 6.8162 - val_loss: 15.4320 - val_mae: 2.5449 - val_mse: 15.4320\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 61/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.1087 - mae: 2.3767 - mse: 8.1087"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.9133 - mae: 1.8762 - mse: 6.9133 - val_loss: 12.9091 - val_mae: 2.3326 - val_mse: 12.9091\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 62/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.9485 - mae: 1.4542 - mse: 3.9485"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.7509 - mae: 1.8198 - mse: 6.7509 - val_loss: 13.1486 - val_mae: 2.3938 - val_mse: 13.1486\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 63/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.7804 - mae: 2.3251 - mse: 8.7804"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4485 - mae: 1.7494 - mse: 6.4485 - val_loss: 14.8389 - val_mae: 2.5481 - val_mse: 14.8389\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 64/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.9892 - mae: 1.6083 - mse: 4.9892"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.6330 - mae: 1.7830 - mse: 6.6330 - val_loss: 13.3076 - val_mae: 2.3590 - val_mse: 13.3076\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 65/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.0009 - mae: 0.9182 - mse: 1.0009"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4051 - mae: 1.8167 - mse: 6.4051 - val_loss: 13.1992 - val_mae: 2.3794 - val_mse: 13.1992\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 66/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.8588 - mae: 0.9972 - mse: 1.8588"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.4886 - mae: 1.7793 - mse: 6.4886 - val_loss: 13.0932 - val_mae: 2.3837 - val_mse: 13.0932\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 67/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 10.6858 - mae: 2.4324 - mse: 10.6858"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2730 - mae: 1.7868 - mse: 6.2730 - val_loss: 13.0315 - val_mae: 2.3573 - val_mse: 13.0315\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 68/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.6576 - mae: 1.1708 - mse: 1.6576"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3058 - mae: 1.7874 - mse: 6.3058 - val_loss: 13.9050 - val_mae: 2.5099 - val_mse: 13.9050\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 69/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.3165 - mae: 1.8470 - mse: 6.3165"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.3884 - mae: 1.7905 - mse: 6.3884 - val_loss: 13.3257 - val_mae: 2.3857 - val_mse: 13.3257\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 70/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.2936 - mae: 1.5829 - mse: 4.2936"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2250 - mae: 1.7537 - mse: 6.2250 - val_loss: 13.8636 - val_mae: 2.3860 - val_mse: 13.8636\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 71/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.3276 - mae: 2.4125 - mse: 9.3276"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2631 - mae: 1.7455 - mse: 6.2631 - val_loss: 14.0767 - val_mae: 2.4031 - val_mse: 14.0767\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 72/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0660 - mae: 1.6098 - mse: 4.0660"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2287 - mae: 1.7631 - mse: 6.2287 - val_loss: 13.2292 - val_mae: 2.3698 - val_mse: 13.2292\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 73/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.0104 - mae: 2.1212 - mse: 6.0104"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.8985 - mae: 1.7454 - mse: 5.8985 - val_loss: 12.8910 - val_mae: 2.3427 - val_mse: 12.8910\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 74/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.1137 - mae: 1.5237 - mse: 3.1137"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 6.2723 - mae: 1.7694 - mse: 6.2723 - val_loss: 12.5268 - val_mae: 2.3041 - val_mse: 12.5268\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 75/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 9.1033 - mae: 2.2568 - mse: 9.1033"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.8947 - mae: 1.7714 - mse: 5.8947 - val_loss: 12.7933 - val_mae: 2.2887 - val_mse: 12.7933\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 76/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.1906 - mae: 1.5643 - mse: 5.1906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.8951 - mae: 1.6732 - mse: 5.8951 - val_loss: 15.5230 - val_mae: 2.5715 - val_mse: 15.5230\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 77/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.2202 - mae: 1.7130 - mse: 4.2202"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.9246 - mae: 1.7435 - mse: 5.9246 - val_loss: 13.4371 - val_mae: 2.4378 - val_mse: 13.4371\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 78/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 15.0750 - mae: 2.4584 - mse: 15.0750"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.7637 - mae: 1.6898 - mse: 5.7637 - val_loss: 12.9896 - val_mae: 2.3241 - val_mse: 12.9896\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 79/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 5.7574 - mae: 1.9001 - mse: 5.7574"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6239 - mae: 1.6565 - mse: 5.6239 - val_loss: 12.9841 - val_mae: 2.4193 - val_mse: 12.9841\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 80/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.9726 - mae: 1.7022 - mse: 3.9726"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6628 - mae: 1.7008 - mse: 5.6628 - val_loss: 14.2899 - val_mae: 2.5081 - val_mse: 14.2899\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 81/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.9900 - mae: 1.9765 - mse: 4.9900"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6927 - mae: 1.6930 - mse: 5.6927 - val_loss: 12.6935 - val_mae: 2.3240 - val_mse: 12.6935\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 82/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.9577 - mae: 2.0217 - mse: 6.9577"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.4944 - mae: 1.7029 - mse: 5.4944 - val_loss: 13.6903 - val_mae: 2.3776 - val_mse: 13.6903\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 83/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.2078 - mae: 1.5954 - mse: 3.2078"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.6522 - mae: 1.7010 - mse: 5.6522 - val_loss: 12.7517 - val_mae: 2.3456 - val_mse: 12.7517\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 84/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.7529 - mae: 1.9461 - mse: 4.7529"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5479 - mae: 1.6726 - mse: 5.5479 - val_loss: 12.3647 - val_mae: 2.3047 - val_mse: 12.3647\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 85/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.5596 - mae: 0.8798 - mse: 1.5596"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.4444 - mae: 1.6182 - mse: 5.4444 - val_loss: 12.5509 - val_mae: 2.3111 - val_mse: 12.5509\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 86/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.4941 - mae: 2.1528 - mse: 7.4941"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.4652 - mae: 1.6338 - mse: 5.4652 - val_loss: 12.5566 - val_mae: 2.3542 - val_mse: 12.5566\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 87/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.4916 - mae: 1.5555 - mse: 3.4916"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.5666 - mae: 1.6390 - mse: 5.5666 - val_loss: 12.4106 - val_mae: 2.3009 - val_mse: 12.4106\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 88/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 17.3554 - mae: 2.8161 - mse: 17.3554"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2077 - mae: 1.6400 - mse: 5.2077 - val_loss: 13.3929 - val_mae: 2.3591 - val_mse: 13.3929\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 89/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 1.2050 - mae: 0.8373 - mse: 1.2050"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3255 - mae: 1.6316 - mse: 5.3255 - val_loss: 13.3948 - val_mae: 2.3415 - val_mse: 13.3948\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 90/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0811 - mae: 1.6029 - mse: 4.0811"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2528 - mae: 1.5911 - mse: 5.2528 - val_loss: 14.7952 - val_mae: 2.4915 - val_mse: 14.7952\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 91/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 8.1721 - mae: 2.1004 - mse: 8.1721"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.2857 - mae: 1.6431 - mse: 5.2857 - val_loss: 13.3228 - val_mae: 2.4061 - val_mse: 13.3228\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 92/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 6.6123 - mae: 2.0193 - mse: 6.6123"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.3257 - mae: 1.6181 - mse: 5.3257 - val_loss: 12.9694 - val_mae: 2.3735 - val_mse: 12.9694\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 93/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.4923 - mae: 1.5790 - mse: 4.4923"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0725 - mae: 1.5739 - mse: 5.0725 - val_loss: 12.1460 - val_mae: 2.3334 - val_mse: 12.1460\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 94/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.5603 - mae: 1.7095 - mse: 3.5603"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0013 - mae: 1.5412 - mse: 5.0013 - val_loss: 12.2631 - val_mae: 2.2999 - val_mse: 12.2631\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 95/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.5386 - mae: 1.6417 - mse: 3.5386"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9984 - mae: 1.5613 - mse: 4.9984 - val_loss: 13.6850 - val_mae: 2.4206 - val_mse: 13.6850\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 96/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 4.0995 - mae: 1.7741 - mse: 4.0995"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9245 - mae: 1.5599 - mse: 4.9245 - val_loss: 12.8912 - val_mae: 2.4394 - val_mse: 12.8912\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 97/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.5955 - mae: 1.6045 - mse: 3.5955"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 5.0209 - mae: 1.6005 - mse: 5.0209 - val_loss: 13.2827 - val_mae: 2.4309 - val_mse: 13.2827\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 98/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 7.3068 - mae: 1.9157 - mse: 7.3068"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.8442 - mae: 1.5370 - mse: 4.8442 - val_loss: 14.1536 - val_mae: 2.5455 - val_mse: 14.1536\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 99/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 11.6336 - mae: 2.6111 - mse: 11.6336"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.9964 - mae: 1.6024 - mse: 4.9964 - val_loss: 12.6009 - val_mae: 2.3386 - val_mse: 12.6009\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 100/100\n",
+ "\r",
+ " 1/36 [..............................] - ETA: 0s - loss: 3.9114 - mae: 1.4947 - mse: 3.9114"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "36/36 [==============================] - 0s 2ms/step - loss: 4.8556 - mae: 1.5872 - mse: 4.8556 - val_loss: 12.0747 - val_mae: 2.3187 - val_mse: 12.0747\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = 100,\n",
+ " batch_size = 10,\n",
+ " verbose = 1,\n",
+ " validation_data = (x_test, y_test),\n",
+ " callbacks = [savemodel_callback])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Model evaluation\n",
+ "MAE = Mean Absolute Error (between the labels and predictions) \n",
+ "A mae equal to 3 represents an average error in prediction of $3k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:38.818456Z",
+ "iopub.status.busy": "2021-01-14T07:11:38.817974Z",
+ "iopub.status.idle": "2021-01-14T07:11:38.841109Z",
+ "shell.execute_reply": "2021-01-14T07:11:38.841445Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 12.0747\n",
+ "x_test / mae : 2.3187\n",
+ "x_test / mse : 12.0747\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Training history\n",
+ "What was the best result during our training ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:38.844554Z",
+ "iopub.status.busy": "2021-01-14T07:11:38.844170Z",
+ "iopub.status.idle": "2021-01-14T07:11:38.846325Z",
+ "shell.execute_reply": "2021-01-14T07:11:38.846653Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "min( val_mae ) : 2.2887\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:38.854473Z",
+ "iopub.status.busy": "2021-01-14T07:11:38.854147Z",
+ "iopub.status.idle": "2021-01-14T07:11:39.841336Z",
+ "shell.execute_reply": "2021-01-14T07:11:39.841667Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/BHPD2-01-history_0</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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\n",
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\n",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_history(history, plot={'MSE' :['mse', 'val_mse'],\n",
+ " 'MAE' :['mae', 'val_mae'],\n",
+ " 'LOSS':['loss','val_loss']}, save_as='01-history')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 7 - Restore a model :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.1 - Reload model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:39.848392Z",
+ "iopub.status.busy": "2021-01-14T07:11:39.844232Z",
+ "iopub.status.idle": "2021-01-14T07:11:39.877884Z",
+ "shell.execute_reply": "2021-01-14T07:11:39.877545Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "Dense_n1 (Dense) (None, 64) 896 \n",
+ "_________________________________________________________________\n",
+ "Dense_n2 (Dense) (None, 64) 4160 \n",
+ "_________________________________________________________________\n",
+ "Output (Dense) (None, 1) 65 \n",
+ "=================================================================\n",
+ "Total params: 5,121\n",
+ "Trainable params: 5,121\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "Loaded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
+ "loaded_model.summary()\n",
+ "print(\"Loaded.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.2 - Evaluate it :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:39.881670Z",
+ "iopub.status.busy": "2021-01-14T07:11:39.881093Z",
+ "iopub.status.idle": "2021-01-14T07:11:39.990990Z",
+ "shell.execute_reply": "2021-01-14T07:11:39.991253Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_test / loss : 12.0747\n",
+ "x_test / mae : 2.3187\n",
+ "x_test / mse : 12.0747\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / mae : {:5.4f}'.format(score[1]))\n",
+ "print('x_test / mse : {:5.4f}'.format(score[2]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 7.3 - Make a prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:39.994475Z",
+ "iopub.status.busy": "2021-01-14T07:11:39.994150Z",
+ "iopub.status.idle": "2021-01-14T07:11:39.996556Z",
+ "shell.execute_reply": "2021-01-14T07:11:39.996222Z"
+ }
+ },
+ "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": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:39.999500Z",
+ "iopub.status.busy": "2021-01-14T07:11:39.999172Z",
+ "iopub.status.idle": "2021-01-14T07:11:40.078554Z",
+ "shell.execute_reply": "2021-01-14T07:11:40.078906Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction : 11.47 K$ Reality : 10.40 K$\n"
+ ]
+ }
+ ],
+ "source": [
+ "predictions = loaded_model.predict( my_data )\n",
+ "print(\"Prediction : {:.2f} K$ Reality : {:.2f} K$\".format(predictions[0][0], real_price))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:40.081882Z",
+ "iopub.status.busy": "2021-01-14T07:11:40.081518Z",
+ "iopub.status.idle": "2021-01-14T07:11:40.083755Z",
+ "shell.execute_reply": "2021-01-14T07:11:40.083482Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:40\n",
+ "Duration is : 00:00:09 336ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GTSRB/05-Full-convolutions.ipynb b/GTSRB/05-Full-convolutions.ipynb
index b29c075..3e9c33e 100644
--- a/GTSRB/05-Full-convolutions.ipynb
+++ b/GTSRB/05-Full-convolutions.ipynb
@@ -134,17 +134,11 @@
"\n",
"VERSION='1.6'\n",
"\n",
-<<<<<<< HEAD
- "VERSION='1.6'",
- "\n",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
- "place, datasets_dir = ooo.init()"
-=======
"run_dir = './run/GTSRB5'\n",
"datasets_dir = pwk.init('GTSRB5', run_dir)"
->>>>>>> dev
]
},
{
diff --git a/IRIS/01-Simple-Perceptron==done==.ipynb b/IRIS/01-Simple-Perceptron==done==.ipynb
new file mode 100644
index 0000000..9457019
--- /dev/null
+++ b/IRIS/01-Simple-Perceptron==done==.ipynb
@@ -0,0 +1,743 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [PER57] - Perceptron Model 1957\n",
+ "<!-- DESC --> Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Implement a historical linear classifier with a historical dataset !\n",
+ " - The objective is to predict the type of Iris from the size of the leaves.\n",
+ " - Identifying its limitations \n",
+ "\n",
+ "The [IRIS dataset](https://archive.ics.uci.edu/ml/datasets/Iris) is probably one of the oldest datasets, dating back to 1936 .\n",
+ "\n",
+ "## What we're going to do :\n",
+ " - Retrieve the dataset, via scikit learn\n",
+ " - training and classifying\n",
+ "\n",
+ "## Step 1 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:15.073410Z",
+ "iopub.status.busy": "2021-01-14T07:11:15.073061Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.472037Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.471694Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
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+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
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+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
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+ "\n",
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+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : PER57\n",
+ "Run time : Thursday 14 January 2021, 08:11:16\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.datasets import load_iris\n",
+ "from sklearn.linear_model import Perceptron\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "\n",
+ "import os,sys\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('PER57')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Prepare IRIS Dataset\n",
+ "\n",
+ "Retrieve a dataset : http://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets \n",
+ "About the datesets : http://scikit-learn.org/stable/datasets/index.html \n",
+ "\n",
+ "Data fields (X) :\n",
+ "- 0 : sepal length in cm\n",
+ "- 1 : sepal width in cm\n",
+ "- 2 : petal length in cm\n",
+ "- 3 : petal width in cm \n",
+ "\n",
+ "Class (y) :\n",
+ "- 0 : class 0=Iris-Setosa, 1=Iris-Versicolour, 2=Iris-Virginica\n",
+ "\n",
+ "### 2.1 - Get dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:16.475780Z",
+ "iopub.status.busy": "2021-01-14T07:11:16.475428Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.495257Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.494915Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " vertical-align: top;\n",
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+ "\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>Length (x1)</th>\n",
+ " <th>Width (x2)</th>\n",
+ " <th>Setosa {0,1} (y)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.5</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>5.2</td>\n",
+ " <td>2.3</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>5.0</td>\n",
+ " <td>1.9</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>5.2</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>5.4</td>\n",
+ " <td>2.3</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>5.1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>150 rows × 3 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Length (x1) Width (x2) Setosa {0,1} (y)\n",
+ "0 1.4 0.2 1\n",
+ "1 1.4 0.2 1\n",
+ "2 1.3 0.2 1\n",
+ "3 1.5 0.2 1\n",
+ "4 1.4 0.2 1\n",
+ ".. ... ... ...\n",
+ "145 5.2 2.3 0\n",
+ "146 5.0 1.9 0\n",
+ "147 5.2 2.0 0\n",
+ "148 5.4 2.3 0\n",
+ "149 5.1 1.8 0\n",
+ "\n",
+ "[150 rows x 3 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x shape : (150, 2)\n",
+ "y shape : (150,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "x0,y0 = load_iris(return_X_y=True)\n",
+ "\n",
+ "x = x0[:, (2,3)] # We only keep fields 2 and 3\n",
+ "y = y0.copy()\n",
+ "\n",
+ "y[ y0==0 ] = 1 # 1 = Iris setosa\n",
+ "y[ y0>=1 ] = 0 # 0 = not iris setosa\n",
+ "\n",
+ "df=pd.DataFrame.from_dict({'Length (x1)':x[:,0], 'Width (x2)':x[:,1], 'Setosa {0,1} (y)':y})\n",
+ "display(df)\n",
+ "\n",
+ "print(f'x shape : {x.shape}')\n",
+ "print(f'y shape : {y.shape}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Train and test sets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:16.499313Z",
+ "iopub.status.busy": "2021-01-14T07:11:16.498843Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.501600Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.501270Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_train shape : (120, 2)\n",
+ "y_train shape : (120,)\n",
+ "x_test shape : (30, 2)\n",
+ "y_test shape : (30,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "x,y = pwk.shuffle_np_dataset(x, y)\n",
+ " \n",
+ "n=int(len(x)*0.8)\n",
+ "x_train = x[:n]\n",
+ "y_train = y[:n]\n",
+ "x_test = x[n:]\n",
+ "y_test = y[n:]\n",
+ "\n",
+ "print(f'x_train shape : {x_train.shape}')\n",
+ "print(f'y_train shape : {y_train.shape}')\n",
+ "print(f'x_test shape : {x_test.shape}')\n",
+ "print(f'y_test shape : {y_test.shape}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Get a perceptron, and train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:16.505354Z",
+ "iopub.status.busy": "2021-01-14T07:11:16.504910Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.513160Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.512822Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-- Epoch 1\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 120, Avg. loss: 0.305583\n",
+ "Total training time: 0.00 seconds.\n",
+ "-- Epoch 2\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 240, Avg. loss: 0.000000\n",
+ "Total training time: 0.00 seconds.\n",
+ "-- Epoch 3\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 360, Avg. loss: 0.000000\n",
+ "Total training time: 0.00 seconds.\n",
+ "-- Epoch 4\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 480, Avg. loss: 0.000000\n",
+ "Total training time: 0.00 seconds.\n",
+ "-- Epoch 5\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 600, Avg. loss: 0.000000\n",
+ "Total training time: 0.00 seconds.\n",
+ "-- Epoch 6\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 720, Avg. loss: 0.000000\n",
+ "Total training time: 0.00 seconds.\n",
+ "-- Epoch 7\n",
+ "Norm: 2.60, NNZs: 2, Bias: 5.000000, T: 840, Avg. loss: 0.000000\n",
+ "Total training time: 0.00 seconds.\n",
+ "Convergence after 7 epochs took 0.00 seconds\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Perceptron(max_iter=100, random_state=82, tol=0.01, verbose=1)"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pct = Perceptron(max_iter=100, random_state=82, tol=0.01, verbose=1)\n",
+ "pct.fit(x_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Prédictions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:16.521767Z",
+ "iopub.status.busy": "2021-01-14T07:11:16.517602Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.525501Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.525207Z"
+ }
+ },
+ "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>Length (x1)</th>\n",
+ " <th>Width (x2)</th>\n",
+ " <th>y_test</th>\n",
+ " <th>y_pred</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>4.0</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>4.4</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>5.7</td>\n",
+ " <td>2.1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4.4</td>\n",
+ " <td>1.2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5.6</td>\n",
+ " <td>2.4</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>5.6</td>\n",
+ " <td>2.2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>4.7</td>\n",
+ " <td>1.6</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>1.0</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>5.7</td>\n",
+ " <td>2.3</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>1.6</td>\n",
+ " <td>0.2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>3.9</td>\n",
+ " <td>1.2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>4.9</td>\n",
+ " <td>1.8</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>3.7</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Length (x1) Width (x2) y_test y_pred\n",
+ "0 4.0 1.3 0 0\n",
+ "1 4.4 1.3 0 0\n",
+ "2 5.7 2.1 0 0\n",
+ "3 4.4 1.2 0 0\n",
+ "4 5.6 2.4 0 0\n",
+ "5 5.6 2.2 0 0\n",
+ "6 1.3 0.2 1 1\n",
+ "7 4.7 1.6 0 0\n",
+ "8 1.0 0.2 1 1\n",
+ "9 1.3 0.2 1 1\n",
+ "10 5.7 2.3 0 0\n",
+ "11 1.6 0.2 1 1\n",
+ "12 3.9 1.2 0 0\n",
+ "13 4.9 1.8 0 0\n",
+ "14 3.7 1.0 0 0"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "y_pred = pct.predict(x_test) \n",
+ "\n",
+ "df=pd.DataFrame.from_dict({'Length (x1)':x_test[:,0], 'Width (x2)':x_test[:,1], 'y_test':y_test, 'y_pred':y_pred})\n",
+ "display(df[:15])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Visualisation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:16.538867Z",
+ "iopub.status.busy": "2021-01-14T07:11:16.532894Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.937457Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.937171Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/PER57-01-perceptron-iris</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def plot_perceptron(x_train,y_train,x_test,y_test):\n",
+ " a = -pct.coef_[0][0] / pct.coef_[0][1]\n",
+ " b = -pct.intercept_ / pct.coef_[0][1]\n",
+ " box=[x.min(axis=0)[0],x.max(axis=0)[0],x.min(axis=0)[1],x.max(axis=0)[1]]\n",
+ " mx=(box[1]-box[0])/20\n",
+ " my=(box[3]-box[2])/20\n",
+ " box=[box[0]-mx,box[1]+mx,box[2]-my,box[3]+my]\n",
+ "\n",
+ " fig, axs = plt.subplots(1, 1)\n",
+ " fig.set_size_inches(10,6)\n",
+ " \n",
+ " axs.plot(x_train[y_train==1, 0], x_train[y_train==1, 1], \"o\", color='tomato', label=\"Iris-Setosa\")\n",
+ " axs.plot(x_train[y_train==0, 0], x_train[y_train==0, 1], \"o\", color='steelblue',label=\"Autres\")\n",
+ " \n",
+ " axs.plot(x_test[y_pred==1, 0], x_test[y_pred==1, 1], \"o\", color='lightsalmon', label=\"Iris-Setosa (pred)\")\n",
+ " axs.plot(x_test[y_pred==0, 0], x_test[y_pred==0, 1], \"o\", color='lightblue', label=\"Autres (pred)\")\n",
+ " \n",
+ " axs.plot([box[0], box[1]], [a*box[0]+b, a*box[1]+b], \"k--\", linewidth=2)\n",
+ " axs.set_xlabel(\"Petal length (cm)\", labelpad=15) #, fontsize=14)\n",
+ " axs.set_ylabel(\"Petal width (cm)\", labelpad=15) #, fontsize=14)\n",
+ " axs.legend(loc=\"lower right\", fontsize=14)\n",
+ " axs.set_xlim(box[0],box[1])\n",
+ " axs.set_ylim(box[2],box[3])\n",
+ " pwk.save_fig('01-perceptron-iris')\n",
+ " plt.show()\n",
+ " \n",
+ "plot_perceptron(x_train,y_train, x_test,y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:16.940357Z",
+ "iopub.status.busy": "2021-01-14T07:11:16.939956Z",
+ "iopub.status.idle": "2021-01-14T07:11:16.943590Z",
+ "shell.execute_reply": "2021-01-14T07:11:16.943262Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:16\n",
+ "Duration is : 00:00:00 474ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/LinearReg/01-Linear-Regression.ipynb b/LinearReg/01-Linear-Regression.ipynb
index d47cb0a..607a88e 100644
--- a/LinearReg/01-Linear-Regression.ipynb
+++ b/LinearReg/01-Linear-Regression.ipynb
@@ -29,95 +29,9 @@
},
{
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- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "output_type": "stream",
- "text": [
- "Version : 1.2b1 DEV\n",
- "Notebook id : LINR1\n",
- "Run time : Sunday 10 January 2021, 21:51:29\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
@@ -140,7 +54,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -180,20 +94,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"width = 12\n",
"height = 6\n",
@@ -229,23 +132,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Theta :\n",
- " [[4]\n",
- " [2]] \n",
- "\n",
- "theta hat :\n",
- " [[3.60312881]\n",
- " [1.99988269]]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"theta_hat = np.linalg.inv(X.T @ X) @ X.T @ Y\n",
"\n",
@@ -261,20 +150,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"Xd = np.array([[1,xmin], [1,xmax]])\n",
"Yd = Xd @ theta_hat\n",
@@ -292,19 +170,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Sunday 10 January 2021, 21:51:29\n",
- "Duration is : 00:00:00 308ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/LinearReg/01-Linear-Regression==done==.ipynb b/LinearReg/01-Linear-Regression==done==.ipynb
new file mode 100644
index 0000000..104c225
--- /dev/null
+++ b/LinearReg/01-Linear-Regression==done==.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [LINR1] - Linear regression with direct resolution\n",
+ "<!-- DESC --> Low-level implementation, using numpy, of a direct resolution for a linear regression\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Just one, the illustration of a direct resolution :-)\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ "Equation : $ Y = X.\\theta + N$ \n",
+ "Where N is a noise vector\n",
+ "and $\\theta = (a,b)$ a vector as y = a.x + b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:10:57.300424Z",
+ "iopub.status.busy": "2021-01-14T07:10:57.300035Z",
+ "iopub.status.idle": "2021-01-14T07:11:00.143853Z",
+ "shell.execute_reply": "2021-01-14T07:11:00.143457Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "div.todo:before { 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+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : LINR1\n",
+ "Run time : Thursday 14 January 2021, 08:11:00\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import math\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sys\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('LINR1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve a set of points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:00.148557Z",
+ "iopub.status.busy": "2021-01-14T07:11:00.148123Z",
+ "iopub.status.idle": "2021-01-14T07:11:00.150555Z",
+ "shell.execute_reply": "2021-01-14T07:11:00.150183Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# ---- Paramètres\n",
+ "nb = 100 # Nombre de points\n",
+ "xmin = 0 # Distribution / x\n",
+ "xmax = 10\n",
+ "a = 4 # Distribution / y\n",
+ "b = 2 # y= a.x + b (+ bruit)\n",
+ "noise = 7 # bruit\n",
+ "\n",
+ "theta = np.array([[a],[b]])\n",
+ "\n",
+ "# ---- Vecteur X (1,x) x nb\n",
+ "# la premiere colonne est a 1 afin que X.theta <=> 1.b + x.a\n",
+ "\n",
+ "Xc1 = np.ones((nb,1))\n",
+ "Xc2 = np.random.uniform(xmin,xmax,(nb,1))\n",
+ "X = np.c_[ Xc1, Xc2 ]\n",
+ "\n",
+ "# ---- Noise\n",
+ "# N = np.random.uniform(-noise,noise,(nb,1))\n",
+ "N = noise * np.random.normal(0,1,(nb,1))\n",
+ "\n",
+ "# ---- Vecteur Y\n",
+ "Y = (X @ theta) + N\n",
+ "\n",
+ "# print(\"X:\\n\",X,\"\\nY:\\n \",Y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Show it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:00.163740Z",
+ "iopub.status.busy": "2021-01-14T07:11:00.160976Z",
+ "iopub.status.idle": "2021-01-14T07:11:00.462084Z",
+ "shell.execute_reply": "2021-01-14T07:11:00.461704Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/LINR1-01-set_of_points</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "width = 12\n",
+ "height = 6\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "fig.set_size_inches(width,height)\n",
+ "ax.plot(X[:,1], Y, \".\")\n",
+ "ax.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ "ax.set_xlabel('x axis')\n",
+ "ax.set_ylabel('y axis')\n",
+ "pwk.save_fig('01-set_of_points')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Direct calculation of the normal equation\n",
+ "\n",
+ "\n",
+ "We'll try to find an optimal value of $\\theta$, minimizing a cost function. \n",
+ "The cost function, classically used in the case of linear regressions, is the **root mean square error** (racine carré de l'erreur quadratique moyenne): \n",
+ "\n",
+ "$RMSE(X,h_\\theta)=\\sqrt{\\frac1n\\sum_{i=1}^n\\left[h_\\theta(X^{(i)})-Y^{(i)}\\right]^2}$ \n",
+ "\n",
+ "With the simplified variant : $MSE(X,h_\\theta)=\\frac1n\\sum_{i=1}^n\\left[h_\\theta(X^{(i)})-Y^{(i)}\\right]^2$\n",
+ "\n",
+ "The optimal value of regression is : $ \\hat{ \\theta } =( X^{-T} .X)^{-1}.X^{-T}.Y$\n",
+ "\n",
+ "Démontstration : https://eli.thegreenplace.net/2014/derivation-of-the-normal-equation-for-linear-regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:00.469467Z",
+ "iopub.status.busy": "2021-01-14T07:11:00.469091Z",
+ "iopub.status.idle": "2021-01-14T07:11:00.472673Z",
+ "shell.execute_reply": "2021-01-14T07:11:00.472310Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Theta :\n",
+ " [[4]\n",
+ " [2]] \n",
+ "\n",
+ "theta hat :\n",
+ " [[4.25724998]\n",
+ " [1.77417902]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "theta_hat = np.linalg.inv(X.T @ X) @ X.T @ Y\n",
+ "\n",
+ "print(\"Theta :\\n\",theta,\"\\n\\ntheta hat :\\n\",theta_hat)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Show it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:00.489250Z",
+ "iopub.status.busy": "2021-01-14T07:11:00.488870Z",
+ "iopub.status.idle": "2021-01-14T07:11:00.797293Z",
+ "shell.execute_reply": "2021-01-14T07:11:00.796952Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/LINR1-02-regression-line</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "Xd = np.array([[1,xmin], [1,xmax]])\n",
+ "Yd = Xd @ theta_hat\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "fig.set_size_inches(width,height)\n",
+ "ax.plot(X[:,1], Y, \".\")\n",
+ "ax.plot(Xd[:,1], Yd, \"-\")\n",
+ "ax.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ "ax.set_xlabel('x axis')\n",
+ "ax.set_ylabel('y axis')\n",
+ "pwk.save_fig('02-regression-line')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:00.800170Z",
+ "iopub.status.busy": "2021-01-14T07:11:00.799750Z",
+ "iopub.status.idle": "2021-01-14T07:11:00.803762Z",
+ "shell.execute_reply": "2021-01-14T07:11:00.803393Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:00\n",
+ "Duration is : 00:00:01 662ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/LinearReg/02-Gradient-descent.ipynb b/LinearReg/02-Gradient-descent.ipynb
index 0fdbc44..da61c70 100644
--- a/LinearReg/02-Gradient-descent.ipynb
+++ b/LinearReg/02-Gradient-descent.ipynb
@@ -43,116 +43,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
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- " margin-top:0;\n",
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- " color:#696969;\n",
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- "</style>\n",
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- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : GRAD1\n",
- "Run time : Wednesday 16 December 2020, 17:11:22\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : ~/datasets/fidle\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Regression Cooker**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- "output_type": "stream",
- "text": [
- "Version : 0.1\n",
- "Run time : Wednesday 16 December 2020, 17:11:22\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"import sys\n",
@@ -180,61 +73,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "### Dataset :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "output_type": "stream",
- "text": [
- "X shape : (1000000, 1) Y shape : (1000000, 1) plot : 1000 points\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-01-dataset</div>"
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X : mean= 5.004 std= 2.886 min= 0.000 max= 10.000\n",
- "Y : mean=-112.002 std= 77.303 min=-425.770 max= 188.861\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"X,Y = cooker.get_dataset(1000000)\n",
"\n",
@@ -250,18 +91,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X origine : mean= 5.004 std= 2.886 min= 0.000 max= 10.000\n",
- "X normalized : mean= 0.000 std= 1.000 min= -1.734 max= 1.732\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"X_norm = ( X - X.mean() ) / X.std()\n",
"Y_norm = ( Y - Y.mean() ) / Y.std()\n",
@@ -279,141 +111,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "### Basic gradient descent :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**With :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "with :\n",
- " epochs = 200\n",
- " eta = 0.01\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "**epochs :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " #i Loss Gradient Theta\n",
- " 0 +17.475 -8.118 +1.568 -3.978 -0.016\n",
- " 20 +8.002 -5.420 +1.047 -2.656 -0.271\n",
- " 40 +3.780 -3.618 +0.699 -1.773 -0.442\n",
- " 60 +1.899 -2.416 +0.467 -1.184 -0.555\n",
- " 80 +1.060 -1.613 +0.311 -0.790 -0.631\n",
- " 100 +0.686 -1.077 +0.208 -0.528 -0.682\n",
- " 120 +0.519 -0.719 +0.139 -0.352 -0.716\n",
- " 140 +0.445 -0.480 +0.093 -0.235 -0.739\n",
- " 160 +0.412 -0.320 +0.062 -0.157 -0.754\n",
- " 180 +0.397 -0.214 +0.041 -0.105 -0.764\n",
- " 200 +0.391 -0.143 +0.028 -0.070 -0.770\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Visualization :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-02-basic_descent</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
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- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
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- "data": {
- "text/markdown": [
- "<br>**Loss :**"
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- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-03-basic_descent_loss</div>"
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imUZh7CKvx+Lr759BTsALwIHqJh58eYvLVYmIyFBTGLts5LDcLoOBPLNyH0u3HnWxIhERGWoK4yRwzawxLJxaEZ//8bPrqKptdrEiEREZSgrjJGBZFl+5fgYVRTkANLaG+MFTa/S4k4hIhlAYJ4mCHD//9KFZeD3OC6+2HKrl4cXbXK5KRESGgsI4iZwzehh3Jjzu9MSy3by545iLFYmIyFBQGCeZGxdMZN7k4fH5//jzGg7VNLlYkYiInGkK4yTjsZzHncoKsgHn+vF3/7CKlvaQy5WJiMiZojBOQsV5WXz7ljnx4TL3HW/kR0+vIxKNulyZiIicCQrjJDV1VDF3Xzc9Pv/G1qM8/rrGrxYRSUcK4yR25YzR3DB/fHz+169sZ9m2KvcKEhGRM0JhnOQ+/Z5zmDG+ND7/wz+tYceROhcrEhGRwaYwTnI+r4dv3jibimJnQJDWYJhvP75SI3SJiKQRhXEKKMoN8L0PzyM/2wdATWMb//LYShpbgy5XJiIig0FhnCLGDi/gO7fMjd9hvf9EI//6xGqCGjJTRCTlKYxTyPnjSvnq9efH59ftrebHz+iRJxGRVKcwTjGXT6/kjoQhMxdvPMz9z20kqkAWEUlZCuMU9OGFk3jfnLHx+b+s3s+DL29VIIuIpCiFcQqyLIvPXzONK6ZXxtueXLabxzQoiIhISlIYpyiPZfG195/PRVMr4m2/fmU7Ty3f7WJVIiLSHwrjFOb1ePinD81i9sSyeNsvXtzCE8t2uViViIj0lcI4xQV8Xr5z8xzOGzMs3vbgS1vVZS0ikkIUxmkgO+Dj32+bz/SxJfG2hxdv47evbtdNXSIiKUBhnCZyAj6+95F5zJzQOY71I6/t4Je6y1pEJOkpjNNIdsDHv946j7mThsfbnli2mx8/u56QRuoSEUlaCuM0k+X38p1b5nDhWZ13Wb+47iD3/GEVre0hFysTEZHeKIzTUMDn5ds3z+aqmaPjbSt3Hufvf7uC2qY2FysTEZGeKIzTlNfj4SvvO5/bLp4cb9t2uJYvP7SU/ccbXKxMRES6UxinMcuyuP2yqXzhmvOwYm1HTjZz90NLWbnzmKu1iYhIJ4VxBrh+7ni+dfMcsvxeAJrbQnz78ZX8acUe3WktIpIEFMYZYuHZI/iv2xcwvDAbgEgUHnhhMz/5v/W0h8IuVyciktkUxhlk8sgi7vvkQs6pLI63Pb/2IF95aCmHa5rcK0xEJMMpjDNMSX42/+8TF3Z549POo/V84cHXWbr1qIuViYhkLoVxBgr4vHzjAzP4wjXT8HudfwJNbSG++8RqfvHiZnVbi4gMMYVxhrIsi+vnjuPHdyygoign3v7U8j18+VdL2afHn0REhozCOMNNHVXM/Z++mPlTyuNtu6qcbuunV+7V3dYiIkNAYSwU5gT47q1z+dxV58a7rdtDEey/beKbj63kWF2LyxWKiKQ3hbEA4LEsbpg/gfs/dTETygvi7at3HeeuB17l2VX7iOgsWUTkjFAYSxfjywu475MLufHCCfFRu1raw9z/3Eb+/jfLOVStR6BERAabwlhOEfB5uevKc/nxHQsYXZoXb9+wv4bP/OI1fvPKdtqCuuNaRGSwKIylV+eNKeG/73oXH144CY/lnCcHwxF+t2QHdz3wKsu3V7lcoYhIelAYy9sK+LzcefnZ/OyTCzlrVFG8/WhtC9/5/Sq+9dibeguUiMgAKYzltEweWcS9f7eQu6+bTkGOP97+5s7jfOYXS7jvrxs42ah3JYuI9IfCWE6bx7K4dvZYfmUu5drZY+M3eEWiUf6yej93/nwxv3ttB01tQVfrFBFJNQpj6bPC3AB3Xzedn3/6YmZOKI23t7SH+c2r27n9Z4t57PWdNLeFXKxSRCR1KIyl3yaNKOKHH72A731kHmPL8uPtDS1BHl68jU/8bBGPK5RFRN6Rz+0CJLVZlsW8yeXMnljGS+sP8djrOzlyshlwQvmhxdv44/Ld3HjhRK6bM67L9WYREXFYbow9bIyJAti2PeTHljMrFI7w8oZDPLpkB0druw6jme338t6Zo7lh/gQqS/J62YOISFqzemrUmbEMKp/Xw1Uzx3DF9MpTQrk1GOaZlft4duU+Ljyrgg9dOIHpY0uwrB7/bYqIZAyFsZwRiaG8eONh/rh8N3uOOc8jR4Fl26tYtr2KySMKuXb2WC6dNoq8LHVhi0hmUje1DIloNMravdU8tXw3b+48fsryLL+XS84dyTWzx3JOZbHOlkUkXambWtxjWRazJpQxa0IZ+0808qcVe3hp/UHaQxEA2oJhXlh3kBfWHWTc8HyunjmGS6eNoiQ/2+XKRUTOPJ0Zi2vqW9pZtOEQz711gL09DKnpseD88aVcPq2ShWePID9b3dgikvJ6PDNWGIvrotEo2w7X8txbB3hl02Fae3gjlN/rYe6k4Vw6bRTzJ5eTm6VOHRFJSeqmluRkWRZnVw7j7MphfOa95/Lq5sMs2nCIDftq6PhTMRiOxG/68ns9zBhfyoKpFSw4q4LSAnVli0hq05mxJK0T9a28uvkwizceZseRul7XO2tUEQvOqmDe5HImjSiMv+5RRCQJqZtaUtfB6kZe2XSEN7YeZXdVfa/rFeUGmDWhjNkTna/hhTlDWKWIyDtSGEt6qKptZvn2KpZur2LDvhrCkd7/DY8ty2fG+FKmjS1h+tgSdWmLiNsUxpJ+GlqCrNx5jFW7jrN693Fqm9rfdv1RJblMH1vC9LGlnDO6mFEleerWFpGhpBu4JP0U5Pi5fHoll0+vJBKNsqeqgbd2H2f17hNs3F9DMBzpsv7hmmYO1zTz/NqDAORn+5gyspipo4qYWlnM1FHFOnsWkSGnMJa04bEsJo0oZNKIQm6+aBKtwTCbD5xk4/4aNuyvZuuh2vggIx0aW0Os2XOCNXtOxNvKCrOZOqqYySMKGV9ewMTyQsqLc3QGLSJnjMJY0la23xu/kQugPRRmx5E6NuyrYdOBGrYdrqOu+dRu7RP1rZyoP8obW4/G23IDPsaXFzjhXFHAhPJCxpblU5gbGLLvR0TSl8JYMkbA5+W8MSWcN6YEcAYbqaprYduhWrYdrmXb4Tp2HKmjrYdBR5rbQ2w+eJLNB092aS/M8TO6NJ/RpXmMLs1nTGkeo0vzGFmSh9/rGZLvS0RSn8JYMpZlWYwozmVEcS6XnDcKgHAkwr7jjWw/XMueYw3OV1U99S3BHvdR3xLsMaQ9lkVFcQ4VRTmMKM6lorjr57D8LHV7i0icwlgkgdfjYWJFIRMrCuNt0WiUmsa2eDB3hPSh6kbaul2D7hCJRjlyspkjJ5uB6lOW+70eKopyqBiWS3lhNmUF2ZQUOJ+lBVmUFmRTmBtQYItkCIWxyDuwLIvSgmxKC7KZO2l4vD0SjXK8roWD1U0crG7kQHVTfPp4fevb7jMYjnCwpomDNU29ruPzOMctKchywjo/m6LcAMV5AYpyAxTlZVGcG6AoL0B+tl/BLZLCFMYi/eR0RedSUZzLnISQBmgNhqmqbaaqtoWjtc1U1bVw9GTss7aZhl66vROFIs417aq6ltOqJR7SuQGK87IozPWTn+2nINtPfo6f/KzYZ3bnV07Aq3dHiyQBhbHIGZDt9zJueAHjhhf0uLypLRgP6hP1rVQ3tFLd0EZ1Yysn6lupaWylsTV02seLxLrSaxrb+lSn12ORn+0nL9tHfraf3ICPnICP3Cwf2QFvfD4ny0duwOtMd5vPzfKR5fcS8HnxehTsIv2hMBZxQV6Wn4kV/i7XprtrbQ9R3dhGdYMT0Ceb2qhraqeuuZ3a5nbqmtqcz+Z2mttOP7gThSNR6mL7GAx+r4csv4eAz0uW30tWx6ffEw/sbL+XgM/TZXnA58Hn9eD3efB7O78S23xei4DPi89jJbR13cbrsXSmLylJYSySpLIDPipLfFSW5L3juu2hsBOqTZ1BXdcSpKk1SGNrkIbYdENsvqk1RENrsMfHuAYiGI7ERj3r3x8HA2WBE9BeD16vhdfjfPk8HjyervMd0x6Phc/bOe/1JE53rtuxfce6HsvCspxLBM4XeGJ/DHTMW5aznSe2nmVZsT8YOrfrso8elnm77bPzGLHv2bKwAOdvkM7tO34eie1Wx/qxaRK2tXAaPbE2EtaPrdq539j61ik1JO67c53EZSTuM/F3F5+xusxbdN0mXSmMRdJAwOdleGFOn99SFQxHnJBuCdLUFqS5LUxLe4jmthAt7R1fXdua28O0dFveGgwPerD3R5TEPwgk3XUGddeWxNzuLcy7h31iY+J+J5QX8pM7Lxqcgt+Gwlgkg/m9HorzsijOyxrwvqLRKMFwhNZgmPZghLZgmLZQuMtne9BZ3hYK0x7sDPGOAA2FowRDkfi80xYhGIp9hiO0hyKEIh1tUYLhcGybKBEXXnwj7un4bXf+2qNdF/S4dt8M1R+ZCmMRGRSW5VzTDfi84NJrpMORaDy0I5EooUiEcCTa+RVOmI8660YSlndZPxwlHIkQjsaWhaNEYstDkSjRaJRIFCKx6XA0SjQ2H4k6X9EoXabD8e2cbaPRaGx9Yut0zieu17EP51id8zj/4Uwmzkdjbc40CetEY/uOQuf6sXZ62LZzvY5te1ifjkB06u2yfuzg8To76kn4vXUP0/i+B/efR78MVe+4wlhE0oZznde5KUzSU/cw7y3Iu67TNdZ7DPte9jtUDwgojEVEJGWccvPXqRMpydUwNsa4eXgREZGhFrVt+5S/HPRaGREREZdZ3fvSRUREZGjpzFhERMRlCmMRERGXKYxFRERcpkebROQUxphLgcXAd23bvsfdakTSn8JYZACMMVGAxEcVjDHjgT3Ar23bvsOdyt5eKtQokkkUxiLSkzeBc4ATbhcikgkUxiJyCtu2m4Gtbtchkin0nLHIAHTvpjbG3AN8p5fV77Rt++GEba8C7gbmAwXAQeAp4N9t267tdpy9scnzgXuADwGVsXXvMcaMAj4FXAVMAkpwzmpfAf7Ntu0tCft6xxrf7pqxMWYK8C3gCmB47DgvxY6zo9u6Hce6DCgD/h6YBrQCLwBfs237ULdtJgL/CFwe+x5bgEPAG8A3bduu7qV2kZSlu6lFBtcrwL2x6XXAdxO+1nasZIz5NvA34ALgL8B9wE7g68AbxpjCHvYdABYBN+AE2b04130B3o0TYLXAH4GfAMuBm4CVxpgZfa2xJ8aYecAq4GPASuA/Y8f5KLDKGDO3t02BR4C9wM+BjcCtwEvGmPj7G40xI2P7vRPYhPNz+W3s+/w4MPLt6hNJVeqmFhlEtm2/EjuLvRtY29OdyMaYy3CCbxlwbeJZsDHmDuCh2PKvdNt0JLAZuMS27aZuyxYBFbZtN3Q71gycM8ofAtecbo09McZYwG+AQuBjtm3/LmHZrcDjwCPGmHNt24502/xqYJ5t2xsStnkU+AjwAeAPseabcM7qv2zb9r2JOzDG5AHd9yuSFnRmLDL0vhT7/HT37uhYN/ZanDPNnnythyDGtu1j3YM41r4OJ6gvM8b4B1AzwEXA2cCyxCCOHef3wOvAVODiHra9LzGIY/439jm/h/VbujfYtt1k2/Yp7SLpQGfGIkNvARAEbjbG3NzD8gAw3BhT2u36aCuwvredGmOuAz4LzMW5Ptv9/+8y4MgA6p4d+1zUy/JFOEE8C3it27JVPax/IPY5LKHtGeD7wM9j19Sfxzmz32zbtm5wkbSlMBYZeqU4/+/1dhNVh3wgMYyP9RZIxpgv4VwHPgm8COwHmnHelX4DMAPI6mnbPiiKffYW6B3txT0sq+2hLRT79HY02La9zxgzH+cmtatxblQDOGCM+U/btu/rQ70iKUNhLDL06gCPbdslfdyutyD24VxjPgrMtm37SLflC/pV5anqYp8jelk+stt6/RK78/vW2Pc1A3gP8EXgXmNMk23bvxzI/kWSka4Ziwy+cOzT28vy5cAwY8x5g3S8Mpyz0aU9BHE+nd3LfamxJ2tin5f2sryj/a0+7LNXtm2HbNtebdv2f+Dc6AXOWb5I2lEYiwy+kzhnsWN7Wf6T2Of/xp4P7sIYk2eMubAPxzuG0yU9Jxa+Hfvx43Rdl/Wjxp68AWwDLjbG3NSt5ptwHq/ajnMjV78YY+YbYyp6WNTR1tzffYskM3VTiwwy27YbjTErgHcZY36HE1Bh4Bnbttfbtv2yMeYfgR8AO4wxf8V5jjYfGAdcghNoV5/m8SLGmPtwnjPeYIx5GucmsMtwHhNaHJs+7Rp7OU7UGHM7zjXp38eOsxXnDuobgAbgEz081tQXtwGfN8a8ivPc9UmcQUyuB9qAnw5g3yJJS2fGImfGx3EG87ga50atfyOhuzjW9fru2DoLgS8DN+OMOPU/wL/08XjfAr6G80jQZ3BufFqF89jQ/v7U2BPbtlcA84BHce4K/wbOI0+P4TxHvKKPdXf3GPAgzshet+D8XGbjPMM817btZQPcv0hS0nCYIiIiLtOZsYiIiMsUxiIiIi5TGIuIiLhMYSwiIuIyhbGIiIjLFMYiIiIuUxiLiIi4TGEsIiLiMoWxiIiIy/4/vbgitVT36c0AAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"theta = cooker.basic_descent(X_norm, Y_norm, epochs=200, eta=0.01)"
]
@@ -427,161 +127,18 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "### Mini batch gradient descent :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**With :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "with :\n",
- " epochs = 10\n",
- " batchs = 20\n",
- " batch size = 10\n",
- " eta = 0.01\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "**epochs :** "
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " #i Loss Gradient Theta\n",
- " 0 +0.313 +3.019 +0.971 +0.071 -0.747\n",
- " 1 +0.229 -4.993 +1.014 -0.033 -0.825\n",
- " 2 +0.210 -0.911 +3.999 -0.022 -0.795\n",
- " 3 +0.237 -1.041 -4.380 -0.022 -0.796\n",
- " 4 +0.339 -1.175 +1.106 -0.002 -0.822\n",
- " 5 +0.937 +3.404 -0.109 +0.011 -0.808\n",
- " 6 +0.460 +5.775 -2.128 +0.025 -0.814\n",
- " 7 +0.717 +10.079 +7.425 +0.004 -0.818\n",
- " 8 +0.584 -6.748 -7.090 +0.005 -0.795\n",
- " 9 +0.622 -1.218 -7.436 -0.001 -0.784\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Visualization :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-04-minibatch_descent</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
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- },
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- "output_type": "display_data"
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- {
- "data": {
- "text/markdown": [
- "<br>**Loss :**"
- ],
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/GRAD1-05-minibatch_descent_loss</div>"
- ],
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- "<IPython.core.display.HTML object>"
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- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"theta = cooker.minibatch_descent(X_norm, Y_norm, epochs=10, batchs=20, batch_size=10, eta=0.01)"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 16 December 2020, 17:11:26\n",
- "Duration is : 00:00:04 240ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/LinearReg/02-Gradient-descent==done==.ipynb b/LinearReg/02-Gradient-descent==done==.ipynb
new file mode 100644
index 0000000..2ddbe8d
--- /dev/null
+++ b/LinearReg/02-Gradient-descent==done==.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [GRAD1] - Linear regression with gradient descent\n",
+ "<!-- DESC --> Low level implementation of a solution by gradient descent. Basic and stochastic approach.\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "\n",
+ "## Objectives :\n",
+ " - To illustrate the iterative approach of a gradient descent\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ "Equation : $ Y = X.\\Theta + N$ \n",
+ "Where N is a noise vector\n",
+ "and $\\Theta = (a,b)$ a vector as y = a.x + b\n",
+ "\n",
+ "We will calculate a loss function and its gradient. \n",
+ "We will descend this gradient in order to find a minimum value of our loss function.\n",
+ "\n",
+ "$\n",
+ "\\triangledown_\\theta MSE(\\Theta)=\\begin{bmatrix}\n",
+ "\\frac{\\partial}{\\partial \\theta_0}MSE(\\Theta)\\\\\n",
+ "\\frac{\\partial}{\\partial \\theta_1}MSE(\\Theta)\\\\\n",
+ "\\vdots\\\\\n",
+ "\\frac{\\partial}{\\partial \\theta_n}MSE(\\Theta)\n",
+ "\\end{bmatrix}=\\frac2m X^T\\cdot(X\\cdot\\Theta-Y)\n",
+ "$ \n",
+ "\n",
+ "and : \n",
+ "\n",
+ "$\\Theta \\leftarrow \\Theta - \\eta \\cdot \\triangledown_\\theta MSE(\\Theta)$\n",
+ "\n",
+ "where $\\eta$ is the learning rate\n",
+ "\n",
+ "## Step 1 - Import and init\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:01.842624Z",
+ "iopub.status.busy": "2021-01-14T07:11:01.842302Z",
+ "iopub.status.idle": "2021-01-14T07:11:03.232629Z",
+ "shell.execute_reply": "2021-01-14T07:11:03.232241Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "div.todo:before { 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+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : GRAD1\n",
+ "Run time : Thursday 14 January 2021, 08:11:03\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Regression Cooker**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 0.1\n",
+ "Run time : Thursday 14 January 2021, 08:11:03\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import sys\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "from modules.RegressionCooker import RegressionCooker \n",
+ "\n",
+ "# ---- Init Fidle stuffs\n",
+ "#\n",
+ "datasets_dir = pwk.init('GRAD1')\n",
+ "\n",
+ "# ---- Instanciate a Regression Cooker\n",
+ "#\n",
+ "cooker = RegressionCooker(pwk)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Get a dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:03.235242Z",
+ "iopub.status.busy": "2021-01-14T07:11:03.234924Z",
+ "iopub.status.idle": "2021-01-14T07:11:03.646303Z",
+ "shell.execute_reply": "2021-01-14T07:11:03.645964Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "### Dataset :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X shape : (1000000, 1) Y shape : (1000000, 1) plot : 1000 points\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/GRAD1-01-dataset</div>"
+ ],
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X : mean= 5.000 std= 2.887 min= 0.000 max= 10.000\n",
+ "Y : mean= 94.953 std= 58.567 min=-100.958 max= 286.268\n"
+ ]
+ }
+ ],
+ "source": [
+ "X,Y = cooker.get_dataset(1000000)\n",
+ "\n",
+ "cooker.plot_dataset(X,Y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 : Data normalization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:03.650294Z",
+ "iopub.status.busy": "2021-01-14T07:11:03.649859Z",
+ "iopub.status.idle": "2021-01-14T07:11:03.669216Z",
+ "shell.execute_reply": "2021-01-14T07:11:03.668834Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X origine : mean= 5.000 std= 2.887 min= 0.000 max= 10.000\n",
+ "X normalized : mean= 0.000 std= 1.000 min= -1.732 max= 1.732\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_norm = ( X - X.mean() ) / X.std()\n",
+ "Y_norm = ( Y - Y.mean() ) / Y.std()\n",
+ "\n",
+ "cooker.vector_infos('X origine',X)\n",
+ "cooker.vector_infos('X normalized',X_norm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Basic descent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:03.672533Z",
+ "iopub.status.busy": "2021-01-14T07:11:03.672189Z",
+ "iopub.status.idle": "2021-01-14T07:11:06.363778Z",
+ "shell.execute_reply": "2021-01-14T07:11:06.363491Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "### Basic gradient descent :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**With :** "
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "with :\n",
+ " epochs = 200\n",
+ " eta = 0.01\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**epochs :** "
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " #i Loss Gradient Theta\n",
+ " 0 +12.189 -6.690 -1.775 -3.278 +0.018\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 20 +5.551 -4.466 -1.185 -2.189 +0.307\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 40 +2.592 -2.982 -0.791 -1.461 +0.500\n",
+ " 60 +1.273 -1.991 -0.528 -0.975 +0.629"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 80 +0.685 -1.329 -0.353 -0.651 +0.715\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 100 +0.423 -0.887 -0.235 -0.435 +0.772\n",
+ " 120 +0.306 -0.592 -0.157 -0.290 +0.810\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 140 +0.254 -0.395 -0.105 -0.194 +0.836\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 160 +0.231 -0.264 -0.070 -0.129 +0.853\n",
+ " 180 +0.221 -0.176 -0.047 -0.086 +0.865\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 200 +0.216 -0.118 -0.031 -0.058 +0.872\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**Visualization :**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/GRAD1-02-basic_descent</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**Loss :**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/GRAD1-03-basic_descent_loss</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "theta = cooker.basic_descent(X_norm, Y_norm, epochs=200, eta=0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Minibatch descent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:06.366958Z",
+ "iopub.status.busy": "2021-01-14T07:11:06.366581Z",
+ "iopub.status.idle": "2021-01-14T07:11:06.894218Z",
+ "shell.execute_reply": "2021-01-14T07:11:06.893876Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "### Mini batch gradient descent :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**With :** "
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "with :\n",
+ " epochs = 10\n",
+ " batchs = 20\n",
+ " batch size = 10\n",
+ " eta = 0.01\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**epochs :** "
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " #i Loss Gradient Theta\n",
+ " 0 +0.314 -7.285 -4.722 -0.016 +0.877\n",
+ " 1 +0.127 -0.315 +1.544 +0.024 +0.852\n",
+ " 2 +0.276 -1.804 +2.065 +0.025 +0.851\n",
+ " 3 +0.196 +2.278 -0.588 -0.005 +0.855\n",
+ " 4 +0.125 -0.103 +0.501 -0.012 +0.861\n",
+ " 5 +0.261 -2.193 -1.318 -0.019 +0.862\n",
+ " 6 +0.189 -1.075 -5.843 -0.015 +0.870\n",
+ " 7 +0.135 -0.662 -5.560 -0.023 +0.873\n",
+ " 8 +0.243 +5.518 -4.847 -0.012 +0.872\n",
+ " 9 +0.170 +3.961 -1.732 -0.005 +0.888\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**Visualization :**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/GRAD1-04-minibatch_descent</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**Loss :**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/GRAD1-05-minibatch_descent_loss</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "theta = cooker.minibatch_descent(X_norm, Y_norm, epochs=10, batchs=20, batch_size=10, eta=0.01)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:06.896957Z",
+ "iopub.status.busy": "2021-01-14T07:11:06.896553Z",
+ "iopub.status.idle": "2021-01-14T07:11:06.900745Z",
+ "shell.execute_reply": "2021-01-14T07:11:06.900449Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:06\n",
+ "Duration is : 00:00:04 672ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/LinearReg/03-Polynomial-Regression.ipynb b/LinearReg/03-Polynomial-Regression.ipynb
index 645f061..1cf5aa8 100644
--- a/LinearReg/03-Polynomial-Regression.ipynb
+++ b/LinearReg/03-Polynomial-Regression.ipynb
@@ -24,96 +24,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
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- "div.todo:before { 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- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
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- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : POLR1\n",
- "Run time : Wednesday 16 December 2020, 17:48:01\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : ~/datasets/fidle\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
@@ -137,110 +50,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "#### Generator :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nomber of points=100 deg=7 bruit=2000\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Datasets :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "100 points visibles sur 100)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-01-dataset</div>"
- ],
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- ]
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- "metadata": {},
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "#### Before normalization :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X : mean= +0.3946 std= +2.7857 min= -4.7119 max= +4.9613\n",
- "Y : mean= +944.8316 std= +2687.1875 min= -4221.2450 max= +11553.9320\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### After normalization :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_norm : mean= -0.0000 std= +1.0000 min= -1.8331 max= +1.6393\n",
- "Y_norm : mean= +0.0000 std= +1.0000 min= -1.9225 max= +3.9480\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Parameters\n",
"\n",
@@ -317,7 +129,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -337,39 +149,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de degrés : 1\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-02-underfitting</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"reg_deg=1\n",
"\n",
@@ -389,39 +171,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de degrés : 5\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-03-good_fitting</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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VKDwEennvz6sK9v2BUD5R85wiWZKkxlbpOQEO23Eqm4VffC9/hxNOhvkvKMtYGkUxgfB2YFrB9ulAD/DHgn1ZoC4a2BVVsC5JkoaskhegjUQuQ3twUqyccwIM2nHqoXvgsd6T8+k0vO1DZRtHoygmEH4UeFMURV8AMoSa4WVxHBeeHzgG2Fi64SXHKZIlSSq9WjnjmgvWX3DUFI6bPZl0UxPzkgzaM5m+2eDTXg8zn1P5cdSZYgLhrwNXA08B3cAY4DO5X0ZRlAZOoW+GuGY5RbIkSaVXC2dcBwrWzztlXnLB+h3Xw8Z1Yb1tDJx9fjLjqDNDvlgujuNfEzpCPAw8Bvx9HMc/KbjJqwllETeUdIQJcYpkSZJKr9IXoA1HpbtFHFbHPvhVQcj1+nfC+EnJjKXOFNV0Lo7j7wDfGeB3NxBaqdUFp0iWJKn0auGMa9WVR17/S9i9I6xPngavfkvlx1Cn7L48CKdIliSptJK4AK1YVRWs79gKN16R337z+2FUa+XHUacGDISjKDqqd3V9HMeZgu3DiuN47YhHJkmS6k4tnHGtqmD9l9+HA51hffYceNkZlR9DHRssI7yG0A7tWGBFwfbhZA/zuJIkqYFV+xnXqgnWV/y57+QZ514ATemBb6+iDRaw/ogQ1O46aFuSJKmuJR6sZzLw0zi/feJpsPBFyYyljg0YCMdx/IHBtiVJklQmt10LTz0R1ke1wjs/kux46tSQ26dJkiSpAvbsgqt/lN9+/btgyvTkxlPHhhwIR1F06hBvd+HwhyNJktTgrvoh7Nsb1qcfAa95W6LDqWfFZIR/H0XRPwz0yyiKJkdR9Cvg30Y+LEnlkunJcveKTVx6++PcvWITmR5L/yWpaqxZAX+4Pr/9ro9By6jkxlPniunusBL4chRFrwDeE8fxxtwvoig6GbgMmA1cVdIRSiqZgaYNvfj8xVXVukiSGlJPT7hALtuboHjhInjR4mTHVOeKyQi/FPgJcAZwfxRFZwJEUfQ54FZgBvCJOI7N30tVquqmDZUk5d11M6xeHtabW0I2WGU15EA4juN9cRy/H/gAMA74bRRFDwFfAVYBi+M4jgd5CEkJG2zaUElSgvY9A1f8d377rLdC+6zkxtMgiu4aEcfxj4DP9t73BcBW4LQ4jh8q8dgklVhu2tBCiU0bKknKu/p/YPeOsD55WugU0Q+v8yitomaAi6KoCbgI+AywF3gIOBm4NYqid8Vx/OfSD1FSqVTVtKGSpGDVo3DLNfntd3wE2kYfcjOv8yi9YtqnzQZuBz4H/Bk4MY7jU4AvAPOBpVEURWUZZan09MC1l8PT65IeiZSI3LShn3vrCbzvFQv43FtP8ANUkpLU3Q0/+nr+ArkXLIJFp/V7U6/zKL1iSiMeIGR//xNYEsfxCoA4ji8BXgFsAb4ZRdGVJR5jaex/BuKLQm++//hyqMWRGlBu2tB3nzqfJQvaDYIlKUk3/BLWrwnro1rhPR+HVP+fy17nUXrFBMJNwNvjOP54HMedhb+I4/iPwIuAXwPnlHB8pbN1EzxyX1jf+BR8/19ChliSJCkJm9bDNZfmt9/8Ppg2c8Cbe51H6RUTCJ8Qx/GA2d44jnfGcfwWoDpnlps9B97/t/ntB5f2ffFJkiRVSjYLP/4GdHeF7aPnw6vePOhdctd5tLWkSQFtXucxYqlstvJXG0ZRlAVIpNvaz78LN16R3/74P8IJJ1d+HJIkqXHdeSP8oHcy3qYm+IdvwFHzDnu3TE+WZSs3s3rTbua0T2DRvBmWuA1Nv3+kotun1by3fQiOPSG//b1/hQ1PJjceSZLUWHbvDIm5nDPfMqQgGLzOo9SKbZ+WAt4OvAaYBbT2c7NsHMevKsHYyiOdhgs+B1/5JGzdCJ374T/+D3zh6zBmXNKjkyRJ9e5n34Zn9oT1ae1w9nuTHU8DG3IgHEVRK3AdoUNECsjSN82cLdhf3cZNCCURl3wKDnSGYvXv/Qt84p/C6QlJkqRy+MufYOkt+e33/A20tiU3ngZXTNT3GeCVhCmVpxOC3n8CjgTeDawDLgdGlXaIZTJ7Dnzg0/nth+6BX//YGVskSVJ57NsL//Pv+e3Fr4QXnJjYcFRcacQ7gPviOP4SQG7ujDiONwKXR1F0D6HX8N8C/6+koyyXk06HtSvh+l+E7d9cxi9WdnBZ99HO2CJJkkrrsv+EHVvD+riJcO4FyY5HRWWE5wJ3FmxngZbcRhzHq4FrgQ+UZGSV8tYPwPNf8uzm25f/imN3P+GMLZIkAM8UqjTuuxPuujm//d6/gQmTEhuOgmIC4S6go2B7D6FEotCTwJyRDqqimtJwwedh1jEANNPDF7dcx5wDWwBnbJGkRpbpyfL5S5dyyZX38+PbVnDJlffz+UuXGgyrOHt2hp7BOUvOgJeekthwlFdMIPwUoVNEzgrgZQfd5gRg+0gHVXFjxsGFF9E5bjIAY7NdXLT5GqZ373HGln6YHZHUKJat3Mzy9Tvp6Mp4plDDk83Cj78Je3aF7UlT4by/TnZMelYxNcJ3Aq8u2L4a+EoURd8HriR0k3g18NNSDa6ipkyn+dNfpeOrn6It08m0zDNcvOXX/PeJH3HGlgK57Mjy9Tuto5ZU91Zt3E1nV6bPvtyZwiUL2hMalWrK0ltCWUTOBz4FY8cnNx71UUxG+KfAY1EUHdO7/e/AMuCDwK+BvwNWAZ8t4fgqKn3UHFou/DI9TWEe76MObOdLW68jnelKeGTVw+yIpEYyd+YEWlvSffZ5plBDtmMr/LRgFt3TXz9olwjPuFbekDPCcRzfCtxasL0viqKXA+cA84A1wDVxHO8r7RArK33ci+Gv/h6++88ApFb8Gb7/NfjoZ+0xjNkRSY1l0bwZLJw16ZCzYJ4p1GFls6FV2r69YXvaTHjHhwe8uWdck1HUzHIHi+O4G7iiRGOpHotfCdu3whXfD9t/uh3GjA1Nrxs8GM5lRzoKgmGzI5LqVbopxcXnL2bZys2s3rSbOe0TWDRvhoGJDu/268LkGQCpFHzw09A2ZsCbF55xhb5nXE00lU9jR3WDee3b4Yyz89u3/zb0/8s29mmKXHakrSVNCmgzOyKpzqWbUixZ0M67T53PkgXtBsE6vPVr4GffyW+/+s3wvOMHvctgZ1xVPiPKCNe1VAredQHsfybf9++WayDdDOd+NPy+AZkdkSRpEAc64duXhJ8ARx4Fb/nAs7/O9GRZtnIzqzbuZu7M/HeoZ1yTYSA8mKZ0OJWRycA9t4Z9N10F6TS8/a8aOhhesqDdUzWSJB3sZ9+GDU+G9VGtYa6CUa3A4HXA1qMnw0D4cJrS8Ff/CzLdcO8dYd8NvwyZ4be8v2GDYdWvgbIVkqTD+NPtcNt1+e13fezZCbvg8HXAnnGtPAPhoUin4SOfhZ6L4f4/hn3XXQ7NzXD2e5Idm1RCXrUsScO0ZSP8z9fz2yeeBqe+ts9NDtd5yTOulefFckPV3AwXfA6OPym/79c/gat+2PAX0Cl5peo9aZ9oSfWioj15u7vhu/83XFcEMK0d3nfhIWeN7UtdfYacEY6iaEYcx439bdjcAn/9D/CtL8PD94Z9114Oe3fD+R8PZRTSYZS69KCUWVz7REuqBxU/u3X1j2D18rCeTsNHPxfarh7EOuDqU0xpxLooiq4Gvh3H8e/LNJ7q1zIKPv6P8J9fgT8vC/tuuy4Ewx/+3+H30gDK8eFcyt6TXrUsqR5UtCfvw/fC9T/Pb7/lAzBnYb83tfNS9SmmNGIF8A7gd1EUrYii6O+iKJpapnFVt1Gt8PEvwZIz8vvuvQO+8Y/QUdMT66nMylF6MJLekwefOnzJnOn2iZZUNYZb3lCxnrzbNsN3/yW//fyXwllvG/Qu9qWuLsVMsfzCKIpOBj5KCIj/FfhKFEVXErLEt5dpjInr91R2czN86O9h3AS46epww0cfgK99Fi68CMZPTHLIqlLlKD0YbhZ3oOz0ReedxH2rt5itkJSokZxBq8jZrQOdEF8Ee3eF7YmTQ5epBp+BttYU1TUijuM/An+MouhC4H2EoPg84F1RFK0A/gv4URzHO0o+0oQM/kZsgnMvgHET4er/CXdYswL++e/gU1+FqdZUqq9yfDgPt+ZsoFOH963e4lXLkhI3kvKGstfiZrPwk2/Bk4+H7XQaPvYFmDCpNI+vihlW+7Q4jncB3wS+2Zsl/gjwTuDfgEuiKPo58K04jv9UspEm5LBvxFQK3ngejJ8Q3hTZLGx8Cr5yIURfhPnPT/hfoGpSjg/n4daceWGcpGo2ks+ostfi3vob+OPv8tvnXgDzX1Cax1ZFlaKP8DZgB9ABjAZGEbLF742i6BrgQ3Ecby/B8yRiyG/E098AY8eHWqFMN+zZCV/7DLz3k3DKWZUdtKpWuT6ch9N70gvjJFWzkX5Gla0n7+N/gcv/K7998qvhlW8q7XOoYoYVCEdR1AK8DbgAOA1IES6muwj4IfBi4H8DZwP/QSifqElFvRFPPA0mTMnXDGW64Yf/BuufgLd/OJw6UcOrlobptvGRVM2q8jNq5zb4r69CpjcmOHo+vOdvnGW2hhUVCEdRNI9QF/wBYCqQAa4G4jiOby646a3ArVEU/RLoO61KjSn6jbjgBfAPXw+9hp96Iuz73VWwYW2YkGPMuMoNXhqEbXwkVbOq+4zqOhBap+7qvQxq3MRQAjmqNZnxqCSKmVDjJuCVhOzvBkL29ztxHG8Y5G73Am8Z0QgTNqw34rSZ8Nl/g+//a35K5ofvha9eCJ/4JzhidkXGLh1OtWSnJak/uc+oRfNmsGzlZi6/Y2VJJiIqWjYLl/0nrHo0bKeaQnJr6tCz06WeTEmlUUxG+AzgFiAGro7jOHOY2wNcQwiaa9qwgoW20WEWul//GH5zWdi3aT185W/gPZ+Al726PIOVJKmOVHyWuP787iq4/bf57bf/FRz74iHfvSr+DepXMYHwsXEcP1bMg8dx/BfgL8UNqY40NcGb3w9HHhNqhQ90QmcHfP9r8MgDYVrmttFJj1KSpKpV0Vni+nPfnfCL7+a3T3oFnPXWoh4i8X+DBjTkrs/FBsEqcNLp8Ln/D2Y+J7/vrpvgok/A2pXJjUtVZ7izKElSvarYLHH9Wf0YfO9fQmkEwNzj4IOfLvriuET/DRpUKdqnaShmz4F/+Cb8NM73Hty0Hi7+FLzjw3DG2V512uA8dSZJh0qs1ePWjfDNL4WzuQDTj4BPfAlaRhX9ULarrF7OA1hJbaPhQ38XpmBsbQv7urtCAf43vwQ7tiY7PiWq8NRZlr6nzqRq49kLVUque1NbS5oU0FaJNmr79sI3/jHMCQCh49OFF8H4icN6uET+DRoSM8JJeNmr4LnPg+9cAmtXhX0P3QP/+FF4x0fg1NcOOzvsVanlVc6/rzO9qVZ49kKVVPE2at3doU3ahrVhu7klZIILyxuLVHWt4PQsA+GkzHxOqBu+4gdw01Vh3/598KOvwz23wfsvDKdhiuCXU3mV++/rqbP6Us8HpV74o0qrWKvHbBZ+8g149IH8vg98Cha8cMQPbbvK6tRQpRFVdyqvZRS86wL431+D9ln5/csfgC99LLRr6RlKl7qgXKfWq+7vlpByly546qx+5A6aLrnyfn582wouufJ+Pn/p0rp573jhj+rWFT+AO27Mb5/zXlhyRnLjUdk1TEa4qrOlC14AX4rh1z+BG66AbE8ozv/Zt2HpLXDeX8PcYw/7MOU4tT6Sv1u9ZcTKXbrgqbP6Ue8ZU89eqC5d9zO4/uf57ZNfDW98d3LjUUU0TCBc9V9Mo1pDg+4TT4Uf/BusXxP2r1kBl3wqHJG+7UMwedqAD1GOL6fh/t2q+sBjmCrx5e+ps/pQ7/XeRU89L1W7W34DV/4gv/2ixfC+vx30ep16S/Y0qoYJhGvmi+mYBfDFb8Jvfw7XXh66SgDc/fvQ1Pv158JZb+t3bvNyfDkN9+9W9Qcew+CXv4aq3jOmnr1QXbn79/DT/8hvL3wRfOwL0DxwiFSPyZ5G1TCBcE19MTW3wJvOD1ngX3wvBMAQyiWu/hH84Xp4W2/2uClf5l2OL6fh/t1q5sCjCH75a6iq8aCp1Nkrz16oUsqaeX3gLvjvr+UnzHju84bUK7gekz2NqmEC4Wr8Yjqs6UdA9EVY/iBc/l/w1BNh/7bNofXatZfB2e+BE05+NiAu9ZfTcP9u/QXQzekm1m7dy90rNtVsAOmXv4ai2g6azF6pVhW+dju6MrSkm5g6vpULzjqOxfPbR/b6ffQB+K+LoacnbM86Bi78CrSNOexd6zHZ06hS2Wzlr2KOoigLEMdxRZ83d1RZDV9MRevJhEzwVT+Cvbv6/m72HDj7vfDiJSOanW6go+7h/N0O/vBKpSBFOOj2S1iqrLtXbOKSK+/vc2Da1pLmc289wS9tVbX+XrsAo5qbOO45k4f/PfL4X+Dfvwid+8P29CPgM1+DSVOHPS7fU1Wv3xdKw2SEocazeU1pOP0NsOj0UD/8+19DZ0f43brV8B9fhqPmwdnnw/GL+5RMDMXhMkbF/t0KM2K3PbyBO5Zv5EB3OOr2FJJUWWavVKv6e+0CHOjuGf73yPIHw6xxuamTJ0+Dv7tkyEEw1OhZZvWroQLhujBmXOgecdZb4fpfwi3X5N/Ma1fCt74cJut41ZtD65fcVM6HUY56p1wAvWrjbrp6g+CcoX4Je1WuNHI1dY2EVKC/127OsA7mHr43fE92HQjbEyfDpy6GaTOLGle1lT9p+AyEa9X4SfCOD4cOEtf/Am79Tf6NvfEpuPRbcNUP4bTXwxlvginTB324cmaMhvslbF2jVBpmr1Srcq/dh9ftoCvTN6FS9MHcQ/dAfFG+G9PkafB3/3fYUyfX9FlmPctAuNZNnAznfhRe+/YwGccffhumagbYtzc0B//dFfDSU+DU18Hzju+3bKKcGaPhfgl7Va5UGvWcvfKsUX3LvXaXrtjEt3/3CNv3dtLV3VP8wdz9fwwXxmW6w/aUGfD3/xdmHFm+wasmGAjXi4lT4J0fCTXCd9wIN/8KtjwdfpfJwD23hWVaO7zs1fDyM/ucCipnxmi4X8LWNUqlU4/ZK88aNYZ0U4qTF85k8YL24R3M/el2+O4/h+9CCN99/+ufYWr9vBc0fAbC9aZtDLz6zaEc4sF74HdXwoo/53+/dRNcc2lYFr4oBMQnnEy6bUxZM0bD+RK2rlHSYDxr1FiGdTB3xw3wo6/nW6S1zwrlEIcpF1TjMBCuV01pOOFlYVm7KnwY3P37UC6Rs/zBsDS3wPNfQvqlp7LkRYur5gvEukZJgxnJWSNLKmrXkP7vsln4zU/hVz/O75s5O5RDFNEdQvXPQLgRHDUX3h2Fi+seuBvuvBEevg+yvUfI3V3w4NKwpNOw8MWhpvj4kxL9wKjnukZJI+eFuI1nSP93mUy4YPz23+bvOHsu/O1XwnU1UgED4UbSMgoWnRaWHVvhrptg2e2hD3FOJhPayzx8b9iedQw8/6VhWfCCw047WWr1WNeooTFjp8Mp5YW4f1m7nZ/cvoL3nLbA11kVO2w5TGcHfPvi0CEi57iXwF9/AUaPTWjUqmYGwo1q8jR4/bvCsmkD3HcH3PsHWPN439utXxOWG68IQfCCF4bOE/OeD89dUPHAWI3BjJ2GopQX4nb3ZPn5nat4ZN0OX2dVbNBymJmtYaKMNSvyv3zZq+H9F4YSwArzYL42GAgL2o+E170zLNs2wb13wkNL4fGH861mIPQpLswWN7fA0fNg3nG9gfHzQveKEUzzPFJ+8NQHL4LSUJXqQlwIwbCvs+o2UDnMcS374JJP5bslQUj0vOX9iXwneTBfOwyE1dfU9jBr3VlvhY79sOIh+Etv8Ltpfd/bdnfBqkfDcsMVYd/4SaEmefbc8POouaFPY5FTPg+HHzz1w9Z5KqdcScVf1m6nuyfb53e+zqpbf+Uw57Rt5kWXfhf2PxNulGoK18W88o2JjdOD+dphIKyBtY2G4xeHBWDrRnj0AVj5CKx8+NDAGGDPzr5ZYwjlE+2zwuw9M2fnf7YfWdKaLT946oet81ROuZKKn9y+gp/fuapPMOzrrLr1KYfZuItT1tzO7D9cQSrb+3/YMgo++lk44eREx+nBfO0wENbQTZsJp742LBCC3pWPhqB41SOw7gno3H/o/boOwFNPhOVgY8bB1BkhEz11RlimzAglFhMnh5+tbUManh88lVeuUhRb56nc0k0p3nPaAh5Zt8PXWY1JN6VYctQEltz8Xbj3jvwvpkyH6B/hmPnJDa6XB/O1w0BYwzd+Ur5XMYSG5Zs3hC4Ua1eG/sXrVsPuHQM/xr69YSnsXHGw1tEhKJ4wCcaOP3QZMw7axnDCgf3cm9nKrp40+1ItdDSNIjVqFHNmjC/lv7r8enqgJxM6ePRkerd7l2xP/vfZLPRke9vgFaznMiPZbP+PnytTSaXCKcRUKuxragr9p5uaQhu9VBqam8OSbj6kzq6cpSi2zlMl6v19ndWoLU/Dt74cLuTOWfBC+NgXwvdEFfBgvnaksgN9WZZRFEVZgDiOK/7c5ebFWv14Zg9sfAo2ruv9+RQ8vS6UWnR3lf3ps6kmUi0t4ZRZbkmnQ3DXlIZ0bwCYC/aamoAUpOgNFAnbZCFLb4DZu062IEDN5gPVbDYfxGYy+f259VyQm8nkA9vcdrXK/c2aW6ClhQ7SbNnXQ0cqzYFUMwdSabqaRjHnqOlMmz45ZPJHtUFbW5jxsG0MjM79HBvWR48NBzLNHpMrz3p/DejPy8J0yYWTQ51xNrzzo1X3OZKLBzzIqhr9/vGr61VT4/zwHsDY8TD32LAU6ukJ5RXbNtOzdRO/velPsG0zkw/sZmrPfqZl9zOp+xnSPd39PuxQpbI9cKAzLBq+TG+g3vt3bANm93e75atheZGP3Toaxo4LQXEu0z9uAoybCOMnhvXxE8P2hElhSaAdkirDev/BNWTCpesAXPlD+N2V+X3NLfCev4FTzkpsWIOxD35tMBAuIT+8i9TU1FsLPIV7uifznab9dExa2Pc22SxT0928ZFqaT512FOmOZ0KG+Zm9vT/3hMxAx77Q5aJzf/iZWz9wID+DXi1JNx9arlC4nepdT6UKliZoShGy2Qd9KaZSfUsmcksuo11YelGYpc50Q3d33zZ65dDZ+/+1fcvQ7zNuQm9Q3FtPPmkKTJoWZkOcNBUm9/40YK451vsPrCETLhufgu9cEsrtciZNheiLMGdhv3dpyIMFDYuBcAn54T18/f3tAEil2NbTwh92pDklPZMlJwzj75jJhGzCs0tXCOxy5QjdmXzQ92yQ2NNbBtEbRGez+eAylQs2KQhA0/myilxA2ieAPWg9ne67nuqty83drtpks/mguLsLurvIdHby9av+xFMbd5DqOsDYdA9zJ7fy3iVH0XSgI8zw1LkfOjuhcx/s3xcOWPbvC22Ocj+f2Tu8g5W9u8OyYe3gt5swOVxEM3VG+Dml9+fUdpg+M2SfVVWGcqFRowY6DZVwyWbhj7+Dn8bh8yTn+JPgg58O16n0oyEPFjRsBsIl5FWiwzdQg/ucER1QpNOQHh3awWl4UqmQWW1uAcLfMQ1c+NE3HVID11TsF01PT8jg7+vN7j+zB/bugb27QqC7Z1dY37M7lNLs3hn2DTV43r0jLIWzTRUaPTZ0RJnWHn5Onxl6X884MgTL6XRx/x6N2OEuNGrkQKdhEi77noGffAPuuS2/r7kF3v5X8KpzBp0ko6EOFjRiBsIl5FWiw1f4t+svGK7FA4pGyFiVpAauqQnGjA3LUPVkQmC8ewfs2g67dsDObfllx9bwc9eOwwfM+5+BdavCcrB0ujc4PhJmHNHbA7u3D/akqdWZva8Dh+vm0MiBTkMkXB5aCj/+Zngf58ycHfoDHzX3sHdvmIMFlYSBcAnZimdoBgoQc3+7lRt3c8ejT7Nh+zMc6O6pyQOKRs5YlUO/r5mJk0Nt8Ow5g9wxEwLlbZth++ZQg7xtc5hKfNvm0LlksIsoM5kwcUx/k8eMaoX23sD4iNlw5FFwxFEhk9wyauT/6AY32EFWIwc6dZ1w2bMLLv8vWHpL3/2nvhbe9bEh95RviIMFlYyBcIl5lejgDhcg5v52550yr6YPKBo5Y1VKmZ4sS1ds4tu/e4TtezvpKjgwGtJBRTrdWxc8HXj+ob/PZkOpxdaNvcsm2LIBNj8demLv3DbwYx/o7D+T3NQE04+AI4+GWUfDkceEn+3Pqbr2TrWqkQOduky4ZLOw7Db46X+GMqic8RPh/E/AiacW9XB1fbCgkvNTWRU11ACx1g8oGjljVQqZnixLH9/Et298hC27O8gUTIFb0oOKVKp3BsPJh7b3g3CBzuYNYdm0Ply9vmk9bHoq1C/3p6cnn0W+/4/5/enmkD2edTQ857m9yxyYPG3QekcdqtEDnVJ9PlZF+daOrXDpt+CBu/vuX3JGyAKPK/7gpi4PFlQ2BsKqqEYJEBs5YzVSubMGjzy1gwPd/df3Vuw109oWSi/6K7/Yuzs/UczTa0P3iqfXhqxyfzLdYSas9Wv6XgA0Zlw+MJ49B2bPDcFyA5dXDBSgFe4/Z9ExnHMSrNm8x0BnGBIv3+o6ADdeCddd3rcjxORp8N5Phs4QI1DryRRVjoGwKqpRAsRGz1iNRO6swUBBMFTJa2bcBJh3XFgKdXaE4HjDk7B+LWxYE4LfbZv7f5x9e2HFn8OSk06Hi4OOmhuC46PmhfUx48r1r6kaAwVoF513El+87B7r7ksksfKtbBbuuxN+8d1DDxpf8UZ42wdDJxepQgyEVVGNEiB6am74Buwp3WtUc1N1v2Za2+Do+WEptP+ZkDV+6okQGD/1BKxbHfYfLJPJZ4/vujm/f/oRISA+el5vcDwvTCpSRwYK0C6743Hr7ksokbNz61aHi+Eee6jv/lnHwPkfhwUvLM/zSoMwEFZFNVKA6Km54Rmop3S6KcX0CW1ccNZxLJ7fXrLXTMXqJEePPXSq8Ww2dLLIBcXrVoWfmzf0/xhbng7LvXfk902eBscsCMFxLgAfQXCcdN3oQAHaI+t2NERZVaVU9Ozc9i3wm5/CH27o285w3AQ4531w2uvs163EGAir4gwQNZiDzxq0NDcxZVwrF5x5HIsXlC4Ahiqok0ylwox3U2fAixbn93fsg3VPhMB4be+yfk3/U13v2BqWwgvzcsHxMQvgmPlw9AIYd/gZ9BL/ezBwgHbc7MmH9BmvihKZGlWRs3M7t8F1P4PbfxtmpMxpaoIzzoY3ne/MjkqcgbCkqlLJswZV2+aubQzMf35Ycrq7Qt3xkyth7crwc93qcNHRwfoLjqcfEbLFz12QzyC3jelzt2r4ewwUoJ13ynweWbej7suqKqWs77Nd2+G3v4Bbf9M3AAZ4/kvh3I+G9oJSFTAQlqpA0qejq02lzhrUVBeT5pZ8XXBOJpMPjp98PCwDBce5soo/3R62U6lwQV4ua3zMAp5Yn0387zFYgNYoZVWVUvL32fYt8Lur4LZrD52oZu6xcM574dgTbBeoqmIgLCWsGk5HN6qa72KSTufbu51yVtj3bHD8OKx5HNasCDXIB2fmstnQ7u3ptXDXTQCc25TmpJYpLG+ZwYpR7axoncHGMTMq/vcYKECzrKpKrX4MbroS/vSH0Ee70DEL4M3vC5lgA2BVIQNhKWHVcDq6UdVlF5M+wfFrwr6uA7D+yRAU55YNTx4StDT1ZJjbuYW5nVt4Aw8DcKCpmZYrFoSSilxpxYxZoc5TjasnA/ffFXoBr3rk0N8fNTdcCHf8SQbAqmoGwlLCaur0fJ0Z6en2milpaRnVW/4wH3hD2NfZEcoo1qyAJx4LPzetP+Suo3q6Q6BTGOyMHpPvUJErrZg204CnEWzbDH/8Hdx5Y/+Txyx8EZz5VgNg1QwDYSlhNX96vsYN93R7zZe0tLYdOiHIvr2hpOKJgszx9i2H3nf/Plj+YFhyxowrCI57W7kZHNeHrgPhwss7boRH7w9lNYXSzXDSK+DMt4RMsFRDDISlhNXl6fkGUJclLWPGhYuZjj0hv2/XDnhyRd/geM+uQ++7b28Ikh69v+/j5Sb/OHp+CJJmHDmssoqayb7Xi54MrHwElt0GS28N/78HGzseTn8DnPEmmDS14kOUSsFAWEqYV8PXppGWtNRMYDdxMhy/OCyQnwRkzYq+F+T1Fyjt2wuPPhCWnNbRMPu5MHtuCIyPmhtaabWMGnAINZ99rxXd3fDYg2HClvvvgj07D71NKgXHnQAvPwtOOHnQ/zepFhgIKxE1EwRUiFfD156RlLTUdGBXOAnIS08J+7JZ2LoxBMW5Nm5Pruw/OO7cHzKNKwtqjpuaQiu35zw3f6Hfc54LE6dAKlWf2fdqsWdXOFD5yzJ44O7+/88glLm8/Ew4+czwfy/VCQNhVVxNBwFSr5GUtNRdYJdKhQk7ph8Bi04L+7LZcDFVLjDOzZDXX5axpyd0sdjwJNxza37/2PFw5NFMSU3izO1p1rRMZU3LFPakR9f1BaVlTRR0HYCVD8PD98Mj94XZCw+u+c2ZMDlkfRedBgteWLWdQkysaCQMhFVxdRcEqCGNpKSlITqFpFIwfWZYTjw17Mtmw6xjuaA4N4X0lqf7f4xn9sDjf2EBsKBg986m0Tw1agrTH5gP+xfAEbNDRnnqdGhKl/tfVlYlTxTs3Aarl+eXNSsOneyi0JTp8JJT4KUvD5NgVPHfM9OTZenjm/j2jY+wbU8nXZke2kysqEgGwqq4Wg4CzDyo0HBLWhq2U0gqFS6qmjQ1tNfK6dgP658IE3+sewKeWg1PrYGOff0+zKSe/UzqWA8ProcHb83/orklnMKfcWTvckT4Of2IEODVQD3rsBMFPT2wbROsXxMy62tXhcC3v64fhZqa4LkLQ93v8SeFdng10Okjd8DwyFM7ONCd74dtYkXFMhBWxdVqEGBJh0rFTiEHaRsNc48LS07uorwNT8L6NfSsf5J9q1fStnU9zZmu/h+nuws2rgtLfyZOCfWt09phSnvIIE+aChOnwqQpoRSgOdmvxUETBfNnhIz6tk2h7GTbJti4HjasgQ1rB8/0FmqfBce9JCzPOx7GjC39P6TMcgcMhUFwTq0kVlQdDIRVcbUaBFjSoVKxU8gQFF6U98JFNAHjIGQ+d2yBp9eFZeO6/Hp/9ceFdm0Py+rlAz/n+EmhU8a4CTBuYu/PCTB+YqhZbh0dJhRpGw1tvT9bR4dsdDo99GxqJhMmNTnQETLiBzqgo4MT96xlx75HGNf1DBMz+5mU2c+U7H4WXNMNP90WanyL0JFqZsWoGTw++giemTWP89//JtJTphX1GNWovwOGnFpIrKh6GAir4mo1CKjlkg5VnyQ6hdRFaU9TE0xtD8sLTuz7u/3PhHrjzRvCsqn357ZNsGMbZA/NHvaRzcLuHWEZrnRzyCqnm0NgnCX05M32hCA+t2S6+73783qXQ+wfwnOPnwSzjoYjj2Z18xS+8egBHktNpicVLnJr60yzYGuGJVOG90+rJv2dWQRoSTfVRGJF1cNAWImoxXZhtVLSURfBjkquIUp7Ro8Nk3ccNe/Q33V3w46tISjethm2bw7bO7f1LttDRnmgDgpDlekeMMgtiTHjQmnHtJnhYGD6zNCHedbRIRDuddftj7P8sRUU/mvq6cD94DOLLc1NTBnXygVnHsfiBe3185pW2RkIS0NUCyUdDRHsaFgavrSnuTnfxWIg3d35jPDe3QXLLti7J6x37IfOfeFnx/5wQV9nR6hP7jlMxrlQKgWj2qCtLZRWtI6G1lYYMx4mTIRxk8LP8RN7yzWmhMB3iPW8tXLgPlzFnlk0QaCBGAhLQ1QLJR0NH+xoQJb2DEFzc+guMWX68O6fK3nIZEJQnemGphSkmkJJR1NTfr25pazdGWrhwH2khnpm0QSBBmMgLBWh2ks6DHY0kHrPEFaFpiZoGgUtSQ+kNg7cK8UEgQZTndPESBqWXLBTyGBHkM8QtrWkScGzEw/UU4ZQfeUO3N996nyWNHDd7GAJAsmMsFRHGuF0qIbHDKEalWdDNBgDYamOGOxoMNVe2iOVgwkCDcZAWKozBjuSlGeCQIMxEJYkSXVhoDZpJgg0EANhSZJU82yTpuGwa4TqUqYny90rNnHp7Y9z94pNZHpGOFuUdBBfY1J1KWyTlqVvmzRpIGaEVXfMCqjcfI1JlTPUWeHso67hMBBW3bF5usqtXK8xp4GV+irmoNM2aRoOA2HVnWrIChjQ1LdyvMbMMqsejfSzsJiDTtukaTgMhFV3ks4KGNDUv3K8xjyTEXgQWT9K8VlYzEGnbdI0HAbCqjtJZwUMaOpfOV5j1XAmI2keRNaXUnwWFnvQaZs0FctAWHUn6ayAAU39K8drLOkzGdXAg8j6UorPwqQTG6p/BsKqS0lmBQxoGkOpX2N+4XsQWW9K8VmYdGJD9c9AWCoxAxoNh1/4HkTWm1J9FlruoHIyEFZDK8eFOQY0Gq5a/8I/+P30kjnTuW/1liG/vzyIrC9+FqoWpLLZys+GFEVRFiCO44o/t5TjhTlS6fT3fmpOp+jq7uFAd8+Q31+5YNrASVKJ9ftB4hTLalhOxymVTn/vp70d3XR29xT1/splxd996nyWLGg3CJZUVgbCaliDXZgjqTj9vZ8O5vtLELL+d6/YxKW3P87dKzaR6en/zPRQbyeNhDXCalhemCOVTn/vp4P5/tJQS9IsXVOlmBFWw8pdmNPWkiYFtHlhjjRs/b2fxrU109rc5PsrQdWWVR1qSZqla6oUM8JqWF7RLJVOf++nXNcI31/JqMas6lB7RdtTWpViIKyGVuvtqqRq0t/7yfdXcqpxpr6hlqRZuqZKsTRCklRXqq0cICnVeEHwUEvSLF1TpZgRliTVjWosB0hKNWZVh1qSZumaKsVAWGVXjtnbJKk/1VgOkJRqnalvqCVplq6pEgyEVVZmZyRVkhdZ5ZlVlQ7PQFhlZXZGUiUNpRygkc5SmVWVBmcgrLIyOyOpkg5XDuBZKkmFDIRVVtV4sYbUn0bKEtazw5UDeJZKUiEDYZVVtV6sIRUyS1hfBisH8CyVpEIGwiorL9ZQLTBL2Dg8SyWpkBNqqOxy2Zl3nzqfJQvaDYJVdapx4gGVhxM1SCpkRlhSwzNL2Dg8SyWpkIGwpIZnLXtjsaVYdfACVVUDA2FJDc8soVRZXqCqamEgLEmYJZQqyQtUVS28WE6SJFWUF6iqWhgIS5KkispdoFrIC1SVBANhSZJUUbaxU7WwRliSJFWUF6iqWhgIS5KkivMCVVUDSyMkSZLUkAyEJUmS1JAMhCVJktSQDIQlSZLUkAyEJUmS1JAMhCVJktSQDIQlSZLUkAyEJUmS1JAMhCVJktSQDIQlSZLUkAyEJUmS1JAMhCVJktSQDIQlSZLUkJqTHoAkSY0g05Nl2crNrNq4m7kzJ7Bo3gzSTamkhyU1NANhSZLKLNOT5fOXLmX5+p10dmVobUmzcNYkLj5/scGwlCBLIyRJKrNlKzezfP1OOroyZIGOrgzL1+9k2crNSQ9NamgGwpIkldmqjbvp7Mr02dfZlWH1pt0JjUgSGAhLklR2c2dOoLUl3Wdfa0uaOe0TEhqRJDAQliSp7BbNm8HCWZNoa0mTAtp6a4QXzZuR9NCkhubFcpIklVm6KcXF5y9m2crNrN60mzntdo2QqoGBsCRJFZBuSrFkQTtLFrQnPRRJvSyNkCRJUkMyEJYkSVJDMhCWJElSQzIQliRJUkMyEJYkSVJDMhCWJElSQzIQliRJUkMyEJYkSVJDMhCWJElSQ0p0ZrkoipJ8ekmSJDWGbBzHh8xpbkZYkiRJDSmVzWaTHoMkSZJUcWaEJUmS1JAMhCVJktSQEr1YTpJUOVEU/RB4P/DcOI7XJDsaSUqeGWFJkiQ1JANhSWocnwOOBdYnPRBJqgZ2jZAkSVJDskZYkkYgiqKrgXOAT8Zx/M2DfncR8A/A9+M4/vAQHuuVwHnAKcBzgBZgFfAL4J/jOO4ouO1zgfuBHuCEOI6fLPjdWOBPwALgjDiOb+vd/0P6qRGOouhs4ELgOGAKsA14HPhZHMfx0P8aklRbLI2QpJH5ELAW+Ncoik7I7Yyi6FXA54FHgE8O8bE+A5wFPAB8G/gecAD4J+C3URSlczeM4/gJ4MPAZOCyKIoKExsxsBD4P7kgeCBRFH0U+BUhCL4G+H/AdcBo4INDHLck1SQzwpI0AnEcb4+i6DzgNuBnURS9BBgD/AToBN4Zx/G+IT5cBDwRx3GfmrWCzPLbgZ8VPPcvoyj6T+CvgYuAz0VR9D7gfcCtvfsO5wJCsP2iOI43H/S804Y4bkmqSWaEJWmE4jj+I/BFYD4hk/sTYCahXOLhIh5n9cFBcK9/7/35mn5+92ngQeAzURR9gpAN3gKcH8dxzxCfuhvo6mc8W4d4f0mqSWaEJak0/hl4BfDu3u3L4jj+XjEP0FvbeyHwFkJ973ggVXCTWQffJ47jjiiKziXUBH8TyAJvj+N4wxCf9lJCOcTDURT9jJDZvjOO4y3FjF2SapEZYUkqgd5M7lUFu/69mPtHUdQC/B74KtBGKIG4BPhy7wLQOsDdVwAP9a4/Atw41OeN4/jfCBfQrSXUMl8FbIqi6JYoik4s5t8gSbXGQFiSSiCKovnA14AdhE4O34uiqK2IhzgHOAn4nziOXxjH8UfjOP5CHMf/RCi3GMxngZOBrcDzCf2ChyyO4x/FcbwEmAq8Afg+cBpwQxRFM4p5LEmqJQbCkjRCURS1EjK4Y4F3ETK5L6S4rPC83p9X9PO70wd57pOB/wM8Bryg9+eXoyg6pYjnBiCO451xHF8Xx/FHgB8SWqmdWuzjSFKtMBCWpJH7GnAC8C9xHN8IfAm4E7ggiqJ3DvEx1vT+fEXhziiK5hDqjw8RRdFk4DIgA7wrjuNNwLmEi98ui6Jo6uGeNIqi1x7Uei0nlwkeascLSao5XiwnSSMQRdGbgU8ASwktzojjONPbUu0B4LtRFP0pjuPVh3moa4CVwKejKHohYbKMo4A3Atf2rh/sv3v3fzKO4wd6n/vBKIr+DvgW8APg7MM87+VARxRFdxCC8RQhC7wIuBe46TD3l6SaZUZYkoYpiqKjCMHoLuC8OI67c7+L43gdYbKNCcDlURSNGuyx4jh+BjgD+CmhzveTwPGEXsDv6ee5/wZ4M/Drg2e0i+P4PwgXvb0piqJPHeaf8VngLuAlhD7GHyTMaPcZ4JVxHB/SVk2S6kUqm+2vZaUkSZJU38wIS5IkqSEZCEuSJKkhGQhLkiSpIRkIS5IkqSEZCEuSJKkhGQhLkiSpIRkIS5IkqSEZCEuSJKkhGQhLkiSpIRkIS5IkqSH9/88K7nTCPllrAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"reg_deg=5\n",
"\n",
@@ -441,39 +193,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de degrés : 24\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/POLR1-04-over_fitting</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"reg_deg=24\n",
"\n",
@@ -486,19 +208,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 16 December 2020, 17:48:03\n",
- "Duration is : 00:00:01 491ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
@@ -528,7 +240,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.7.9"
}
},
"nbformat": 4,
diff --git a/LinearReg/03-Polynomial-Regression==done==.ipynb b/LinearReg/03-Polynomial-Regression==done==.ipynb
new file mode 100644
index 0000000..73716b9
--- /dev/null
+++ b/LinearReg/03-Polynomial-Regression==done==.ipynb
@@ -0,0 +1,586 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [POLR1] - Complexity Syndrome\n",
+ "<!-- DESC --> Illustration of the problem of complexity with the polynomial regression\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Visualizing and understanding under and overfitting\n",
+ " \n",
+ "## What we're going to do :\n",
+ "\n",
+ "We are looking for a polynomial function to approximate the observed series : \n",
+ "$ y = a_n\\cdot x^n + \\dots + a_i\\cdot x^i + \\dots + a_1\\cdot x + b $ \n",
+ "\n",
+ "\n",
+ "## Step 1 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:07.865209Z",
+ "iopub.status.busy": "2021-01-14T07:11:07.864885Z",
+ "iopub.status.idle": "2021-01-14T07:11:09.185133Z",
+ "shell.execute_reply": "2021-01-14T07:11:09.184748Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "div.todo:before { 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+ " margin-bottom:20px;\n",
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+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
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+ "}\n",
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+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : POLR1\n",
+ "Run time : Thursday 14 January 2021, 08:11:09\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import math\n",
+ "import random\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sys\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('POLR1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Dataset generation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:09.193342Z",
+ "iopub.status.busy": "2021-01-14T07:11:09.192895Z",
+ "iopub.status.idle": "2021-01-14T07:11:09.480766Z",
+ "shell.execute_reply": "2021-01-14T07:11:09.480483Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "#### Generator :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nomber of points=100 deg=7 bruit=2000\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "#### Datasets :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "100 points visibles sur 100)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/POLR1-01-dataset</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "#### Before normalization :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X : mean= +0.0861 std= +2.8096 min= -4.6939 max= +4.9015\n",
+ "Y : mean= -2806.4826 std= +4822.4781 min= -23157.5333 max= +4297.1028\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "#### After normalization :"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X_norm : mean= -0.0000 std= +1.0000 min= -1.7013 max= +1.7139\n",
+ "Y_norm : mean= -0.0000 std= +1.0000 min= -4.2200 max= +1.4730\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Parameters\n",
+ "\n",
+ "n = 100\n",
+ "\n",
+ "xob_min = -5\n",
+ "xob_max = 5\n",
+ "\n",
+ "deg = 7\n",
+ "a_min = -2\n",
+ "a_max = 2\n",
+ "\n",
+ "noise = 2000\n",
+ "\n",
+ "# ---- Train data\n",
+ "# X,Y : data\n",
+ "# X_norm,Y_norm : normalized data\n",
+ "\n",
+ "X = np.random.uniform(xob_min,xob_max,(n,1))\n",
+ "# N = np.random.uniform(-noise,noise,(n,1))\n",
+ "N = noise * np.random.normal(0,1,(n,1))\n",
+ "\n",
+ "a = np.random.uniform(a_min,a_max, (deg,))\n",
+ "fy = np.poly1d( a )\n",
+ "\n",
+ "Y = fy(X) + N\n",
+ "\n",
+ "# ---- Data normalization\n",
+ "#\n",
+ "X_norm = (X - X.mean(axis=0)) / X.std(axis=0)\n",
+ "Y_norm = (Y - Y.mean(axis=0)) / Y.std(axis=0)\n",
+ "\n",
+ "# ---- Data visualization\n",
+ "\n",
+ "width = 12\n",
+ "height = 6\n",
+ "nb_viz = min(2000,n)\n",
+ "\n",
+ "def vector_infos(name,V):\n",
+ " m=V.mean(axis=0).item()\n",
+ " s=V.std(axis=0).item()\n",
+ " print(\"{:8} : mean={:+12.4f} std={:+12.4f} min={:+12.4f} max={:+12.4f}\".format(name,m,s,V.min(),V.max()))\n",
+ "\n",
+ "\n",
+ "pwk.display_md('#### Generator :')\n",
+ "print(f\"Nomber of points={n} deg={deg} bruit={noise}\")\n",
+ "\n",
+ "pwk.display_md('#### Datasets :')\n",
+ "print(f\"{nb_viz} points visibles sur {n})\")\n",
+ "plt.figure(figsize=(width, height))\n",
+ "plt.plot(X[:nb_viz], Y[:nb_viz], '.')\n",
+ "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ "plt.xlabel('x axis')\n",
+ "plt.ylabel('y axis')\n",
+ "pwk.save_fig(\"01-dataset\")\n",
+ "plt.show()\n",
+ "\n",
+ "pwk.display_md('#### Before normalization :')\n",
+ "vector_infos('X',X)\n",
+ "vector_infos('Y',Y)\n",
+ "\n",
+ "pwk.display_md('#### After normalization :') \n",
+ "vector_infos('X_norm',X_norm)\n",
+ "vector_infos('Y_norm',Y_norm)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Polynomial regression with NumPy\n",
+ "### 3.1 - Underfitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:09.484770Z",
+ "iopub.status.busy": "2021-01-14T07:11:09.484449Z",
+ "iopub.status.idle": "2021-01-14T07:11:09.486798Z",
+ "shell.execute_reply": "2021-01-14T07:11:09.486466Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def draw_reg(X_norm, Y_norm, x_hat,fy_hat, size, save_as):\n",
+ " plt.figure(figsize=size)\n",
+ " plt.plot(X_norm, Y_norm, '.')\n",
+ "\n",
+ " x_hat = np.linspace(X_norm.min(), X_norm.max(), 100)\n",
+ "\n",
+ " plt.plot(x_hat, fy_hat(x_hat))\n",
+ " plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ " plt.xlabel('x axis')\n",
+ " plt.ylabel('y axis')\n",
+ " pwk.save_fig(save_as)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:09.510076Z",
+ "iopub.status.busy": "2021-01-14T07:11:09.500879Z",
+ "iopub.status.idle": "2021-01-14T07:11:09.782362Z",
+ "shell.execute_reply": "2021-01-14T07:11:09.781988Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nombre de degrés : 1\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/POLR1-02-underfitting</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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KeqQknfHGtDi2Rueet07PuXtWOtwyi90hgH/v5wuc6gso6g92Tr8iTlG8vkdt9lwazYDvKsIwmrQ77eN50tWXTepJT1wX6htYzw/8dEbKn1O9hG1pqXPJSLfZ7fqix26hu3EaPUyet2IRZErSttpl2YKkY/t93+0uSZ9XulZrPaZCKqtCOqsNH7tVGt/ks5d2m9va9M4eFY0f7DPHF1SuVJROpTQ9MxfK7wenXxGnqILrKM2eS6MZ8F1FGEaTTqd9rnn6RaG/gcX6gT82Jo2tk85ZF+79el6tPV67GeuGAN3aRaSxtV+nhZOlxXDH2iCrirKVos5T8eyVcw9Lc/3ft5fNKZXLN5eL9Ls5TX5t10WQg5BJp7RzalxfvPO+0MsZOP2KuI1acI0Kz9NoIAyjySBP+4zkB34qdTbIhdxsRJVyj4Ddboa7oXd2h7ISr/i4UpXoFkGmSkWpVO2dHe4d1xdB5ppLQDoF7I6z2S0hPbfW9yLIqMoZOP0KAINDGEaTQZ724QM/oHRGWntu9RKilOepvLioHxx4QMceekSXbMjq8olzlCm11mu3bFCzuPK20ydPaeHRk8pXFpX3lrTWi3ARpFeRCmeql7A19c7uUB6SX6vzZs/oFQ+f1JlaO796W7/TPyxJmamV9drZNb7KRjj9CgCDk/Kiqm/swhjjSZK1duCPjeFSbx/FB/5o2Hf7IX3ymwdVf1dJeZ5y3pJueO6T9YorLmwJ1Y07QLab2e5Sv71YjLZ3dpRWu0FNx04ktdntmMtGAGDIdXyDZGYYsQqz3oq+rPFrLX3xah0wNl+yWbog5Jq6dr2zG4Oz324j7b4nwt7Zy1uun3ws3PtNpzsH7Kbw3aHVX8e67Xz1rARCxfsVMDwIwxgJ9GUdDgMtffHZOzuweu/sxjDdZbfHyuOPa3buUZ1eOKmNmYrOXyOlFhtmvRtnvBeLvR9/tSoV6fHT1UvYxrId+2JX1uT1i6J0opTSeRvW6cLJ85XOd1kM2RjS2/TOdgHvV8BwIQxjJNCXdTiMRK1rY+9sbej57WlJF/q970qlS2u+bgsh28xet5aTlMu9H3+1lkrSqZKqffuapSWN1y6BtemdvWKzmW4z1t02rxnL9vdvjhDvV8BwIQxjJNCXdXjQaqiLdLoa3vJrJW0K976bdoJsDcyNgbqxVrtXycjj1fA+wN7ZocmM9ajH7hKwu7X6W5Pru3c271fAcCEMO6SfGrWgPxt1PVzr/T914rzQ2rRRy4dEGstWL+eG3NOvsXd2ywLH235wRPv/6WfKVUpa65WUr/35jIvW6dIn5mLtna3yknTmVPUStjW5HqG6zXbqDWH7imJBPyw/rIVyWo/XOpBoTV5bxsPuxwjAD8KwI/qpUQv6s1HXw7W7/0sv2qBLN2/UvX3WqlLLB7To0js7n7lQ373/nKaD0Hw2o6e85ArJzwxnudx+AWPTosjiyhlsP2UlEfbOXl4Eucqc/TRJN7e53rs53X62uldJSP22bosiffbOBlxEGHZEPzVqQX826nq4dvd/74OP6caXPUPpXam+alWp5QP863vBZCaa3tnLiyCLrQsZOwXs1t7ZHeq062UjEUkNrHd2r9nsLmUkTZ1IzvbO5owakoww7Ih+atSC/mzU9XCd7v/owyd1/dXb+noMavkA/4Z2wWTjIsh1IZceVCrV8o7W1n2NM9SFNhvUdGr1t3xbxL2zl0rVSwRlI14urzOVjC7xMppIZVVMZ3Vk7VpNPWVCqXynkN2h1R+9sxEDwrAj+tn6OOjPRr3NcpT3P5JbRAMRcm7BZLqhlCFsS0u1biMtYbp1c5puZSWFQptZ8EKkvbNTxYLOU0sVTVHS/H393fHyc92mLV/bkpGGgE3vbARAGHZEP6czg/5s1L1mo7x/togGEJuxseolit7ZpcWWEhCfM9btQvhAe2efqV7Cll3Toca6YWa6W512Y8huvA9He2cnHdsxO6SfrY+D/mzU2yxHef9sEQ0APlXK+se779cHv/x9pRaLWustKl9Z0nmZsvY852Jden6+x06QLTPhjX24o+ydHZVOvbOXS0Fy7bdTbzfr3boQcoz5yz51/CAnDAMABoaFVqMnsi48nXpn+91qvVtZSQzZp2/temf32umxU+/setiu/73P3tkJ0fHFyGEGAGAgaF04miJbSDnI3tl+AnbbNn4tvbSjXAQ5iN7Z9dnoThvUdCwr6RDKE7IIkjAMABgIWheOrkQtpOzSO7tvnXpnd9t+vd3mNO2+dyC9sx8L935TbXpnb3qi9Ob3hPs4fSIMAwAGgtaFGHmR987uErAXW2/rsEhyRVvACBdBtuudHcXMdp8IwwCAgaB1IbBKTb2zQ/59ads7u90Cx5aQ3a1Ou1vv7NzacMcfAsIwAGAgaF0IDKHIe2e3BOchrCEmDAMABmJod6wDEI2xMWlsnXTOurhH0hVhGAAwMIlaaAXACU40lgMAAADaIQwDAADAWZRJAH1iRy0AGE28v7uBMAz0gR21AMC/JIVL3t/dQRgG+sCOWgDgT9LCJe/v7qBmGOhDtx21AABnNYZLT83hchjx/u4OwvCIKlc87T84q323H9L+g7MqV7y4hzSS6jtqNWJHLQBYKWnhkvd3d1AmMYKSdioqydhRCwD8Sdp23L3e35NU/4zuCMMjiDqnwWFHLQDwJ2mTB93e35l0Gi2E4RHU7VQUYTh87KgFAL0lcfKg0/s7k06jhTA8gtqdisqkUyqVKypXvKF+4wEAjK5RmTxg0mm0sIBuBNVPReXGzv7vXap4+sL++3TTvjtZTAcAQB9YXDdaCMMjqH4q6hXP3aKxhlngYW9jAwBAEtQnnfLZjFKS8kNe/4zuKJMYUZl0SmPp9IpZYE7jAADQnyTWP6MzwvAIS1obGwAAkmJU6p9BGB5pSWtjAwCAa3r1K6afcfQIwyOM0zgAAAyvXv2K6Wc8GCygG3H10zjXX71Nu7dP8MsDAMCQaOxX7GnlQvdetyMchGEAAIAYdOtX7Od2hIMwDAAAEINe/YrpZzwYhGEAAICAyhVP+w/Oat/th7T/4OyqNrTq1a+YfsaDwQI6AACAAMJa2NZroTsL4QeDMAwAABBA48I2qXlhW9C+w736FdPPOHqUSQAAAATAwrbRQhgGAAAIgIVto4UwDAAAEAAL20ZL3zXDxpgnSrpa0hlJ37DWlnv8CAAAQGKxsG20+A7Dxpjfl/RaSf/CWnuidt2zJP1fSU+ofdv3jDHXWmtPhz1QAACAYcHCttERpEzi1ZK8ehCueZ+kTZI+KulrknZK+tfhDQ8AAACITpAwvE3Sj+tf1MojrpH0EWvt6621L5Y0Len6cIcIAAAARCNIGD5f0lzD179S+/OLDdd9S9JT+h0UAAAAMAhBFtCdkPTEhq+vkVSRdEfDdZ6kfAjjAgAAGAnliqfpmTkdPr6grZMsths2QcLwTyW92BjzDkllVWuIp621jR2mL5F0PLzhAQAAJFdYWzcjOkHKJP5C0oWSHpB0v6RJSbZ+ozEmI+kqST8Kc4AAAABJ1bh1s6fmrZsxHHyHYWvtV1TtFHG3pHsl/Xtr7acavuWFqpZIfD3UEQIAACQUWzcPv0CbblhrPyTpQx1u+7qqbdYAAACgs1s3FxoCMVs3Dxe2YwYAAIgIWzcPv44zw8aYJ9f+esxaW274uidr7c/7HhkAAEDCsXXz8OtWJnFU1VZpl0k62PB1L16P+0UfaM8CAECysHXzcOsWWj+harB9rOVrxIT2LAAAAOHqGIatta/t9jUGr7E9i9TcnoWjTQAAgOBYQJcgtGcBAAAIl+8wbIy52uf3vWX1w0E39fYsjWjPAgAAsHpBZob/3hjzR51uNMZsMsZ8WdKf9z8stEN7FgAAgHAF6fowI+ndxpjnS/ota+3x+g3GmCsl3SLpSZK+GOoIsYz2LAAAAOEKMjP8LEmfknStpB8aY14kScaYt0u6TdK4pDdZa18R9iBxVr09y/VXb9Pu7RMEYQAAgD6kPC9YtzRjzA2SPihpraR7JD1d1T7Er7bW/tjnfXiSZK0N9NgAAADAKnScPQzcTcJa+wlJb6v97C9J+oWk5/kNwgAAAMCwCBSGjTFpY8wfS/oLSack3SHpAkm3GWMuj2B8AAAAQGSCtFZ7kqTbJb1d0j9Jera19ipJ75C0TdKdxhgTySgBAACACASZGb5L0pWS/krSbmvtQUmy1v6JpOdLeljSB4wxXwh5jAAAAEAkgrRWS0t6pbV2Rdi11t5hjPllSR+V9NKwBgcAAABEKUgYvsJae7TTjdbaeUkvN8a8qd9BAQAAYKVyxdP0zJwOH1/Q1kn2GwhD4NZqYaC1GgAAQDDliqeb9t2pA8fmVSyVlavtRLt3zy4CcW/htVYDAADA4E3PzOnAsXkVSmV5kgqlsg4cm9f0zFzcQ0u0IGUSMsakJL1S0q9J2iwp1+bbPGvtr4YwNgAAANQcPr6gYqncdF2xVNaR2QXt3j4R06iSz3cYNsbkJH1N1c4RKUmemqecvYbrAQAAEKKtk+uVy2ZUaAjEuWxGWybWU0vchyAzwzdKeoGk90r676q2UnuXpA+pGpD/i6qbcPx2qCMEAACAdk6Na8fmjStqhp+55QJqifsQJAz/pqQfWGvfKUn1/TWstcclfcYY84+q9iL+d5L+LNRRAgAAOC6TTmnvnl2anpnTkdkFbZmozgA31hJLzbXElE/0FmQB3VZJ32n42pOUrX9hrT0i6auSXhvKyAAAANAkk05p9/YJXX/1Nu3ePqFMOtW1lhi9BQnDJUmFhq9PSrqg5Xt+JmlLv4MCAACAP/Va4kb1WmL0FiQMP6BqB4m6g5Ke2/I9V0g60e+gAAAA4E+9ljifzSglKV+rGd45NR730BIhSM3wdyS9sOHrL0n6z8aYj0j6gqqL6F4o6dNhDQ4AAADddaolZvGcP0Fmhj8t6V5jzCW1r98vaVrS6yR9RdIfSjos6W0hjg8AAAA9tKslhj99bcdsjBmT9FJJU5KOSrrVWnvGx8+xHTMAAAAGpePRQaAd6FpZa5ck/U0/9wEAAADEJUiZBAAAADBSCMMAAABwFmEYAAAAziIMAwAAwFmEYQAAADirr24SAAAAiF+54ml6Zk6Hjy9o62TzphvdbkOAMGyMGbfWzkU5GAAAAARTrni6ad+dOnBsXsVSWbnadsx79+ySpI63EYirgpRJ3G+M+awx5trIRgMAAIBApmfmdODYvAqlsjxJhVJZB47Na3pmruttqApSJnFQ0m9KeqUx5rCkv5b0MWvtI5GMDAAAAD0dPr6gYqncdF2xVNaR2QV5njretnv7xCCHObR8zwxbay+XdJWkT0raLOl9kh4wxuwzxjwvovEBAACgi62T65XLZpquy2Uz2jKxvuttqAq0gM5ae4ekO4wxb5F0g6Q3SLpO0muMMQcl/Q9Jn7DWPhr6SAEAALDCzqlx7di8cUVd8M6pcUnqehuklOd5fd2BMeZKSb8n6VWS8pKKkv63pL+01n6vw894kmSt7euxAQAAcLZjxJHZBW2ZaN9Not1tDun4Dw6jtdojkh6VVJC0VtIaVWeNf9sYc6uk37XWngjhcQAAANBGJp3S7u0TbeuAu92GVYZhY0xW0iskvVHS81RN2wclvVfSxyQ9Q9J/kPQSSR9UtZQCAAAAA0J/YX8ChWFjzJSqdcKvlXS+pLKkL0my1tr/1/Ctt0m6zRjzeUm/HsZAAQAA4E+33sME4mZBNt34hqQXqDoL/KCqs8AfstY+2OXHvi/p5X2NcERwdAYAAAalsb+w1NxfmHKJZkFmhq+V9A+SrKQvWWvLPb5fkm5VNTg7jaMzAAAwSN16DxOGmwUJw5dZa+8NcufW2p9I+kmwIcUjyplbjs4AAMAg1fsLFxoCMf2F2/MdhoMG4SSJeuaWozMAADBIvXoP46wwWqslXtQztxydAQCAQcqkU9q7Zxf9hX3wvR3zKOs2cxuG+tFZPptRSlKeozMAABCxen/h66/ept3bJwjCHTAzrOhnbjk6AwAAw8r1jleEYQ2mrobdXwAAwLCh4xVhWBIztwAAwE10vCIML2PmFgAAuIaOVyygAwAAcFZ93VQj1zpeEYYHoFzxtP/grPbdfkj7D86qXPHiHhIAAAAdr0SZROQoTAcAAMOKdVOE4chRmA4AAIaZ6+umKJOIWNQbegAAAGD1CMMRozAdAABgeBGGI0ZhOgAAwPCiZjhiFKYDAAAML8LwALhemA4AADCsKJMAAACAswjDAAAAcBZhGAAAAM4iDAMAAMBZhGEAAAA4izAMAAAAZxGGAQAA4CzCMAAAAJxFGAYAAICzCMMAAABwFmEYAAAAziIMAwAAwFmEYQAAADiLMAwAAABnEYYBAADgLMIwAAAAnEUYBgAAgLMIwwAAAHAWYRgAAADOIgwDAADAWYRhAAAAOIswDAAAAGcRhgEAAOAswjAAAACcRRgGAACAswjDAAAAcBZhGAAAAM4iDAMAAMBZhGEAAAA4izAMAAAAZxGGAQAA4CzCMAAAAJxFGAYAAICzxuIeAAAAAEZDueJpemZOh48vaOvkeu2cGlcmnYp7WF0RhgEAANC3csXTTfvu1IFj8yqWysplM9qxeaP27tk11IGYMgkAAAD0bXpmTgeOzatQKsuTVCiVdeDYvKZn5uIeWleEYQAAAPTt8PEFFUvlpuuKpbKOzC7ENCJ/CMMAAADo29bJ9cplM03X5bIZbZlYH9OI/CEMAwAAoG87p8a1Y/NG5bMZpSTlazXDO6fG4x5aVyygAwAAQN8y6ZT27tml6Zk5HZld0JYJukkAAADAIZl0Sru3T2j39om4h+IbZRIAAABwFmEYAAAAziIMAwAAwFmEYQAAADiLMAwAAABnEYYBAADgLMIwAAAAnEUYBgAAgLMIwwAAAHAWYRgAAADOIgwDAADAWYRhAAAAOGss7gEMUrniaXpmToePL2jr5HrtnBpXJp2Ke1gAAACIiTNhuFzxdNO+O3Xg2LyKpbJy2Yx2bN6ovXt2EYgBAAAc5UyZxPTMnA4cm1ehVJYnqVAq68CxeU3PzMU9NAAAAMTEmTB8+PiCiqVy03XFUllHZhdiGhEAAADi5kwY3jq5Xrlspum6XDajLRPrYxoRAAAA4uZMGN45Na4dmzcqn80oJSlfqxneOTUe99AAAAAQE2cW0GXSKe3ds0vTM3M6MrugLRN0kwAAAHCdM2FYqgbi3dsntHv7RNxDAQAAwBBwpkwCAAAAaEUYBgAAgLMIwwAAAHAWYRgAAADOIgwDAADAWYRhAAAAOIswDAAAAGcRhgEAAOAswjAAAACcRRgGAACAswjDAAAAcBZhGAAAAM4iDAMAAMBZhGEAAAA4izAMAAAAZxGGAQAA4CzCMAAAAJxFGAYAAICzCMMAAABwFmEYAAAAziIMAwAAwFmEYQAAADiLMAwAAABnEYYBAADgLMIwAAAAnEUYBgAAgLMIwwAAAHAWYRgAAADOIgwDAADAWYRhAAAAOIswDAAAAGcRhgEAAOAswjAAAACcRRgGAACAswjDAAAAcBZhGAAAAM4iDAMAAMBZhGEAAAA4ayzuAQAAAGB0lSuepmfmdPj4grZOrtfOqXFl0qm4h7WMMAwAAIBIlCuebtp3pw4cm1exVFYum9GOzRu1d8+uoQnElEkAAAAgEtMzczpwbF6FUlmepEKprAPH5jU9Mxf30JYRhgEAABCJw8cXVCyVm64rlso6MrsQ04hWIgwDAAAgElsn1yuXzTRdl8tmtGVifUwjWokwDAAAgEjsnBrXjs0blc9mlJKUr9UM75waj3toy1hABwAAgEhk0int3bNL0zNzOjK7oC0TdJMAAACAQzLplHZvn9Du7RNxD6UtyiQAAADgLMIwAAAAnEUYBgAAgLMIwwAAAHAWYRgAAADOIgwDAADAWYRhAAAAOIswDAAAAGcRhgEAAOCsWHegM8bE+fAAAABwg2etbbsHNDPDAAAAcFbK87y4xwAAAADEgplhAAAAOIswDAAAAGfFuoAOADA4xpiPSfodSU+11h6NdzQAMByYGQYAAICzCMMA4I63S7pM0rG4BwIAw4JuEgAAAHAWNcMA0AdjzJckvVTSm621H2i57b2S/kjSR6y1r/dxXy+QdJ2kqyRdLCkr6bCkz0n6U2ttoeF7nyrph5Iqkq6w1v6s4bZzJX1P0nZJ11prv1m7/mNqUzNsjHmJpLdIepqkJ0h6RNIhSZ+11lr/zwYAJA9lEgDQn9+V9HNJ7zPGXFG/0hjzq5JuknSPpDf7vK8bJf1zSXdJ+mtJH5a0KOldkv6PMSZT/0Zr7X2SXi9pk6RbjDGNkxtW0g5J76kH4U6MMW+Q9GVVg/Ctkv5M0tckrZX0Op/jBoDEYmYYAPpgrT1hjLlO0jclfdYY80xJ50j6lKSipFdZa8/4vDsj6T5rbVP9WsMM8yslfbbhsT9vjPkrSb8v6b2S3m6MuUHSDZJuq13XyxtVDdy/bK2da3ncJ/ocNwAkFjPDANAna+0dkv6jpG2qzuh+StKkqqUTdwe4nyOtQbjm/bU/f63NbX8g6UeSbjTGvEnVWeGHJe2x1lZ8PvSSpFKb8fzC588DQGIxMwwA4fhTSc+XdH3t61ustR8Ocge1Wt+3SHq5qvW+50lKNXzL5tafsdYWjDGvVrVG+AOSPEmvtNY+6PNh96laGnG3Meazqs5wf8da+3CQsQNAUjEzDAAhqM3ofrHhqvcH+XljTFbS30v6Y0l5Vcsh/kTSu2sXScp1+PGDkn5c+/s9kv7W7+Naa/9c1UV1P1e1tvmLkmaNMf9gjHl2kH8DACQRYRgAQmCM2SbpZkmPqtrh4cPGmHyAu3ippOdI+ri19nJr7Ruste+w1r5L1dKLbt4m6UpJv5D0dFX7Cftmrf2EtXa3pPMl/Yakj0h6nqSvG2PGg9wXACQNYRgA+mSMyak6k3uupNeoOqN7uYLNDk/V/vybNrdd0+Wxr5T0Hkn3Svql2p/vNsZcFeCxJUnW2nlr7destb8n6WOqtlm7Ouj9AECSEIYBoH83S7pC0n+11v6tpHdK+o6kNxpjXuXzPo7W/nx+45XGmC2q1iOvYIzZJOkWSWVJr7HWzkp6taoL4m4xxpzf60GNMb/e0patrj4j7LcTBgAkEgvoAKAPxpiXSXqTpDtVbX8ma2251m7tLkn/0xjzPWvtkR53daukGUl/YIy5XNUNNZ4s6V9K+mrt763+V+36N1tr76o99o+MMX8o6S8lfVTSS3o87mckFYwx31Y1kKdUnQ3eKen7kr7R4+cBINGYGQaAVTLGPFnVQPqYpOustUv126y196u6Icd6SZ8xxqzpdl/W2tOSrpX0aVXrft8s6Z+p2iv4t9o89r+V9DJJX2nd+c5a+0FVF8K92Bjz1h7/jLdJ+q6kZ6ra5/h1qu58d6OkF1hrV7RcA4BRkvK8di0tAQAAgNHHzDAAAACcRRgGAACAswjDAAAAcBZhGAAAAM4iDAMAAMBZhGEAAAA4izAMAAAAZxGGAQAA4CzCMAAAAJxFGAYAAICz/j9jmovtlqJEwwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "reg_deg=1\n",
+ "\n",
+ "a_hat = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)\n",
+ "fy_hat = np.poly1d( a_hat )\n",
+ "\n",
+ "print(f'Nombre de degrés : {reg_deg}')\n",
+ "draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], X_norm,fy_hat, (width,height), save_as='02-underfitting')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 - Good fitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:09.789919Z",
+ "iopub.status.busy": "2021-01-14T07:11:09.789590Z",
+ "iopub.status.idle": "2021-01-14T07:11:10.087761Z",
+ "shell.execute_reply": "2021-01-14T07:11:10.087473Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nombre de degrés : 5\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/POLR1-03-good_fitting</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "reg_deg=5\n",
+ "\n",
+ "a_hat = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)\n",
+ "fy_hat = np.poly1d( a_hat )\n",
+ "\n",
+ "print(f'Nombre de degrés : {reg_deg}')\n",
+ "draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], X_norm,fy_hat, (width,height), save_as='03-good_fitting')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.3 - Overfitting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:10.091237Z",
+ "iopub.status.busy": "2021-01-14T07:11:10.090859Z",
+ "iopub.status.idle": "2021-01-14T07:11:10.408090Z",
+ "shell.execute_reply": "2021-01-14T07:11:10.407799Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nombre de degrés : 24\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/POLR1-04-over_fitting</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "reg_deg=24\n",
+ "\n",
+ "a_hat = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)\n",
+ "fy_hat = np.poly1d( a_hat )\n",
+ "\n",
+ "print(f'Nombre de degrés : {reg_deg}')\n",
+ "draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], X_norm,fy_hat, (width,height), save_as='04-over_fitting')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:10.411214Z",
+ "iopub.status.busy": "2021-01-14T07:11:10.410713Z",
+ "iopub.status.idle": "2021-01-14T07:11:10.414418Z",
+ "shell.execute_reply": "2021-01-14T07:11:10.414686Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:10\n",
+ "Duration is : 00:00:01 231ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/LinearReg/04-Logistic-Regression.ipynb b/LinearReg/04-Logistic-Regression.ipynb
index dbba445..1476d53 100644
--- a/LinearReg/04-Logistic-Regression.ipynb
+++ b/LinearReg/04-Logistic-Regression.ipynb
@@ -60,78 +60,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
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- "div.warn { \n",
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- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { 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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "FIDLE 2020 - Practical Work Module\n",
- "Version : 0.6.1 DEV\n",
- "Notebook id : LOGR1\n",
- "Run time : Wednesday 16 December 2020, 12:52:18\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Update keras cache : False\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
@@ -159,7 +90,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -270,7 +201,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -292,7 +223,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -308,28 +239,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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PKKYIIYQQQnxAMUUIIYQQ4gOKKUIIIYQQH1BMEUIIIYT4gGKKEEIIIcQHFFOEEEIIIT6gmCKEEEII8QHFFCGEEEKIDyimCCGEEEJ8QDFFCCGEEOIDiilCCCGEEB9QTBFCCCGE+IBiihBCCCHEBxRThBBCCCE+oJgihBBCCPEBxRQhhBBCiA8opgghhBBCfEAxRQghhBDiA4opQgghhBAfUEwRQkiXUVoqYVQZReaGDEaVUZSWSlGbREhXQzFFCCFdxvieccwemUW1XsXskVmM7xmP2iRCuhqKKUII6TLmjsyhVq8BAGr1GuaOzEVsESHdDcUUISR2MEzlj6F1Q0in1D/v6VQaQ+uGIraIkO6GYooQEjsYpvJHcWsRw+uGIaUkDK8bRnFrMWqTCOlqMlEbQAghRhim8kdudQ775H1Rm0FIz0DPFCEkdjBMRQhJEhRThJDYwTAVISRJMMxHCIkdDFMRQpIEPVOEEEIIIT6gmCKEEEII8QHFFCGEEEKIDyimCCGEEEJ8QDFFCCGEEOIDiilCCCGEEB9QTBFCCCGE+IBiihBCCCHEBxRThBBCIqG0VMKoMorMDRmMKqMoLZWiNokQT1BMEWJFqQSMjgKZjPq7xD/2hATF+J5xzB6ZRbVexeyRWYzvGY/aJEI8QTFFiBXj48DsLFCtqr/H+ceekKCYOzKHWr0GAKjVa5g7MhexRYR4g2KKECvm5oCa+scetZq6TQgJhKF1Q0in1GkonUpjaN1QxBYR4g2KKUKsGBoC0o3/Jum0uk0ICYTi1iKG1w1DSkkYXjeM4tZi1CYR4olM1AYQEmuKRTW0NzenCqki/9gTEhS51Tnsk/dFbQYhvqGYIsSKXA7Yxz/2hBBCzGGYjxBCEgDLCBASXyimCCEkAbCMACHxhWKKEEISAMsIEBJfKKYIISQBsIyAfxgqJWFBMUUIIQlAX0YgtzqHcrVMUeAShkpJWFBMEdKF8Bt496GVEai8r4KslEVpqURR4BKGSklYUEwR0oXwG3h3Q1HgDYZKSVhQTEUNG+mSEOBk291QFHiDFddJWFBMRQ0b6ZIQ4GTb3VAUeEMfKt0n70NudS5qk0iXwAroUcNGuiQEiluLGN8zjrkjcxhaN8TJtstgGxZC4gXFVNQMDakeqVqNjXRJYHCyJYSQzsEwX9QUi8DwMCBJ6m820iWEEEISBT1TUcNGuoQQQkiioWeKEEIIIcQHFFOEkJ6FxU0JIUFAMUUI6VlY3JQQEgQUU4SQniXo4qb0dBHSm1BMEUIiJyoREnRxU3q6iBcowpMPxRQhnYYthNqISoQEXUmcbXyIFyjCkw9LIxDSabQWQrXaSguhHi+PEZUICbq46dC6IcwemUWtXmMbH+IYivDkQ88UIZ2mwy2EkhBC6JZeguyZR7zQLZ//XoZiipBOMzSktg4COtJCKAkhhG4RIWykS7zQLZ//XoZhPkI6TbGohvbm5lQhFXILoTBWrBmbKPsVDewlSHoZfv6TDz1ThHQarYVQpaL+zoXrveCKNUIICReKKUK6HK5YI4SQcGGYj5AuJ+4r1sIIGxJCSCehZ4oQ4oqgPV0MGxJCkg49U4QQVwTt6WLYMFzo+SMkfOiZIt0Bq4onFtbYCRd6/ggJH4op0h1oVcWr1ZWq4iQRsMZOuNDzR0j4MMxHuoMOVxUnwcEaO+HCFjeEhA89U6Q76HBV8W5E1HYmCa1oiDX0/BESPql6vd7xi8qyXAcARVE6fm3SpZRK7VXFQy6G2W2MKqMtHozhdcMA0PYavUiEkB4lZfYGw3ykO9CqihPPGHNrphenkUYaNTDfhhBCrGCYjxACAMJcmoyU6YqVdgxXEkLChGKKkB7CSlSIcmmqtWpbvk0ShYm+PMD04jQ23rgR0k4pMfYTQuINxRTpDlhnyhFju8cwvTjdFBVju8ea7+VW51DoL7R5ovbJ+1B5XwX75H3Irc611S0a2z0We3GlD2Fq1FBj3SUBSRTLhEQNxRTpDlhnyhELxxYst52s/DLmVi0cW4i8KKSdADALTzIPrB0W+STEPRRTpDtgnalA0Go+6T1RRowVywFEXhTSTgAUtxaRlbJtxyU1DyxM7xGLfBLiHoop0h2wzpQj8mvylttOMHqv8mvykSep2wmA3OocZiZmUOgvQEpJyEpZpJFObN2lML1HbO9DiHtYGoF0B8Vie50p0sbUtqm2prduMVYsFzXS7TROqnx3U6X1ML1Hxa3FyJ8nIUmDRTsJIYlHJOi0EKXVe0lFVGC1W4QiITHGtGgnw3yEkI4RVq6PVa5XNyZUs0UMIfGCYT5CSMfQhE2tvlKWIGyPSjcmVMctZNmN3j9C3EDPFCEJRu/pGdw1iMFdg7GuDxSFsGFCdfh0o/ePEDdQTBGSYPST2MKxBSwcWwh8QgsyNBeFsGFIzDlen3U3ev8IcQPFFCEJRlTZGwh2QgvS6xCFsHFSOytphJV75vVZ0/tHeh2KKUISjH4S0xPkhBak14HCJhjCCqt5fdb0/pFeh2KKkASjn8Tya/LIr8kHPqHR62AtmKLIFworrOb1WXejSCbEDRRThCQY/SQ2v30e89vnA5/Q3HgdjKJj78N7u6JprpVg6qakenqYCPEGi3YSQgLDWEwyk86gUqskrrikcan/7OIsaljJTZNSEirvqwCIpoBmnEoRxMkWQkKGRTsJIeFj9NKUq+XQvTZh5CwZPVEZKWPqCeqUN0d/n+N7xlHcWoxFWI1lEQihmCJRUioBo6NAJqP+LiUzBERWMIafslI29HyrMCZzoyis1qqmgqlT+UL6+5xenMbGGzdGFjrVC7vpxWmWRSA9D8UUiY7xcWB2FqhW1d/j/EabdIxemqltU6F7bcLIWTKKQiktRR7GEpXBiMoTpBd2enp1gQIhbCdDomNuDqg1JodaTd0miUbU5iTs/KGhdUMtOUtBTObFrcVmHpCUlrBcXUYd9Y61wNHQ5yNJaQm1ag11rOS5RuUJEgk7KSU1xSZphXll3Q89UyQ6hoaAdOMjmE6r2ySxBJG75OUcYeQs6UN31Vq1KWA6LV70HqBKrYI+qa/l/ag8QUbPXaG/EGiYM4raXWHCvLLuh2KK2BNWblOxCAwPA5Kk/i7yG22SCWLC8HKOIHKWrCbvKOtsiXK3DlxzAIX+glA8dkqEhJ10323ig+12uh+KKWJPWLlNuRywbx9Qqai/c13m9u6xBHvRhOF2co9q0rGavN0Kh7B7GVqJx06JkLCT7rtNfLDwbfdDMUXsYW6TN3oswV40Ybid3I3nGFg90BFPi93kXa6Wm6voxnaPWdoRZS/DbhEh3SY+WAy1+6GYIvYwt8kbPSRCS0ullppSudU5TF482bZsfnpx2lIYGScdAB3xtFhN3uN7xrFwbKG5vXBswdKOKHsZdosI6TbxwXY73Q/FlBk9FqKxhLlN3ughETq+Z7wpjrT6UhN3TAj3tRJGxknn4NLBjnharCZv0TWt7OiUoBGFE72KkLglfFN8kKThqZ2MLMt9AEYAPAvAcQAziqIsuzg+/u1kRkfV0Eytpk6Ew8NqXg/pDKWSGhabm1NFSLGYvJyqiO4himXYmRsyLTWHpJQEAG11iPToW7KYEUarFrfjM6qMYnpxuuW1Qn/B1I5OjX+QY+PkXFzeT4h5OxlXYkqW5bUAPgLgjQBO0L31FICbAfyVoihHHJwn/mIqk1FzXTQkSU2UJp0hqWI2BiIwil5x+mtqZKVsS18+L3363EzgTvd1Oz6lpRLGdo81Q335NXlMbZvqiJCwuieRgLUTp2Y4OVcUnytCYob/3nyyLD8HwP8DcCWAMoB7ANza+F1uvP7Dxn7Jp4dCNLEkqflGESWdR93eQwsv6VmuLiOTzviqhq6Fe/Zv3w8A2LRrk2kYymnit9ucptzqHOa3z6P+/jrq769jfvt8xzwyVi1kggwnOjlXtyS3ExIGbnKmPgwgB+BTANYrinKBoihbFUW5AMB6AJ9uvP+hwK2MAuYJRUtSxWxEItBPe48g8mU00aOF9wCgjjqqtWoz72XLhi2e82CcCCWnk32SkrStWsgEmaTt5Fx+xi1uOVmEBI0bMfUHAL6rKMq7FEX5tf4NRVF+rSjKOwHcC6A71n93ew2kuJNUMRuRCDRr7+Fkkg1yKf/A6gHLba84EUpOJ/tOrRQLQkDo70lDu/8gk7SdnMvPuHVbEU5CjLjpzXcSgO/Z7PNdAGd5N4eQBpqYTRrFYnvOVAcw9qdzk8+ShPCNk/57+n56Vj3iRP0Dw0ATELV6zXNPP+2e9AnwUXnT/IxbEj5jhPjBjWdqFsDzbPZ5HgD+LyG9S0QeTT9egyC9SQeXDlpue8XJ/cVtOX0QAkK7J6sWMk6IOsyWpNAqIV5wI6Y+DeByWZZPF70py/JmAJdBzakipPuJUS2yuAiJsCbNuNyfG4IcC7/3H3WYrduKcBJixE2Y7yCA/wDwI1mW/xnqKr4nADwHwPkA3gzgGwAelmX5PP2BiqLcE4y5hMQIbeVerbayci+E0GSY9X1KS6WW6t6AP2+S01BbJ4mqPpKTseiUbVGH2ToVWiUkKtyIqb0A6lDrLFwFtRSChlZ74Q8bP0YkwWuEBEcU9Z06tHLPa+6Nk4la5KFw60HxIgg6KXCCyF0SYXcPTgREWLYZ7ZTSEqqNunkppBhmIyRg3IipG6CKKULiR4e8RC0MDbUWFg1p5Z5Xr4Jxoh7bPYaslG2Z/EXncutN8iIIrI4JWmiF5ZUJQgh1wmM0vmccldpKAc4+qS8WHkNCugnHYkpRlB0h2kGIPzrhJTJ6vyYngYmJ0FfuOVnJJsI4US8cW0A6lW6Z/EWrAN0KFy+CwOqYoL01XscPsBZ2QQghP7Y5xVg2o1qrJiLnjJAkwUbHJH54SewOur6TyAZjdfOJiY6s3POavGtMgAbQMvnPLs4GkhjsJdHa6pigvTVh1UcKIsFcL85q9RrK1XLgK+24ko6Q8HHd6LjR5PgiqI2On6koygcar58A4HcAHFEUpWZximT05iPR4aUvX9A5UyIb5uYS1a/R6FWZPzqP5dpKP/KslMXTf/O0cF83obWgc6bi1APOqmddUOHIsO+XDYoJCYzAGh2/BsDnADy3cdK6oihS472XQK2Avk1RlD0256GYIubEocm0yAZjjlRSmi83sBIGogldtBot6ua+naYTwi7IhsWEkFAJpNHxiwF8FWoS+jsB3Kx/X1GUH0Itn3CJJxMJ0YhDXz6RDUltcdPAbWgtqtpEcaop1Yn6SN0Whou6QCghUeAmZ+p6AP8F4MWKotwIYF6wz30AzgjCMNLDxEG0iGxIeL9GK2EgqoIedW2iOEzKnRB23VbQ0okID+LZxuHzkQSbSGdwI6ZeBuCriqI8brHPL2DfcoYQa+IgWoK2IQbV0t0KAyuPSWmphMFdg0jtTCG1M4XBXYOuJg4nk07UVbs19LYO7hrE4K7BQCfLOHnigsCJCA/i2cbl8xF3m0hncCOmngngiM0+/8PlOQnpDYwrAcd1f2RjILREPfWsPCbje8ZbKqcvHFtwNXGYTTp64TK9OB2L5rh6WxeOLWDh2AKq9SqmF6cxtnssEpusiNo74iRsGYTXM2rPqYg42kQ6gxvh858ARm322QyAfk0SDTEQJaZY1cGyElo6wpwkRROglcdENEm4mTjMJh29cNETZS6RsU6THmMrnqgpLZUwMjmC6cVpVOtVzCzOxLIPXxB5YnHMNYujTaQzuBFT3wAwJsvyy0VvyrL8+wB+D8C/B2EYIa5xKEoiwSqp3iC0KjPTQrGkFxrTi9MYmRwJTFC5zdsRTRJuJg6zSUckXDqZSyQSrHpb4874nnGUq+Xmdh31yPrwWYUtg8gTi2OuWRxtIp3BcWkEWZafD+CnUEN5uwBsAPAGqL34zgMwAeA4gDMURbEMB/ZkaYQoesd1K2ZjGYeSCmZYPX9dTatqCphdB5z+jvZl+MYl9ABQ6C9EUoOptFTChV+4EIeOHwIA9KX78K03fwtbNmxxfLyo/IHXEg1h1nzSXz+dSrfU6sqvyWN+u2gtTjTE6TNCSBfivzSCoij/CeDVAB4FcC2ASxsn/lpj+zEAr7ETUj1LlF6TOIe/vGA2lmbenzjcv1VCe2PlYKUhpMa3ivMtzHJPoiC3OodnZJ/R9NhU61VM3DHh6niR90L0zd5JUm9Qib+i8KNm6/7t+7H+2eub++bX5DG1bcrTdcLC6EXLStlYeEeizuMiJGxc+a4VRfkJgCEArwfwdwD+L4BPQBVWI4qi/CxoA7uGTvSOMyPO4S8vmI2lWUmFuN9/Q2idcVMBp78jjYNrxPkWxa1FZKVsczuFlK+cDL8T3OzibFt7Gr+IRJaTpF67fZzeq1XOy/ie8eZx6VQaWSkbyco7q3vRi9FCfwEzEzOxWB3IVW6k23HdTiYIejLM56VFSlDEOfzlBbdjmZD7dxKqCrI6uN/q3qs+uKolP0ffnsYPxnvU+tVZ2Tm4a7AlGdwYfnN6r1bjG5dK5XFqt+OUuIydFXGqvE9ii/8wnxFZllfLsvy7Xo/vOaIsRBmHiuJB4nYsO3X/PsOJThJ3jfsA8Oxd8ruMu1qrWm57xejFAOA7qdfpvVo9g7is1Eri8vu4jJ0V9J4RP7gSU7IsP1OW5f8ty/LjUGtOHdS9d64sy3fIsnxW0EZ2BVEWooxDRfEgcTuWnbr/CMKJfiYALxOcPsQkpSWkGl/UgpwgjWLh4NJBW5EpqpOlx+5enYQB7VZqdSovKAnCxEgSVrklUaSS+OCmN9+zAPwAal++RwHMoNXl9TMArwCwNUgDIyEOCctBEqWQi8NYdur+I8iL8zMBeJng9OKtUqugT+oLfIL0IhbsjrG7Vyei1M4rOLZ7LHTPRmmphHK13HzmudU5TF486UrERZEMrh87bVGB1+uHZX8SRSqJD248U9dBLdr5VkVRzgLwJf2biqL8F4DvALgoOPMiIu4Jy0mil8Yy4HCik0nDzwTgpY2JUbxVa9XA26BMXjyJTDoDAMikM5i8eNL2GDuxZHevXkSpUYAtHFsI3bMhSoKfuGPClYiLOpzl9/ph2Z8E7xmJL27E1B8BmFIU5Z8t9jkE4Pn+TIoBUa686zZ6aSwDDic6mTQ6PQF04tv7xB0TqNTU5ORKreKo5ILf/nZe7ssowLRj3ZzDLSLR51YIRh3O8nv9sOzvth6JpLO4EVOnAnjIZp/fAHiWd3NiQrclbEeJ1VjGIQQYJAGEE932puv0BODFa+QWJ5OlyGvnNvyj379cLSO3OudKlBoFWH5NPnRhKxJ9boVg1OEsv9eP2n5CRLgRU08CONlmnwHYN0OOP92WsB0lVmPZCyFAl4Ixjr3p9HjxGrnFWHhSSkuWrXU0r53b8I+ogbGbJfFGr+DUtqnQha3IE+nWOxl1OMvv9aO2nxARGRf73gfgD2RZPklRlCeNb8qy/DwAF6MbevNpHgbiDaetc3ohBKgJxlptRTBafLbMetNpkzzQWg9HSkuoVCsY7h/uSF2cToSIiluLGJkcadawWq4uY3zPeEstJTM7vIa7NDQRZle3ae/De/Hqf3l1s7XMb8u/dXGH1ljVO9I8kUb2yfuax23atclSFJqdo1P4vX7U9hMiwo1n6tMA1gK4Q5blEf0bje0vATgBwI3BmUcSiVOPUzeGU42eKE1IAY4EozGEUegvtHk69B6VcrWMGmodSyTW25dCClJaCmRVlT7kNr5nHJXqSkFHY7Pe0lIJUlpqbgcR7tJwKhDHdo+19Og7dPwQNt64MZDVZV4TrKNOLCekl3HTm28KwA4ALwPwcwB/BQCyLB9pbP8egL9SFOX7wZtJEoVTj1M3hlONQjKTcSUYnYQwRB6VTiUS6+3rk/pQqVVcT96i3CajEMhIGcu2LsvVFSEjpSSUq2W1EXHjT1qtXsP80XnsfXiv7b3ocRpO1Vd+1xOEiPHq/Ys6sdwP7N1Hko7b3nw3QC198DUASwCqAOoA7gDwSkVRPha4hSR5OPU4hVX/KcrEdqOQrFZdCUYnCeUij0qncqr09lVrVU+J4iIPiqjkgpmonDsyhzpW2mAt15ZRWiqpta/qlZbXx3aP2d7LgWsOoNBfcJWDo++RqCcIEWN8vtV61ZHASHJiNr1qJOmwNx8JHqc5U173tyPKPogduHaUOVN6nPSIE+0zd2SurU/b0Lohx/3mjOc0eumM1N8f/N84Y86URhC98rTnO7047eq8cegt59UGt7374nCvpCcx7c1HMUWiJ2gB4qWxcVCCLmhh2CH0k9PA6gEAaksWq4nKyYQmmiRFwkmriu1kcrRqhGwkrObLevvCmtiT0BzYiNcmzG6PS2KzZ9IVUEyRGONF/FjhRZxF6c2KAfrJSY/TicpMUIgmPTfCyQn6a5/6O6fi0ScfxXJtGVkpi6ltU9iyYYtru41EMXknUTB4FYBuBWkShSbpCtyLKVmWawC8KK26oiiWJRcopkgLQQsZL96hoAVdwjBOTnqcTFRmE3/cwzFOBYtofA5ccyCUe7EL48Z5TDslAJMoNElXYCqmrBLQ7/H4891ATCadJ6rE7aBX9XlJbO9UmYaYVn03S1b22lpFS8L2UqE9qJVdTs7jdAWcaAzCSpI2NpMe7h82LY0RZkNlL8+gUwU1WbiTxA2G+cgKvRzqCjrXyex8HRpjt96L0lIJG2/c2PZ6ob/gyPNh5Skw2jJ58SQm7pgw3TbmQGnhurcV34aFYwsAgPyaPKa2TVna5TVBXuThEI2P39CS2TOyC2F1IsRFzw8hQjx5pkiv0QsVyc0IukyDWeFSYxHP2Vl/1zG7vEvvRW51DoX+QlvBUC2/yc5DYeUpMNoytnusuT29OI0LvnABphenm+8vHFtoyd0qV8sY2z3WFFIAsHBsAeN7xi09KE68Tk49HKLxsfLYOfHsmD0juxIHdu8H4dlLcs0qQqLAt2dKluV1AF4B4L8A3KkoijjxovUYeqbiSC97ptxi58ky5mAB6pjWWhO8AQAHDrQc62VlnREv3guRp0Sb8P14KKzysfxgV1Jh1QdXtRXXdOppE+HG2+fEsyMal0J/oc1TZ7yOnR1BeJXomSJEiP/VfLIsXw3grQB+X1GUY43XXgTgmwDWNHb7MYALFUWxbFRFMRVTErqs3xde79lOeI6OAtPT5sfryeeBbLZpw9jlZdyJ9qX+dmEos3IBfiZDO1FmF8ITCbJMOoNKrSIsZZBOpZFbncMjxx9pCqEUUuiT+oTCSFSzSrNP2imhBm+rE/3iRMyKVlB6sc/4DKzGxOs545TkHgadvt9eG98uIpAw3+VQV+od0732MQCrAfwT1CroZwN4uxcLSQwIqyJ5nHHaR9CIXUjUTRL9wkKLDZ+4aUEoNKzCLcaQEYBAEnTtQkqiEN7M4kwzhDcyOYLJiycxvG4YaawIqUxavOB3eN0wprZNYWZiplmVfOOajXjeM5/Xsl9+TR7FrUVL+4b7hz333vOLk2rkonY2XuwzPgMpLfmuhO5l4UAc8Bri7HQFdlZ87z7ciKlBAA9pG43w3vkAPqcoylWKoowDuA/AG4M1kZAQ8ZonZrf6L5cDCoWVfTRSKfG2zoaho2gTAYD1xGjMcTm4dDCQydAup8h43XK13NLqpVwtY+KOCeyT92G4f7jpkarUKsivyTcFU6G/gAPXHGjaqp/Ms1IWv/j1L5pjUOgvYH77PHKrc5b2+em95xcnuVjaPbrJxRJhfAaVaqUrVrp5EUZeRUqnc8SYk9Z9uBFTawH8Urf9ssbvr+he+y6A9X6NIgkhzGX+nSoh4LUkgpNyDvp9sln1/CMjwN13q0JLktTtfL7FhtpgvjkZ5tfkkV+Tb5sYjRPNwOqBUPqy2XkojB4YUc86baLwKvisJh4r+/z03rPDbqJ349nxu8zf+Ay0UgpJ9yrpFyo4FUZeRUqn+xomuY8iEeNGTB0DsE63fT6AGoDv616rAzghALtIEvAaIov63BqlElAur3iFcjnn4TknIVH9Pk8/rd7Lvn3Ali2tx05NtQiz7B1Tzclwfvs85rfPt02MYYX13GIUAlPbploElX6i8DqBeD1Om5w37doEANi/fb8jcaGf1Fd9cBWknVKbYHLjAfEivNx4ZczEmNNzBFXXyy/GMdWv6nQqjLx+Vjpdt6rX6mTF5TMWJm4S0L8NYBjA6QCqAPYBOKQoyu/p9vkSgDMVRcnbnIsJ6N1AmFXDgzi3XXJ5QlcvhlHzyO56bmtWifZ3ep69D+/F2O4xlKtlZKUs/ul1/4QPffdDrlc3eq3K7iQx3M1qSS8r4zq5Im9UGcXM4kwzPJuVspiZmOm4R0u0ulFrZO23rRGJli5aHRpIAvqnATwPwGEAvwDwXABNNSTLsgTg5QB+6s1GkjjCrBoexLntvFt+6mpFWMlc5AUJM0zgpWaVKMzkNPSlCSlAzbn609v/tCV/qrRUarNF9M3XLORjdz/64zSMnhE3HhAvoSfRMW6/2Tu97tyRubY8tygSovVjmkIKfem+pv1afpwdSU2c73Z6IUfMsZhSFOVrUFfq7QMwB+DdiqLs1u3ySqghvqlALSTxJeg2MEGf204s+RFsIqHmU2BpgkDaKWHVB1eZTpxmxSfDotN/CI0lEMrVclNITC9O2wqkmcUZjEyOtHg59ILH7n70k7roeMBdmMZL6MkoLLTVkdr9ORE7Tq8rej2KyU4/pn1SX/P5abl4UQqjXghThUkv5IixnQxJFm7qQtmF8fzU1RKFIYeGfIUNndYd6rTLPKzraSGZ2cVZZKQMqrUqhtYNYf7oPJZryy37auEe42uaLXaFQfXFOu3up2mX7lloLW22bNji+T7dhJ6MzY6NAtNLEVazYqCisHFWyuLpv3na9b0GRSda5rihi8JUkdBF4Ve2k4kNMW10mxjcJKbbebf81NUSebV8tuNxEl4CuidZtilYoJZU0MJup5x0SjOJPStlhULKaIvIm6TfVx/yMd7P5MWTLV4HbR99Lazl6jIm7pjwdJ9eQk/6Y6q1dpFYrVctPSR7H96LkckRTC9OQ0pLuO4V12Fs91hL2x7Nu5VbnUPaMBVUqtEJFyB+noxeCFOFSS+EX+mZ6jQJTXqODWEmvbtB5NUaHwdmZgDt/1Q2q247FGl+KmLbffOL4zdDM2+S0QvhxCtg9ORo9azsqsYbe/5p+wPA9GJrBfuovCOitjiA9WfD7Bg9+vuJ2vPi1IsWFVGPD4kN9EzFhqQ3E47asxZm0rsbRF6tYhHo61vZp1JxVdJB85ikoeaIuPEE2SVVR1Vx2SrXxEluEuCuAGblfRXMTMw48qSN7xlvEVLAitdB5HmIyjti5iWq1WuYXpwWeqfshBQAzzlgYWD8fGqFXr2WiwiaqMeHxB96ptwQRO+6pHum3NofdL8/0fmA+PQUjMhzZpdjElUOyuCuwRbBkl+Tx/z2eQDinKmB1QNYri7j0PFDzf2ntk21eSX8NoMW5QlpFPoLANCWMxVFuQBA7LHUU+gvtHlJnHimDlxzIHLvpIbV51N71npPIb1DJCLomQqEIApJhrkCrhO49awFXXxT5BHqRIFPp0TkObPLMYkqB8Xo+dFva96k6vurePpvnm6WPtCElLa/yIum92QsHFvAwrGFFq+bnRfDzDOXlbIobi22eCIK/YXAhJQX74relvya9hJ+Ii+avnhqVsoiJZgDNu3aFLqHx+n9Wn0+tWeth3lLJG7QM+WGuOTrRIlbz1QnxixOzyVoT5zTy8Y0Zyq1s30SL/QXTO0Q5VGJvGhWq/eklNS2As7oWRIdb+YFCxKnKwmtnpPfIqB6wvbwOLXV6r7NinnGxTMVx3xEEhruPVOyLB+TZfn/022/T5bl84K2LFHEJV8nStx61joxZlE/F30e2fi4Oiaa5+yRR4BVq9SGxqtWAXv3BnNJ3Tf+wV2DGNs9ZvnHPKrVNEZPSl+6zzJ3S+QxM3vNrBm0qJSAsRCl0ROib54cJnarwpzktnnJ3zGb4MP28DhdBWf1+RQ96zjlLUWVj0jihVWY79lo7bO3A8CWEG2JP0kP0QWB23ICnRizqJ+LVZhxbEzt/weov8fGgrmkTZgrLkxtm2o2GS70F1CtVS0n1+LWYosAy6/JCydNY+hL3wzaLGFbf62oEortwq1OxIfXUgtamQk9YYd8gwgvG0OuB645EKvl9SybQAAgY/HeEwBO7ZQhiUATEsQ5nRizqJ+LVR5Z2ZAEbNz2eklBTSogfn/MtYlfwxj2GVg9gFFltMWrpiWoOz2vFmap1+tqc1y0j0sKqZaJ3GiXHUGFcopbi23n0V9DSkuoNkLWRpv9IvrMmAnJIO9XKz9Rq6v1xEpLJVfncvusOs3QuqGWz3TUNbFINJjmTMmy/BUArwHweQCPQfVM7W38WFFXFOUDVjskNmeKEBH6PLJUSi2PUK2q4cb5eWC5tZo3CgXfuVRB5sAEMXE6PYdxP21yDaqhrxlOVuNZrRAMwk7RdfRjZbwPpysInY69m1yrIOsqBV2jKW45SnGzh4SKac6UlZjKA7gdwIjLi9UVRZGsdqCYijkRJVEnFv14acnvWoL+7/4u8OijrYIqgJIYfksD6AlisvN6jiBKNti1knF6XiuBKqrA7rW0hNlYeR2LIJK8jQRZSiPoshxxLaBJUdUTuE9AVxRlAcALAQxiJVfq8wAusPm5MACD/RN1cckkE0apgW55HqL70OeRVautIb/Dh4HBwdZzBFCsVZ83M799HvPb5z0nlweR82F2DmOi/OCuwZZl8l5yaozL7QdWD5i2kgGch8usQqd6RHa6KXlgNlZe84uM5zMr5Okm1yrIUhpBl+Vw8lmLohkxE9F7G8elEWRZrgHYoSjKDX4v2hHPVNKLY0ZJGKUGuuV52N2H6P25udbxBNRQX0zuX+SR0TcGdnsOvbfAzNuTQgoj/SPCHCK7axqvpSVXa8U/jSv5nIbLnIQLAXEJhSBCaF49G6PKaFvrG1EhTzd4DduK9gvaY+PksxaFxypuzZlJKLgP84VJR8RUnGoPJY0whE+3PA9JWvE8Aer46O8rhJ59YSOqMA2siAY/k6pdPSgvk43fau9tffyqFQz3Dzf7wRnHwYhIqLiZSIMWF6Jq7p2ayKMQME4/a50WM1GLOdIRgq2ALsvyqbIsj8uy/GZZlv9QluX4rfqLuvZQkgmj1EAUzyOM0GImY71t1rNvZEQdz0IhVkIKMF8tpVUfdxK6MAshmdWDAgApLXkKxfit9q6/p3K1jBpqLf3gpJRlymdbGFRbhadhF1Z0W9rALnyVW51rq+el5dG5xW2ozE2IOKgwnJPPmptwYlB2sX9fb+NKTMmy/AJZlr8J4BCAr0LNofoKgEOyLH9TluUNQRvomahrDyUZt7WknBDk83AqksLI/TKG64zbIsIYTxcYJ4u9D+/1nd9jdw3tnNoEI6JcLWNkcsT15GU3adm9L8qNMstdMmImziq1FQ9In9QX6ETayVwct9dyI2DCvg/jc5+8eNLR5zwou6IqjEvigWMxJcvycwHcC+DVUMXUvwD4aOP3wcbr32vsFz0RT2DEQJDPw6lIcttH0IhItIXhYQvIg2YmaIyTxdjusbbJQzTR9KX7HE+WZhOSNsEcuOZAs4GwHmNlcif3aBcis5vU9F4kDf29WQlAvTjTxnt6cbpFnFVrqsC2m8idekScCNqDSwctt53idjGCG29M2Injxuc+cceEI5HEopskCNx4pq4H8HwA7wEwqCjKWxVF+StFUd4KYAjA/wfgFAB/E7iVhOhxKpKshI8TASMSbWF4PAXX0U8wqz64CtJOyXaiMRM0xsmiXC23TR6iiaZaqzqeLO0mJG2iEwkqN5NXEF4EUYV0/b1pthrDfVJKahFnoga8mihzYuf4nnHMLM6gWq9ienHa1EvnRNAGtWLO7XmCWCEYlsfKqUiKqgk46S7ciKnXAviWoigfUxSlJbahKEpVUZSPA/gWgD8I0kBCmmgCSBRqEwkiK+HjxLslEm1heDwF1zHL69G8SGM3DGLfySlUpBTKw4NAqeR4yb2+rYg2eYgmmuH+YceTpZMJqbRUaltpp+UX+fXSuPFuDPcPt42HJij1x7lp/dI8d0OUOZnI547MoY6VBUBmXjo7QauNq3a93Oqc5zCj/lq51TmUq2XTshZ+zq2/j7A8Q05FUhC5TlGXZSDR40ZMPRfA/Tb73N/Yr7volhpJSUcTQCJmZtoFkZXwceLd6lTSvOA6Vnk943vG8YmbFjB8BMjUAGn/AjA+bjp5GCeLqW1TbZPH0LohpHQLVbJS1tWk4mRCEoUT9eURnHgnnHg3rLw8Rlsz6QyWq8vC69rdk6hZsiY4nXqTjOiFhDZBb9q1CQCwf/t+oaDVj6smDr3m6+jFc1bKorRUCqz/Y9CJ43Y4FUlB5DqxxhRxI6aOA1hvs88LGvt1F2EkMhP36AWQkXrdXU6UE6HkJKTnRWgbj5mcbLuOKAla70UaOgJIDaeGVAcqs9OYOzKHTDqDNNLCsJU2WWzZsKVt8ihuLWKkf6TZTNZJXSY9TiYko0DUh82ceieceDcA61wsva3VWrXpHTJe1+6erCZrJxN5cWsRWSnb3DYKCacTdFienU71fwxrFVwnE8KZd0XciKnvAXiDLMu/J3pTluVzAVza2K+78JvITIJBL4DM3rdDEzKzs6qYsRJKdiG9UkkteTA9rQptkXdMhFGcT0y0XUc/wWSlbItAGlo3hLl1QLXhSKqmgLm1QLVeRaWm1kxyOnk49X4EgXG5vn7bqXfCyrthxMmEZhSt1XrVcZjGarJ2MpHnVucwMzGDQn9BKCScTtBW4+oHs1WNQecVdcMqOOZdkYz9Lk0+BDVv6juyLH8RwN1QGyA/F2q7ma0AagA+HLCN0TM01FrEkjWrokErgKn1wFtebi2E6SQZXBMytZoqXpwUJDXrVTg+DpR1OUBOvWMOxLlZ7SdA/SZ/9ZExfOKmBQwdVYXU+NbG6Vx+K9a8H7X6Sk5WIIUGRWNm4NCvDjVFizHnR1vW7rSwZXFrESOTI82cLKctZLQQo75Qp5txKC2VMLZ7DAvHFgCIq6Nr+4lWIVo956F1Qy1FIDs9Qeur04v6P5IVRJX8SW/hqgK6LMt/ALW21BoA+gNTAI4BuEJRlK85OE+yGh2z8W/88PpMvFRiN6sIbzwX4KxNTMAV5v1UXg6tarTgHjOXz7VVQ8+vyautYAz2A3B9T06FjQg/TYadtHLx8oycVkqPuvJ3Uoh7I+K420cABFUBXVGUf4eaN7UNwCcB/GPj95sBrHcipBIJa1bFD6/PxEtSuZknyRh2dOodC7i8gp+ck9DCE4IxE5174diCMJzlJQdF69Gn3Y82OdnhtoK5HrNVeqLXvNyPVfhLC9HqhVQcQ0xxWekW9yTxuNtHrHET5gMAKIryWwA3N34ISRb6UKFJ+KmNgQFgYaF12+xcTkSdJgQDwipUZEdo4QlBaLy4tdjWQw4wD2eJXrP79m4lWsyOtatgbnXNoXVDbZ4ps5V7+ibKWm6WH++DqM5VHNuYmIWSO+2J6XSSuNv7YxJ7svHUm490Ab1a7iFIL2MXeCxDS/4VeN9EPeTya/JCz5qZt83u27uZp620VMLI5AimF6dRrVcxszgjLGoKqMVK9eNgdc3i1mLLPWn30zYcgqrqfr0PVqsj9UTtGTITCZ32xNh5YYMepzBb85D44SpnKigSlzPVjQSct9PVeMmzsqKHc/D8eiOM+UEAmmLm4NJBYaJ0bnVOmNuk5RbZ5TOJcpKaJSpc3oPoXPu377ccE7Mxc5qH5SenLgjMrt/pXC+7z17Q4+T2/pgzlQiCyZkiXQTLPaiYeej0r0tSsMU7g6pbFpJ3Mehv6Przje8ZR3Fr0bMnTLRcX19QsrRUQlbKtp1fFDIxK2pqV5xTSkuePSoi74OdB8Ps/aBa/YSNmZ1uPDFBfCbtvLBBj1OYrXlI/KCY6lU6Vd077pgJG/3ry8v2NancYBSy09PehFBAosw4URkbIY/tHnM0kTlttOwnnGPVhBgwnwSNIkxf4V2bxPZv3w8A2LRrU9P+vQ/vxfzR+eYk+7u/87uoVCu2k67ZWIiEhd0kbva+2eRrvLZRfIoaPYeJmZ1uFk50IiQYdJgtrGKkJJ4wzNer9HCoqQWzEJ6X0J7TMR0dVQWUkULB3XNwYaNVCMEY3hBVvdZe9xJOCiOco7+WnhRS6JP6UK1VW+7TSQhFZP/CsYWWfoJZKYv8mrxtOMhNyMhuX7fhJ7vnmUYa1fdXTY72R1ihKi+fIbe2ON2f4biehmE+YqALkqcDwcxD58VzNza2Ug19elrdFmHm2XLrXXJho9U3e6PnA0DLN3T961bhD6eNloNIrDXzUNVRb/bam1mcwcjkiOPwosh+Y2PmcrXsyONgPNf04rSpZ8/ufFbvizxgds9zuN/cs+eXsDxIXj5Dbm1xGmZjCQMigmKq14njqr5O2qT3BNVqakXzUsldLSjNXn35BKB9WyOXU71QxtY4bnPXXNhoGkoqlTD7GQnLO4GfTwIbl1LIr8k3J+/c6hz60n0t55LSkjDU57TRslm4wyovxvgeAOyT90FKtYestF57ddRRrpYdhytF9ut75wFoNhG2m3RFuV1mk6/d+azeF03sxvvQP8+ww01h5Wd5CZl1omchSxgQDcdhPlmWNwAoAPhOo9YUZFnOALgewOsB/BbAxxRF+YqDczHMFxfiuKqv0zb5vZ7+eCNm/7+0kKA+3BfivZqGikZHUZ+dRapWQzUFVNJAFhJSjTDl6DfH28JpKaQw0j/SFmra+/BejO0eQ7laRlbKYmrbFLZs2OLfRov3zMJ9TsivyWN++3xzWxS+eeT4I3j1v7way7VlAMD6Z63HXW+5yzasoz+XcfVhkKvW7FYHmq1uDIuoVw52wpY43SPpOIGE+d4P4F8APK177W+giqkXAngJgFtlWX6JFwtJRISxqs+vZ6nTKw39Xk9/vJ58vv01DS3MeuCA6qUKKrndBNNv9nNzSDVsl+rAqiqQ0iW0G2sZAarHZ3pxGhs+tQGpnSmkdqYwuGsQbyu+rVn8slwtY2z3mKtVV1bf+M3es0pI11dDFzXs1drOaIg8QFs2bMHg2sHm8b/49S8chXX05yr0F1yFqNysXBN50/TXzkpZlJZKHQtJxSnpOixb4nSPJD64EVMvBfBtRVEqACDLchqADGAWwAsAnAPVO/XOoI1MHHEMnZkRxqo+v6vMvNjkZ8z9joGxrQygCqSpKftjg85dMxkH01CRyHagpQWMSIgAwKHjh5r/1soT6IVXuVr2XDYAaA0nmoUQtfsShfumtk21THpeEQk5N4LH7eTrJidn8uJJZNJqI4tMOoPJiydtbQ+TMJb3ey2LwFIDpJO4EVPPAXBIt70ZwDoAk4qiHFYU5ccAbgdwdnDmJZSg6gh1goD7xAHw7+nxYpOfMfc7BsYVePm8r9WRvmrquB0H/b1ns0Cq4cXWtYDRhIAX7CZv/b2Wq+WW6yxXlx3XVNLCWXom7phomUxF1dedoBdyKaQgpSVsvHFjs5q6neDJrc6huLXYLPQ5vmfc8pm6EUATd0w0vYGVWgUTd0yY2u42+T/qyukacUv4dmNPXMaQhI8bMdUHQJ8A8rLG9l261w4DeF4AdiWbJBXEDGNVn52nx86L5MUmP2OuXW+/WmcImzY5925puU/6ZHPtNY/4mjysxkE07vqxnpkBRkbaWsAYw1UDx9RkdS1pfeCYevr8mnxLwraThsH6ey0tlZq5SYAaTrSrqWQ5FAYRMrVtCoX+AqSUhEJ/AVPbVM+h3YSnF3J9Ul9LHz9ALHjsandpqwydJsJb3aOV8PIakrJqv9Np4pbw7caeuAlBEh5uxNRhAKfrti8GcERRlBndaycD+HUQhiWaXi+IaefpCdpzVyqp19LwOuZe7NKO0eNExFjga/Kw+uzZ3Z+NiNUm5uIeYPgIkKmrv4t7VCE1tW0KU9ummoKqT+prCzvZ3aseTUg4+XZ/cOlg+1AYRIhIkBlFw/TiNDbeuLHlOvrjqrWqqZ16jJOoPgRaq9eaqwy16+lzzyYvnmwTQGZjYCe8vIa6xveMt5SF0AvbThO3nnVBil3SPbgRU/8O4FWyLH9cluUPAngVgK8Z9hlGayiwNwkjdJYkrCblUkldwRak5258XK1SrpHJOCtlYBQ3XrxbouRztyLGwEUYaPH8XIT2EJYpVp89nx5TbWIePSZBaviopTowekzC/PZ55FbnzMNOJmNulZOl5QA5+XY/tG4IKd1CG32FcyuMokHD6jpGe0UeHyuRaMXCsQW8rfg2oZ1+Wsq4xar9TqeJW8K33p7c6hzK1XIgXkaSbNyIqY8COAjgXQD+GsBjUFf4AQBkWV4P4PcA3BOkgYmEBTHNEQkJv567ubnWEgTVqvWYm4kbLx5FUQK3vl6VZp8LEVO82eD5udnejCZWnz2L+3OV26E7Tz2dxkK/1DxudnFW/E3cZMytVuNpYszJt/vi1iJG+keaIbyZiRlPoUANq+toE2mhv4AD1xwQenxEtZ6csnBsoU04uW0p4xer9jtu8JszFMdq425WS8ZNCJLwcCymFEX5JdQSCH/Y+CkoivKobpdnQhVa/zdQC4l/4rS6UCQk/Hru3IogM3HjxKNoHMvJSXECtz5vyqV92YWDLZ6f7EJ7CMsTFvfnKrdDd54DJ2fwmsuXm8dlpIz4m7jJmGsTk758gIYmGpx8u7cTFVZhspRJ6RhRUrtZqNB4buMkquVrmXnhjBiFU6c9HEbR6FScGvGbMxT3nCM7oc8Vhb2Dm6KdfwLgCUVRHKz3tj0Xi3Z2kjgV5gzDFrd9Bv3YYHWsWa+8oOyzOY+fb/Gmvc9srik6Tlu11mKDzZhrtk8vrhQx1QoiFrcWfXsnzAot6scsnUq3JMAbi3q6PbcRLT9LFFbU6Ev3tdgAAIX+QiBjEAV++zKG0dcxSFjAs+cIpGjnPwJ4jX9bSMeJ0+rCMPLJ3IZV/dhgNZZmHiijfQAwOKh6sVIp9d96b6GZfTa5V36+xZt6PmyuaTxOSkviCd9mzLVv8AeuOdBccacJKV/f7huexJ++YxoP3VTDwLH2MJlWtsAoYrSkdrtQldMk49zqHKq19gbD6VQahf4C6u+vC/OrfI9BhPj1qMU954hhPKLhRkw97nJ/EhfitLowDvlkfmwwG8tSSc2T0oRWLmcu0oylFBYWWkWKmX02otjPyiHT4o8219RPJpl0ptlkuE3MORxzJ6LBTQ+/8sVjwOxsy8pD46SsiVA96VQaA6sHMKqM2taUMpvwRXaKEtitmkIX+guhCqew6yD5FRtxFytJFbkkeNyIo28CuKBR+ZwkiSSuLoxTnpceK6+RZmM6reZPmYk0kWfQibfQRhT7+RZvugrP5prGsgFak+Egl4Hb1WzSixujdy49v9AUg1IdGDrSvvpO1DJHS4o3iiyntZzM6jSJEu69NIX2Mm4ioWTnzfQrtpyKDf11Vn1wFaSdUktDa4oVEnfcCKPrAJwE4HOyLK8LyR4SBnHwBrklblXkNXG3aZO6vX+/K69RCyLPoBNvoY0o9jMRm3q1XAjxsEIydjWbLHv4rUWLGMyMFNomZZE3aJ+8DweXDtrWlDLLUzOr02QVzgSC9XQ4CfvaeTM7lQCuv065WkYNtVgmnBNihhsxtQfAcQB/AuAXsizPyLJ8tyzLdxl+vh2OqSQRmHmU3Hqa7MRJpz1XduLOTSi1WGxtgtzXp57T7j5sRLGfidhUCFlUhzd6LUTFJtsQPDe3OUmajW22Cu7jXe/IN8VgeTCHscvbawKZiVAnNaXMxIZdnSa3LWa8IBJKVmFHkQDuVNFJkXeQRS5JknAjprZArYCeArAKwBCA8xuvG39Ir2ImOtx6muzEif5809PAxo3hiio7cecmlJrLAfPzam2sQkG9h1pNPC4dEo22Xi3B8zMKCWMvPKGYc3Aeu5yk/Jq8qa3G+/jM9qmmAD3zmizuRHtNILNyB+VquWWCz6/Jt62iM4qN2cVZjCqjLSvQAHGdprC9PqLFAcbr2T33TiWAi4RrHBPOCTHDcWmEIGFphC7GrDyA2etm2JUTMJ4PCLfsQ1jlJezGZXRU7Zmn/T/NZtXtTodqBXZm3gf3y9Y9nMeq5ENpqYSx3WNYOKYm9GttbQC0HbNp1ybH9uqXvGuIlr4bl8Zn0hlUapWW47JSFtVatc1247J/YKUMQhC5QcZxm12cRQ0rdjl5Xp0qmqm/jpSWUKlWMNw/HNr1/BDHQqKkY5iWRqCYIsFiJjq8iBErQaU/nx47keYVt7WinGI3LiLRWCiEIhgtJwmBnaMTcF9jZ3QU9dlZpGo1VFPAwedk8ft//QKUlkqeavWMKqMttakAVZAA7baJXjO7jkjoAKoA2b99f3OctMKeB5cONkN2xuPSqbTwmk4FW1CwJpI/zGqh5VbnkJWyFFe9QSB1pgixxyzc5WVFoVVoUDufnjDLPoSVxG83LqL7CaJOmCB8aBl2EtjpKeG9WMSBkzOopIDZdcBrLl/G7x5ZxtxnMljeCcx9JoNvvNS6ObIeUU7N3JE5Ya6PG3vNegZW61Vc+IULm+NUWiohK2WbIUJjWEy7vt6O5lAIVvbV6jVML04Hnj+lv17QZQbCLq8QF0QlNGr1mrD9D+k93FRAP8/pSRVFucfmXPRMEXuchAbD8hjFgNJSCVfvGsPtNyxgVbXxlSioEKPA05S5fK49DLZtPzA2tlIXK58HpqZ8jbHR6/PzSaBwNI2UhxCqyDOVX5NHVsr68sLovXQiD5UefbjM6N0rV8u2Xjcz71pSvEa94vESeSs1r6OeuFVpJ4ESiGdqL4C7Hf4Q4h8nK+TiUvbBS6K4zTHje8ZxJ0ooTADT/UAlDfd1wsyuIUioFyYb2xUY9YAxqXjoCFQhpbPFKcWtRfSl+4Sv+/HC6JPSRZglZRuT2ae2TdnaIXotSavYwl7xFxfPl9nqzvyafKyrtJPO4EZM3WDycyOAH0BVbP/eeI0Q/ySp2KiXulg2x2iT1ME1wGkTwAnvl5wJRr2AGhlRE9aN1xAIVaEA8Vpg1IJmdXXtdOuAqvZ9TyeanUyiudW5Ns/AwaWDgdZrykrZlu2+dJ9joebEjtzqXEsT5KRNyGGv+ItLs2Nj8+cD1xxwLJhJ9xNYArosy28FsAvASxVF+bnNvgzzke7C7WpFB8d4Dp+YJefrr+E0PDo6qpae0OMzAd4Y1ho4Bnzr1izyi9UWW5zev91+fldffeqHn8I7p97Z3P7k2CfxFy/5C+H5B1YPYLm6jEPHDwFYWV1od70krxAL2/a4NzsmPUX4CeiKonweqofqw0Gdk5DE4KX/oc0xnkNV+hCeHrPmy8WiKqwyGbXp8uDgSljwuuvUoqIa69e79hAaPUyzi61JvAfXAId/MIXRG4eQuXwOo99UC1iahY/cFgvVezae3j+DWmHEVTj2PXe+x3Jbf/6FYwtNIQUAC8cWHHlSktzjLWzbg/Z8xSVsSLqLoFfz/RSA40R10mN0ump5J6/nJSRpc4znScoo0rJZa7v04caFBfVHCwv+6Z+ueM/SaeAZz3Cdl2YM02SkTFv7lok7JtpCOfpJNIUUpLSEzA0ZjEyOYGZxprnv2O4xS6+IXpTdfnMdA0+UXYVj9W1hRNui6t3G942ENaF7PW+cBYbVlwovdsclbEi6i6DF1O8CyAR8TtItdLrfntvr+RFfXhLhw0qen5xU7wFQRdTznme9v5knq1YDyuW2RHUnE5h+n+nF6RYPU7VWbZsc7UoZ9El9qNQqzd5t+obK5WrZcmLUi7KhI2rDY+1+KrPTthOxMWfKuK2df+CYujJxeaf6e+CY+v7A6oG28QprQvd63rAFhh+xZvWlwovdnWqRQ3qLQMSULMuSLMtXAXgDgB8HcU7ShbhpBhwE+rwhrV2LFXFrruyViYmV3KvlZeDQIet70nuy9GheLUMo0skEpt+n5ZSNMI1xcjQKkqd2VpF7+Tj2vaaIyvsqqNaqlt4fwHxi1Iuyh5+TRb1xP9UUMLcWLR4u0YQ/tW2qKaCyUrZZYd14/uIeYPgIkKmrv4t71JwpAG3jNbs42zKhG0OfXvEqFKyOC8JrFZZY83K/nWqRQ3oLx2JKluWSyc8jAP4LwN8DWAbw12EZSxKOl7wiP2Qy1ttGOi329AQZkrTyNGn3pL9euax6xSRJrSOVz6+EBaem2kKRTiYwUejLLKdJ3wevKUhqaIq/0lIJUlpq7p9CClkpCyklIStlkWrkhJpNjHrPRv7eGaQa9zO7DhjfiuZ9iIovlpZKmLhjAtVaFYX+AmYmZrBlwxbh+UePSU2vl1QHRo9JmNo2hYVjC23jlZFaP4vGba94FQpWxwUhhMLyBnm537CKl1oR5zBq0onL2LrxTKWhZrIbf5YB/AyqmDpLUZTvB20k6RI6XerA2IbFuG3ETuyFmYMVhFesVFKTx83uU39P+uuVSqoHqlJRGzDPz6+EHrdsaQtFGuvtVOvVlT9ijTF6ame1GerSmhNr7VY0kdK8dd22MQyHOXX/5epyc/8+qQ8zEzOovK+CmYkZjPSPOM+n0YVWz7omi4NrWofIOOG7EhKCz49Z2LFaa31Gxm2veBUKVscZhZCbCu3aM9B7KIP0Bnm53yiS/ZmnFR5xGVv25iPeiXv1cbf9AO3uJ6xmx4C30gpGRGUMAHUF3uHDrffk43pWPcq+8eFHMPBEGVJdDaPNrgNef30eh351CMu15ea++vIF+qXvP59UPVNSHdaV2S2WxpvZZyyZYFXRWrsfrYGy8drCcgBLaPv8ZHZvarvGgWsONCcAUTkHu8bOnS6h4KdCu6j/YJDNnJMCyzuER4fHlr35SIMgvStxzzFy6wkzKxegjZNVGNDvuAYRAjULSz7jGe1J7j6up32zl1IroTctTLahIaSAlVBXVso2hZS2rz7Mo/d0ve6NKfxibaMUQyMB/iIMuArlmPVQM4aWjCGi/Jp8i5dDhHZt4bdhwYIC4zUK/QXkVucsPSpW37TD+hZuFSrxU6HdGO6VUpIrb1BcQjh+YZ5WeMRlbD2JKVmW+2RZfqEsy6+QZfl0WZbb+zmQeBKkAIoyx8gJflbLicbJSoD4HdcgQqBmgkj0XAK4nqipr76aeTUFLPRLbV4N7dimKTphsWrTCE5Zu35lnEslFG9Gm/CwmmRF+VqiP7J674i2f3FrsRn+Obh0sM1urdee0xwg/b3lVudQrpaRuSGD8T3jLdfSiwurcwede6SN48YbN2J6cRrVehXTi9PYeONGDO4axOCuQWzatQlZKRt4LpYT4hLC8UsUeVq9QlzG1lWYT5bl3wHwUQBvBnCC7q2nAPwLgPcqivIrB+dhmC8qgggnaYQZ9ooa0Tjt398axpmcVFfOzc215ylJEnDnnWqT4HJZzUmamlJzkMKiVGptSgyE+lxETX3rBw7g9pvrGDqiCqvxrWjLS8pKWcxMzJh7Jxx8Rq2qnotCS2aVyN2eR9sHgOvq9F4qumtooTGr8KAXRNcSkUIKfVIfqrWqq/Ci37Akw2MkZvgP8zWE1L0A/heACoDvAri18Xu58fr3GvuRuBLkirok9c5zi2icjJ6uiYkVb5QebX9NSAHq77ExXybZhjxyOTV5/MABteVLEM/FInwpauq7atMIzniHhGfckMVpE+1CSlsR15xQRed38Bm18tAUtxaRSa+sjkshhdwSkHv5eNt92J3HGO4T1cBy+m3YrTdLj+aV0V83k85gdnHWU/hL+yzpa4BZUUcd1VrVddK232TvuIRwCLHDTZjvrwCMAvgMgPWKomxRFGWroihbAKwHMAmg0NiPxBW3AsgqFyisopNxwMk4iUoQ6Pcvt1bKbtt2iWXIQ/+cxsfV65s9Fzf5XSbhS5Gw00+cotVpWtJyy4QqOr9o7A02G/Oo9IUxx/eMo1Jd8V5sOFbH7TcsqMn51ara+LlxH0PrhpqlFQB1ZeLgrsGW+9E3IdYqsW/atQkAsH/7fsciwakwMMtJmzsy13xvaN0QKrUKaqi5Cn8Zw3pO6YSQEX2m4hLCIcQON2LqjwD8UFGUCWMoT1GU44qibIfam+9/BmgfCRq3AijuSeZWhF3R3OhBKRRa98+2Vspu2fZgm6Vnw81zcrOvSV6cXS6LUaRkpax4IhScv7QaGJ0AMu9Tf5dWt9tszKMCWgtj6lvWFPcAq/Tarl5v3kdxaxF9UmvKp7GfnlkldrciRqulBaCZhG7FwOoB0+3y/lk8dFMNyzuBh26qobzfWdFPUXK+RqG/gLvfcjcK/QVIKQn5NXnk1+Q7JmREnymRZ6tbktJJd+FGTL0AwF6bfb4DtaUM6RbinmRuRdhC0M57NTW1IqC0nCkftll6Ntw8Jzf7moTc7EJWxa3FZv2nttCezfmFQs1gc3ZuoVkdXUsW19tTqVWaob6hI4JEh8Z95FbnhF40/f0YPW5eEsD1tbTSqTSyUtbWm6WvrWXc/sYtmZZq69+4xVnRT7PkfM1ruGXDlua9zm+fx/z2+Y7VY3IaBk1aUjrFX2/gRkz9F4CTbfbpb+xHuoVOVy0PkrCFoJ33assW4OmnVU/I00+3Jp97sM0y5OHmObnZ10QwCoWdztumbwVjORELzi+cVEU26gSo0Z5MOoNKTQ316VcY1gFV2OqEryh8ZfQKmV1HdKxo8vSyCu/Q8UOm2xsXqy0lKDYuOiv6aSy4CiA24TOnYdCk9dZLmvgj3nAjpu4DcKksy4OiN2VZ3gjgssZ+pFtIcpJ5nIWgB9ssk3ndPKcAnqlQ2HnxBDqozTS0bsg8Z83Enkq10pxwx7eqxUMrKeDAc7NqzpRO+Ba3FtGXdlbdxUkOj2jyNN6TqPmxG1KGz0/K4Wdbb3+hv4AD1xwI3evk1DPjND8qaUnpSRN/xBuOSyPIsnwRgG8BeBLALgB3A3gMwHMBbAGwHcCzAIwpinKnzblYGoGET5wrtLu1Lap7cVP+IqCyG6bL6V3YYrbk32xpfZBL8EXn2r99f1sZidJSqVniIJPOtJUdGNw12FKBPb8mj/nt841BivFn24DTkhBOiaIKvB+Cvn8SKaalEdzWmfozAJ8GYPwap/Xo+wtFUT7j4DwUU4S4IaqaXnYCST+pa++Z2ehXALg43mlLGY0gJzwn5xK1sjHamDTRYEav14rqludIAAQlpgBAluUXQC3aeSZUT9RxAA8A2K0oyiGrY3XnoJgixA1BFlt1g17EpVJAX59qhyZmtNCe2fsh9zYUTVQAmq9puU8Hlw5aTmRBTnhOzmVVLLPbxAY9M52Dwi10ghNTQUAxRYhLovJM2XmejNXfrUReCIJQP1FvXErhazfXsUlXff3QWueTdycnIv210ql0S+9CfTivGybHbriHpEDhGjpsdExIoolqIYA+QbxabV+BqE+EBtR9zOpmiZLufTaI1if3am1stHIBxT0rCb9OkqA7uepKv5hg/bPXm+7XDSvB/FZBJ85hsnt0WBYnaYT0XKMoyiPezCGECNFETZQMDbV6x/ShvmldNW1tJZ/R3snJlRY7mYy6rQ8Tzs6ifPEYzrwm69iLMbRuqPlNfOgIWsoFDB1ZWe2l72mniRLjN/YgJyI33hhjQ2X9NidH4gb9/4ckrHTsJuw8Uw8DOOjyhxXJCAkCn16bwK+h947lcqoo2qS2VYG00vrEtG7WxMRKWK9SWWkSrfN2pecXXHli9Mvp9+vqSVVTaqhPW2IvFCU2LWr8TERuPEpWS/1F7yWtCGTS7E0ybL8THXZi6hHBz6+hxg1F7z0C4BdhGUtITxFEBXc7seTmGvqQXzarnks7TpJaQ3gDA+3XFRUqNYT+5tbClScmtwTsmwQqNwD5k9bjF2v7UEkBB5+TxUlTdzfDSkLBYtOixs9E5MajZDUB6t/Lrc6hXC03++olJfTXDaHKpMCQanR4Wc23A8D1iqJIdvtanIMJ6ITYEUTCtl3iutdrGI8DgHweOHhQFUjlsiqg9NcF2m3RwoRzc8DAAB4+fginHlnG3DrgdW9MYdWmEesE2sFBYGGlFhPyeWB+vm03Yditf1NoKyTDSAR2WzsrLvR6aQTSVQSagN755X+E9CJBVHC3a1vj9Rqi/R7RpUoePNh+XWMSvZYzNbfSLmb90Wozgfybt/TZe4f0Qkq03UD4jd3lvbsJV4URbjHrqxf3vJikVSwnxAtczUd6m07kJXkliBV8doLB6zVE+5XL5mE/kVB529tWwmzT08DCAlINASbVgfxiNdwwhct7dxOuCiPcEue+elYwj4f0AhRTpLcJIi8pLOwaKTvBSjD4qUieywGFQmtZBI1aTbXZeN2xMVU06cRT03tlxMZTpHmJ9q8xuMrzeWf2a/fgYnz1nqH1R2u4dcd0R0W4XpRkpSzSEf35duKh0+8zvmccxa0Oml4TkmAopkhvYxcG80vUni8rweBXSGpCzSioUqmV3Cz9dU1CcG048BRpXqLXbAOm+4FKGqq4m5pydw8u0HuGinvUUGQoItzkM6N5u4bWDaFSq6AGtczD2O6xjq6Wc+KhY9I56TUopkhvE0RekhVx9nz5FZKaUDMKqr4+5+HCbHbl36mUKojsPEWlEm7dMY2nd9RQ3KNWOj/h/ZK9d8mnsNV7hoaOrtS0Qq2Gyux0cCLG5jNjXCm4cMxdOQm/OFmpyPpYpNegmCK9TdiVxf0KFjMBEITHKyghqb9HQBUBIlFjDMHl88DMjCqgJAkYGXE2/uPjGDZUOneU1OxT2DbzoLbtRyaTbYYXqylgbi2CEzGGz4xRqBkTugF35ST84iShPKikcy81qljXikSBZWkEWZbb25rbU1cUxa6yOksjkN7Ab089s+OD6NXnJ2fKiY1hXc9QlqGSBh45csA+Fyeo3oCjo6oIrNdRB/C0BBQmgEfWBrTkf3AQ9YUFpKDmg82vAS75QKFZWsFY5qFcLaO0VDItw2Dcf/LiSUzcMeG5V56T6u5B9ePzUmKC/ek6Q4/2XPTW6FiWZZPsUGsURbH0eFFMkZ7Br4AwEwBWwiAo0eIUUTNkzcsXxnW9CkmXx+knCyktoVKtYLh/GD+/Zg4p3djXoeZtvesdeUy9r73GlWsEYqrw5+ZCzW5SM4qLTDqj5lx1UGx4nXi91KhiXavO0KOi1ZuYCguKKRIanRYSYePFMxWE1ypIW4PG6zN2eZyoSGY6lcbcZzLI/7IC1GqoQ/3rWk0B1U15ZGcDEFNGz1sKOOOmgueJyigujIjERtBeB68TLz1T8aVHRWugRTsJiS9WeTFRr6zzgllOl/71TEa9V6u2LZ24bz/5YW6ejdeSET5KIWjU6jX8/uWVZkV37S+rVAeyCwcRCENDqDdy2aop4OHnZH3VZjLmL2WlrG0+U9Cr8bwmpHupUcW6Vp2BxVhboZgi3YXVhB7Vyjo/Is5MAGivDw2p79VqK/ekTywHVu7X733b3Ycood3pvcdw1aOoSGY6lUZ2U8Pjpq+zFeRK0GIRqYZQlkYKyN8748srZBQXU9umbMVG0KvxvE68Xoqfsj9dZ6BobYVhPtJdWIWaRP3k7r4bmJgINywYZvhLlDu1f7+6Mq5cFh/jJ/Ha6j5EYTRNJNnde1DJ4QFiljPVDHk5DRsmMPQcdKisR5OVSffBMB/pApx4OaxKHYg8B2Nj4XtEwiwMalbewExIacd4wct9aEJKO2Z2VrhbOT8ATUrVAdSktPj52n0GtPclCVi1yldoU+/hePpvnkb1/dVWT4fTsKFPr5vfpf6OjjeM6zdeOonhdcNIQ01Ynzsy56vMAL1FpNuhmCLJwcmkZDXBmfWTC7MCOuCunpPbkKBIPNpN1l5radndh+j5ZAxVUozb2qFvBJYlNBO668vL4vuw+wzoPWH6XoFRhg19imm/+UuOjjeM6wu2TWCfvA/D/cOo1CqsZE6IDRRTJDkEUbHbmOeSzYZbAR1wVxjUTiwYxRbQLh7NxiWdVu/fa4jJrs/f9HTr85mebveQGcOsDb6Ng8jUWhO6hfdh9xkwFhA126+T+CyOasxfml6cduWlcpT/ZDKurGROiDMopkhyCKJit1EQTE2FWwEdcLeKzE4sjI+rBSO1ZsEjI9aJ4EY7/Nyf1X2Mjdkfb/HMhtYNYW6dunoNaDQvrlbbvXN2nwHRvZtdt1OrO31W2RclwrvxFDlK/jYZV67YIsQZFFMkOQTR+sUoCLZs8bbMPizsxMLcHKBfNFIuq0JGLwomJ5tL95toXjjt/oIWEk6aGA8Pq7YJrlvcWsS73pHH7DqgZUmM0Ttn9xnQN1/OZtX9cjl1nIz32qkVhF5LOTTQr5rS49RT5GjVlcm4Rr1ii61hSFLgaj5C4oTdyq/RUdUjZSSdbl8xZ7VCLugVhinBIheRTU6uG/TKPrNrxnAFoR1RFqSMYkUeC3CSmMHVfIQkAjsvRrGoelw0NC+WKDRo9HINDKx4hYz5TXY5RXaeLGMT4/XrxR4kszCm/vyStCLOgkjYN7tmUI2eQ0bvnSlXy8itzkXiKQq6kKcTmLNFkgLFVJxIYoVu4g03z1q/7/i4mudVKKwIlXxeLAqMoRtgJaylx4mQsAuJ6W0qFIC77hKLQjMBoz9/pQL09QWTsG91zSDCxh1AL2JKSyVkpWwkJQaiEDbM2SJJgWG+OBFVTzXSedw8ay/FMkV5OaKipZLkrJCkn2P1mNnqNeRmPE4bH/35gfgXzbR4hqLeelJK6njxyyhCbiz2SWIGw3yJIMzijiReiJ61mbfK7nPhNMHZ6KEpFMyPMdoyMNC+Ss5L4raZrV5DbsbjtD6FZrZpCfsBeX8DS5AWedgaz+CpnVX8fBIYOLayu9NQW5AJ3FEko7PYJ0kKFFNeCCscl5Acjq6lk2FW0bM2C1kF9bnwU+8KWDlWT1Ci32vIzXhctdouPPX3srCg/gS0gi+wPCKRYG7YnakBw0eA4p7WQ5yE2oLMc2oTNktgWgIhDSimvBDWkuqE5HB0LZ1stit61mYeqKA+F37qXR08uHJsGA1+vZYPMB4nEp6iQp7afTkVgiZCO7A8Ihu7pTowekxCob/gKocorDyn0lIJCy8bQXVmGqhWUY+6yjwhEUMx5YWwwnE+69F0PWF7jjoZZhU9azMPVBSfCytvmF9xF+ZzFNlmJvbcCEG90NYVSw0sQdrMbsMzcBtqCyuBe3zPODY8UVYr1QNIMS2B9Dr1er3jP1dffXX96quvrieWQqFeT6frdUD9XShEbVFv4GbcDxyo1/N5dV9A/feBA8GdPwwOHFCvKUnqbzt7w9zf7bnd0OlxPnBg5XOg/3FzX5IkPP7AsQP1wmShLu2U6oVJdTtQu30+g7Dsk3ZK9Z/3o15JqWNRSYF/B0kvYKpruJrPC05XT5FgcbPiS1TcslCwXh2ZtOfqdvVnXFaLRlEs0++9iz5PCSjyGRajyiie3j+D22+uY+gIsH8d8Lo3ppHdNIzJiycxcccEV+CRboSr+QKF4bhocJOIbdYk14okPFd9iExUeNMqhNbJMKaVHVEstPAbmjQWS02lenqBSHFrEas2jeCMd0h4xg1ZnDaRwsLqGmaPzGJs95hl0jtbxJBuhGKKJAc3E6JoouuGyU+fu6PHbkUg0FkRY2VHEAn1bvOu/ArlXE5tMK0VJh0Z6ekFIvqVfdVaFfVGR8VavYZytdyS9F7eP9vyrK7eZS22CEkiFFMkObiZEIvF1hYn+Xx3TH6ilWlOVgQCnV0tGrYXzCQhPFSS4LmMAGOSe1bKtmx/45bW2l+fuGmBLWJI10ExRbqTXA6Yn19JF56fD3byi6r1j13hTSvvkxsx4Pf+rOwIogSFUVRqxTiDeiZs7eQY4wrDqW1TLdsbF1trfw0dBVvEkK6DCeiEeCGqZG59kvzAgPrawYPBt07xe39WyfxOWsDY2SxKCNfOVaupOU19fep1vIxDXJL1uwHDWJYHczjzmiwT1EkSYQI6IYHiNoxl5+lw6gnRe5eyWXU/vYcnqFCU3zBdLqcKmIEBVfRs3AgMDqr2um0BI0KUEK7ZCqjeyHJ5JQw4NubO/qS2doqjR80QXs7eMRV+i5g4jgPpaiimSDu98IcozDCWCLvQlpfQV5gTfhDJ6uPjausWjYUF9TUnLWDsECWE5/Pt/QP119Ywe/b61yVpRaAlqbVTJ6v4OyWKXLM4jgPpahjmI+30QogjzDCWCLvaSl5qL4X5nIKouWW8J0B8X0Hdh95m43UB1VtldT3j65r9Sag5phFFDa84wnEg4cAwH3FBUkMcbggijOXm27adp8eLJ8jp6jwvXrggvAmie9DyvPSE0XtQv5ITaN02e/bG16vV5K3cY7N0FY4D6TAUU6SdXvhD1Ol7tBMMXgSFU8ETVcijWFSTwPUsLLQLujDCQFNTKyHAQkHd1jB79t3wuQ9TYCcJNo0nHYZhPtJO0tqqeKEX7lGjkyEP47hqYTM9UYeOzZ59L30meiGUHya99FkhekzDfBRThHQ7nZw4RXlHlYq40ChzWKKDOUX+oBjtVZgzRUjPIgp5hBXmEeUdDQ+37pPUEFo30Q0hzSjphbxS4gqKKUK6HVFOUlh5VKJJet8+4MCBlRwm5rBED3OK/EExSgxQTBHSi4T1zdo4SU9Oqp6vTZvU9/fvT9bquG6FfQb9QTFKDFBMkeDo9hVC3YTTb9Zun6lxkp6YYPFE0n1QjBIDFFMkOFh12B6vgjNooer0m7XfZxpVbklchH1c7CCEhArFFAkOpxNnL08wduLEbGyCFqpOv1lbPdNSSe23l0qpP1rvPT1R5ZbERdjHxQ5CSKhQTJHgcDpxuplguk142QlOs7EJysMjGk+rMbZ6pma99/RElVsSl9VWXu3ots89IV0OxRQJDqcTp5sJptu+2dsJTrOxCcrDIxpPqzG2eqai52Z8LarckristvJqR7d97gnpciimSHA4nTjdTDBx8TBY4caLYCc4zcYmKA+PaDytxtjqmYqeW6dFi9nYx2W1lVc7kvC5J4Q0oZgincfNBBMXD4MVbrwIdoLTbGz0xxWL6jW8hIBE4+l1jIvF1gbC+XznRYvZ2Cd9tZWxIbSoQXQQMJxISCBQTJHO42aii4uHwYogvQhOxsZPCEg0nl7HOJcD5ueBel39mZ/vvGjplAfHq+iIe7guTPso1EgPQTFF4k0SPAxuPDtBTDBuBITxekD7eMZ1jJ2MldXYBzmZexUdXsXewYPW20ERphiNu5AkJEAopgjxixvPThATjBvxluQJzYntVmMf5L17FR1eQ6idCm+HeR3mfZEegmKKEL+48ewEMcG4EW9JntCc2G419kHeu5+8Ms2mWg0ol515yDoV3g7zOknIdyQkICimCOkkQUwwbsSb1+tFne9SKqkTvIaXsQpyMveTV5bNrthRKjnzkHUq9BrmdZKQ70hIQFBMkXainki7mU5PMF6vF3V4cHwcWF5e2c5k3I9VkGPtR3Qk1Tvo9+9AXHPxCAmBVL1e7/hFZVmuA4CiKB2/NnHA6Kg6gdZq6jfq4WH1jyFR0bwLc3Oqt0MfyukWMhlVSGlIkjop9sr1RXh97kn9/5RUuwkJj5TZG/RMkXaS+k26U0TttekEUee7RH19EV6fe1LDXfw7QIhjKKZIO3GcyOJEL0wyUQuAqK8vwutzDzLc1ckQPP8OEOIYiinSThwnsjjRC5NM1PkuUV9fRByeeye9ovw7QIhjMlEbQGKINpERMVo7F33uDOl+4vDcO+kV5d8BQhxDzxQhbomj14SEj/G5A51f9RoH7xghpA2KKUKSDMtYREcUCxEYeiMkljDMR0iS0Sb0Wm1lQmdopjNEsRCBoTdCYgk9U4QkmV5YWeiXsLx3DLkRQhpQTBGSZDih2xNWOK6TIbewBCHDxIQEAsUUIUmGOTT2hOW96+RChLAEYS8UoCWkAzBnipAkwxwae4aGWtuiJNF7F5YgZJiYkECgZ4oQ0t10g/curHAuw8SEBALFFCGku+mGumBhCcJuEJqExACG+Qgh3UOp1F6lPIniyUhY4VyGiQkJBHqmCCHdAxOqCSERQDFFCAkHq2X3YS3JZ0I1ISQCKKYIIeFg5SUKy4PEhGp/sO4UIZ6gmCKEhIOVl0j0XhATOROq/cEwKSGeoJgihISDlZdI9F4QE3k3rNzzQlAeJYZJCfEExRQhJBysvESi9ziReycojxLDpIR4gqURCCHhYLXsXvReN1Qqj4qghGix2F5aghBiCz1ThJBWokpCZr6Td4LyKPVqmJQQn1BMEUJaiSoJmRO5d/RCNJcDymWuyCOkg1BMEUJaMYaMpqc5KccdvRDNZtVnFacVeSy5QLociilCSCv6kJFGXCZlYk8cE/lZcoF0ORRThJBWtJCRnrhMym7pRY9IHFfkxVHgERIgFFMkHvTipBdXtJBRoRC/SdktvegRiWMifxwFHiEBQjFF4kEvTnpxJ46Tslt60SMSx0T+bvgsEWIB60yReNCLk17csaoTlRRYuyoedMNniRAL6Jki8aBXwwAMb4YLPSKEkA5AMUXiQa9OegxvhkscQ16EkK6DYT4SD3o1DMDwJiGEJB56pgiJkl4NbxJCSBdBMUVWYP5O5+nV8CYhhHQRFFNkBebvdB7m9MQTfrEghLiAYoqswPwdQlSS+MWCApCQyKCYIiswf4cQlSR+sUiiACSkS6CYIiswf4cQlSR+sUiiACSkS6CYIiswf4cESZLDTkn8YpFEAUhIl0AxRQgJhySHnZL4xSKJApCQLoFFOwkh4cCwU2fp1cK3hMQAeqYISXI4Ks4w7EQI6REopghJcjgqzjDsRAjpERjmI4ThqHBg2IkQ0iPQM0UIw1HdCcO3hJAOQTFFCMNR3QnDt4SQDsEwHyEMR3UnDN8SQjoEPVOEkO6E4VtCSIegmCKEdCcM3xJCOgTDfISQ7oThW0JIh6BnihBCCCHEBxRThBBCCCE+oJgihBBCCPEBxRQhhBBCiA8opgghhBBCfEAxRQghhBDiA4opQgghhBAfUEwRQgghhPiAYooQQgghxAcUU4QQQsSUSsDoKJDJqL9LpagtIiSWUEwRQggRMz4OzM4C1ar6e3w8aosIiSUUU4QQQsTMzQG1mvrvWk3dJoS0QTFFCCFEzNAQkG5ME+m0uk0IaYNiihBCiJhiERgeBiRJ/V0sRm0RIbEkE7UBhBBCYkouB+zbF7UVhMQeeqYIIYQQQnxAMUUIIYQQ4gOKKUIIIYQQH1BMEUIIIYT4gGKKEEIIIcQHFFOEEEIIIT6gmCKEEEII8QHFFCGEEEKIDyimCCGEEEJ8QDFFCCGEEOIDiilCCCGEEB9QTBFCeotSCRgdBTIZ9XepFLVFhJCEQzFFCOktxseB2VmgWlV/j49HbREhJOFQTBFCeou5OaBWU/9dq6nbhBDiA4opQkhvMTQEpBt/+tJpdZsQQnxAMUUI6S2KRWB4GJAk9XexGLVFhJCEk4naAEII6Si5HLBvX9RWEEK6CHqmCCGEEEJ8QDFFCCGEEOIDiilCCCGEEB9QTBFCCCGE+IBiihBCCCHEBxRThBBCCCE+oJgihBBCCPEBxRQhhBBCiA8opgghhBBCfEAxRQghhBDiA4opQgghhBAfUEwRQgghhPiAYooQQgghxAcUU4QQQgghPqCYIoQQQgjxQSbKi8uyHOXlCSGEEEKcUlcUJSV6g54pQgghhBAfpOr1etQ2EEIIIYQkFnqmCCGEEEJ8QDFFCCGEEOKDSBPQCSEkKmRZHgTwUQAvBfAcAMcVRXl2pEa5RJblDQAOAviCoihvjdYaQnoXiilCEo4sy3UAMFtl0tjnYQDrAQwoivJwZyyLL7IsSwC+CiAP4F8AHAbwVJQ2EUKSC8UUIaQXGQBQAPAPiqL8r6iNIYQkG+ZMEUJ6kVMavx+N1ApCSFdAzxQhPY4syxcBuBbAOQD+B4BHAPwbgL9VFOW4Yd+HAUBRlA2C8+wA8H4AFyiKslf3eh3AdwD8MYAPAvh9AM8FcKWiKJ+XZfk5jeuPAzgVwDKAJwD8AMANiqKUHN7HiwD8NYBXAHgWgMcBfB3ABxRFecxgj8b7ZVl+f+PfOxVF2WFy7mcCOAbgPkVRXqZ7/UQASwBWAfgTRVH+RfeeDGCycZ//qHt9EMD1AC4C0A/gCIA7G3bOG667A40xhSoA/xzAKIAjomegOy4N4FMAtgP4CoA3KorCMCYhIUHPFCE9jCzLfwbgPwC8DGoO0aegiob3APi+LMvPDuhSawD8EMBLoAq1mwA8Icvy/wBwL4C/BHAIwGcAfA7AzwC8Dmoozsl9/AGA70MVZHcC+ASAOQBXA/hxI1FbYyeALzT+/Z3G9k4Ae83OryjKbwD8CMA5siyfpHvrZVCFFKCKIz0XNn5/W2fn2QB+DGAbgPsAfBzquLypYeeLTUz4SwD/CFXo3gTgG2a2yrJ8AoBboQqpSQBvoJAiJFzomSKkS2h4Mcx4tmD/9QBuBPAbAOcoijKre0+BKkQ+CiCInKIXQk30vkJRlIruOuMANgL4lKIo7zTYl8WKUDGl4TX6PNS/Z1sURfmu7r33APgIgP8D4NUAoCjKDlmWtwB4C4C9Zt4oAXdBFU/nQfV4AaqAqgK4Bzox1fAMbQFQUhTlUOO1FIB/BvA7ALYpivKvuv0vB/BFALtlWS4oilIzXPtCAC9VFOUBKwNlWV4D4PaGne9VFOXvHN4bIcQH9EwR0j283+LnWYL9twHIArhJL6QaXAfgSQBvlmXZVtA4oAzg3XohZeC/jS8oilJWFOVJB+d+HYC1AG7RC6kG/xvAwwBeJcvyC1zYK0LzMOk9UBcBuB/AlwGcKsvypsbrmxs2fVu37+8BGAbwA72QAgBFUW4B8D0AQwBeLrj2/3EgpNZD9fKdC+DNFFKEdA56pgjpEhyWRtBzVuP3XYJzLcmy/ABUL8wwgJ/6NO9hRVF+KXj9OwD+E8B7ZVk+C8AdUAXBg4qiVB2e2+o+KrIs3wNgA4AzoYbJvPIDqKLvIgCQZflZjWt/VHftiwDsx0qIT2+TqZ2611/esPMew3s/srFtqGHfMwD8vqIo37bZnxASIPRMEdK7aN6qx0ze115/dgDXelz0oqIov4aaR/VPAF4E4NNQc4oel2V5pyzLfQ7O3ZH7UBSlDNV79EJZlk+GGsaTAHxbUZQZqCsDNa/VRQDqaBVOfuwUjp+OTQCeB6AE4Cc2+xJCAoZiipDeRVup91yT959n2A8AajD3aD/b4lqmHdUVRTmsKMqVAE4GcBqAawAcBfC+xo8dXu7DK3cBSEH1PF0E4GmonjQAuBvABY2w6CsA7DN44/zYadeRvgh1JeNmAN+WZXmdzf6EkAChmCKkd9FycLYY32is4tsMtSr4jO6tJQDPMfEYma1Ec4SiKHVFUfYpirILwKsaL7/ewaFW95HBSg5SEB4bfd7UhQDu1a2U+zbUVYtXQw23GUNtpnYaXvdkp6IofwvgnVDDhHc3Sk4QQjoAxRQhvctuqDWdtsuynDe89wGoq852K4rytO71H0H1TP2pfmdZlt8KdQWZK2RZPs1QtkBDEwL/5eA0X4VazmGrLMsvMbz3FwByAO5UFMVPvpTG/QB+BTXpfRStgkn79181fhtzo+6FWq7h5bIsv0H/RmP7PKj5Vt/zapyiKJ+CKuZGAXxHluVTrI8ghAQBE9AJ6VEURXlYluW/gFqL6CeyLN8KYBHA+VCb/85CrTelZxdUIfWZRrHPXwA4A+pKtX8H8AcuzXglgE/Isvz9xvV+CbVw5+ughhQ/5uA+fiPL8hUAvgRVQHwJaqL5i6CWQ3gcwJ+5tMvsWjVZlr/TsA/QiSlFUR6RZfkA1FIPVajJ9fpj67IsvwVqXa9bZFm+Heo9D0H1wD0JtfCnsSyCWxs/K8vyU1Drdd0jy/KFAQlJQogJ9EwR0sMoiqIAGINaOPJ/AngX1Nylj0Gta3TMsP80VAF0L9QCmf8LatmDl0L12rhlCmqh0BOgCpS/hOqh+Q8Ar1AU5TaH96HVVrqjcT/vBjAC4LMAXuS0irpDNAH1a6jJ8qL37jdWj2/Y+f8AnA3gZqhjdi1UIboHwNmN932jKMrnoZa+WA9VUOWCOC8hREyqXrfLaySEEEIIIWbQM0UIIYQQ4gOKKUIIIYQQH1BMEUIIIYT4gGKKEEIIIcQHFFOEEEIIIT6gmCKEEEII8QHFFCGEEEKIDyimCCGEEEJ8QDFFCCGEEOIDiilCCCGEEB/8/5Xd+iZegmQrAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset X : ndim=2 shape=(1000, 2) Mean = [4.9518 6.9841] Std = [0.9994 1.4956]\n",
- "Dataset y : ndim=1 shape=(1000,) Mean = 0.622 Std = 0.4848876158451565\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"plot_data(x_data, y_data)\n",
"vector_infos('Dataset X',x_data)\n",
@@ -351,20 +263,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train : ndim=2 shape=(800, 2) Mean = [-0. -0.] Std = [1. 1.]\n",
- "y_train : ndim=1 shape=(800,) Mean = 0.625 Std = 0.4841229182759271\n",
- "X_test : ndim=2 shape=(200, 2) Mean = [-0.0325 -0.0741] Std = [1.0732 1.0111]\n",
- "y_test : ndim=1 shape=(200,) Mean = 0.61 Std = 0.4877499359302879\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Split data\n",
"\n",
@@ -401,54 +302,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**This is what we know :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**This is what we want to classify :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.display_md('**This is what we know :**')\n",
"plot_data(x_train, y_train)\n",
@@ -466,25 +322,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "convergence after 14 epochs took 0 seconds\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s finished\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Create an instance\n",
"# Use SAGA solver (Stochastic Average Gradient descent solver)\n",
@@ -518,27 +358,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy = 0.924 Recall = 0.893\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x720 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plot_results(x_test,y_test, y_pred)"
]
@@ -568,7 +390,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -594,31 +416,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "convergence after 2238 epochs took 0 seconds\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s finished\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Create an instance\n",
"# Use SAGA solver (Stochastic Average Gradient descent solver)\n",
@@ -643,46 +443,18 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy = 0.950 Recall = 0.934\n"
- ]
- },
- {
- "data": {
- "image/png": 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tC21je2g7iyct1nZBEclMW5t1QbtjhxXEtLW5vSLJlzxk39555x3mzZtHd3c3p5xyCueeey4HDhzgd7/7HZdccknsuFAoBMCjjz7KSy+9FLu9v7+ft99+e8RjNjQ0cNlll/GpT32KT3ziE0nPe/rppzNjxgyA2PkrKyt58cUXOffccwEruDrqqKPo6+vj7bff5oMf/CAAl156aSxAGRgY4Etf+hJbt26lrKyMl19+OaPnP3nyZCZMmMAVV1zB4sWLufDCCzP6/mx4KpDRhYuIgPWGRvuBdgYZHHVf9N+H9gPtLJ+8XG9wiIh9s2Y59q68FLk8ZN+iNTL79+/nwgsv5I477uCyyy6jsrKSrVu3jjo+Eonw+9//nsMOOyzlY95555384Q9/oL29nXnz5rF161amTp064phAIBD7vKysjMHBQUzT5MQTT+T3v//9iGN7e3tTnuu73/0u73nPe3juueeIRCJMmDDB5jO3lJeX8/TTT/PrX/+an/zkJ9x+++1s2rQpo8fIlGdqZOIvXKJBTFSECIMM0n6gnb5wnzsLFJGC2RzaHPt3oGd/Dy3rWli1ZhUt61ro2d8DWP8ubAltcXOZIiJSrNraoK4Oysqsjw5m36ZMmcL3vvc9vv3tb3PYYYdRW1vL/fffD4Bpmjz33HMAnHfeedx+++2x70sW7OzcuZMzzjiDm266ierqav7yl7/YWsPs2bPZu3dvLJAZGBhg27ZtVFVVccQRR/DUU08B8JOf/CT2Pfv37+eoo47C5/Nx7733Eg6HATjiiCNGZYqSOXDgAPv37+djH/sYt956a9Ln4zTPBDLxFy6p6MJFZHzoDHXG/j1Y27aWPb17iJgR9vTuYW3bWsD696Az1OnmMkVEpFhFs2+Dg9bHWbMcffj58+dz8skn85Of/IT169fzwx/+kJNPPpkTTzyR1tZWAL73ve/x7LPPctJJJzFnzhzuvPPOUY9z/fXXM3fuXOrr6zn77LM5+eSTbZ2/oqKCBx54gBtuuIGTTz6ZefPmxYr3f/jDH3LllVfygQ98ANM0mTJlCgBNTU38+Mc/5swzz+Tll1/m8MMPB+Ckk06ivLyck08+me9+97spz/n2229z4YUXctJJJ7Fw4cK0xzrFiHY2KKSmpiYTIBgM2v6eYG+QAQbGPK6CCq6uujr7xYlI0but97bY56vWrCJiDr/J4TN83HLNLbGvr626tqBrG6cKN9nNhmxeY0TE27Zv384JJ5zg9jI84cCBA0yaNAmAb33rW7zxxhvcdtttY3xXYaT4PaZ8jfFMjYydIAbgEIfyvBIRcZsff+zfhGlV09jTuwfTNDEMg2lV02LHRecFiYiIiKW9vZ2WlhYGBwf5u7/7O+6++263l5Q1z2wt8+O3dZwuXERKX12gDt/QP18rG1dSU1WDz/BRU1XDysaVAPjwUReoc3OZIiIiRefTn/50bHBne3s706ZNG/ubipRnMjJ1gTq2hbalrZMZbxcuakUt49WCwAK2h7YTIUL1lGpWr1g96hgfPuYH5ruwOhERcUM0My/elE25i2cyMgsCC2LvwKYyni5cuge6Wd+/nm2hbbEtNtFW1Ov719M90O3uAkXyqLKsksWTFlNO+ah/F3z4KKecxZMWK6AXERknJkyYwL59+7K6GBb3mabJvn37Mm/5nKf1OC564ZI4RwasCxcfvnFz4aIZGiIw0z+T5ZOXsyW0hc5QJ4c4RAUV1AXqmB+Yr7/7IiLjyIwZM9i9ezd79+51eymSpQkTJsQGe9rlmUAGdOESlThDY23bWvb27mVa1TRWNq6kekp1rBX1oomLXF6tSP5UllWyaOIi/T0XERnn/H4/tbW1bi9DCsxTgQzowgWSz9AwTTM2Q2P1itWxGRrj+eckIiIiIqXLMzUyMiy+FfXe3r2x/aCmabK3dzilqlbUIiIiIlKqFMh4UHwr6mlV02IdOjRDQ0RERETGCwUyHqQZGiIiIiIy3nmuRkYKP0ND82pEREREpNgoI+NBhZyhoXk1IiIiIlKMFMh4VLQVdX2gPlYLU0EF9YF6lk9ezkz/zJzPET+vJn5uD1jzagYZpP1AO33hvpzPJSIiIiKSCW0t87B8t6LWvBoRERERKVYKZCSlUpxXo3ofERERkdKgQEZSKrV5Nd0D3bQfaCcy9B8M1/tsD21n8aTFjmzJExEpKv374KUO2LUVBkLgD8CseTCnASZPdXt1IiJZUyAjKfnxx4KZaVXTYhkZL86ria/3SRQNbNoPtLN88nJlZkSkdOzeAU9sgHAYzKFax4EQvPwM7NwMC5fBjNnurtENCu5ESoKK/SWlUppXk1jv07KuhVVrVtGyroWe/T0AsXofEZGS0L/PCmIGB4aDmCgzYt3+xAbruPFk9w5oW2MFcwMh67ZocNe2xrpfRDxBGRlJqdDzavKpFOt9RETSeqnDysQA7OmFW++Hv+6D6VPhukugpsq6/6UOOPMid9daKPHBXSIzAoMR6/7Ga5SZEfEABTKSUnReTWJdCVgBjA+fY/Nq8q3U6n1ERMa0a+twJubW++GNfWCa1sdb74ebr7Tu37XVmUDGC9u1FNyJlBRtLZO0CjGvphD8+GOfT6uahmEYAJ6s9xERsSW6bQqsi/WhN3AwTevr2HEOvIHjle1ayYK7SFxwB8PBnYgUPWVkZEz5nldTCHWBOraFthEhwsrGlaNm4oB36n1ERGzxB4aDiulThzMyhmF9HTsuxzdwvLRdq5DBnYjknQIZGRdKqd5HRMSWWfOsjIgZsbZNJW6jAjB81nG58NJ2rUIFdyJSENpaJuNCtN6nnPJYJ7YoHz7KKfdMvY+IiC1zGqCszPq8psqqifnRautjTZV1e1mZdVwuvLRda9Y8K3gDK8g6air4DOujk8GdiBSEMjIybkTrfbaEttAZ6uQQh6iggrpAHfMD8xXEiEhpmTzVmhOTOEcGrIv1sjLr/ly3e3lpu9acBmt+zmBkOLhL5ERwJyIFoUBGxpVSqPcREbFtxmyrNiXWTeyQtW3KyW5iXtquVajgTkQKQoGMiIhIKZs81apNyVd9SqFqcZxSiOBORApCgYyMK33hPjaHNtMZ6mSAAfz4qQvUsSCwoCBby9w+v4iI49zarpXL3Jp8B3ciUhAKZGTc6B7oHjXcc4ABtoW2sT20ncWTFud1Lo7b5xcRyQs3tmvt3jH6fNG5NTs3W+ebMdu584lIUVIgI+NCX7iP9gPtDDI46r5oYNF+oJ3lk5fnJTNSqPOXWsan1J6PSMkq5HYtL82tEZG8UiAj48Lm0OZYFqRnf8+ogZjVU6qJEGFLaEteGgEU4vyllvEptecjUvIKtV3LS3NrRCSvNEdGxoXOUGfsYnht21r29O4hYkbY07uHtW1rASsz0hnq9OT54zM+0fNERYgwyCDtB9rpC/fl9DwKpdSej4g4yEtza0Qkr5SRkaKRz21EAwxvQdjbuxdzaM6BaZrs7d0bu+8Q+ZlzkO/zu51xclqpPR8RcZCX5taISF4pIyNFoXugm/X969kW2ha76I9uI1rfv57uge6cHt+PP/b5tKppGIYBgGEYTKuaFruvgvzMOcj3+d3OODmt1J6PiDjIHxj+fPpUa14NFOfcGhHJKwUyRaov3Memg5sI9ga5rfc2gr1BNh3cVJJbaQqxjaguUIdv6K/7ysaV1FTV4DN81FTVsLJxJQA+fNQF6rI+h5vndzvj5LRSez4i4qBZ86xuaGDVxBw1FXyG9bEY59aISN5oa1kRGm9FzoXYRrQgsIDtoe1EiFA9pZrVK1aPOsaHj/mB+Tk9F7fO78cfu/ifVjWNPb17ME2zYBknp5Xa8xERB7k1t0ZEio4yMkWmmIuc85UlKsQ2osqyShZPWkw55bHMSJQPH+WUs3jS4ry19M33+d3OODmt1J6PiDgoOrem3D+cmYkyfNbtTs+tEZGipIxMkSnWIud8ZokKtY1opn8myycvZ0toC52hTg5xiAoqqAvUMT8wP+9zSfJ5frczTk4rtecjIg7LdG5N/764Y0NWnU0+ZtyISEEpkCkyybITpmnGshOrV6yOZScKFcjke5hjIbcRVZZVsmjiItc6XeXr/NGMT2KwCdYFvw9fXjNOTiu15yMieWB3bs3uHdaAzHB4uG3zQAhefsbaorZwmRUYiYjnaGtZkSnGIufELFHLuhZWrVlFy7oWevb3AMSyRNnQNiJnRDM+9YH6WNBXQQX1gXqWT17uubqqUns+IuKC/n1WEDM4MBzERJkR6/YnNljHiYjnKCNTZIqxyDnfWSJtI3KO2xknp5Xa8xEpCV7apvVSh5WJAdjTaw3M/Os+q03zdZdYzQLCYeu4sTI7IlJ0lJEpMsWYnch3lsjtQnwREbFp9w5oW2Nty4oOpoxu02pbY91fTHZtHc7E3Ho/vLEPIqb18db7rdvNiHWciHiOMjJFphizE4XIErldiC8iImOI36aVyIxY7ZCf2GAV4RdLZiYabIGViRl6Iw7TtL6OHaeZVCJepEAmB33hPjaHNtMZ6mSAAfz4qQvUsSCwIOsL72Iscq4L1LEttI0IEVY2rhzVSS26tlyzRNpGJCJSxLy4TcsfGA5mpk+1MjGmCYZhfR07TjOpRLxIgUyW8tmOeKzsBMCmg5scDaDSKcYskYiIFFiybVpm3Datm68c3qZVLIHMrHnWtjczYgVbicEXWLNnZs1zc5UikiUFMlnIdztiSJ2dyGcAlW4txZYlEhGRAvPiNq05DVaL5cGIlTG6+crRx5SVWceJiOeo2D8L+W5HnEp8ABUfTETPN8gg7Qfa6Qv3OXpeUCtcEZFxzx8Y/nz6VGt7FhT3Nq3JU605MeV+K/MSz/BZty9cVjw1PSKSEWVksuDW0MrEACqxVqV6SnUsgMpHnYlqWERExjGvbtOaMdtqQBBrGX3ICraKtWW0iNimQCYLbg2tdCuAEhER8fQ2rclTrbqdMy8aOQdnxx+Kew6OiKSlrWVZ8OOPfT6tahrGUHo930Mr3QqgRERESmKbltfm4IhIWgpksmBnaKWB4fjQSrcCKBEREWB4m9bxpw3VzBjWx+NPs26fMdvtFaYWPwfHHFlnas3BGbDu79+X/PtFpOhoa1kW7LQjNjGZ5puW5LuzV6h5LlK88jG7SEQkI/HbtLzEi3NwRCQtBTJZqCyr5JyJ5/DowUfTHvfEO08wwz/DsQtMr81z0UW3s9xovS0iUjK8OAdHRNJSIJOlN8NvYmBgYhasg5iX5rl47aK72IOuQswuEhHJSXwR/UCo+IrovTgHR0TSUo1MljpDnZhY/whGO4hFzEisgxgQ6yDmJC/Mc3Fz3k02uge6Wd+/nm2hbbGGCtGga33/eroHut1dIO7NLhIRscULRfRenIMjImkpkMmSmx3EovNcrq66mmurruXqqqtZNHFR0bwT76WLbq8EXclabxcicBYRGZMXiuj798HEKcNfX3cJHDUVfIb1sZjn4IhIStpaliU//lgwM61qWmymizqIeWvejdtDRu1S620RKVrFXkS/e8dQoBW3Nddrc3BEJCllZLJkpwXzeO0g5qWLbq9kOtR6W0SKVrIi+khcET0MF9EXWny2aGg7eFJemYMjIiMoI5Mlr3UQKyQvZau8EnSp9baIFK1iLqK3ky0CmFwNH16hIEbEYxTIZKkYO4gVS+ctL110eyXoUuAsIgWVSQcyf2A4mJk+dbitcaoi+kJ2N7PTchngwFsKYkQ8SIFMDqIdxLaEttAZ6uQQh6iggrpAHfMD8wsaPBRTu2O7A0NfCr3E86HnXW117JWgqxgDZxEpUdGaknB4OAiIdiDbudnafjVj9vDxs+ZZ95kRK8uRmPWA4SL6TB87V3azReFBK8BSMCPiKQpkchTtIOZmIXixzRhJd9Ednb0DxNbr5nwZL2U6iilwFpESNaKmJIEZgcGIdX/jNcMX/XMarCBkMJK+iP7YE+GxezN77FwlZote7xm+z+eztptFt5e51YxARLKmYv8SUIztjpPNu4kvWDcTii7danUcDbrKKY81b4jy4aOc8qLKdBR7620R8bjEmpKv3AWXt1gf9/Rat0c7kEVNnmplUsr9VuYlXnwR/avbMn/sXMW3Ur7uEigvG/46HBluRgDuNCMQkZwokCkBxdp5K/Giuy5Qh4HVbatYAi7wxpBREZGCyLYD2YzZVibl+NOGBk8a1sfjT7NunzHbne5m8a2Ua6ogEjfnZlQzghDcdxM89ZC7M29ExDZtLSsBmXbecqspQDHPlymGLYIiIq7LpQPZ5KnW1qxU27Pc6G42eSqU+SE89DqZrhlBdI35qtcREccpI1MCMpkx0j3Qzfr+9WwLbYsFQNEalfX96+ke6M7bOr3S6lhEZNzyB4Y/nz7VutiH1B3IiuWx0zl2zvDn110CR00Fn2F9jDYjiGdGrDqeJzYoMyNS5MZdINMX7mPTwU0Ee4Pc1nsbwd4gmw5uKmhdhtPsDuesraiNNQWIL8CHwtSoaKijiEiRmzVvuM4l1UV/tANZMT12LgpVryMijhtXW8uKqUWxU/rCfYTMUOz5pOu8hcmIpgCJrYarp1THalTyscXKK62ORUTGLbsdyOJrT4rhsdPZHVcfmmyWDCSfLxOt11EnM5GiNW4CmWJrUeyE+MAslfgZIw8feNhWjcrzoefZHtrueN2Ml1odi4iMS9EOZImzXsDKlpSVWfdn0h45fgBmstbLuTy2HXZqcwpRryMijhs3gUxii+JCZyOcli4wi3ec/zjOPOxMKssqbdeoQH4yVRrqKCLiAdEOZNHgY+CQVbcya56VLckk0Eg2ADORP5D6seODoIFQ+mNTSZwlk6zYP1UDgPh6HSfWIiKOGjeBTDF3zMqGncDMh4+ALxALDPz4Y8HMtKppsZ9BYo1KVD4yVRrqKCLiAWN1ILMj3XDNqHI/XPjF5IFAsiAom65is+ZZ32NGrFqcW++3si7T42pzkt0WX6/j1FpExFHjJpAptY5Z2QRmY9WoFCpTpVbHIiLjQOJwzcRgoaZquKA+MWBKFwSZEavO5okNVuZorGyIndqcZLeZETj0Lry+07m1iIijxk0gYzcbkW3HrELPZskmMBurRqVlXYvnM1UiIlIkkg3ATFZQ/6fNowOZXIKgROnqfsbS/YL1J1pDk4rdtYiIo8ZN+2W7LYqz6ZjlxmyWbFoZR2tUyimP/SzilUKmSkREioTdAZjhgdHzWpIFQZGETmPRrmJ2ROt+jj9taJ6NYX2sPRlmpHndNyND6xgjkMlkLSLimHGTkclXxyy3uqFl0sr41YFXeeLgE7wVeSv2/QEChAmPWHe6TJUPH33hvqKoYSl09ktERLKQqsgewOezMi01VdbXidkMu0FQJl3FUtX9PPWQVQ9jRlJnf+xQhzORghs3GZl02QgfPsopz6pjVmLRfcu6FlatWUXLuhZ69vcAxGpMnLQgsCD2PKKB2S3X3MLqFaupnlINDHcC23hg44ggBiBEiEEGqfHVjJmpij6HfGWXMpHv7FcpDkwVEXFF/GDL6y6BsrjX3sHwcGYFRmcz/IHhz6dPtbqJQfquYtmyk/2xw4m1iEhGxk0gA8Mds+oD9bEtVxVUUB+oZ/nk5Vm1GE5WdB8xI7EaEyBWY+IkO4HZaYHT2BramvZx9kT2xD5PFRBFDTJI+4F21y7q47NfibNzIkQYZJCHDjzEqwOvZvX4bmwRFBEpWfGDLWuqIJJQm5IuszJrnpUlASsIOmoq+AzrY7KuYrmwm/1Jx6m1OGXXLjjxRCgvtz7u2uX2ikTyYtxsLYtyumNWLt3Qct0iNVYr47YDbbFjX9n9Cnc+eCfhSJgyXxlXffwqjptxHACHG4fzjvnOiG1mxThrx07LaROTBw88yEWTLsooMC3FgakiIq6aPBXKyiE89O9qqhkuMDqbYafTWFnZyGApW3bmzIzFqbU4pbEROjut4LGz0/p62za3VyXiuHGVkcmHbIruIbN3/9Ntd4oGZldXXc21VddyddXVLJq4iMqyyhHbyaJBDEA4EubOB++M3fe2+TbLJy8fkdkpZHbJLjvZLwATM+PMkVtbBEVEStr7TwGGtoVlklmJdhor9w9nZqIMn3X7wmXOtDu2lf0xhv7keS1O2bFjOAMWiVhfi5SgcZeRcVomRfdRmbz73xexjo3eDsMBz/bQdhZPWmwr8xANYlJ9XVlWOWK7VjF2MEuX/XrzrTdpWdeSdeao1Aamisg4UswT52OZlYHMMyvRTmOx53bIytw4/dxsZX/KYdFn4NVt+V2LU2bPHs7I+HzW1yIlSIFMjrLphmZni1SECE+9+xQ7D+10ZLtTma9sRPBS5isbdUyus3by3U0s1fqicgk6Sm1gqoiME8U+cT7dDBfDZwUx6bIZqTqNubHGo99n/fHCrJi2Nms72Y4dVhDT1jb294h4UFaBTFNTUyXQAPQCvw8Gg2bcfYcD/xwMBm9yZIVFLlp0n5g1geGuYYnd0Oy++//KoVdi35NNzcqRviNj28uu+vhVo2pkoqb6rBcQO9klgKPLjx51ru6BbkcyR+kkW9+bb70Zuz+XoCOXIE7toEWcpdcYm/r3eWPifKEyK7nwwhozMWuWamJkXMg4kGlqajoReBSYhlVjs7mpqeniYDD456FDJgH/DhTli0w+LjrHKrpPfFy77/7HB0XZbHdaOHEhGw9sBOC4GcfxnS99J+n6z554NmAvuwTwl8G/jJgpU6hC+WTra1nXknHmKJlstghCYQI4kfHE668xBfVSh5VBgNTzT4pl4nwhMiu5SrfGYt6+JzKOZZORaQF+D3wGmAzcBnQ0NTUtCgaDr6T9Tpfl86Izk25odt/9j5fNdqdj/cdy5oQzeerdp1Ku5cwJZ3Ks/9jYc5hRPoPuwW4gfWew+CyQ3a1yuXY7i2a/HjrwEObQlOVMgo50stkiqE5nInnh2deYgks2/8SMm39y85XDE+edCCDG68V8sW/fExnHsglkzgQWBYPBvwF/Az7V1NR0C/B4U1PTImC/kwt0SjFddNp99z+6NsiuZgXgjMPO4Kjyo3jy4JPsiwz3w5/qm8rZE8+OBTFRrw2+FvvcbhaokIXyM/0z+fikj/PggQcxMW0HHWPJZotgoQI4kXHGk68xrrA7/8SJifNuXMwXQ+Dkle17IuNUNu2XA4AZf0MwGFwF/DfwBHCCA+tyXDG1110QWBALVFINofTh4zj/cbHjVjaupKaqBp/ho6aqJqPMw7H+Y1kxZQXXVl0b+7NiyopRQQxkV/Re6EL5Y/3HctGki9IOA00MOuyY6Z9J46RGKn0jv6/SV0njpMZR2Tq3hqGKlDhPvsa4wh8Y/nz6VKs9MIw9oyVT8Rfz8YXwMHQxP2Dd329zeKQdu3dA2xorUIoGbNHAqW2NdX8hxG/fSyW6fU9ECi6bQGYHcGrijcFg8MtYLzStuS4qH4rpojP67v9YF+JnHnamrYAnk8zDWOzOxfHh447eO7it97YR35/JLJ1cROuS6gP1scetoIL6QD3LJy/Paotg90A3bQfa6Iv0jbi9L9JH24G2EbN9oDg6naWbMSTiUZ58jXGFrfknDkycT6zF+cpdcHmL9XFPr3W7kxfzbgROqcRv30slun1PRAoum61lG4FlwD2JdwSDwWubmprKgatzXZjTiuGiM57dBgGZbnfKld3OZYnriXKqZsWOTOqSxpLN1sNc21XnSo0GpER58jXGFbbmnzgwcb7QtThuNTHo3wdbfw3dL4wdvCRK3L5XDNviRMYBI34OR6E0NTWZAMFgsGDnDPYGYxediZ2uaqpqYnUWFVRwdVVxvUb2hfvGDHic6sbWF+5jff/6pBf0uSinvKiL3jcd3BQL4FLVu/jwUR+ojwVO2XyPU+z8nor9Zy4lxXB7AfHceI1xTbLaFRg5/yTX2pUff3X488tbIBJ33eAz4EfROkUDPvfN3M4FcN9Nw9vJvnLXcOBkDGWbogGbPwCXfj3384H1c3xsPUTG2EaWSvxaCvE7ERlfUr7GjJmRaWpqujkYDH7F2fUUXrbtdYvBWJkHJ9+ZT1f0Hi964b7nrT34fD4iZiRWuxPd9gb5yxw5LZuGBdl0OnOKGg1IqSiV1xjXFGL+iT8wHFhMnzoysHCyFicqmyYGuWRA+vfB4/dlH8TEb99TcwCRgrKztezGpqamqmAwWFxpigy5edGZT/noxpZs21v8NiqIu9jHJDz0j3/8RX9UfaA+6SydbJ5nPodOZrP1MJtOZ04pZKe4fNAQUYlTEq8xrnJqRkuqYGBG3fB2q+suGb3VC5ypxYnKNHDKtaPaSx0QHnoN3dML3/7JcO1PTRX8yz9YH1OJ376XSXOAYp6pI+IRdor97wH+samp6b6hvcmjNDU1NTQ1Nf3O2aU5y26BvdcuovLVjS2aBbq66mqurbp2RKaqZ38Pb771JonbEhMv+gEWTVyU88+0e6Cb9f3r2RbaFgs4ohmn9f3rRxXhZ8Nuk4PEepd8NB2wo9hqvjJRiN+neEpJvMZ4XrouYa9uG+6IFq3F+dFq62P0At+JWpyoTJoYONEYIL5Q/9b7h4MYGK7RScbwQbnfCpSi2RU1BxApqDEzMsFg8LKmpqZ9wJeBKUMTlt8FaGpqOh74FrAkv8t0ht0Cey8p1Dvz8d3col3eEuWjyL1Q839y2XroZNMBu9xuNJCtYprnJMWhlF5jPGus7VDhCPjKoKwcIpHUdR+TpzpT5J5JEwMnGgMkbmVLNOo2I/X2vfjHSseJ2T4iYq9rWTAY/OehF5pvAv/b1NR0BXAtcAXgB54FRu/VKkJuXHTmk1PvzI+11SfxPImiTROSXfTnso2oULUgXtt66NWaL9X2SDKl9BrjSXaCAdOEv6uHigmpa3GcGpo5eap17FgF85OnOtNRLXEr2+s9I+8fsZ1tjAYD8Y+VjlP1RCLjnO32y8Fg8Oampqb9wBpg+9DNO4CvBYPBn+VjcV7g9l5/J96Zt9MsIN154ru+RUUv+nNtRJDPjFPi766MMoyhxhhm3Dy+YmxY4LXAK8rrtT2SP3qNcZHdYGB3p3URnywgcLrI3W4TA9uNAULD3dASs0Sz5sGOP1jHXXfJ6BqZ6HY2GLsOaNY8K3BLt70s03oitXIWSclWINPU1GQAnwH+eegmA3gDOCsYDBZgIlVxKoY5Hrm+M29nq88vDvyC9/vfzysDr4w5XwaI1RsBOW8jylctSLLfXZhwLJApp5xBBot266GbjQZy4eXaHskfvca4LJsuYYnyMfvFThMDu40B4p9nYpZoTgP86Y9WwX9NFfzfFH0nysrHrgOK3xaXSib1RE5luURK1JjF/k1NTR8HXgD+C5iOtV/5n4c+/3VTU1NNPhdYrOIDgMQWxREiDDJI+4H2vE9YXxBYEGteEH1n/pZrbmH1itWxNsjp3pm30ywgTJgD5oExzwMw2z87VuTuRCOCbIvw00n3uzOH/gP43OTPcXXV1Y40LMgHtxoN5CIfv0/xNr3GFAF/YPjz6VOHC/szaa+cLKsTicvqQH6K3O00BkgmvhEAwDmXWnVAqfjKrGPGyoBEt8WV+4fXFZWsOUA6TjQyEClxdrqW/Ryow+osc1wwGPxKMBj8LrACOAHoaGpqmpm/JRYnOxfpgwzy1DtP5XUduXZjS7bVJ2JGYlt9ol4bfI1zJp6T9jxLJi3hgkkXxM5l57Gj24hSqQvUxc63snElNVU1+AxfynocO/LV6c0Nid3lijnwgvz8PsXz9Brjtky6hKXiRFYnG3MarAwHpO6oBlaW6Ct3WQM9v3LX8NaxaJZoxmxYci3UnjwyAPGVWbctudZ+5iO6Le7404aCRMP6ePxp1u12Hycxy5Vu/SLjlJ2tZY8A1weDwefjbwwGgxuG9jPfD/y2qanp/GAwuC0fiyxGdvb6A+wY2EHdQF1e3x3PpRtbuq0+b771Jj37e2IZlz3hPbbP0z3QndE2ott6b0taX5SPWhDVabjHq7U9kld6jXFbJl3CUin00MyodI0B4tlpBDB5Kpz9KeuPE+uK3xYXrXP5xR3261ycaGQgUuLstF8+P819Dzc1NZ0P/AJ4AqhOdWypsXuRDoxZB+JEw4Bsu7ElFvG/+dabI+6PD8qiF/ZjnSe6dSteukYEUcnqi/JRC6I6Dfd4tbZH8kevMUUgky5hqcQXuRdiaGa8VI0B3MoSJcq2zqVY1i9SxGx3LUslGAz+tqmpaSHwKwfW4xmpungBRMwILetabLWTdbthQF2gjhdCLwDWVp9v/vibI+7P5sI+WYvdPW/twefzESEyYhtRoujPofVAK0AsqGuc1MjOgZ2OzP/x6gyWRG53zMtWKc5zkvwZr68xBWe3S1gqTmR1cpGsMUC0SxkUNksUz043t033QpkfBg+NzNS4leUS8RA7NTJjCgaDzwEfcuKxvCLZXv94dupAiqFhwILAgtjn1VOqec+R78m5ADvp1i1MImYk1qo5ul0tVY1KVDSoazvQRq2/1pFakFKo0+ge6GZ9/3q2hbbFgrLoz2p9/3q6B7rdXeAYvFbbI+4aj68xrogGA5d+HT73zeFWy3YK050scneKE7U/udr66+EgJlWdi2laQQwMZ2ra1kDNTPfXL1Lkcs7IRAWDwZ1OPZYXJNvrv2rNKiJDaWM725SKYThgZVklM8tn0j3YDeDIcMVMtt2lqy+KcnrieyHrNPKRNbHTMtupn5VIsRhvrzFjKsbZIrlmdZzmdpZo9w7oem7461R1LomimZq/7gSfD8IurV/EAxwLZMab6F7/6BYoyHybUrEUnS+cuJDd/bsZZNCRC3u7W7cgeaCT76CusqySUyecylPvpu4od+qEU3MOAvK1bbAYAmARcVExzxaxM/ulkGvJtfYnnXTBJAy3do5KVeeSau5OJAJHHwdv7srP+kVKgCNby4rNrt5dnBg8kfKbyjkxeCK7enfl5Twz/TM5vuL42NeZblMqlqLzXFs4J7KzdQusi/DoNjYAAyvQybZVs1194T6efffZtMc8++6zOW3py+e2QSfaWouIR2m2SGacaoWcaPcOa/vXy8+MHrTZtgae/sXo1skRc/j74+tc0s3d2dOdn/WnsmsXnHgilJdbH3fl5/pJxCklmZFp3NBIZ08nETNCZ08njRsa2daUn66dH5jwAXYd2pVVNqOYis6dLMC2s3ULrIvwcCQc+9rn87GycSU333NzXoO6QmQ08nmOYgmARcQFibNFkr2TH50tUgxZETvyvU3O6SyRnQL+114evi0aqMSLr3MZqyNZsvXn62fW2AidnVY2qLPT+nqbup5L8SrJjMyOnh2xWpWIGWFHz468nSuXbIbdzMUgg2w6uCmvRf/gXAF2up9JvMR6GdM0qZ5SnXbiO0CwN5jTz6MQGY18nsOPP/Z5up9VsXddE5EsJJstkuyd/F1b3VphZsbKbOzO3+t31uKDSTviAxWwCvbjB3ZOn2plaMBeR7J8/sx27LCCGLA+7ijCn79InJLMyMyunh3LyPgMH7Or87tXONtsht3MRYQIL4Ze5MXQi5RRxiCDRd9qN9XPZIABTKx/0FNloVI1HIjKtc6kEBmNfJ6jLlDHttA2IkQcac4gIh5SSrNF7GQ2nthgbaEqpjqQ+GDSjnStkyGzuTt2fma/vgfKK+B98zPP0MyePZyR8fmsr0WKWEkGMm3L2mjc0MiOnh3Mrp5N27K2vJ8zm4GU6YYDJope/Ec7VRVy1kwqY3XkSvYz2XRw05gX4emCuqhcunMVYktfPs9RyK5rIlJkSmm2iFe3ycUHk3akClSiMulIZjcbNHgou+YPbW3WdrIdO6wgpi3/108iuTDM+HRngTQ1NZkAwWCw4OcuRn3hPraEtvBi6MW0wUwq5ZQXvNVuso5cMHI6e7Lgqi/cx/r+9UlbB6eTqs7Eh4/6QH1GAWR8MOXk4xbyHNn+/EXyxBj7kMIp6deYpx6yLlDNSOqLf8NnFYMX08V/MvEDK79y18ig7Kipwxf3/oA106ZYxK87F74y67lGIqk7kiUGINmcu9xffFktkcykfI0pyYyM10QzF9F32VMplla7ucwxSZeF8uHDwOC95e/l9cHXR2y7crI9dSEyGvk+h5PNGUTEQ9yejeIkr26TmzVvOJhMxfDZbJ1cndncnXRBjJeyWiIOUSBTROLrKpJxc9ZMvFw7ctm9CL+t97bY94xVZ3Jb722264bGCqaiGY1cgoFCnSPT7Ywi4nH5no1SSF7dJhcfTKZSVganL7Y+HytQyaSjWvzPLFGqgZvR5g8KZKQEKZApIvF1FckUS6tdu4M8t4e2p7zItnMRnslgTcisbqgQGQ1lTUQkL6KzUTJ5J78YxWc2Mil4d1umwaSTrZ/TZYO8lNUScYgCmSIS340qGaeLxscq1k/FbkeuAQa4vfd2woSz6rJmpztXokyaABQio2H3HNn+LkRknHJ6NoobvLxNzq1gMl02qBBZrXzP/BHJkAKZIhJfV5GMk612kxWL281oZJIpCRPO6LHj2akzKZa6oVzk8rsQEfEsr2+TcyOYjP+Zxbdg3tMLg+HhjMy0ypHd0eKzWonBiOGzSqkjkfSBye4do39X0fk1mXZHE3FISQ7E9KqxBklGL+ZvueYWVq9YTfWUaiDzovH4Yv3EoClChEEGaT/QnnLgZLJBngYGPsPHnt49tKxroWd/z6jvs/PY8ewM1szXQMtCyfV3ISLiadHMxvGnWRfRGNbH40+zbteF8WjRn5kv7nXx1vthb5/1uWFAednwwE0YzmolG6ZpRoaHYKYarBk/vyZxW5sZsW5/YoN1nEgBKSOTR5lsF4o/dpDBWEF4hAjllMcyG9F5MjCyaByslr92zpVrsX6yTEnLupZRtTLJMkjVU6oJE7adLUlWZxKvWOqGspXr70JExPNKYZtcoU2eOhx8QPr6mOjx6YZpxks2jNSrM3+k5CkjkyfdA92s71/PttC22Das6Hah9f3r6R7oTnts9OK2nHIWT1rMZyd/lrmBubFamAoqqA/Us3zycgDb54LkxfqZZDSimZJ4yQKKVI9tYvJS6CXbP8toncnVVVdzbdW1+PHH7ptWNQ3DsNqLOznQsi/cx6aDmwj2Brmt9zaCvUE2HdzkeGYk19+FiIiMU/7A8OfTp1qZGEhSHzN0XGIw8pW74PIW+NfvW38ub7Fu29NrHRMNTMDahhbNxES7o0XiuqPBcHc0kQJSIJMHmWwXsnssMOJi/uqqq2Pv0Ge6NclusX66jMZM/8wxA4p0jz3IYNZBQbKtbT7DR01VTU51Q1GZBKG5cuJ3ISIiBdK/zxpKet9N8OOvWh+fesidLVWz5ln1LWBlRY6aCr6hYaLJur6lCkb29Fp/0gUmXp35IyVPW8vyIJPtQibmmMcOMsgTB59gyRFLcjpXNPDJpFj/1YFXOdZ/bNLnOVZXsbVta9nz1p7YdjjDMOjZ3xOr7bm3/15ODJw4ZmeuZFv0oo/p9LDJXIZ9ZsPu7yKX7JKIiDig2IrdM+36lioYiZcqMPHqzB8pecrI5EEm24XsHAvQPdidNIORzdak+EzFysaVHDn5SMDKAoTD4RGF+g8eeDBlBmJBYAEGVhYmWSOClY0r8cUVI4Yj4RHPKUJkzCxHquxIfK1QPB++2Ha8bAKNxMCwZV0Lq9asGtHAIBoYOiHf2SURkaJVTNmNsRRjsXu0g1m5fzgzE2X4rNvju76l2oo24vtSBCaZZn9ECkQZmTzIdrtQ4rFvvvXmiAxGsoLvTM51W+9t+PEzyz8rdnv1lGrKy8oxDAPTNNnXvy821BKsepZUGYi+SF/an0P1lOrYeuLXGC9dliNddiSZcsqZE5gzathkJk0X7A777Ax1Ji2+z3QejJ0W09lml0REilaxZTfGUqzF7pnMs0k1gLS60rq/py/1MFIvz/yJ2rULGhthxw6YPRva2mDWrLG/T4paSQYyu3p30bihkR09O5hdPZu2ZW3MqircX9ZMtguZmCOOffOtN0c8VnxQkeziOZNtYmAFPq8MvDLitmQBVMu6lrRds6JBRrSbWiqJa6qaXEXLuhZbnbnsbJuLFyEyKojJdEZLLjUr2cyDiTZOSPw+GNmVTkMxRaRkpOueFe2Y9fh9cOyJsLuzOAYvJqsvMeNqSm6+crimJFUgk69hkna7vtkJRuLFByZen/kDVhDT2Wl1e+vstL7ets3tVUmOSnJrWeOGRjp7OgmbYTp7Omnc0FjQ82eyXShxm1eisS6e7ZwrUWJTgPhC/aixumbZ2YKVbE0Ghu3OXCm3zb21h5Z7k2/5eurdp2Lfn82Mlmw7ouUyDybaYro+UJ+0K52GYYpISUnVPWtEx6xB6HpuuC4j1XyTQsm12D3Z/JZCP6d0W9FGMEZvS4PUM39mzoX3zoEnf1rcWwR37BhuWR2JWF+L55VkRmZHzw4iQ+8WRMwIO3oK+5c10+1CL4ReAKytWO858j0ZFXzbOddY2YxoYX58NmisDISdLVjR5xS/plVrVtnOcqTMjmASjlgvgm++9Sbff/D7fO1zXwPglUOvcMHhFwDZNUIYq4EBJK9ZyXUeTLTFtGbFiEjJs5PdSCbZfJNCyaXY3U4GqlDPKRqMPN0Or6W4NjIMOO3C5Fv7ErM/XtoiOHv2cEbG57O+Fs8ryYzM7OrZ+IbebfAZPmZXF/Yva7qJ9InF6JVllcwsnxm7P9OC73TnikrXRACGg433HPke2xkIu1uwEmWS5UiVHUm0b//wuz7x2ZBsGiEsCCyI/RyTNTCA5DUrmgcjImKT3exGymxN3HyTQrFT7A7Wc0vMSNjKQBX4Ob25K/V9ZgSe+cXYGZVibICQTlsb1NVZ2+Dq6qyvxfNKMpBpW9ZGXXUdZUYZddV1tC0r/F/WTLYLLZy4kDLKgMwuntOdK57dQCOTIMruFqxonUc250i1bc6ubOpdMglCcz2XiMi4ZHeQYzENXpzTYF0Aw3B9yY9WWx9rqkYem7hlrNiGScYHVqnYCayKMUBLZ9YsqyZmcND6qEL/klCSW8tmVc1iW5P7BVx2twtVllVy4aQLcyr4TjxXsDdoqwlAOeWECWNiZtQ1y+4WrPpAPfMD81nfv55BBjM6R6ptc9/88TdH1OHEb5OLDz6yndESDQy3hLbQGerkEIeooIK6QN2oZgK5nitXmXZJExFxXaruWdMTshvFNHgxXbF7MvFbxuK3lBXDc4oPrFIZq3FB4uNk2wDBLflqvCAFV5KBTDbc7nSWzcVzOnYDjTmBOdT6azMOojKpA8q2M1f898W3YL7q41clfT4Ax/mPy/hnkGrLXiY1K7mcK1vZdEkTkQLTBdNodrtnuTF4Md3vK1pfsvXX0P3CyGAgXUtmwzd8bDEMk4zf2pf2uDECq1wbILjFS3U9MiYjcc5HITQ1NZkAwWCw4OdO5cTgiXT2dBIxI/gMH3XVdUWR1clWX7gvlgVJpZzy2OyWvnBfyiAKSPquf42vhifeeSJtcBJ/IZ3uHOkCtb5wH0+9+xQ7DqVv2lBGGSsmr4g9VqY/g1wU8lxunE9kDMkL2FxSNK8xyS6YYGS72vF6wZTqZxMvVXBg+KzOWU6/y2/n9wXJj/nKXSMDlKOmDgdovjLrdjNS+OeUzPpvwKCN4KK8Apb/e+r777tpOJhJ9/z9Abj067mv2wn9+6wtf8kaL0SV+5M3XtCbEm5K+RqjjMyQQnc6y/eWoEyzIKkyEGnf9Wc750w8hz3hPbaCk2w7c1WWVXLB4RdQV1GXckBmstqVQs5oKfQ8mFy7pIlICk5drBRTp6pilGqQ44w6eHWb1X65kIMX7c62AWttidJlJCJh6+K4WIZJHl4J+/dYn6cKrKLHpWNni2D8UM10ChUkZFIfFB9UKotTtBTIDJldPXtERiafnc4KtSVorO1qAJsObkoZTMXPRkkUXfvjBx9n+eTlBblYzmb7ndNb9pxeX7bstL+OdklTICNik5MXK8U6Cb6YpBrkaCcz4nTwZ+v3FfdamHhMdSXs7UuxZSwAZ3+6eIZJ/q1v+PN07a/jj0vGzhZBOwFaIYOEbOqD9KZEUVMgM6RtWduoGpl8sBMctB9oT7klKNNMTlaZlqFgatfALkff9XciCxX/fKKPtz20nedDz6d8vELOaCnUubzSJU3NCMQznL5Y8XIhdKElezf+vXOs+3Z3Dmdr8rmNJ9PZNonHTKu0tlOlykikykC5sTUpfltZukxSuu1XkL4Bgt0ArdBBQjb1QdlmcaQgFMgMKVSns1y2BDmVybEbTBkYjr3r73QWqhQK3XO5yHerS1omSuF3JOOI0xkUrxZCF1qqd+O7XyhsHZHd31eqY3r6rHbMieIzEqkyUIWWy3DPRLkGaIXOXMY/97THxT13p7q8JbNrFzQ2wo4d1oDOtja1hc5QSc6RKWbZDk6MDz7i6y+ixw8ySPuBdvrCfWOuITGYalnXwqo1q2hZ1xJraxwh4ti7/k6uPR+P54bugW7W969nW2hb7Occvchf37+e7oHutN+fasaOnSGqhVAKvyMZZ5ye9WF3VkqhOlUVo2IaqGj392X3GMNn1cUUcsuYXXaGe9qtbYHhAO3Sr8Pnvml9PPOi7DOX+ZyxE//cU0l87k51eUumsRE6O61grbPT+loyooxMgWUbHDhZ3G23viJeLu/6O12Y7vVC91y3F0Jm7a/d4PXfkYxDTmdQnCyELlXFVEdkd7ZNVLpjfGVw3KnF283KqdoWJxQ6cxn/3FNJfO7ZZHHs2rEDIkNriUSsryUjysgUmB9/7PNpVdMwht7RGSs4yDaTk4zdYApw5F1/J9eej8crNLsZsS2hLSkfI9olrZzyEUNAwfqdlFPOwsMWsjm0mWBvkNt6byPYG2TTwU0FyYJ4/Xck45DTGRQ7k+AL2amqGBXTxHtbv69y60+6Y8r9sORa+xkJN0RrW8r9o7MThc4kFTpzmc1zzyaLY9fs2eAbemyfz/paMqJApsCy3RLkZHG33WDKjz+21ui7/rdccwurV6ymekp1bK2p3vXvC/ex6eAmxwvTvVLonopTF/nRLmn1gfpY4FtBBfWBes6ZeA5PvPNE1lvXcuX135GMQ/nYblMsF4vFqpjqiOz8vs651Prjld9p/z546iFr3suPv2p9fOoh6/Zobcvxpw0FE4b18fjTrNsL1UrY6f/v7Mj0uccHualk+6ZEWxvU1VnfX1dnfS0Z0dayAstkS1B8MXi8XIu77U6hPyFwArX+2qxmo8QXeju5dvBGoXs6Tl7kJ+uSlm5Ypt2ta4kybUzg9d+RjEP52G7jZqcqLwzvc7Lo3Al2f1/F0n0sHbstjd1uPuDWNrdMGi840Z0tlVmzYJt3h68XAwUyBWZ3cGJfpC/pMUDa4MNOcXcmwVRlWWXGs1HS1YDkunawH4i5Veg+lnxf5Dtdn5JN9zGv/45kHMrXxYobnaq8MryvGOuI7Py+cvmdFiLA9NLck3wGCU4qpvbZMoIRfTe4kJqamkyAYDBY8HMXi75wX9Lg4H3+97Ht0DZePvRyxo9ZTrntd9mTXZzCyGAq29a4mw5uil3EprqQzmXt6TIO2Txeodn5+fjwUR+oz6oQPtgbjAVKLetaRgRKNVU1scC1ggqurro67WNl+7P2+u9IMma4vYB4Ob3GjLjQ9ODFSv8+aFuTfgZIub84LmK9tFYn2Bn0mUuAGf27+8ozwwXkqZooGD5rK5XbraCjvP7/neRbytcYZWRckmxLUPdAN20H2kZc/NkJBMba5pVMPqfQ2+mKlu3ao1ucTJIH4Nn8LAot3x3HnNy6lm12x27msVh/RzKOFcusj2wVUyewsXjl3Xgn5DtLkipI8sowVq//fyeuUSBTJFJtxxorEMgl+MhlCn26molMuqLVB+ptrz1VFimqnHLmBObkHIjlW74v8p3cuma3VXeyoaj5DJYlN7kMY5UiZ2dCfTFdxI6XLTv5DDDTBUluN1EQyTMFMkUi1Tvfkbh3VpIFAmNtDcqHsWomyigjjPUP9lgX0naDqHR1N/G8coGcz4t8J+tTcs3u5BIsS35kU/MkHlJMncDsGg/vxuczwEwXJFVXwt4+d5soyPi0a5c14HPHDqutdFub1dzAYQpkikSqd77jJQYC5Ul+ffl+p9XOMEdj6D8T07FCbztbnMKEaTvQxtuRtz3xLnO+LvKd3Lqm7mOlxYlhrFLkiq0TmFhyCTDHahCQLkiaVmm1Mna7iYKMP42N0Nlp1Wt1dlpf56FDmwKZIpHqne8on+EbEQgAhAnTF+6LXXAU4p1WOwFFPKdqQOxscTIxeSvyVux7xuu7zE5uXVP3sdLidEc7KULF2AnM65IFEjOG/s3b3Wmv+1i2AWa6DnR/+iNMf1/6IKmnzxramagUhrF6ocX4eLZjx3DTiUjE+joPNBCzSKQbUlk9pZppVdPY27uXtW1rY9Pfgdj09/h3WhPrRyJEGGSQ9gPtOU91tzPM0cSkjLK0U+czrQHJpO4mnpPP3UvSDctcPnm57aBuQWBBTkNRpbg4NYxVipitCfUlcBFbKLt3WJ3VXn5mOGAYCEHXc9af+NtefsY6dneSCzY7gx9hOECCkbUv5sjXdcwIhAfhtYRzTZ9qBUcwOkiC4hzcmY1Uv5d0vwNIPyRUnDV7NviG/s77fNbXeaCMTJFI9873YHgwZQYiWmQd/05rKk6802o3oBhkkM9N/pxjNSB2tzjpXeZhTmxdU/ex0uJkRzspUuOpE1i+pSuiTyZd9zE7gx8Bup+HV7dZQYrhG/79pWoQkChVFg6GWy57PWORbQc4r8xXKhVtbaNrZPJAgUyRSFfXsGrNqjEvOOLfaU0lVXepTNgNKPz4Ha0BsbPFCTLvrCVjU/ex0qGap3FivHQCy7exOo2B/e5j0QDz8fusICUV0xy+304b5WTrShYkldI8nmw6wHlpSGipmDUrLzUxiRTIFIl073zbueCIf6c1ncR3WjNtDmA3oBhkkO6B7tgWplybENgpYAe9y5wv6j5WGlTzNI6Mh05g+TZWpzHIrPvYjNlw7InWljSwn2WB1A0CUgU4UaWYhcumA5yX5itJRsZVILOrdxeNGxrZ0bOD2dWzaVvWxqwq51vBZSv+ne8XQy/Gghk7Fxzx77SmE/9OazbNAewGFCYmvzjwC1ZMXkFfpC/nJgTpAr14epdZJLV8D2MVKSl2Oo1l2n3szy8Mf53sIjzZ1rCaqtQNAtJ1QIPMt5J5oYA+mw5wXpuvJLaNq2L/xg2NdPZ0EjbDdPZ00rih0e0ljRJ95/szkz8Ta69sp8i6LlA3qrA+UXzgk21zgGhAEa9nfw8t61pYtWYVLetaYs0IwoR58uCTjjUhSFXAfqTvSAys4saVjSupqarBZ/ioqarRu8wicaL//zrZiEOkZPkDw58nK6JPV1ifrPtY25rhLk6Q/CI8epEdScj8pGoQkHYNAeui3G4Akm0BfaGN9XuJHRf3O/DifCWxZVxlZHb07IgNmIyYEXb0uPM/pZ3MUKZF1vHvtKYSH/jk0oZ1pn8mPnyx77/zwTtjwcubb73JnQ/eyb997t8A6B7sjgUZThTiJ9vi1BfuY33/egYZ1LvMImNQzZOITXZaWdtpb52qPiNZliXVRXaqBgFOtdj2Ug1JNi3GNV+pZI2rQGZ29Ww6ezqJmBF8ho/Z1e50p4hmhiJmJJYZ2tY0uiAqkwuOTAMfO3NZ0hXIxz9+fDvoxK/Nof+yPY8d6qwlkhnVPInYYKfTWLLbEttbp6rPqK60Blb29A1fhMdve0rWPjlRqnVl2mLbSzUkdn4vic9f85VK1rgKZNqWtY3KhLghk8xQJhccmQQ+brRhzed59C6ziIg4Kl0r62RSFdanqs/Y22dtE4sfWJmufbKvzNqa5q+Ampnw153W10602PZSDUk2LcazCX7EE8ZVIDOralbSzEeh5TMzZDfwsduGFWDTwU2juosZGLFMy9QpU9m3f3iP6dQpyf/xzHchvt5lFhERR6VqZR0dXLm7c+z21nbrMyD1RXay9skjCvNzbLHttRqSTFuMa75SyRpXgUyxKIbMkN02ykDS7mLHVRzHy4deBuDqj1+d8vunGFN423w7L+1ec23pLCIiMqZcW1nbrc8AwIChNwmtL9NcZDvZYtuLNSSZPn/NVypJRnSrTyE1NTWZAMFgsODnFkt8gbxd5ZSzfPJyKssq6Qv3cW//vWM2F1gyaQltB9rSnif+ce1K1jo6es5oTcxYLZ1FxDGG2wuIp9cYKSpPPTRcn5Gq/sTwwcy5UDHBnYtsu2s8/jT3t5bJeJTyNUYZmXEqXYG8ne5ilWWVNE5q5BcHfkGY8KjHL6OMCyddyLH+Yx0vxI9vHZ0oeo72A+0ZB0ciIiKOs1ufMe/vh7MMxbpG1ZBIkRlXc2RkpPi5LPGi3cUiZiTWXQyIdReL//4Vk1dwUuCkEXNdTgqcxIrJK2IZkVTzX+oD9SyfvDzjzEli6+hkM2yiQZeX9YX72HRwE8HeILf13kawN8img5tszdwREZEiEa3PKPdbWY14hs+63e36DC+sUSQJZWTGuWiB/POh52O3ZdJdzG6BvZOF+Lm2jvaCZFvnBhhIWq8kIiJFzgv1GV5Yo0gCBTIC2O9i5kR3sVy50Tq6kLR1TkSkBDlZnJ8vXlijSBxtLRPA6mLmG/rrsLJxJTVVNfgMHzVVNTl3F3OaH3/s82lV0zAMqwasGIOubIyXrXMiIiIiuVAgIwAsCCyIBTLVU6pZvWI1t1xzC6tXrKZ6SjVgBTLzA/PdXCbgraArG8m2zo1VryQiIiIy3mhrmQDpu5hl210sXxYEFrA9tJ0IkVjQlahYgq5slPrWORERKQEjBnKGrFk0qqeRAlNGRmKc7i6WL9Ggq5zyWGYmyoePcsqLJujKRqlvnRMREY/bvQPa1lizZ6KDNAdC1tdta6z7RQpAGRkZwcnuYvkUDbq2hLbQGerkEIeooIK6QB3zA/M9G8SAtXVuW2gbESKsbFw5aqYPjL11ri/cx+bQZjpDnQwwgB8/dYE6FgQWePpnIyIiLuvfB09sgMGB0feZEWsWzRMbrA5oysxInimQEc9KDLqiF+/39d/nyMW7W8FArlvn1LpZRETy5qUOCA8Nwt7TC7feD3/dB9OnwnWXWAM1w2HrOHU/kzxTICMlwemLdzeDgVzqldS6ufgpWyYinrZrq5V5ASuIeWMfmKb18db74eYrrft3bVUgI3mnQEY8z+mL92IIBrLdOpfYujlxW1r1lOpY6+Zi3z5YipQtExFbirmQPloTA1YmZqghDaZpfR07Tg1pJP8UyIjnxV+8p5LJxXuxBAPZ1Csla91smmasdfPqFatjrZsVyBRWMQTIIuIBu3dYNSbh8HDmI1pIv3MzLFwGM2a7tz5/YDiYmT51OCNjGNbXseNKoCFNMQeUAqhrmZSA+Iv3VDKZu+LlOS5q3Vy8NOhURMYUX0hvJryumRHr9k3r4PWd7qwPrAt5Y+jy8bpL4Kip4DOsj9ddYt1u+KzjvEyd2TxBgYx4XvzFezp2L969HAyodXPx8nKALCIFklhI/5W74PIW6+OeXut2MwKP/pd7F9JzGqCszPq8psqqifnRautjTZV1e1mZdZxX2Qkon9hgHSeu0tYy8Tw/flvBjN2L9/jHm1Y1LbY9ywvBgBOtmwtpYGCA3bt38+6777q9lLw7JXJK7PO7T7171P1Vr1fFPt/+1+2FWFLGJkyYwIwZM/D7/WMfLCKZs1NID9ZtbrU4njzV2t6WuP0NrExMWZl1v5e3Xqkzm2cokBHPi794TyWTi/e6QB0vhl7ExEwZDBgYRRMMxMu1dXOh7d69myOOOIKZM2fGskelas/gHkys7N6EfRMYCA8H3/4yP9OnTgesv1s15TWurDEd0zTZt28fu3fvpra21u3liJSmdIX0r/dYmZliuJCeMdsKomL1I4esmphSqR9RZzbPUCAjnhd/8Z5KJhfv7/e/nxdCLwCkDAZMTN7nf192C86jXFo3u+Hdd98dF0EMwARjAu+Y7wBQXVlNT18PA+EB/GV+qiurRxxXjAzDYOrUqezdu3fsg0W8zq0i71SF9FHFdCE9eap17lK8kFdnNs9QjUwJ2NW7ixODJ1J+UzknBk9kV+8ut5dUUNGL93LK8SX8lfbho5zyjC7e/zTwJwysC+tURdkGBjsHXCy2TCPaurk+UB/b/lZBBfWBepZPXl507X3HQxADMNE3Mfb3qrysnOlTp/Pemvcyfep0ysus95QMDCb6Jrq5zLTGy+9Kxjk3i7yTFdLH04V0YfgDw59Pn2p1ZIPS7MzmcQpkSkDjhkY6ezoJm2E6ezpp3NDo9pIKzsmL985QZ2wLUKqibBOzqIuyo62br666mmurruXqqqtZNHFR0WRistEX7mPTwU0Ee4Pc1nsbwd4gmw5uoi/c5/bSbCk3ypnimxILZhIZGEzxTaHcUKJcxDVuF3knK6Q/uloX0oU2XjqzlQC9YpaAHT07iAz9gxsxI+zoGZ8tAbOZu5KMl7uWlapiGiS5fv16zjrrLI499tiMvzfgC3CkcSQHIwd513wXExMDgwnGBCb6JiqIEXGb20Xe0UL6TeuGA6nrLhm9DtCFdD7NabBm9gxGhgPKRF7vzFYilJEpAbOrZ+MbeufAZ/iYXe3ioKwSoBbGxSV+kGRiHVSECIMM0n6gPevMTFlZGfPmzaO+vp5LLrmEgwcPpjz2hz/8IXv37k0ZxFx22WU88MADAFxxxRW89NJLANx8882xY8qNciaXTaamvIb3lL+HmvIaJpdNdjyIef311/nkJz8JwNatW3n44Ydj9z300EN861vfAmDv3r2cccYZzJ8/n9/85jeOrkHEc5IVeUfiirxhuDYlX2bMho9cNpyFKdUWx8UsGlCW+4czM1GGz7rd653ZSoQCmRLQtqyNuuo6yowy6qrraFvW5vaSPK0uUBertVnZuJKaqhp8ho+aqpqibGFc6uIHSaaSyyDJww47jK1bt/Liiy9SUVHBnXfeOeL+cPTdWeALX/gC1113na3H/cEPfsCcOXOAkYFMoRx99NGxoCoxkLnooou48cYbAfj1r39NXV0dW7Zs4ayzzir4OkWKSrEUeR/9PvjwZ3Qh7aZoZ7bjTxuqmTGsj8efZt0+Q28aFwPtYygBs6pmsa1pm9vLKBlea2Fc6uIHSaYSHSSZ67bCs846i+eff57HH3+cb3zjGxx11FFs3bqVF154gRtvvJHHH3+cUCjEF7/4Rf7xH/8R0zS55ppr2LRpE7W1tbFtiADnnHMO3/72t3nggQd45513mDdvHieeeCJ33XUXn/rUp9i9ezfhcJivfe1rfPrTnx6xjnPOOYd58+bx9NNP09/fz49+9CNOP/103nrrLS6//HJ27drFxIkTueuuuzjppJN44oknuPbaawErc/jkk0+yb98+LrzwQjZv3szXv/513nnnHX7729+yevVq3nnnHZ599lmuuOIK/vVf/zW2vo6ODr74xS/y7LPPYhgGl19+OV/+8pdz+pmKeEqqrmFu1KaUeotjLyjlzmwlQoGMlLRdvbto3NDIjp4dzK6eTduyNmZVzUr7PYVoYdwX7mNzaDOdoU4GGMCPn7pAHQsCCzxdkJ8PdoadQu41S4ODg/zyl7/kggsuAODpp5/mxRdfpLa2lrvuuospU6bwzDPPEAqFaGho4LzzzmPLli3s2LGDF154gTfffJM5c+Zw+eWXj3jcb33rW9x+++1s3boVgJ/97GccffTRtLe3A7B///6k6/nb3/7G7373O5588kkuv/xyXnzxRf793/+d+fPn8+CDD7Jp0yY++9nPsnXrVr797W9zxx130NDQwIEDB5gwYbiFc0VFBTfddBPPPvsst99+OwB33303APPmzRtx3x//+Edee+01XnzxRQD6+vpy+pmKeM6seVZ3MjNSHLUpupAWSUtby6SkZdvRLZ8tjLsHulnfv55toW2xi/Ro4fr6/vV0D3Rn/dilKL5mKZ1sa5ai2YhTTz2VY489li984QsAnH766bHBj//7v//LPffcw7x58zjjjDPYt28fr7zyCk8++STLli2jrKyMo48+mg9/+MNjnm/u3Lk8+uij3HDDDfzmN79hypQpSY9btmwZAGeffTb9/f309fXx29/+ls985jMAfPjDH2bfvn3s37+fhoYGVq1axfe+9z36+vooL8/uPapZs2axa9currnmGn71q18xefLkrB5HxLOSdQ1TbYpI0VIgIyUtl45ub/W/xZfu/hLXrLmGuzbcxfmcn3ML43wXrpei+JqlVHKpWYrWyGzdupU1a9ZQUWEFRIcffnjsGNM0WbNmTey4rq4uzjvvPCDz2SrHH388f/zjH5k7dy6rV6/mpptuSnpc4uMahjFi61r87TfeeCM/+MEPeOeddzjzzDPp7MyuNXhVVRXPPfcc55xzDnfccQdXXHFFVo8j4lkq8hbxFAUyUtJy6eiWj/k8+S5cL0ULAgtsBTL5rFk6//zz+f73v8/AgJVBe/nll/nb3/7G2WefzU9+8hPC4TBvvPEGjz32WNLv9/v9se99/fXXmThxIitWrOBf/uVf2Lx5c9Lv+elPfwrAb3/7W6ZMmcKUKVM4++yzWb9+PQCPP/441dXVTJ48mZ07dzJ37lxuuOEGTj311FGBzBFHHMHbb7895vPs6ekhEolw8cUX8x//8R8p1yZS0lTkLeIZqpGRkta2rG1UjYxd+ZjPU8jC9VJRiJqlsVxxxRV0d3ezYMECTNNk2rRpPPjggyxdupRNmzYxd+5cjj/+eBYuXJj0+6+88kpOOukkFixYwGc/+1muv/56fD4ffr+f73//+0m/p6qqig9+8IOxYn+A5uZmPv/5z3PSSScxceJEfvzjHwNw66238thjj1FWVsacOXP46Ec/yhtvvBF7rEWLFvGtb32LefPmsXr16OYVUa+99hqf//zniUSsn3FLS0tWPy8Rz1NtiognGMm2KuRbU1OTCRAMBgt+bhG7TgyeSGdPJxEzgs/wUVddl3N3uNt6b7N97LVV1+Z0Li/Yvn07J5xwgq1j+8J9bAltoTPUySEOUUEFdYE65gfml1yDhGjHs1NPPdXtpYyS4neW2f66PNNrjIhISUn5GqOMjEgKuWRzUvHjt9WFS8M2R6ssq2TRxEXKVImIiAigQEYkpXzM56kL1LEttC3t9jIN25THH3/c7SWIiIgUPRX7ixRQMRSui4iIiJQCBTIiBRQtXC+nfFRA48NHOeV5L1wXERERKQXaWiZSYNFhm+OlcF1EREQkHxTIiLhAheve8fbbb7Nu3TquuuqqjIdfioiISP5oa5mIjGtlZWXMmzeP+vp6Ghsb6evri9136NAhmpqaWLhw4ZhBzN13382XvvSlPK92pK9//es8+uijgDVL5uDBg7H7Pvaxj8Wey/e+9z1OOOEEli9fXtD1iYiI5JMCGREZ1w477DC2bt3Kiy++yJFHHskdd9wRu6+iooJ7772XOXPmuLjC1G666SY+8pGPAKMDmYcffpjKykrAmqfy8MMPs379ejeWKSIikhcKZESkYPrCfWw6uIlgb5Dbem/j7cjb9If7GTQHbX3/rt5dnBg8kfKbyjkxeCK7enc5ur4PfOADvPbaawDs3LmTCy64gFNOOYWzzjqLzs5OAPbu3cvFF1/MaaedxmmnnUZHR8eox7n//vupr6/n5JNP5uyzzx51/+OPP87ZZ5/N0qVLmTNnDldddRWRiNWSe8OGDcydO5f6+npuuOEGAMLhMJdddhn19fXMnTuX7373uwBcdtllPPDAA3zve9/j9ddfZ9GiRSxaZG1XnDlzJj09PVx11VXs2rWLiy66iO9+97s88cQTzJs3j3nz5jF//nzefvttR3+GIiIypH8fPPUQ3HcT/Pir1senHrJuF0eoRkZECqJ7oJv2A+1Ehv4DMDF5x3yHd8PvMsU3hYAvkPYxGjc00tnTScSM0NnTSeOGRsdm/YTDYX7961/zhS98AYArr7ySO++8k+OOO44//OEPNDU1sWnTJq699lq+/OUv86EPfYhXX32V888/n+3bt494rJtuuon/+Z//4ZhjjhmxVS3e008/zUsvvcTf/d3fccEFF/Dzn/+cD37wg9xwww388Y9/pKqqivPOO48HH3yQ9773vbz22mu8+OKLAKMe85/+6Z+45ZZbeOyxx6iurh5x35133smvfvWr2H2NjY3ccccdNDQ0cODAASZMmODIz09EROLs3gFPbIBwGMyh2XEDIXj5Gdi5GRYugxmz3V1jCVAgIyJ51xfuo/1AO4Mkz7yYmOyP7OdI40jKjdT/LO3o2UFk6AUhYkbY0bMj57W98847zJs3j+7ubk455RTOPfdcDhw4wO9+9zsuueSS2HGhUAiARx99lJdeeil2e39//6isRkNDA5dddhmf+tSn+MQnPpH0vKeffjqzZs0CYNmyZfz2t7/F7/dzzjnnMG3aNACWL1/Ok08+yde+9jV27drFNddcw+LFiznvvPOyfr4NDQ2sWrWK5cuX84lPfIIZM2Zk/VgiIpJE/z4riBkcGH2fGYHBiHV/4zUweWrh11dCtLVMRPJuc2hzLAuTionJwcjBtMfMrp6Nz7D+2fIZPmZX5/5uVrRG5s9//jOHDh3ijjvuIBKJUFlZydatW2N/olmXSCTC73//+9jtr732GkccccSIx7zzzjv55je/yV/+8hfmzZvHvn2jtxEkNg8wDAPTNJOusaqqiueee45zzjmHO+64gyuuuCLr53vjjTfygx/8gHfeeYczzzwztmVOREQc8lKHlYkB2NMLX7kLLm+xPu7ptW4Ph63jJCcKZEQk7zpDnWMGMgDvmu+mvb9tWRt11XWUGWXUVdfRtqzNqSUyZcoUvve97/Htb3+bww47jNraWu6//34ATNPkueeeA+C8887j9ttvj33f1q1bRz3Wzp07OeOMM7jpppuorq7mL3/5y6hjnn76abq6uohEIvz0pz/lQx/6EGeccQZPPPEEPT09hMNhNmzYwMKFC+np6SESiXDxxRfzH//xH2zevHnU4x1xxBG26l127tzJ3LlzueGGGzj11FMVyIiIOG3X1uHtZLfeD2/sg4hpfbzVel3BjFjHSU60tUxE8m6AJOn1JEySZySiZlXNcqwmJpn58+dz8skn85Of/IT169dz9dVX881vfpOBgQH+4R/+gZNPPpnvfe97fPGLX+Skk05icHCQs88+mzvvvHPE41x//fW88sormKbJ3//933PyySePOtcHPvABbrzxRl544YVY4b/P56OlpYVFixZhmiYf+9jHWLJkCc899xyf//znYw0BWlpaRj3elVdeyUc/+lGOOuooHnvssZTP8dZbb+Wxxx6jrKyMOXPm8NGPfjTHn5qIiIwwEBr+/K/7IJptN03r69hxhwq7rhJkpNrKkE9NTU0mWC1BRaT0BXuDSYOZM18/k5mzZ8a+NjCoKa8p4Mrc8fjjj/Ptb3+bX/ziF24vJWPbt2/nhBNOSLy5qCaF6jVGRFx1303DwcxX7rIyMaYJhgFHTYWbr7Tu8wfg0q+7t07vSPkao61lIpJ3dYE6fDb+uZlgqIOWiIh43Kx5MFTPyXWXWMGLbyiIuW6oiYzhs46TnGhrmYjk3YLAAraHtqetkzEwmOibWMBVueecc87hnHPOcXsZIiKSD3MarBbLgxGoqRrOwMQrK7OOk5woIyMieVdZVsniSYspp3xUZsY0TQwMpvimpG29LO5zYyuyiIjnTJ5qzYkp9w9nZqIMn3X7wmVqvewABTIiUhAz/TNZPnk59YF6KqgA4KD/IAO9A1T5qsYchinuMk2Tffv2aYCmiIgdM2Zbc2KOP82qhcGwPh5/mnW7hmE6Qm9/ikjBVJZVsmjiIhZNXATAwKQBdu/ezZ/2/cnllYkdEyZM0ABNERG7Jk+FMy+y/kheKJAREdf4/X5qa2vdXoaIiIh4kLaWiYiIiIiI5yiQERERERERz3F1a1lTU5ObpxcREWeYwWCwqIZigl5jRERKRMrXGGVkRERERETEcwzNBRAREREREa9RRkZERERERDxHgYyIiIiIiHiOAhkREREREfEcBTIiIiIiIuI5CmRERERERMRzXJ0jI+J1TU1N/wucC1wcDAZ/Hne7AfwX8Dng/wSDwRtdWqKIiHiUXmNE0lNGRiQ31wMR4JtNTU1lcbd/G+sFZq1eYEREJEt6jRFJQ4GMSA6CweBzwL3ACcBnAJqamr4CrAL+G7jKvdWJiIiX6TVGJD0NxBTJUVNT0wzgFeBNrHfJ1gD/A1wUDAYPubk2ERHxNr3GiKSmQEbEAU1NTS1ANL3/O+DcYDB4MOGYs4F/AU4BjgY+HwwG7y7kOkVExHv0GiOSnLaWiThjb9znX0h8gRkyCXgRuBZ4pyCrEhGRUqDXGJEk1LVMJEdNTU3LsNL9fwWmY72IXJ14XDAYfBh4eOh77i7gEkVExKP0GiOSmjIyIjloamr6GPBjYBtwEtAJXNHU1FTn6sJERMTz9Bojkp4CGZEsNTU1fQh4ANgNnBcMBvcCX8PKdH7LzbWJiIi36TVGZGwKZESy0NTUdDLwC2A/VtHlGwDBYPAB4FlgSVNT01kuLlFERDxKrzEi9iiQEclQU1PT+7FaX5rA+cFgcGfCIauHPv6/gi5MREQ8T68xIvap2F8kQ8Fg8E9YBZep7n8UMAq3IhERKRV6jRGxT3NkRAqkqalpEvD+oS9/h7XH+SHgrWAw+KprCxMREc/Ta4yMR9paJlI4pwJbhv4cBnxj6POb3FyUiIiUBL3GyLijjIyIiIiIiHiOMjIiIiIiIuI5CmRERERERMRzFMiIiIiIiIjnKJARERERERHPUSAjIiIiIiKeo0BGREREREQ8R4GMiIiIiIh4jgIZERERERHxHAUyIiIiIiLiOQpkRERERETEcxTIiIiIiIiI5yiQERERERERz1EgIyIiIiIinqNARkREREREPEeBjIiIiIiIeI4CGRERERER8RwFMiIiIiIi4jkKZERERERExHMUyIiIiIiIiOcokBEREREREc9RICMiIiIiIp6jQEZERERERDxHgYyIiIiIiHiOAhkREREREfEcBTIiIiIiIuI5CmRERERERMRzFMiIiIiIiIjnuBLINDU1mU1NTaYb5xYREREREe8rd/n8CmZERLzPcHsBIiIy/mhrmYiIiIh4lmEY5xiGYab5M+j2GiU/3M7IiIiIiIg4YQPwcJLbI4VeiBSGAhkRERERKQWbTdNcl+k3GYZxhGmab2d6n1PnkOxpa5mIiIiIlDzDMGYObTVrNgzj04Zh/NEwjHeANUP3m4Zh3G0Yxt8bhvFbwzAOAG1x3/9xwzA6DMM4MPSnwzCMJUnO020YxuOGYcw3DON/DMPYDzw/dN+EofPvMAzjoGEYfYZhvGAYxv8r0I+hpCgjIyIiIiKlYKJhGNVJbj9kmmZ/3NcfB/4J+D5wJxB/36nAxcBa4MfRGw3DaALuADqBb2I1rLoMeNAwjH80TfOuhHMeC2wC7gd+Bkwauv0O4HLgHuC7QBlwHPDhzJ6qgAIZEcmzvnAfm0Ob6Qx1MsAAfvzUBepYEFhAZVml28sTEZHS8Y2hP4nagQvjvj4ROMk0ze1Jjj0RONc0zUejNxiGUQX8X2AncEY0KDIM4/vAFuA7hmH8t2mafXGPUwusNE3zBwmPvxT4pWman8vomUlSCmTE0r8PXuqAXVthIAT+AMyaB3MaYPJUt1cnHtU90E37gXYiQ/8BDDDAttA2toe2s3jSYmb6Z7q7SBERKRV3YWVAEu1N+Lo9RRAD8Fx8EDPkXOBw4HvxmR3TNPsNw1iDlVn5CPBA3Pe8BfxXksffD5xoGEa9aZovpn4qYocCGYHdO+CJDRAOgznU2GMgBC8/Azs3w8JlMGO2u2sUz+kL99F+oJ1BRne9jAY27QfaWT55uTIzIiLihFeSBCHJvJzhfbVDH7cluS8ajMxKuH2naZrhJMdfB9wLvGAYxi7gMaw6nDbTNNVdLUMq9h/v+vdZQczgwHAQE2VGrNuf2GAdJ5KBzaHNsSxMz/4eWta1sGrNKlrWtdCzvwewApotoS1uLlNERMafgxnel83Q36TnME2zFZgJfAarhubvgQeBxw3DqMjiPOOaApnx7qUOKxMDsKcXvnIXXN5ifdzTa90eDlvHiWSgM9QZC2TWtq1lT+8eImaEPb17WNu2FrACmc5Qp5vLFBGRQmhemk0wUCx2Dn08Mcl9c4Y+7rL7YKZpvmWa5jrTNFdiZXL+L3AWMKoDmqSnQGa827V1OBNz6/3wxj6ImNbHW4e2mZoR6ziRDAwwEPt8b+9eTNMEwDRN9vYOb1c+xKGCr01ERAruCZqXrqd56bk0L/Xa9ecjwN+AawzDOCJ649Dn1wAHho5JyzCMMsMwKuNvM60Xx+jWhCOdWvB4oRqZ8W4gNPz5X/fB0MUmpml9HTtOF5uSGT/+WDAzrWoae3r3YJomhmEwrWpa7LgKlEkXESlpzUvrsDIOAJcCr9K89L+AIM0b9zh4pgWGYaxIcd+D2T6oaZp9hmH8K1br5D8YhnH30F2XAe8H/tE0zf02HuoI4A3DMB7CCl72YNXfXA30EjezRuxRIDPe+QPDwcz0qVYmxjTBMKyvY8fpYlMyUxeoY1toGxEirGxcydq2tezt3cu0qmmsbFwJgA8fdYE6l1cqIuIdrR1dFVjXb76hP2VYNRx/W9JQG0r3vS76TMLXxwL/DtxA89J7gO/QvDFdAb5dy4b+JHMcJOk+Y5NpmkHDMN4ArsdaO8BzwFLTNB+0+TAHgVux6mI+gjVb5g3gIaDFNM3Xs13feGVEt3sUUlNTkwkQDAYLfm5J8NRDVncyM2LVxNx6v5WJmT4VrrsEaqrA8MHxp8GZF7m9WrGpGGa39IX7WN+/PmnXsqhyytW1rDR4ee+7iKtaO7rKgWOwLu4T/xwDTAYmxv0pS/NwB7He2X8ryZ/o7X8BtgPdSxpq838RaNXGdGM9n1QiWNmI/0fzRhXlim0KZMa7/n3QtsbqTpZKuR8ar9E8GY9INrsFrOyHD19BZ7cU01okrxTIiIyhtaPrcOAU4AzgZKzOVccCR5M+OMmXg1hT6l9K+LNzSUOtc22Am5eeg9Vi2K7fA98GHqR5o9oRS1oKZCT5HBmwMjFlZZoj4yHFmAXpC/exJbSFzlAnhzhEBRXUBeqYH5ivTEzpUCAjEqe1o8uH1c3qjKE/pwP1uBOwZOpdrFkqm7HaA/96SUNt9luempf+ELg8i+/cDnyV5o0bsz63lDwFMmLp32e1WN611Srs91fArHkwp0GZGA/ZdHBTrC6lZ3/PqLqU6inV+PBRH6hn0cRFbi9XSocCGRnXWju6yoAPARcAZwKnYtU/lIpO4NdDfx5f0lDba+u7mpceBvwVa3tctn4P3EDzxt/k8BhSohTIiJSQYG8w1imsZV3LiE5hNVU1rF6xGrA6hV1ddbWbS5XSokBGxp3Wjq7DgPOAjwMXAtWuLqhwIlgdt6KBzZNLGmrfTXpk89JPAz9x6LytwL/QvPFPDj2elAB1LRMpIZrdIiKSP60dXUdiBS0fB87HKr4fb3xYtT6nAP8KvN3a0dWKFbD875KG2vii2886eN4lwEdpXvo94D9o3tjv4GOLRymQESkhmt0iIuKs1o6uKqzZJxdjzULRtdNIRwArhv7sa+3ouh9oWvLIqmlYGSsnVQD/AnyW5qVfpnnjfQ4/vniM1yarikgadYE6fEP/W69sXElNVQ0+w0dNVY1mt4iIZKC1o6uhtaPrHuB14HZgEQpixjIVeN9QW+dl5O/nVQOsp3npgzQvnZ6nc4gH6H9IkRKyILCA7aHtRIhQPaU6VhMTz4eP+YH5LqxORKS4tXZ0TQI+B1yF1WVMMnfP0MfEIZj5sAQ4m+al19G88Z4xj5aSo0BGclYMwxfFUllWyeJJi8ec3aLfiz36uy0yPrR2dM0ErgG+AExxdzWe9jdgI81LT8CqoSmEKuDHNC/9FPCPNG98rUDnlSKgQEZykmzg4QADbAttY3touwYeumCmfybLJy/X7JYc6e+2SOlr7eg6BfgK1jv7XpjxUux+vqSh9m88UpBsTKLFwDaal66ieeOPXDi/uEDtlyVrxTh8UcQJ+rudMbVfFk9p7eg6Hvgm8En099dJ5y15ZNWjwJ+B97q4jgeBy2jeuN/FNUgBKCMjWdsc2hx7p3pydw8XLVtL1Z/20vv+aTy0YSX9M6uJEGFLaIuGL4qnxP/dTjVYVH+3RbyntaPraODfsSbN6xrIWa9jzZU5B3eDGLDaY2+meeklNG/c7PJaJI/UtUyy1hnqjF3sXbRsLVWv7MEXjlD1yh4uWrYWgAgROkOdbi5TJGPxf7fXtq1lT+8eImaEPb17WNumv9siXtPa0VXZ2tH1LeBPwJUoiMmH+5Y01EYoTJG/HbOA39G8tNHthUj+6H9kyVr88MWqP+3FF7G2KfoiJlV/0vBF8S4NFhUpDa0dXYcB/wTcgFUULvlzD81LD8Parlcs9gJ/cHsRkj8KZCRr8cMXe98/zcrIREwiPoPe93tj+KK6UkkyGiwq4n2tHV2XAv8XOMbttYwDzy1pqH2BR/gHrAGZxeAQcAnNG/e4vRDJH20tk6zFD198aMNKeo+rIVLmo/e4Gh7aUPzDF7sHulnfv55toW2xi9ZoV6r1/evpHuh2d4HiGg0WFfGu1o6uo1o7uh4C1qMgplDuHfpYLNvKAL5M88an3F6E5JcyMpK1+OGL/TOrWfd77wxf7Av30X6gPWlXqmi73fYD7epKNU5psKiIN7V2dF0GfBeodHcl40oYuI/mpe8BznN7MUPuoXmjWuOOA8rISNaiwxfLKY+9ex3lw0c55UU7fDGxK1XLuhZWrVlFy7oWevb3AMS6Usn44+W/2yLjUWtH14zWjq6Hgf9CQUyh/XpJQ+0bwDKK4w3y54Cr3F6EFIYCGclJdPhifaA+Vi9QQQX1gXqWT15etAMD1ZVKxuLVv9si401rR9cVwDbgo26vZZwqpm1lfcAnaN74jtsLkcIohshZPK6yrJJFExd5ap6GulKJHV78uy0yXrR2dP0dsBY41+21jGMHgJ/TvHQOsMDltZjACpo37nJ5HVJACmRkXHK6K5W6n4mIFE5rR9cS4B5gsttrGed+vqSh9iCPFEU25ps0b2x3exFSWNpaJuOSk12p1P1MRKQwWju6jNaOrm8AG1EQUwzupXmpASx3eR2/AppdXoO4QBkZGZec6kql7mciIoXR2tE1Gase4yK31yIAvAZsAhYB73VxHd3A8tZzb5lAR9fyJQ21a11cixSYAhkZl6JdqdoPtMcCjijf0H92ulIldj9b27aWvb17mVY1jZWNK6meUh3rflbIOgttdRORUtLa0XU80ApoeFPxuG9JQ23E5W1l7wIX07zxLTq61gHLWzu6PgJ8fklD7UEX1yUFoq1lMm450ZWqGLufaaubiJSS1o6uC4GnURBTbO6heelhwMUurqGJ5o2bWzu6rmF4e9ungN+3dnTNdG9ZUijKyMi4lmtXqmLrfqatbiJSKlo7ugzg34BvAIbLy5GRti5pqH2RR1gGHOHSGtbSvPG/Wju6Pgh8J+G+k4BnWzu6PrGkofZJF9YmBaKMjEgO/Phjn0+rmoZhWK+12XY/y5UGfYpIKWjt6JoA3A/chIKYYuT27JhngGtaO7reg/X3xJ/kmKnAr1o7uj5W0JVJQSmQEcmBk93PnFCMW91ERDLR2tF1BPBL3N2yJKmFgftoXvoe4DwXzt8DfLL13FvCwE+Bo9McexjwYGtH16cKsjIpOG0tE8mBU93PnFJsW91ERDLR2tE1FSuIOc3ttUhKjy5pqP0rj/BloKzA544Ay2je+CodXd8BFtr4Hj+wobWja9KShtof5Xd5UmjKyIjkINr9rJzyWGYmyoePcsptdT9zSrFtdRMRsau1o+so4AkUxBQ7N7eVfY3mjY+2dnRdAqzK4Pt8wA9aO7quzdO6xCUKZERy5ET3M6cU21Y3ERE7Wju6jgGeBE50ey2S1gFgI81LTwQKs9VgWCvQ0trRdQKQTWbFAG5t7ej6urPLEjdpa5mIA3LtfuaUYtvqJiIylqEg5nHg/S4vRcb2syUNtQddmB3zCvC51nNvmQT8HJiUw2N9o7Wja/KShtp/cWZp4iZlZERKSLFtdRMRSUdBjOfcS/NSH8MzWwrhIPAJmjfuB/4LZ+YJ/XNrR9edDjyOuEwZGZESM9M/k8ZJjTxx8AneirwVu73SV8nCiQs51n9sTo/fF+5jc2gznaFOBhjAj5+6QB0LAgsUIImIba0dXUejIMZLXgMeAxYBMwp43pU0b3yxtaPrepztZPePrR1dPUsaav/NwceUAlMgI1Jiuge6aT/QHmvDHNUX6aPtQBuLJy3Oum4n/rGjjz/AANtC29ge2p7TY7tJwZlIYbV2dB0O/AIFMV6yfklDbaTA28rW0LzxvtaOrkVASx4e/6utHV1/XtJQuzYPjy0FYETbsxZSU1OTCRAMBgt+7oLp3wcvdcCurTAQAn8AZs2DOQ0wearbq5MS1RfuY33/egYZTHlMOeUsn7w84wt0px672IKGZMEZWFvxfPg8G5wVmAYWim2tHV0+4EGg0eWlSGbmLnlk1S7gTXKrUbGrA1jUeu4tNcBmoCZP5wkDFy1pqH04T48veaSMTD7s3gFPbIBwGMyhC6OBELz8DOzcDAuXwYzZ7q4xFwrSitbm0ObYxXjP/h7Wtq1lb+9eplVNY2XjSqqnVBMhwpbQlowbEzjx2MWW0ekL99F+oD1pcBZdY/uB9qwCPxFJ6RYUxHjN1iUNtS/yCJdSmCDmr8AlrefeYgAPkL8gBqxZOP/d2tG1cElD7R/zeB7JAxX7O61/nxXEDA4MBzFRZsS6/YkN1nFetHsHtK2xgrKBkHVbNEhrW2PdLznpC/ex6eAmgr1Bbuu9jWBvkE0HN9EX7hvzeztDnbEAYW3bWvb07iFiRtjTu4e1bVbmPEKEzlBnxuvK9bHjg4bEbW8RIgwySPuBdlvP0ymJwVnLuhZWrVlFy7oWevb3xNa2JbSlYGsSKWWtHV1fBDTLw3vuGfpYiG1lg8Cnad74BvBd4MwCnPNwoL21o2tmAc4lDlJGxmkvdViZGIA9vXDr/fDXfTB9Klx3CdRUWfe/1AFnXuTuWjMVH6QlMiMwGLHub7ym6DIzxbadKZVcMxYDDP9u9vbuJbp11DRN9vbujd13iEMZry3Xx85ntihbyYIz0zRjwdnqFatjwZnbrbVFvK61o+ujwG1ur0MyFgY2lPt8fzlh2qQZZYZBuc/Hs1eeM+Ig0zS59lcv8PArbzLRX8bdH1/AgqMq2fu3EEt/+gf63h3gmx8+gY/XHQ3Akp88xfcXn8zRRxyWeL4baN74ZGtH12eApgI8v6j3AL9s7ehqWNJQ+9aYR0tRUCDjtF1bhzMxt94Pb+wD07Q+3no/3Hyldf+urZkHMm5v6fJokFbI7Uy5BExObHPy448FHNOqpsUuzA3DYFrVtNhx0cGdmcj1sYsxaMhn4Cciw1o7uk4Cfoq1jUe85ZElDbV/nVRRdvhjn/sQ1RMDSQ/65Z/e5JW3DvDKNR/hD6/1cnX7c/zhioVseHE3nzv5WP6h/hguWP97Pl53NG073mDB9MpkQcx/07zxltaOrpOB/8z3E0uiDmht7ej6yJKG2pAL55cMaWuZ0wbi/t7/dSiIAevjX+O2kw1keGFUDFu6kgVpkbggDYaDtCJRyO1M3QPdrO9fz7bQttgFcjRgWt+/nu6B7rTf78Q2p7pAXWx+zMrGldRU1eAzfNRU1bCycSVgFbHXBTJvw5/rYxdj0ODHH/t8WtU0DMOqWXci8BMRS2tH11FYHcqOcHstkpV7AQ6vKD883UGtnX/lsycdi2EYnDnjSPreHeCNt9/F7/PxzmCYUDiCzzAYjES49Q87ub5hVMO6l4AvtHZ0VQI/A0ZFOQXyIeAHLp1bMqRAxmn+uHcqpk+FoQsjDMP6OnZcBhdGxVJ3k68gLY8KVQPhRMDkRH3LgsCCWLBRPaWa1StWc8s1t7B6xWqqp1QDVrAxPzA/4+eY62MXY9CQz8BPRKC1oysAPAS81+21SFYOAA/SvLS+osxXcd69v+OUux7jrj92jzrwtbff4b1ThmOPGZMn8Nrb73Dp3Bn8z849XLDu9zQvrCP4TBefPelYJvpHbAp6G/hE67m3/A0rcHpfXp/V2Fa0dnRd5vIaxAYFMk6bNQ+MoR/rdZfAUVPBZ1gfr7vEut3wWcfZlbil6yt3weUt1sc9vdbt0S1d+ZSPIC3P8ln8Hs+JgMmJjEVlWSWLJy2mnPLYBXqUDx/llLN40uKs6oJyfexiDBryGfiJCAA3A6e6vQjJ2s+WNNQeBD7TcflZbP7HRfxy+Qe545ldPPnnnhEHJhvmYWAwZYKf9ks/wLNXnsOCo6bwi5f/ysVzjmblQ1v45H8/ze//8hbAZTRv3AH8G3Bh3p+VPWtaO7qOd3sRkp4CGafNaYCyoS3ANVVWTcyPVlsfa6qs28vKrOPsKpYtXfkI0vKsUNuZnAiYnMpYzPTPZPnk5dQH6mPHVlBBfaCe5ZOX51QPlMtjF2PQkM/AT2S8a+3o+nvgy26vQ3JyD81LfcDyaD1LzeEBltYdxdOv9Y44cMYRh/GX/e/Evt7d/y5HHzFhxDE3PbGDr541mw0v7OaUoyv50ZL5fL51819o3vjz1o6u84HmPD+fTEwCNrR2dBXPu7Myior9nTZ5qjUnJnGODFgX+WVl1v2ZFOcXy5auOQ3WHJzByHCQlijTIC3P8ln8Hs+JgKkuUMe20DYiRFjZuHJUVy+wn7GoLKtk0cRFeSmaz/axo0HDWMMnCx00RIOzLaEtdIY6OcQhKqigLlDH/MB8BTEiWWjt6DoS+DEaluplu4HHgUV/OzR4TMQ0OSLg52+HBvnfnXv5+sKR8/Aumj2d25/ZxT/UH8MfXutlSqCco+ICmVf2HeD1A++ycGY1W/+6n8P8ZQxGzCd37DtQMdT2+D6K7w32BcC3gFVuL0SSUyCTDzNmWy2IYx3GDlnbrbLtMOYPDAcz06cOd0Ir9JaufARpNmXbDczJ4CAdJwKmBYEFbA9tJ0IklrFI5PVtTsUaNOQz8BMZp/4TOMbtRUhO1i9pqI3wCJ99c6iFMsBgxOTS+hlc8P73cOezXQBcdWotHzvuPTz8ypu8f80jTPSX819LRr5WfXXTS/x/H54DwLK5M1h83+8PffHh594z8fAjmrGGXh5ZwOeWietaO7oeWdJQ+0u3FyKjGdF3jgupqanJBAgGgwU/tyc99ZDVncyMpG57bPjg+NMK0/Z4RBvoHIM0G5K1T4aR7+Kn2tLUF+5jff/6pC2NE+UyW2bTwU2xgCnVjBQfPuoD9WkvlnN5riIu0rvuEtPa0fU54G631yE5q1/yyKou4E2sbVZOOgQspHnjU60dXT8ELnf48Z22Bzh5SUPtX91eiIykQMYL+vdZLZaTDaKMKvcX5SDKXNkJRMopTztbJVVwkEy2AYMT64x/rGLLWIiMQYGMANDa0VULPIdaLXvdliUNtQtoXrocWJeHx/8izRuDrR1dK4G78vD4+fC/wAVLGmoLf+EsKWlrmRfke0uX24M203BiGnyy7Uyp2B08mcjJ+g9tcxIRL2rt6CrDuuhVEON99wx9/ExeHtsKYk4F1uTh8fPlPKxame+4vRAZpoyMl+RjS9fuHWMHSDNmp/7+PAv2BmO1Jy3rWkbUntRU1cTqSCqo4Oqqq209plPbwJJRNkXGKWVkhNaOrn8D/sPtdUjOwsAxSx5ZZWAV/Jc5+NjPAR9oPfeWicBm4FgHH7sQ3gFOXNJQ2+X2QsSijIyXTJ5q1cA4VQcTP2gzkRmxupM9scHVLWv5aJ+crFWyaZqxVsmrV6yOtUrOpjOXsikiMt60dnQdB3zd7XWII/53SUPtmzzCP+NsENOHNfQyBLTivSAG4DCsLFKxzLoZ9xTIjGeJgzaTNRGIDtosRBOBJOx2AyvP4K9yoWbLSPbd5kTEc74DccOwxMvuHfro5LYyE1hB88ZddHT9f8C5Dj52oS1u7ehauqShdqPbC5Hi69cthVQsgzbTqAvUYQztWkk1DR4gTJi+cJ+tx3Rq8GQ2+sJ9bDq4iWBvkNt6byPYG2TTwU221+4l3QPdrO9fz7bQtljwOMAA20LbWN+/nu6BbncXKCKOaO3oOhdodHsd4oi3gQdpXjoXONnBx/0mzRvbWzu6LgJGzxbwnttaO7oOd3sRokBmfCuWQZtpLAgsiH1ePaWalY0rmVY1jb29e1nbtpae/T2x+7eEtth6zLpAXWyKe6rgyInZMoncvLAvdADVF+6j/UA7gwyO6hQXIcIgg7QfaC/JAE5kPBkq8P+u2+sQx/xsSUPtOzibjfkV0Nza0fV+rCYCpVBT917g39xehCiQGd/8geHPp0+1BmxC4QdtplFZVklZ3BbdaE1LxIzEaloATEw6Q522HnNBYEEskIkOnrzlmltYvWI11VOqAecHT7p5Ye9GAJXYba5lXQur1qyiZV1LLPiMdpsTEU/7R+BEtxchjrmH5qU+4FKHHq8bWN567i0TgJ8DUxx63GLw5daOrpluL2K8UyAzns2aZ3UnA6sm5qip4DOsj9ddYt1u+KzjXBQ/m8WJmpZoq+RyymMBTZQPH+WU226VbJdbF/ZuBVDJGiokBp/Rhgoi4k2tHV1VwE1ur0Mc8xfgceDDwDEOPN67wMU0b3wLa1bMXAces5gEgP/n9iLGOwUy49mcBqvFMliF/TdfCT9abX2sqbJuLyuzjnNRPmpaorNl6gP1se+roIL6QD3LJy/PaBimHW5d2LsVQKmhgsi48O9AaU1hHt/WDw17/KxDj9dE88bNrR1d1wDLHXrMYvPJ1o6us91exHimrmXjWSaDNl0cmlkXqIvNfVnZuHLU3BfIrqYlsVVytMPWff33Od5hy60L+3y2mk7Hbre5fDRUEJH8a+3oqgO+6PY6xFH30rz0cOATDjzWWpo3/ldrR9cHKf0BkrcAp7q9iPFKGZnxbsZsa07M8acN1cwY1sfjT7NunzHbGprZtgZefma4QcBAyPq6bY11fx4VoqYl33UkdrNKgKNF+G4FUG41VBCRgvkOejO0lGxe0lD7ErAUyLUb1zPANa0dXe8B7qf023Kf0trR9TG3FzFe6R8hST9oswiGZkZrWtoPtBMZ+i/KN/RfLjUt8XUkiaLnaz/QzvLJy7M+h52sUtS20Da2h7azeNLinLe4uZUZWRBYwPbQdiJEYsFnIqcbKohIYbR2dH0A0IVbaXFqdkwP8MnWc28JAz8Fjs7x8bxiNfCw24sYj4zoO7SF1NTUZAIEg8GCn9szXNzKNcJTD1mZFzOSemim4bMyOHkemtkX7mNLaAudoU4OcYgKKqgL1DE/MD+nrV+bDm6KBRk9+3tGBRnVU6oxMKjyVfF25O2stp31hftY378+abCUSjnlOQVPdp+bDx/1gXpHt5aBleUaK/h0uhZJXFMK7VTFptaOrjY02byUDALHLHlkVRlWwX/ZGMenEgHOp3njo60dXd8BVjm1QI84a0lD7W/dXsR4o0CmGO3eMXbdyozZhVnLfTcNbyf7yl3WsEzTtFo0HzXVagwAVqB16dcLsyaHBXuDsaxFy7qWEVmLmqqapNkEyPyCPNWFfT4DDDsBlBMBU7rz5yP4lKKjQGacaO3omgs8h37npeThJQ21i2le+s/At3N4nK/SvPHm1o6uS4D/dmhtXmL9HKWgVCNTbOK3cpkj2+VaW7kGrPv79yX/fqc5PDSzGCfb260jSZRp++L4Tmnx8tnFzI1W04nnXzRxEVdXXc21VddyddXVLJq4SEGMiHetRkFMqYluK8ulW1kr0NLa0XUC8KPcl+RJH2vt6DrZ7UWMN6qRKTYvdViZGEi9lSscto7L81YuwMq0RIOZ6VNHZmQyHJqZLCMRLah3qiYkG3brSFJlTgYZ5J7+e6gP1I+51Sx6Yf986PnYbfkuwo8GUMqMiEguWju6aoFPub0OcVQ/0Erz0pOAk7J8jFeAz7Wee8skrKGXk5xanAfdCCxzexHjiTIyxWbX1uFMzK33W4FDxLQ+3nq/dbsZsY4rBIeGZro52T6d7oHuEduuUnXYgtSZEwATM6MOZ/mYjZOOMiMi4oBryb5+QorTz5Y01L5D9kX+B4FP0LxxP/BfwHhvRXlJa0fX+9xexHiijEyxcXgrV87mNMDOzVZ3sujQzEQ2hmYmDmZMltmIDmZ0uug8lWhwZTJcJ5aqwxaMve0skw5n+ZqNIyKSD60dXZOBy91ehzjuXpqX+oBLs/z+lTRvfLG1o+t64GIH1+VVZcD1wFVuL2S8UCBTbBzcyuWITIZmpuHWYMZ07ARX8RK3nR05+Uha1rVkFZCpPbGIeMzlwBFuL0Ic9SrwOPARsmuTvIbmjfe1dnQtAlqcXJjHXdba0fWNJQ21b7i9kPFAW8uKjUNbuRxlZ2jmGJwazOhks4BkwVWybWNRidvOTMysi/TdLsIXEbGrtaPLB1zj9jrEceuXNNSaZLetrAP459aOrmOAn6Ath/ECwGVuL2K8UEam2Di0lctx0aGZcxqG59vs+IP10cZ8GycGMzrdLCDTbmWJmZNVa1blVKSfTRF+X7iPzaHNdIY6s5pnIyKShfOBWW4vQhx3L81LDwc+keH3/RW4pPXcWwzgAaDG8ZV532dQlqogFMgUG4e2cuVFsvk2AyFrYObOzWnn2+RaExLfLCBRJrUp8ewGV6mM9T3dA91jBlbRInw72+lyDeQUBIlIlrKtn5Di9cclDbXbeYTPAIdn8H2DwKdp3vgGHV13AGfmZ3med0JrR9cpSxpq/+j2QkqdApliFN3KFc18DByyamJsZD7yJn6+TSIzYmWQnthgrTvJ+nKtCcmkWcD8wHxbF+x2gqt0xvqeTAOrdOwEcr848AtWTF6R9HzF2vpaRIpba0fXYcDH3V6HOC46OybTbWU30LzxydaOrs8ATQ6vqdSsABTI5JkR3RpTSE1NTSZAMBgs+LklS089ZGVezEjq+TaGz6qbSTHfJtVke9/Qf+kupoO9wVj2pGVdy4hMSE1VTSwwKh+KzVOd45yJ5/Bm+M1YkDOWV3a/wp0P3kk4EqbMV8ZVH7+K42YcN+KYVIGVDx/1gXpHmhdsOrgpFnSla0wws3wmS45YMuJ7+8J9rO9fnzQIiiqn3LGgS8YlDUgsUeN4SnspGwSOWfLIqnLgL9ivl/5vmjd+emjo4++Bw/K1wBLxJnDMkobasNsLKWUq9hd7HJhvEz/ZPloLU0EF9YF6lk9enjYjYLeeZXDov1Szah49+Cgvhl60FcQAsSAGIBwJc+eDd446JlWjADtF/3bZbUzQPdg9qvFBYjarZV0Lq9asomVdCz37e2Jr3RLa4shaRaSkaLhf6fmfJQ21e7C2DNq9DnwJ+EJrR1cl8DMUxNjxHuA8txdR6rS1zCv698VtNQtZXcMKudXMofk2mdSExMu0niVd1iJ+bsxYokFMqq9h7C5sTtSmpAvk3nzrTXr298SeX2Lr52JsfS0ixa+1o2sK8DG31yGOi24r+6zN498GPtF67i1/Ax4CNPDRvs8Av3R7EaVMGRkv2L0D2tZYW7uiAUW0yL5tjXV/vvkDw59Pn2rNtYGM59tk2z65LlAXa1Oc2AY5WT1LuqxFqqyEDx8nBU4a8ThlvrK0X4MVWBlDP4/EwKqcctb3r2dbaFssGInWpqzvX0/3QHfa5x3lxz/ifMmeb1RiFiiT1tdOtrcWEc9bitVKVkpHP9BK89KTgbk2v+cymjfuAP4NuDBvKytNH2/t6NL8pTxSIFPs4ovszZHbpawi+wHr/v59yb/fKTPiuollOd+me6A764v6BYEFsUAm2izglmtuYfWK1aMGV0L6C/axtoId6TsyduxVH78qFrxEa2QSpQuswoTTbnVrP9BuK0iI7+aWLHBL1/o5MQjKd9AlIiVD28pKzwNLGmrfxX6R//+jeePPWzu6zgea87esknUYcLHbiyhl2lpW7F7qsNodQ+oi+3DYOi5FkX3Odu+AV7cNf53FfJtc2ydHB0imaxYAxB4/3fazsbISCycuZOOBjQAcN+M4vvOl76T4wYwtuo1trE5rY23pWhBYwAuhFwArkHvPke+xPYvHTnc2A4Mw4aTb7rJtby0i3tXa0VUD/L3b6xDH3Uvz0jLstdR+DFjd2tE1E7gPvfmdrUuBu91eRKnSX8pi50CRfU6iGaFw6o5XAJSVp51v40TB+VjNAk4InGBr+1m6rEQFFRzrP5YzJ6Rvje+L+18n3Ta2sY6x2xCgsqySmeUzY1+nen7JZvFkms3KV0MAbVsT8ZRL0LT2UvMq8ARWgHrUGMfuBv6h9dxb/FhDL48c43hJ7ezWjq4Jbi+iVCkjU+wcKrLPmp2MEMCxJ6YchgnOFZynaxZgZ1YNpJ7/Eh8EnHHYGRxVfhRPHnySfZHhn/NU31TOnnh2LGMD6TM8do5J3AqWysKJC9ndv5tBBjOaxRPNZv3iwC8Ik7wLZHwmJh8NATTHRsRzFru9AHHcuiUNtebQEMx0DgGX0LxxDx1dPwROKcDaSlkAaAB+7fZCSpECmWKQriOZPzAczEyfamViTHN0kX2535r14nRXs2QZITMuIxTdYha/9SyJTArOs5Vu+5mBEbtYTxUEmJi8FHqJ50PPxzqLXTjpwqRbqTLtopbumMStYNk8v/hZPEm35vlG35aK07+fXLcVikhhtXZ0lQEfcnsd4rh7aV46CauJQzpfpnnjU60dXSuBywuwrvHg71EgkxcKZNy2e8fQ1q3wcMAQ7Ui2czO8pxZe/5N133WXjM6IAGBYW7+iAysTH2PhsrTZkrTsZoTCg1ZAliJosnvhb/eiPpXo9rMtoS10hjo5xCEqqKAuUMc03zSeeOeJtEFO9GJ7rGzBWHUn0ZqYPW/twefzxQZqRoiMuRUsmfgWzoMMYmDgw0eESOz5zQ/MTxkIbA5tHrNeJ8rp30/itsJcaoVy4UQbbJFxYj6gTkul5dklDbWdPMJngcPTHHcPzRuDrR1dpwJrCrS28UD1ZnmiQMZN8R3JEpkRGIzAX3eBzwfhSOoie8zhACPZYzyxARqvyS4zk5gRer1n+D6fz9puFt1elqbhgJ2Cc7sX9WNJt/1shn/GiCDHjz8WvCQWuqfLFoy1ja1lXYsVDGDGZs9ETCuIiT822VawRMm2ZZmYGBiUU85HJ310zG1Zdrb2RTn9+ymGOTba2iaSkYVuL0AcF50dk25b2XPAVa0dXVOxhl6q9bZzTmnt6JqypKF2v9sLKTUKZNxkp/4kEoGjj4M3d43M2oDV7pi4DEk+uprNmgc7/mB9ft0l8JW7YHBozeHIyO1lu7amPIed+hU7F/W5SgxyNh3cxLbQNkxM29mC6Dv7qfTs7+HNt94cdXuy+plBBrmv/76UmQGntmXZ3doHqbfeZfv7KcS2wnS0tU0kYwpkSssg8BOalx4DfDjFMX1YQy9DQCtwbIHWNl6UYf1/9ZDbCyk16lrmJrsdyfZ0WxmV408bGkxpWB+PPw3K/MPZmHx0NYtvpxwNrKJGNRwIWXU6SWbaROs7yikf0fELrAvkcspT1ndA/jpeJcsWpOssFj8LJ9mFcfRxkklVP5NuVkuu3d6iP7d46bq2AVn9ftKxO8cm122FqTjRMU9kvGjt6PIBZ7m9DnHU/yxpqN2D1QY42XWfCaygeeMu4D+Acwu5uHFE28vyQIGMmzLpSDZ5qpXtuPTr8LlvWh/PvAgGD9l/jGxMngrx0+ynT7UaDcDohgNg1eW0rbFqfxKM1T451daeXAZpjiXTqffRd/YTB1zGS9a1zDCMUW2g46UakJlpoBUv/ucWL13r5tn+2Rn/fsZSF6gbsy22U9sKk8nlZygyDp0EVLq9CHHUPUMfU20r+ybNG9tbO7ouApK3+xQnKJDJA20tc5PdjmT+NO9UO/EY6fTvG1l/k7LhwJAx6nLS1a8kk+9tQZk0IbBTtJ7scRLrYtJ9f+I2tmy3ZaX7uaXbOnbmYWfGfkdOcXtbodtb20Q8RtvKSst+4CGal84D5ia5/1dAc2tH1/uxAh6jgGsbb05s7eh6z5KG2tF7zyVrysi4ada8oToXrIDgqKngM6yP0QDB8FnH5fMx0nmpY/jzZDU4YNXNXN5ifdzTa90WrcvJkZ1tQYMM8tQ7T2X1+JlkC+y8s5/uceLZzQxkuy3Lzs8tKtetY2PJdVthrtze2ibiMQpkSssDSxpq3yV5NqYbWN567i0TgJ8DUwq5sHFK/385TBkZN81psNojD6bpSFZWNrJOJR+Pkc5Yc2Qg+WyZaF1ONg0G4tjttrVjYAd1A3Wx7U92W+1mki14PvR87LbEd/bffOtNevb3UD2lOu0wzlTfn5gZCPYGqQvUUVtRy58O/Snjbm+ZdCmrD9Snbd3shHRtsfN97kJ2zBPxstaOLgPVx5Sae2leWoZVHxPvXeBimje+RUfXOpJna8R584H/dnsRpUSBjJsmT7VmvCTOkQEri1JWZt0/Vtvk98yC10bXpGT0GKnYqeNxui4n/vQZdNuKbjHri/TZbrWbyZDJxG1oiZ3JEgOExMcxMQkTjn1/uiGaAwzwQuiFEbdlsi0rk59bPme3RCULLGcHZuc9iAH3t7aJeMixQPWYR4lX/Bl4EjgPmJ5wXxPNGze3dnRdAywv+MrGr5PcXkCp0dYyt82YnbojWeM16QdZ7t5hFda//kry+48+bvRj9O+zOovddxP8+KvWxxSdxoChNQ1JVuifrvi/3J/ZuZKdPsW2ILDmsiR2nnrqnadSFuSnKqi324Qg/h37ZNvFkhX5xz/OFN9w1v6T53wS39CWQJ/h45PnfNLujyQm3basYtpOlc9mDXa4vbVNxEOOd3sB4qh1SxpqTUZvK1tL88b/au3o+iDwHRfWNZ4p8+Uww4wv5C6QpqYmEyAYDBb83CWjf58VxCQbphlV7h9ZcL97x9jZn8TA6amHrE5kZiR1jUyy2TWxgMOwf64konNeIkRiBfLxmZDEYvrohWr88YkF9T581AfqM85E9IX7+HH/j2Nfx4ZeJinqr6CCq6uuHvH9d/TeESu+T/a9ybY9RRsIJBprW9amg5t4MfRi2vk4BgZzA3Nt/RzsbtVL9n3r+9enbFUNUE55QWa49IX7XNnaNk6oQLgEtHZ0fRG43e11iGPqljyy6jXgTWDi0G3PAGe1nntLJbAZONqltY1nVUsaavvcXkSpUCDjVU89BC8/nX4QpmHA8adbdSrZBD5g7/uykexcSSS7EF61ZhWRuODIZ/i45ZpbRn1vpoGGHa1vt9I92A2k7jyWKlC6rfe2lM/BMAx8ho9wxNp6ZmDg8/kwTTOrIOzVgVfZeGDjmM9n6aSlHOtPP/ese6B7zK13qVozJwtEnQospagokCkBrR1dtwH/5PY6xBHPLGmoPZ3mpZ8D7h66rQc4pfXcW14HHkWF5245e0lD7W/cXkSp0NYyr9q5xcYgTNM6DqwOYmHrIpk9vfY7jUXreMr9w93RMrBroJcTX7uL8u4WTnztLnYNZNbVLLotKN5YAx2j8tFqd+HEhZQPlZZF6y1uueYWVq9YHcuepKq3SLfdKz6IAax6mkg4aVez50PPjzkM9E8Df8IYurZM1bXMwGDnwM60zzfd7JxUW/XiaYaLiKeMnSYXr7h36GN0W1kEWEbzxleB/4OCGDfp/zMHKZDxqvhBmNGuYTDcPSzxuGTdx0YFPkOdxhKlquOZURe3hSy5xj330zmwjzAmnQP7aNwzxrmSmOmfyfEVw1u307VJNuLeGB4r4An2Brmt9zaCvcExA4OoTOot+sJ9bDq4KXae+KxS4nOIRFIP2ExWoD9WfUlnqBMT6+9EqgDCxBwzgLDTxjk6+yYZzXAR8RTVyJSGQeAnNC89Boimur9G88ZHWzu6LgFWubc0AY5zewGlRF3LSkGZDwbDI79OZKf7GKTuNDZ5qrVFLb6d8lMPYe0oSb29bcfAPiJDF9QRTHYMZNfV7AMTPsCuQ7sYZDBt56lZFbPGbFccO31C4XliR7NU7LQSTrYdKxpYwOgOZC3rWkZ1QYtKFoSNNQzUqQDCThvnaEYl2dawTAaOioh7Wju6KoC/c3sd4ohfLWmo3csj/CvWG9atQEtrR9cJwI/cXZqgNwwcpUCmFITD6b8GK4MSDWamTx3O4iR2GvNncEE51oyZm69ktn8qnUPBjA+D2f7szmW7TbKvkl2HdqVttZvMWIFBsvUsmrgo6cV7/HYsuxKDrk+e80keePyBEV+3rGsZVV8SzYYkrsOpACLXgEgzXEQ84/1ol0apuGfo42eAV4DPtZ57yySsoZeTXFuVRCmQcZACGa8qrxjeNnZU9cjA5KipI48DmDVvuPvYdZck7z5m+Kzj7LKR5WmruYTGPfezY2Afs/1TaavJ8lzYH6oYDXjiA4nJ3T1ctGwtVX/aS+/7p/Hjuz7J/33uAduBQSYSt2Ol6kTmwxc7LlnQlZixSZUNeT70PCbmiA5iTgUQuQZEmuEi4hm6uCoN+4E2mpfOB2YBZ9C8cT8dXQ8AeseoOLyvtaPLGGqNLTnSuy9e9b75xBoFXXeJFbz4hoKYaGCCMXQcMKfBansMVkezm6+EH622PtZUWbeXlVnH2TXWjBlglr+KbcdcyeDM1Ww75kpm+bM815BoJuTqqqu5tuparq66mkUTF43IoEQDnvgalouWraXqlT34whGqXtnD4k/fmbfCczsF7tFzzSyfSRllI2p7khlrqGVizcyCwILY88+0KUG8ukBd7HHS1SalCog0w0XEMxTIlIb7lzTUvouVjVlJ88YXWzu6rgcudnldMiwATHZ7EaVCGRmvmtMAOzdbbZGjgUmi8vLhYCHafWysOTJjtEMewU6WJ1G258pQZVnliO1nVX/aiy9iBQK+iMn73gzHJZDsbZOyO0vF7nYsgFcHX8WHj78r/zteH3ydQxzCj5/JvsnsiwzXE6XLhkDyrXG2tuKNEUDYyagYGIQiIYK9waQ/F7uZNBFxlepjSsO9NC8tA/bSvPG+1o6uRUCL24uSUY7Eyp5JjhTI5Kp/n9VGeNdWa6uVP2Bd4M9pyOuFelaBSbT7WGy9h6w6lWzXGwumIqmDKcMHZeVWwJXLubIQvy2q9/3TrIxMxCTiM9hZ48MwIra3SSUr3k/VJMDudiwYDkB2D+4eVZsTP4MlWQ3NWDUzTgQQYwVEUa8MvJL255KupkhEikL+/1GWfPsz8BugCvh2a0fXMcBPgDJXVyXJVAFdbi+iFGQ1ELOpqakSaAB6gd8Hg0Ez7r7DgX8OBoM3pfn+0hiIuXvH2IGEjen1ORkRSOUYmGTDxZ/BWBmSX/7tl7x86GXAfo1MVPxjAWNOpy+jjBWTV1BZVsmmg5t4IfQCYNXIfP/B77Nvv5VdqTqiCp/PR29/74jzGhjMDcwdcaGfbBhoVC7DPu1mlhK/JzEgqq2oZeehnWl/LuWU22qeIJ6ngZge1/r/t3fvcW7Vdf7HX8kkHSm1nWHKyKViU0o7QOXSBRc3rmxVZLW0+alb1grKygq7zeqC6O7PouIuImVXZMsDjQj+VJRSsa4wLVURqVYdqYItKG2n9DLlTqedzjBCaZrb74+Tk5zcT64nybyf++hmJjmTfHJm5HE++Xy/n8/A0M+AC5yOQ6rypYDf9zlIdaHbCJznbEhSwAUBv+/nTgfRDsquyASDwdMxJsIei7HHZnMwGPxAKBR6OnnIFOALQMFEpi2MjxgX8Pkm3ifiRpVi42pb0+urkq8tciPVuspjU6kKyTmvO4c9R/akjh+fOZ27H0kvi+oAlr+5cEcz63PN8MwouXk/RoyNhzYSeH2A+Z3zU4nM9GnT8XR4cLlcJBIJRv88mnoN66b9BAm2hbdlJDLFqiGVtlQup7Jkla+isuHQhpLnpRbNE0SkIY5xOgCp2nctX/8PSmKaWbfTAbSLSjb7rwAeAaYBJwJ7gIFgMDixBvxsG0i3OR4ehWvvgMtXGLfD5U2vb3lmMvWh6+CyG4xbM7HatBbuuR7u+qxxu2mtkQRWwc60+U2HN5XV/jgf87n2Rvfa2ry/N7qXsdhYTvXBmnRYZScgUaL89JWfZgzm7HJ3cbL35JyfLTbss1AHMTvnbf0r620NBgV7TQ1q0TxBRBpCiUxr+33A73sKoH9g6MNA0OF4pDj9761GKtkjcx6wIBQKvQq8ClwcDAZvAX4ZDAYXMFE2L9mYoZKaXl9utcSpfTe1lG/JWSRsNAfYvbmqJWd22xvbedzKzrHZlZB9B/dx4OUDqePMqkyhfTLZXC5Xxs/viOxgd2Q3C6csBMhbjYHcuTN2OojZOW/WCkqpJWi1GropIk1hmtMBSFW+B9A/MHQm8A2HY5HSVJGpkUoqMp1AxhVZKBS6BvgBxnrMU2sQV/OzMUPFOK7Mi7jndsC624wLfvM1zARg3W3G45UaH6lLhSTv65jL7hKZF+DGsruI8XiFr2u3vXHq8YPG4/sO7mPF91Zw4OUDqcetG9ZLPReQs1nf/DmTWZU50XNi6j5r2+Lp06bjdqVfMxaP5byOWRl54JUH8lZPoHBLZWsHsVtHbyU0GmLDoQ2MxcbKqqDsjexl1fgqtoa3phIWcwma2ebZizfjvJRbIRKRpnK00wFIxSLA9/sHhrqA/wWOcjYcsUGJTI1UksjsAM7JvjMUCn0SI5nprzaolmBjhopxXBkXcfVMAOqZIGWzLLvbExnl9OfvwLN3Bac/fwd7ItUvuyunvfH+0f0kLHl3duJgd9+Jyax8WJlVGdOmw5t4Lvpc6ntr0vG5yz6X9+evue0aVtydTrKiRIlhnMMDLx9gxd0rco7JZiZlOyM78yYf5VRQ7CxB803yVTVjRkSaQ//AkBvjQ0ppTT8FRjCqMrlrkaUZaWlZjVSSyNwHLM33QCgUugq4m4nQwWbWWUZnLig8kLLc6fX12ndT5wpJDsuyu0XDaxiMjBAjwWBkhEXDa9Kvu+fxip7ebiXAfDxbvgTFznOBkZS84Zg35NxvTY52HtmZsYQrOwmxvo6pWBUoo3pycJgV38uf1MSJEyNWMPmw+17duIvGbz6nK+GqydBNEXHcZKcDkKp8D/gccJHTgYhtqsjUSNmJTCgUWhEKhd5T5PF/CYVClSRIreU0v9FeGNIzVL613LjtrXB6fb59N3HLvhuoLAGod2OCF3bD/bcay9Xu+mzGsrsdkRHiyYpInAQ7IlnL7ipY7mZn2rzpikVX0OHObKGffdFe6LnMWS3ZF/H5qjLW5Mi6pyXfEi7r61gVqgJlVE9IEIvHUkvlvn7/13OOt1PBKVZBMd9DofjNx4ciQyycshAPnowleubzePDYGropIo5TItO6xoAjwH84G4aUyVv6ELGj5Gb/YDB4YygUurYRwbSUSgZSllKvfTf1bEzw+AZ44uGCD8/19jCYTGbcuJjrtZyPjg5jWVuZDQHsTJs3TZ82neUfXp53Y7wLF6dMOoXdR3bnfS7rrBZrq2SzKlNs0KUp3xIu6+tkz4PJ9zzFmgWY82mszOQjO+6c85LnvLlxZ1RvSi1BK2foZiXza0SkIbSRrXU9CnyLylbYiHPCpQ8RO+z84X8mGAzmfuwr6Rkqc85N7plxGbdzzjXuL7crVz323UB5CVI5FZIXdhdNYgDW9S6hz9tDBy76vD2s600uu8MF8XhFy93M+SrFKgFnTjozdV+hZU8ddHDe684r+FzFLuKLVTSsz1NquVqpilL2MXaU2utTqoJS7iZ+c8bMsu5lXNV9Fcu6l7Fg8oKM5MRO8wARcYwuqlrX36D9Fq1I7TxrxE775e8C/xQMBqcBHwmFQjnDOYLBoB/4cigU+qtaB9j0Kh1Ima/F8uRpMH7AuJC/eolRLXlpxEhiKt13A8Zzm8nMcT3pikx2gtThKa9C8vsH0l9vfxq+8n2IxsDTAZ/6IJz6JmZ5u9l64pW5MVm2iOyJjLJoeA07IiPMTSY7s7zd6eVuec5toUqAz+sj4Uqw7ci2gqfDhYsOOlLLnro6ulLP9cfwH1PHWSsh2RfxhSoaicyGfgXbJJd6Hg8eYxkZsYxjbrjrhoylYvlaSReLexKTWDp1adEKSl9nH1vDW4kTr6jNczbr/Jps5jK89a+s55Kpl6gyI+KMQ04HIBXTEqXWpESmRlz5lqtkCwaDXwE+CfwE+EAoFDqcvH8OcBMQAAiFQh0FnyTz+RLJ4yuLutXlm7ECGFf3JX4fHq9R7SlnydqmtUYykogbe2KyE6Te7mTjgkS6WmPnte/6bPqxj/2XkcSkju2Ab/7f3Ocwl90lgJjxyfzpz9+Rsfysz9uTTn68ncaATRvyTa3PVmjZk2nDoQ2pi/hyZtC4cJEgkbotxazcZMdq3m+dI2NNAOzEVOgYN27mdc5jweQFRWMbi42xanxV0YGiHjy2Ew8759RubNK02r/BSxvrHxhyQYH/aIpIPdwe8PuWOR1EO7A1EDMUCn0qGAyOADcAPwsGgx8DrgI+hvFpwGNA4Y0KkmbtIJbDcgHscufuu3G74Q2z4IGvlTco8zS/UVGJxtONCfIxX75QslOkQpKRxOT7HpexJM6M975bUo+UbAhgQ7FP/U0ePCydupSuji7GYmNsOLQhZ7/GbO9s2/tv8imVxFgTFTduNh7ayMH4wdTjXe4uzp98Pid5TwLgkqmXsOnwJnYcMdpjF4rJhdFBLLuCk/3adjqImUv38iWF1vjtVk/yza/J3r9jzq+pZSKjPTki9gT8vkT/wNBh4HVOxyIyQagiUyO2EhmAUCh0YzAYfBm4DdievHsH8PlQKPS/9QiuLWV3EMuXMOCCqdPh0MvGhbx3EvTOhJd2wws7y9oYD9hrTJBIQCyZBFTSEMDTkVuRsbrshszvLcvdijYEsLkfqJyp9T6vL+ci3dyvsT28nXNedw6PHX6saGUnm5nAFKuYmFWHszvPZiw+xrpX1uU8v3n/wikLmemdSVdHF3979N/SN6mvZKKWIEEHHSRIVJ18lNrED+RNBPMlCeXMr6mVfNU56+/YPL8ikvIaSmREGkX70mrE1u7hYDDoCgaDHwE+lbzLBbwE/LWSmDLZabFMwkhiPnSdkQBc9C+wb4+RaFQ6B6ZUY4KY5QK5aEOAcLoBwBTL/sJPfTCdvJh7ZEzTenPjsczhKdgQwM5+oGRzgrfdt4GPr32Jf/7xfu5Z8w2GD+ZvGbw9vL3ksMfHDj/GoimLmNc5L2cqfanWxoXaFYNRETIrDnYGTo7FxlL3z/TOZNGURbgKrOCxJi+neE9JxT2JSczrnMclUy8p+8K90Cb+sfhYWRv3y20eUC1rdc7u+RUR7ZMRaSBVZGrETvvl/4OxpOxUjAzyJmA/cDPwcDAYfHcoFBquZ5BtpZIWy3aqOMWWfZmKNSaw2xDAfA9PPZoeCApw6pvy74kBeEueGV2W5W4FGwKUmsNj2Ws0KXkeO6MJnj50IFUhyf7UP0Ik1bGrWOVmd2Q3CyYvYMHkBYRGQ6kL9lKtje1UHMqpHlmXWu2K7ErtwSn0cwkSdLo7WdZdn6W3lWzct9M8AIzfzYZDG6pe+lXp+RWZ4JTIiDSOKjI1Yqci8yOgD6N72SmhUOjaUCj0P8ClGMnNQDAYnFm/ENtMJS2W6zUo08pSIeHqJXB8D7hdxq3ZMc0qEYd4NDOZyefMd8IJJ+feby5383hzn8PlNu4vNofHutcoq0o119uDO1m5cJHb8tjOsMfB8GDqeLtLo6B0xWEsNpa6qC8nBsi/18TOz9VSdpKQrzplJgmm+Z3zU8ljoVbYYFSV7LRjNvc3hUZD3Dp6K6HREBsObUhVWJrhPIm0ICUyIo2jikyN2ElkHgLmh0Khj4ZCoefNO0Oh0GrgfcAJwG+CweDpdYqxvdhJGLKXVNVrUKbVaX6jAgLphgDfWm7c9nYblaBr74DLVxi3w6Ppn53Rl7t8bFovXHA5nPWOwq9ZzRye7CqVJbafuC5MLVXzTZ6edzYL2N+vYXdpFBSfL3OC5wRWja/KWO5Uzp4RJ/aaZKskSSg29ydbqaVfdubRNMN5EmlBSmREGmfc6QDaRcmlZaFQ6MIij/04GAxeCDwAbATy96aVNDsdxLKXVNld9lXuoEyrYg0BoHgDgH1Dtlsk531d63I3c75Oqc5s+apUydhO+uqDbE2e17DHxe0FWiaXmrdisrs0Cgp3FXPj5tnos8TI7OZmNwYwEirzIr2cn6ulSpMEa/OAJ8NPVrT0y+6ytmY4TyIt6FWnAxCZQJ51OoB2YbtrWSGhUOg3wWDwfOCnNYin/dnpIJa9pGrWWek5MLUclJnNrJBYB3Wa6lUJsso3XycShh2/M/5BOrGxGdukaGY7ZOuFc/fUbnqm9nBw/GDRYY/zO+eXbMlcaC6MKUYsb2czuzHsjezNuICvxaDKSlSTJJjNA8xzCeW1Y7a796XL3cVYfKxo4lnv89Ts1Jpa8ni+9CEiUiPPOB1Au6g6kQEIhUJPBIPBt9XiuSaEnIThSOaMlex9IZVUcSplrZDcc339K0GmovN1LMxGA1ZFYjviyezyZb1wPjh+kN7uXm75xC0Zx2TPW7E9V8XdxZbwFraFt+VUDazzZcqNwaxEWJ+j2lkxlbJTnSqVJFRa1bE7j+bP8T+nkkqnzlMzU2tqKWCP0wGITCBPOx1Au6hJIgMQCoV21+q5JoRiHcTyHVtuFacWGlUJAnj84XQSU3C+TlL2srcCscVdLgZnGGMRzE/w9x3cl36aPJv1PXiY4ZnBPeP35HxSXWyuivkp9tmdZ7MtvK3oWy3VMMCDJ2Pmi51KROo9kWBzeHPdPlm3W50qliRUWtWxmwBFiBCYEqjZQM92UknXOZkwlMiINMZ4wO972ekg2kXNEpkJx9zLYS7BKrSXo1bKreLUQqMqQc/tgKEn0t9n78e5+fvGbJpCiU2B2GJu2HLyUUD6E3yrfJv1EyR4JvpMwU+qzZbMhdhJOopdvLtx51xE2qlEpN4zsbp+sm67OlXkIrjSqk45CVCpgZ4T9SJdramlCCUyIo2hZWU1pESmEoX2cjz1qHHhf/7S4h23KlVOFadWr1fLSlC+5G9GHzyzNfO47D0vw6PGkrHsRgPWWPLEtv4vJvPy0cafuPUTfFMikSAai3Lg5QOpqkb2Znwo75NqO0lHsYv3eZ3zcp6/nPbP5cZbiWqThEqrOuUmQOaeHF2Qp9ldnpe9P0kmBCUyIo2hZWU1pESmXMX2ciTiRvVi42qjelKPKkmj1aoSVCj5s1ZizCVlcUvCYSYwhRoNgNGuOU9sL8TuhTyf4FsdHD+YU9WYuvcAi5feSfeu/YzOPpa1q69gfKa9T6rtJB3lLskqVYlw4pP1apKESqs6tVjWNtGpNbUUEvD7XuofGDoETHY6FpE2p4pMDSmRKVf2/JJ8ezliMeO4RlVO8qnl0rdqK0F2N/KbS8qsju+BaAz2jxVoNNCZjs36nnf8jis9HWybMYnNJx+V8Ql+3FK9yVfVWLz0Trp3DuOOJ+jeOczipXdy9yPGJ9Xbw9tJkCjY7cnu8qd8Znhm5L2/VCWiFT9Zr6SqU4tlbROdWlNLCUOAZsKJ1JcSmRpSIlOuIvNLMmar7HncuUTGqaVvhViSvz2RURYNr2FHZIS53h7W9S5hlje538W6pAyMQaE3Xpk/YYTMRgN53rMnGuPNe1/jzXtfI+qGvzvjX9hy8mQ+c9+XiyYY3bv2405WhdzxBN27MjeSm0mF9XtzT0pfZx9Php8kQaLkzJlsz0SfYdX4qpy9LaUqEa36yXolVR3tfalOLbrOSVvbgxIZkXrT0rIaUiJTrkbPVilXMy59syR/i4bXMBgZIU6CwcgIi4bXsPXE5H6XQm2USzUaKPKezebL3jjM23uY0549zOG3XsZ/PHJXwQRjdPaxqYpM3O1idHZmopM9J8a6J2XRlEX8KfwnwFhCZr1YvHPdnTldxgo9j3VvS7FKBEy8T9a196VyWp4nJWifjEj9DTkdQDtRIlMub2fjZqtUohmXvlmSvx3JJAYgToIdEUvyV6jFc7bsRgOb1tqq+HQAHTH4x0EPr3vfv6UaAWRbu/qKnD0yVsX2pOyO7MaFKzXz5fb7b+fAywcA2HdwH7fffzufu+xzJZ8ne29LvkqESZ+si11aniclaIyCSH3FgD86HUQ7USJTrkbOVqlEMy59syR/c709qYqMGxdzvZbkr1DlxeWCDq9RccnXaKBAxSe87wDxW++AA/GMRM4dh7N3H+KXZ0wFjAs4V7J2EyPG+Mzp3P1I7ifVpmJ7UsxlZSYzicn3fbl7W6yViLHYGKvGVxElqk/WpSxanidFPFH6EBGpwmDA7zvkdBDtRIlMuRo1W6VSzbj0zZL8retdklMxKchaeSm2p6dAxad/NfgOxCBBRiLXkYC+58L88gwyLuDG4mMFP6m2fl9sT0r2sq9iKt3bMhYbY3N4c0bCZKVP1qUULc+TAh7FaPXodToQkTb1B6cDaDdKZMpV69kqtdaMS98syd8sb3d6T4xVhwdOOh2eGyy/xXOBis/cA9BhXutnJXKdUbiq+6qMp+nq6Cr4SfX28PaKupH1TOth5OWRjO9Nlext2RvZW3CvDIAHD6d1nqZP1kWkbAG/77X+gaEtwFucjkWkTSmRqTElMpWo1WyVemjGpW92k79KO6kVqPjsPdbNyfvjuMpI5Ap9Up0gkbfb0zFTjyEai3LNbddk7HMxLfs/ywp2Lit3b8tYzKgYRYkWPR1OJjFmtahQe2oRaXoDKJERqZfHnA6g3biyBwQ2QjAYTACEQqGGv3bbGx+BdbcVn9ni8TozsDNjtk0Nk79C77lQswMA35nw9osLxJY7d2csNsa6F+/ijN3j9D0XZlI0wRGPi9NeuIOhQwdIYFRVert78+5XKYcHT0bXMtOGQxtSyVShRgFu3MzrnOfIkqFC1SLrUjdrW2lpK67Sh0gr6B8Y+gDwQ6fjEGlDYWBawO8LlzxSbFNFpt0089K3agdrFnte8z2XGrppZSYvu/4AsawqR9bcnS7gkl8eJBGPppardUYTPJ1MYiD/cM183LhTX5fTNWowPJg6vtmGYBarFhVqKy0iTem3Tgcg0qb+oCSm9pTItKNmXvpWL+Z77l8J8RJd2wCe2QbPboNoFApsmk/N3fnlPQC4s5MdMvfkuMjc5+LFy6mdp+btDAWU3TXK3KMDzTcEc3N4cyrJKqettIg0l4Df92L/wNAQ4HM6FpE2M+B0AO1IiUy7qlf1o5lN7UknMVC8a1ssT+Wm4NydaMFjfvLxC3mP90F2REZ40+TpfMiyz+XUzlOLdoYqt2uUF6+thgNODMFs5mqRiJRtACUyIrWmRKYO3KUPEWkh3s7018f1GJv8IXezv2l4FK69Ay5fYdy+OAJxSwUnm1nlSR5z0lcfZOuJVxKduZxtJ1yZ2uhfjxkufZ19qWVpVyy6gt7uXtwuN73dvY4PwWzmapGIlE3Ly0RqK44SmbpQRUbqq8Qm+pqz07XNyrr8LB5L359dwTEVqfJMiibqOsNlfud8toe3EydecAhmnDjheJix2FjR1691d7Fyq0XqbibS1HTBJVJbjwT8vgOlD5NyKZGR+nluR27TgaxN9BW3XC7EzsBSK2tiYlWoglNkNs8Rj4t5nfPq1v64q6OLhVMWFp0jA7AzspPdkd0Fu4Tl6y4WIcLW8Fa2h7dX1F2sr7Mvb3vqfG2l6/H67UjJnjjoT8CLwPFOByLSJu53OoB2pfbL7abRFZBicTjVBjpfAgXprm2JRHrfi7mczPzfgafD2GeT3a7ZVGgfjcsNc85tyJ6ksdgYmw5vYseRHUWPy9fGeSw2xqrxVUVn0RRq/1wqJjvPu2jKIta9sq7mr99uWrCVtdovt5n+gaHbgX9yOg6RNjE74PftdjqIdqSKTDtxogJSyLYBIw4osok+ZhxX64v/Ul3btg0UX36Wnbx0JP9nEosWrvJ0dBjP3QBdHV1Mck3CjZs4cabuPcDipXfSvWs/o7OPZe3qKxifmb9LWL26ixWrFlkvvndFdqm7WQlqZS1N4n6UyIjUwp+UxNSPKjLtwk4FxOWCDi9Ej9S/UnPP9UYSBZlVD5cLju9JJwPeTvjQdZnvo94VJTvnCjLn7kD+Ko9VA6tfodFQak/KpW9dQffOYdzxBHG3i9FTern7EWP/zCQmsax7Wd6fW3H3ioy9LNZhntk/Z9dYbKxoW+l6v347aPbBpwWoItNm+geGJgH7galOxyLS4r4Y8PuuK32YVEIVmXZhpwKSSBhJDNS/UhOxzHwq1gY5Yuli1aiKUrGhoSkumPlmOOud6aQko8qTZ6ZVA6tf1i5h3bv2444b59cdT9C9q3CXsHp3F+vq6CraVlrdzUpTK2tpBgG/70j/wNCPgQ86HYtIi7vP6QDamdovt4s9j6cvyLNaBOdtIwzJgY8R44J+PE+HrmrYbYPsTc48GR8x4ohGchOLesRpLj874ZT8j7tcxsDMcUuTEXM2z0X/Yuzvyaee59TCS/r1R2cfS9xtnN+428Xo7MIzZaw/d2z3sbiSv5dGzaJx+vVbgZI9aSI/dDoAkRb3dMDv2+J0EO1MiUy7sFMByZ6ZMjxq3G/uVamlWWcZS7PAqAgd3wPu5LIyaxvkWAQ2rYXHH86sKDUqzn178t9fLCHJrn41KlYL60yZtauvYPSUXuIdbkZP6WXt6sIzZWZ5Z6W+LjSLBsDnrc8svGaehdMslOxJE1kPjDsdhEgLu9/pANqdEpl2YacCUqhSk4gbFZ1aOs1v7C+B9Ab5by03bq2b6eNxYznW0BOlK0q1jtOSkOyJjHL683fg2buC05+/gz2RIgmJnepXPc6pxfzO+amEYHzmdO5+ZDm37b+Fux9ZzvjMwkM5Ey6be+LqtOPBGrc5C+eWT9zC8kuX13WYaCtRsifNIuD3HUYXYiLV0LKyOpt4e2SapT1xrdkZBGl3r0ol8p3XN/jgpT3JZKHIBXT2UrJ6xmllSUgWDa9hMDJCnASDkREWDa9h64lXphMSa2e1Svb/1Fi+LmHTXo1y9u5DnPpcGG80QcLjxX3yrzL+toeODKWeo9D+i9RxRzcmblM9h4m2EjuDTyd6sicNtRr4iNNBiLSgF4HfOB1Eu5tYiUwztSeuNTuDIIsMc0ztValEofP6wi7jk30XmXlMoWYE9Y4zmyUh2ZFMYgDiJNgRKZKQeDvTP9uoWPOY6Z3JJVMvYUt4C4ee2cK7HzuIOw4dyXPtikZy/rabYf+FNe5C3c0mMiV70mR+jtG97NhSB4pIhm8E/L6Y00G0u4mztKzRm8kbzezE5fGm96ZkK7RXxeU2KjqVKHVe4/F0xcJUqhlBPeLMx5NONOZ6e3An11O5cTHXa0lIsjf229n/U+tYC+jq6GJB9AwW/mEcbyydxKRk/W178TLt1Sh/88dx5nqOyXjPx01r3P4Ls7vZsu5lXNV9Fcu6l7Fg8gJdnCeZyd68znmp38UkJjGvcx6XTL2k2YZhShsL+H1R4JtOxyHSYiLAHU4HMRG0XkWm0qVhTg5obJR8gyA9XmOQY6JIpaaaYY52zmv2Y3HL1Xb2cixo3NDJo7vg5WEAfuK6kCOh7zNzf4y9x7qZdPWFmcdZ2al+FYu11ssbs/b6LBpew47ICHO9PazrXcIsb/pv+6+Onc7pjzyBOw4P9C5JHTvH28N9U9/P9n1hnn3DUSX3X4zFxtgc3sxgeJAIEbx46evsY37nfCUjNVKqlbVIA30N+DTgLXWgiADwo4Df96LTQUwErVWReW6HMcjwqUfTS3vMpWHrbjMeL6QJNmg3hNki+EPXwWU3wCVfgHdcmr9S43Ib95+/tPL9QXbbPlsfy4ghazmWGVet48zn1bHUlyd99UFm74/jScDs/XFO+uqDeY8Dile/SsVazd9wIXn2+sQse30A4/HdWzjzka2pys0sbzdbT7yS6MzlbDvxSua6u1n42Mt0vRovuv9ib2Qvq8ZXsTW8NbVULUKEreGtrBpfxd7I3vLfg4g0rYDf9zxqxSxSjq86HcBE0TqJTLVLw5pgg3ZDjY8YbY3vuR4e/i7ggqnTk8upXEYVYM65RgWnmn1Bds+r9TFTvuVYvjONuLydtY0zn6jld10s9miEHGb1q5xY67W80e5en+gRXHHjdQt1aXPHIfDM9IJVlbHYGOtfWU+UaMbeDeP14kSJsv6V9YzFxsp7DyLS7FY6HYBIi3gi4Pdpk3+DtM7SsmqXhjXJBu2GyLf5PnrEGO7Y0QHv/HDtkgK75zX7seN7cpdkdXTAWe9MV5Xqrdq/CTNOu7HWa3mj5X3M9fakuq/l7PWBkl3aOhIwde9u+Kv8L7U5vDmVwEzde4DFS++ke9d+Rmcfy9rVVzA+czpx4mwJb9GSKJE2EvD7ft8/MLQJOM/pWESa3NecDmAiaZ2KTLVLw5pog3ZdNbqpgd3Bl8Ueg/osHSul0X8T9VreaHkf63qX0OftoQMXfck9MkDOEriyurRZDIYHU4nM4qV30r1zGHcsTvfOYRYvvTP5fHEGw4PlvQcRaQUrnQ5ApMmNAqucDmIiaZ2KTLVLw6rdoN0qGt3UwM55heKP+c5MV2IaqdF/E/Va3mh5H+a+lxwdHYAntZyuaOWmSFXS2r65e9d+3Mk9T+54gu5djWnfLCKO+V/gWeCNTgci0qS+HfD7DjkdxETSOhUZO5ProfgyoEo3aLeSRjc1KHVe3R3Gv0Ln/J0fgbdf7Mx5b/TfRLV/w4XYfR8nn22vclOkAuW1NC0anX0scbfxHuJuF6OzG9e+WUQaL9mKWctmRPKLAyGng5hoWqciY2dyfallQPnaE3snVdf6NlutW+uWy4mmBqXOK9T3nNtR7PdS778JUy3+hgux87c9dbq9yk2RClRfZx9bw1uJE2ft6ity9siAMbSxVPtmEWlZdwDXAZOdDkSkyawK+H27nQ5ionElsjtJNUAwGEwAhEJlJK7jI0Z72nwdpEwer3Ex51RVJd8mezAuTjs6UtPV6+qe69PJzLV3FN5g7+00WjRPBM3we4Hm+Buu8lyMxcZYNb6KKNGCx3jwcMnUSzRPZmJxOR2ANE7/wNBXgGucjkOkiRwB5gb8vr1OBzLRtM7SsmZfGtboTfaFtENTA2vr6Ls+a9xuWlvZuWuW3ws0x99wJW2jLbo6ulg4ZSEePLiz/vPhxo0HDwunLFQSI9LevoSxqVlEDF9XEuOM1qnImDKWCDm0TCmfTWvTy4YKbbJ3uY0Lxnq2Fm6GT/2rUcvqyfgIbLgbXh42vnfy95IdVzP+DZdhLDbGlvAWBsODHOEIk5hEX2cfZ3ee3XRJzFhsjM3hzQyGB4kQwYuXvs4+5nfOb7pYW5gqMhNM/8DQp4CbnY5DpAn8GTg54PftL3mk1FzrJTLNqpmWdDXLUqpy1TIJM8+B9bmc/r1Iw+2N7GX9K+uJJ//P5E7+38IpC5npnelcgO1DicwE0z8w1AlsB3xOxyLisC8E/L7rnQ5iomqdpWXNzolN9oVUuXzIMdmto6+9Ay5fYdwOJ1cxmK2ji7EuJ7Ny+vciDTUWG2P9K+uJEs1IYsCYdRMlyvpX1jMWG3MmQJEWFvD7wsC1Tsch4rBh4Bang5jIWqdrWbOrdkp8rZU7db4Z5GsdnbC0jr7xynTr6GLvq9AsHbcb4sn7nfq9SMNsDm9OJTBT9x7I6bA2PnM6ceJsCW9hweQFDkcr0pLuBT4JvMXpQKq17gff5qF195JIJLhg8d+z+OLLWf3/VvLQunuZ2nUMAJf+06c55625/63YvGkj37z1euLxOBdcdDEf+PAyAO4K3cTm323EN/s0rv78VwD4xU/v45XxMRZd/NHGvTmppy8G/L5XnA5iIlNFplbaYZO908qtahVqCrB7S/5ZOrE4eDr0e5kgBsODqURm8dI76d45jDsWp3vnMIuX3gkYlZnB8KCTYYq0rIDflwA+7XQc1Xp6zw4eWncvX77zPlZ+Zz2PDWzghWeHAFh88eWs/M56Vn5nfd4kJhaL8Y1bvsB1N3+b2+5+kF//fB3PDu3k1VfGGXxyM7fe9RPi8Rh7dw8SDh9mw09+yHvef2mj36LUxx7gG04HMdGpIlMrjZ4S3+yy57Z4JsHRXfDqmDFdPt98nXKqWvn2AUXC6YYLpuyEKB6Hby3PjLVZfy9OzyRqcRHSSwu7d+3HHTf+DtzxBN270nsyj6BlhSKVCvh9v+4fGOoHAk7HUqnn9u5mzuln0fm6owA4/ey/ZNOvfmbrZ3duf4LjZ7yJ4048CYC3vesifvebh1j4gY8QjURIJBIcCR/G4/Fy/z13cNHf/QMej7fEs0qL+HzA7yuyqVcaQRWZWmmG1rrN4rkdxqb9px5NJybRI0b3sGjyotFMOtbdZhwP9qpa5s8+/N3CLZWtjusxEiHITYigeX8v+c5hvnMGtW1X3Ua8pC8WRmcfS9xt/B3E3S5GZx+bemwSWlYoUqV/hyLDpZrcSbPmsO3x3zP+8ijhw6+x+ZFfcmD4RQDW/+i7XHXZe7jtxn/nlfGXc3724P6XmN57fOr7nmOP5+D+fRw1eQpv/Zu/5ZMfvYje49/I5KNfz87tf+Qv//qChr0vqauNwGqngxB1Lau9NmitWxU7nceymZ3IoPyfLdRS2c7j03rhHZc23++lnO5t4wdas0NdA2w4tIGt4a3EiRfcI+PGzbzOedojUz11LZvg+geG/hv4N6fjqNRDD9zLT350N687ajJvnDmbSZ2v4wOX/jOvn3YMLpeLe+68hdGRYT5x7X9n/NzAhh+z5fe/4uOfuQkw9sDs3P4EV37yPzKO++pNn+G97/8wu3c8yZbf/5qZJ/dx8T98vFFvT2rrMHBmwO97yulAREvLaq8VN9nXUqGN9sf1wIcvhO89mJtUmJ3IzltsXHj/8h6I2fxwL19TgKuXFE9uwEgEmjGJgeLn0HrOHn8Ynt2WP+FJxI1ljhtXN+/MoDqb3zmf7eHtxIkzPnM6dz+yPOcYN27O7jzbgehE2s7ngfcCpzsdSCUuuOjvueCivwfge9/4Mj3HHkfXMenK7QWLP8iX/v1jOT/X03tcqnoDMLL/RY6Z3ptxzJ6ntgJwwht9fPPW67nxa/dy8xf+lReeHeKEN6p7dQv6opKY5qGlZVJb+TqPxZNJxle+n/n9yjXGcWYnMoCp02HytPTzWdsw//vXjX/Wlsz5mgJkv675OqZSy8mcXqpV7Bxaz9nTT9amXXWb6uroYuGUhXjw4M76T50bNx48LJyyUEMxRWog2Y75I7ToErOx0QMA7H/peTZtfJC3v2sxBw8Mpx7/3a8e5KRZc3J+7pS+M3jx2b3se+FZIpEj/ObnD/AW/7syjrnnm7fwoY99kmg0Sjxu/Lfd5XIRPny4ju9I6uSPwH+XPEoaRhUZqa1inceisfRj+TqR5Rtiaa24mBfnkL6oz9cUoFjHMyheoSjWRGD35sYs1bLbvS1uOZ/VtKtuYzO9M7lk6iVsCW9hMDzIEY4wiUn0dfZxdufZSmJEaijg923uHxj6EvAFp2Mp1399Nsifx8fwdHi48pr/ZMrUafzPF69haOc2XC4XvcfNYNm/fQmAgwf28dWbPsN1N3+bDo+HK675D/7zmsuIxeO8a+GSjIRn069+xuy+Mzhm+hsAmHv62fzrR/6WmSf34TvlVCfeqlQuCvxjwO9ryWS9XSmRkdoq1nmsw220QM7XiczjLT3E0sq8qL/pn3OXXlkv6nM6nnUWr8TkiwEau1TLbvc2Kw37LKiro4sFkxdoH4xIY9wAXAT8hdOBlGNF6Ac5933y8/nnHB4z/Q1cd/O3U9+f89YFeVszA5z39ndz3tvfnfr+ox+/lo9qjmir+lLA73vM6SAkk5aWSW0V6zz2qQ8Wnq9zdFf+ZVJud7rrmJV5UW+2uv7WcuO2t7vyOT7Ze1OcWqpldyaRtTtese5sGvYpIg2S/LT6I0C41LEiLeQPGEm6NBl1LZPaqrRrGa50a+Zr70hXIcAYYhmPw/Qu4/sDY4U38Zd6nWLVlHuuT1dCrDG4kkmEORvI2wkfus7+65bLbteyN54Ge/9kVIsKNQVwuWHOuRN2aZk0jLqWSYb+gaFPA192Og6RGjgM/EXA79vmdCCSS0vLpLbMeTr5WgJns7YIfvi76fuzl5PlG2KZT4cX4lFIQPL/5b5OsSVhdvem1HupVrFzmPFepie7lmkIq4g0nVswhmS+zelARKr0GSUxzUuJjNTejLlG5cM6T8fjNZaPvTpmVBqy5+tUsi8k36yUaub42I2hEUu18p3DfO/FVsIz8Vovi4izAn5fvH9g6B+ALcDrHQ5HpFL3BPy+W50OQgrT0jJpDpvWGp3Bii2TsvJ21n7QqJ0YmnGp1kQfwirNQEvLJK/+gaH3Af+L/kak9WwG3hbw+15zOhApTImMNIdyptnX6+K8GWIQaU26SJWC+geGvgh8zuk4RMqwHzgn4Pc943QgUpy6lklzMPeFeLyZ3bjA+L7UEMt2iUFEpP18AXjA6SBEbIoCS5TEtAYlMtI8zH0hc841lo7hMm7nnGvcX+9BlM0Sg4hIGwn4fXHgUmCH07GI2HBNwO/b6HQQYo+WlomISLW0tExK6h8Ymg1sAlTWlmb17YDfd7nTQYh9qsiIiIhI3QX8vl3A+4A697AXqcjvgGVOByHlUSIjIiIiDRHw+34N6BNvaTbPAu8P+H3hkkdKU9EcGZFyZbQ7DtenFbSISJsK+H2r+geGTgb+0+lYRIAXgHcE/L4XnA5EyqeKjEg5ntthtGh+6tH08MxI2Ph+3W3G4yIiUlTA77seWOF0HDLh7QPemVz2KC1IiYyIXeMjsHG1MWcmEc98LBE37t+42jhORESKCvh916JkRpxzAHhXwO8bdDoQqZwSGRG7tg1ALGZ8PTwK194Bl68wbodHjftjMeM4EREpScmMOGQUeHfA73vS6UCkOkpkROza83i6ErNyDbw4AvGEcbtyjXF/Im4cJyIitiiZkQYbBy4M+H1bnA5EqqdERsSuiKWZyUsjYM5gSiSM71PHqbOoiEg5lMxIg7wCvCfg9z3qdCBSG+paJq3Hqa5h3s50MnNcj1GJSSTA5TK+Tx03qX4xVEPd1kSkiQX8vmv7B4YAljsdi7SlQ8BFAb/vt04HIrWjioy0lkJdw3b8Du5fCU89Vr/XnnUWuJL/k7l6CRzfA26XcXv1EuN+l9s4rtmo25qItABVZqROXgL+JuD3bXQ6EKktVWSkdVi7huWTiMMj9xlfzzmn9q9/mh92b4ZoHHq74cYrc4/p6DCOaybFzlsibryfjath0SdUmRERxyUrMy8C/wN0OB2PtLwngYUBv+8ZpwOR2lNFRlqHna5hAJvur08L5Kk9cP5S8HjTlRmTy23cf/7S5ksG1G1NRFpMwO+7DXgPMOZwKNLafgr4lcS0LyUy0joKdQ174UDmRXkiUb+L8hlzjcrFnHONPSa4jNs55xr3z5hbn9ethrqtiUgLCvh9DwF/CTzldCzSkkIYe2LGnQ5E6kdLy6R1FOoaBhCNGRfl5nKvPY/DeYvrE8fUHuO56/X8taZuayLSogJ+31P9A0PnAT8A3uV0PNIS4sCnAn7fSqcDkfpTIiO1U++uWNldw144kPm4Lsrza/VuayIyoQX8vtH+gaH3YOyZ+bjT8UhTexVYGvD71jkdiDSGlpZJbTSiK5a1G9jVS8CTtQdUF+X5tXK3NRERIOD3RQN+3yeAZUDU6XikKT0N/LWSmIlFiYxUz9oVy9yLYUrEjfs3rq5+A/5p/vQFudk17ITpxkX5CdPTF+W4dFFudZrf6KYG6fP2reXGbW+3cX8zdlsTEckS8PtuB94NvOB0LNJU1gBnBvy+LU4HIo2lpWVSveyuWCvXGMu8jkt+4t/bne6KVc2+kqk9cF4g3WK5UAtkj0cX5VZmt7WNq43fgzXZdLmNJKYZu62JiOQR8Pt+0T8wNA/4GrDU6XjEUYeAqwN+351OByLOUEVGqtfIrlhzzoG3vs/Y35HD1bwtkJ3Wit3WREQKCPh9owG/70PAxUAd+u1LC3gcOEdJzMSmioxUr9FdseacA8f5LI0Fjhh7YmrZWKAdtVq3NRGREgJ+35r+gaFfA3cCFzkdjzREFLgJuD7g9xWYkC0ThRIZqZ4TXbF0US4iIkDA73sJWNQ/MPRRYCUw1dmIpI62A5cF/L5HnQ5EmoOWlkn11BVLREQcFvD7vg2cAfzC6Vik5o4A/w3MVxIjVqrISPVO88PuzRCNF96Ar65YIiJSZwG/7+n+gaF3Av8EfBGY7nBIUr21GAMudzkdiDQfVWSkemZXLI83XZkxudzagC8iIg0T8PsSyTbNpwC3orkzrepPwAUBvy+gJEYKUSIjtaGuWCIi0kQCft9YwO+7Gngz8BOHwxH7DmAMPj074Pf93OlgpLlpaZnUjjbgi4hIkwn4fYPAe/sHht6B0e3qXIdDkvwiwG0Y3chedjoYaQ1KZERERKTtBfy+DcBb+geG/g64AdBSgeYQB+4Dlgf8vp1OByOtRUvLREREZMII+H0/BE4H/gF4wtloJrTXgNuBvoDf93dKYqQSqsiIiIjIhBLw+2LAXcBd/QNDC4CrMQZq6gPe+jsAfA34WsDv2+90MNLalMiIiIjIhBXw+34B/KJ/YGg28K/AR4EpzkbVlnYCtwB3Bfy+15wORtqDEhkRERGZ8JItfv+1f2Do88A/Ap8AZjoaVHv4LXAz0B/w++JOByPtRYmMiIiISFKyY9Yt/QNDtwKLgUuB9wBHORpYa9kOrAa+r70vUk9KZERERESyJPfR3Afc1z8wNAVYCCzBSGomOxlbk9oLfB9YHfD7/uhwLDJBKJERERERKSLg970C3Avc2z8wdDTwXoykZiETO6l5CfgBRvKyyelgZOJRIiMiIiJiU8DvexVYA6zpHxiajJHUvA84HzjRydgaIAY8CjwMPAT8WvtexElKZESksPER2DYAex6HSBi8nTDrLDjND1N7nI5ORMRRAb/vEPDD5D/6B4beBPiBv0renkFrt3SOAluAAWADsDHg9407G5JImhIZEcnvuR2wcTXEYpBIfuAWCcNTj8LuzXD+UpihwdgiIqaA3/c08DRwD0D/wNDrgfMwkhp/8utmbe0cBYYwNupvwug29mgyWRNpSkpkRCTX+IiRxEQjuY8l4hCNG48v+oQqMyIiBQT8vj9jLMF6CKB/YMgFvBGYA8xN3s7BaPN8EvXfb5MAngOewpjr8pTl31DA74vW+fVFakqJjIjk2jZgVGIAhkdh5Rp4aQSO64Grl0Bvt/H4tgE4b7GzsYqItIiA35cAnkn++3n24/0DQz3AmzCSnW6MxKbQv6OTtwngz8B48t+fs27Nrw8AuzWMUtqJEhkRybXn8fRyspVr4MURSCSM25Vr4MYrjcf3PK5ERkSkRgJ+3wgwAmx2OhaRVtDKG9BEpF4i4fTXLyWTGDBuXxqxHHeksXGJiIiIJCmREZFc3s7018f1gMtlfO1yGd+njpvU2LhEREREkpTIiEiuWWeBK/mfh6uXwPE94HYZt1cvMe53uY3jRERERBygPTIikus0v9FiORo3NvbfeGXuMR0dxnEiIiIiDlBFRkRyTe0x5sR4vOnKjMnlNu4/f6laL4uIiIhjVJERkfxmzDXmxGwbMLqTRY4Ye2JmnWVUYpTEiIiIiIOUyIhIYVN7jPbKarEsIiIiTUZLy0REREREpOUokRERERERkZajREZERERERFqOo3tkgsGgky8vIiK1kQiFQi6ngxARkYlFFRkREREREWk5rkQi4XQMIiIiIiIiZVFFRkREREREWo4SGRERERERaTlKZEREREREpOUokRERERERkZajREZERERERFqOo3NkRFpdMBj8GXAB8IFQKPQjy/0u4NvAZcB/hUKhzzgUooiIiEhbUkVGpDr/BsSBG4LBYIfl/psxkpg7lcSIiIiI1J4SGZEqhEKhJ4DvAacCHwYIBoPXAtcAPwD+2bnoRERERNqXBmKKVCkYDM4AdgL7MCoxtwEPAotDodARJ2MTERERaVdKZERqIBgMrgDMJWS/BS4IhUKHso55O/Bp4C+AE4CPhkKh7zQyThEREZF2oaVlIrWx3/L1P2YnMUlTgCeBq4DXGhKViIiISJtS1zKRKgWDwaUYS8peAo7DSFSWZR8XCoV+DPw4+TPfaWCIIiIiIm1HFRmRKgSDwfcCdwFbgTOAQeBjwWCwz9HARERERNqcEhmRCgWDwbcBPwSeA94dCoX2A5/HqHTe5GRsIiIiIu1OiYxIBYLB4JnAA8DLGBv7XwQIhUI/BB4DAsFg8K8dDFFERESkrSmRESlTMBicjdFeOQFcGAqFdmcdsjx5++WGBiYiIiIygWizv0iZQqHQLoxN/YUe/zngalxEIiIiIhOP5siINEgwGJwCzE5++1uMfTRrgYOhUOgZxwITERERaUFaWibSOOcAW5L/jgL+M/n19U4GJSIiItKKVJEREREREZGWo4qMiIiIiIi0HCUyIiIiIiLScpTIiIiIiIhIy1EiIyIiIiIiLUeJjIiIiIiItBwlMiIiIiIi0nKUyIiIiIiISMtRIiMiIiIiIi3n/wOZFGRC3SxRCgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 1008x720 with 4 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plot_results(x_test_enhanced, y_test, y_pred)"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 16 December 2020, 12:52:20\n",
- "Duration is : 00:00:02 592ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/LinearReg/04-Logistic-Regression==done==.ipynb b/LinearReg/04-Logistic-Regression==done==.ipynb
new file mode 100644
index 0000000..8f2122f
--- /dev/null
+++ b/LinearReg/04-Logistic-Regression==done==.ipynb
@@ -0,0 +1,823 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [LOGR1] - Logistic regression\n",
+ "<!-- DESC --> Simple example of logistic regression with a sklearn solution\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - A logistic regression has the objective of providing a probability of belonging to a class. \n",
+ " - Découvrir une implémentation 100% Tensorflow ..et apprendre à aimer Keras\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ "X contains characteristics \n",
+ "y contains the probability of membership (1 or 0) \n",
+ "\n",
+ "We'll look for a value of $\\theta$ such that the linear regression $\\theta^{T}X$ can be used to calculate our probability: \n",
+ "\n",
+ "$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$ \n",
+ "\n",
+ "Where $\\sigma$ is the logit function, typically a sigmoid (S) function: \n",
+ "\n",
+ "$\n",
+ "\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
+ "$ \n",
+ "\n",
+ "The predicted value $\\hat{y}$ will then be calculated as follows:\n",
+ "\n",
+ "$\n",
+ "\\hat{y} =\n",
+ "\\begin{cases}\n",
+ " 0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
+ " 1 & \\text{if } \\hat{p} \\geq 0.5\n",
+ "\\end{cases}\n",
+ "$\n",
+ "\n",
+ "**Calculation of the cost of the regression:** \n",
+ "For a training observation x, the cost can be calculated as follows: \n",
+ "\n",
+ "$\n",
+ "c(\\theta) =\n",
+ "\\begin{cases}\n",
+ " -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
+ " -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
+ "\\end{cases}\n",
+ "$\n",
+ "\n",
+ "The regression cost function (log loss) over the whole training set can be written as follows: \n",
+ "\n",
+ "$\n",
+ "J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
+ "$\n",
+ "## Step 1 - Import and init"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:11.438449Z",
+ "iopub.status.busy": "2021-01-14T07:11:11.438123Z",
+ "iopub.status.idle": "2021-01-14T07:11:12.802876Z",
+ "shell.execute_reply": "2021-01-14T07:11:12.802490Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "div.todo:before { content:url(data:image/svg+xml;base64,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+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : LOGR1\n",
+ "Run time : Thursday 14 January 2021, 08:11:12\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn import metrics\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "# import math\n",
+ "import random\n",
+ "# import os\n",
+ "import sys\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('LOGR1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.1 - Usefull stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:12.810267Z",
+ "iopub.status.busy": "2021-01-14T07:11:12.809885Z",
+ "iopub.status.idle": "2021-01-14T07:11:12.819897Z",
+ "shell.execute_reply": "2021-01-14T07:11:12.819592Z"
+ },
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def vector_infos(name,V):\n",
+ " '''Displaying some information about a vector'''\n",
+ " with np.printoptions(precision=4, suppress=True):\n",
+ " print(\"{:16} : ndim={} shape={:10} Mean = {} Std = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
+ "\n",
+ " \n",
+ "def do_i_have_it(hours_of_work, hours_of_sleep):\n",
+ " '''Returns the exam result based on work and sleep hours'''\n",
+ " hours_of_sleep_min = 5\n",
+ " hours_of_work_min = 4\n",
+ " hours_of_game_max = 3\n",
+ " # ---- Have to sleep and work\n",
+ " if hours_of_sleep < hours_of_sleep_min: return 0\n",
+ " if hours_of_work < hours_of_work_min: return 0\n",
+ " # ---- Gameboy is not good for you\n",
+ " hours_of_game = 24 - 10 - hours_of_sleep - hours_of_work + random.gauss(0,0.4)\n",
+ " if hours_of_game > hours_of_game_max: return 0\n",
+ " # ---- Fine, you got it\n",
+ " return 1\n",
+ "\n",
+ "\n",
+ "def make_students_dataset(size, noise):\n",
+ " '''Fabrique un dataset pour <size> étudiants'''\n",
+ " x = []\n",
+ " y = []\n",
+ " for i in range(size):\n",
+ " w = random.gauss(5,1)\n",
+ " s = random.gauss(7,1.5)\n",
+ " r = do_i_have_it(w,s)\n",
+ " x.append([w,s])\n",
+ " y.append(r)\n",
+ " return (np.array(x), np.array(y))\n",
+ "\n",
+ "\n",
+ "def plot_data(x,y, colors=('green','red'), legend=True):\n",
+ " '''Affiche un dataset'''\n",
+ " fig, ax = plt.subplots(1, 1)\n",
+ " fig.set_size_inches(10,8)\n",
+ " ax.plot(x[y==1, 0], x[y==1, 1], 'o', color=colors[0], markersize=4, label=\"y=1 (positive)\")\n",
+ " ax.plot(x[y==0, 0], x[y==0, 1], 'o', color=colors[1], markersize=4, label=\"y=0 (negative)\")\n",
+ " if legend : ax.legend()\n",
+ " plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ " plt.xlabel('Hours of work')\n",
+ " plt.ylabel('Hours of sleep')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def plot_results(x_test,y_test, y_pred):\n",
+ " '''Affiche un resultat'''\n",
+ "\n",
+ " precision = metrics.precision_score(y_test, y_pred)\n",
+ " recall = metrics.recall_score(y_test, y_pred)\n",
+ "\n",
+ " print(\"Accuracy = {:5.3f} Recall = {:5.3f}\".format(precision, recall))\n",
+ "\n",
+ " x_pred_positives = x_test[ y_pred == 1 ] # items prédits positifs\n",
+ " x_real_positives = x_test[ y_test == 1 ] # items réellement positifs\n",
+ " x_pred_negatives = x_test[ y_pred == 0 ] # items prédits négatifs\n",
+ " x_real_negatives = x_test[ y_test == 0 ] # items réellement négatifs\n",
+ "\n",
+ " fig, axs = plt.subplots(2, 2)\n",
+ " fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
+ " fig.set_size_inches(14,10)\n",
+ " \n",
+ " axs[0,0].plot(x_pred_positives[:,0], x_pred_positives[:,1], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
+ " axs[0,0].plot(x_real_positives[:,0], x_real_positives[:,1], 'o',color='green', markersize=4, label=\"Réels positifs\")\n",
+ " axs[0,0].legend()\n",
+ " axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ " axs[0,0].set_xlabel('$x_1$')\n",
+ " axs[0,0].set_ylabel('$x_2$')\n",
+ "\n",
+ "\n",
+ " axs[0,1].plot(x_pred_negatives[:,0], x_pred_negatives[:,1], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
+ " axs[0,1].plot(x_real_negatives[:,0], x_real_negatives[:,1], 'o',color='red', markersize=4, label=\"Réels négatifs\")\n",
+ " axs[0,1].legend()\n",
+ " axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ " axs[0,1].set_xlabel('$x_1$')\n",
+ " axs[0,1].set_ylabel('$x_2$')\n",
+ " \n",
+ " axs[1,0].plot(x_pred_positives[:,0], x_pred_positives[:,1], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
+ " axs[1,0].plot(x_pred_negatives[:,0], x_pred_negatives[:,1], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
+ " axs[1,0].plot(x_real_positives[:,0], x_real_positives[:,1], 'o',color='green', markersize=4, label=\"Réels positifs\")\n",
+ " axs[1,0].plot(x_real_negatives[:,0], x_real_negatives[:,1], 'o',color='red', markersize=4, label=\"Réels négatifs\")\n",
+ " axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+ " axs[1,0].set_xlabel('$x_1$')\n",
+ " axs[1,0].set_ylabel('$x_2$')\n",
+ "\n",
+ " axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
+ " autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
+ " axs[1,1].axis('equal')\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.2 - Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:12.822855Z",
+ "iopub.status.busy": "2021-01-14T07:11:12.822538Z",
+ "iopub.status.idle": "2021-01-14T07:11:12.825080Z",
+ "shell.execute_reply": "2021-01-14T07:11:12.824782Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_size = 1000 # Number of observations\n",
+ "data_cols = 2 # observation size\n",
+ "data_noise = 0.2\n",
+ "random_seed = 123"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Data preparation\n",
+ "### 2.1 - Get some data\n",
+ "The data here are totally fabricated and represent the **examination results** (passed or failed) based on the students' **working** and **sleeping hours** . \n",
+ "X=(working hours, sleeping hours) y={result} where result=0 (failed) or 1 (passed)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:12.830573Z",
+ "iopub.status.busy": "2021-01-14T07:11:12.830249Z",
+ "iopub.status.idle": "2021-01-14T07:11:12.832790Z",
+ "shell.execute_reply": "2021-01-14T07:11:12.832458Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "x_data,y_data=make_students_dataset(data_size,data_noise)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Show it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:12.850073Z",
+ "iopub.status.busy": "2021-01-14T07:11:12.846555Z",
+ "iopub.status.idle": "2021-01-14T07:11:12.945412Z",
+ "shell.execute_reply": "2021-01-14T07:11:12.945073Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset X : ndim=2 shape=(1000, 2) Mean = [4.9888 7.0105] Std = [0.9916 1.4667]\n",
+ "Dataset y : ndim=1 shape=(1000,) Mean = 0.636 Std = 0.4811486256864921\n"
+ ]
+ }
+ ],
+ "source": [
+ "plot_data(x_data, y_data)\n",
+ "vector_infos('Dataset X',x_data)\n",
+ "vector_infos('Dataset y',y_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3 - Preparation of data\n",
+ "\n",
+ "We're going to:\n",
+ "- split the data to have : :\n",
+ " - a training set\n",
+ " - a test set\n",
+ "- normalize the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:12.949877Z",
+ "iopub.status.busy": "2021-01-14T07:11:12.949469Z",
+ "iopub.status.idle": "2021-01-14T07:11:12.954849Z",
+ "shell.execute_reply": "2021-01-14T07:11:12.954552Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X_train : ndim=2 shape=(800, 2) Mean = [0. 0.] Std = [1. 1.]\n",
+ "y_train : ndim=1 shape=(800,) Mean = 0.6425 Std = 0.47926375827930073\n",
+ "X_test : ndim=2 shape=(200, 2) Mean = [-0.0122 -0.0446] Std = [1.0495 0.9943]\n",
+ "y_test : ndim=1 shape=(200,) Mean = 0.61 Std = 0.4877499359302879\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Split data\n",
+ "\n",
+ "n = int(data_size * 0.8)\n",
+ "x_train = x_data[:n]\n",
+ "y_train = y_data[:n]\n",
+ "x_test = x_data[n:]\n",
+ "y_test = y_data[n:]\n",
+ "\n",
+ "# ---- Normalization\n",
+ "\n",
+ "mean = np.mean(x_train, axis=0)\n",
+ "std = np.std(x_train, axis=0)\n",
+ "\n",
+ "x_train = (x_train-mean)/std\n",
+ "x_test = (x_test-mean)/std\n",
+ "\n",
+ "# ---- About it\n",
+ "\n",
+ "vector_infos('X_train',x_train)\n",
+ "vector_infos('y_train',y_train)\n",
+ "vector_infos('X_test',x_test)\n",
+ "vector_infos('y_test',y_test)\n",
+ "\n",
+ "y_train_h = y_train.reshape(-1,) # nécessaire pour la visu."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.4 - Have a look"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:12.958155Z",
+ "iopub.status.busy": "2021-01-14T07:11:12.957780Z",
+ "iopub.status.idle": "2021-01-14T07:11:13.138600Z",
+ "shell.execute_reply": "2021-01-14T07:11:13.138263Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**This is what we know :**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**This is what we want to classify :**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.display_md('**This is what we know :**')\n",
+ "plot_data(x_train, y_train)\n",
+ "pwk.display_md('**This is what we want to classify :**')\n",
+ "plot_data(x_test, y_test, colors=(\"gray\",\"gray\"), legend=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Logistic model #1\n",
+ "### 3.1 - Here is the classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:13.141960Z",
+ "iopub.status.busy": "2021-01-14T07:11:13.141571Z",
+ "iopub.status.idle": "2021-01-14T07:11:13.146942Z",
+ "shell.execute_reply": "2021-01-14T07:11:13.146568Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convergence after 13 epochs took 0 seconds\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s finished\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Create an instance\n",
+ "# Use SAGA solver (Stochastic Average Gradient descent solver)\n",
+ "#\n",
+ "logreg = LogisticRegression(C=1e5, verbose=1, solver='saga')\n",
+ "\n",
+ "# ---- Fit the data.\n",
+ "#\n",
+ "logreg.fit(x_train, y_train)\n",
+ "\n",
+ "# ---- Do a prediction\n",
+ "#\n",
+ "y_pred = logreg.predict(x_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### 3.3 - Evaluation\n",
+ "\n",
+ "Accuracy = Ability to avoid false positives = $\\frac{Tp}{Tp+Fp}$ \n",
+ "Recall = Ability to find the right positives = $\\frac{Tp}{Tp+Fn}$ \n",
+ "Avec : \n",
+ "$T_p$ (true positive) Correct positive answer \n",
+ "$F_p$ (false positive) False positive answer \n",
+ "$T_n$ (true negative) Correct negative answer \n",
+ "$F_n$ (false negative) Wrong negative answer "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:13.150944Z",
+ "iopub.status.busy": "2021-01-14T07:11:13.150106Z",
+ "iopub.status.idle": "2021-01-14T07:11:13.587738Z",
+ "shell.execute_reply": "2021-01-14T07:11:13.587310Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy = 0.914 Recall = 0.959\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x720 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_results(x_test,y_test, y_pred)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Bending the space to a model #2 ;-)\n",
+ "\n",
+ "We're going to increase the characteristics of our observations, with : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$ \n",
+ "\n",
+ "$\n",
+ "X=\n",
+ "\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
+ "\\vdots & \\dots\\\\\n",
+ "1 & x_{m1} & x_{m2} \\end{bmatrix}\n",
+ "\\text{et }\n",
+ "X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
+ "\\vdots & & & \\dots \\\\\n",
+ "1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
+ "$\n",
+ "\n",
+ "Note : `sklearn.preprocessing.PolynomialFeatures` can do that for us, but we'll do it ourselves:\n",
+ "### 4.1 - Extend data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:13.591354Z",
+ "iopub.status.busy": "2021-01-14T07:11:13.590874Z",
+ "iopub.status.idle": "2021-01-14T07:11:13.594295Z",
+ "shell.execute_reply": "2021-01-14T07:11:13.594652Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "x_train_enhanced = np.c_[x_train,\n",
+ " x_train[:, 0] ** 2,\n",
+ " x_train[:, 1] ** 2,\n",
+ " x_train[:, 0] ** 3,\n",
+ " x_train[:, 1] ** 3]\n",
+ "x_test_enhanced = np.c_[x_test,\n",
+ " x_test[:, 0] ** 2,\n",
+ " x_test[:, 1] ** 2,\n",
+ " x_test[:, 0] ** 3,\n",
+ " x_test[:, 1] ** 3]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.2 - Run the classifier\n",
+ "...and with Tensorboard tracking and checkpoint recording."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:13.598056Z",
+ "iopub.status.busy": "2021-01-14T07:11:13.597585Z",
+ "iopub.status.idle": "2021-01-14T07:11:13.772221Z",
+ "shell.execute_reply": "2021-01-14T07:11:13.771908Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "convergence after 1492 epochs took 0 seconds\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
+ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s finished\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---- Create an instance\n",
+ "# Use SAGA solver (Stochastic Average Gradient descent solver)\n",
+ "#\n",
+ "logreg = LogisticRegression(C=1e5, verbose=1, solver='saga', max_iter=5000)\n",
+ "\n",
+ "# ---- Fit the data.\n",
+ "#\n",
+ "logreg.fit(x_train_enhanced, y_train)\n",
+ "\n",
+ "# ---- Do a prediction\n",
+ "#\n",
+ "y_pred = logreg.predict(x_test_enhanced)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.3 - Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:13.775582Z",
+ "iopub.status.busy": "2021-01-14T07:11:13.774667Z",
+ "iopub.status.idle": "2021-01-14T07:11:14.096530Z",
+ "shell.execute_reply": "2021-01-14T07:11:14.096790Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy = 0.923 Recall = 0.984\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x720 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_results(x_test_enhanced, y_test, y_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:14.099836Z",
+ "iopub.status.busy": "2021-01-14T07:11:14.099399Z",
+ "iopub.status.idle": "2021-01-14T07:11:14.102779Z",
+ "shell.execute_reply": "2021-01-14T07:11:14.102447Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:11:14\n",
+ "Duration is : 00:00:01 301ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/MNIST/01-DNN-MNIST.ipynb b/MNIST/01-DNN-MNIST.ipynb
index 5ba721a..fccd0cb 100644
--- a/MNIST/01-DNN-MNIST.ipynb
+++ b/MNIST/01-DNN-MNIST.ipynb
@@ -38,97 +38,9 @@
},
{
"cell_type": "code",
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+ "execution_count": null,
"metadata": {},
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- " font-size: 1.1em;;\n",
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- "\n",
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- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { 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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : MNIST1\n",
- "Run time : Monday 21 December 2020, 14:47:57\n",
- "TensorFlow version : 2.2.0\n",
- "Keras version : 2.3.0-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Running mode : full\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@@ -155,20 +67,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"\n",
@@ -187,18 +88,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Before normalization : Min=0, max=255\n",
- "After normalization : Min=0.0, max=1.0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print('Before normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))\n",
"\n",
@@ -218,56 +110,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-01-one-digit</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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a2jLEQDUCgHNV4mrMv7mfPMYuiiITl5g8Ncy1HhwcnMiRkOA9kJKGjD2QLscgw6m8pxdh8KQ01Wq1BsKz6/U6isUiZmZmzKtYLKJcLpuMUdsFGkcFstUrwEsOSSBV5GCvgGGuNELaHOidWFlZwa1bt7CysoJ6vW7cdpxcmUxmwODlmmwSrlVbqgEy1Dgu+D17e3uRD7LtUeD32vki/JzHSJuKfW/PEzQ88vqoZkxPT5t8C5JDuVxGtVo1rmV6PliUJs49dV3XZQVJecnhnOF62MPAWINOp2NyGhjf0Gg0Tuzf6/UMQUgRm5PctQLZrr44qkjUNQ4jZdjfmQZ/v2sicFwyl6Xb7RqPEJOzKDXQc8S4CAZMFYvFoSQxl93hMuDJ4RxxmkkmE4UYfchw5qBjZEpznPHY4woaJ3VkGVAFnKzUdJrQ6SQJwTaIys9d3+NyrdqGVJeR1KUWZLNZk+fCyZ/JZEz4dblcRrfbHUhhTyrs+rzh1YqUQa7urkKpF4mpqSljcad0wklDw6UrdfoyYJd1C0KQLQQ4mUEJIJL4KBHYlaoYTcrAMVkha1TIwUsO5wh7FYurj9MvLl1iuVwuMhApSbBWY6lUQrFYRDabRSaTMZIKLfbAsa0kSQQZLcP2ddlTgPBgNPm5HWYel5hpqKQEwYxWZs/S88NCvEwnD7v2y1YpOI5xQerIAYifjs19aNm2C6i2Wi2TTn0RYH4BjWwzMzOGHBiLwYeYFv0kYcdESDE/CHJCBe3nMnbyf1fuSVzIyFZKCpIsZIp9NpsNDLm2ieEyJ6hXK84J9moWV3KgWFoul1Gr1TA/P492u42dnR1orbGxsRGa35AUaEir1WqYnZ1FqVQy5NDr9ZDNZgeiBff39xMdl0tFCJr4UqqgJ8dGUPyFdK3a5xs2+lISJQOoGKwmyWF3d9fYcWy4npPLIggvOZwjKKq6HkAXpM5aLpcxNzdnjFlaaxOizCjJ8wodlhLL7OwsFhYWUCqVzGrX6/WQy+UG0qI54ZIgCJfuH0e0B6JXO9c9c9kcpBQRR7VggBSlB5ICI00Zddrtdk29iWw26xxvWiZlWsaRBFJFDrb+G1etkKXXXcYuNnPJ5/PmYXOJ9DyXLOAKwOQlsOyZfQwlloWFBSwuLmJxcRFzc3OoVqvI5fqFe0gOrNMo7QNMc5ZuO7sylYScdHJChhkCXa5XKTkMCyltBHky7FXeLs3Hz2S1Lru3B38vGnjpCZIEEeVmvkh4teIccRqDJN1g5XLZ1IfkxJPly2ThFdYc4ConC77YbkjGJJAg6JKUto5arYbFxUVcu3YN165dw/z8PCqViskR2NnZMQFAsiIzQ4SbzeZAzofMFOW+0gBIlyjFcUpKce8tr+ssCLNRsHCO7e4EBklCenFIDpIgZGXuvb29gfgUl/p02UjLOJJA6sgBwFDSg22QlOQgOz2Vy2WTlcm+DbIrlGzI4iIHBvRQeuAEZtBOtVrF3Nwc5ufnsbCwgNnZWUMOExMT2NvbQ7lcRrFYNJIMS7fz+zguu7waX5Q66P2gCN5sNmN5P+RKboeJJw3+dnYJfUphsj7lzs4OMpnMADlQpZA2B9pp5Dnt77xspGEMSSFV5HCaG8uJTfVB6tEsX0ZykJmZzLWQkoNdLs4mB1mqnd/BCUybw+zsLGq1GqrVKkqlkrEzHBwcoNfrmdyCfD5vXJ0yTbnT6ZigHwYFybZ1mUzGVHDa3t42mY9R3g9ZxIb3zY4GTRryvvJ/aQ+RdSWmpqZCjZGyarjdni8uLsLV6dWKlEESRDabHZj0VDlKpdKJ5jF80GQtRFclateKBxwbQ2XIb7lcRrlcRqlUwszMjDGkAf2QYvk5ax1wEjF8eG9vz7hF6fmYnZ01jWcODg7QbrexublpqkhTZw+yO8hrk5+dd66F7eXgS46TUg9/F/m33WT4tPkhFxVe7SWHFEIa+ChFAMeBSeVy+UQfSTvQR57D1pUZMyATs2S1o6AO1pQOGJhEySGXyw3oz1Rn8vm80a3ZperatWtYWFhArVYz5NdsNrG6umokCcYJsKq0hFRd7NU7jleB94fHxIXt3pQNgGxIKUISgE0Glx1VGgUvOaQMtogqpQalFKanp088ZEHRgNwmxe+gXAG54suuT0wukpWOCEZuyjLwHO/U1BRKpZIhh0qlgoWFBdxxxx244447UKvVkMvlcHBwgEajYUq9y76WAEyKOu+BrN8o7xfHH+atCNLroyaplBDkdQYV2KUqJ9UsV1Pe067McUnwrPCSwwVArmphN1yK/KzuLPVTafWXFaalTUGex0UUch85Nm6Xqog0agYVKsnn88bHT7FZKYVsNovt7W3T8bpSqWBxcTGQHCgdyWzSyclJNBqNgb4Rsk4jv/O0D7ErStKGdFMSlLpcoHQnaz1IyYvkFtRXZBic9+T15HDOGFY/ZCAN9W6qD8DgiiTLklGkJ4IKr0oSsD8L+z+qOCpzMOg1mZiYQC6XQ6fTweHhIaanp1EulzE/Pz+gVpBsisUipqamBkhtcnISuVwOm5ubxvNB0pCZq3Ef4LDVVhpybUh3a1QMBWs8VKtV1Go186pWq6acHO0zss7naQjiIiauVyvOES5jU5TkQLFaxubv7e0Zb4JSyngNgmL0SSIXCa6W9E7MzMyYkO/p6WkUi0XMzs6agCqJUql0Qi2R8R6NRsO4BGngk12+iKDJG3ciuSaqtM/EuX5GtjKATAaRkSCkZyesCPCw408aXnI4J7hi+aNuNh9EBtLQFcYVkquy7FGRFtBdqbU2BkhZDYkh4cVi0Xl8sVjE4uLiibiOYrGIjY0N1Ot1NJvNgRqatg3FhWEecGnEBeIbDCk5MT5EBpBdu3bNEAQrVdOAa7tk04a0jus0SBU5AIPurjg3muRAFxh95ZQcWGEon8+fu19/WEjJZnp6eiC4SpZ2D0OlUjHSAOM6aBCl6sQiOHZeR1L3wla5onpiZDIZFItFQwy0qdxxxx2GGBgvQnJkEJgddUmk5XdN0+JzVqSOHCTiPsBStaDtgaskDX0ykCZqwl0kuBJyTHHsFTaKxeJADIYsesuoQ9s7Iis2SZx15YvzmzG1nsRw/fp13LhxA9evXx9QKRj4JWNC0j75xklySPedjgnb1WgHLPHvuOXZeLyrjH2SkPENMt9i2Akg7Q3S2k+XqW0gDCoEc1oDn33/w4rCShsD3bQ3btww5HDHHXcYyaFSqRipwTYgpxUyXsb1GuI8E0qp1yqlvqyU6imlvqmUelApFbsTsVJqSin1s0qpzyul2kqpxtHfr4pzfKolh7iQq610JcofRa6YYTUJSQzS5hFmmXfZRs7iJuTklaQmYzfktfJ7qFLJ4C754rkk6QV5ZVxwbbPtDLZE4gJVCaa0X7t27YRKwUjQQqEQmJ4dNMY0qBYJEtjb0e+N+SH0u16xV+YzlFL36oiWeEqpaQD/E8Bz0W+8+x705/uTAXxLnAGkjhzkjxyHbWUgEnXt/f19M/llcpP0v7vIQVrZbXKI00OD5x/WYMbJK9Ui2QdDWv55vdI4p7U2vSG2t7dNF3F29+Z5ZD5JUECYvBfcJonApetHlaSbmpoyxlW6Zm0bw/z8PGZnZ00FrTSpfsMgCbVCKXUXgFcDeFRr/SLx+dcAvBPASzDYTNeFfw/gXgA/qLX+k9OMI3XkAAxfJo7EwAAhZkECx1WNZZRg0EPsCoIi4hhJuV3ma0SBoc9BRU6kRMBrpY2CgUGMPJS9O9bX1423gjklsuOX3Q5QXoMNV2xBUNSoDcYxsGnNtWvXjPogiYHGx7MQQxr0/YTG8FIACsA7rM/fC+BtAF6GEHI4Uj1eA+D3tNZ/ovqDKup+/8zYSBU5uIKJ4kgOtOwz0nB6enogCIq9GGnUAk4GWAWtpMOqC8MGcLF1XKPRQL1eR71eH6g7IRv+yqxRGRREcuh2u6ZnKPt2sAIWC7aG2VGCoh0pOQRJDUFgRqwkBhogr1+/bupeSOPjqEoMREJqxTMBHAL4tPxQa91TSj12tD0MPwCgBOBzSqlfAfCvABSVUuvoE8ybtdaRxrdUkQMxrORA/z6JQcbvc0K5cgxc5+Jks4kiiiTkMXG9LIeHh2a1X1tbw+rqKtbW1kyMgl3fQeYeSLeeDAJrt9toNpum27fsLM4gqGErP8nrlbkYYeQgq3OxxgVViRs3bpioT2ljGIYY4pLvRSOhMd0AsK61dlVGfgLAPUqpaa11UI7+dxy9PwBgF8C/A7AB4CcBvAHAnQBeHjWI1JHDMMTA/Tjhp6amnDYD21gZxO42Mdhjkv/bUkdQNqGdwyHRarVQr9exvr6OW7du4YknnsDt27exsrKCra0tE90IYKCtvfRq0LBoB4HJjt+yB0RUklWYRCGvM8wrMTExYYiBxkeSAiUHWRCHBXDiRqimwfAYhCGe28+Kfx/SWj8k/i8ACCqZ3hP7BJFD6eh9FsBTtdZfPvr/YaXUnwD4KaXUf9JafylsjKkjh9NAhj6fNupvWFKyj5XGzqAiMiQl2ge2trawurqKW7du4fHHH8fjjz9uCGJra8sUpbGDokgUtG8wyEmSgHxRsogzqVwEYUsI8hptTExMmAI1s7OzRpW48847jTohJQYSQyaTiRybLdGlUXqIq1Zore8O2dwBsBiwLSf2CUL36P3PBTEQ/x3AcwA8G8D4kEMcv/xlPSx8WGXFqKCCquxhYXcF/+Y3v4mbN29ibW0N9XrdJGFJcshmswNEQxsCG8BIr8QwLffCVCXbgBkEqhJ0V0o1gkbIhYUFzM/PDyRVMbXeBSm12J+njRiAxJ6/mwCeopTKOlSLO9FXOcJqAj5+9H7bse3W0XstahAjRQ5AOpJrXJBp47JTk0wfl7aBZrOJzc1NIzncvHnTdAZfX19Hs9kcKIpCuwl9/3b/C9a3PK3I7ap8xb/jQGZXSomBqoSdLyE7goURU5C7NY6x+jJwmjL/DnwGwPMAPAvAJ/mhUioH4OkAPhFxPA2Zf8+xjZ+tRg1i5MhBIk2rhxTv7bLqshYi3ZbsBr6+vm4MkWtra9ja2jpBDAAGJBK7YOuw3bqDYKtWYZGOEvRKVKtVzM/PmyQqGh5tG4OUGFyQtg05nlFAQmP9AIA3om9Q/KT4/BXo2xreL77vOoAKgG9orTsAoLX+mlLqT9E3XP5DrfXnj/adPDrHPoA/jBrEyJLDRawcwzycMvGL3oJGozFQ8ZpkQY9Cs9l0ujDDJntSRCDBe2l7ZOJEHTLASfbtoG2BodCsRWGrEkEIkg5HgSSSGKPW+gtKqXcDuF8p9SiAj+A4QvLjGIxxeCv6nofnAviY+PzV6BPLR5VS70TfW/Fi9KWRX9BafyNqHCNFDhf1cNgeC0ZIBoH6PaUGqgzr6+vY3NzE1tbWQOyCfDHgiZ6Fi+rrGYRhDbMMiWY4tCuJahjjo4sYXOSVViQYPv0AgK8DeCWAHwGwDuBd6McoROomWuv/q5S6B8AvHZ0rB+CvAfxLrfX74gxgpMgBuBiCcOm7YSqMVClklOLq6ipWVlaMusDYBTbVoTdCGhAvw01nE6H9WRDY/k+qEXwxwKlWq52ouB03fkX+PQrEACT3fGqtD9DPqXgwYr/7ANwXsO0vAfzoaccwcuRwURhmktIwSHKgJ2Jtbc3ELWxsbBjpgTaIMLcr3aD8n9/jqpUwzAMZFh7OEG2bHG3IKlXMlZDEQK8EiYFJVGFeibBrGRViAEZrrFEYG3KwIxQJWzQd9pzyHEGQNSw7nY5pNrOxsYG1tTVDDtvb25FqgywhLwkCgHEnusrqy3f7PshoUXmsPIdNOPa1M9NVdvgiMdieicXFRdRqNVQqFRP5KCtwRyGtMQxxMApp5XExFuRgRyXa285q1IqSImy1ggZJ5jcw1yEq7kCp4/oOrsY6nNxhhVp4vXKbfGBlFWp5fUHXKCMz8/n8QEj0/Py8cVNK4yNtDKz9GDVhXCQ8isQAjO64XRgLcnDhrCtPmI/dhiQH6Y2gt6LRaMQKSLKbxNqTxZYiOE45Dn4m/+ax8nyMk4hyV8p+oLQxMFeCsQv0UszNzZ3wSoTduzih6qOGUR+/xFiQQ9gPIifaaX84PsRBx8salsxvaDabxoXZ6/Wcx0mQGGQBFVnDQY7DpQLYhOAyMkp1xTa2BoHp4cyulElU9EiwFkOlUhmQGMZposSFVytSiiCx9LS2hriSA1dg2TuDwU7dbjf0WACmezbzQ+xQZa78tmQgSSFMNeAxkuCGSBAybfpkoNPCwoIJhWaRFml8jKNK2Nc4DmQyDtdAjA05nCVx6qyQZd0YxiyToMLA1Gsp5rta+9n2B37vMK5HwmXHCILsGJbP5w1JlEolFItFZ9fwcVo9h4UnB48BSBcjCYLJSmERjTKVHDhuJmtnULpUCQnXqpt0vIT0nrhew6z84yo1AF6t8AiAXMWjYgUkaBR0EUMcSMlCxinY+Qm25MHvDIOUYmRSGSUjvphOHlYzw5XMNS6kQIzT9XhySAC20VN2hg4T26mOsMjMaSMkKX3YxkaXLSLo7yBwXDS0bm9vG08E1QhJCByDbCIcRZbjNKHG6Vo8OSQEGaMge1BMT0+Heitki7qzfDcNh3w4g4yX/Iz7RH2v1nogLFwGacnz22XuuV1W1xpH16UNr1aMOYY1bsr6jiyPT8NdPp+PdGWe1T4QFHLsIh1btI/6bhaSabVaxmAqG/5IYpA9Q6J6WspgrVGNhnRhXK4D8OSQCGQR21wuh5mZGWPJL5fLxq15XmBOBFUU24sRpmYA7vBr7mPbQ2QhG1nJmr002KtT1rmUq6kdYzFOkwnwksOVwDBZgSSHXC6HQqGAUqlkaih2u10cHByg0WiY7t9Jgyu4/WC6DIBBkoJts6CUQJuDjACli5atAGT5f7o7mYHJnApXdyxg/AhinK7Fk0MC4KqZy+VMcdW5uTmTkk0DnV0eXurp9sS2E8Y4aWnE5LvMsxi25Lz8LjuWQsZZSOzv72N7e9tIKcy7oLRULpcHUrTt7mLDujxHbbKN2njD4MkhAMNEWDLEOJ/Po1QqoVarmQ5TSikTftxqtQZWXZllyfPIWALbyKi1HmiZJ1vdJXG9w1xzu9023cTK5TIajYbplSHLzUf1CnHBlnRGacJ5tWLMIbMb+X8YaIwkOcgJS4miVCqZyk/cTnHdFrFp0GNkosyktJO7SBhJXjsQz0gq80j4arVaaDabpjv2sFGTrgCpUcKojTcMnhwCYFv6o2wOUq3ghGfbOmY0sgoUpQpWp5YWe2ntl3EEAEy8AYvTKqWwv7+PTqdz6vBpIm7AlsTBwcFAshmJgQlnzLMYRnoYJhs2jRjFMQfBk0MAhlUraJC0rff5fB7FYtFMGNmBSpaIo3FPxknIiQX0yaHVaqHRaCCbzQKAKWrbbrdjX5freuxJGZco6MWQ0sz29raRHKha8JrC4HKvjtpk82rFmMNWK+Lsz4AnEoMskFIsFk8Qg3QJ0sBIyUG6BHO5nFl19/b20G63sbm5afpKyl4ZcTwh9Eq4MjtdrswwyMjOXq+HVqtlqm4Xi0UUCgVDDiSIKNjfO2o2h1EaaxQ8OcRAnB+cKoRUDejem5mZGchDkPkJtt3BDqZihytJDvl83kgTDEo6ODjAxsZGqP1B6v4yjkH+Le0bUQRBkqFqQXKQmZq5XM6EWlNNGmd4crgCGPZHJilIcpDRkpQS+M4XVQvb5sBjpb7OHpvZbPZEnUl6S+r1uomnoBuR+8qJH1QwJq46QbsIyYlqRb1eN4QgX8OQw6hJCxJerfBwQiZdyTwLqgckAkkKUnLgBKaXguI4J/f+/j7y+fxAshNtE6zUVK/XTYVrGcHIdHLZli/I9mBfk/03r48kw34dHLckBQZFSfsD7SVh93FUMcpjt+HJ4RxgkwQnsWxp52q4S3KQOQpcoYH+ik9Jguek2sIKTfV6Hdvb2wOeEUosUnpxSQ9BkFIKrw84jruwDYiSEAuFgnlRgpDXFOdejhJGbbxh8ORwjrBVDU5oeifsCEepJpAk7LqSNjHIjlNsnEOC4Eu6GJVSRnqI8yC7oieBY9XETsQCjpv+khxmZmYwMzODQqFgJJ98Ph+YMDbKSEqtUEpNAHgNgFcBeBKANQAPo9/xKp5ravB8DwP4cQBf1Fo/Nc4xnhwuADLykXYAV6ozgBMrtO16lMZOqhJzc3PGGFiv100p/M3NTfOi3YKrvav+QxDscZDMpCGVgV3SJctS9qVSybxYfJZqk/09MltzFIkiwTG/Hf3emB9Cv+sVe2U+Qyl1r47REk+M6QUAXgRgqOw/Tw4XBDnB7CQkbue7K6OS26XUABzHGfR6PbTbbTQaDdNQh23uSUbS7UmCiFND0h63lHgkmKmplDLkVSqVjPRCd24+nzdFde1mN6NMDEAykoNS6i70G+E+qrV+kfj8awDeCeAlGGymG3auIoBlAO/GkK3xPDlcMIZJPLJTrO30Z+CYLNiFiquz7e6k67Tb7SKXy6HX6w0UbYkaM787qg7E4eEhms2mCSWXEZOdTsfYQPb39wNTuu3vHiUkNN6XAlAA3mF9/l4AbwPwMsQkBwBvQX+evwmeHC4XSZZBGyZSEeiTR7VaHQi3lrkYrVYLhUIB3W4X2WwWOzs7mJycDI2NkB4KeR1h42I8Br+z0+mYDuIkBmmIPY2kkFbpIqExPRPAIYBPyw+11j2l1GNH2+OM5VkA7gfwUq319rBj8+SQIKJW1dPETsi/4x5fLBaNe7FSqaDZbBqdn5O10+kgk8nEarjjsn1EQQZ72aTAiNCguIooUjxr5azzRFy1Qin1WfHvQ1rrh8T/NwCsa61djVWfAHCPUmpaax3Y90ApNYW+pPGHWuuHYw3KgieHC8BZVrlhcjzkfmxhxzoLVDeYOp7L5dDpdIxoH9fuwPNHTWB6Mez4ChlsleZJflrE/Y201neHbC4ACOq43BP7hDVF+TkATwbwz2MNyIHxCedKAc5LzB121QYwkNshC7HInAcZTHUeY5Zjt6WPNKoEScB1vaeQvDoAgiLFcmKfoDH8AwBvBvAWrfVXYw/egpccEkbSD/1pJxPdiQySku7E7e1tE7EYVWvhtN/PXBPGPMjy9XZQlY24OR1pREJxDjcBPEUplXWoFneir3KESQ0PAtgE8KEjoiCmAEwffdbWWt8KG4Qnh3PAeRFEXHDiyJTxcrlsgqHq9bpxJ8b1WMj4gziQSVeMjGRmpqvPxbggoWv5DIDnAXgWgE+Kc+cAPB3AJyKO/xb07RZfDNj+FQC/D+AFYScZSXJIctVI84MZZKuQpeVchVF4nCSHbreLVqtlpAdZpSnsHthG1ji2Ala+Yj9NRkbK2g5BnbHk94TFPKT1d0toXB8A8EYAD0CQA4BXoG9reL/4vusAKgC+obWmqvE6AFXHeZfRt1n8GwChUgMwAuTgejjl+7DncRn40qgL25OEf8uwZTsQSYZcU6yX9RZk8VcpOUTZHFjZ2q4B4UI2m0W1WkW1WkW5XEalUjG2DqnK0BAaRH72tY8KklArtNZfUEq9G8D9SqlHAXwExxGSH8dgjMNbAbwcwHMBfOzo+I+6zquU+mUALa31B+OMI9XkYK9WtpU77kMUlGkoo/7k5xeFoHG5Cp7wndZ/6Q3gsXYeh/RE9Hq9gVWcIcyuwCrXOPk9dEO6kMvlUKlUUK1WUavVMDs7a0iCNR5s6SHonowiMQCJPkMPAPg6gFcC+BEA6wDehX5uxenKjA+JVJMD4CYGlytsGL+4lBRcEsVFIeg7g3R7ed2yPD1wLDXwb56bodZctSkxSNE+zrXL5jb8m5CGz2q1irm5OczOzmJ2dha1Ws1ILLIylB02zeuT76OIpJ4jrfUB+obFByP2uw/AfTHP+aRhxpB6ckgCcrKF6a9pWqnssYRJPzK8WVarJmTvTqnrh7Wrk99rSy5ScmA9CdnMh1JDrVYzKoZNDsMYQUcJvtjLBUJOgiipIUx/DTrnqFjMOVattUmkAo4nEAvaulZk4DhPg5M7qGmNC9zHlhqkbaNQKJiGPlQtqtUqKpXKgJ2D6kzYd12mNHdWjOKYg5BqcrBX9CBR+yzntv9OMxi7QDJwFYlxXQtrV8pWdrLqddg9ZOFZaYwkSAws7CL7g0rjp6wnOWwNyVGTHkZprFFINTnYuCiXVtI6b9JjHCaqsdPpmIrQ7ErF/ApWhIq6Xpc6IQ2fLKTL4i6MyuSLcQ5BRshxglcrxhhRMQTDIMroGScS8LTfzX4WzWYTW1tbWF1dxdraGtbX1wfqTO7t7YXmVbgkBjk2WSeTgU40epIQZNftcYeXHMYYLrtGmAoSNMGjjKDyu4KODzs2DCwT32w2sbm5ifX1daysrODmzZuGJLa3t9HpdAa6ZQeNxUUedhk7/k8SkO+UMOJKDXIcozbZvORwBeCauGEuRrmP/ExG+bniF6L8+sPq3Ds7OyZEenNzE2tra1hZWcHKygpu3bqFlZUVbGxsoNFooNPpnCgQG3ZtLkQlGo2CsTdJjNO1enKwELRiB30eJ7zX9XeQa/IsODw8NKXiKC3cvn3bvFZXV7G+vo7NzU1ToZp2B3uMUTEHUvWK87oq8OQw5qDLMCo2ImpVDyIY+xxxCCYKWmsjMayvr2N1dRW3bt3CzZs3cfPmTaysrGB9fR1bW1vY3t42lZnosQDcrfLCvs9+yehN213Kv8e1JD3h1YoxxkWJwXGDgCR5BB2zu7truk1tbGxgdXUVt2/fxq1bt/DEE0/g9u3bWFlZMRJDt9s1rkyeWyZB8bvCem/KKE0SgozalK+ofIygezOKBDGKYw5CKsnBtSLJBy8siEf+OEGisTSgyZb3rizBy4RSKvCaeV9YTbrVaqFer2Ntbe2EKkE7Q71eN3YGCSkhSWkmCiQIrfUAQUhSGDbgatThyeEcYRMCy6nL/pIM4pFFSgH3qm+LyFwlGfYr27bRFZcmHBwcnOiryWvn30zHJjnQ1iBViUajgWazGbvLVRxIlYEE4SKEYSWHUUaaFpezIpXkQFGVpNDr9UxRVL663a4pWkqDmswzkOeS/SLtNnKlUgnlcnmgGUtafmDZk0JeNyMeeW9IDo1GAxsbG1hfX8fa2ho2NjawtbVlgp7CiIFp2UD8prpBtgV5v6MMm2EYtehIwEsO5wZbauCq2G63TWs3GeVHo5otFUjxWBY3BfrMLrtF1Wo17O7umpwFZhimASQGxiw0Gg20Wq2Bcu/dbhedTmdgH9nxqtlsGiINg00IcSQMl0ciKm4jLkaRGABPDrFxmh9YSg5cGeWquLGxgc3NTWxtbRlXnJz4MnWZK5rsNs1w30qlgtnZWXS7XSM1yBRmVydouUoCJzMi41wbEXVfdnd30el0Tkz4er1uVAQ2iiGBtttt00SG7zQ+xhmbjMcYdqW3s0SvYowD4NWKoTAMQbgkB7vN2+rqKlZXV43lvdPpGKu6HbUnrens6zg5OYlcLodqtYp2u22kBvkgs9U9w32lBT6IHOxoQfuaXXq33J8P1eHh4UDoM3tfkhipKnDyU92gekFJgqoHvRJRCHKzhkEacqVhVxaR4f0ZlihGlVRGddwunBs5nNb4ZBsjKT1IgqBOTemBUX6sZyD99SQZKTnk83lDDNJdRzWk0+kYclBKOa3wRJjXg5PC9rzYEoQkExogKQUw0pHEwNyIer0+0GJONo9hSDRJkd8T9JvYBCUncpB6QfsMszKDwqXDCDMKozjRRnHMQUiVzYGwjZJs6WaL2RsbG2i1WsbuoJQypdZlzUNOFBJILpczkYHSELe/v49ut2sKk0jJwfbl8/tIDnY+gSQpGVhkqyZSsuJYeb1SciBBSNXCbjPH67Sbx1BlsuFSBeRvQFeqnZHJa7QrTLOQiyQLeQ/GaeIEwasV5whXfIMkCVru2Ydxe3t74MHtdrsDk8HV5p6SBh/YyclJMymbzSaKxSJyudwJtcJu5QYcp08z+1C+bCnGLg5rW/WltEQPBI2wlBZomKU9odfrDbSZs4uxAPGK2gRFaMrmuTyebmCZms0qT0EVpq8CMQBechgapzVMSoKgesCJsLu7a1Z/17Fh0X2UEOTk3dvbQ6fTwdbWFgqFwkBPBzkG6csHYKQGO2WZf0vXqLR/kPDkealS2O5LeihoY2Er+52dnYF74roPJElJDPK3cK10MtdC2g2otklXMN3BrqIuJIizkMOoTbZRG28YUic5AO7qy9LFlkRQDVdnVlRilGGhUDhRmIQrvCQHfrc9YfL5/ECFZ9lRyo7fkO/8nMQnYxhoXJQveUyQXUDGdtgEHUQKtj2EtgW+WLhFttmjW1jWiZT3cBhRe9Qnl1crLhlJPEBaa+P7ZzxFo9EYeKjl97jUAeDYIMloSzavzefzA70heA5GNUoDIiUAaUzk/yQLKXFI6SWKIElqkiD4mQ1JutKeQlWC0gKvkRIDy8PJepGyynRcV6+NUSSKURxzEEaCHOwVz7bwh4EeB+BkzwW5Une73cA2bVLF4erPz5VSxsZAQuDkyWazhmy4P4mBpEBPA/+X9gMaUfndPAcw6EUI80TYAUryvkn7jquWJCc1bQiyiCx7UZTLZfM5u3mzP4ZNtFG/F69jlCeYlxzOGUHGMfs9LP1Xei6kesDJLScbV/PTotfrd0VnFWZJEpxYtF1I46okB2lYjIKcaJOTk8ZGEKZqyfBmPsBxistSwpiamkI+n0e5XEatVkOtVhuQFGTtSNnARna3igNJdKNIEkmNWSk1AeA1AF4F4EkA1gA8jH5Tm3bEsTUAP4V+M5zvAjAP4Bvod8v6Ra31N+OMIZXkYCPI5Rb2Q9DYSILgqrm3t2cmVNKwbQWdTsd4PaQ4Lw2qJIdh7SdSAqDKYEsXNmwpJO41aa1PNK6Zn583DWtIBPIle2MOa5SU+40aQSQ43rej3/7uQ+g3tmE7vGcope7V4V2v/tHRMX8M4FfR75b1VPSJ5ieUUvdorb8UNYCRIAf5sMedRDJSz7a8nzd6vZ4hgV6vd8IoSS8FyWRYYrDF9LMYZuNARpZScpibm8Pc3BwqlYpRIWRBWem9CeuLaSNOZGaakYRaoZS6C8CrATyqtX6R+PxrAN4J4CUY7Jdp48sAvkNr/bfWeX8fwB8B+AUAPxY1jpEgB+B0ST72w2gff54gAWQyGezs7JyI2iRJnFZiiBOenRTYMIcdu5mXMj8/P0AOMkKU3g2XcTcKoyYtSCQ09pcCUADeYX3+XgBvA/AyhJCD1vrrAZ9/VCm1ib4UEYmRIQfCTg12QYrPcnWVwUYXNdadnR1MTU0NfOdpxHsAA94D+1znSXpsWuNqeVetVo1XRhKXXZX6qgRCJXSNzwRwCODT8kOtdU8p9djR9qGhlKoAKAH4qzj7jwQ5BD34UcY0O6/AjlG4KOzv7zvDk4eBS4WQhBf3fLb9I+o42g9k7Qu73R1DzaVKII2Kw0oOo4yEvBU3AKxrrXcc254AcI9SalprHZ6HfxJvApAB8Btxdh4JcpATS65OYQ/c3t4elFJmYl6kSuGC63vj6teSGJheHnbeINBzIL0HtI24jKKZTAbVanWgIS57YcqWd7lcbmBS2DEYVyWvAogvOSilPiv+fUhr/ZD4vwDARQwA0BP7xCYHpdSPAfi3AP43gF+Pc8zIkIMr+zGTyaDb7QYet7e3d8KN5soXsA18p32Q7VXTFaMQRBJxzmd/DhyvVDLQyvU9MueDhkIeQ6mKcR+0lRQKBczOzuL69etYXFzE3Nyc8VAwlqFQKJwYt0x8u2oEEfcatdZ3h2zuAFgM2JYT+8Qd0/MBvB/A5wD8hI65oqSSHOQDZWc9ygec1vGgGAU+9FLndaUm2zrxWR5i27YhCYJ2h7DJPiyCgrbs/A8Gacl4BLuxrV33gnENCwsLuH79utN9GQaOaZS9D8MiIbXiJoCnKKWyDtXiTvRVjlhSg1LqhwE8CuCLAJ6ntd6OO4gLIYch2HQgQk6Sg50JWCwWTfmz7e3tUN8+A39kXIAkCmlEc4132PFzYjKegqoNgFhBTnHBe0MpwJULAsCEdxcKBadKkM1mzSTm2JVSppwe4xoWFxdRrVZRLBYHIj+jcBUkBiKha/0MgOcBeBaAT4pz5wA8HcAnYo7lh9CPk/gygHu11lvDDCKVkgMwuKq7iKFYLJpwY631QEUoG3aKtV2lKMpgNkzwDsmNlaj4nUmSAsF8BwYbyeIyMtqSAUzFYtHEKMzOzqJWq5loRjnRKXFMTEyYe874hmq1aiI/r9Kkj4uE7skHALwRwAMQ5ADgFejbGt4vvu86gAqAb2itO+Lz5wH4XQB/A+CfaK03hx1EqslBpkNLVxqjCu2iK2HRhgcHByYD0xb9pG7sKsIyrKgo9f4kVQgJruyMPZCRmHaBG4Y+Mz5hYWEBi4uLWFhYMATByldSLeJvwKQymZYdV2q4akhCrdBaf0Ep9W4A9yulHgXwERxHSH4cgzEObwXwcgDPBfAxAFBK3Q3g99CPlfh1AP/UEfPzW1HjODdyOK2uafvJaWcoFAool8smD4KTXNokZJ0Du9qy1PkZQk2yIOwAq7PYIKSKERWXcRqQLGWqdDabNddkF9Vl3UyqB9evX8e1a9cwPz9vpAGZAyJtFlK1o80nblu7q4YEpakHAHwdwCvRz5FYB/Au9HMrogJknopjw+XbA/a5PHIgTnuzlFIDdRIKhcJA7QIZgUcDJUu3t9ttTExMYHd3d2DyU9SWBGS7Be205bNehx2X4LpOqdbEiT/I5XIm1oCiPqUH5lnI2hOSHOh9uHbtGm7cuIGFhQXMzs5iZmbGOXZpVLWJ2+MkkrovWusD9PMjHozY7z4A91mfvQ/A+846hnMlh2EnlBTjaWCjSmFXXqI7jiKvrGdIf36n0zF2CYm4EYpRE/u0oCuWkpE92bhqy7qQVA9s28Hc3Byq1SrK5fKAuC+rS8m8iNnZWSwuLhqpYWFhIdDrQIKOA/v+XFV7xDhdd+psDnJ1ymQyZiVnxSVpf7BrGMpMQDvQJy3uNEo7JDWOlSI9MFjQltKPVA8Yvlyr1YzdgJGKbOcni9OwJgPzIuiSrFQqZ27g47KtJOESHlWM0zWnlhwoHgM4QQyyGpFNDrI0mwwTZs2Fy8bU1NSJMXPFlw+WXTNTa21UrGKxiNnZWczOzhrPQ6VSwczMjPEi8LopHfFY5kbQCHkWSGKQsSly+zhNljgYJ3UrdeQAwETXcfWX/S05aYrFIjqdjplkNjnYpdlOUzMhaUxNTQ0kL3GScsV31X2QhkW6FmVmJF2SJIdsNjtgd5CSFwOh6HGIqzLExVUjAhfG6R6kjhzsqEWumNKLwAlPHz3JQYrp0urOSdZsNi/12uhxob1ATmraSvhwyZL80muQzWZNOTYaJGXZNqZO8xwyqImqGu01Sa1ythrh1YrxQKrIQdob7Jh8O9mKBCHtDLatwY5Y1Fqj1WpdyrUVi0UzmRcWFrCwsGCMiaVS6cSElbEK9urPmo2MdGS0I0lGltTneexcEldF6mER5M0ZpwkyLLxacY6Qq44MabbBVZQ1GvmyC6jyXNJuwQa8rnO66h0GJRBRBdBaB5aIz2Qyhhjm5+dNnMHCwoIxDFL/DyqHLxsFS9cuo0VlHceoiWkbD4MIwmVDcOEqE4EL43Q/UkcOgLs9W9i+MzMzgROaE555ApVKBe122/R+AI67VsnSchJ21KRru6wYLYuy0oBIqYEEQclBGgdlOTkp8chcDWmYZSJVUHZk2L3luO1oTtvrYMeExP1drirG6d6kihzOIpoyFkK+lFIDFv5arWa6RrFXBHBMDiQSOQFc1nj54iR2lZOni9K2EdDDQJWChkSbHFxBWTJSkZmpp3VH2gRkk4PMP+G1ByWnefTh1YpzxmkePOYazMzMnEhVZmZhs9k0UoNspScTsFxJWEFqhdwuS95LA6J0v5ZKJeOpoJ2AXhZZpdk+r60e2SnsZ30gg1yS0gh8Fd2Sp8E43aPUkcNZbq4kA0b35fN5lEolzM7OmtZy7CBlk0NQ8VYAJ8Rte5u9CgMn4zMY00BXIvMh7Ka79nn5txTxpSp0FthG4ChDsEc4xuk+pY4czgKqEcDxqk2pgWXgZTu5IP3aNfnt77G3B62+sgs3X6429UFdoVzfHWWsHeZ+2WqMfY2eIIaDVytSDJICjYG2HUKSQpA0EDVB7X3CcjBsg56symwb+YaZfElMVNnsJyxAzLsp42Oc7s/IkUPQQxzXBTrOiBMBaj+8Vzlg6TwwTvdxJMjBNozJzwBPCHx3uSMBryJcJMbpGUw9OYQ9/LYF/apa1MNiFYCTHpareI8uCuN0b1NPDoBbXHbp+VeNHFxEELavjN24SvfpIuElhwuG9Lm7HuqrLibHCXF2GVmv8j07L4zTPU09OZAYZESiJAvuI9+vClzGxTheh6tOpueJcbqvqScHwLvSwnBViTGt8GrFiMA2XobFIxBnmWRR8RCuldtP6vHCOP2eY0kOtuU+yprvQlCQU9zt9r5xXh6jj3H6HceSHAB33wiXKxRI/ge1zxsW8hxlJ3BhnB7AcYNXK1KOqJXehaQmnO0FiJIOTuM18J6G9GKcfpfxobkjSM+GK5fBzmi8iFdS+RT2dXqkD0mpjkqpCaXUa5VSX1ZK9ZRS31RKPaiUOtl9KPgcz1dKfUop1VZKbSqlHlFKfWvc48dOcpDuTvk/cVmTKsgwedZzeaQLCaoVb0e/N+aH0O96xV6Zz1BK3asjWuIppV4I4IMA/gLAz6HfbPcBAH+qlLpba30zagBjRw6Ad+95XB6SeOaUUncBeDWAR7XWLxKffw3AOwG8BIPNdO3jM+j31fwmgB/QWreOPv9fAD4H4D+i34MzFGOnVnh4XCYSUiteCkABeIf1+XsBdAC8LOL4ZwO4AeDXSAwAoLV+DP1O3C8+IpBQeHLw8EgQLhuXfMXEMwEcAvi0/FBr3QPw2NH2qOMB4M8c2/4cQBnAt0cNYmi1YmlpadhDPDyuDO6///4kTnMDwLrW+mT/BOAJAPcopaa11rshx3Nf1/EAcCeAL4YNYixtDh4eaYdS6rPi34e01g+J/wsAXMQAAD2xTxA5sE+B6xw9a5/gMXqXmIdHuqCU+gKARa31Nce2hwH8OIBskOSglHoXgPsBPEVr/dfWtiUA7wbwQ1rrPwwbh7c5eHikDzcBzCulso5td6KvcgRJDTye+7qOB9wqxwA8OXh4pA+fQX9uPkt+qJTKAXg6gM86jrGPB4DvdWz7HgDbAP4mahCeHDw80ocPANDoBy1JvAJ9W8H7+YFS6rpS6juVUtKG8HEAtwD8tFKqKPZ9GoDnAHhEa70XNQhvc0ghlpaW/hOAu9F3N80D6AL4OwC/C+BXl5eXNy5vdB4XAWE3+BCAj+A4QvJPAfxjRkgqpd4H4OUAnqu1/pg4/sfRJ5m/QD8+ogzgteiTzndrrb1aMaJ4LYAZAH8E4FfQXyn20Y9s+8ulpaW/f3lD87ggPADgdQDuQt+A+BL0ox5fEBU6DQBa60cA/Cj6HotfBvB6AJ8E8H1xiAHwrsy0ory8vNyzP1xaWnoLgDcCeAMAH3AyxtBaH6CfU/FgxH73AbgvYNuHAXz4tGPwkkMK4SKGIzx89P7kixqLx9WFJ4fRwj87ev/LSx2Fx5WAVytSjKWlpdcBKKKfbns3gO9Hnxjedpnj8rga8OSQbrwOgIyS+wMA9y0vL69d0ng8rhC8K3MEsLS0dA3APehLDCUAL1heXv785Y7KY9zhyWGEsLS09C3oR7Z9ZXl5+amXPR6P8YY3SI4QlpeX/w7AlwDctbS0NH/Z4/EYb3hyGD0wV//gUkfhMfbwBsmUYWlp6TsB1JeXl29bn08A+EUAiwA+tby8vHUZ4/O4OvDkkD78MID/srS09AkAfwtgA32PxbMBfBuA2+gn4Hh4nCs8OaQPHwXwEIDvA/A0AFUAbfQNkb8J4J3Ly8ublzY6jysD763w8PBwwhskPTw8nPDk4OHh4YQnBw8PDyc8OXh4eDjhycHDw8MJTw4eHh5OeHLw8PBwwpODh4eHE54cPDw8nPDk4OHh4cT/B+rV9IkXtfPOAAAAAElFTkSuQmCC\n",
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\n",
- "text/plain": [
- "<Figure size 864x291.6 with 36 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_images(x_train, y_train, [27], x_size=5,y_size=5, colorbar=True, save_as='01-one-digit')\n",
"pwk.plot_images(x_train, y_train, range(5,41), columns=12, save_as='02-many-digits')"
@@ -287,7 +132,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -316,48 +161,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/16\n",
- "118/118 [==============================] - 1s 6ms/step - loss: 0.5797 - accuracy: 0.8426 - val_loss: 0.2592 - val_accuracy: 0.9272\n",
- "Epoch 2/16\n",
- "118/118 [==============================] - 1s 4ms/step - loss: 0.2261 - accuracy: 0.9352 - val_loss: 0.1886 - val_accuracy: 0.9436\n",
- "Epoch 3/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.1696 - accuracy: 0.9516 - val_loss: 0.1523 - val_accuracy: 0.9547\n",
- "Epoch 4/16\n",
- "118/118 [==============================] - 1s 4ms/step - loss: 0.1357 - accuracy: 0.9610 - val_loss: 0.1255 - val_accuracy: 0.9634\n",
- "Epoch 5/16\n",
- "118/118 [==============================] - 1s 4ms/step - loss: 0.1122 - accuracy: 0.9675 - val_loss: 0.1231 - val_accuracy: 0.9613\n",
- "Epoch 6/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0956 - accuracy: 0.9720 - val_loss: 0.1074 - val_accuracy: 0.9679\n",
- "Epoch 7/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0798 - accuracy: 0.9766 - val_loss: 0.0936 - val_accuracy: 0.9724\n",
- "Epoch 8/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0701 - accuracy: 0.9794 - val_loss: 0.0957 - val_accuracy: 0.9717\n",
- "Epoch 9/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0609 - accuracy: 0.9821 - val_loss: 0.0879 - val_accuracy: 0.9725\n",
- "Epoch 10/16\n",
- "118/118 [==============================] - 1s 4ms/step - loss: 0.0534 - accuracy: 0.9844 - val_loss: 0.0957 - val_accuracy: 0.9700\n",
- "Epoch 11/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0484 - accuracy: 0.9857 - val_loss: 0.0817 - val_accuracy: 0.9742\n",
- "Epoch 12/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0423 - accuracy: 0.9876 - val_loss: 0.0927 - val_accuracy: 0.9708\n",
- "Epoch 13/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0374 - accuracy: 0.9891 - val_loss: 0.0796 - val_accuracy: 0.9763\n",
- "Epoch 14/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0323 - accuracy: 0.9909 - val_loss: 0.0796 - val_accuracy: 0.9757\n",
- "Epoch 15/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0290 - accuracy: 0.9923 - val_loss: 0.0799 - val_accuracy: 0.9775\n",
- "Epoch 16/16\n",
- "118/118 [==============================] - 0s 4ms/step - loss: 0.0254 - accuracy: 0.9933 - val_loss: 0.0827 - val_accuracy: 0.9747\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"batch_size = 512\n",
"epochs = 16\n",
@@ -379,18 +185,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Test loss : 0.08268913626670837\n",
- "Test accuracy : 0.9746999740600586\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
@@ -407,58 +204,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-03-history_0</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_history(history, figsize=(6,4), save_as='03-history')"
]
@@ -472,32 +220,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-04-predictions</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x1652.4 with 200 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"#y_pred = model.predict_classes(x_test) Deprecated after 01/01/2021 !!\n",
"\n",
@@ -516,32 +241,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-05-some-errors</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x507.6 with 15 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"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",
@@ -550,53 +252,18 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/MNIST1-06-confusion-matrix</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_confusion_matrix(y_test,y_pred,range(10),normalize=True, save_as='06-confusion-matrix')"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Monday 21 December 2020, 14:48:29\n",
- "Duration is : 00:00:32 930ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/MNIST/01-DNN-MNIST==done==.ipynb b/MNIST/01-DNN-MNIST==done==.ipynb
new file mode 100644
index 0000000..effdf04
--- /dev/null
+++ b/MNIST/01-DNN-MNIST==done==.ipynb
@@ -0,0 +1,1957 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [MNIST1] - Simple classification with DNN\n",
+ "<!-- DESC --> An example of classification using a dense neural network for the famous MNIST dataset\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - Recognizing handwritten numbers\n",
+ " - Understanding the principle of a classifier DNN network \n",
+ " - Implementation with Keras \n",
+ "\n",
+ "\n",
+ "The [MNIST dataset](http://yann.lecun.com/exdb/mnist/) (Modified National Institute of Standards and Technology) is a must for Deep Learning. \n",
+ "It consists of 60,000 small images of handwritten numbers for learning and 10,000 for testing.\n",
+ "\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ " - Retrieve data\n",
+ " - Preparing the data\n",
+ " - Create 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": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:41.080151Z",
+ "iopub.status.busy": "2021-01-14T07:11:41.079109Z",
+ "iopub.status.idle": "2021-01-14T07:11:42.401579Z",
+ "shell.execute_reply": "2021-01-14T07:11:42.401907Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
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+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 2.0\n",
+ "Notebook id : MNIST1\n",
+ "Run time : Thursday 14 January 2021, 08:11:42\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sys,os\n",
+ "from importlib import reload\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "datasets_dir = pwk.init('MNIST1')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve data\n",
+ "MNIST is one of the most famous historic dataset. \n",
+ "Include in [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:42.405201Z",
+ "iopub.status.busy": "2021-01-14T07:11:42.404766Z",
+ "iopub.status.idle": "2021-01-14T07:11:42.616713Z",
+ "shell.execute_reply": "2021-01-14T07:11:42.616979Z"
+ }
+ },
+ "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",
+ "print(\"x_train : \",x_train.shape)\n",
+ "print(\"y_train : \",y_train.shape)\n",
+ "print(\"x_test : \",x_test.shape)\n",
+ "print(\"y_test : \",y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preparing the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:42.620358Z",
+ "iopub.status.busy": "2021-01-14T07:11:42.620040Z",
+ "iopub.status.idle": "2021-01-14T07:11:42.829269Z",
+ "shell.execute_reply": "2021-01-14T07:11:42.828948Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Before normalization : Min=0, max=255\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "After normalization : Min=0.0, max=1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Before normalization : Min={}, max={}'.format(x_train.min(),x_train.max()))\n",
+ "\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()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Have a look"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:42.832752Z",
+ "iopub.status.busy": "2021-01-14T07:11:42.832434Z",
+ "iopub.status.idle": "2021-01-14T07:11:45.845534Z",
+ "shell.execute_reply": "2021-01-14T07:11:45.845240Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/MNIST1-01-one-digit</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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\n",
+ "text/plain": [
+ "<Figure size 864x291.6 with 36 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_images(x_train, y_train, [27], x_size=5,y_size=5, colorbar=True, save_as='01-one-digit')\n",
+ "pwk.plot_images(x_train, y_train, range(5,41), columns=12, save_as='02-many-digits')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Create model\n",
+ "About informations about : \n",
+ " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+ " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+ " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:45.850610Z",
+ "iopub.status.busy": "2021-01-14T07:11:45.850274Z",
+ "iopub.status.idle": "2021-01-14T07:11:45.889160Z",
+ "shell.execute_reply": "2021-01-14T07:11:45.889428Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "hidden1 = 100\n",
+ "hidden2 = 100\n",
+ "\n",
+ "model = keras.Sequential([\n",
+ " keras.layers.Input((28,28)),\n",
+ " keras.layers.Flatten(),\n",
+ " keras.layers.Dense( hidden1, activation='relu'),\n",
+ " keras.layers.Dense( hidden2, activation='relu'),\n",
+ " keras.layers.Dense( 10, activation='softmax')\n",
+ "])\n",
+ "\n",
+ "model.compile(optimizer='adam',\n",
+ " loss='sparse_categorical_crossentropy',\n",
+ " metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:45.892862Z",
+ "iopub.status.busy": "2021-01-14T07:11:45.892530Z",
+ "iopub.status.idle": "2021-01-14T07:11:54.138929Z",
+ "shell.execute_reply": "2021-01-14T07:11:54.139306Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/16\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 2.3057 - accuracy: 0.1074"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 14/118 [==>...........................] - ETA: 0s - loss: 1.8231 - accuracy: 0.5212"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 28/118 [======>.......................] - ETA: 0s - loss: 1.3413 - accuracy: 0.6590"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 42/118 [=========>....................] - ETA: 0s - loss: 1.0627 - accuracy: 0.7250"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 56/118 [=============>................] - ETA: 0s - loss: 0.8963 - accuracy: 0.7657"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 70/118 [================>.............] - ETA: 0s - loss: 0.7850 - accuracy: 0.7932"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 84/118 [====================>.........] - ETA: 0s - loss: 0.7059 - accuracy: 0.8128"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 98/118 [=======================>......] - ETA: 0s - loss: 0.6462 - accuracy: 0.8277"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "112/118 [===========================>..] - ETA: 0s - loss: 0.5996 - accuracy: 0.8391"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 1s 5ms/step - loss: 0.5845 - accuracy: 0.8427 - val_loss: 0.2623 - val_accuracy: 0.9226\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 2/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.2776 - accuracy: 0.9062"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.2605 - accuracy: 0.9264"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.2558 - accuracy: 0.9271"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.2440 - accuracy: 0.9294"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 57/118 [=============>................] - ETA: 0s - loss: 0.2356 - accuracy: 0.9324"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 71/118 [=================>............] - ETA: 0s - loss: 0.2319 - accuracy: 0.9335"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 85/118 [====================>.........] - ETA: 0s - loss: 0.2262 - accuracy: 0.9351"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 99/118 [========================>.....] - ETA: 0s - loss: 0.2220 - accuracy: 0.9366"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "113/118 [===========================>..] - ETA: 0s - loss: 0.2181 - accuracy: 0.9378"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 1s 4ms/step - loss: 0.2163 - accuracy: 0.9382 - val_loss: 0.1891 - val_accuracy: 0.9446\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 3/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.2315 - accuracy: 0.9434"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.1841 - accuracy: 0.9471"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.1805 - accuracy: 0.9489"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.1746 - accuracy: 0.9504"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 56/118 [=============>................] - ETA: 0s - loss: 0.1704 - accuracy: 0.9510"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 70/118 [================>.............] - ETA: 0s - loss: 0.1677 - accuracy: 0.9518"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 84/118 [====================>.........] - ETA: 0s - loss: 0.1661 - accuracy: 0.9521"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 98/118 [=======================>......] - ETA: 0s - loss: 0.1634 - accuracy: 0.9528"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "113/118 [===========================>..] - ETA: 0s - loss: 0.1612 - accuracy: 0.9533"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.1603 - accuracy: 0.9535 - val_loss: 0.1489 - val_accuracy: 0.9560\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 4/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.1128 - accuracy: 0.9746"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.1480 - accuracy: 0.9590"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.1364 - accuracy: 0.9630"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.1375 - accuracy: 0.9623"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 57/118 [=============>................] - ETA: 0s - loss: 0.1350 - accuracy: 0.9623"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 71/118 [=================>............] - ETA: 0s - loss: 0.1353 - accuracy: 0.9618"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 85/118 [====================>.........] - ETA: 0s - loss: 0.1339 - accuracy: 0.9621"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 99/118 [========================>.....] - ETA: 0s - loss: 0.1313 - accuracy: 0.9627"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "114/118 [===========================>..] - ETA: 0s - loss: 0.1291 - accuracy: 0.9632"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.1289 - accuracy: 0.9632 - val_loss: 0.1295 - val_accuracy: 0.9602\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 5/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0794 - accuracy: 0.9805"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 17/118 [===>..........................] - ETA: 0s - loss: 0.1100 - accuracy: 0.9663"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 33/118 [=======>......................] - ETA: 0s - loss: 0.1090 - accuracy: 0.9691"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 49/118 [===========>..................] - ETA: 0s - loss: 0.1091 - accuracy: 0.9689"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 64/118 [===============>..............] - ETA: 0s - loss: 0.1087 - accuracy: 0.9688"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 79/118 [===================>..........] - ETA: 0s - loss: 0.1088 - accuracy: 0.9688"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 95/118 [=======================>......] - ETA: 0s - loss: 0.1076 - accuracy: 0.9688"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "110/118 [==========================>...] - ETA: 0s - loss: 0.1084 - accuracy: 0.9687"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.1088 - accuracy: 0.9685 - val_loss: 0.1109 - val_accuracy: 0.9661\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 6/16\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0888 - accuracy: 0.9785"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 14/118 [==>...........................] - ETA: 0s - loss: 0.1022 - accuracy: 0.9694"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 28/118 [======>.......................] - ETA: 0s - loss: 0.0975 - accuracy: 0.9711"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 42/118 [=========>....................] - ETA: 0s - loss: 0.0946 - accuracy: 0.9720"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 57/118 [=============>................] - ETA: 0s - loss: 0.0947 - accuracy: 0.9725"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 72/118 [=================>............] - ETA: 0s - loss: 0.0961 - accuracy: 0.9721"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 87/118 [=====================>........] - ETA: 0s - loss: 0.0952 - accuracy: 0.9723"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "102/118 [========================>.....] - ETA: 0s - loss: 0.0945 - accuracy: 0.9724"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "117/118 [============================>.] - ETA: 0s - loss: 0.0937 - accuracy: 0.9726"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0936 - accuracy: 0.9727 - val_loss: 0.1040 - val_accuracy: 0.9688\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 7/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.1124 - accuracy: 0.9629"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0834 - accuracy: 0.9742"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.0852 - accuracy: 0.9749"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.0834 - accuracy: 0.9755"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 57/118 [=============>................] - ETA: 0s - loss: 0.0848 - accuracy: 0.9753"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 71/118 [=================>............] - ETA: 0s - loss: 0.0844 - accuracy: 0.9756"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 85/118 [====================>.........] - ETA: 0s - loss: 0.0832 - accuracy: 0.9760"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 99/118 [========================>.....] - ETA: 0s - loss: 0.0829 - accuracy: 0.9760"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "113/118 [===========================>..] - ETA: 0s - loss: 0.0822 - accuracy: 0.9760"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 1s 4ms/step - loss: 0.0816 - accuracy: 0.9762 - val_loss: 0.0959 - val_accuracy: 0.9707\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 8/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0853 - accuracy: 0.9766"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0697 - accuracy: 0.9799"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 28/118 [======>.......................] - ETA: 0s - loss: 0.0666 - accuracy: 0.9801"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.0683 - accuracy: 0.9800"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 58/118 [=============>................] - ETA: 0s - loss: 0.0692 - accuracy: 0.9795"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 74/118 [=================>............] - ETA: 0s - loss: 0.0703 - accuracy: 0.9791"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 89/118 [=====================>........] - ETA: 0s - loss: 0.0706 - accuracy: 0.9792"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "105/118 [=========================>....] - ETA: 0s - loss: 0.0703 - accuracy: 0.9794"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0708 - accuracy: 0.9793 - val_loss: 0.0917 - val_accuracy: 0.9711\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 9/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0478 - accuracy: 0.9863"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0567 - accuracy: 0.9835"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.0584 - accuracy: 0.9832"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 44/118 [==========>...................] - ETA: 0s - loss: 0.0571 - accuracy: 0.9843"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 59/118 [==============>...............] - ETA: 0s - loss: 0.0595 - accuracy: 0.9834"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 75/118 [==================>...........] - ETA: 0s - loss: 0.0599 - accuracy: 0.9828"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 91/118 [======================>.......] - ETA: 0s - loss: 0.0615 - accuracy: 0.9826"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "106/118 [=========================>....] - ETA: 0s - loss: 0.0614 - accuracy: 0.9823"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0618 - accuracy: 0.9821 - val_loss: 0.0875 - val_accuracy: 0.9740\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 10/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0317 - accuracy: 0.9922"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0553 - accuracy: 0.9857"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 30/118 [======>.......................] - ETA: 0s - loss: 0.0565 - accuracy: 0.9852"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 45/118 [==========>...................] - ETA: 0s - loss: 0.0551 - accuracy: 0.9848"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 60/118 [==============>...............] - ETA: 0s - loss: 0.0544 - accuracy: 0.9846"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 75/118 [==================>...........] - ETA: 0s - loss: 0.0554 - accuracy: 0.9843"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 90/118 [=====================>........] - ETA: 0s - loss: 0.0557 - accuracy: 0.9843"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "105/118 [=========================>....] - ETA: 0s - loss: 0.0556 - accuracy: 0.9843"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0559 - accuracy: 0.9843 - val_loss: 0.0869 - val_accuracy: 0.9733\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 11/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0294 - accuracy: 0.9980"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0444 - accuracy: 0.9891"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.0462 - accuracy: 0.9877"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.0461 - accuracy: 0.9875"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 57/118 [=============>................] - ETA: 0s - loss: 0.0460 - accuracy: 0.9873"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 71/118 [=================>............] - ETA: 0s - loss: 0.0479 - accuracy: 0.9865"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 85/118 [====================>.........] - ETA: 0s - loss: 0.0474 - accuracy: 0.9865"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 99/118 [========================>.....] - ETA: 0s - loss: 0.0482 - accuracy: 0.9861"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "114/118 [===========================>..] - ETA: 0s - loss: 0.0487 - accuracy: 0.9859"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0490 - accuracy: 0.9859 - val_loss: 0.0848 - val_accuracy: 0.9725\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 12/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0283 - accuracy: 0.9883"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0411 - accuracy: 0.9882"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 30/118 [======>.......................] - ETA: 0s - loss: 0.0439 - accuracy: 0.9876"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 46/118 [==========>...................] - ETA: 0s - loss: 0.0434 - accuracy: 0.9879"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 62/118 [==============>...............] - ETA: 0s - loss: 0.0430 - accuracy: 0.9882"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 77/118 [==================>...........] - ETA: 0s - loss: 0.0437 - accuracy: 0.9879"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 93/118 [======================>.......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9877"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "109/118 [==========================>...] - ETA: 0s - loss: 0.0440 - accuracy: 0.9877"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0447 - accuracy: 0.9876 - val_loss: 0.0823 - val_accuracy: 0.9732\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 13/16\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0367 - accuracy: 0.9863"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
+ " 16/118 [===>..........................] - ETA: 0s - loss: 0.0412 - accuracy: 0.9868"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 31/118 [======>.......................] - ETA: 0s - loss: 0.0397 - accuracy: 0.9879"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 46/118 [==========>...................] - ETA: 0s - loss: 0.0379 - accuracy: 0.9886"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 61/118 [==============>...............] - ETA: 0s - loss: 0.0390 - accuracy: 0.9882"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 76/118 [==================>...........] - ETA: 0s - loss: 0.0398 - accuracy: 0.9881"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 91/118 [======================>.......] - ETA: 0s - loss: 0.0405 - accuracy: 0.9879"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "106/118 [=========================>....] - ETA: 0s - loss: 0.0400 - accuracy: 0.9879"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0404 - accuracy: 0.9880 - val_loss: 0.0821 - val_accuracy: 0.9745\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 14/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0312 - accuracy: 0.9922"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0346 - accuracy: 0.9914"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.0347 - accuracy: 0.9908"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 44/118 [==========>...................] - ETA: 0s - loss: 0.0348 - accuracy: 0.9906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 59/118 [==============>...............] - ETA: 0s - loss: 0.0338 - accuracy: 0.9909"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 74/118 [=================>............] - ETA: 0s - loss: 0.0347 - accuracy: 0.9906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 89/118 [=====================>........] - ETA: 0s - loss: 0.0355 - accuracy: 0.9903"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "104/118 [=========================>....] - ETA: 0s - loss: 0.0357 - accuracy: 0.9902"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0367 - accuracy: 0.9898 - val_loss: 0.0826 - val_accuracy: 0.9745\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 15/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0440 - accuracy: 0.9941"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 15/118 [==>...........................] - ETA: 0s - loss: 0.0309 - accuracy: 0.9917"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.0335 - accuracy: 0.9906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 43/118 [=========>....................] - ETA: 0s - loss: 0.0332 - accuracy: 0.9901"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 57/118 [=============>................] - ETA: 0s - loss: 0.0327 - accuracy: 0.9904"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 71/118 [=================>............] - ETA: 0s - loss: 0.0330 - accuracy: 0.9905"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 85/118 [====================>.........] - ETA: 0s - loss: 0.0330 - accuracy: 0.9906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 99/118 [========================>.....] - ETA: 0s - loss: 0.0329 - accuracy: 0.9906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "113/118 [===========================>..] - ETA: 0s - loss: 0.0327 - accuracy: 0.9906"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0326 - accuracy: 0.9907 - val_loss: 0.0758 - val_accuracy: 0.9769\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 16/16\n",
+ "\r",
+ " 1/118 [..............................] - ETA: 0s - loss: 0.0309 - accuracy: 0.9941"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 14/118 [==>...........................] - ETA: 0s - loss: 0.0271 - accuracy: 0.9936"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 29/118 [======>.......................] - ETA: 0s - loss: 0.0246 - accuracy: 0.9936"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 44/118 [==========>...................] - ETA: 0s - loss: 0.0265 - accuracy: 0.9929"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 59/118 [==============>...............] - ETA: 0s - loss: 0.0262 - accuracy: 0.9932"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 75/118 [==================>...........] - ETA: 0s - loss: 0.0270 - accuracy: 0.9930"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ " 91/118 [======================>.......] - ETA: 0s - loss: 0.0268 - accuracy: 0.9930"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "107/118 [==========================>...] - ETA: 0s - loss: 0.0274 - accuracy: 0.9928"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
+ "118/118 [==============================] - 0s 4ms/step - loss: 0.0278 - accuracy: 0.9926 - val_loss: 0.0800 - val_accuracy: 0.9756\n"
+ ]
+ }
+ ],
+ "source": [
+ "batch_size = 512\n",
+ "epochs = 16\n",
+ "\n",
+ "history = model.fit( x_train, y_train,\n",
+ " batch_size = batch_size,\n",
+ " epochs = epochs,\n",
+ " verbose = 1,\n",
+ " validation_data = (x_test, y_test))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Final loss and accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:54.142442Z",
+ "iopub.status.busy": "2021-01-14T07:11:54.142096Z",
+ "iopub.status.idle": "2021-01-14T07:11:54.411581Z",
+ "shell.execute_reply": "2021-01-14T07:11:54.411880Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test loss : 0.0799570381641388\n",
+ "Test accuracy : 0.975600004196167\n"
+ ]
+ }
+ ],
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('Test loss :', score[0])\n",
+ "print('Test accuracy :', score[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Plot history"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:54.415628Z",
+ "iopub.status.busy": "2021-01-14T07:11:54.415112Z",
+ "iopub.status.idle": "2021-01-14T07:11:54.897515Z",
+ "shell.execute_reply": "2021-01-14T07:11:54.897170Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/MNIST1-03-history_0</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_history(history, figsize=(6,4), save_as='03-history')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.3 - Plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:11:54.900826Z",
+ "iopub.status.busy": "2021-01-14T07:11:54.900459Z",
+ "iopub.status.idle": "2021-01-14T07:12:08.974659Z",
+ "shell.execute_reply": "2021-01-14T07:12:08.975002Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/MNIST1-04-predictions</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "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) Deprecated after 01/01/2021 !!\n",
+ "\n",
+ "y_sigmoid = model.predict(x_test)\n",
+ "y_pred = np.argmax(y_sigmoid, axis=-1)\n",
+ "\n",
+ "pwk.plot_images(x_test, y_test, range(0,200), columns=12, x_size=1, y_size=1, y_pred=y_pred, save_as='04-predictions')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.4 - Plot some errors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:12:08.981892Z",
+ "iopub.status.busy": "2021-01-14T07:12:08.981496Z",
+ "iopub.status.idle": "2021-01-14T07:12:11.308076Z",
+ "shell.execute_reply": "2021-01-14T07:12:11.307732Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/MNIST1-05-some-errors</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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qOp0O86wPh0PmvePTRXQ6HVwuF2KxGNbW1rC6ujoRaaKUKr7w+TFzUzUO3kurll/Of6l1PbtL1NKHHgIP2nhVpgnwN77X6yGbzWJ/fx8//PADfvzxRxwfH6Pf77PXBAIBbG5u4tmzZ9jZ2cHa2hoikchMkfmHNLCPBb4ikwxKHgpZlstllEol5PN5JJNJnJ6eskrZeYYrcKUf6nK5mGfVYrHAaDQyIXyj0Qi73Q6v1wuHwwGDwcBaC2u1WgQCASaRFY1GJ65NC5Qo/JvdPnQ0GqFeryObzeLs7AwnJyfMCBoOh6wZhNfrnUgX4BUmnsom+akYDodotVpM77hSqaBer6PT6TBFjWQyiVQqhUKhgF6vx0KgjUYDqVRqbkX6h6BUnna7zeYLj9KLJbgZfCiZGI/HKJVKuLy8xNnZGcv/r1Qq6Pf77OBOkSbaa8mINRgMcDgc8Pl8UylSw+FwovBV8J5Op4NKpYJqtYpWqzXRml6j0cBiscBut8PlcjFnyceizJvl0+/43PPb5OR+KR608ap0ofOUSiXWhOBXv/oVXr9+jUKhwH7u8XiwubmJ7e1tbG5uYnl5GeFwWHRHuoeQ8aq2oVWrVVZ8dXZ2houLC6RSKWSzWRQKBVSrVbTb7bnXp0r2WCyGhYUF+Hw+OBwOVnhlMBhgtVphtVpht9tZKHQwGECj0cBqtcLr9cLn801dW+Rhfpher4darcbyXdPp9MTPDQYD65uuRMjYXY95OaODwQD1ep0d/GgOkd415UFWKhVUKhWmdUzXIgm5j2U8Hk/kkqvp+Ip5dH3UtEOJwWDAtFyPj4+xv7+Pw8NDJBKJicOJVqtlUY3hcMiMUvII6nS6qXF6KnPyNnnYsiyjUCjg9PQUl5eXKBQKTOUGuKqpcDgcCIVCiMfjiMViTMWB8sTH47Hqff/Q71UWk6m1Tycewjx70MarWjh2PB6jWCzi5OQEr169wq9//Wu8fPkSmUyGvcbv92Nrawtff/01nj17htXVVZbjqoTCVQ9hMB8r/GLJU6lUcHl5yaTP9vf3cXFxwTZdauMKTLcZpev6fD5EIhGsrKxgY2MDS0tLWFhYgNvtht1uZ6FRo9EIk8kEk8kEo9HIKuX566uFdh57vtdtUM6ldrvNQpdU9ENQIwir1arqgRBz83rM2mjH4zFqtRrLWT04OMDR0RFOT09ZxKLT6TDDhd/w6HlXE6a32+1wOp2wWq0s5ExpCL1eD81mcyIKRh4f0uOuVqvw+/3s4CdyXW/OvHAwFTFTLcj+/j6TDaT3UetXMlzJ80qeQZvNNndOPvZ5edO/bzgcIpVKsTTGw8NDNsd6vR4AsChTLBZDPp9HtVpFKBSC2WxmeeLk9aY5ZrPZPrjPqP2cvqcm0/UQeHDGq9IA4Re1TqeDYrGI4+Nj/Pjjj/j1r3+NN2/eTBiugUAA29vb+Prrr/HVV19ha2sL0WgULpdrYoD5pHXK8Xkog/rQ4UOE5HFV86yfnZ1hb28PL1++xOvXr3FycoJcLscWAiWkFGAwGFhRSDgcRiQSwfLyMlZXVxGLxRAKheB2u2G1WlnqwHXh88LEhjuJ2oY2Go3QbDZRqVSYGgRPMBhEIBBQDZ89lU3yU0IKD4eHh3jz5g3bVBOJxMw0AJ1ONyEHRx44i8UCh8MBp9PJQp40h6iLU6lUQjabRSqVQi6XY2usxWLBeDxGuVxGKpWCx+NhShNqc1CoD3wYfv3kGQ6HEweWk5MTnJ+fI5vNTnlNe73ehFfdbDYjEAiwKJXD4VAdA/qdj2181KK8o9GIdWRsNptotVrMyCTbQZZlll5zdHSEt2/f4vj4GNlsFo1Ggzla9Ho9HA4HLi8vkclkcHl5iUAgAKPRCFmW2UHRZDKx1AK73c7k5ShSaDAYJqTn+DoB4kPGqpoH/T6N54MzXpV5bsRgMEA+n8fBwQG+//57/OpXv8KbN29wcXHBXuP3+7G5uYmvvvoKX3/9NXZ2dhCPx+Hz+WA0Gtn1B4MBO2UqK2UFnx5eA1It5F4qlXB6eoo3b94wzd6TkxNks9mZ7QlJq9DhcCAYDCIajSIWi2FxcRELCwsIhUIIBAKsKMtms4nxvmPUUig6nQ5qtRpKpRJKpRKazSb7mcPhwPLyMmKxGDwej+oCTBuDGKv5qN2fdruNXC6H4+Nj7O7u4uXLlyx6MUuFQ6fTwefzwel0wmw2s4iEw+GA1+uFx+OB2+2Gw+GA3W6Hw+GA0Whkhunp6Sl2d3dRrVYnoiGSJDFlmPPzc5aeQ7nnPJRGJKIa10M5P7rdLiqVCmtpfXFxgWKxOOEJ59/L4/F4sLKygmfPnmFjY2OqiyBBkanHzng8Zl0bKSc8l8uh0WhgOBxOyMWRcy2ZTOLy8pJ5XXmHXLfbRaPRYPUbZ2dnrM6C9JS1Wi1MJhNsNhvsdjusViubhxaLhTlm6OBPh3+9Xs/sJzKq56GmU3ufeJDGKzDtxanX60gkEvjxxx/xO7/zO/jxxx8nKl+DwSA2Njbw/PlzfPXVV9jZ2cHy8jL8fv/EIPKSLPxGe98G7jHDi2krN6hqtYrz83O8efMGP/zwA16/fo2joyMUCgXWBUYNnU4Hu93Oqpm3trawubmJpaUl+P3+qdPrbcdbTXj/KaOcr7wXiNcHzWQyrMsZcOVZWF1dxdbWFlZWVuDz+VQ3yXmSaU8d/jlUplY1m03kcjkcHh7i9evX+OGHH7C3t4dkMqlqxABXXrdoNIqFhQV4PB44HA4WQvZ4POzwR3PJarXC6XSydKxcLgej0YhsNstSCQhSCimXy0in0/D5fAgEAggGg6p/l0jnuh5qc6PdbrP7zHcXJMjTzaddAWDKKpRyt7W1hXA4PLNhyGNcB/m/p9/vo9FoIJ1OM2/q0dERLi8vUalUmJONT71oNBpoNptMshF4nzLD67l2u11cXFwgn8+z5hD8oY28q+Rp1Wg0rLDY4/EgHA5jeXkZy8vLaDQaTDeZ8mZ1Oh2sVitrxKSGcm29b2P54IxXtRyoZrOJZDKJg4MDvHr1Cru7uxOGq9vtxtraGp4/f846aJHYuTKkIsK9XxbecFWOQ71eZ54b3nDlw48Ev2kbjUZ4PB5EIhFsbGzg2bNnePbsGdbX1xGNRlWL9GjS8p4h5c/m6eoJrpgVuhyNRixkfXFxwSSYut0ua7G7tbXFDpkej0eEjm8AGXhqkQuSJTs4OMDLly/x/fffY3d3F8lkkhmUNpuN5RnrdDrYbDZ4vV6Ew2GEw2H4fD7m9XE4HHC73cwjSznhFNoknE4nCoWC6nzr9/tsYx0MBhgMBiwFR3B71HKcW60WyuUycrkccrkcyuUyM6T0ej0zZpTpVz6fD/F4HBsbG+zgT4Yrf6B47B21gKvUC6q52N/fZyk31Jms0Wh8sD04pa9RlEGWr9qNN5tNdDodlltOh0k+AtztdlmUmJ8jer0eXq8X0WiUKfCk02m4XC7odDp2TZIgpEOny+VizhuS5pq3l92HtffeG69KnUylJ67VaiGVSuHw8BBv377FycnJRLWyRqNBLBbDxsYG87iurKxMGa73YTCeMsouIkpDpVarTRmuJycnKJVKqouELMsstOLz+bC4uIiVlZUJdYmFhQXVCnbgvcSVMmWE/l/NoyhkYabh75ey2rlcLuPi4gJHR0c4OztDLpdDv9+H0+lENBpl+pELCwvMCCIjh09BEPd8Gj71Ru0QSK2yf/jhB3bYJ1ky6lUfCoXgcrlgsVhgsVjgcrlYaoDT6YTFYoHJZGIGrMPhgMlkgiRdNSFQa9LBF3EpIc+R2+2Gy+WCzWabmasnxvzDqHnde70eWq0Wa8OsTNWhiKNyTbXZbIjH41haWmJ1AbzHlT8sPUaUfxulKZIU58uXL3F6eopcLnct5Q3KHQ4EAvD5fCxNjdIGOp0O83yTZ5Yvluz3++h2u6jVaqhWqxNFdvl8HsDVnlqr1XBycjKRFglcGbl0IA0Gg4hEIggGg/D7/XC5XCwVQc2Rx68tX9Jhc++NV+C990uZozEej5HP53F4eIjd3V28e/cOl5eXE3mP8XichR83NjawsrKCUCg08SCS4SQ8rl8O3sBRUxU4Pz9n4c1Xr17h8PAQxWJxZqqAJEmw2WyIRqNYX1/H9vY2tre3mZYvnTTnQZNSabzy//KvE0wzS9u12+2iWCwikUgw45Wk7EgqhlqF+nw+VtBDmoi8iLdgGl7HkZ9PlDJAlebn5+colUoYjUZwOBxMdH5lZYVFp8gLS0YsGa0k12MwGGA0GpmxOuvAQpD8Eg9JzlG4kzqpmUymqfcL+bnbQy1gG40GarUay83kkWV54nt0oIlEIkxKcFa06rF6ypV/13A4RLlcxtnZGd69e4fDw0Pkcjl232g+0NzjQ/4WiwU+nw8LCwus3sLpdEKn07Guj71eb6LFMqWjDYdDdLtdVitQKBSYLGS9XmdOG4PBgE6ng1QqxbpJUhE65btSZCQYDCIej7N5H4vFWGSF8tqVMlr3Ye49CON11oQgjU+SSjo6OmItJU0mE6LRKDY3N/H8+XMmgxQIBCYGQunZVfud92GgHjPz5KaoyIOKs8jjOs9wpSplCj1T97SNjQ1EIhFVj4HSqy8OMneHmiHTaDSQz+dZygAfLbFYLPB6vfD7/fD5fMw7rtFooNfrmdGq5pUTvEfNeKQNkDYynU7HQv6hUAhra2vY2NjA+vo6lpaWEAwGmUeVmhLwEQa130GyWiTpw39fKWFHUDFlOBzG0tLSlPE6T7NUoI7S80pdCElBgL54KE+T7jevIkGV7SaTaWYr1KcCheypiUer1WL7EclY8Yc+KjwkzWqXy4VgMMgKhV0u10RRFo0DyTNSYSr/OyuVCrLZLNLpNLLZLKrVKtMep8YSjUaDedrb7bZqmoHT6UQqlUKxWGQNSsLhMLxeLyu+tFqtMJvNbNzvg9PgQRivBL9QttttJJNJHB0dsZNPOp1Gt9uF2WzG4uIitra2mLLA2toagsHghPdWTYqp2+2iXq8z4WCtVsuKD0hy4j4M3EOHn0CyLKtWPlYqFZycnGB3d5epCpydnaFUKqkariQP4nK5sLCwwPJbv/rqK2xubiIajU4ZPLSJS5IkxvYTwo83Febk83nmNeDbSdJGSaoPPDcV536qzPNOmkwmeDweLC0tAbhKvbJarQiFQlhcXGShYdrAPvR7eJTpNrRmD4dDJJNJZLNZ1Gq1qaIwWmc9Hg/b0OnQQga3UmFG8GGUDhlqS8qnafGMRiPV3HLKR6YvUuN5Kiifc61Wy5rThEIhFItFjMdjtNttNu+MRiPLEw8Gg0x+keSraJ2j4kc66PFqSmS80piQ57XVaqFer09oY9O8onGmTl65XA75fJ4ViZHWMnl4y+Uyaw7SbDZRKBTYZw6FQgiFQggGg8wbe18Ojvd+JeAXLMqlarfbSKfTOD4+xsHBActzpVOk1+vF8vIyvv76a3z77bfY3t5GNBqdyG+kE4gyL6tWqzFPULPZnHgA/X4/k35RInJmbwbv8VTzclYqlYkc11evXuHk5ASVSkVVx5VavNIGvLa2hq2tLayvr2N5eRmhUEhVcof35AhJtLtH2Y6y3++jWq0in88jl8uhWCyi0Wiw19hstgmN0FmVsLMQ43fFLONVkiQ4nU6srKzAZDJhbW0No9EIJpOJ5bTyHpebQGsghTqpWrnb7aJQKLAOeIVCYUqKy2g0MieBzWabaAMMvM85FOP7cfBRC51ON7UmKhUqhsMhOp0OayJSKpVY22B+P33sHnHl36XX6xEMBrGzs4PBYMAMzVQqxTrQjcdjWCwWRCIRbG9vY3FxEW63m3VoJIUA6h5ItgjtS2rjA7xvAOLxeFj6QavVQqfTYXbNaDRiObHlchnlcpmlibTbbdRqNdYxr9lssgK0breLTCYDj8fD0glWV1fR6XRY2sO8LqSfc37eS+OVrzgHJh8c0gI8OTnB27dvcXh4iGQyyXrX2+12LCwsYG1tjeU5rqysTDwEdPJQO8VTYdDe3h6KxSKMRiNisRharRYAsAdqVtjksU7eu2be/arVajg7O8ObN2/w8uVL7O7u4vj4GPl8fqKQgPTzdDodnE4nFhYWWMvf7e1trK6uIhKJwOPxqLYw5A9GaqkjgtvBL2B8XupwOESr1UKxWEQ2m0U2m0WpVJoIXVIRg9frVS3YIS/cLFUQMX5XzDoUUkoNpVVReJ/yUPmvm0Qh+Jw+pfQgpXcdHR0xYXZaTwkKSVsslolcQf7v4f8VfBhl6huNMemBkheQhwpPAbCOanT4cLlcTNKuVqvB7XZPNCN4zOunmvEaDoeh1+tZHjjlhqbTaYzHY9bqleyRra0t+P1+aLVaDIdDltdKeePXTVUjry79bo/Hg9FoNOVNpxQRaqBAMl3U6rlQKLCUA1qLq9UqCoUCkskkXC4XcrkcWq3W1OecVy/yuQzYe2m88qEn5YCSzMvR0REODg5YwQFwVcFHrT7X1tawvLyMSCSiGiqe1Tu70WggmUxib28P5+fnMBqNKBQKkGWZeQaUrSp5L6LgZijvmVIO682bN0gkEqhUKlMVsFRk4vF4WHU6X5ilJoNFYTNlCFKM3d0wK4cYeN9NK5/PM0HvcrnMPOl+vx+RSATRaBSBQAAOh4Ndg4wgyt3SaDQwm83MS8eH1vjPAoixBSbvBRmnau2wle9RU9xQKr9QGBqYzoXrdDpMbJ1akGaz2Ym0AYqYzDqw0O8U3By+YE+SJJjNZua54xUilJqjypSCVquFQqGATCaDdDqNTCbDJNLo+o+9iI6eczo4m81mxGIxWCyWCX1vh8OBbrfLVG6o8DQUCrFrUfrTLDuHmKVuQ/Pvprn/pKdMjWEymQzOz89xeHiIw8NDHB0doV6vM49tp9NhurK0zkqSxA7ASoWdzzn+99J4BdQrF6mbRTqdZm3tMpkMut0urFYrIpEINjc3sbOzg7W1NYTD4SnjRfkwKOl0OhMSPsBVfq3ZbGZFJCT4q3ZdUegzG/6eKxc6kvU4Pz+fkMM6Pz9ngs9KJEmCx+PB1tYW+1pZWUE8HkcoFJoaez7XS4zTp0MZOSGogCCfz+Py8pIZr1QhGwqFsLy8jLW1NcTjcbhcLgDvDSDqSkNtSym3mTaGWYV4j3lDvQk39YjQfKENlt84eUOWl87hjaXRaIRSqYSLiwscHBywSFmxWES322WducLhMOt053a7Vb06Ygxvjprqg9FoZCFninAUCgWW80iGK38gIaiT1OXlJUKhEMt/pHzNxz5Gs2wHr9eLlZUVDAYDmEwmxGIx9Pt9OBwOJi/m8/lUrzmvyYry8AioN3u5CXq9Hm63G263G0tLSygWiwiFQnA4HEztoN1uI5vNMjUnm83GDjoGgwHD4RDtdps1KSF1g8/NvTZelZBhSW3taCOTZRkejwfLy8vY2dnB9vY2lpaW4Ha7Va89q6Vkp9NhlXzVapWFtk5PT+Hz+bC8vIzV1VXVDjQiJ2s+8zxyw+GQbXJ7e3vMcD0+Pka5XJ6pH2i1WrG4uIgXL17gZz/7GVZXVxEIBJjEhxq80SzG6tMw6772ej1Uq1XkcjmkUimk02nU63UAV7mu4XAYa2trWF9fRzweZ4cPKtzb29vD4eEhstksACAUCmFnZwfAlbyWcsxnPW9PlZs+62p5s7PC92qqBvV6HYVCAZeXl8zZkEgk2NputVrh9/sRj8exvLyMaDQKr9c7NY4iZeB2zDK2HA4H/H4/wuEwFhYWUC6XMRgM0Gq12OFe7X39fh+VSgUXFxfwer3MeKFUD7Xf/5jGbF7zBZfLhdXVVTidTmxubmI0GsFoNMLlck0opqgx7x6pGbd3eU99Ph8b+0qlwvKam80mK1onm8tms7F5TUowvO6zmqzdp+TeGq/KRVOWZZRKJaRSKdaPOZfLYTAYsEVweXl5okCHr1YlT5AkSaydmvIhIKkIEgkmqCNJrVZDu92eOpF+yJsruGLW/Wk0Gri8vJxq+cp3zlLbLOPxOJPC+uabbxCNRiekdZSeN6EL+umZNQ9IF5FSBpLJJNN1BcByw5aWlrC0tMS8ruPxmOkpvnz5Eq9fv8bl5SUAYHFxEZIkwev1IhKJTFXGi/n4HrUN70Ob4nXnCwnb89eg1pnUoz2bzSKXy03J9LjdbkQiEcTjcdZ2Vm0TFG2Ab4fauFssFnZoyOfzqFar6HQ66Pf7Ew0L+EM+8D51J51Ow+PxwO/3s4p0Jbx6yGMxYOd5PA0GAwKBANxuNzPaKf+b5sdNjfmPPbTxv49qBfiUTMpnrtfr6Pf7LBWLZL56vR7Lmy0Wi9Dr9Sxtk5obxGIxLC4uQpZlVozG//6P+fwf4l4ar8rOOaPRCOVyGclkEmdnZzg7O5tQF3C5XIhEIlhcXGQhYz5fjqQ9KM+Ecjhoo6XTxfn5OZLJ5EQeHkEivyRjoYZYXOejFsLt9/vI5XI4OjrC69evmeFaKBQmclz5e+twOLC8vIwXL17g22+/xdbWFmKx2FRlOp+H99jzse4L/DiRx7zf76NYLOLy8hLn5+dIJBLI5/MsgmEymRAIBBCJRFinF6LX66FUKuHy8hInJyd49+4di4gMBgNEIhEUi0U0m030+33mAXrsBSQfA4WElSkBlMt3m/mifC3l1lWrVdRqNaaFqXyPzWaD3+9HKBSC3++HzWZTrUUQ43hz+EO78vskldZoNFCv15maAI9Op2NrKrXsrVaryGQyrAtevV6fki2k15Kx91jHjuYOcHWPSdZKDbIzaE/j59rHGKdq0nTKn5NiBOWyNptNdLtdJntGzrlUKoVqtYrhcMhkJ2n8ms0mczgYjUbYbDZWiDYYDCaKPCkHl1KKPhX3xnjlB5XfdMbjMWq1GlKpFE5OTnB0dIREIoFyuQzgqkiLJB2i0SiCwSDrVkHwOTzKIh3aWM/Pz7G/v8+q2vnUAL66j05SgpuhdmodDocoFApIJBIsJ+7k5ASFQmFmAwKXy4Xt7W38/Oc/x3fffYfnz5+rGq6CL4MyLaPdbrNQI1WbJ5NJVmQJXIWuwuEwIpEIAoHARAiSql/Jc8cbQLQYt1ottNvtCeMVEI0m5jErf/UmzPMkUQegarXKGhMoCy7J00M5mFQ8NOvzCm7GvDA35WNSKgA5hpSQs4cOOt1uF6VSCblcDrlcDuVyGa1WayJF7ylKDl7nb1VLrfmQcTcvYnKddAKSqiuXy8x5QOtvt9tlslpk3JKsVrfbZcWdknQlmUY6sv1+H1qtFplMBr1ej7WZdbvdsFgsE972T/kM3DvjVektIf2xRCLBmhFkMhn0+31IkoRgMMg8ruFwGE6nc8qQ0Wq1qlWzwJXnr1Ao4Pj4GHt7ezg4OJiqhqWBsVqtrNuFGk9psl6HWYU7ANih4ezsDAcHBzg4OMDZ2RkymczUJscTjUbx4sUL/L7f9/vw9ddfIxKJqFZN88aySBX4tPDzip+/ZLxms1nWTOTo6AiZTIal5ZhMJtbdiRQGiG63y1IMSJ6Hh7wOvV6Pfc3TIBRcQQcMqpDmue6mo/TY8pAcWiaTQSaTYdrM1J+dsFqtsNvtEw0plC1mP8a4fuoo0+4oakgeP/K+5nI5HB4ewm63s3xy4L0WuvIZoSYj2WyWzU9SMADepwo89nzzm/xtkiRNOL4+5plWFksqc9L5iPVoNEK/30e5XEYikcCrV6+wv7+PZDLJ9LWpBogaIFAjA1mWmQcWAFtrKSUkk8kgEAiwtBOS3ZvFXR9o7oXxylfVKb0lg8EAxWIRp6en2N/fx+npKfO6BoNBrKyssFaG1IdbeQ0yXikZnb+BpBt7dHSEt2/f4vj4GIVCAf1+H3q9nuXT+nw+5hlQ0yAUC+s0atWuwHuPK7V93d3dZYU48wzXWCyG9fV17OzsYGtrC0tLS0zMnFJDKM/osS+c9wWqUgYwFZWgcFMqlcLR0RGTSuLDk7zYNn/w7PV6OD4+xrt373B8fIxMJoN2uz3xu6nCmRZe8gqQBA09D4A4wCj5WINwVkiw1WohnU7j5OQEBwcHbOyazSarZgbeK0WEQiH4fD64XC5YLJaJgw+lnYi19eOh8DEwGZEIBAJMsSMYDOLs7Gwi6tXpdFiKHU+9Xsfl5SXevn3L1uDl5WX4fL6JHGj63bMOOo8J3o4h+KLHT2EnzLsm2T1U6J5MJpk+fi6Xu9b1KRfWYrHAZrPBbDazSAr9P6VLGI3GCa+rMuf1rv/2e2G8ArMNHV4n8PT0FJlMBsPhEE6nE4uLi9je3sbW1haWl5dZS0G1CaI88RDtdpvl452dnbFiEOAqJYH3CpGMyyzxb7XrC9S1XFOpFN69e4dXr17hzZs3rDOJ8n3UeScYDGJ9fR3ffPMN1tbW4Pf72YmQvApPrWXhl4RPxVHbFIErw7ZeryOTyeDk5ITNX8pVt1qtTLKHDodarRatVgupVAp7e3t4+fIli4Yo89BJooVCm/Q76Xmgw/Bj3jC/FPymTPe71Wohl8uxKNbu7i729/eRyWTY3LZarXA4HOwgurS0xGoUbqJXKbg5s7xibrcb4XAYsVgMFxcXuLi4YD+jA6HSA05REUq9o0MJtT3loVxPfp4+NtTyT9VScz43SiWBQqEwkbJ1XUiLWa/Xo91uo9vtwm63M2UYipop9e+BJ1CwNeskX61WWXiCciwAMJ2yzc1NbG5uYmlpCX6/n71PWWmu9hDJssySlalHMI9Wq4XD4WBdfzwez4R3QPn5BZOo3RPKv7m4uMD+/j729vZweno6VSwAXI0P6eZtbm7i66+/xvPnzxGPxyd6LPNKEsJT83ng5xN/73kqlQry+TyTxeINVwCs0nxxcRHRaBQ+nw86nQ6NRgOJRAJ7e3vY29vD8fExSqXSVB40nfipWxBpDtIGIg4zd8s8T0qn02Fyd4eHh3j79i3evXuHk5MTZrhqNBp4vV62bj9//hxra2uqmsyCu2Xe2mi1WhEKhbC6uopcLod+vz+RPgC8X2PJOKHXNBoNNr5OpxPhcHjKeFXzSD5G+DVQLY3qU8HXCym/PxwOMRgMWGQSAOvwxaPT6WCxWCDL8pQTyeVysSZA1M2r1+vBYrGwvVmZvvc58p7vhfGqdK0TVNmYTqcnct50Oh1cLhfC4TDi8TgWFxcnDFfgfWs7qoDj5SFokKlVGlVckuYk/7kcDgeCwSAWFhZYNaxa9eZjPVHeFP6BVR4UKPcmnU7j7OwMJycnODs7mzo08JhMJgSDQWxsbOD58+fY2trCwsLCxALJd4V57AvkfUKtw4ssy+j1eqhUKjg/P2cFWvl8fmJ+kcLA+vo6nj9/jp2dHUSjUWg0GmQyGVxeXuLo6Ii1f1Yr4DMYDDCZTLDb7UxrEHjf4nKWR1hwO2h+Ke/neDxGu91GPp/HxcUFzs7OWGEIvxGOx2M4nU4sLS3h2bNnzHj1+/1TOqHiIHq3UK6r2v3k19hWqwWtVguTyYRsNjtx2NRqtazegzytrVYL+/v7E90tY7HYxH5Ia/Nj3iPva+qgTqeDyWSCzWZj8mYejweZTIa9RpIkFl22WCzo9/uoVqtoNBowGo3ssLm9vY1QKAStVovRaASDwQC/349YLDZlfwGfXuf3ixmvylM8P7FkWWbSDIlEAtlsFrVajSUKG41GWK1W2Gw2uFwueDyeqevTiYMMWLounVKoyrJSqaDRaEyFJOn1lPMajUZZVxG1SSh0CN+jZsD2+33UajXkcjlcXFzg8vISmUxmKoSh9MAbDAY28cgDrjzZK99LhQn3dUF5iPBzZ9ZhTZbliSYiR0dHTB1EeZo3mUwIh8PY2dnBz372M2xtbbGfNZtN5HI5ZDIZFIvFCcOVxpiUP2hhVmtIMquwUnA7ZqV2ybLM8uqoT3qlUpnQDAWuxs5utyMSiWB9fR3r6+uIRCKqAvdi7t498zRKfT4fVldXMR6PYTKZ4HK5cHZ2hmw2i2q1yqrP6YvyKQlqM3pycoJwOIzl5WW2RlDqjjhEfhpmzRVJumoH7PF4EIlEUK1WUS6XWQMmciYYjUa43W6srq4iGo3CYrGg0+mg1WpBlmV4vV4sLy9jZWUFgUCA1SXo9fqZay/9/k/JF13deQOW/0N7vR5bCGkDa7fb7PV8+8FZBT6Uf0LyWPQe8gyRIZXL5VCtVlW7Zmm1WlitVgSDQZbQTo0P6Pq8kLdYbN+HMNRksarVKlKpFPPKVCqVKY8ab7gqcxn50McseO+rGI/PC99sgrphpdNpVUNGo9HA4XAwbw1BzQwKhQLbNNXgPXPkaRV8WuZFNrrdLtMKpepjJUajEXa7nWn6hsPhCcOVz1MWc/dumXc/dTodm4v0336/H4FAAMfHxzg7O0Mul2ONDNSaV3S7XWQyGRwcHMDhcKDT6cDv97PuW4+pWcF9Q3mQ5FMWqG6HlAIajQZbX8l4HY1G0Gq18Pl82NraQjQahcFgYFqwRqOReW2dTieTz9LpdMyJoPaZPrWn/Yt6XmcZGdQ7l0L5rVZrQoZhNBqh3W6zgSgWi4jFYhPXIPkHvV4/dbJvtVpMX/T8/By5XE51k6SThdfrhd/vn2jxxhvOjzkcchN475xah7RarcZaRV5eXqJWq81VF6AFj/LpUqkUrFYrxuMxPB7PVIEHX9EqFsq7Zd59HQwGqNVqSCaTePv2LX744Qe8fPkSZ2dnbIyV84siI6PRCIPBgJ3mySufz+dZu0oeXuNwOByyg2ixWGRpA/TzwWDA1CcEH88szyulDdCmWC6X0el0pp4VipJ5vV54vd4pTVcSthfz9/Oi1WpZITJ56oLBINvzSPKO9mHg/fpO83E8HqNYLGJ/fx/AVcof6a57vV7Y7XamASr4dPCRR7rXJpMJCwsLrKiSbKZqtYpKpQLg/cGR0rmo/oAOk2azGRaL5UZ66o/W80oP/ix3N+XXqDUF6HQ6aDabTIXA6XRiPB4jEAiwCnQ67alNFko2Pzw8xMHBAS4vL9FoNKDT6SY8gU6nE06nk+kQ8pChJjw+k/CSZ/zYSpKERqOBVCrFjNdKpTJRhUoGiSzL7FRHRu/FxQV0Oh2TaCFRc7PZzCaWxWKB2WyeeqbUNCMFH4Y/WCgNik6ng0ajwRbAQqGAZDKJw8NDvHnzhkkkzWo2QakB+/v78Hq9CIfDaDabODw8xO7uLtLp9JS3loeMXmod63Q6US6XYTKZ2IZqNBrhcDjgdDqZnI/g9vAV1fyBnVrBFotF1kyiVquxqJckSbBarQiHwwgGg/B4PFMFWiJf/ctB+y2to+Rls9vtrLtlKpWakFdSqgfIsoxqtYqzszN0u13kcjlEo1FWk0Jpd06nc66nUPBp0Ol0CIVCaLVaqNVqaDQa6Ha7ODs7Q6/XY7rryWQSLpcLWq0WLpcLNpuNFcOqwRfife5UvS96DJr1h+r1elaEQac23ggdj8esZaTNZsNoNEKlUsHi4iLC4TDcbvfcZgLtdhuZTIZpT5JME79Zk3yIz+ebEM/+0Gd/6tDmpgYZLJeXlxMLoVarhV6vZ1420ueU5ffdQWhxPD4+ht1un3g+QqEQIpEIFhYWpjo0AUIz8raopcYA73NbqVUztXulPFVqsTzLcKVrJxIJ/M7v/A7y+TxcLhd6vR7LiU4mk6qhZ/4z0Dze3d1FpVKB2+1mYSybzcZ6b8diMUiSBJPJJMb/I5h170iKh5Ql+EOLyWSC0+lEIBDA0tISIpEIPB7PlAfnsRf03GfIgKR5rtFo4HQ6EY/HUSqVcHp6Co/HA7PZzOpOgMnQ8Hg8ZjmSrVaL5cDG43EUi0V0u10YDAZYrdaJiBkZP2JtvhvmRS7MZjPC4TBrKCBJEiwWC2sMRNJnlUoF0WiU6f9SB0s1R53y2fmcfFHPqzL0BIDJcXg8HtbXnHQAeajdmSzLqNfryOVyKBQKWF9fx+LiIrxeL2w229QiORwOkc1mkUgkcHp6itPT04kqaKvVygS0l5eXEQ6HYbfbRWOCj6Tb7aJer6NQKKBQKEz9nO4lpWNQhw/egOW1es1mM7xeLyKRCNbW1rC9vc3ylpUGrBir2zFL7oQaD+zu7uLly5eswQT1r+92uxOb3Cyo8cC7d++YEHqv10On00Gn00Gv15vpjSPjNZlMolqt4t27dyz3laqn19fX0Ww2mWeJjCbxLNwO2hj5TWowGKBUKrHDi7K1M9335eVlbG5uYnl5GR6PZ8qxQGNE/y34fPCycvzY0qFjYWEB0WgU+XyetRKlIiylDNZoNEKtVmO5kul0Gu12GyaTCaFQCOFweEqdROnJF9wefh6p4XK5WDGdXq+H2WxmtQnNZhPv3r3D+fk5U3NaX1/HV199hX6/j9XV1anrfcnmE/cmAYXvwiFJV+L05Dmh3BmHwzFhaKZSKZbzVq1WmXhup9PBwsICvF4vHA4HLBYLNBoNWq0WLi4uWCety8vLKXksu92OeDyOzc1NPHv2DPF4fCplAHg/ycWkm2TWqY82t3K5PFV5TukCpA5BDAaDuUbQ6ekp0uk0KxBpt9toNpvsebHb7cxYEekdN4cMFf7e9ft9pFIpHB4e4vXr1/j1r3+No6OjqTHlr8G3i+QVRXq9HorFoqrG7yz4PDtqCUsd93g8Hg8ajQY76JjNZhgMBublVyKK/D4MPQt0j3hPeSqVQrFYnHoOyNuzsbGBnZ0drK6uwu/3T40BzdFPLa8jUEep8kK43W62H3a7XVit1om9lop6iMFgwJwI3W4XtVoNJpMJi4uLLJWEh8+jFtw9lMvKr+Ver5f9NxmwVqsVFxcXyOfzyOfzODo6gtVqxdHREXK5HBqNBvr9PpaWllhqJjmaaF1/Mp5XnlkPsNlsZjpiS0tLSCaT2Nvbm3hfoVBghR8knkue2GAwCLfbDaPRyHI6zs/PcXBwgKOjI5aszEPG6/b2NnZ2drC4uDghvksIhYFpJElieaq8SkAul8PJyQlSqRSq1erU+6i4Zl6YeRapVAoajYad+HO5HJaXl7G6uorFxUUEg0E22QhRaKeOmnwdT6fTQTqdZiL0BwcHOD8/n2m40iGU8qaoIxbw3hM/T1GAULYcvM5zQo0wfD4fKxpxOBxTYUtgMnQpeI8yH5G/P71eD4VCgWn5Xl5eolqtTh023W43Ezjf2NjA4uIiXC4Xa+ELTObKifX09ij3z+vey3kyjw6HA4uLi+j3+7BarYhEIkilUszIoVbq89LFSqUSarUaOp3OzAJMwaeB5jBvZEqSBJfLxcbLZDLB7XbD5/Ph5OSEyRtWq1Xk83k0Gg20222mVhCNRmGz2dhBlg4+n7tR0L0wXudBEh4bGxsolUrodDo4PT2deA0ZRCSum06nEQgEEAqF4PV6odfr0Wg0mPg5yW+pbZoWiwXhcJgttgsLCzAajROV9LSpi81u/gbX6XRQKBRwfHyM/f19Vb1PYL7k2XUoFosYDAZMQYKUDGRZZkVcPMpNU/CeWR5I6ll/cHCA169fY39/f0qEXg3q3hMOh+HxeFgHrGazyQq9isUiyuWyqlydkps8J5RaUK1WmbdIzfAVnp/Z0KanPMi0221ks1kcHx+zQ4zyYEoNXiKRCJaWlrC4uIhAIMB+Tg4HXvxecHv4A95N3zfr/lutVsRiMTaPl5aWkEgk2LgPBgPmgaWxVDIcDtm8E/Ps8zLrYKLRaGC32xGLxeBwOFiaZCwWY0V1+/v7GAwGePv2LVsjqVg+EonA5XLBbDYzVQKlgfypuRcrBm/0KP9ovvtHu93GeDyGwWDA+fn5hPFZrVZRr9eRTqeZyLLP54PX62VV6rlcjhmtvDHKQ7mUsVgM8Xh8YlJTkrNILn8P5UupiVBTmsb+/j7evn2LZDKJVqs1dQ3KebqNAavRaFiqCOUyFwoFyLIMu90On8/HCnn4zyyYZpZ8XafTYRqOL1++xO7uLk5OTljUYxZWqxU+nw9LS0tYW1tjJ3bykmezWaRSKSSTSZhMJlQqlQk5HoLm3XXGjRqYkNwPRV4kSXoSLSrvkllGPSmAJJNJ1gXt/Pwc5XKZzWGz2YxQKISFhQUsLCwgHA5PGK4f+h2C2/Oxe5MyMkUFsuFwGLFYDAsLC0yJoFqtolQqodvtMvUd5VyliIuas+e681pwO+Y52PR6PVNSorGlRkyk5Xp8fIzBYIBkMsmUfEajEfr9PutsajQaYTAYPrtNdC+MV2C+8oDH42HJwnq9HlarFQ6HAxcXF6hUKqw7FhX69Pt91Ot1XFxcsGIrOh3yKE+bOp0OZrMZDodDtaiAThYiL+s9ah5potPpMDmz8/Nz5PN51qlFuWDZbDaWo0r3nfeQ8otit9tFr9djp35eUqnT6eD8/BxutxuRSATRaJQZMvT5RG6dOnQ/yNAj/c5cLoeDgwPs7u5id3cXBwcHrBADwMTBhbwvFouFVZhTH/vl5WW4XC4Mh0Pk83kcHx9jNBqx9AGj0ciaUdDvV/PY6HQ6GI1GJu1DOa2UnmC1WpnMHclwUbW0mmdIPAfqzFqT6/U68vk8kskkzs7OcHp6ilQqxTznJpMJPp8P4XAYCwsLCAaDcDqdqm21RfTj4+Dnxaz7qDwg8B5a5f3naw+0Wu2E0WkwGLCwsACLxcLm8MnJycScGg6H0Ol07FBLMmmzqtXnfW7B3cPrZPNOQ7J9+EJnvV4Pp9OJZDKJbreLYrGIw8ND9n6SV1PKn9HPZ9kFd8W9MV5nnRA0Gg1sNhurUqRe5nRiODs7YzdXjQ+FNXnIgLLZbFMC2oDw2M1ilveEWsJSbhS1+FUar3q9Hj6fj+WoklYn5VJRZaQkSaxAr1KpsHAwGbJEt9tlTQ3IkNVqtXC73aoegKdcrDNLo284HKLRaCCbzeLk5AR7e3t4/fr1hC4y3XNen5dk7qhF5MbGBp49e4adnR2srKzA4/FgOBwikUigXq/j+PiYNRyhpgR0eFHLgya5HafTySIrHo8HLpcLXq8XTqcTDocDNpuNaf86HA643W6Wa6lEGFHq8IdGAEwGKZfLIZlMMkmzbDY7kfJhMpng9XqZ19Xr9U6l7tD1RRTr9lx3P5rl3VQasdSRkr74nEgel8vF8shtNtvEnOINYlmWmd46FQyJsf6y8PdfbSxsNhtisRhLKwgGg9jb28P5+TkqlQqy2SzLbyUd7VkNg6hZwqM3XnmUxgS5t00mE8xmM+x2Oyu+oJBgIpG4Vs4cQe5vwmg0IhwOMyFltcEQOa7TzKsWHY1G6Ha7aDQaaDabrL2gMj3A6XRicXER33zzDVZWVlhIim9HSAtku91GsVhEOp1GOp1m+ZPK8DVvwFKnGL5giP/8fK7OU2NWR7TRaMQ0Hl+9eoXd3V3s7++rdkbjx57aDK6uruLZs2fY3t7G+vo6lpaW4PV6AVzNZzpQNBoN5PN5pjUIXM1FZa6WyWRih0u32w2/349gMIhgMIhAIIBAIAC/3w+Xy8UOoEajEXq9nnXZU2t4AjzNQ8sslGsvf2/IcD07O8Px8TGrTlY2k6DDC4nTq21udG1h0Nyem9w3mk/z3qOMcNFrlVJWZNzOWjP5bpi9Xo8pElDluxLhFPr8qI0bpf+Rtr3P52PNKnQ6Hd68eYNSqYRerweNRgOLxcJSSlZXV1W7b33KuX0vjVdA3RtGbc7MZjMzRMgT4/F4UC6XJ4yY8XjMvDcku9Tv9zEYDNj1qcd6OBzG9vY21tbW4Pf7VZsS0EIrFtvrQacvauOppttpNpuxsLCAtbU1fPXVV9jY2GCVkK1Wi+U5k9FB4QuHwwG9Xo9Op6MqtUTvL5VKKJVKqNfrLL1E+RmfcgrBLOOd9DuPj4/x5s0b7O7u4vLycsJQ0el0UxuSzWbDwsICNjc38c0337CiR7/fP/F7e70eK9rK5/MTxjAtjrRpUuEPHS69Xi/cbjfrBESLrM/nY15XNWNJ+Xc/1TGfB9+Ygjf0x+MxqtUqzs/Psbe3h7dv3+Ls7GxKsYXy5xYXF7GysoJYLAafz8eKXpWeH0rhEXw8Ss1V5eHgQ8/7rMItkrWjNK1KpcIOLpQ6RKgVCFWrVVUHA11b8OlQpgkAk554/oDCe0kpgqbValGtVnFxcQGj0Yher4d2uw29Xs+iWyRDGolEWIMo0uv/lNxb43UWkiTB7XYDALP+Q6EQtra2UK/XJ06Ew+GQdX8pFArIZDLI5/MTk8hqtSIej2NnZwdff/01tre3EQ6HVW88VdUJbo7SO6vT6WC1WhEIBLCysoK1tTWsra1hfX2dGa/NZpMtejSxxuMx03AF3jerUPt9g8EAnU6HCecrZVr4z/VUx5WMFeXfT4eERCKBo6Mj1vaRoPQLSZLYfKKKZBKk39rawtLS0pROcrfbRSaTQS6XQ6VSmdoA6XMB79U/nj9/ju3tbSwuLrIFkryxTqcTbrcbTqfzRgumMGCnodAx5bQRpMl7cnKCN2/eYG9vj6WPAFfzmaqXV1dXsb6+jtXVVcRiMZauw+ew8548MQYfDzlnSDYSAIs43KQf/Syo4DmfzyOdTuP8/Bz7+/vI5/Msz5UiG8qi3Fqthna7zRrPKHmqa+/ngN/flPeZP6DOiiqTTCFfg9Dr9dgBhrqdJhIJbGxsYHNzU1VelG+CcVfz/V4ar9c5ITocDtbGbmlpCa1WC51OZ6KdJVU1X15e4t27dxiNRsjn8xPXslqtWFxcxNdff43vvvsOGxsbrIJOeToRaQM3gwxOtfvGqzosLy+z1r4+n49NKDJQ+v0+JEli4WSXywWNRoNyuaya4gFgyuNLRUCCSWZFEprN5kR6hjKnnO9prtPp2CGSJObW1taY7Aohy1fd8Kh1ZDabRb1en6nbajKZ4Pf7sbm5id/4jd/Az3/+c6ysrMBms7GFlJ4Lq9V6I8NVGEw3o9lsIpvN4uzsDPv7+zg6OkKtVgMA1hGRxn9ra4t1OqQcdmDyoCTW0psxKy+f5ItarRY7pA+HQxgMBpbv/bGV4FS0eXx8jOPjY5yenuL8/JzlO3e7XVZEKbhf8Co+SuP1Q4eGer2Oy8vLiQYkFMnudDpIpVIstezo6AiFQgEajQZer1fVeKWaiEdtvAIf3lx0Oh1cLhdcLhcATFQpU4HPeDxGpVLBwcEBer0ezs7OpjZKaltHi+7i4iL7GeX28DmXguvDG6/K8TQajaz9IJ/8z3t7JOmqOxL1q+fHgK9eVfOokvIEpYqo5doK3p+EeWOi0Wggl8sx76hSv5MiEFSVbDabWZ7r9vY2Njc3EYvF4PF4Jt5HaRyZTAaZTIaJ2pNHnTAYDLDZbPD7/VhbW8OLFy/ws5/9DN99992UF5dSgD6UJiC4HmrPQ7fbRaFQQCqVQiKRQDKZZIYrvcflciEej7MIitJwpdfxounCgL0e8/LyW60WisUi8vk8yuUyOp0OK7bx+XzQ6XSquf789fjKcH6tlmUZ1WoVqVQKJycn2N/fx/7+Po6Pj5lBQ2lEs/LJAbDUEYvFMvX5RRrep4fm2bzCKTpYDgYDFq2mTorU/rvT6TCDl1dvymazKBQKMJlMWFtbUy2epz36Lr3sD8Yi40ObasYQ9TXn0Wg08Pv9qNfrTO9RCeV2kBGl/J1qhpHgetAmqLawabXaCbkjXouTJhstsMpqV/IKFgqFCak0HpqINMn4cBrPU0sZUHpw+HGRZRnNZpNJIFFeG78Y0Xjy1chmsxnRaBRbW1t4/vw5VldX4fP5pn73cDhkLXwpjAhcGauUemA2m1k/9ZWVFWxtbeHFixfY2tpSbdNMHd0I8sjyucz0HNKXYBL+mVDO036/j1wux5p/UKqHEovFAp/Px+Tp/H4/bDbb1OsoIvaUiyRvyryC2HK5zLoiZTIZ9Ho9lr5DHljKT9RqtczJw3u/Kb2KWr5S+kGn00G1WkU2m8XFxcWELJpyXeAbEUiSBIPBALvdjmg0im+++QY7Ozvw+/2qe7R4Dm6OMh1AKYV23WLIbreLVquFZrOJRqOBarWKcrmMXC6HdDqNi4sLXF5esk5qtIYqHUHUQW2ek+iu99kHYbyqubtv8rDP6+BE4Q6j0ThlJAkPwcfBSxDNGi/+lE8FXsD7MVN61NrtNi4vL1mLXzoRKuGL9Mgj/5SM1HmQ4aB8rrvdLtNuPDg4QCKRmDJUyIPD5417PB4sLi4yr+vCwoKq4cIX6FBUg5/LRqMRfr8fKysrePbsGZ49e4atrS2srq4iFArN/HvIe8QbrLzhKjbG2XxI0oba7JK6gLIolq5hMBhYJMXj8cBqtar+PhoP8vSIMboeap3O+v0+CoUCDg4O8Otf/xrn5+cYjUbw+XxYW1tjextJnJHxSmshzcVOp8PqQkqlEiuwajQaTJaQuuCVy2XUarW5yj42mw3xeJzJ5D179gyrq6sIh8NTEUxRsHc7+OI8NeP1OuoSFAmjKFs2m2X/rfYs0DqhNl7BYBB+v39mGt+nmN8Pwnjlwxv0/9eFCrWKxaJqdyfSdJ012GJhvT00weblm/IGB3nP+W5rSgMrnU7j7du3THM0m83OVBEgDwBJJgmZpMkQpJrxSlJIp6enyGQyE94VkpfjPdg+nw/xeBzr6+tYX19n6QJqBz5K/6BiSpLR4b2uwWAQ6+vrePHiBb799lusrKyw1KB58Hnpszr5CNShOajWZCSTyeDo6IgdZprNpuoaaTAYmJ6u2+1W1XUleG+bGJcPM8vz2u/3UalUkEgksLe3h8PDQ8iyjGg0ygTkNRoNGo0G+2/+0ECGY7PZRKFQQDqdZl5VMlJJ8aXT6UwVhKkZMiaTCZFIBF999RV+8zd/Ez/72c+wtrYGt9s9kW7EKxuIZ+Dm0Bzi7z9vH82bW8PhELVajY15IpFgjYSSyeRECgqlhvF7Of87ac3e2trCzs4OwuGwqkb+rA6qH8ODMF4J5R/OV8YSdJNIIJ+8BolEAqVSacKIcrvd8Pl8rB0aL8lE1xI6hLeHPx0qQ/b8BKNQNBVkAepGSDqdxv7+Pt68eYM3b97g7OwM1WpVdWEniSWfz4dgMAiPxzNTh+6pjfEsTwdp6KZSKeRyOWaomEwmFhbkvW6Uk/rVV19he3sb8Xh8wnBVS1HgVUD4JhPA+4VwZWUFm5ubWFtbg8PhmPqM1MiAf16IpzSOn5pcLofT01O8e/cOBwcHSKVSaDabU94zu90Op9MJj8fDGkWQ94VvNKHT6VjBhhinj2c0GqHdbqNSqUzlptvtdpjNZvT7fTgcjon+85QyQMZru91muejpdBr5fB6VSgXNZnNm8x8ALGKp0+mYk8Dj8WBjYwM/+9nP8POf/xwvXryYiMJQOhcfHRHcjuvMI1q3+/0+SxGo1+solUrI5/Msj53SxNLptKr0pPL32u121oiE1GWePXuGaDSqenAlu+rJGq9KRqMRK9igIi2dTofxeMykXc7Pz3F0dITz83OUSiW2kJpMJkSj0YmmBMpN/SkaNneNUnuQhzYz+ndWtSrl3R0cHOD169d48+YNTk9PUSgUMBwOmfHLF+PZ7XZWiLe2tjY3lP0Ux1htPCjHjcJFg8GAzYtOpzNxfx0OB1ZXV1kh1cbGBisQATARmiSoiI70XYvFIkql0sQ1/X4/wuEwE8fmIaFz8h6JlJ67Qfn8y7KMUqmEy8tLlk95fn6OYrHIFB7MZjNGoxGMRiNCoRBCoRCCwSATOCeGwyEzgEhebVbhiMiDvRl8qhXvHKjVashms9Dr9ajVajAYDBPpAmQ08lJ3NCd5j+ssFRBCp9NNaC27XC7WEnpnZwdLS0tTay7vEBLjfDNuauyTlnaj0UClUkGpVEKhUGBfxWKRFfvlcjnV4lw1KC2EV5ZZWVlhue5qntdP0ZL9QRuvvPeOjCDgvTwEVUrSqaJcLrM8Sp/Px76cTifMZrNIGfgEXPceziqOG41GSKVSODo6wuvXr7G7u4uTk5OJggG9Xj/xXmpPSSfC9fV1hMNhdiK8bnjlKUEGar1eR7lcRrVaRaPRmKhC5aHQ4M9//nN88803iMfjsNlsbDMlCS2e0WjEPACdTmeietVkMsHn87GGBrOKfYxG44QcnuDmKAXLeWNyNBqxtsCXl5dMKiebzbLKcqoPMJvNcLvdWFlZwdLSEsLhMNPgJvhxEnPt41C7d7zThqdUKkGWZeRyOTZ/ZzUwoJ9TIwKSFvwQdrsdkUiEGS80d6n4Wa3AEvh0ve4fOzeZO0qDNZ1Os/mcTqdRKBRQr9fR6XTQ7XbRbrdV0yqVGAwGRCIRbGxs4MWLF6ztdzgchsvlmmi/roy83fXcf9DGK53i+fwZot/vo1wusx7cmUyGLb4kbu5wOFil+6ycSECEIm8LnxKgHB/KfaLTIVU4RqNRVhQ0HA5xeXmJ/f197O7uMo9ruVyeyHPlDStJkuD3+7G0tITV1VWsrq4iGo1OeHX5MKYY2yv6/T46nQ5b9PL5/MxTeCwWw/Pnz/Htt9/i66+/xurqKjM2qZpZzZtNRpNer4fFYoHL5YLb7cZoNMLCwgJWV1cRj8cRCARgMpkwGo0mTuyfo2vLU2GW17rX66FarSKXyzEJHCra4F9jNBrhcrmYIsTa2hqCweBUyJDWVuXv4ov11OS5BJPMK3hVO8g1Gg3W0VCts9V1IaUYPudRp9PB7XZjcXERGxsbrLkPdViitu1UIMYXZYlx/nh4BwHw/p6Ox2O0223UajUUi0XmTc1kMri4uMD5+TnOz8+RTqcnpO4Ifr/mDVCSC7XZbIjFYmzMv/nmG6yvryMSiUxFyei9dN1PwYM2XueFfEni4/LyEslkcmIjVhsgUfF4e2Z5VMjYIDksXmKj1+uxasdkMgmj0cgKhux2Oys0uLi4wP7+Pt6+fYvT01OmLqDValW9A36/H8vLy1hfX8fy8jKCweBUOsJTH2u18A0VUJHGn5rhSqk2z58/x89//nM8f/6ceVz5awOzvUQkqbS8vIxut8uM10gkgufPn2NlZQV+vx9msxmyLLOqZq1WK7SW7wilMgMPhZCr1SrrjDSrrafX68Xa2hrzvng8npnjzkMdevr9PoxGI2w225Q6AX0+YejMhtfMVSuInacIcF0ov5wkt6iffTgcxsbGBnZ2drCzs4PV1dWpwwvlW1KqiZqcpWA2s9bS4XDIuk92Oh2mAjAcDlGv11EsFifyl+kgmkqlcHl5qbr/SZLEitcpj5kceuTs8/v9iMViWFlZwerqKkvHm2e4fkoe5W5AlXTZbJa1hFUiyzJLZKYKSpFAfnPmhd5JX5AKOYrFIpNearfbKJfLrFCu0WggmUyyFA7ql0xdfRKJBOulTQn/Sux2O+LxOJNsmnUiVOu//dThN8FZIcOlpSV8++23+O677/DixQssLS1NFVTNO1Dq9Xo4nU7EYjEAgMvlQrlcZobQ0tISFhcX4fF42CYoZM4+L3SIoXAiGR5KdDodkzXb2NhAPB6H0+mELMusFTMZPErK5TLS6TR6vR7rn640XvmuQGJN/vB9+FTzQ6vVTqzjpMG8tLTE0gXi8biq153fG0TKyM2ZZbxSak8ul0O5XGb5yd1uF+VyGdlsFslkEul0mhXekeTZrOfE7XbD4/EwA5YK8Gw2G7xeL8LhMCKRCCKRCEKhELxeL/Oyq/E5xvrBG69qpxIS2i2Xy6jX61MnUF6wXJzsP455xorRaGRSSvl8Hq1WC+PxmOkEkvpDp9NBqVSaSOEA3nto8vk8kzojb4zS06DT6RCNRrG9vY3nz59jY2MDwWBQNcz81Md81kZCwuLKzljAVarAixcv8Ht/7+/FixcvsLq6Cq/XO2WcUBqPGjqdjnnVKV+O8qwsFgu8Xi98Pt/EgqjMZxbcDbM8r3z+I/WiVxtPl8uFYDCIWCyGWCyGQCDAfsY3iVBSLpdxeXmJTCbDDkter3fu5xNGz/x8V7PZrJojTpEK5WGUrjXLkKHiWWoxa7Va4XK5EA6HEY/HsbKygpWVFdZFzeVyqRbbCrWej2PW+FBEiiTOSqUS6vU62ytTqRRSqRQKhQLa7fbc+eh0OuF2u+H3++H1euFwOFhXS5PJBI/Hg0gkwhwLwWAQVqv1WuoBn3rcH4Xxyk9GSj6mDj5q4RTqhe5wOGC322G1Wu+05+5TYlbOFXBlkCwsLKDRaLDuLb1ej+l7drtdVKtVJvlCY0BeQGpV12w2Jw4gyjE1mUxYXFxkeTg7OzuIx+NwuVyqYaqnbLzO8oJotVrY7XbEYjF8/fXXGI1GuLi4wGAwgN/vx7Nnz/Cbv/mb+Pbbb7GxsQG/38+uQcYl3etZ91ej0cBsNsNoNMJut8Pv97OFdVYbS/66gk8Pb7zSHFWKk1utVsRiMVago9ThNZlMTAWGjOBms4lSqYRsNot8Po9Op8MOKWrrLv/7njqzCl70ej0cDgfC4TBWVlbQarVQLpcBXK29JpMJer2e9aLn9VpJAs9kMk2k41CI2O12w+VywWazsVSBQCCAaDSKpaUllpuujGxRlER4Wz+eeXnO5BnV6XRM7zeZTLICy1wuN7Fn6nQ6WK1WmM1mlsZnsVhgs9ngcrng8/mYzB0ZrwaDAW63GwsLC4jH44hEIlOfhY/Ufe7c9QdtvConNd1IktQBpjc+SjwmV3g4HJ4IUyqvL06O81FWK/OQ8UoVre12G9VqFZVKhSkFdLtdlr9KMmcUKu73+xNtB9Vwu90sVeDbb7/F8+fPsby8DJ/Px8aUX1CfsuFKqD3TOp0OXq8Xm5ubMBgMWFpaYtJyZNSura1heXl5KreRjIzrhnhJNWCWNJrg06LWlYegeUeFH9T2kYpvnE4nlpeXsba2hkgkwiqMlej1ejQaDRQKBVxcXODi4gK5XA6tVgs6nY5tmA6HY6b+slh336M2Z41GI7xeL1ZXV9Fut2Gz2ZDNZtHv95lH1mg0YjQaoVKpIJ1OI5fLTTQFcbvdLHWDIi+hUAjRaBQLCwtwOp0s/cNqtU5IY6l5e/nnSozfxzFrDuh0OjgcDgQCAfT7fVY7QocUKtQjeAPV5/PB4/HA4XDAZrPBbDYzjWaPxwOn08kONNTowuPxqLb7BibHm9b/z5Xq86CN11nwA6f00mk0GlitVgQCASwuLk64wpUIY+dmUB4xAFYQZ7VasbKywvpwp9NpZDIZljpA3hmqaOR1PJUNKJQEAgEsLy9jZ2cHX331FWtDGAgEYLFYJj6X4IpZhw3SbDSbzVhYWGA5j/QzOqVbrdapwqlP0T1F8GmZZbxStKNcLqNUKqFUKqHRaAB4f1Dc2trC1tYWYrEYnE6n6vPUbDaRzWZxcHCAly9f4u3btyiVSjCZTFhaWkIgEIDX64XX652Yq4RwHLxnlhfTYDDA5/NhY2MDRqMRgUCASZrJssw8q4PBAPl8nnlhgavDhd/vx8LCApv3lOa1tLSE9fV1LC4usuiVMkIyq4OaqCe4O5QOAkqZo8OfwWBg+uak2EORTKvVClmWWZTZ6/UiFAohHA4jEAjA7XbDYrHAbDbDarXC6XTC6XSytAEaR0pNUctdByY7fX1ub/uDNl55DwLJZvEqAsPhEO12e+I9o9EIZrMZoVAI8XicGa90+uc7kIjT/83hxenp3mk0GkSjUcTjcUSjUSQSCaZBR12b5sm58LlTVP3o8XiwtLSE7e1tPHv2DNvb21heXobf75/aDClHVozlbCRJYt7Q67Rj5U/Xtzngzap4FXx65nle+/0+q1gmqaxut8vylIPBIOLxOGKxGMLhMJxO50RXw2azyd6fSCTw9u1b/Pjjj9jf30en00EkEsHy8jLcbjcr/CBRc6G/rM48JwodHqganPQ7h8Mhy10dDofI5XKskrxYLEKn0zEPKx0gKO+cirH8fv+tPqsYt7uHL1qlZ4Eim4PBAJ1Oh6l3UIMZinBRimQwGEQwGITf72ceVlrzyTmh1mAAeC+ZBdyfQrwHbbwCmDJKKFdDkq46hyiNV1mW2SQNhUIIBAKqGqBiEt4tFouFbVjhcBjlchmj0Qi1Wm2u4WowGNiXxWKB0+mckOzgu3v4fD5VLw4gDKO7hO9udVPDdZ7hxI+RmH+fDsopV7u//X4f1WoVhUKB5aYSFFo2mUzM2KHNrtlsMimedDrNlF4SiQTOz8/RarVgNBrZGqA0XIHJ3Gkx9pPMK4Kk1AsK61P1OSkFjEYj5m1bWFhAtVplkRbqjEWNJ5xOJzNwPoTSeSTG7NMxKwfcYDAgEAig1+tBr9cjFAqh0+mwVvd0gCHvqtvthtPphMViYc0teFmsWVA6HzktSPbsS475gzZe1TZBSmQmYWzlz+12OxNHd7lcU8bOvM1V8GHonisfajo0+P1+LC4uotVqsRBIPp9XLawzmUysR7fVaoXP50M0GmXNB5aWlhAKhVje1qxTIyCM17vkYwzL67xPjNWnZ9b6Rt7TcrnMZO2I8XjM+qO3Wi1WfNnv95FMJvH69Wu8fPkSx8fHyOVyqNfrrLOT1+tlXdk2Nzdn6kMKbo5Go4HJZGI5inTPycgYj8es7fLm5ia63S4kSWIHECr80el0zNC5CbxhJebup2FeGo3VakUkEoHD4WDqAsD7iCUZp3Tw5O2j6x466Pfz6QFfWsbuQRuvBL8QUxWl3W5np/xcLsd+znfxmbV4Cq/P7eDDW2oSZiaTCYFAACsrKwDAwhk2mw35fB71eh3AldFKunNkvDqdToRCIayurmJrawsbGxsIhUKw2WxTzSbE2H1a5hXpXff9Yoy+PGoGLOm8kjIIodFoMBwOWRFWNptlRRzD4RBHR0f4u3/37+J3f/d38e7dO1SrVWg0GnZYXV1dxc7ODp4/f47NzU0EAgHV3GnhNLgdVIE+rwMdX1hD71Fe46a/U8zjz8O8e00dzz6U7vUxY3UfZUUftPE6a0BNJhP8fj/W19dRr9fhcrnQbrfhcrlYR5hQKKQaYhYL6Mcx6wGXZRlmsxnBYBDAVQVkIBBAPB5HLpdDsVhEs9nEeDxm6QEU3jCZTLBarQgGg1hcXMTa2hpisdhUErkIYX0cfKEcFc4B741VqkAV9/bxQmNNOo9UtEdFO1SIZbfbIUkScrkc2u02jo6O8Otf/xovX75kqQbj8Rh6vR6RSARff/01Xrx4wfSX1bx7s6I2gtkoHTfzmCW5pQapvNAaoNVqJ7TRxRh9WfimStfRXJ0Ff6BRRp3Jvrqv4/3gjVc1Y8lkMiEUCuGbb76Bx+PBt99+i36/z8LWkUgE4XB4prAyXVtwc9TuG4WwzGYzk7Dy+XyIxWJoNBpoNBpoNpvodrsYj8cwGo1Mi44qKkkb1Ov1IhAIzKx+BD59T+XHzKyUmXktRQUPj1kHf4PBAIfDAb/fD7/fj2w2y7pskS4z1RMUi0WYTCa0Wi1cXl7i8PBwIkdWr9cjGo1ibW0N29vbTKGA99qTcSTW3ZtzU4m6216f/vtDnlvB5+MmB5FZKMdULWXyPqQHzOJBG6+AuqePkpgdDgdWV1eZJBOfwMx3cuIRguh3jyRJzGtnMpngcDgwGo2YHi/9q/T08ad8UpOY1XKS3ncXk/qpwis6qC1iIkz4OKD5NKuxSDAYxNLSEhO8J11mOmBS6sDp6SkAoNPpoF6vTxiuVquVGa2bm5ssP51fX6mC+b6FIx8Kn3KtU4uwiPX1/nAXUQq13FWl4+I+j/ODN17V4GV/PsRNwi6C28EvenSA+FiU0iHCsLobxH18/JCxqGY0UkMKak4wGAxYERcA1kO9VqtNvVeSJFatvrCwgI2NDTx//hxra2sIhUJTBZU0h0Wa1t0yL3LCF8aqHUj57wlHzv2GH7d5c4k/cKit78rxfyg8KuP1NuEMPk9S8HCg3Ew6OYpcTIHgepBhojZf7HY74vE4RqMRut0u02zli15nXXNxcRHLy8usD/rS0hJrJepwOFTfd1/z6R4q/KEemOw+qWz8wke2eANHjMnDgmwYGlu18efH9bGM76MyXm8zIMLT9DD52ER1geCpws8XXvNVo9GwBi6SJKHdbiOfz+Py8hKXl5eseEsJdc2izlurq6uIxWIIhULw+XxwOp0z6wvuaz7dQ2TeveSNlnnNIMRYPEzmpXvx/82nBzz0sX5UxqsaH8rheOgD+Fi4acclEdISCO4GZdGUTqdDMBhEo9FAJpNBOp1GvV5HIpFAq9ViaVkkfB4KhbCysoKNjQ3WVjQYDDK1EKUkFiEcB3eLyEd9mjzVefRojVeS+1EWAlHhDxUECT4vfO4V/avM11F6BJR5WGLcBIJPi06ng9/vx9raGtrtNmw2G7LZLBO459tO+nw+LCwsIB6PIxKJsPaTvOaosr2kmMMCgeBjeLTGK2/wKI0gwZflOt7wWV4EIdckENwd8wxJu92OpaUlGI1GxONxNJtN1r2HBPFNJhNsNhvTZXY6nbBarTO9rWLuCgSCu+DRGq+87I8SYcR+OZSb5XUqjUWqh0Dw6ZiVgqPX6xEMBuHxeLCxsTEhjE5ffCRrXkSLn/di/goEgo/l0RqvgDBS7yuiQEAguJ/w6TsajQZ6vf5W0na8JJMwWgUCwV0jEo8EAoFAAAAT+egfcw3+OkLHVSAQ3DWP2vMqEAgEgutzF8VUohubQCD41AjjVSAQCAQAplOtlJ2ZZgmgKw1WoSYgEAg+JTc2Xn/xi198is8h+MyIcXw8iLF8PIixfDyIsXw8iLG8f4jjsUAgEAgEAoHgwSCJZHqBQCAQCAQCwUNBeF4FAoFAIBAIBA8GYbwKBAKBQCAQCB4MwngVCAQCgUAgEDwYno7xKkn/JiSpC0mKfeR1/hVI0gCStHVHn0xwU8RYPg7EOD4exFg+HsRYPh4e8VjeT+P1SjDwX4Ak/VeQpAYkqQ1J+hGS9FuQJPVG3POvFwPwpwD8Zcjy5Qde+0chSfJPX39c5RW/BJAH8G/f+HM8RSTpnLufyq/sLa734bGUpH8AkvRXIUkZSFLvp3//U0jSP654pRjL6yBJXkjSH4ck/TVI0jEkqQNJqkGS/g4k6b8HSbr5OiLm5JdBkv7YnPlIX6MbXlN9LG/33IixvAmS9GchSf85JOnyp/tb/mmv/NchSd5bXG/2vLz5Wi7G8rp8znl59bObPjf3bizva5OCvwLgj+LqZv3fAbQA/EEAfxHA74ck/ZEb9hz81wAYAfy5ua+6Gux/F0ATgE31NbLcgST9RQB/FpL0+yDLv3ODz/FUqQH4Cyrfb97iWvPHUpL+5wD+lwCKAP7fADIAfAC+BfAPAvjr7LViLK/LHwHwv8XVvfwvAFwACAL4wwD+9wD+62JOPhheAvg3ZvzsHwDwDwP4Gze85qyxvPlzI8bypvyPAfwA4G/iar+0Avg9AP4MgD8BSfo9HzwcTvKheXn9tVyM5U14ic83L4GbPjf3cSypg8q9+QL+GzIgy8CpDPi47+tl4K/99LM/doPrOWWgJQP/6QdeJ8nAfyYDJzLw5376PX98xmsXZGAoA//hF79f9/0LOJeB8zu61vyxBP7IT+P2N2XArvJzvRjLW933f1gG/kkZ0Ci+H5KBi5/u+X/rzsbx/evEnPy84/y7P93jf+pOxvK2z40Yy5uMmWnG9//XP93fX97JWF79/OZruRjLuxjju52Xt31u7tlY3se0gT/8079/HrJcZN+V5QGuThIA8D+8wfX+GQAWXHlw5/FbuDrd/PO48vTORpbTAP42gH8akuS4wWcRfByzx/IqBPlnAbQB/Hcgy42p11w9Q8rvibH8ELL8tyDL/xFkeaz4fhbAX/rp//7BG1xRzMn7hiQ9x5XnJQXg/3ODd84ey9s+N2Isr48sd2f85P/x07/rN7jadefl9RFj+XF8inkJ3O65uWdjeR+N19BP/56q/Iy+9x0kyXXN6/3Bn/79OzNfIUnbAP4tAH8Rsvzb17zuf4krl/zvv+brnzJGSNI/C0n605Ck/xEk6R+6Ve7y/LH8fQCWcZUWUIEk/ROQpH/1p9/3ez9wXTGWt4cOBMMbvEfMyfvHf/+nf/8DyPJNcus+PJbqfOi5EWP5cfyTP/37+gbvuc5Y3mYtF2N5ez73vPzQc3NvxvI+5rySt3VZ5Wcr3H9vAfivrnG9vx9AA8Ch6k8lSQfg/4yrfKw/fe1PCfzqp39/P65yKwWzCeHqHvOcQZL+ecjy/+8G15k3lr/x0785XOXyfDXxU0n6bQD/NGS5oPJeMZa34Wru/HM//d9/fIN3ijl5n5AkM4B/FsAYV7moN2H+WKr/vus8N2Isb4Ik/Slc5YQ7AfwcV+PyGlcHwOtynbG8zVouxvI2fI55efPn5t6M5X30vNIN+ZchSR723asFj09odn/wSpJkwFWBQBayPKuY5H+Bq2KePwZZ7tzgc1J1ZfwG73mK/B8A/CO4WvSsuDIq/30ASwD+BiTpm2td5cNjGfjp338JgBlXJ087gOcA/hNcTbb/54yri7G8Hf8Wru7vX4cs/yfXeoeYk/eR/zYAF4C/gZsU91xvLNW4znMjxvJm/CkA/zqAP4krA+Q/BvCHZhzWp7neWN52LRdjeTs+x7y86XNzb8byPhqv/zdcVdWtAngLSfrLkKS/gKtqvH8cwNFPr7uOC50kHyqqP5Wkvw9Xnp0/D1n+3Rt+zvJP//pu+L6nhSz/Gz/lvuUgy23I8hvI8r8E4N/BlZH5Z655pfljCVDoSsKVh/U/hyw3Ict7AP6bAJIA/sCMFAIxljdFkn4LwL8CYB9XyiDXRczJ+8ef+Onff/+G7/vQnJzm+s+NGMubIMshyLKEK8PyD+MqSvkjJOm7a17hw2N5+7VcjOXt+PTz8ubPzb0Zy/tnvF4l9/9TuDoRZHG1wP0LuDI+/n4ApZ9emb/G1chrY5r6yfvQ5CHeF4LdBLPidwhuBhVsXDd3ZvZYXkET9RSy/GriJ1feO/Lw/H0q7xVjeRMk6X+AK9m6twD+Ichy+QPv4BFz8j4hSTu4yhdPgpeRux4fmpPK33WT50aM5W24Miz/GoA/hCsj5v90zXfebCwn+dBaLsbypnzOeQnc5Lm5N2N5/4xXAJDlIWT5z0OWX0CWzZBlB2T5H8PVovcCVzdu7xrXqQLo4/1JhMcGYAPANoDuhBjwlRsdAP53P33vL6i8n655HSNaMA3dN+u1Xj1/LAHg4Kd/qzN+TsatWeVnYiyviyT9SQD/HoA3uDJAbtZoQszJ+8ZtC0KuMyffc/PnRozlxyDLCVztl88gSR/2kt1kLKf50FouxvLmfJ55Of3eDz0392Ys72PB1jz+KK5OE39FVfZInV0A30KSHJDlOvf9HoD/YMZ7vsNVzt3fwZVRpBa+pDZpL6/5OQSTUPheTVViFrPGEgB+G1eVy+uQJANkua/4+fOf/j1Xua4Yy+sgSf8qrvIVXwL4Ryek7G6GmJP3AUky4WpNHWP2ff8Q8+Yk/Z7bPDdiLD+ehZ/+va7x8+GxVOdDa7kYy5vwueblbOY9N/dnLL+00OwMoVyHyvd+QwbKMtCQgZUbXOvf/kl09w/e4D1/Zq4g+tVr/spPr3n+xe/Xff0CnsmAR+X7izJw9NP9+9N3NpbAf/jTz/9Xiu//ozIwloGqDLjEWN5qLP+1n+7Rr1XH9GbXEnPyPnwBf/Sn+/UffbKxvO1zI8byOvdoSwZCKt/XcGLz/+WdjOXHrOViLG86rp92Xn7Mc3OPxvK+el7/JiSpg6sQUwPAM1wVa/UA/GHI8k28dX8VVwUC/zUA/9mdfLorQfx/BMABZPnNnVzzcfJHAPxPIUn/BYAzXI3lKoB/Alce9L+Om/VK/tBY/ssAfhPA/wyS9PsB/D0Ai7gq2BoB+BdxFVJ5jxjLDyNJ/10A/yau7uHfBvBbkCTlq84hy//Ha15RzMn7ARWE/OWPuMbssbztcyPG8rr8YwD+3E8ygCe4qgcJAvgDuCq8yQL4F29wvXnz8nZruRjL2/Bp5+Vtn5v7NpZf2nqeYd3/T2Tg+588ZT0ZOJOBvyQDS7e83g8ykJYB7TVfP9/LA/yhn37+J7/4vbrPX8AfkIH/qwzs/zSWAxkoyFftW/85GZDufCwBjwz8Oz89M30ZKMnA/0sGfo8Yy1uPI82HeV//3zsdx9mfQczJuxnT7Z/u1+W1x+CmY3nb50aM5XXv+3MZ+N/IwEsZKMpXrTtrMvCrn+79zSMks8fyD9xqLRdjedP7/znm5e2em3s2lpIsy1/afv70SNI/A+D/giuv7V+7g+v9VVydUlYhy7WPvp7g+oixfByIcXw8iLF8PIixfDw88rF8KsarhKsCDzOAF/iYP1qSXuCqg9NvQZb/vTv5fILrI8bycSDG8fEgxvLxIMby8fDIx/J+SmXdNVeD9icA/DW8r6S7LWFcaVD+pQ+9UPAJEGP5OBDj+HgQY/l4EGP5eHjkY/k0PK8CgUAgEAgEgkfB0/C8CgQCgUAgEAgeBcJ4FQgEAoFAIBA8GITxKhAIBAKBQCB4MAjjVSAQCAQCgUDwYBDGq0AgEAgEAoHgwSCMV4FAIBAIBALBg0EYrwKBQCAQCASCB4MwXgUCgUAgEAgEDwZhvAoEAoFAIBAIHgzCeBUIBAKBQCAQPBiE8SoQCAQCgUAgeDAI41UgEAgEAoFA8GAQxqtAIBAIBAKB4MEgjFeBQCAQCAQCwYNBGK8CgUAgEAgEggeD7rov/MUvfiF/yg8i+DC//OUvpY+9hhjH+4EYy8eDGMvHgxjLx4MYy8fBrHEUnleBQCAQCAQCwYPh2p5X4pe//OWn+ByCOfziF7+482uKcfwyiLF8PIixfDyIsXw8iLF8HHxoHIXnVSAQCAQCgUDwYBDGq0AgEAgEAoHgwSCMV4FAIBAIBALBg+HGOa8CwZdElmXIssz+W5KuChHpX/51/PeUPxcIBJiYS/y/ACbmlpg/AoHgPiGMV8GDYjweYzgcYjQaAbjaWDUaDTSa90EE5QZMPxcbsEDwHlmWMR6PMR6PMRqNMB6P2eGQ5o1Wq2Xzh59jAoFA8CURxqvgQUGbK2+gzkMYrAKBOkqPKhmz/P+TISvmkUAguE8I41XwoNDpdNDpxGMrENwF5FHV6/Xse8qUG4FAILhvPHgrQOmFE16CxwU/vncRthTepM8L7ylXesvp/qu9hsaHHydljrMYw0/DrHuqttZ+6D2Chw154/l/Z8HPSUrTEs/FJMr7x+eaK+s5eOatd091LXzwxqskSWxSzSreETxsxuPxnY3pddMNBHeH2sYnSdLEQk15l/xBRavVqhqvT3Wxvktu613lDxeEGIfHjSzLLCd6ngFLc1Kr1UKWZTZ/Be/h1z2CjFY+55y/z8q6Df4adH+fYj76gzdegauBU1tUBY+Du5yY9KyI5+TzwHthZnntZFlmxqrS8zpr7J/iYn2X3Ob558dDzJ+nARmjvJNIyayIiHhG1FEe/PhoIKCuqPMhz+tT5MEZrx8KLQoePmrjS9/vdDpot9vo9XqQZRk6nQ4GgwE6nY4tsLTIkkGk0+mg1+unPHmCmzMrtPWhcJa45/eP8XiMwWCAXq+HbreLXq+HwWDAvGYGgwEmkwlGoxFGo5HNMcHjQ21e854+rVYLrVb7JT/ig2BWMfG8NfCu1ke16NZjjkY/SOOVwovzJpTwxD5cyAAlmR5iOBzi8vISR0dHyGQyGAwGcDgcCAQCcDqd0Gq16Pf76PV6GI1GMBgMsNlscDqdcLlccDgcE4UpgMiBvSm8vBJfma68d59rwxPz/Hoo79NoNEK73Ua5XEYmk0EqlUImk0G1WsVoNILZbIbf70ckEsHCwgLC4TCcTufcYkl6HsRcelgo5zQ9K3dVHEtr7GOPlszKD+bD/p/yHoxGIwyHw5m/87HNyQdnvAKPbxAEk9AkVIaNB4MBEokE/vbf/tt4+fIl2u02/H4/1tfXEYlEYDKZ0O12Ua/XMRwOYTab4fP5EI1Gsbi4CIPBoGq8jsdjoQN7TWZ5U9WM189xP8WYXQ/+PpHhms/ncX5+jrdv3+LNmzc4PDxEJpNBv9+H0+nE0tISnj17hq+++gqj0QiSJMHlcn1wAxZj8rBQzmmRVnU7lPdvVgTxU38GtVSDxzieD854nXV6UXoWHuNgPRVGo9FE8Q4xHA6RzWbx8uVL/K2/9beYgZpMJrG2tga73Y5ut4tarYZutwuTyYRQKIRqtQoAsFqtsNvtE9e8qW7sU+c2HlX+3s7aGGdVss8LtQlmMy+i0Ov1UCwWcXZ2ht3dXfzwww/4/vvv8e7du4nXJRIJ1Go1Fsmgojq32z3lkbtLRRDB52eeV/BDlfA8SmPpMRtPatzGu6pcH5XfIz6UeqDX66ecM4+ZB2G8zgs79Pt9tNtt9Pt9aLVamEwmmM3mqdeK8OLDYZYsiyzLzGNE4ZFOp4Mff/wRzWYTHo8Hw+EQ1WoV7XYbWq0W2WwWvV4PZrMZHo8HHo8HVqv1S/xZDwa1MKJGo4FOp7vxwtzpdNBsNtFqtTAYDACA5SFT8Vy320Wr1UKz2WTz2Gq1wuFwwGKxQKvVYjgcYjAYQJIkGAwGWCwWWCwWGAwGMadVoPQqyvvmabVaSCaT2Nvbww8//ICXL1/i4OBg6hqVSgVv3ryBLMsYDAbo9/vo9/uIx+PMgOVTBYT+8v1HTZJu3pzudrsol8sol8uoVqsTedG0ThCUI2232+FyueB2u2G326eeP1KPeejzVi1H+EOv5x0z1C2y3++j2+1OfQ0GA4zHY+h0OlgsFlitVpjNZlbYSql1FosFTqfzSRmuwAMxXvkJoqReryObzaLdbsNoNMLn88FgMEy1C30sE+apMKsAiApJeIbDIZLJJNrtNmRZRr1eR7PZxGg0Qr1eZ+HOQCAAn8+HWCzGNlq+Gl5wBW1MfMtQ+v5N6HQ6yOVyyGazKBQKaLVakGUZBoMBRqMRBoMBo9EItVqNva5Wq8FgMCAQCCAWi8Hv98NoNLKiIo1GA4fDAZ/Px3KdhQE7jbI2gKfRaOD8/ByvXr3Cjz/+iKOjo5lrbKFQwN7eHgaDAYbDIXse+v0+zGYzgKtN22AwwGq1qnrkRRj6/qBUDfjQuFSrVRwfH+Pw8BAXFxeoVCpot9tTh1sysPx+P2KxGFZWVgCAHT4J3nh+DAVgyo50s+7nYDBAp9Nhxj+lxtHhvlaroVqtolqtolKpoFqtotVqsfxzn8+HUCgEt9sNo9HIjGCDwQC/349oNIpAIPC5/ux7wb03XvnTnfJh73a7SKVSOD09Rb1eh9PphCRJ8Hg8U6cQ8kKIsNb9Z1aoSZIk2Gw2+P3+qff0ej022VutFvPMlstlJJNJnJ6eIhQKwev1wmAwwOv1wmg0zkxBecqb7Sw9wevMHfImkIf84uIC5+fnSKVSrBhIr9fDarXCYDBgOByiVCohkUjg/PwcxWIRBoMB0WgU6+vriEajsFqtzDuh1+vh8/kQj8eZceZ0OmE0GqfG7ClHW5ReMZ5ms4lEIoG9vT3s7e1N/Iw8O91ul82hQqHActDJ69NsNmGz2Zjah8PhYGMx6/M8xXG4j1y3gKfVaiGVSuHt27f41a9+hYODA+RyOeYYIM+gJEkwm80IBoNYXl7GaDSCy+VCOBxWfQYfS4oWvyYqpa2AK5uj1+uh2Wyyr06ng36/zzyurVYL1WoVpVIJ+XwehUIB+Xwe5XIZtVoNo9EIVqsVkUgE8XgcwWAQJpOJGcAmkwkLCwsYDAbQ6XTweDwTn/ExO+3uvfFKKENgo9EIFxcXODg4wP7+PlqtFsLhMNxuN1t0iXkLueD+wQvUK79vt9sRi8WwubmJk5MTDIdDlsuq1WonwtNEsVjExcUFvF4vM3Q0Gg38fv9UqJMPgz5lyFBRQkYk5UEOh8OJfEiSXqrVashkMkgkEkgkEshkMqhUKuj3+9DpdDCZTCzsXC6XkUgkkM/n2e9JJBK4vLxELBZjhtFoNILRaITf70etVkO/32cGrMfjmfLIC+NVfc0jj/j5+fnE961WK5sTjUYDhUKBXaNSqeD4+Bh6vR6j0QiFQgEOh4OFif1+P/r9PiRJgsPhmPgcT3kcvgS8ITVLg5Wn1+sxryCN92AwQKFQwNu3b7G3t4fd3V28e/cOzWZT9XfWajVIkoRwOMyiY3q9XtUBwf/70OH/nuFwyJworVYLjUYDtVoNtVoNzWYT7XabpQPQa5vNJhqNBkqlEgqFAorFIorFIiqVClqtFgDAZDKhWq2yKLPRaGRpVEajEZlMBrVaDeVyGeFwGCaTie2hRqMRVquVOQseE/feeKXTDWkS0uaYzWaxt7eH169f4/j4GKPRiBkvykWbD1UID8D9Z1YoX6PRwOl0IhaLYWdnB263m4VONBoNGo0GxuMxarXaxOmeL1BxOByw2Wyw2+1ThSdkIF3Xy/jUGI1GKJVKyGazKBaLaDab6Ha7GI1GAK7uHy3elUoFuVwOmUwGxWIRpVKJ5bRSyFCn02E0GrEFXMnFxQWy2SyTaKL8rmKxiHa7zQ6per2e5b/yPBYPz22YV4g4GAzQ7Xan7jkZHZTfrNfr0ev12M+r1SrOz8/R7/eRSqVgs9lYJCQWizEHgyzLLFz8WL0+9x2l7BUdRtWiE2QU5fN51Ot19Pt9DAYDVKtVnJ2d4ejoCMlkcqbhSni9XsRiMSwuLmJhYWGmtNpDXVvnFUFSPUaxWEQ6nUYmk0Emk0E+n0etVkO73Z5IveFtmXa7zQxdWgv5edftdlEqlTAej1lkipwGer0e2WwWmUwGR0dH8Hq9sNlssFgscLlc8Pv9CAaDCAaDLC/2sczHe2m8Kk/qpPfZ6XRQr9fZQL1+/Rpv375FPp+H1Wqd8riqXVNw/5kV1iLPayQSQafTQTQahUajgV6vZykkw+EQ9XqdnVqJWq2Gi4sL2Gw2BAIBxOPxmR76pyoX8yEPWbFYxMnJCY6Pj3FxcYFisThxWKTiq2azyUJhtBBTsQ8tuko5mVn0+30UCgU2zgaDAfV6He12m4XNXC4XfD7fTI/fU2Seh4sMU7PZjE6nw75PYUxJklSdAKPRiI15MpmE2WyG0+nEwsICer0ey3s1m83Mu/5QDZWHzKyKf7WxqFQqSCaTOD4+xunpKasfoQNOuVzGxcXFBw3XQCCA5eVlrK+vs3Qf0t5WwnfEfGioGa7D4ZDl7ScSCRweHuLg4ADn5+fIZDJoNpss7YbeS+lVtB6SMUuFkUp6vR5KpRLzcNM1NBoN+71ms3nCMROJRLC8vIy1tTV2vx0Ox6OpEbiXxiudGvkKVhr4SqWCo6Mj/Pjjj9jb20M2m2W5kB6PBzabbWrCiKKch8U8z6vNZkMkEoFer8dwOGSdf6rVKiwWC5rNJgtPU3U6cJW/pdFokE6nUSgU0Gw2VcOqtCA8RcjzDLw/MAJXnrpMJoPj42Ps7e3h3bt3LD+10Wgwb8J4PEa320W73Va9v+TRo7xJZT4t/Yw2N0pFIA/ScDhEo9FAo9Fg4+n1ehGNRrG0tDQ1dk/xAMIza82j/MR4PI5yucy+T+keANhBg9Zgfh7xHlun04lOpwOTyQSfz4dwOMzCz7Pu/VMfl7tETV4OmC/ZRDq/1WoVyWQSR0dH2N/fx/HxMTO2aO51Oh00Gg1mUNEhkryHAFgNgc/ng8/nYyoDFAlR81g+xPHnPzMpcHQ6HVQqFaTTaZyfn+Pg4ABv3rxhxqvSiXKd38GPHV8gyRu18w7+VLgej8dRr9fR6/Umii1dLpdqlGqWV/m+ci+NV9qoaEMjNBoNarUaTk9P8fbtW5ydnQEAK+5YW1tDMBicKtaikMldGrC8t+k6PDW9u49h1smcPK8LCwtwuVwsjKzRaFh+XqFQQKlUwmAwYKFl2kyptSwVozzU0/+nYjweT+S0UgoAeV9OTk5wcHCAs7MzZLNZJkmm5ilQw2KxwG63w2w2Q6/XT1Qdm81muFwu1glNo9Gg3++j0WigUqlMFDIAVxXzFOqkytx+vw+TyfQpb9GDgXLe1AwYm82GpaUlvHjxAsPhEBcXF8yTzXvYSJaMNlM17eVarYZ8Po9wOMzSQugQ8VQPgZ8aXqZKaXSopQbw9Ho9VCoVZLNZNqePjo5wenqKVCqFUqmEbrfLxpnWhPF4zPJYdTodm7cU/dDpdBgOh+xw02w2WaRkVpj9Ie+F4/EYjUYDmUwGFxcXODo6YvU3h4eHSKVSN74mdTQjw5QO7mrM27t6vR5SqRR7P3l4acxI6YW/1kOs9bi3xqvaoPX7fRSLRSQSCZyenqJQKCAcDmNxcRHPnz/Hzs4OwuHw1KlC6T24C64T8hTcjlmnc0mSmLEqyzKMRiPMZjNkWYbZbEa9XkcqlUKxWESn04EkSWg2mxPjRIv7U95YZ20c5Dktl8vI5XIsZzWZTOLi4gKpVArZbBblchnNZhO9Xu9ac0CSJDidTpZ/5fV6YbFYALwvxORzJ0OhEFMYKJVKODs7w7t37/D69WtUKhX2O2mT5IsheOP1IS3Edw1/YFficDiwvLzMcsMtFgtLA+FTaWjNpJoD+p6yIJIOhVT0o2bkEmpV2YLbo0yPmXdf+TSAw8ND7O/vY39/fyKKwhuuvGQeAOZI4vNpR6MRO2Rms1mcnZ3B5XIx9Q+Px/MoimKV93U0GjHj9eDgAK9fv8a7d+9wcnKCYrGoeg2TyTShisJraGu1Wuj1era3kX69cq4R/JycRbVaZQcMOnzY7XZ4PJ4JVRCyZeizPBTupfE6y2tQLBZxeXmJ09NTXF5eot/vY3l5GdFoFFtbW1hZWWGbFw3YXXtcCeFF/bSo3VtJkmAymWAwGFiImXC73fD7/VhYWGCV7RS+Jkjcniovn+L4zav+JuO1VCrh5OSEpQgcHx9P5MLRe2cd4IxGI+x2O7vPOp0ODocDwWAQkUgECwsLsNvt0Gg0zFiyWCwIBoNYXFxEPB6Hx+PBeDxGJpPB7u4uKxLS6/XM09vv95mHuNvtsmKwhx6evAvmeeGsVivi8TgLRVJLZT6FAHifdkBrKZ/ewRu5lM5BFdOlUgkOhwNGo1FVOF0Yrh8Hn8OqnH9qc7rf76PZbKJcLrN6kTdv3mBvb4/N7W63C+D93gtgyvOnHHP6Ga0ZJpOJRVfsdjtsNttU0RbvmHpIhpISWitrtRoKhQJyuRxLR+OhdEaXyzWx75DhT/eUbwDDS2iVy+Wp1APeEaeW208/o3qBTqeD8XgMm83GnH1KHmJB+70xXpVVkcq81Xw+j4ODA7x79w6np6eo1Wqs6MDj8cDr9U54XWji8RNEzQ2vJiPCPxhqoZmPMYbpmvwEFvm414OeDTUvuslkgsPhYM+C3W5nsj6E1WqFy+WC0+mcWUzyFA4ls4xXOvFXq1UkEgm8fv0af+/v/T1UKpWJ15HHW/k9qnB1u91wuVyw2WwwGAwsLcDr9SISiSAYDMJut08YRkajER6PB5FIhLXw1Wq1iEajKBQKLE+LX6ip2IFkY0j7kM+Tf8rMMg5MJhO8Xu+EJ045nsD7+0cFJXxeMvDemBmNRqhWq7i4uGDyWbQZ+/3+qTzkh7RB3nfU7iPlYtLhnQ4mhUIBqVSKNR04Pj5GMpmcmFP8nqncKykErebxoyLNfD6PfD6PSqXCjCYlj2H8+YMcpTxRymKv14PRaITD4YDX64Xf74fb7YbVamV7F+UU88WrwNU97vV6qNfrKBaLTPuVWp4rux7y9ggfYaZnoN/vo1wuQ6fTIRgMIpfLMUUJZerAQ+PeGK/K0ARPsVjEwcEBXr58ib29PWQyGQBgGoNqhoiakUmTjx8o5QPAv4dOR/wD9rHyL/w1gfeNFx5Dt5EvCXX5MZlMzDvLjxEtJm63G06nU7WF8FMv7KNc806ng2q1imw2O2W4ms1mhEIhWCwWtohSsc7CwgLC4TB8Ph/sdjssFgszdsgbS4cHs9nMFnG+zSEZrvxnogpcMlB5+MMlfygknupYqnnggPeHZbPZzDxkpA6g5sGhsCNdg4wbimjR98rlMo6Pj9mBAngvveVyuZ7sOHxuqOiuWCyiUCiwL0oDyuVyTMqpUCioGi2z0j7o2Znn7bsuD9FY4tFoNLBYLExlQa/XIxQKsQY5RqMRTqcTbrcbXq8XLpcLFouFSVWRLcI7V4D3xmuj0UCxWEQ2m0UqlUI6nWaHgmazORUBUzr9lHYUqb+USiVUKhXU63X4fD72c3rfQ/KG3wvjlfdwKkNdg8EA6XQab9++xevXr3FxcYF+vw+9Xo+FhQXWMYk2NwpTqckt8SER/rTDn1z4waMHgn/9xxo3ykKGp2ws3Qa+yIeHvEMUBlWKblutVuaV/ZDnlf/3qcF7FJS54w6HAxsbGwiHw9Dr9azHud1ux+LiIlZXV7G4uAiPx8N0V2lB1ev1rICDDFo+7WCWykMul2OFWsqQHJ/XKSIYH0Z5aOfvnVoKCP0//3NSIKD8PPLy1Ot19kUhaJLkAgC73T4RGlX+HjFuH8d4PGbFlblcDslkEpeXl8zwyWQyKJVKqFarqNVqbH1UY55hSXNOKTNIjgOz2Qyz2Qyj0Ti3eOyhjbfy82q1WjgcDkSjUVgsFsRiMabKQPuTyWSCzWaDw+GAw+Fghaq855U/VNLeNhgMWNpALpfD5eUlTk5OcHZ2hkQigVwux34XPw500KTmLzz9fp/JeSWTSeYlpvSth+g8uxfGK6EWUmo0GqwFbDqdRr/fRyAQgN/vx9bWFjY2NuByuZj8Bw0G/zDQ9dTSEeYxK4UBeO+F4IWg+ffR30P/z2+0at5lwYfhDznKgwblH+XzeaTTaVYNTxXopAXq8/lmel6Bh7eo3iUajYZ5qEOhENbW1tDr9VAul2Gz2ZiOo8/ngyRJrDjH7XZjaWkJa2trrCPWbcSwqSqW5m6r1UKxWES5XGYdZnh4g5gqoZVz9aF7eO4SfjzUpMqURona2kbvHQ6HTDGC3yjL5TL29/cn1k1ZlhGLxaZaV9J7n0Kqzl0hy1dayvTV6/WY2D2FmrPZLC4vL3FxcYFkMskahZBsEo/ScKExUR5Y6Gf8M6MshKWaAovFwvKdZ9UuPHQ0Gg3LYXW73awBAS/tR/eEDPqb/N2yfNWCORQKIRAIwOv1wuv1wu124/T0FMlkEqVSCfV6fcIBQF5ztXlbq9WQSCTg8/lYoXMsFoPL5ZrYC2ftsfeNe2FFzcslLZVKyOVyKBaL6Pf78Hq9CIVC2Nrawvb2NpaWluD3+2EymVj+G4WMeePyukYrbaCSJM0s6iGZjHa7zTZw5d8CvN84+YlNxrUaylQCwTR86FJpvJZKJSb/kkgkmLFDOX7BYBCBQABut3tm2sBjWFjnoczr5qHQvd/vx8bGBvR6PWKxGFqtFqxWK0sLoGYAVJlMhQCRSARut/vWn00Z9qeCIv6Lh299SN4etTEVzIbfYE0m04Rxw+skK+HHSmnIVKtVvHv3jj1nRqORFa4or0FqE/d5k/wSzKr+piYslFtaLpeZ7jHJyhWLReRyOdYJj0LNatAhg77IucIXD5GRTClvswxSMoTp67HPPfo7Z8nzfUxEQZIkVrdB6ZE+nw+hUAg+nw9WqxXHx8esWJWgOaVGs9nE+fk5i4aQQ43WUQDMhpJleWak5L5wL4xX4L3Bxg82tZjM5/NoNpswGAwIBAL45ptv8Bu/8Rt49uwZPB4P0yCkHCxlxfF1oarMXq8HSZJgtVqncvAAsMR0qgTkF3w1A1yv17Ow9Xg8nrnBP+XuTtdl1qlyPB6j2WyysAiJrQNXIS2Px4NwOIxwOMzC2moTc5bG7GNi1oKk1WphtVoRDodhtVoRjUZZEwIKMVERFgC2yJFxojZXrovSC0ihaX4BVY6L0WhkoUrKcxZcD7q/VOjodrvhdrsnPDkUwlSmeND7+SYGACbykcvlMl6/fg2tVguPx4Pl5eWpzzBvo33qzNrHBoMByuUyTk9PcXh4iEQigUKhwAxYKtIiY3ZeegBdjw4wNpuNHQQpLajdbqNSqaBWqzEDVm1/Io8teR5nee2fEnexh+v1etb0IRQKIRqNMs8pcJXjnE6nJyImNKf4lKDRaIRWq4VUKsW89VRsZrVaEYlEJtbPh2B/fDHjVVnxrKxIbbfbLARM0keyLMPj8WB9fR3ffvstIpHIxHuo1SHv8r7OyYHE2PmcLUm66tpFrnqazMViEalUCslkcqIXNJ+7wnteNRoNTCYT07lstVrodrvw+XxTMjJqBrxAHeW4kpg6hdWUP6Nq0GAwCL/fD5vNNmUUzfNKPgXoWTWZTMxLpjRY1LiL+8Xnu/P56uTtUataJuUJNTk8PiR9n70HXwpKEXE6nQgEAlhcXGRrbKlUYhElunf8Wsp7XclDYzKZYLVaJ4yler2Ow8NDbG5uolKpYDQaTYWo+d8heM8s6aJ+v49KpYKzszP8+OOPePv2LdLp9ES+pVr+MvB+DJX5qlqtFk6nE6FQCKFQCE6nExqNBu12G7lcjhk+vBGsvD557Ph/n7rx+jHw6RpGo5F5R30+H9u7+DQrXhKL12Tmmx5Qwx5qYEB5ymSHBAIBFsFS+zz3bV/8op5XNckeWZZZxWQul2MJ54VCgb3OarXC6/VOXIt+NmvStFotVCoVNsn5TY0SpJXGK0n/OJ1O6PV6dLtdFItFpNNpJoZPeUR8nhD9HePxGHq9nl3H5/Mhm80iEAggEAiwVno2m0015HkfH5j7isViYdWdbrcb2WyW/Uyn08FiscDr9SIcDsPr9TJvES+pJtI13nObwjVaIJUi5LwHSS0fXa1Ycjgcsla/VGCi9NLReNGCzf+cz/8iNQPlIYX/3Mp8PuXXY4QOdbyH3Wg0Ip1OTzWi4D1pSoF0Cj8aDAZWLU1UKhUWtu73+xOSXMK4mQ3/3PIMBgPUajWkUikcHBxgd3d36mDHjxOlvtG8owMd7yW32+2IRCLY2NjA8vIy3G43ZFlGoVCARqNhWqZKlM8BP4+Uc+oxMu9vVfNMK9cYNWgNpLVL7WAXi8VYsSyF/am1Lx9x5AvOlWSzWbx9+5Z5ZekQGY/Hpz43pQ/ddaOnj+WLG69qg9zv91nyOaUNVCoVGI1GVmGn5omZFa4Yj8fI5XI4OjrC+fk5G2BKKqfWodSxh4xbCqVYLBZotVom5k36a+VymeW9Esp8V8p1dTqdLOxK+SuLi4tYX1/HysoKQqHQxGee9/A+VeYZFAaDgfVzXlpaQjKZnOjBToVIPp9vomCOJq6amLrg4+DnofK/+UIftYNDvV5nKSC0KCs9RsCk4aq2kfIeQoJXF+ENM/79fDU+8PCjIWqfX6fTMcNFlmW2VtlsNlxeXiKbzbJwMfD+Hn6os4/y95KHVTl+j/1w8DHMMv7oUEd746yxoP2Lb8+qdk2bzYZQKMSimZubm3C5XOh0Ojg/P0ez2UQymVQ1gtSMV8EVSg+48r7Puld0uACmlYl4FhcXWdqP0WhkjUOUObDz0nIuLi5Y4Z9Wq4XL5cLCwsLEXjgej5nG833LY/6iaQNqp0tyh5PXpVKpME+o2WxmeRmdToe1mCRm5dmUSiWcnp7iV7/6Fd68eYNcLgcArApQo9Gg1+uh2Wyi0+kwV7xer2eC7DqdDqPRiBmwtVqNeYQoXAO89wbRQ0PvJ+kg4Gph8Xq92N7exng8hsfjmTJe1TQrBfOlxUh3j4TwyXilIgM6RPCQ8Sq8rh8PnfTn3Us+9My/j4e8S6RxmM/nVY1XpWC3ch1RfgHvU3mUXmHlQZHPvb1PC/ZtUfsbNJqrtryU+0Y6lBT9ora7vPeVvx6/MVMnJ6UOr9VqhcVimQhPCq6H2l42Go3Q6XRQq9UmDuc8fBtXZZEjH47W6/Xwer2IxWLY2NjAV199hc3NTdhsNpRKJVaoed3CHT5a8xQOJmrF2WrcxqhX6skru5JptVrEYjFmv5A+d6/XQ6FQmGjTTLYIqbGQrCRw1fyp3+/D6XQyZ1owGJz4G2/7N3xqvqjxSkaFcrOjispyuYxutwuz2YylpSUEg0Gsra2xwiceMkCU4cdarYbT01Ps7e3h1atXePnyJbLZLGRZZgurJEmsJRuFJ8kdb7FYmEabVqtlbeFoYW80GnMHttvtot1uo1arTYTUSHUgHo9PtX8DJitxnyL8xqg0PNS8EbVajd1HOpQQ9KzMalUpDgmT8J5IZXhfafgBk5vVdZ9XfhyV7xkMBuzwSjJZat166P2kScvnalHV9IcOJY95c50FjSndI5vNxgrxSLWDWvEqPXb8/eL/m8ZG6emhZgjUeYuHlxsUXA/aQ7rd7sRBwWw2w+l0QpIkFkFUg+aowWCA0+lk7UKXl5exuLiIcDgM4Koyne8CpebBE+P2nnnryF2sMSTFpVQ38Pl8ePbsGdrtNtrtNosa5/N51pGLoNx03ngFrgrQqXvpysoKXC4XW0vpWbmrv+Mu+aLGK+8i56Gk9HK5jMFgwBLJ19fXsbGxgcXFRWi1WrTb7YnNlATQKaenXC7j/PwcL1++xKtXr3B8fIxUKsUKu1qtFhsY5QmVXPCDwYBV5FqtVkiSxNzy1IJN6W1QotYZiBaYwWAgFgEVlOFcGmfec0fqEPSsZDIZpFIplMtlNsbA+xDwpyw4eiyQkUIbllLzT+0+Ko3a6zLLMzMajdDr9dBut1mB46xcdgqd2Wy2KUP1U3jTH0MeOj+neDweD8vB5+UGeW8d8H7cruONoXxzNSUKMo6E7rU6s3JG+UIdAHA6nVhaWoLT6WQKA7OgGgy73Q6/34+lpSXE43GEw2G4XC4AV/sVSW6VSiU0Gg1V41X52Z5azuvnhuatcu6Gw2E8e/aM2SuDwQCNRmOqcJkKXNXGJJfL4d27d6x5wfr6Oqsr4p0+/Jh+acfaF1s15m0A1Fkin88z4/X58+f45ptvEI/HYTQamXt8NBoxHTR+gWy1WkgkEnj58iW+//57HB4eolQqscICfuOclxdCEhXxeBwejweSJKHZbLKCICou46EUg3kT1+Vywev1Mi03tfvz0DfJj0Ftg+TvB3lbM5kMLi8vkUwmWVeZk5MTlEol9lo6eKjdZ2Xe1lOH7jsvj8QvVvOey5sadnRtNSOTUnTa7TZLzVGOE+Vn2u12plN4nc8zGAyYQUyehVlajY+RWZsgpQ+QfI7JZJoq0lB625XzU62GQchh3ZxZewfdT3J6SJIEt9vNjM9SqcQatCgxGo2sXSkVr66uriIej8Pv9zPh+mKxiIuLC5ydneHy8hKVSmXKufOhzyi4O2iNnCUZKEkSlpaWMBwOWWOXi4uLqdbewKSOPX+ddruNw8NDFrEcDofY2dmZ0mYmh8Z9SLP7osar2om7VCrh8vISiUQCmUwGo9EI4XCYtVnT6/XodDooFAool8tMN9VgMKgar2/evMGbN2+QzWah0WgQjUZhMplYlR0JMJPaAD9JPR4PVlZWsL29jbW1NQQCAWg0GpRKJVgsFnS7XVYNzUMnHHpQCNooXS4XlpaWsLGxgYWFBdhstqn7QGHVp2rA8vmTyvvAT9JEIoG3b9/i6OiIPTPFYhHNZhM6nY4VcQWDwYlUAuI+JqJ/afi8qk8FbcK8V5f//ZTb12g0WHctPnrhdrsRDAbZAVCp8UqFBrTOKIsyKcQmyzL0ej3L+SSdSz6Kw/NYnpNZnlO+oUc4HGYFrMq17LrQGpnNZtHv9yfGibxAX9qD89DgDx98rQWlDgQCAZTL5YlCYpfLhVAoxKQC/X4/gsEglpaWsLi4iFAoBJPJhOFwiFKphPPzc1bgTNJp/J5NGs88yhzzp+6AuSuUthKffkjhfZ1Oh5WVFRSLRRwdHcHtduPi4mLqWsPhcKJGh+becDhEKpVi3yOFpFld8e7DnP2sxit/aldW0tGkIfmPo6MjpFIpaLVaeL1eZDIZuFwuJshcKBRQr9dhMpkQjUanbnKn02F9gUmrLhQKYXl5GX6/H1qtlhmsFJ5sNptMSstsNiMYDGJ9fR07OzvMeJUkCZlMBsPhELlcTlUYnUJh/ESmyl5SGaAE+eXlZdaxiIfXV3wKzPKw8v9P4ct2u83SBE5PT7G/v483b94gkUgwrUqTyYRQKITNzU28ePECKysrU8VawHsD7anc5/sChaTJIOXzVengR4oD6XSaeRHo8LewsIDFxUV4vV4mY0eHV1mW2XzudDrodrvodDqs0JK641HKDxmwvKSdz+eD1+ud6En+WJiX5mGz2RCNRtFut9m9IcOVOtbxBw4+rUAtTNzpdJBMJvHy5UtYrVZsb2/D4/GwCNht2gg/dfh7D1zVddTrdbjdbrhcLmxubsLj8bBwP9Vt8BKNJCu4sLDAoorA1dxrt9vIZrO4uLhAKpVihWHUillNOUKr1bKmIlQYJNbUTwdp6VJhOXC1NtKhxOv1wmq1sjoQeg1/CDUajTAYDKzYq9Fo4PLyEg6HA7FYDCsrK4jH4xP2yX06kHxW45Umm5pHp1Ao4ODgAN9//z1++OEHvHv3Dvl8noWwrFYrC/vX63VWKOX3+2EwGLC4uDhxvdFohH6/j16vB51OB4fDgfX1dbx48QLxeJxdh7pkkWQEeWSMRiO8Xi/i8ThWV1fZRkmSMqVSCR6PRzVcSW55Xu7KYrHA5/NhbW0NOzs7eP78OTY3NxGNRmcar/flIfkcXKeqkbxxlUoFmUyGhbZOT09xdnY2ESojNYef//zn+Pbbb7G+vs5yuniEh+DLwXuQeDQazUThEB/+MhqN8Pv9iEajCAaDMJlM6HQ6yGaz6HQ6bHNtNpsol8soFossd49vpUnGa7fbZUUO1Bp3cXERa2trWFlZQSQSmZLNeugoG8LwTgWHw4Hl5WVmsJNAfb1eZ0U8SqjwVm0se70ezs7OYDAY0Gg0kM/nsbW1hcXFRdjt9qmiL6UahGASSZKYgo3BYGD5qfl8nrUPXV1dnchZliSJdchzu91wOBzsGmpKN71ej3W3pAML8L4IctbnUip0iDH8NPDzVQlJQgYCAfj9fhY1oQgmP8fUpLharRaq1SrK5TJKpRIqlcqU8Xpf5udnN17VFqfhcIh8Po+DgwNWXHV+fo5utwur1YpCoQC9Xs9CT9RIwGw2Y21tDQsLC1M5ORRGcbvdrKXa8+fP8e2337KCr2q1ikqlwrwMg8GADTZ5Sv1+Pwu3kKHa7XbZCScQCDDNV4ImOJ/TZ7VaEQgEsLS0hK2tLezs7GB5eRkul2umHMZ9eEA+N1QQRzmOtPBSHnG73UahUEAikcDx8THOzs6QSqUmDFdJkrCwsIBnz57hZz/7Gb766iuEw2GWNqDshvYU7/N9QW1D7Ha7qNVqTE+ZhzZhp9PJct/T6TTq9TrTbQbAnhNqcpLP55n8D+XQUtU2Fb9QS1wq0nS5XGyzf6x5fLIsM++3wWCAVquFzWbD2toaE6hPp9O4vLxEqVSaKAKhjUzpieNz8/r9PtNcLpVKbO3W6XRYXV2dWQzy1JllJNBz6vV64fF4mGet3W4z2cXnz59jaWkJdrsdsiyzZ1yn08FqtcJoNLLrWq3WqQhoo9FgDiK1z6W2XpJhxKsTiFqCTwel1KkdJsju8fl8rE2wUjWCb4agJkFI6QW9Xm8i3ec+HUw+u/FKp3R+0Wq32yx/8fj4mBmuwPtJDFxtatVqFYVCAa1WCw6Hg50ulEUBRqMRwWAQ29vbzHjd2dnB+vo6FhYWoNFo4PF4WIcsmmwU/tJoNKxnOxX8EBaLBR6PB7FYDJubm0yagry2fC6tLL/v/e7xeFiYJhqNwu12T3xm+hvuy8PxKaH7rcznkWWZeVbr9Tp0Oh08Hg9rp0vFcsfHx9jf38fJyclENy3gSj4kGo0ydYrl5eWpwxIAEbL8xPByZ7y6CEUlALDQMU+hUEA2m0WhUJiS/CEDi7Rg+/3+xPhTLmen00G5XGaFn8VikaULqBlI1JCEZLfo99wXL8OnRE0GCwAikQgikQhCoRA8Hg9yuRw7VCoNTf4aer2e5ezTAaHb7TJDiwrtjEYjy7UE7tfG+KWZdS90Oh3cbjdisRjW1tYAXHlKXS4X2w9XVlaws7Mz8T7yiivXWuX1Ly8vWYc1NdWCWYcLinSSZBNVvqu9/inXctwFlCowa65QO3qXy8Va2CvVjvhideXPKPWD1ub7qgbyRT6V8kRG4XsK8fGne6fTiVgshmg0Cp1OxxQIyNNKIT1lKoLVasXS0hJMJhMT4Y1Go/D7/WwwnE4nTCYTm2RKPUPKy1LmtVI3p/X1deh0OoTDYeRyOZRKJRaeLJfLqNfrTA3BZrPB6XSyLzXpmKfkdaBJo3Z6zOVyePXqFS4uLqDX67GysoLNzU243W5UKhWkUikcHh5if39/4qADXJ06Y7EYlpaWsLCwAL/fr1oBTf+KRfTTwntkBoMBO7xS8aJybtVqNZydnSGZTKpWTMuyjEajwQrzSHSbNs/hcMg0EXk95lmSdnQ4CoVCLM9rdXUVS0tLCIVCTA3kKT4nRqMRdrsdHo8HXq8XLpeLGSi8I4K/N/Tfaver2Wyyuet2u2E2mzEej+H3+1m/dp554dHHzqyoEDUWWFtbQ7fbhdvtRqfTgcPhwOLiIgKBwJRThK6nvL/8tekQuLe3h9PTU5RKJdUGB8o2zATJ2/V6PSaWP09x5yntdXfNvNC9Xq+H1WqFw+GA3W6fqCVQXoMKWZWQ6ojVamV69Dz3Zew+q/GqVsXcbreZwUcdq9iH+8kw3NzcxM7ODsxmMzKZDGw2G87OzgBcVR2rDZLFYkEsFkMgEADwvk2rUhKHkpZ5g4b/vLMekEAgAKvVikgkgnw+j1QqhdPTU5ycnGA0GjEFAr6qT6fTfbBbyVNZqGdVLcryVU/t169f49WrV9DpdCgWi0wpolQqsVzXs7OziYpat9uNxcVF7OzsYGVlBYFAYKbCAPB07vWXgu6vmnIGHQx5arUaE8u+uLiYyLcjOp0OSwHgq635HHfaXPnQGkVO+J+ZzWb4/X7E43GsrKxgbW0Ny8vLWFhYYOkClFv4WJ8VPrKlHKPBYMBy/xcWFlAoFFjKBV+xTKoMvDeWdwLwa2qr1UImk8Hx8TFcLherZ1Ae5nkpr8d67+cxy6vGO06oeU+73WaRxmAwqFpEPI9+v49SqcTkBrPZrGrKAJ8zqfZ5eS3uD43ZUxzTu2SW44UUJ0gOjYrc+X2S4MdLacTyaj/3lc9qvJKHjW56r9dDsVhEJpNBNptFpVKZMF49Hg8ikQg2Njbw4sULuN1u5PN5OBwOeL1edLtdRCIRBAKBqVaxRqNxpnaj8kR/nVAVf+LU6/XQ6/VwOBwIh8NYXl7G4eEhut0uLi4uJiqb6fV0KuWFhJWG1VOc0ErjdTgcolwu4+zsDLu7uywnx2azodVqoVar4fz8HJeXl1OG6+bmJra3t1mbQ1KVUELJ64K7Rc1TxhdyzKrYH4/HyGazOD09xZs3b7C3t4dkMolOpzO1sNbrdVWjVgl5DSnKQW0u6TNRZ6lQKISVlRWsr69jdXUV0WiUdSp6KijXZWIwGOD/396fR0m2b3d94PfEPM/zkHNWVtW9de99V3qSwLRAWJZtsGnA0Mu4kdu4jcx6tGUaqYHl1ZhhLXdLGGipwVoyzSAMeGEDLWzL2AiD1AxCb9C779ZclVPkFBnzPEdknP4jcv/qxIkTkUNVZmVF7M9auaIqhhMnzj7n/PZv//b+bofDgXg8jvX1dVQqFZEPSVE5tdSZOtdRr9eLNB2yZa1WQ7FYFMoxVOCqhJzXRbKDEq0JH6lieDwe0Yu+2Wyi1+uJomCv1ys6QapzWalzpFoaUFk4qawHuIrjQlKVfr8fwWAQHo8HNptNM1CzqDZ9Vyhl0tQ2MpvNY2oslUoFrVYLZ2dnExOSaTnJVFdEq+EOh0Pcu7WCfO+LG3VelXluWrMxKqqgGV8+n0er1RrtmMGAUCiEaDSKWCwmnNRAICCiAc1mEx6PB/F4fKLqf9oFQrIuwJullFkXE50oyotfPQhTWkC320U2m8Xh4eFYARcNuLSEqW7tt4iQM6OOElBlc6VSQaFQAABsb2/D4/GI7jFUPKIkFotha2sLn3/+OT7++GOsrq7C6/WKvs/qyADzblBGY5SrDJeNmLXbbRweHmJ7exsvX77Eq1evsLu7i1wuJ5RCtATS1Si1Wmmpm+SuaHWGIqi0CmKz2eD3+5FIJJBMJhGPxy+UxJq3VBP1xF2pv6vT6YTCSq/XQ7VaFfKD6m1QQSWhzG+m5UnKhQUwVjwyLadu0Qu4tM4zWkUwmUxwuVxiXALe3FPpmiHHRt0ogmxLr1Pzgm63i1KphOPjY7jd7olrYda90+FwiKYH9+7dw8rKCrxe74Rt+f779ijTG5X+FTA6BzweD5aWlsTqCJ0XJycnqFarosHFNB+k2WwinU7j5cuXsNls6Ha7CIfD4h6q1bjkfdwTb9V5Vb9GuWuHh4c4OjpCPp8X+YuBQADRaBSRSARutxtWqxVms1lUSlKuD+k+qiOvs5iWA6llAPVz04yUz+eRSqXw+vVrZLPZsdfIAaZcXe4486aRg/p45nI5VCqVsahqoVDA3t6eUIXIZrNjea52ux0+nw+JRAIbGxvY2toSPbqpjSwAkbbBvHvUjsZlb2apVApffvklHj9+jO3tbXEfaDQaF4rim0wmWK1W0WHP5/MJcX3KdybH1WazjelP0g2d8sM8Hs+ltFznyXHVgnKGqWCVWmPLsoxsNovnz59PRHvo/k6DGOlPAuOOEw2kVBBCKVt8TV4OWsqlY2ixWMYcGeX7gDdOBX1OaSdyeil4YLPZsLa2hl6vh9PTU7x48ULT8ZzW0IW0QUkGcm1tTQSUlCli7Ly+PUo7KleEyT408aDxjqLtdN+rVCrCD9FSs2g0GkilUtDpdOj3+yIVa3V1dWI1W5mGddvc+F1j2sy50WiIKOXe3h4ODw9FNI2Kqyj/zOVyjR1kn88Ht9stlAG0oqHTUFe3X/b9yuUvNbu7u3jy5AlevXolulQoiUQiCAQCY4PoIt6wlTM09Q2MCgZev36N09NTobXb7XYxHA5RKpXEuaReTqYogsVigdfrRTAYFNulC/aidr3M9VAOqMrnCLX8m5Lt7W185zvfwTe+8Q08ffpUtDQkaR9a8VBCkQW/3w+32y06YlEbZyrWSyaTQqibJLSUN3HKxZymd7io0ERb6aRIkiR0dV0ul+a9S9mwgD5D0HPKKCE5VdMiQKw8oM1ljslFk0il86rOawyHw/B6vZopd0rHU5nLTDrq8Xgcm5ub2NraGltRo0LNRR333jVqnWblSrIymk4rHlRk2Wg0hMY1BYdoTKZVqbOzM6HAROkGNpsN4XAYq6urY/tB301jwG1zo2fStFlWu90WBU7b29vY29tDJpNBp9MR8ikbGxtC5sjv98NkMo3l8VyUTKzM47nKIKXO+1FerNSalrpykQNOzRUODg7EBe1wOETaQzAYRCAQELIzTqdT8yKe5xu1shUo/U5qc0fLkZTzuL+/j2azKaLsVqtVHC+dTger1QqXyyWq0bvdrrgwO53ORC4PXcTTJIGYy0HXFKFMDdA6pr1eTzQFoFatJHOl1HV+/PixqHBut9uaxQNms1m0vozFYohGo/D5fEJyyW63IxgMIh6PY2lpCdFodGrO+zRoKVuZx7mojq36N1ssFthstpnFa8prW+s6oygRVaRXq1WR+0oRcOW2uG3z9bhMsRQ5KtRNkiAd5G63e+l2wJSCQ4EDrVbN5Cgzl0edX6o1cSG/RAvqGki5/w6HAxaLRfN+Rvdyuv91Oh2cnJzA7XajVCqh3+9P2JVWabRyrG+DG3delTeg4XAoqk1J6ojawFIyMVWMb21tYWtrC6urqwiFQlceiADt3JCLUIr2qpUBZFlGrVbD8fExUqkUDg4OxF86nUaj0RCaiIlEAsvLy4hGo3C73aJFHyWzT4te0HGbJ5Q2UOYQU99zmshQpfnu7i7y+TyGwyEcDge8Xi98Pp9w+lutlshlpd7r+XweR0dHSKVSQvKImNUZhrk8Wnqt087VwWCAcrmMdDqNTCYjdFbJcSkWizg9PRVNJkhXUquIwG63i6gqKQLE43H4fD5YrVbo9XpRoEXi3NMkYi7z+5jrFWQoHVeK3tLz5MzShIZqHbxer5iAyLIMu90uouNsi5uFJhLNZhOyLKNarYpgUqlUmnBe1QEhggJJd706/UNDLd15Fb+AVJwKhYKIpF4kW6Z2PpUrXwaDYeKeOk027bZ45yP6rC5R9Xod6XQar169wpMnT/D06VPhqAwGA1gsFvj9fsTjcaysrGB9fR3JZHLM46d8qsvMLtVLX5c1vjJPSEmz2UQ+nxfLnc+ePcPu7q5oX+n3+7G6uoqHDx8KuaZgMAir1SqOBxWWaF3k8+a0EsqZPjkvjUZDNCPY398Xk5mdnR2cnJwIB4maOySTSQSDQRiNRtTrdZhMJiGN1O/3USwWsb29jUAgAKvVil6vh0QiAYfDAWC8KIRuCIsYUXsbZulEKun3+8jn86IQa3t7G6lUCplMRsjhUStmipYrIzNqB9bn82F9fR2ffvopPv74Y6yvryMUCo1psFJ6D+VSXgel08TL1uPQEuFlonFqHWWj0ShyXweDASqVioimK6WyLBYL9Ho9bDab5rHnVZN3Dzk5uVwOR0dHeP36NZ4/f46Tk5OpnZfUkF17vd5YLQLB19L1UK9qXbYAtlQqIZ/PiwYtmUwG2WwW1WpVpOFpoVYKoYJqiqxSKh/xvlem3rnzqlxaVEcty+Uydnd38eWXX+KLL74QOaIkj0WdqNxuN3w+H3w+30SomhoK0EA1i8vkYqhviJTkrD5ROp2OuMD39vbw+vVrbG9v4/j4GP1+H36/H6FQCA8ePMBXv/pVfPrpp1hdXdWM+E2LAs/DxT2t+E2SJNEViW6Wp6enODk5EZqtqVQKx8fHY4PfcDiE0+lEMpnE8vIyLBYLKpUKzGazWOLI5/Po9/s4ODiATqcT4vSfffYZ1tfX4Xa7x/ZFK4WBmURr1q+8WanTCM7OztBsNlEoFHB0dIRXr17h6dOneP78+dikRAtl1xiluoDb7UYsFsP9+/fx2Wef4bPPPsPy8vLMAk3ld6hTRabJ5PEAOxtyXC8bnSZnlVavKFpH28nn86IPezAYRCwWg9/vH0sdUG+Pndd3B03Uut0uTk9P8eTJEzx58gQvXrxAOp0WLUWVua3kpKpt0Ov10G63RXctLdhuV0frfqRcvaTIJ00aqAVzPp9HLpdDPp8XDZNIS7/dbmtOQLR8EkoRajabyGazOD4+xvr6unhdGTh4Hw7sjURelV10CFmWUalUsLe3h8ePH+PJkydIp9NipkbVrXSzm6Yzp6xeVTqnV7041MvYtC2t72w0GiJCuLu7K7r/kFary+VCIpHAysoKVlZWkEgkEIlEpi5Vz3PEjwYZ9XGUZRnNZhO5XE6kXBwdHeH09BT5fF7kO6oHRorIh8NhUcFarVbFjNBms+Ho6EjMLJ88eYJKpYJ2uw1JkkTOj/KYk3OjXNpkJlHOxLWaa9CgRa2QadZ/enqKvb09PH/+HE+fPsX29rbQPJ6GOtd8OBzCYrEgFAphaWkJ6+vr2Nzc1Kx4VaOUe5rmvAJvcvLfdwThrjGtyEerLSy9Ro/K19THWvn/wWCAarWKRqMhJASpSE8LTiGYzSzFHK330Fin1tV++fKlmDxSnvNgMBhroa61vEz3AZJh4mvp3UJydZ1OB+12W/zV63VUKhWUy2UUCgUUi0WUy2VUKhVRm9NqtVCv11GtVlGv1zWvMS3VCmAUmSfZLIvFgna7jXg8DpfLpdkd8za5scir1g2O8kUpr0aJ1WqF0WgUSxPU4lENLetdRUtSC2UeLn2PVqSWZCOUy5/pdBpnZ2cIBAKiHV88Hsfa2hrC4TBsNtvC3myn/W5anjo8PMTLly9FF6VisYhutysc0VAohFwuJz5nMplgt9tFlx+SNaIWdl6vF36/X6QdVCoVvHz5UvQApxzjSCQitqklAM5oMy1CTc4HtUQmUWtl7vHOzg52dnYmeqRbrVY4HA5IkiQKHwGIqnMqNPB6vYjFYkgkEqLa/TK572Tfi6KEV1mOWyS0jpnyGGnd26cd71nHnyKxSqd4Ue+b1+Uqx0t5z1OmctGkM5fLCcfVZDLB7/fDbDaLqnNKDaA0ECVKNRd2XN8t/X5fpNpR86NqtSpslk6nkU6nkc1mRaMnWuEgVQl1kyQl6oml8nvL5TL29/fFqlqlUsHW1hZWVlY0u7ldZhL1rrhV57XVaqFQKCCTyYy9Rh2raNChDlTTZuHk8b/tRaLUPqP9UNJqtbC/v4+nT5/i8ePHeP36NXK5HDqdDiwWC6LRqNCUDIfDCAaD8Pv9MBgM4j3v07jvA7WjMxwOReSaJgHPnj3D06dPkUql0Gw2YbVaEY1GRSV5OBwWBVuk10mC86T5q9PpYLfbEQgEEAwGYbfbIcsynj17hkajgXw+j52dHeHwAhBpKOrZorIAiXnDtIjkYDBAoVBAOp3G8fEx0uk0crmcSAXJZrPiUem4ejweLC8vIxaLwWw2o1qt4ujoCIeHh2OpAnq9Hg6HQ2g90zmgbiEKvHF4lak+lP/KvDveZmnwIqeUVusosscO7OW5KNKqfF09AaFjrg4UmUwmodih1+tRKBREZBWAUA4hO1HDBLvdfq3CamYSWZaFXBVFTSuVioiyUqDg5OREFI2r77dKn4qusVnpcmp/q9frCU1YpeNcqVRQrVaxsbGBcDg81oHrNsfQG3Fep92sqMKcIi2EcoBUytVM27Y6QqpeJgQmL1RlboZak1LttFJ+7sHBAZ49e4YvvvgCjx8/xu7ursi3XFpags/nw+bmJjY2NhAKhWC320WS89nZGTqdzkRhEO3DvDhK6uOt/K2kJqB0XElhYnd3V9wMqdkEOa7D4RCVSgWdTkcU64RCIXGDpPauVqsVHo9H9EinivPj42OcnZ2JKCzlR25sbCASiWgOwhyJxcT1o3WcGo0GcrmcuGlSgxFSFaCWnyS3Q21Yqc3z/fv3EYlEcHZ2hv39fSHJooRaLwcCAQQCASGJpZ4Idrtd9Ho9SNJIL3SWjBNzebSOITk40wIK1/0e0nntdrvodDro9XoYDAaczvOOUEdbCXJaKUhEXSsNBgP8fr9YRaTrK5/Pj6X+UADJZDIhFAohkUggEAhMdLoE3qQBMZNM08GmqGcmk0Emk0Eul0O5XEatVhPObLFYRDqdxsnJiWY9wbQOWsrCVErBVGs0078Hg4FQKyBnulKpiBW3zc1NLC0tIRgMaqoRaP22d8WthidmObY0O1CGutXGoAOsVYWqnLUrL1bldylbqE2j3W4jl8vh4OAAr169wrNnz0Qi++npKYDRTDMSiYicvOXlZRFCV+rK0vdrFb7MC8rfpj6ujUYDh4eHePHiBV6/fi2KspS5zoTL5cLy8jLu378vWtJ1u11YrVYkEglEo1ExOSAJLVpedjgccLlc8Pv9SCaT2Nvbw8nJCWq1GnK5HJ4/fy6kPux2+0QB17zZ5Loob2ZakctisShSAmi2T5HXbDaLQqEgCjbMZrNwPuPxOLa2tvDJJ59ga2sLPp8PpVIJer0e+/v7E9+l1+uFnTwej5BS0urSpzVJZa4P1Rqoj3WxWBRKEWp5HOV9Vh3pU/9fnRPb7XbFYFyr1dBsNuFyua6tGMFcDkoDIMfV6/ViaWkJg8EAkUgEa2tr8Hg8ItKmnjhKkgSPxyNy0re2tsbUXZSQVCGnE0xCPo76WqFW869evcKrV69EnQ1N8KhAq1wuo1qtXmlyoC7AVeeyT5u80vdTQCqbzaLZbEKn08HpdI5ds9Nqn94l79x5neWg0XKe2Wwec17owJEDquwIoRyQpuW50nZnRV7puWkHstvtCqMcHBxgd3dX5Lnu7+8jn8+PvZ8krygHk5aklVW5yhaoyn2YpwGW7KZ1bJvNJo6OjvD48WM8ffoUR0dHIplcSSgUQjweF/q+4XAYOp0OvV5P9PL2er0isqbX62EymaDT6WAymWCz2eDz+RCPx7G+vo5UKoWXL1+KNI9MJgOLxQKLxQKDwYDV1VV4vV7NiMS8p3VcxLSq+2w2i93dXTERSaVSotiuUqkIDVedTjfWTCCRSGBtbQ0fffQRHj16hPX1dRHJSafTmqoBkiSJtqQej0fcGNX7RMtiADQdLubqUDdBOpZU0LO7u4uDgwNUKpWZBR9KlEVyWgELyqMrFovIZDIIBoOie6LNZuPUjxtEWVhrt9sRCoWEjai5jsViQb/fh9lsnoiEDwYD2Gw2xGIxbG5uYnNzE7FYbGrkFVjce+o0lIEt9fFtt9vIZDJ4+fIlfu3Xfg07OztCAYI+S+3mlSlXl0G5sn2ZNB0aa2k1s1KpoFAooNfrwel0Ih6PY3V1dSIodNOFezd2d9A6UWmZwWKxjC1BKGUfut0uarUaMpkMjo6O4HQ6EQ6HhRNIEklKvVetQquLICe50WiIxOfT01MRhqck6Hw+j3a7DYPBgMFgALPZLHJdg8GgaFFJM1OS2pqVbzRPkPOqdZJ2u13kcjns7u7i6dOnInINQDiTgUAAm5ubwrFZWloSUW3avlYEh2bysiyL8wqAyJ2lnvavXr1CNptFJpMRwvnZbBbr6+uIxWITeZQ0MC/isqU6pabf7wuZFHJcnz17JmSvSqWS6GhGs2+v1yuUN5aXl0Wzjo2NjTGZFa/XKzo2qW+epDTg8/kQCoVEm1f1Ocai6G8PrXgp05noWut2u8hkMtjb28OXX34priV15JUKr9TXqlJSTXk/pH9TNOf09FRMUP1+PwKBANxuNzuvNwiNmSRXFggERAqH0WhEp9MRUTat5iHkcLlcLoTDYdHxTitiPm2llR65cHISkhw8PDzE69ev8fr165nvp/RH5URBrbBDPpYy/Uc9tqonmjTOktYrQTmwtVoN7XZ7IkXhNvLWb+TuoHUS0sGknFA1dAOk4p5Xr17BYrEIJ5OKPAAIiY/BYKCpyXoR/X4f9Xpd6FHu7e1hb28Px8fHKBaLovcvbd/r9cLlcomoEulOUuvaadGjRWGabm2v1xOtX9WO6+rqKtbX17G2tobNzU3RCpiaOhBaKSLqgVaZs6zT6YTig8fjQTAYxOPHj/Hy5Us8ffoUOzs7SKVS+OpXv4rv/u7vxsbGhhgklQP5okAzb7UT2Ov1kMvlsL+/j1evXuH58+diFSKdTovGHMBIPYCiNcvLy1hbW8Pa2hri8bgotFOqPQAYS61Ro2xYEo1GEQ6HhTTLokfGb4JpuWmdTkesnHzrW9/Cs2fPcHp6OhbpURaEANqpG8rVGeVKFClWUN97sncymRQyhMptsM2vxqzjRWotpKlOKRukwZ1Op9HtdlGpVETrdjXUGpRSt+x2+1jgRu0IKc8TdcRPeT9fNDurfy8pQNDqc61Wu3AbdA+3WCxCclTpG9E2G43GWHEe2UDpnGqtElOAgsZat9uNQCAAr9cLi8WiuRp+07a8MzmvwJvkYJLKoiT+4XAIg8GAZDIp3kvtQa96U2u1WqhWq6ID0MuXL8fEmXu9npDp8Xg8sFqtMJvNMBqNIjpAHcBWVlamtq5dpJvttN9JlZLqAr1QKISPP/4Y3/u934tPPvkEa2trCAQCsNlsl4qkKSPt05YlnE4nPv74Y8RiMZhMJuTzebx48UIIN9tsNiSTSSSTyYnuW4tiN2CyBSFRLpexs7ODb33rW/j2t78tcr4rlQq63a54n9VqFeka9+7dw71790RnPJ/PJxQ31NdIqVSauJESkiTBarUiEAggEomMXWMUNeCo67tB6VCo6fV6KJVKotjy4OBA5NcpWy4r89uAyeY0SkeFovuyLAuHqFQqwWQyIRwOo1AooNFoaBabsArB9VGOuxRJoyV+ksrS6/VCAYSuT9JvVsvdEZS6ZbVahfb2rBVHcmqn5UkvEhSEUd97KXCm0+lE+pRaoUkNpdfRJMJmswm/BRhdy7VaDYVCYezaU0KpAbRvwJvrlxxXqkOgAN7m5iYCgcDEajON0R+c8zptmYDSAtRdOMhD7/f76HQ6YhZI1fo0+JnNZoRCIQBvOvKohZO18lsHgwGazaZIPi+XyygWizg+PhbFJxR1NZlMYlZBFZQOhwM2mw0ul0ssZdJyJkVktX7/IjhCyiioEhJPbjQaYzc+k8mE5eVlPHr0CN/7vd+Lzz77bCzJX3lhUa6N+vu0jinNLKmYiz7n8/nw6aefipbEmUxGSH60Wi1NzbtFQj1DluVRM5GDgwM8ffoUv/Zrv4ZvfetbY3JWdD3abDYEAgGsrKzgwYMHePDgATY3N7GysjJWfaqOvpCSByX8q4+52+2G3+9HMBjU1HZVNhVh3g2z7tkUTKB7rXJgmuVQakXfKPqqvu4oF54cJqWsD/P2UKRbvZxMcoOZTAaSJKFarQoFEYr4TVvd1HKW1NekVkoA/ZuvX+3xjDrTud1uJBIJlMtl6PV6FItF1Ot1cS0CEDUfpLrjdrvhdDrhcDjGCl07nQ4KhQIcDgcsFgtKpdLE+KeVSkDXvNFohMViEcV8W1tbePDgAdbW1sSquHIlnO4RNznhvDHnVWunqVOHehZHP1b5uXq9jpOTE5hMJhiNRnEgSGqHHKZOp4NSqSRufDRjoc8BEHm0pVIJ1WpVOMZ0o7TZbAiHw3C73XA4HEgkElhdXcXS0hICgYCQaHI6nXC5XHC5XOIkmKbjSr9r3tGaYQ2HQ2SzWSGarFxmXFpawsbGBu7du4e1tbWJ6lQ6R4DRsdSqMteC0kvoAlKmciQSCdy/fx+pVEooFPh8PlEUpvwt8yRjpoVa+UKd46p0XB8/foznz5/j6OhIHCeavNHKBFUbU9EGyaYoUzmUOZTlchnHx8dCfaJUKk1cM6Qu4ff7NbVdmXfHrOU9o9E4ttLUbreh1+vFoKfMdVfewy/7nUpIPF3ZtIB5d8xKiXK5XLBarWI8pVxGQr06otPpEAwGEY1GEYlEEA6HxSqlOojEzEZtDwrE2e12RKNR4Z8sLy+jVCoJ34VWncmpJN+EnFan0yl0d5XO6+HhoZicUFdKivQSFJlXq884HA4Rcf3KV74ifDGr1SrSDkjm7jaK9G4t55Wep5m3kmknPOW/Uji71+uJG2YkEsFwOESpVMLr16+xvb0tcnMoD4ciNu12W1TJtVotsSzpcDhENfPm5iZ0Oh0cDgeCwaBYriQNUavVKqK/5BhPi7jOOgbzgFoBQjkYUXWysuCNIqkejwfJZBLLy8uIRCKajslFQsrTUObjaL22vr6O7/u+70MkEsFgMBCKBrVaDRaLRejHqvWDiXmxp3KZWH0dtlotkeP49a9/HV988QVSqZRQEYjFYmJSR73ofT4fwuEw4vE4IpEIvF7vVDtQterh4aHospfL5cSN02KxYGVlBZubm0gkEvB4PBPbWtS8uJtkWgTMarViaWlJ2N9sNuPZs2finAAgojK0jWktRC9CqSTD9r1dlNKUs1Y0KCjkcrkQj8extLQkCmy9Xu/EvfNtbbgIaQVaaRW0PC9Jkqjmp1bKpJ+tzFM3m83CP6E/8n8olaPb7aJYLCISicDn84mJBqVnKlEHcGhspeK85eVlUa9CqSfKQrDbUn25MedVyyiUxxaNRlGv18Vr5AhqOYPU4YEq2gwGgyjkkmUZqVQKT548wRdffIGDgwO0Wi0xSyBNUOoB3Gq1IMsy3G434vG4eAyFQmKmYrPZYLPZYLfbxczFZDJp5vNo/e5FgKJ36tmVLMtot9uioj+TyaBcLqPT6YhqdFrWoAtK7cBSIQH9+7IXgjJZXX3zlWUZXq8X9+/fh9PpFHI/lUoFJycnkGVZ05m+aZHl94Fy+VDLeT04OMC3v/1t/Oqv/ipevHghlpVCoRA2Nzfx8ccf4969eyI1gFYrnE4nrFYrDAbDmBKIEorsnpycYHd3F/v7+8hkMmN6vltbW7h37x4SiQTcbrdm2si8R8dvE7Wzory2LRYLlpeX4XK5YLPZMBwOUS6XcXR0NLENynmj+zLlJWuhtTJHDvBN58ktKuSAqI8tyRmenJyI4jnKs1QXCtE54fF4hMIA3QMuqvsYDociut7v98U5RpHDaRPeRYOcV7qf+nw+JJPJsdauFHUF3mjoUudIuoYowEa+Fd17yXEdDodoNpsiXUcdqFE2llDayW63w+Vywev1jsmi6fX6qc2lbop37rxOE7mWJAmBQAD3798XBTOFQkG0mgNGzorRaBQnN3UBodA2RUZJakun02Fvbw9PnjzBs2fPcHJyIpa2KN+u1+uJTjwAhNC9clazvr6OSCQiLkKtwgMtFjkna1qOnLKVHMloUGRNkkYddahLB2nnKgWOqUryKvtBdtC6AXa7XbHUQtTrdRSLRciyjHw+j8FgIBwwJcqmF/Nk52kFOq1WCycnJ3j+/DkeP3489prX68XKygo++ugjfPrpp9jY2EAgENDcDgnZ06QPGNmpWq0il8uNNTqgAZKWydbW1rC6uopYLAaHw6E5CWbn9eagdAC6F+v1egQCATx8+BCnp6d4/PjxxH2RBlCSWlJPbtX3Cq17hzL9aN6ut/eFOkVIOZmgAMPR0ZFoHnN4eIh6vS5WLvv9/kSKHwWPKLBDtSrtdlsEHUiWicZwCiBRrQF14AMgUriofkRLiWjRIFsp/Zi3xWw2IxwOw2g0CpWP09NTHB4ewmAwzNSKVTYxUObFau33B+28kuOq5bxGo1F813d9l2gXubOzg3Q6jUqlIqpVJUkShV3UeYWKePL5PF69eoXhcIhUKgWdTodsNoudnR3kcjmhVCBJEprN5oRBaMmDCkGCwaDoAhQKha7sNNHjot5op8niKOWXqFED5TpTSztaEqbZWiAQmHocaTlLKz+IPq+sfiZo4kMi6EdHR9jZ2REpJpIkYWNjA1arFcvLy5rfOyuC9CEyK/dNqbupxmw2i4gLSWDN+g5ldIAc13Q6jePjYxwdHeH4+HhM69lgMMDn8yEajSIWiwmlAvU5ps7TZd4t5HCoj7Hf7xd5jdNSOejzhJY26LR6CGV+HfNuIMdRnQ41HA6RyWTwxRdf4IsvvhDNXNrttgj2AJPBG2UXy36/j0ajgXw+D6fTibOzM7jd7rGVL1pKprG8UCggm80inU6jWCxiMBjA4/GIZehkMgmv18v6vjcIFZ37/X7RDGTa/VRZtEWr3yThpaVWcNuTzhuJvCpR5q2Ew2FRABCLxRAIBETnpV6vJ6qYZVlGo9FAoVCATqcThViNRkMUedjtdkiShFarJdQJyImhCKASi8WCWCyG9fV1IZgei8Xeera3qI7rtBOV8qKovafX64XD4RA3sJOTE5HgDUDIoFEOshpyIGdF26Y9TyLb+/v7Quf1O9/5Dl6/fo1mswmLxYKzszNsbGxoXozKCMKiQ8n4tHw1C+XyJA1ymUxGFAucnp6iUCiMfYaWKv1+vyjUoo5q09JUmJtD6xibTCaxQpHNZsXz5HhqLRtq/V/rmlJG6vh6ezcoJczUqSHFYhGvX7/GN77xDTx9+lREXCn1h5b3lZDtOp2OmIxSMVCpVBLNJSiFQLnqSeP54eEhdnZ2cHh4iE6ng0AggEajIdIRnE4nO683iFKXl9QIqCmF+n3Ka5Ei55VKRUTPO53ORLrIbTqwN3qWUAW4JEki/G0ymZBIJGC320WExmQyodvtih71kjSS7MhkMnA6nchkMqIFJUVgp6HM1SBodkcV0STvQEsVkiSJtAT1Ute0ZeNFHkBnOZHK6HY8Hkc2mxWdmGq1mhj06MaqFK0OhUITk4lZTqtWhB9403Yyl8vh4OAAL168wDe/+U18/etfRyqVEu/rdDpCt3TWgDlPg+msyCUVCoTDYbx8+XLstV6vh0KhgP39fVgsFrRaLYRCIaHNS+lClGdF0TnS+k2n02JpMpfLodlsjm2f5LECgQA8Hg8cDsfYBAdY7FWO22Ja2hc1jgiHw1hdXUW9Xkcul4Msy0LaCpi+SnKZ71Url7Ct355p967BYIB6vY5SqYRSqQQAQqqSAkhq59VoNIq6hkKhgOFwiEajgWKxiFAohGAwKDrn0WoprcCcnZ2hXq+LFTAKIFBnro2NDbFqqt73RT0PlD7ILNTHjK4/5cok1Y9Q7RHJaWm1YdYaI2hVrlQqoVwuo1arodVqaeY63xY36rzOWibyer1IJpMol8siD4NC2iaTCc1mE+l0Gn6/H1arFXt7e2NFXtNQOq4GgwGBQACrq6u4f/8+Hj58iM3NTVGspVQOoHwtLW0yZR4W52NN73olSaOe9BRdbzabqNVqYtJBTiVNPmjgo1l5JBIR/c3JgVE7r8o0AS0nrF6vo1wuo1AoCEmmL7/8El9++eWY40pQwZFWvuy82nvahMBisSAajeLjjz9Gq9VCKpVCu92GzWaDJElCKDubzeLJkyciQupwOOD1eoVKh8/nE9uUJAn1eh2np6c4OjoSOrtKqIgzEokI51XdOGIec4/vIpR+o762hsMhPB4PNjY2UC6XRWEPFdwpHR1ygLWiqOSgqFMErFYr7Ha76A7EqSFvz6z7l81mE3JX2Wx2LCBEUTgqxKMJBckhNZtNsRJaLBZxenoqlEco+ED3dgCi+BmAiMRSlXuv1xOKFTQeE4ucmqcsfLxo9U892aNrR6kAQLKTAETRFTmv6rxacl7V30k6+SQ3ql6Bm+br3RQ36rzOkt0AIHTDyHun6Asli2cyGbjdbgAQUTutzivT8Pv9WF9fx6NHj/DRRx/hwYMHWF5eht/vH1uSBCalWqYZYdFng2roAqNjR+LKlLrRarWELqQkScjlcqLtIE1IcrkcDg8PkUgkROcymiGqu/Uo9e3UtNttZDIZHBwcIJVKYW9vDzs7O3j58iX29/cn3r+6uoqNjQ2hVadmHpep1dEtJdR17PPPP4fNZsPR0RGKxaIoums2m9jZ2cH+/j7MZvNY4w5Kyel0OjAYDHC5XADeyNRls1mcnJwgk8mMdVzzeDxYXV3F6uoqEokEgsHgRPrIPEW+7zp0fmgVZVHBLdnj7OwMtVptYhCjz2vlsFKQQB1k8Pl88Hq9cDqdsFgsc5Vn/r6g+7GWLf1+P7a2tlCr1aDT6fDq1Sucnp6O2ZI+T2lAVEBNEdpGo4FKpQKLxYJcLidsR6ufNMGhSa3H4xG588FgUBRphsPhsVVQQp0utEiQDzLNkZz2GXqkyKvW50j2yu12i8K7adtSQxMOkld7n9yo86pVRENQjiud1Hq9Hk6nc0yCIRQKwWg0otVqiXZ1R0dHYonfZrOJpYxOpyOWf00mE/x+P9bW1vDxxx/j008/xcOHD7G6uopwODw2u6OZzUWONqON1uxYkiR4vV4MBgOhSydJEhwOB46Pj5HNZlGpVFAoFFAqlZDJZFAoFNBut2E2mxEIBETV8rTv0yreyufzSKVSePbsGV68eIHXr18jlUpN3JQ9Hg/W1tbwySef4PPPP0cymRyT/SCU1c/zwizn1WKxIB6PiwrzTCaDbDaLXC6HXC4nWuvW63WRq2yxWOBwOHB6eiq6tnS7XSwtLcFoNAqn9ejoCKenp8jn86jX6zAYDEL3l7pzrayswO/3a3bpYQf2dlBPFum6NhgMYhXMYDAI0fOTkxOUy+WxbZDDREvPahkeeqRqalqFo1Quu93OeY/vALKlVvF0MBjEgwcPRJQcGDkmymJNmmBQ9I4mHWoHk6SUqFiLIrSkDiRJ0piIfjweh8lkQr/fRzweFwEEmvAqt72oaOUpX/ZzBCmA0L1aicPhgNvthsvlEmo/yjFymiKN0WgcS0GY9t23wY3fIbQKuAaDAXq9nujgQpE06o9MkDwPtfK02WxYX19Hv9+H2WwWM/R+v49WqyUiRGazGT6fDysrK7h//z7u37+v6bjS/i3izO5dMcuxoB7Ig8EANpsNkUgEx8fH2Nvbw+vXr7G7u4tms4l6vQ6r1Srs6nA4JqKu9F0UBVC+1m63kcvlkEqlsLOzg1evXuHZs2fY3t5GLpcT73M6nSL3+d69e9ja2sL6+joSicRYRy5C3YBhHpj1e2jSZ7Va4ff7kUgkkM/ncXp6iuPjYxE5rVQq6HQ6ooir0+ng+PhYRGJyuRySySQcDgdqtRpev36Nw8NDpNNpUail1+vh9/uFduyDBw+QTCYnBjAAwhGaJzt8CFCuIjmvkiTBbrcjHo8jl8thaWkJ0WhUKL0AEFrLNPhqaS6TY0NFOiSRpmxeor5Ps+2vjtbYS8eRnMjBYIByuYyTkxPs7+9PKI1oFbJqFdtRIafRaBSaoxS1VUYOrVYrrFYrQqGQkG9aX19HKBSa2lqWud6xUErYqY8tFciS2lI+n0ehUBjLUSanl6QuKcWOVq2VxdQAxKRlLgq21NBSAt0QqXc9HWStyCclcxsMBiwvL4uoD+WqSpIkBlDqPGEwGOB0OkVOTzQaRSAQYA25G2KaQ0QFQMlkUhTN5XI5BINByLIsoq0ARDEIDWKBQGBM+JwcVvU5UigUkE6ncXh4KFIFDg4OJiraqYvaJ598go8//nislSnlP1/2d80rVG1M/bLdbjd8Pp9Y9ksmkyLySpXElUoFmUwGmUxGRNVTqZTQTT47OxPPKSvUdTqdOCe2trawsbExVa6Onde7hdlsFoV9NMHR6XSiNbPShsoIDU12gDfnGomwr6ysiOvR6XSyaP07Rn0fJVUYciZpLH5b6NxQtlL3er0iLUuv14sla7/fj3A4jFAoJNIDlXBQ6d2gdd/U6/Ww2+0IBoOIxWIolUro9Xqo1+tjEXby02iFxOFwCOnLer2ORqMBp9Mp3qs1Wb2p+/atOq/KJUu6wV0U3TKbzYjH4/B4PKJ6kT5HJ7eycl2p+alsm8aO680wy3aUJO73+xEMBiFJEmq1GhwOh6hAbzQaQoHi3r17IgJHUN9lWpZSUqlUsLOzg2fPnmF3dxenp6coFosol8sYDocwm81ot9twuVxYX1/Hp59+is8//xwfffQRlpaWEAgEJpYoOad53Im12+1Cf7XRaIhr8OzsDO12G+l0Gi9evMCXX36Jly9f4vT0FKenp9jf3xc3uk6nIwp7CEmS4Ha7EYvFsLKygkQiIexAqzPK+8Qi2+N9Ma3ghwpsqDCzWCyK7luk50wo82dJv5uuZ1mWhUg9OTE+n08UCDLvDvVqFaV0tNttdDod0Xr9bbBarfB6vYhGo4jH4wgGg3C5XKIgmvKgTSYTQqEQlpeXEQ6HRWtuNey8vhumXUtOpxORSARLS0solUpoNpuiPoX8LGVTCrqfk9rE6empiMJqtXqf1dDgXXDrzquWDIMSpUIBvZdatr4tymq4RYuq3TY006e8NjrWVqsVsiwLyZR+vw+TySQkzBKJxNh2SCBZkqQJ5zWTyeDJkyf4lV/5Fbx8+RLNZlPINfl8PjEj9Pl8uHfvHh49eoRPPvkEa2trCAQCU2U+OMo3QtnlRWs5X5ZlnJyciEILulkVi0VUKhXUajXRiELdqcdqtcLtdiMQCCAcDo9NIKhKlnPQ3z9auW00MaSmFaFQaExCR43JZBqLugIj6R2qWqfJkt1un0gdY66PVoMPgiYSnU5HiM4r7QOMAkcUBSf1gMFgIJQClOkDdrsdTqdTFG9SC2m3243BYIBCoYBKpSKWpUmVRnlf4fqTd880X0un08HtdiORSAgVgVKphEKhMJYqoh7DKUWPUjYplU/d4vc2lAfuTFY8/VilNMS7zkdVSrfQTZlnd+8W5eRj2swrFAphbW0Njx49EstV6+vrCAaDmrIdWo6kspXpt771LWxvbwMAotEoVlZWxIzebrfD7/eLLi4rKyszHVet/V0kriJLJUkSEomESANyOBwIhUJIpVIiD1KryAMY5U95PB4hiabeB2UuFfP+0DoPdDqdcGyUwvLTmldMaz1Zr9dFq1CK+nFQ4XZQjrUAxEol5TgajUZEo1EsLS0hHA7DYrGg0Wjg9PRUpGURtEJDxT9erxexWAwbGxsIBoNot9uiQIs0gbVUBJQBD+bdMO16ovSNWCwm5CuPjo5weHiISqUi3kfnB8lcDgYDtFot0cGUtuPz+Sac15u+h98Z55VQSkMo5SLeFnU1PN8kbwb1TF9r9qXT6RAOh/HgwQM4HA4Mh0NEo1FYrdaJrh0USVXf0KjdIBUT0cBpNpsRjUbx8OFDxONxeL1eeDyeMQF8dlxnc9VjQHJ30WgUm5ub2N7eFkoPmUwG5XIZnU5HzOitVqsQNJ9WWc52uBtopdFQTjTlLrpcLlit1mvZjGR3lPI7vPJx81B0k7qmUf5ptVpFrVaD3W5HJBLBw4cP8ejRIwSDQdRqNTx58mRClaDf74scWlpi9nq9iEQiiMViQnGmVquhWq2K9AH1pIbGfrb9u0N5LJUFVjqdDna7HTqdDq1WC+l0WtR/ZDKZsXGbHNZWqyUCFdlsVhTRx2IxLC0tTaQO3HT09c44rzftTPJSxO2hnByoKxIJt9uN1dVVeDwe9Pt90TO91WoJhxWAqHJWnxvU9IA065rNJrxeL5aXl0WKwOrqKnw+n1iO1HKCmXEuew1SpIxsFQ6HRfclmjQYjUZR1KWWKgsEAkIWSSuyx4PY3cVgMIhIm7KgLxaLicJZgnLVtVJHaFJpt9tF7hwAjrq/I2ZdP5RPbrfbRaEsSVCm02kAo3s0NS3Z3NxEq9WCyWRCKpXC06dPheY6SVVSahfJMHk8HlGsFQqFUK1WRYSXCvyoyA+YT13tu4SyaI8g28RiMSQSCUSjUZRKJaHSQzZRtwavVCqQJAnxeFwUe6m/a2Gc11koVQqUy5pqlAeKBkCqUOcL4vYhp1O9hDAcDoW6gMvlEjdBSuynCB05m1o5d3q9Hl6vF+vr62i1WiiXy/B4PHj48KG42SaTyamJ5LQcMo9yWO8S5bGi/1NCvyRJYlJAuFwubG5uolar4fj4GCaTCa1Wa+zm5nK5EAqFREMSNcprl7mbmEwmoeiysrIiNJoTiQTq9brQ26be6dQqnFIESDc2mUyK5eVphTsAa/2+a2jZ3m63IxaLibbeTqcTz58/FwoSJI8WjUYBjNRdkskk3G73mJoLtXt2Op0IBALw+XxC49VqtQpJNJJApNeUzivARVo3jdY453Q6EQqFsLS0JMZTo9EomsnQNal2UCnHXdkeWgk7rxg/CNNaxqkPktLz52Wo9wcNOqQdR5hMJlGooVSK0IqQa93Q7HY7lpeXIUkSlpeX0el0hJZsMplENBrVdFyB8bxOPjemo3XdEbOW+GhZ2Ww2Q5blsYgbFQoEg0HhvGptgyOv759Zx1+n04n0D1KO2NjYQLVaRafTgSzLQjaLWm5TpI0mn+TUkBqJ1+sdK+68yv4wV4OuZyqGpsibx+OBJEl4/Pgxzs7OUC6XUa1WcXZ2Br1eL6rTY7HYmPPa7/dhsVgQCoWQSCQQCoXGJqYOhwPRaBQejwcAhBIQ34Nvj2npkiaTSTSMqVQqODs7g9VqRaFQEA2GAAiVAbqf07Wt1ZHvNvggnFcayK7qxStzXJn3w7SiOHJSpy0vzLoYdDqdEDIPh8OiPzblcJFQ+qzPq/OfGW2UdqBrkNo/TkvFabfbIvJGOpIUmSFdwVgshkAgAJvNNvX8YN4/sxxJi8UiNLXj8biIyCsLNdUBBuV1rtfrhZA9/Wm1M521H8z1IIfRYrGIGgCfzwez2YxarYZSqYRsNotOpyOUJAKBABwOB5LJJLa2tsaKe7xeL0KhEJLJJJLJJILBoFCNIMnEQCAwlkNN9mfb3h7Txjun04lYLIZutwu9Xg+HwzHRyrvb7SIQCIg0gUgkgnA4rNlURPl9N8UH4byyg/Fhc1Hu1XVsS1EdrbauhFLxABhPEeDz6WK0jhHlyqmhyDo1L6B+9cvLy6hUKjg6OoLRaMTa2ppoBUs6u1qdddg+dx8q0mFpqw8TkkBSkkwmsbm5iVwuB6vVKmTwaEJKqSFf+cpX0G63sbOzg7OzMyQSCWxtbWF1dXVi1YsaB3FKwPtl1thHua+Ut+x2u5HL5dBoNETK32AwQK1WQ61WQ6/Xg8fjwebm5lSVoJtePfsgnFeGuQ6UK62UZeHl6HcPaXhSXrper4fH48Hq6iqAUbS1XC5Dp9MhEolgbW1NFNNNi5CzjRjm5piV659MJoXCgMFgEK1bqUA2Ho/j888/h8PhwKNHj9Dv9+Hz+bC2tob19XV4vV4xwVXKXjLvl1n3VFpBobbNXq8X1WoV7XZbOK/UBrjVamEwGMBqtSISiSAajU51XhdKKoth3hXKlAWO5t0sSt1kg8EgtB/D4TA+/vhjtNtt6PV6IbHkcrmmymQB7LwyzE1CKVta+Hw+PHz4UNQUUAMCWnWhlIB4PC7kkyha5/F4JlbDOGBw96EovF6vh8VigcfjEfJ1yvOEGsjIsizu5w6HY2L15TbUndh5ZT54lBeX8ibJFeu3g9Zxpg5aVKXMzD/XqSxmp+b9oJWPTM9brdaJTodKHA4HHA4H4vH4hd/D998PA3I2yYmdVux81W3eJOy8Mh8kVL2slFBTFgGwPBrD3DwkpUYpOrRMDIwXzCodW2UHN6UsGsvWvR9IBYIUIpTSd+8SOjcojYBtPV9oNTS5Sdh5Zd470yKnF6EcAC/b0pRhmJthWkGIUu+Z/q98nPYcc3u8ax3dWfJXbGPmXcDOK/Peuc7NTLnMoeX88g2SYW4eiqDNyqG8CL5m3y9Knc53ZYNp2s2s6Tq/3LZd2XllPlh40Lu7kEwZ/ZFjo14mZj58+Dr8sNGKllMKCADRpZKga1vd8ZKc4FnXNZ8jdx91g6dpr6tTf24bdl4Zhnnn0A1OOcjR88CbZUoezBjmbqGceAKT16ryula+R/nIfJgo79tazqv6ufepJMHOK8Mw7xylTBktFyqf50GOYe4u6oYu6vxkksTj63r+UNaRqJnWJvx9wM4rwzDvHOWSEsMwHw4XaXQq6w2Y+eJDiqBf2Xn92te+dhP7wdwybMf5gW05P7At5we25fzAtrx7cFiEYRiGYRiG+WCQ3qW2G8MwDMMwDMPcJBx5ZRiGYRiGYT4Y2HllGIZhGIZhPhjYeWUYhmEYhmE+GBbHeZWkPw1J6kCSkm+5nR+DJPUhSfff0Z4xV4VtOR+wHecHtuX8wLacH+bYlnfXeZWkBCTpr0KS0pCkLiQpBUn6KUiS9xrbSgL4cQB/CbJ8NOU9/ztI0t+DJJ2ef98pJOkXIUm/RfXOnwGQA/Bnr7wfi4Yk+SFJ/xEk6echSTuQpDYkqQpJ+ueQpP8zJOnq599FtpSk33put+Pz79uDJP0dSNKv09ga2/IqXO3YXrQtbTte75xhO16V27Dl6LXfBUn6C5CkfwZJqkGSZEjS35yxNbblVeHrcj6QpJ+EJP1jSNLR+fEtQZK+gCT9CUiS/xrbm2bL/+D8Opz1d6ba2t2zpbqP7Z34A9ZlICsDsgz8fRn4CRn4J+f/fykD/itu7y/JwJkMJKe8/n8/33ZeBv6aDPw/zj/zTRn4Mxrv/yPn7//17/1Y3eU/4A+cH6e0DPwtGfh/ysBflYHK+fN/Vz5XvHgntgR+8ny7BRn4y+fnzd+VgZ4MDGXg97Itr23Lqx/b69jxuucM2/Hu2XL02nfOv6suAy/O//03L9ge2/Ku2ZKvy9uwZU8GfvX8uP6EDPyFcx9EloGTqf7L1W35mQz8ySl///j8+37hrtvyve/AlIP+D88P0n+iev7Pnz//s1fYllsGmjLwi1Ne/93n2/xHMuDUeN2o8VxMBgYX3oQX/Q/4zTLwb8uATvV8RAYOz4/7v/NObDna5pkMZGQgpHrtB86/a49teS07Xu/YXs+O1ztn2I53z5ZvtrkpA5IM/KZLOq9sy7tmS74ub8OelinP/xfnx/dn3oktZ3/uX55/12+767a8e2kDkrQG4IcApAD8V6pX/wSAJoAfhiTZL7nF3wPABuC/0/guHYCfBNAC8O9BlusT75HlvsZzaQD/DMDvgiS5Lrkfi4cs/xPI8v8EWR6qns8A+Nnz//2mK2xxui2BZYzSYL4OWc6pvu+XANQBBDX2kW15Mdc7ttOZbsfrnjNsx8tye7akbcryNmRZvvQW2ZaXha/LeUKWO1Ne+e/PHzevsLXZ16UWkvQxgO8DcALgf9bYvztly7vnvAK/+fzxFzUulDqAf4GRUb7vktv7wfPHf67x2q8HsArgHwAon+cO/VFI0n96iXyhfwHADOD7L7kfzDg0KRhc4TOzbLkNoAfgeyBJgbFXJOn7ATgB/G9Ttsu2nM3bHFstZtlxFhedM2zHi7krtrwItuXF3BVb8nV5s/zb54+Pr/CZ69jyPz5//CuQZXXOK3FnbGl43zugwdb54+spr29jFJm9B+AfX2J7vwGjGajW9r56/pgF8G0Aj8ZelaR/CuB3QZbzGp/95vnj9wP4hUvsB0NIkgHAv3/+v//1Cp+cbktZLkGS/iiAPw/gOSTp7wMoAlgH8NsA/CO8uTjVsC1n8XbHVotZ16Q2lztn2I4XcRdseTnYlhdxF2zJ1+W7R5J+HIADgBvAd2Nkl8cAfuIKW7maLSXJCuD3AhgC+Msz3nlnbHkXnVf3+WN1yuv0vOfCLUmSCUAYwLRlq9D54x8AsI/RbOXrGC3H/DkA/zqAvwPtpe3M+ePShfvBqPkJAB8D+AeQ5X94qU9cbEtAln8KkpQC8FcB/H7FKzsAfm5iae0NbMuLuP6xHecydtTmMucM2/EyvH9bXga25WV4/7bk6/Ld8+MY2YL4XwH8B1OCaJNcz5b/B4x8qv8Z0xSZRtwZW97FtIGLkM4fL2MUkpcoT3ldr9jm74Is/2PIcgOy/AzA7wBwDOA3TkkhKJ0/BjReY6YhST8K4McAvATww1f45EW2BCTpjwD4uwB+DqPogx3AdwHYA/C3IEl/Zson2ZYXcf1jq+ZiO05+92XPGbbjZXiftrw8bMvLwNfl/CHLEciyBCAC4HcCWAPwBSTp80tu4TrX5Y+cP/7XF7zvztjyLjqvFFl1T3ndpXrfLNrnj5Ypr5Nx9yDLX469IsttADST/B6Nz1pV38FchCT9QQA/DeA5gB+ALJcu+ISS2baUpN+EUfHd/whZ/sOQ5T3Icguy/G2MJiInAH7svCBQDdtyFm93bNVcdE2qv/sq5wzb8SLepy2vBtvyIvi6nG9kOQtZ/nmM0iT9AP6bS37yqrZ8iFH9zzFG9T+zuDO2vIvO66vzx3tTXqeKu4tzOWS5glFC+zSBX/quypTXybm1arxG27zcssyiI0l/CMBfBPAUo5tdZvYHVFxsy3/r/PGXND7bAvANjM73r2h8lm05m7c5tur3VzDbjm+4+jnDdryY92PLq8O2vBi+LhcBWT7AaJLw0URhnvb7K7jadXmZQi3iztjyLjqvdCH+0ETXDklyAvhXMPL6f/WS23sCIDpF2uGfYlQhuXmeJ6Lm4/PHlMZr1CbtO5fcj8VlVFTw/8LoWP3ApfOwJpllS/P54zRpGHq+p/Ea23I2b3NstZhlxxHXO2fYjhdz+7a8HmzLi+HrcnGInT9e5FwSl7suJcmCUcrHEMBfucR274wt757zKsu7AH4RwAqAP6h69U9hlNPz30CWm5fc4i9j9Dsnl/5luYCRDpobwH8+9pok/WsYFWxVoV1FSVJdk7Ne5g2S9McxSur/NQD/6vkxvy6/jGm2HOnPAcCPQJLiqn34NzGa9HQA/IrGZ9mWs3mbY6vFL2O6Hd/mnGE7Xszt2vL6sC0vhq/LeUGS7kOSIhrP6yBJ/wVGxeW/Alm+bB7rL+Ny1+XvBuDFqOBuVqEWcWdsKb37ItF3gCStY3TBhQD8DwBeAPheAD+AUbrAr4csFy+5rV93vq0/C1n+v2m8HsJIu2wDo5vBNzBSG/gdGBWF/XuQ5b+j+owOwCGABmT5PhhtJOn/hFEhwRmAvwDtPOUUZPnnLrm96bYc2eQfYqQYUQfw8xhVRj7AaHlNAvCHIMs/rfE5tuUsrntsp29vlh2vd86wHS/Hbdpy9PpvB/Dbz/8XwSggsIc3jlcBsvzjGvvItrwIvi7nh1Eqxn+J0WrwLkaSZ2EAvxGjgq0MRhOG55fc3uzr8s37/hlGslq/DbL8P12wzbtly/fd4mtGm7KkDPw1GTiVRz1/D2Tgp2XAd41tfVse9WTWT3ndJ49az+6ff1dRBv4HGfi+Ke//ofMWan/ovR+nu/w36pUsX/D3y+/MloBRBv6QPOoPXTtvZZeTgV+QgR9iW76VLa9+bK9jx+ueM2zHu2fLy9kzxbb8AGzJ1+VN2/FjGfivZOA7MlA4t2NVBr55fuxvwu95cG6bo6nvucO2vJuR13eNJP0eAP8tgN+JUfXe227v72E0I1qHLF9G9YB5V7At5wO24/zAtpwf2Jbzw5zbclGcVwnAv8RINeAzvM2PlqTPMOrG9aOQ5b/4TvaPuTxsy/mA7Tg/sC3nB7bl/DDntrx7BVs3wchoP4JRTlDsgndfRBTAHwfws2+7W8w1YFvOB2zH+YFtOT+wLeeHObflYkReGYZhGIZhmLlgMSKvDMMwDMMwzFzAzivDMAzDMAzzwcDOK8MwDMMwDPPBwM4rwzAMwzAM88HAzivDMAzDMAzzwcDOK8MwDMMwDPPBwM4rwzAMwzAM88HAzivDMAzDMAzzwcDOK8MwDMMwDPPBwM4rwzAMwzAM88HAzivDMAzDMAzzwcDOK8MwDMMwDPPBwM4rwzAMwzAM88HAzivDMAzDMAzzwWC47Bu/9rWvyTe5I8zF/MzP/Iz0tttgO94N2Jbzw7uwJcMwDHN5OPLKMAzDMAzDfDBcOvJK/MzP/MxN7Aczg6997WvvfJtsx/cD23J+uAlbMgzDMBfDkVeGYRiGYRjmg4GdV4ZhGIZhGOaDgZ1XhmEYhmEY5oPhyjmvDHNZZFkee6R/S9JkcbbWc1rbUj8nSZLma7Q9SZIu3DbDMAzDMB8O7LwyN4IsyxgOh2N/SieTnMrLOJlKJ1j5p36d/k3b0ul00Ov10Ol00Ol07MQyDMMwzBzAzitzq8yKkl7ms2rnVcuJlWUZOp1u4v3svDIMwzDMhw87r8yNIEkS9Ho99Hr9rX2nOrLLMAzDMMz8caecV3WkTL1cPA3lEjQtF7PzsniwzRmGYRhm/rlTzivwxlGlHEn1v9UoHVb6YxiGYRiGYeaTO+W8khOqzE/UisKqI2wcdWUYhmEYhlkM3rvzSk7pu5Y2UkdqWTLp9pBlGWdnZxgMBuj1euLv7OwMw+FwLB/WYDDAYDCMRc2VdlKqFpydnYltKKPx6ug8bd9kMsFkMsFsNsNoNMJgMPA5cAe5qPhOjda9gu3KMAyzOLxX51XLwXyX2z47OxP/Z7mk20GWZfT7fTSbTVQqFeTzeeRyOZRKJdRqNQwGA+h0OlitVjidTjgcDtjtdlgsFphMpjEnlmzY6/XQ7XbRbrfRarXQarXQ7XaFI6t0lAeDAQwGA1wuFwKBACKRCCKRCHw+HxwOB0wmk+Y+83nxftCafCj/PxwOxfvUKyzqVCG2IcMwzGLwXp1X5WDzrgcedaSVI6+3w9nZGdrtNkqlEg4PD7Gzs4PXr1/j4OAAhUIB7XYbBoMBTqcTfr8fgUAAHo8HLpcLVqtVRGGp+UCv10O73Uaj0UClUkG5XEalUkGz2US/3wcADIdD4dwOBgOYzWZEo1Hcu3cPH330EQDAbDbDarVq7rM6+s/cHtMip+rmExfdK9h2DMMwi8N7TxtQDzqzlhAv2o56OdFgeO8/by6ZFqlURl3z+Tz29/fx5MkTfPnll3j9+jUymYx4r9VqRSAQgN/vh9frhcfjgd1uh8lkgl6vhyRJGA6Hwnmt1WoolUrI5XLIZrPodDoz93F5eRkmkwmRSATdbneiSQJzd6A0D4ZhGIa5DLfq3SnTBNSR0LOzs7Gl4Xa7jU6ng263i263i8FggLOzM80OSkajEVarFXa7HU6nE06nEzabbeZ+0D4w12OaA9vv99FoNFAoFJBOp5FKpbC3tzfmuAJAu93G0dERTk9P4XA44HQ6YbfbYTabx4r1BoMBut2uiLxe5LQSkiTBYrHAZrPB4XDAYrFMnczweXB7KFMBOI2HYRiGuQ7v1XklhsMhWq0WyuUyCoUCCoUCSqUSKpUKKpUKarWayHOkgh+dTgeDwQCz2Qy73Q6fz4dIJIJYLIZEIoFwODx1mZh5O6Y5/+Rstttt1Ot1YcNGozF1W4PBQNjZZDLBaDSKJWNlHmS32525TxaLRRSA+Xw+3L9/H1tbW1hbW0MkEoHb7YbRaBzbVy72YRiGYZgPj1t1XrWqgweDAer1OvL5PE5PT0U0LpPJoFAoIJ/Po1gsolQqodFooNfriYiryWSC1WqFx+NBNBrF2toa7t27h36/D1mWEQqFYLfbNfeDl5BvBmUBFRVUXRZSJbgKVJDl9XphsVhgNpvh9XqxsbGBjz/+GPfu3UM0Gh0r1KJCPl6qvn1o4kn/ZhiGYZircuPO67QIlyzL6HQ6qFaryGQyODg4QCqVwsHBAU5PT0UEtlgsIpfLXRh5C4fDKBaLaDabIv2gVqvB5/PB5XLBbDbDYDCIfEpyYHkAfbdQGofZbBbOpMViGXsPpXjQMj6lg6glsJQRduXER6fTwel0IhwOI5FIIBqNwu/3w2azwWw2w+l0IhaLYXl5GYlEQlNhAGCVgdtAudqi5bQOBgPxR1Jo/X4f3W53TGKNPmcwGGA0GmE0GoUMGkmi6fV6blLCMAyzANyo80oOiTLaQgyHQ9RqNRwdHWF7exuvX7/G3t4ejo+PUSwWUa/X0Ww2UavVLnRcASCbzY4tW5fLZcTjcUQiEYTDYQSDQXi9XrhcrglnmnNgr8a04yRJEkwmE5xOJzwejzjeZrN57H1KKSu73Q6j0SiKvSg1hLZHkw1yYkliy+/3izSRYDAIt9st8lqtVitcLhc8Hs/Edyv3n+1989CERClzRZydnaHRaKBWq6HRaAgZtFqthnK5jGKxiFqthk6nA1mWRW67w+GA2+2Gz+dDKBRCIBCA1+sV5xLblWEYZr55b85rt9tFLpfDzs4OvvzyS7x48QIHBwfI5/NotVoiEnMZx5UolUqQJAnNZhOnp6eIRCJIJBJYX1/H5uamkGhSoxxcmYtRR9CVRXSUxhEMBhEIBOByuTRtb7VaEY/HkUwm4Xa7IUkSut0uOp0OBoMBAAjHlSYXFFX1er0Ih8MIh8Pw+XzweDxCZovONYrOXbT/zO2hPu6NRgPZbFastFCOdD6fRyaTQTqdRqFQQKvVwnA4hMlkgsPhEPZPJpPY3NzExsaGyH9X5zVrfS/DMAzzYXMraQNaVKtVHB0d4cWLF3j8+DFevXqFXC6HZrM59j6dTieW/JXRG+XSP+VYDodDMQAeHR3B6/ViZWUFrVYLZrNZROuUuY6Uo6kUO2cuDx1/WtLV6XTweDwIhUIIBoNwOp0TVf69Xg9GoxHBYBAbGxuIxWIwm83o9XpotVpie2STwWAAWZZFVDcQCIhIutPpnOqkKh1rJezM3B4UPVdfW/V6Hel0Gru7u9jf38fR0RFyuRyKxSIKhQJyuRxyuZxmsZ/VakU4HMbm5ib6/T4sFgvcbreIvvOKCsMwzHxz486rVkSz0+kgk8kglUrh9evX2NnZwcnJiWaxjl6vh9frRSQSQTAYhMPhgNlshk6nw3A4RL/fF5XtuVwOmUxGyGuRmL3ZbEYkEsHq6iq63e6Ys6Nc1mSuDjn/al1dpXar1qSAUgfC4TCWl5fhdDpFswHqwkWRV5qYGI1G2O12uN1uzXSEafunzJ9lO988ygmDlu1rtRqOj4+xvb2N58+f49WrV9jf30c2m0W1WhVSedSEQk273UYqlQIAuN1uRKNRJJNJdLvdqecbwzAMMz/cqPOq5TD0+31ks1kcHh5if38fBwcHyOVyU6vMdTodfD4ftra2cP/+fUSjUdhsNuj1epydnaHVaqFYLOLo6Ag7Ozs4OzvD8fGx+Hwul8PBwQFOTk6Qy+VQLpfhcDjGts9OzdsxHA4nHAabzSaaDmg5ExSNMxgMsFgs8Hq9MBqNY5MJipDTc6QwMUuzVYlWniVzs8xKFQJGqQLHx8d4+fIlnj17hmfPnuHly5c4ODhApVIR7zObzfD5fEL7VymnR6lEpVIJ2WwW+XxeaACfnZ1xcxKGYZg5553f5dVNCJRL9O12G8ViEalUCru7u0K8vlarifeoZaxMJhP8fj82Njbw+eefY319HU6nE5IkCUH8YrGIaDQKq9WKwWAgBO2J09NT7O3tYWlpCYFAAEajEYFAQCxzU0oCc3WU/ejVGI1GofCghiLm5XIZjUYDPp8PTqdTLPsqO60pq9WvaieOwt0+01KF2u22cFy//PJLPHnyBNvb20ilUmPpAWazGfF4HMvLy0Kj9+zsDIVCAfv7+0ilUqhUKqINcTabRaFQQLVahcvlEuec2vacRsAwDDMf3IjzKsvyhKMhyzLq9TpOTk6EusDx8THK5fKbnTkfdJRFWm63Wyz5379/H+vr62ODUrvdHsutpJaiT58+RbVaBQBUKhVsb28LofqzszOsr68jFArBYrGI7SkdMY7Gvj16vV40DlDTarVE6ojT6YTD4dBUgphmAy0NWXV7YHZc3w9a0e5msyly3L/zne+IlsHpdHrMcbXb7VhaWsL9+/dx//59LC8vw+PxoNfr4eTkBDabDf1+X3Tfq1QqorgrkUiIvFf1hIkiwnxOMAzDfPi8c+dVmWOoHsDIed3b20MqlUIulxtr90kOI0knOZ1OEYFZWlpCNBqdGHysVquI8AEQWpGSJOHly5coFAoAgN3dXUiSNPa60WhEOBwec16p0p2Xmy/HrKV5vV4Ps9kMh8MBo9EochglSUK1WsXx8bFoNOHz+RAOh8dSOgitoit1xFdLt5d1XG8f9WrL2dkZms0mstksdnZ28OzZMzx9+hQvXrzA0dGRuP51Oh2CwSCWlpZw7949PHz4EPfv38fS0hKcTifa7TZcLhfa7TZyuZwo6qrVashkMjg6OkIymUQoFILH45nQFlbrBzMMwzAfLjcSedXKeZNlGc1mE/l8HicnJyJdQKnpSQOMXq+Hy+VCLBbD5uYm1tbWEA6HNbtlAaOIrcfjGWspSukAz549Qz6fhyzL2N7eRr/fh06ng81mExE/ks/iQe16zHJeHQ4H/H4/QqEQ0um0cDYrlQqGwyEGgwFsNhsSiQTW1tYQDAbHtkHFWpTiQUyLjKujr8zNM2s5vtfroVqt4vT0FKlUCnt7e9jd3cXh4eHYCgspTzx8+BAPHjzA1tYWVlZWxHXfbrchSRIKhQL29vZgt9vFak4ul0M6nUYmk8Hy8rJm/jxHXhmGYeaHG6lsmJbz1uv1RD5qvV5Hv98fk7yiqKfNZkM4HBYRmJWVFbjdblG0Q99BgxRFXr1e75jzqlQkoBzYVCoFu90On88Hv98v0g2sVqsQ2edB7vIoJa3UUNFNIpFALpcDAKEA0W630W63MRwOEQwGkc1mUalUJiJjSjUDtfPK3B2mRcd7vR6azSYqlQpKpZJo9azWb3Y6nVhaWsKDBw/w0UcfYXl5GcFgEB6PBzqdDhaLBYPBAIlEQqiOEJVKBeVyGbVaTWhEa+2f8pFhGIb5cLnVslyKtFG+orLSXzmokIA9KQwkEgnY7Xb0+33xWfqjbVCk1e/3TzhUw+EQT548ETmwVDRCubJmsxmxWExIaKlzYJXPMePMcl4tFguCwSBWVlZQq9WEHq9Sy5fE6UulEsrlMqrVKjwej3idbKBMR2HuFtMcQrLbYDBAv98X176Wc2k0GsVER+mgKs+rYDCIcDiMQCAAm80mnqeJUKfTEd9zlf1kGIZhPixu1XlVOjq0DEzpAkocDgcikQjW1tawvr6OWCwmBivKm1SqBCgHOHJgSScUeDNoPX36FJVKBdVqFbu7u/B4PHA4HHA4HLDb7QgEAmP7QQL87LhOZ5bzarVaEYlEsL6+jlarhUajIarDCVmW0W630Ww2Ua/X0Wg0xpxXnU4nIunMh4WyWYBy8qHVVIK6p3k8HqEPrFXoZ7PZYLFYJrSaO52OKOLq9/vcnIJhGGaOuVXnlRxKg8Eg/qYJ2FP/+kAgAJ/PJ15Ttw7VghobkFyOLMvodrtoNpt4/vw5ut0uDg8PYbFYYLfb4fF44Ha7YbfbYbVaxXZmyUAxb5iWf2qz2RCJRNDv94Ws0cnJyVjxFgARmaP2sL1eDyaTaWzbrP5wt5lWDDUYDNDpdNBsNtFsNtHpdCYmq9SRjZxWk8mkKa/WbrdFEwN1A4N6vY5qtYparYZms4l+vy/OIYZhGGa+eC+RV5JQmuWQUKGOGq1oDEVIKbpDklterxc6nQ69Xm9MUocif/v7+3C5XAgGgwgEAnA6nUgkEmOOE0f9ZqO239nZGQCISYrP5xPFeplMBru7u3A6nSiVSuIzWkvL6u1fxXFVTjiUE5CLJiLKKDIXfF0eZZGm8phJkoROp4NSqYTT01McHx8jm82i2WyKCCwVaj148ADxeFy0+1XmrHe7XdRqNZycnODly5dIp9NotVpj+1AoFJDJZFAoFKY6rzwJZRiGmQ9u1XlV5i+SowlMNibodDqo1WrI5/MoFArw+XwT0jfq7VJhllKxABjpxCYSCaysrCCZTIriIGCUgnB8fIz9/X0kEgn4/X7YbDbRwEDd8pSZjVrtgRwZv9+PSCSCaDQqImxK57Xf76PX66HX603kLF4n8k3nk/JcU59zWihb0vKE5fJMq+SXZRmNRgOZTAY7OzuiDWyj0YDVasXS0hIePnyITz75RGg4+/1+0f6ZKJfLopXs8+fPsb29LfLXiWaziVwuh2KxiEajMTXvlWEYhvnwuXXPTOlUkKOidija7Tay2Sz29vaE47qysjJ1GZC0Jae1pfR4PAiHw0gmk0gmkyJCA0B06CLtSNIa1dIbZS5mWnGVx+OBz+eDz+eD2+0e+0yn00Gj0RA5r51OB1arVUwgrhMBpag5nQ+0X9P2WdnYgB3XqzEtqk0rHtS6+fXr10LX1W63Y21tDd/zPd+D7/7u78bq6ip8Ph9sNtvYdU6dtV6+fIlvfOMbePHiBTKZzITzCkDkvWrdUxiGYZj54dbTBiiyRUv9WpqM3W4X2WwWL1++hMFgwGAwQKvVQjweh8vlEsuJwMhJMRqNIhVBC1q+XllZwf3791Gv1/Hs2TNUKhXo9XoR+VNWQw8GA+j1+rFBUKlJq1QkuOg3Kx/nnWmRUqvVCpvNBrvdDofDAavVina7DWBk73K5jGw2i2w2C5/PB6vVOlFtDow3wVA7peqGCW+79M/tRC+Plt1p9SSdTo81JAAgrkeloogWtVoN2WwWu7u7ePHiBV6+fKnpuDocDni9XtjtdpjNZs17AduRYRhmPrgR53XaIGE0GoVTQm1Z1dEwnU6Hfr8vGgu0221UKhVks1msrKwICR2z2Qyr1SqcIa0KZiVerxf37t1Dr9cTg2wqlRINC+jPYrHAYDBMSPool6Iv67yqo3mLMnhOax5AaRhms1kIzwOjCF2xWEQqlUIgEIDb7YbL5dKMfisl0shxpRxZZT7120KTKy4WuxitYzQYDFAoFMSEhHSWgZGEWjweRyKRQDQahd/vn9jmYDBAo9EQubLpdBqnp6djjqskSXA6naKhyYMHD7C6ugqv1ztxP1i0SSTDMMw8886d11ntQi0WC7xeL0KhEPx+PyqVClqt1kT0lZoKtFotVKtVFItFnJycYHl5GbFYDIFAAF6vF36/XyxFa7WEBCAiqH6/XzjNw+EQjUZDVLc7nU643W6xHavVClmWxyR3lJE+9e/VcmAXdbCc9rvJ4TSZTLDZbHA4HCJFoNfrIZfLwWq1wul0wuv1IhgMIhKJaG6Htv+uI63q37GoNrwq6gK3wWCAYrGITCaDTCaDcrk8NhFcXl7G8vIyIpGI5sRzOByKrlz7+/s4PDxEoVCYKNKSJAk+nw+rq6vY2trCo0ePsLW1hVAoNJFipHXtMgzDMB8mN+K8akWqJEmC3W5HOBzG8vIyKpWKEBVXdtyhTluk3dhqtVCv11Eul3F6eopoNIpwOIxIJCIEy/1+v3Bk3W636JZFcjpUdGU2m3Hv3j2USiW8fv0au7u7GAwGIvXAYrHAZrOJyJ1SbYAdmMsx6ziZzWa4XC74/X6Ew2F0u11h+2w2i+FwCKvVKs6RpaUluFwu8Xll1FVpo2lFdequSrPE9GnflX/vIoI7ryjVBZSpHTQxzOVyODk5wenpKSqVinBeA4EAYrEYwuEw3G43zGbzxLZ7vR5KpZLIlT08PBzbBqHX6+F2u7GysoJHjx7h0aNHWFlZESojSngiwjAMMz+8c+d1mrwULfHF43H0ej0x+BkMBqRSKZRKJdEuVOlkDAYD1Go19Pt9NBoNlMtl5PN5ZDIZ+P1+eL1e4RAFg0EEg0HR9pWEz9UDpMPhEJEZcpApCqhMY+DCnauhdGTUToJOp4PL5UI0GsXa2hqazSa63S7a7Ta63S5kWUYul4PD4cDR0RHS6TQKhcKY83p2dnapLlu9Xk8I1isnQBTlJzUKSZLG0kOsViv8fr9YGVA6r1QEpHScFxVlgZb6WJydnaFeryOXywlZunq9DlmWYbFY4PF44PV6xzRd1fZsNpvIZrNIpVLY29tDOp1GrVabmHzodDpYrVYEAoGxzlxaKzDstDIMw8wPN5Y2QCidDbvdjmg0CqPRONakgAalfD4/VtRB2wNGRT1nZ2eiMj2Xy4k8VVpuDgQCiEajSCQSiMfjiEQiiMfjsNvtY9ukbk4kap7P55HL5ZDP51GpVMZaTzJXY5rDbzAY4PV6sbKygm63i1arhUqlgnQ6Pfa+UqmEQqGAXC6HbDYLt9sNr9cr0jMuch6p+KtaraJaraJcLiOXy+H09BTZbBaNRkNoAVN+dbfbhSRJCAQC2NzcxCeffAKj0Qiv1yu2q9QyZTD1WAyHQzSbTaG7msvlRGtgyncnB9blcsFqtY7Zs9/vo1gs4vT0FIeHhzg6OhJ2U+r/Enq9XuS/22y2iYmq8v7DtmMYhpkPblRtQFn0Qk0JbDYbksmkkEEaDAbodrtjf8oIC71vOBwKRYBWqzXWYlav14uoTjQaxcrKCjY2NkTEx2QyCUfk+PhYRHNKpRJKpRIMBgNOTk5wcHCAcDgsHC3KxaPoHEX+6LfQb1Q+EjRQUrHWoqQeKH+jUhmA2vYaDAYMh0NUKhWkUqmJfMd2u41Go4FKpYJyuYxarQar1Sqks6bJXQFAq9USBUKkEZzNZnF4eIi9vT0cHBygVCphMBgIIXxSvDAajYjFYsjn8+I8NZlMYuLD+q/jTFOV6Pf7qNfrQmWAcl77/b6QS4vFYojFYgiFQnA6neKzJJF3eHiIg4MDHB0d4fT0FOVyGc1mcyJtgO4vdG3SH6WRKPOjGYZhmPnhxp3XacVMkUgEvV4PzWZTtHSkpV5lRXG/35/okT6NSqWCRqOBVquFdrstdENLpRI8Hg+63S6Oj4/x7W9/G7u7uygWi6LlZC6Xw/7+PpxOJ1qtFtxut9COJSkt+m7qHqX+jcoIj16vF8VJLpcLTqdTM6I7z3JMpASg7Kbm8XiQTCZxfHwsjrESZZtYZUoBRfSp1S85KmQfZbTv9PRURNIzmQwODg6wt7eHfD4/c38rlYpwXM1mM2RZRjKZhMfjGdN/Vdt8Hm13XUgdhHJej4+PRe65zWZDPB7H5uYm7t27h2QyCbPZLHJcs9ksjo6OsLe3h+3tbRweHorILTUgUUKd2ZQT336/v/BpHQzDMPPOjTqvs5QHdDrdWKSUchK73S4GgwGazaZ472WrhPv9PqrVKiRJEsvHJycn8Pv9sFgsQr5nf38fR0dH4jtkWUa9Xsfx8TF0Oh0ymQxMJpNoTUmdn5R5umrnVflbqQDM5XIhHA5jaWkJS0tLopCMUBYgzaMDpIy8K/F4PCIXWR3JpDQS0tpVR9u63S4qlQoqlQqq1SoajQaazSaq1apIN8jn8yiVSqhUKsIpushxBd50XPvOd74jvqvf7+PevXtjubcAxH7RygAzsnetVhOTiJOTk7E0IKXzurGxIZxM6sCVSqVwdHSEg4MDpFIpIY01rVW0UpNZ2Vp4nq8phmEY5haaFMwaRAwGA9xuN2KxGJrN5lgh1+npKWq12qW/h1IIer0eyuUyWq0WcrmcyIejJWeKtNZqtbGOXFTx3m63sbe3h8FgIKJ/7XZbDKDKFAhadgbGVRZ0Oh0sFguCwSA2NzdxdnYGj8eDUCg0ts/KSPI8LkdP67ykzHdWQ9HVfr+PVqsl8pMpxaRUKiGTySCdTouirlKphHK5jGKxiHK5LGTWKBqnnAhdRLFYRL/fF1Ffo9EoOq5NK0ZbRLRsV6lUxASCWrUqMZvN8Hg8Y8VwlUoFOzs7+OKLL7C9vY2TkxPk83mhDUvOL6VtKFFGv5XpOXQdsjQWwzDMfHIrkVctSKLK4XAgFArh7OxMOH2k9ZnJZMQApqw0p4GJnqPtEZ1OB/V6/VL7SNXOnU4H+Xwe+XwevV4PrVZL/F23T3o8Hken04Hf78fGxsZEpfwiFAFNSx2ZlkNKUfNKpYKTkxNYrVZ0u104HA4hq5ZOp3F8fIzj4+OxFAGKxmo5yxaLZWziQd+lLAgktYtKpYKXL1+K89Nut8NgMCAej8NqtQIYNdy4jPLBPKKWw5NlWaRtUGFcsVhEo9GY+Gyn00G5XBZ5sK9fv8aXX36JL774Aru7u6I4i9Q/ZFnWlNMCRs6wxWKB3W6H3W4X6R7K/aL9ZRiGYeaHW20Pq+x7T0t7JHVjMBhgtVrhi2UUkwAAKPtJREFU9XoRiUSwurqK09NTZDIZ4ZgoI6CU69hqtd4qwkLFYJQnSzJL1P3pbTg5OUEoFEK5XJ5QUQAwdhzmlWkTGEq/UIvJA0Cj0cDJyQlkWUapVILP54PdbhcyTDTJyGazIspXLpen7oPT6YTH4xF/TqcTFosFsiyj0WigUCjg6OhozOadTgeHh4f48ssvodfr0el08NFHH2F5eRk+n2/sdyknIfNqS7UjqPydNNnIZrPCea3X6xNL/c1mE/v7+/j617+Og4MDdLtdHBwc4Pnz53j16hXS6TSq1apIAVB+t1pCD4CY5Pp8PqFeoMx3nedVDYZhmEXmVp1XYLKoidQCTCYTnE4ngsGgaGKQz+dxfHyMVCqF4+Nj5PN5NJtNMbiRfI6WY3hZKK+VdDwHg8FExy/mesyKvOv1elitVrjd7rE2wbIso1qtYjgcolgsjkkgUbEWKRI0Gg3UajXNXvcEdeqiphaxWAyRSARutxvD4RCZTAbPnj0T2qRKh6tSqeDVq1fodruiI5terxdthIlFmIQoHUe1Tbvdrsh1zeVyKJVKaLVaE0v3lUoFT548QaFQgNVqFTnqFKmt1WoTaiP03VScRRiNRgQCgbFmJUpJvEVY1WAYhllUbtV5VTozyqVb6m7ldrtFOkCv1xNLx7FYDLu7uzg+PkalUhG5jNVqFVarFblcTjOvkYp/pkVmKY+SorgANAtDrvtbqX+7z+ebKpw+z4UlylxENRaLBX6/H7FYDEtLS0in00IKrVKpjOU7K7tdUaoILdlPs6/JZBrT/I1Go+K7EokEAoEAhsMhUqmUKOSjnE1ykgaDgUhdabVaMBgM8Pl88Pv9iMfj4rum5fbOG9OW4QeDAdrtNmq1mtDY1VoRqVaraLfb2N7eFkoUwJvCRTru6mV/Ksyi/5vNZtGYYGlpCbFYDH6/f2JflTJ283qNMQzDLCK3HnkllIOJOkJnMBhEdyyXyyXkpkKhkHBee70e6vU6isUiCoWC6H1ORV9K6RxSClA6isoBU+mwqguy6DkqMjKZTKLJglI4X9l4wWw2i1a49+7dw8rKClwul2bL3GnO3Twwyzl3Op1IJBLY2toSur35fB71el1MJC4L6cCazWaYzWY4HA74/X5EIhERaQ0GgyLyGovF4PV6MRwOYbFYRJqI2WwWEyTKnwVGKQS7u7sIhUJYWlpCMBiE2WyG1+sVubtaBUXzxjQnkKKcNOmkIkc1vV7vwlUN5fVH21Ren3Tck8kk1tfXsby8jHA4DIfDMbEdhmEYZj55b87rZXG73UgkEjCbzQgGg2g0Guj3+2KgpNaf5HBoVTzT+9WRX60oq7IojNDpdDCbzXC5XPB4PHC73bDb7WOtcEnQnhxuqqqm6B91iVIy79Xrs5w6h8OBRCKBWq0mlAFIm/cqGAwGeDwehMNh0daVWrxSq2Cfzwe32y26dbndbtEcIRqN4uzsDCaTCeFwGDs7O9jd3cXe3p7IgQZG+Zqnp6fY3t6Gz+eDwWAQ+a9aXZ20WiR/6ExLA9Hr9TAajWLyoG48cdXvUE7o1MWSJpNJpBatr69jaWlJTCK09ou2yTAMw8wPd955BUYOrMViQSgUGpPTAiDyVFutlmgrub+/j1evXsFoNArRe4rWKtuLajmvysgsRX0MBgNsNhvcbjeWl5eRSCSEcD0NriQDFAwGEQwGhcNEkWOtwiTlcvg8Mkvnl1oFt9ttlMtlZLNZnJycXGn7JpMJoVAIiUQCKysrwjYUafV4PHA4HCJH1Ww2w2QyjTmVVqsVa2tr8Pl8SCQSiEajsNvtYik8nU6L5edisYidnR1YrVaYTCYR5VU7r0r5tHlxnGb9DqPRCLvdLiZsXq8XTqcTpVLpyt+jTDXQkryyWCwIBAJYXV3F+vo64vH4WJcu5f7q9fq5T+VgGIZZRO6U86qVO0jOps1m0+xQpf78yckJfD6fkMBSdmIiKSRl20i1g0GRQmU6wWAwEN2hVldX8dFHHwnNVtIDNZvN8Pl8CIfDiEaj8Hg8mo6p+rvnnWm/kSLZPp8PPp8PTqdTNH5Q4nA4xhpC0LngcDjg9XoRDAZFLuvy8jKWlpYQiUTg8/lgs9lgNBon7ECi9tQpTa/XC6fLarUKnVlZlmE0GlEoFIQUFPCmwUYikcDq6urEPpPzOm/5zNPOWaPRCLfbjXA4jGQyKdQfut2uyCGmlBrqWkd/Stk7tRIJOaDK6Cul4ywvL2N1dRXRaFSkDNDn6bOLco0xDMMsGnfGeaVBi/5o+Z4GvMsgSRISiYQYDIE3zq9erxdqBcrvVDo3tGSpHEQJqm5eX1/HJ598gng8DlmWUalU0Gw2IUkS3G43QqEQ3G73zN9J37UITMuTJKeF7K3U7CVIn9Vut8NqtcLpdIqUAGU6gMfjQSAQEKkDgUBAs0BOuU9kW6XDrNPpsLa2JlJMKL/57OwMjUZDFHCZzWasrq5qykFN6yr2oaPVIICeI6WQcDiMZrMptJENBgNOT09FHrPy+lZe58rCLeUjXbvKbmYulwvBYBDRaFSoRhBU1KVs38wwDMPMH3fiDq+W4aFc0us6AKFQSDRBoMIqUhSg6CtB76H9IIdZ7ZQYjUY4nU5EIhGsrKwgEAgAAPx+PyqVCvr9PqxW60ThiJp5z3NVM82O/X4f9XpddMeiDmtKKPpttVpFm9319XWsr6+L1A2z2SwK6ej4z3JcgTfRdYrMKydHRqMRDx48gMVigcFgQKvVwuHh4djnqfsTyWctIspJmCRJQq+ZIqh0vRwdHQklh2q1inq9LnKJlek/yokjbV89oaG0BOXkRb1Pw+FwrlNxGIZhmDvivNIAdp1BhwZApYNkNpsRDofF8i85SdSJSekkkeIApRdMa1sKvBmwlVEdku3pdDpj3Zsu+q2LgDINRP271R2ZarWaiMApIUWAaDSK+/fv45NPPsGDBw+QTCbH8ogpenuZIillQVC/30en0xENM4CRjdbW1tBoNPDq1auJfGXq4FapVFAsFhGJRCacsEWws9quFosF4XAYkiSJtJBoNIpsNot0Oo2joyOcnJyg1WqNaTOTXB5NGs/OzsYUJyinNhKJIJlMIhKJwO/3T+Qa06SXYRiGmW/uhPN6GZRRGFpyJhUBWio0mUzCsbRarXC5XKK63G63w2g0TjgVJKVF9Ho9EX1VQl2EMpkM0uk0PB6PeE2v18Nut4+lG1AESkt6a5HQcl4HgwHq9ToKhYIQqCddUIPBMJbjaDAY4Ha7EYvFsLGxgXv37mFtbU1TuYEid8ronTpCqLaBLMtiMkMtZAmv1wubzaZpt0ajgVwuh6OjIzidTkSjUeHkUrX9vDuwWr+P9HvpmggGgygUCggEAjCZTOh0OiJ3mFCueAwGgzFNV+DNBHFlZQVra2uIx+OaqTlvu2LDMAzDfBh8EM6rUmx81nPqz6iFyq/yfUoRfGBUmHV6eoqXL18K2aulpaWxrj5KbUpyqiltYZEHVPWxp65o9XpdLCVTZyW1o2i1WoU00srKCqLR6MxjSSki9L1qO6qdm1mdo4A3RUBqOp0OstksUqmUkE0LBAKwWq0TKwjzmOc865y2WCzweDywWCzw+XwIBoNwOBzo9/soFAo4Pj6eUCIwGAzC6VenYlgsFkQiEZEyEo/Hx647YtFSchiGYRaVD8J5BTBRQUxOCDkKVJRF0HJwq9USigBUQa6EnEtl9FVr8CPn9enTp9Dr9Tg7O0O328X6+vqYVA/tJ0UCafuLyLSUAWURHrXjJfuoc429Xq9YLo5Go5qySErU0VX192s1ipiWKtJsNtHpdDQl1SgdpVgsolQqIRAIwOVyidQD9THQ+u4PmYsmEKT5Svq61ITC6/VOLPcDEKsTWtt1u91IJpPY2NjA5uYmIpHIxHGm713E1Q2GYZhF44NwXpX5hMrnZlUUDwYDdLtddDoddDoddLvdCSeEIkQkqUQRwUajISqmiW63i2KxKP6vbFygdqhoEOaKZ+2It16vh9PphN1uh9lsFtHSbrc7dsw9Hg9CoRBisRjC4fBYqgbZV5ZlmEymsbzUWc6qGqpo13J6arUaOp3OhFA+fY66qZEM1CzHaZ4cVzXqFRFK46HfbLfbhdau0WicmVOuPl/sdjvi8ThWVlawvr6OZDIp0hKASVmyeT7ODMMwzIi59a6U7V+pal29BEwRHapcHw6HyOVySKVSODo6mqh+bzabQvOVilGWlpYQDoc1lzEXnVmpGl6vF4FAAH6/H06nEzqdbqxgy2g0IhqNIplMIh6PIxAIjOn8KnMjSTv0OlE3dcSeoHSGVqulGXml6vp4PI5kMilSBtQoVwrmFS1tZqUTSRPCRqOBTqej2f631+vBYDCMvWaxWJBMJrG2toa1tTUkEgkEAgFhL6UsGUdcGYZhFocP3nmd5rTQgKaURQLGHSqr1YpIJIKPPvoIKysrkCQJh4eH0Ov1QgpJiSzL6HQ6KJVKyGazQoy90WiMOa/T8isXjVlC8UajET6fD5FIBKFQSETAG40GDAYDYrEY1tfXsba2hlgsBpfLNeZkKuXMLqPyMGsf6bNkr36/j3K5jGq1KiSd1NhsNsRiMWxubmJzc3PMsaZJj7JAcBHOAy0nst1uI5vN4vj4GCcnJ8jn82NKAwSlj9CxdjqdiMfjePDgAR48eICVlRUEg0HNTnUMwzDMYvFBO68U8dFyYKkAhNp4Us91dccem82GcDgsHBCXy4VKpYLj42OUy2Ux0Co1KNvttogkkW4libLTe+c50nZZKO9X7bhRioDJZEI4HMba2po41larFWazGcvLy3j48KHIcVR3V6Nl+utEXJWTCmVRHTDKZS2VSshkMigUCmg0GpoSXmazGX6/H8lkcmzfSAWD0kYWwWml61BLyaFUKiGVSuHly5fY3t7G6ekpWq3W2Puoox1BBVpbW1v45JNP8PDhQywtLU0oDChz3hmGYZjF4YN2XoHpVc/KnESr1Sr+jEajqGZuNpsiFcBkMsHtdiMSiSCRSCCZTKJcLiOXywnZK3Je+/0+er0eOp0O2u22EF0n53URHJZpqKWpCCpuKpfL6Pf7MJvNsNls8Pv9ePjwIXQ6HZxOJzKZDIxGI5LJJO7fv4+1tTX4/X7NiNtF8mPK5Wx6HzWrIAdWnYOpVBFIpVIoFouaOa9GoxE2m02zgIz0Zhd9AtNsNpHJZLC9vY1nz57h1atXSKfTwnlVtotVpgu4XC4kEgk8ePAAn3zyCba2thCNRjXTMvR6/cKvcDAMwywaH7zzOg2dTgej0Si6LjmdzgnNzkKhgHQ6jZOTE6TTaaEi4HA4EAwGEYlEcHZ2hlqtNpFCQPqw5MgqHaJFHkiVXY7oOAyHQ5RKJTx//hw7OztoNpsIBoPY2trC6uoqHj58iGAwiFgshmw2CwAIBoNYWlpCNBqFy+USaQJazupVqvlp/waDAfR6/UTle71ex/HxMV6/fo2dnR2k02m02+2x9yg1g7Uc1EVpUnARlUoFJycn2N3dxatXr7C/v498Pg/gTcGlXq8fc1zNZjN8Ph+WlpaEusDS0tJEdJu2oXxkGIZhFoMP2nmdNXhRxySXywWPxwOn0wmj0TiWvyjLMtLpNHZ2duB2u9FsNqHX69FqtYSSQLVa1ZRLomIRKhyi6KxyGXMRB1WtivFer4dcLofnz5/jm9/8JiqVClZWVuBwOLCxsYFAIIBAICCc116vB6fTCZ/PNxbZnCa9Ne04a00kKFqr1+sn1CBkWUaxWMTBwQF2dnawu7uL09PTiYmL3++Hw+GAJEnodDoTLYEXrfJdK7e5Vqshm83i8PAQBwcHODo6Eo4rfUZZeAWMIrFer1cUQq6srCAej485rqShrGzrzDAMwywWH/zdXzlgKrtt0TJ0v99HIBCA0+mciPIAo+hQKpWCyWRCPp+HxWJBu91GpVIR0TktJ4QG0X6/P9V5VbLIS5u1Wg2np6fY2dnB48ePRX7r1tbWmKIDaYJ2u11NSSV10d204znrWEuSBJPJpPl6NpvFwcEBDg4OcHh4iHQ6jXK5jG63K7RLPR7PmLqAOh+WnLJ5d16Vx1hpp7OzMzQaDWQyGXEcT05OJpoSkLoEpQwYDAa4XC7EYjGsrq5iZWVlQh6NvnfR0zEYhmEWnQ/eeVVC3a2UUR3q8DPNee31eqJd5cnJCUwmkygoarVaIidW67uUqQPK9rXA4kRd1Y6kOupI+rjZbBanp6fIZDKoVCrwer0olUqoVqsT29QSsVd/JxUIaR3ni5xbrefz+Txev36N7e1tpFIpZDIZ4bgCo9xKj8eDpaUlrK+vY3l5GV6vV3OyMu9FRJR6oaUuQAVvFL1OpVLI5XITqRe0akEYDAbRSY26aHm93qlKIgzDMMziMnfOq1Y7T5/PB5fLBZvNBrPZPDGQNhoNIXhP0ABJ0VU1NPhSvitFX7WY90iRukiLjt1gMEC1WkU+n0c+n0e1WhXOYLfbRaVSQTabRafTgcViufT3XZTreNWoZz6fx8uXL/H48WO8ePECBwcHyOfzY21K9Xo9vF4vlpeXsbm5idXVVQSDQc0iokUo1prlvBaLRRweHmJ3dxdHR0eoVCpj14ZWrjCpfqyurgp5NG4ByzAMw2gxV84roO0oOhwO+P1+RCIRxONx7OzsiNcMBoOIsqqbEgCYusRMkki9Xk84sOQ4L9LAOqv9aa/XQ7VaFSoD3W5XyEeRjNLBwQEikQiWl5eFs0L5xWr9VXWLYK3v13JcqUmFWr5KlmVks1lsb2/jyZMnePr0qZBzqlQqY9swm80IBoNYWVnBvXv3sLa2hlAopOl0z9K3nRemLd/3ej2Uy2WcnJzg6OgImUwGzWYTwOhaIpuqtV49Hg/i8TjW19exsrKCUCg0IY8GvMlZnvfJAcMwDDOduXJelfmGSsxmM0KhEDY2NpDJZKDT6UTEbzgcotPpTB0Me73e1Ap3irx2u130er2pkVd6/7w6M7OOXaPRQKVSQbPZxHA4hNFohNlsRr/fRz6fx/b2Nmw2G9rttohkGgwG4Rwpc4lJ9oycIGrnS1Fz2jZ9Xjm5UOZCU1pIqVTC0dERXr9+jefPn+P169c4PDxEsVicKNBzOp0IhUKikCgWi8Hj8Wjm4c6rnQmyjVYBXa/XE7JohUJBRNupSI7aMCujrx6PRxRpLS0tIR6Pw+fziQJLsoWyQGvejzHDMAwznblyXikyp3Y2dTodQqEQPvroI/R6PRiNRrx48UIsaV6ElnMmyzJ6vR6azSYajQba7faYXNZFn58npjnmZ2dnQkuXGjlQPmir1cLp6SlevXoFnU6HbreLRCIBn88nNF3J8aTPORwOuN1uOBwOoddbr9dFIwFSlzCbzTg7O0Or1RIpIRR1lWUZrVYLxWIRp6enODg4wN7eHlKpFE5PT1EqlSbUBSwWC8LhMOLxOGKxGMLhMLxe75jjqs61XgS0bE6tYOv1Our1OjqdDgaDgYiWKvPSJUmCw+FAPB7HysoKlpeXEYvFhONKkPM6zxNAhmEY5vLMlfNKjoPWABcIBPDgwQMhat5qtcakewiz2Twmmk7LlOqirX6/L6KKVHgUDAYnHNVFqY7W+o1nZ2fo9XpotVrCeaW/RqOBdDoN4E3HrWq1ikAgAJvNBlmW0e120el00Ov1hPNK+csmk0moQtTrdZydncHpdMLr9cJqtYp8W5I6I2mls7MzVCoVZDIZHB4e4ujoCOl0Gvl8HvV6fczORqMRbrcb8XgcW1tbIhdT6bjSb18kx0o5QVT+5n6/j1qtJv5oQge8KaYk++t0OrhcLtGM4uHDhyIVQ5nrqkwTWKRjzDAMw0xnrpxX5cBGQvTURclqtWJpaQmSJAl5LOroRBgMBpHjajKZJopMlIVbg8EA9Xod+Xwe2WwW8Xgc7XZb04mjfMt5ZJZTQTZQplaQ8wIA5XJZRC07nQ5KpZJwPoHRMe50OqLdqtVqnYi8KhtIOJ1OeDweWCwW4UiR8wpANKGo1+vIZrM4Pj4WbWDVeL1ehMNhJJNJrK6u4v79+7h//75mq1pgMdIFCK0IM+UwFwoFFAoFlMtltFotYWv19aPT6RAIBLC5uYnPP/8cn332GTY2NuDz+Sa+j7toMQzDMErmynlVolyeVC5BUuesUCgEr9cLg8EgBlgq7KH0A+UATYMnOaeU20eDdbVa1XReFzlipDwWtGSsziWtVCqQZRmdTgf5fB5WqxVms3lM7UFpR5PJJF4fDAbodrvo9/vQ6XSwWCxwOBzCpiR1RjnNZD9SOiiXy5pSXV6vF6urq9ja2sLm5iY2NjawurqKeDwOv98/4bgtQkctLUk0YjgcolarIZfLIZ1OC5kxagNLn1OeD0ajEeFwGA8fPsTnn3+Ojz76CKFQaOxaVX523o8vwzAMc3nm1nkFtAc9i8UCt9uNUCiEWCyGdDqN4+Nj8ToVl6g/r3a6hsMher0eOp0O2u02Op3OhNYrMc+pA7Mq66nIymazwWazwWq1ajontVpNFFDRtmi5GMDYhIL+lAVDtB9UECRJkoj60p/afv1+f6LiHRg1SlhZWcFHH32ETz/9FA8fPsT6+jrC4TAcDsdYVyhlO+B5d65maRhTCs7R0REODg6QTqdRqVQmJOmUUPvfra0tPHz4EPF4XLxGaTvU4plhGIZhlMyt86p2gJQ4nU7E43Hcu3cPzWYTJpMJpVJJ5Ogp8x6V+XZKjEajcMgoWjitG9e8OzfTfp/JZILb7UYkEkEikUCpVEKtVkOn0xGOI+WiUh6yVkOIm8BgMMBgMAin2GKxwOv1IpFIYGNjA48ePcLHH3+Mra0tJBKJifOIZNEWqUBLOYlQUq/XcXJygp2dHezt7SGTyaDRaIxNGOizJpMJfr9fOK2bm5tIJBIT3zNLuYNhGIZZbObWeSVnQ8t5dTgcSCaTaDaboo3s0dERstksyuWykHUCJiOuAGC1WuHz+RCJRIS0DxUaqQd2pT7pPDLr91ksFqGNStHpfr8PvV6PUqkk5K3IRoPBAO12G61W68JI9bRCOi2MRuOYzJJerxdRYbvdPhaJTyaTWFpawurqKlZXVxEOh6dKpS0S6kg3MRgMUKlUcHR0JJxXavCgThWw2WyIRCLY2NjAV77yFTx69GjCcSXm+ZphGIZh3o65dV6B6QOgzWZDLBaDTqeDzWaD1+uFz+fD7u4ujo+PkcvlNHMhgTcND6LR6FheZCwWg9PpnHB0tKS75gl11FWZ30vOK+mrUktXq9WKfD6Pfr8Po9EoHNh+vy+cV2W+6tnZGfR6PSwWC6xWKxwOx1gb30ajISK6SqjAy+VywWq1CmeKonoWiwV+vx/xeByrq6tYWVkRExGfzwe3263ZqpZ+x6Kh5bwqmxLs7+/j4OAApVJJKETQ5EKn08Hr9WJzcxNf/epX8V3f9V148OAB/H7/xPdQNJ6dV4ZhGEaLuXVeZw18VqsVer0eVqtVVKi73W44nU5YLBYhgK/U+9TpdPD5fAgGg0gkElhdXcX6+jo2NjawvLyMSCQCu90+NW1gUaDldIp0mkwmRCIRUTFOkle5XE40gFA2Fuh2u0I7t16vo9lsot/vw2AwiCipy+WCxWLBcDhEq9VCuVxGuVxGrVYTMkzk5LpcLqFQQGoD3W4Xg8EAJpMJoVBIdM2ilq82m21qCggw/2kgWszqqNVsNlEqlZDL5VAsFlGv1wGMr3q4XC7EYjHcv38fX/nKV/Dpp58iHo+L84TOG4qoL+LkgGEYhrkcc+u8qlF3QDKbzaJjk91uh8PhgM1mEwUi/X5fSDiZzWYEAgEkEgmsrKwIx3VpaQnRaBSBQAAOh2OsSl7Jojk6wKSgfCAQEJJXLpcLpVIJ3W4Xer0eJpNJHHeSxyIHlhoQ6HQ64bzSJGM4HKLZbKJSqaBaraLZbI5FaS0WC5xOJ5xOp3BIyXmlyKDf70cikcDy8jLi8fhEDqsy/3JWHvW8M81hHwwGopNZt9sdK9KijmqULrC6uirUG5LJpDiOpAcMjCY7i5RHzDAMw1ydhXFeAW0n0mq1IhqNjkk0UdGWwWBAq9WC3W5HLBbD1tYW7t+/LxzXQCAAu90Ok8nEy5zn0DHQyv31+XyQJAlut1sUZ1HbUDp+VGlOSg7dblc4pCaTSRTIKSO11ASh1+uJwiDS7KVJitlsFq1hyb7KtBEtCSxi0fJbr4JS/owUHpRYrVaEw2Gsrq6KVYpAIDAxAVhUOTmGYRjm6iyM86ocGEnSSrlE6fP5YDAYhASWLMuw2+3CeV1ZWcGDBw/w4MEDrK+vIxgMji15cnX0iFlRSbPZDL/fD7fbPdEAQi2BRU4RLScro57KJX069mRTtXyV0sZaElskrzVNkonaytK/mXHo2FPkVY3P58Pa2hoePHiAe/fuiYmiElIhYAeWYRiGuQwL47yqUebYEdSustVqQZIk+P1+dDod2O12JBIJbG5uCsdVCeXIcoRuEorKkTNpMplgMpne815pQ86y0vFdxPxWLaapDQyHQ3Q6HdTrdZTL5bHXPB4PVldX8eDBAzx69AgbGxsIBoMT9ldOEBiGYRjmIhZyxCBnRMvZ9Hg8WF5ehslkQjQaRb/fF1Xp0WhUszqatslOzjjKKCpFTO8qpEJAzis7rpNMa33cbrdRLpeRz+fHXlteXsb9+/fx6NEjoZfr8XhgMBhE1zQ+xgzDMMxVWVjnVdkpSTl4Go1G+P1+WK1WxONxnJ2dwWAwwGKxwGazTV0WZ13KSdQRzLuMej/v+v6+D7Qir9SmlwquiEgkgocPH+KTTz7B/fv3sby8DK/XC4vFInKbaaJwlyc1DMMwzN1j4Z1XLaid6VVYxAr0y3Dbx+Vt8ibZiZrNtBbANptNNO3IZDIIBoP4yle+gs8++wwPHjwQRVrKdAHS/WUYhmGYq7KQzqsaZcX0LH1Pei/lt5ITzI7r5aFcY3XnMnUqh1aOpdYys/p9akm0aZFUZWHXospfXYVpx8lisSASieDjjz8GANRqNQQCATx8+BCffvopVlZW4Pf7NfOc2YFlGIZhrgM7r28JD76XR+1gXoaLoqjTunspn7+M9i5Xus+GnFb1MbJYLFheXoYkSdjY2EC324XD4UA4HEY8Hoff79fsUkbwMWcYhmGuCjuvGE8juGgw/RDyN+8q1ynQ0cqxvMz7Lnr/dd+3qExLtbFYLEgkEgiFQuj3+6KLGsmPzVIR4GPOMAzDXAd2XqE9iNLytnp5edr7mctzlePHzufdRJmeQU0gLvt+hmEYhnkb2HnVgHIylc4r5efx4MswV0uz0MpdZhiGYZjrws7rFNTFJDzgMswbrpr+wdcPwzAM865g51UDFk5nmNmoC+WmpdnwtcQwDMO8a67svH7ta1+7if1gbhm24/zAtmQYhmEWCRa3ZBiGYRiGYT4YJNYpZRiGYRiGYT4UOPLKMAzDMAzDfDCw88owDMMwDMN8MLDzyjAMwzAMw3wwLI7zKkl/GpLUgSQl33I7PwZJ6kOS7r+jPWOuCttyPmA7MgzDMNfgw3BeJemHIUny+d9/dI3PJwH8OIC/BFk+0nj9t0KSfhGSdAxJakOS9iBJfweS9Os0tvYzAHIA/uyV92NRudrxvWhb020pSSnFeaL+y2hsjW15XW7ymhyJw/6HkKRfhSTVIUktSNIXkKQfhSTpNbbGdmQYhlkg7n6TgtEg9xcANAA4rrmVPw7ADOC/1Nj+TwL4IwCKAP4+gAKADQD/ewD/DiTp34cs/03xflluQ5J+GsBPQpJ+PWT5V665T4vBVY/vxUy35YgqgJ/SeL4x8Qzb8nrc9DUJ/HUAP4yRQ/rfAWgC+EEAPw3g+yFJv3us/R3bkWEYZqG421JZo9Y8/wjAKoD/L0aRmt8PWf7LV9iGG0AawL+ALP+Q6rUIgBMAeQCfQJZzitd+AMA/AbAPWV5TfS4G4BDA34Ys/96r/qyF4brHd/r2ptty9HoKACDLK1fYR7blVbj5a/K3A/h5APsAvgeyXDh/3gjgvwfw2wH8Psjyz6k+x3ZkGIZZEO562sCPAvjNAH4fRtGX6/B7ANgwiuCoWcboGHx9zLECAFn+JQB1AMGJT8lyGsA/A/C7IEmua+7XInC94zudWba8HmzLq3LT1+TvPH/8c8JxBQBZ7mMUrQWA/2TiU2xHhmGYheHuOq+S9ADATwD4acjyP32LLf3g+eM/13htG0APwPdAkgKq7/9+AE4A/9uU7f4LjJY9v/8t9m3eeZvjq8UsWxJmSNLvhST9Z5Ck/xSS9ANT8iSVsC0vw+1ck5Hzxz2N1+i5zyFJHo3X2Y4MwzALwN3MeZUkA4C/gdEy4H/2llv7DRhF+F5PvCLLJUjSHwXw5wE8hyT9fYxyM9cB/DaMlkf/4ynb/eb54/cD+IW33Mf55O2OrxbTbfmGCEbnjpJ9SNLvgyz//6Z8hm15Ebd1TY5yooFRWoIaZXrJfQC/qnqd7cgwDLMA3E3nFfjPAXwFwG+ALLevvRVJMgEIA9jGtOReWf6p81zJvwrg9yte2QHwcxPL3W+g6vWla+/fInD94zvOZWwJ/DWMlo6fYeQcrQH4vwD4EQD/CyTp10GWv9T4HNvyYm7rmvwFjNIK/jAk6W9DlkvnnzMA+FOK93k1Pst2ZBiGWQDuXtqAJH0PRpGdPwdZ/pdvuTX/+WN5xvf9EQB/F8DPYRQRtAP4LoyWKP8WJOnPTPlk6fwxMOV1Bnib46vmYlvK8p+CLP8TyHIWstyCLD+FLP8BjCK/VgB/cson2ZazuN1r8m8D+F8wOleeQ5L+EiTppwB8B8BvwSgVBQDOND7LdmQYhlkA7pbz+mZp8jXeFGe8DRQhskz5vt8E4CcB/I+Q5T8MWd47d3q+DeB3YFQp/2OQJK1qeKvqOxg1b3d81cy25Wx+9vxxWi4k23Iat31NyvIQo5SSH8cokvrDAP5DAMcYpRsUz9+pFbFnOzIMwywAd8t5HWlG3gPwAEBnTGQe+BPn7/n/nD/3UxduTZYrGBUM+ae84986f/wljc+2AHwDo2P0FY3P0jYvt+y9mLzN8VW/v4LZtpwF2cg+5XW25XRu+5oEZHkAWf5zkOXPIMtWyLILsvxvAHgO4DOMnNNnGp9kOzIMwywAdy3ntQvgr0x57XOMnJx/DuAVgMsuXz4B8BVIkguyXFO9Zj5/nCbXRM/3NF6jVpTfueR+LCJvc3y1mGXLWVAnL60KdoBtOYvbviZn8cMYRWz/+rl0lhq2I8MwzAJwt5zXUSGIdqtJSfqTGA2Uf/1KgujAL2OUY/k9mJRl+meggh5J+q8hyyeK7/s3AfwrADoAtDr2fN/542RUkSHe5vhq8cuYZktJ+gjAqSjwefP8MoC/eP6/aZ282JbTuP1rEppOrSR9FSOZrgaAPz1lu2xHhmGYBeBuOa83w98D8GMA/nVMDpR/9/y5HwTwApL08xjl2T3AaMlbAvDHIMvFsU9Jkg7AvwrgFWT56Y3u/YfN9Y7vdGbZ8ncD+GOQpF/CqDtTHaOin9+KUbTuHwD4sxNbZFu+D2bZEQD+ESSpDeApRnb8CKNirS6A3wlZnoygsx0ZhmEWhvl3XmX5X0KSvgDwf4Qk/THI8pnitSEk6bcA+IMA/l2MiohsGFUt/wMA/2/I8i9qbPUHAcQB/F9vevc/aK5/fKdtb7otR9G2LYwigb8Oo/zWCkZL2n8DwN+YIs3EtrxtZtsRGE16/l0AvxejIqw0gL8M4Ccgy6kpW2U7MgzDLAjSdMnMOUKSfg+A/xajqM3Pv4Pt/T0AvxHAOmS5+tbbYy4P23I+YDsyDMMw12RRnFcJo2ISK4DPZojcX2ZbnwH4NoAfhSz/xQvezbxr2JbzAduRYRiGuSZ3TSrrZhgNjD8C4OcBxN5ya1GM9C5/9qI3MjcA23I+YDsyDMMw12QxIq8MwzAMwzDMXLAYkVeGYRiGYRhmLmDnlWEYhmEYhvlgYOeVYRiGYRiG+WBg55VhGIZhGIb5YGDnlWEYhmEYhvlgYOeVYRiGYRiG+WD4/wMq1paC0yiOhQAAAABJRU5ErkJggg==\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",
+ "pwk.plot_images(x_test, y_test, errors[:15], columns=6, x_size=2, y_size=2, y_pred=y_pred, save_as='05-some-errors')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:12:11.311054Z",
+ "iopub.status.busy": "2021-01-14T07:12:11.310677Z",
+ "iopub.status.idle": "2021-01-14T07:12:12.940150Z",
+ "shell.execute_reply": "2021-01-14T07:12:12.939857Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div class=\"comment\">Saved: ./run/figs/MNIST1-06-confusion-matrix</div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x576 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pwk.plot_confusion_matrix(y_test,y_pred,range(10),normalize=True, save_as='06-confusion-matrix')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-14T07:12:12.943270Z",
+ "iopub.status.busy": "2021-01-14T07:12:12.942791Z",
+ "iopub.status.idle": "2021-01-14T07:12:12.946491Z",
+ "shell.execute_reply": "2021-01-14T07:12:12.946761Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "End time is : Thursday 14 January 2021, 08:12:12\n",
+ "Duration is : 00:00:31 548ms\n",
+ "This notebook ends here\n"
+ ]
+ }
+ ],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"todo\">\n",
+ " A few things you can do for fun:\n",
+ " <ul>\n",
+ " <li>Changing the network architecture (layers, number of neurons, etc.)</li>\n",
+ " <li>Display a summary of the network</li>\n",
+ " <li>Retrieve and display the softmax output of the network, to evaluate its \"doubts\".</li>\n",
+ " </ul>\n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.ipynb b/README.ipynb
index e99d2ec..fbc482c 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -2,13 +2,13 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2021-01-12T10:47:42.374568Z",
- "iopub.status.busy": "2021-01-12T10:47:42.374015Z",
- "iopub.status.idle": "2021-01-12T10:47:42.382550Z",
- "shell.execute_reply": "2021-01-12T10:47:42.382062Z"
+ "iopub.execute_input": "2021-01-14T07:24:35.970555Z",
+ "iopub.status.busy": "2021-01-14T07:24:35.970025Z",
+ "iopub.status.idle": "2021-01-14T07:24:35.973458Z",
+ "shell.execute_reply": "2021-01-14T07:24:35.973069Z"
},
"jupyter": {
"source_hidden": true
@@ -43,7 +43,7 @@
"For more information, you can contact us at : \n",
"[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top) \n",
"Current Version : <!-- VERSION_BEGIN -->\n",
- "2.0\n",
+ "2.0.1\n",
"<!-- VERSION_END -->\n",
"\n",
"\n",
diff --git a/README.md b/README.md
index 9104f3e..9ef5b26 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ The objectives of this training are :
For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
Current Version : <!-- VERSION_BEGIN -->
-2.0
+2.0.1
<!-- VERSION_END -->
diff --git a/fidle/01-update-index.ipynb b/fidle/01-update-index.ipynb
index 7d1ec0c..53beba0 100644
--- a/fidle/01-update-index.ipynb
+++ b/fidle/01-update-index.ipynb
@@ -85,13 +85,46 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Read : LinearReg/01-Linear-Regression.ipynb\n",
+ "Read : LinearReg/02-Gradient-descent.ipynb\n",
+ "Read : LinearReg/03-Polynomial-Regression.ipynb\n",
+ "Read : LinearReg/04-Logistic-Regression.ipynb\n",
+ "Read : IRIS/01-Simple-Perceptron.ipynb\n",
+ "Read : BHPD/01-DNN-Regression.ipynb\n",
+ "Read : BHPD/02-DNN-Regression-Premium.ipynb\n",
+ "Read : MNIST/01-DNN-MNIST.ipynb\n",
+ "Read : GTSRB/01-Preparation-of-data.ipynb\n",
+ "Read : GTSRB/02-First-convolutions.ipynb\n",
+ "Read : GTSRB/03-Tracking-and-visualizing.ipynb\n",
+ "Read : GTSRB/04-Data-augmentation.ipynb\n",
+ "Read : GTSRB/05-Full-convolutions.ipynb\n",
+ "Read : GTSRB/06-Notebook-as-a-batch.ipynb\n",
+ "Read : GTSRB/07-Show-report.ipynb\n",
+ "Read : IMDB/01-Embedding-Keras.ipynb\n",
+ "Read : IMDB/02-Prediction.ipynb\n",
+ "Read : IMDB/03-LSTM-Keras.ipynb\n",
+ "Read : SYNOP/01-Preparation-of-data.ipynb\n",
+ "Read : SYNOP/02-First-predictions.ipynb\n",
+ "Read : SYNOP/03-12h-predictions.ipynb\n",
+ "Read : AE/01-AE-with-MNIST.ipynb\n",
+ "Read : AE/02-AE-with-MNIST-post.ipynb\n",
+ "Read : VAE/01-VAE-with-MNIST.ipynb\n",
+ "Read : VAE/02-VAE-with-MNIST-post.ipynb\n",
+ "Read : VAE/05-About-CelebA.ipynb\n",
+ "Read : VAE/06-Prepare-CelebA-datasets.ipynb\n",
+ "Read : VAE/07-Check-CelebA.ipynb\n",
+ "Read : VAE/08-VAE-with-CelebA.ipynb\n",
+ "Read : VAE/09-VAE-withCelebA-post.ipynb\n",
+ "Read : Misc/Activation-Functions.ipynb\n",
+ "Read : Misc/Numpy.ipynb\n",
+ "Read : Misc/Using-Tensorboard.ipynb\n",
"Catalog saved as ../fidle/logs/catalog.json\n",
"Entries : 36\n"
]
@@ -121,7 +154,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -291,7 +324,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -366,7 +399,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
diff --git a/fidle/02-running-ci-tests.ipynb b/fidle/02-running-ci-tests.ipynb
index a4cc95f..1edc05e 100644
--- a/fidle/02-running-ci-tests.ipynb
+++ b/fidle/02-running-ci-tests.ipynb
@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -59,11 +59,71 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Run profile session - FIDLE 2021\n",
+ "Version : 1.0\n",
+ "\n",
+ "Load profile :./ci/full_jdev.yml\n",
+ " Entries : 8\n",
+ "\n",
+ "Create new ci report : /home/pjluc/dev/fidle/fidle/logs/ci_report.json\n",
+ "\n",
+ "Notebook : LINR1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:04\n",
+ " Saved as : 01-Linear-Regression==done==.ipynb\n",
+ "\n",
+ "Notebook : GRAD1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:05\n",
+ " Saved as : 02-Gradient-descent==done==.ipynb\n",
+ "\n",
+ "Notebook : POLR1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:03\n",
+ " Saved as : 03-Polynomial-Regression==done==.ipynb\n",
+ "\n",
+ "Notebook : LOGR1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:03\n",
+ " Saved as : 04-Logistic-Regression==done==.ipynb\n",
+ "\n",
+ "Notebook : PER57\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:02\n",
+ " Saved as : 01-Simple-Perceptron==done==.ipynb\n",
+ "\n",
+ "Notebook : BHPD1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:11\n",
+ " Saved as : 01-DNN-Regression==done==.ipynb\n",
+ "\n",
+ "Notebook : BHPD2\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:11\n",
+ " Saved as : 02-DNN-Regression-Premium==done==.ipynb\n",
+ "\n",
+ "Notebook : MNIST1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:33\n",
+ " Saved as : 01-DNN-MNIST==done==.ipynb\n",
+ "\n",
+ "End of running process\n",
+ " Duration : 0:01:16\n",
+ "\n",
+ "Complete ci report : /home/pjluc/dev/fidle/fidle/logs/ci_report.json\n"
+ ]
+ }
+ ],
"source": [
- "profile_name = './ci/smart_cpu.yml'\n",
+ "profile_name = './ci/full_jdev.yml'\n",
"# profile_name = './ci/full_gpu.yml'\n",
"\n",
"cookci.run_profile(profile_name, report_name='./logs/ci_report.json')"
@@ -71,9 +131,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 318196 100 -rw-r--r-- 1 pjluc pjluc 100935 Jan 14 08:11 ../LinearReg/03-Polynomial-Regression==done==.ipynb\n",
+ " 318138 240 -rw-r--r-- 1 pjluc pjluc 245591 Jan 14 08:11 ../LinearReg/02-Gradient-descent==done==.ipynb\n",
+ " 313937 44 -rw-r--r-- 1 pjluc pjluc 43926 Jan 14 08:11 ../LinearReg/01-Linear-Regression==done==.ipynb\n",
+ " 318210 260 -rw-r--r-- 1 pjluc pjluc 264920 Jan 14 08:11 ../LinearReg/04-Logistic-Regression==done==.ipynb\n",
+ " 843697 72 -rw-r--r-- 1 pjluc pjluc 72520 Jan 14 08:11 ../IRIS/01-Simple-Perceptron==done==.ipynb\n",
+ " 843701 856 -rw-r--r-- 1 pjluc pjluc 874407 Jan 14 08:12 ../MNIST/01-DNN-MNIST==done==.ipynb\n",
+ " 843699 208 -rw-r--r-- 1 pjluc pjluc 209016 Jan 14 08:11 ../BHPD/02-DNN-Regression-Premium==done==.ipynb\n",
+ " 843698 216 -rw-r--r-- 1 pjluc pjluc 219264 Jan 14 08:11 ../BHPD/01-DNN-Regression==done==.ipynb\n"
+ ]
+ }
+ ],
"source": [
"%%bash\n",
"find .. -name \"*==*==.ipynb\" -ls\n",
diff --git a/fidle/03-ci-report.ipynb b/fidle/03-ci-report.ipynb
index b75c4e3..77445d1 100644
--- a/fidle/03-ci-report.ipynb
+++ b/fidle/03-ci-report.ipynb
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -34,14 +34,14 @@
"data": {
"text/markdown": [
"**Version** : 1.0 \n",
- "**Output_Tag** : ==ci== \n",
+ "**Output_Tag** : ==done== \n",
"**Save_Figs** : True \n",
- "**Description** : Smart profile, for cpu \n",
+ "**Description** : Runned notebooks (done) \n",
"**Host** : Oban \n",
- "**Profile** : ./ci/smart_cpu.yml \n",
- "**Start** : 12/01/21 11:26:19 \n",
- "**End** : 12/01/21 11:33:06 \n",
- "**Duration** : 0:06:47 \n"
+ "**Profile** : ./ci/full_jdev.yml \n",
+ "**Start** : 14/01/21 08:10:56 \n",
+ "**End** : 14/01/21 08:12:13 \n",
+ "**Duration** : 0:01:16 \n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
@@ -66,170 +66,97 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- " #T_a95ec_ td {\n",
+ " #T_fad5b_ td {\n",
" font-size: 110%;\n",
" text-align: left;\n",
- " } #T_a95ec_ th {\n",
+ " } #T_fad5b_ th {\n",
" font-size: 110%;\n",
" text-align: left;\n",
- " }#T_a95ec_row1_col7{\n",
- " background-color: OrangeRed;\n",
- " color: white;\n",
- " }</style><table id=\"T_a95ec_\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Id</th> <th class=\"col_heading level0 col1\" >Dir</th> <th class=\"col_heading level0 col2\" >Src</th> <th class=\"col_heading level0 col3\" >Out</th> <th class=\"col_heading level0 col4\" >Start</th> <th class=\"col_heading level0 col5\" >End</th> <th class=\"col_heading level0 col6\" >Duration</th> <th class=\"col_heading level0 col7\" >State</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_fad5b_\" ><thead> <tr> <th class=\"col_heading level0 col0\" >Id</th> <th class=\"col_heading level0 col1\" >Dir</th> <th class=\"col_heading level0 col2\" >Src</th> <th class=\"col_heading level0 col3\" >Out</th> <th class=\"col_heading level0 col4\" >Start</th> <th class=\"col_heading level0 col5\" >End</th> <th class=\"col_heading level0 col6\" >Duration</th> <th class=\"col_heading level0 col7\" >State</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row0_col0\" class=\"data row0 col0\" ><a href='../LinearReg/01-Linear-Regression.ipynb'>LINR1</a></td>\n",
- " <td id=\"T_a95ec_row0_col1\" class=\"data row0 col1\" >LinearReg</td>\n",
- " <td id=\"T_a95ec_row0_col2\" class=\"data row0 col2\" ><a href='../LinearReg/01-Linear-Regression.ipynb'>01-Linear-Regression.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row0_col3\" class=\"data row0 col3\" ><a href='../LinearReg/01-Linear-Regression==ci==.ipynb'>01-Linear-Regression==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row0_col4\" class=\"data row0 col4\" >12/01/21 11:26:19</td>\n",
- " <td id=\"T_a95ec_row0_col5\" class=\"data row0 col5\" >12/01/21 11:26:23</td>\n",
- " <td id=\"T_a95ec_row0_col6\" class=\"data row0 col6\" >0:00:03</td>\n",
- " <td id=\"T_a95ec_row0_col7\" class=\"data row0 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row0_col0\" class=\"data row0 col0\" ><a href='../LinearReg/01-Linear-Regression.ipynb'>LINR1</a></td>\n",
+ " <td id=\"T_fad5b_row0_col1\" class=\"data row0 col1\" >LinearReg</td>\n",
+ " <td id=\"T_fad5b_row0_col2\" class=\"data row0 col2\" ><a href='../LinearReg/01-Linear-Regression.ipynb'>01-Linear-Regression.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row0_col3\" class=\"data row0 col3\" ><a href='../LinearReg/01-Linear-Regression==done==.ipynb'>01-Linear-Regression==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row0_col4\" class=\"data row0 col4\" >14/01/21 08:10:56</td>\n",
+ " <td id=\"T_fad5b_row0_col5\" class=\"data row0 col5\" >14/01/21 08:11:01</td>\n",
+ " <td id=\"T_fad5b_row0_col6\" class=\"data row0 col6\" >0:00:04</td>\n",
+ " <td id=\"T_fad5b_row0_col7\" class=\"data row0 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row1_col0\" class=\"data row1 col0\" ><a href='../LinearReg/02-Gradient-descent.ipynb'>GRAD1</a></td>\n",
- " <td id=\"T_a95ec_row1_col1\" class=\"data row1 col1\" >LinearReg</td>\n",
- " <td id=\"T_a95ec_row1_col2\" class=\"data row1 col2\" ><a href='../LinearReg/02-Gradient-descent.ipynb'>02-Gradient-descent.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row1_col3\" class=\"data row1 col3\" ><a href='../LinearReg/02-Gradient-descent==ERROR==.ipynb'>02-Gradient-descent==ERROR==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row1_col4\" class=\"data row1 col4\" >12/01/21 11:26:23</td>\n",
- " <td id=\"T_a95ec_row1_col5\" class=\"data row1 col5\" >12/01/21 11:26:26</td>\n",
- " <td id=\"T_a95ec_row1_col6\" class=\"data row1 col6\" >0:00:02</td>\n",
- " <td id=\"T_a95ec_row1_col7\" class=\"data row1 col7\" >ERROR</td>\n",
+ " <td id=\"T_fad5b_row1_col0\" class=\"data row1 col0\" ><a href='../LinearReg/02-Gradient-descent.ipynb'>GRAD1</a></td>\n",
+ " <td id=\"T_fad5b_row1_col1\" class=\"data row1 col1\" >LinearReg</td>\n",
+ " <td id=\"T_fad5b_row1_col2\" class=\"data row1 col2\" ><a href='../LinearReg/02-Gradient-descent.ipynb'>02-Gradient-descent.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row1_col3\" class=\"data row1 col3\" ><a href='../LinearReg/02-Gradient-descent==done==.ipynb'>02-Gradient-descent==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row1_col4\" class=\"data row1 col4\" >14/01/21 08:11:01</td>\n",
+ " <td id=\"T_fad5b_row1_col5\" class=\"data row1 col5\" >14/01/21 08:11:07</td>\n",
+ " <td id=\"T_fad5b_row1_col6\" class=\"data row1 col6\" >0:00:05</td>\n",
+ " <td id=\"T_fad5b_row1_col7\" class=\"data row1 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row2_col0\" class=\"data row2 col0\" ><a href='../LinearReg/03-Polynomial-Regression.ipynb'>POLR1</a></td>\n",
- " <td id=\"T_a95ec_row2_col1\" class=\"data row2 col1\" >LinearReg</td>\n",
- " <td id=\"T_a95ec_row2_col2\" class=\"data row2 col2\" ><a href='../LinearReg/03-Polynomial-Regression.ipynb'>03-Polynomial-Regression.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row2_col3\" class=\"data row2 col3\" ><a href='../LinearReg/03-Polynomial-Regression==ci==.ipynb'>03-Polynomial-Regression==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row2_col4\" class=\"data row2 col4\" >12/01/21 11:26:26</td>\n",
- " <td id=\"T_a95ec_row2_col5\" class=\"data row2 col5\" >12/01/21 11:26:31</td>\n",
- " <td id=\"T_a95ec_row2_col6\" class=\"data row2 col6\" >0:00:05</td>\n",
- " <td id=\"T_a95ec_row2_col7\" class=\"data row2 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row2_col0\" class=\"data row2 col0\" ><a href='../LinearReg/03-Polynomial-Regression.ipynb'>POLR1</a></td>\n",
+ " <td id=\"T_fad5b_row2_col1\" class=\"data row2 col1\" >LinearReg</td>\n",
+ " <td id=\"T_fad5b_row2_col2\" class=\"data row2 col2\" ><a href='../LinearReg/03-Polynomial-Regression.ipynb'>03-Polynomial-Regression.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row2_col3\" class=\"data row2 col3\" ><a href='../LinearReg/03-Polynomial-Regression==done==.ipynb'>03-Polynomial-Regression==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row2_col4\" class=\"data row2 col4\" >14/01/21 08:11:07</td>\n",
+ " <td id=\"T_fad5b_row2_col5\" class=\"data row2 col5\" >14/01/21 08:11:10</td>\n",
+ " <td id=\"T_fad5b_row2_col6\" class=\"data row2 col6\" >0:00:03</td>\n",
+ " <td id=\"T_fad5b_row2_col7\" class=\"data row2 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row3_col0\" class=\"data row3 col0\" ><a href='../LinearReg/04-Logistic-Regression.ipynb'>LOGR1</a></td>\n",
- " <td id=\"T_a95ec_row3_col1\" class=\"data row3 col1\" >LinearReg</td>\n",
- " <td id=\"T_a95ec_row3_col2\" class=\"data row3 col2\" ><a href='../LinearReg/04-Logistic-Regression.ipynb'>04-Logistic-Regression.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row3_col3\" class=\"data row3 col3\" ><a href='../LinearReg/04-Logistic-Regression==ci==.ipynb'>04-Logistic-Regression==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row3_col4\" class=\"data row3 col4\" >12/01/21 11:26:32</td>\n",
- " <td id=\"T_a95ec_row3_col5\" class=\"data row3 col5\" >12/01/21 11:26:36</td>\n",
- " <td id=\"T_a95ec_row3_col6\" class=\"data row3 col6\" >0:00:04</td>\n",
- " <td id=\"T_a95ec_row3_col7\" class=\"data row3 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row3_col0\" class=\"data row3 col0\" ><a href='../LinearReg/04-Logistic-Regression.ipynb'>LOGR1</a></td>\n",
+ " <td id=\"T_fad5b_row3_col1\" class=\"data row3 col1\" >LinearReg</td>\n",
+ " <td id=\"T_fad5b_row3_col2\" class=\"data row3 col2\" ><a href='../LinearReg/04-Logistic-Regression.ipynb'>04-Logistic-Regression.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row3_col3\" class=\"data row3 col3\" ><a href='../LinearReg/04-Logistic-Regression==done==.ipynb'>04-Logistic-Regression==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row3_col4\" class=\"data row3 col4\" >14/01/21 08:11:10</td>\n",
+ " <td id=\"T_fad5b_row3_col5\" class=\"data row3 col5\" >14/01/21 08:11:14</td>\n",
+ " <td id=\"T_fad5b_row3_col6\" class=\"data row3 col6\" >0:00:03</td>\n",
+ " <td id=\"T_fad5b_row3_col7\" class=\"data row3 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row4_col0\" class=\"data row4 col0\" ><a href='../IRIS/01-Simple-Perceptron.ipynb'>PER57</a></td>\n",
- " <td id=\"T_a95ec_row4_col1\" class=\"data row4 col1\" >IRIS</td>\n",
- " <td id=\"T_a95ec_row4_col2\" class=\"data row4 col2\" ><a href='../IRIS/01-Simple-Perceptron.ipynb'>01-Simple-Perceptron.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row4_col3\" class=\"data row4 col3\" ><a href='../IRIS/01-Simple-Perceptron==ci==.ipynb'>01-Simple-Perceptron==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row4_col4\" class=\"data row4 col4\" >12/01/21 11:26:36</td>\n",
- " <td id=\"T_a95ec_row4_col5\" class=\"data row4 col5\" >12/01/21 11:26:40</td>\n",
- " <td id=\"T_a95ec_row4_col6\" class=\"data row4 col6\" >0:00:03</td>\n",
- " <td id=\"T_a95ec_row4_col7\" class=\"data row4 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row4_col0\" class=\"data row4 col0\" ><a href='../IRIS/01-Simple-Perceptron.ipynb'>PER57</a></td>\n",
+ " <td id=\"T_fad5b_row4_col1\" class=\"data row4 col1\" >IRIS</td>\n",
+ " <td id=\"T_fad5b_row4_col2\" class=\"data row4 col2\" ><a href='../IRIS/01-Simple-Perceptron.ipynb'>01-Simple-Perceptron.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row4_col3\" class=\"data row4 col3\" ><a href='../IRIS/01-Simple-Perceptron==done==.ipynb'>01-Simple-Perceptron==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row4_col4\" class=\"data row4 col4\" >14/01/21 08:11:14</td>\n",
+ " <td id=\"T_fad5b_row4_col5\" class=\"data row4 col5\" >14/01/21 08:11:17</td>\n",
+ " <td id=\"T_fad5b_row4_col6\" class=\"data row4 col6\" >0:00:02</td>\n",
+ " <td id=\"T_fad5b_row4_col7\" class=\"data row4 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row5_col0\" class=\"data row5 col0\" ><a href='../BHPD/01-DNN-Regression.ipynb'>BHPD1</a></td>\n",
- " <td id=\"T_a95ec_row5_col1\" class=\"data row5 col1\" >BHPD</td>\n",
- " <td id=\"T_a95ec_row5_col2\" class=\"data row5 col2\" ><a href='../BHPD/01-DNN-Regression.ipynb'>01-DNN-Regression.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row5_col3\" class=\"data row5 col3\" ><a href='../BHPD/01-DNN-Regression==ci==.ipynb'>01-DNN-Regression==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row5_col4\" class=\"data row5 col4\" >12/01/21 11:26:40</td>\n",
- " <td id=\"T_a95ec_row5_col5\" class=\"data row5 col5\" >12/01/21 11:26:55</td>\n",
- " <td id=\"T_a95ec_row5_col6\" class=\"data row5 col6\" >0:00:14</td>\n",
- " <td id=\"T_a95ec_row5_col7\" class=\"data row5 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row5_col0\" class=\"data row5 col0\" ><a href='../BHPD/01-DNN-Regression.ipynb'>BHPD1</a></td>\n",
+ " <td id=\"T_fad5b_row5_col1\" class=\"data row5 col1\" >BHPD</td>\n",
+ " <td id=\"T_fad5b_row5_col2\" class=\"data row5 col2\" ><a href='../BHPD/01-DNN-Regression.ipynb'>01-DNN-Regression.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row5_col3\" class=\"data row5 col3\" ><a href='../BHPD/01-DNN-Regression==done==.ipynb'>01-DNN-Regression==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row5_col4\" class=\"data row5 col4\" >14/01/21 08:11:17</td>\n",
+ " <td id=\"T_fad5b_row5_col5\" class=\"data row5 col5\" >14/01/21 08:11:28</td>\n",
+ " <td id=\"T_fad5b_row5_col6\" class=\"data row5 col6\" >0:00:11</td>\n",
+ " <td id=\"T_fad5b_row5_col7\" class=\"data row5 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row6_col0\" class=\"data row6 col0\" ><a href='../BHPD/02-DNN-Regression-Premium.ipynb'>BHPD2</a></td>\n",
- " <td id=\"T_a95ec_row6_col1\" class=\"data row6 col1\" >BHPD</td>\n",
- " <td id=\"T_a95ec_row6_col2\" class=\"data row6 col2\" ><a href='../BHPD/02-DNN-Regression-Premium.ipynb'>02-DNN-Regression-Premium.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row6_col3\" class=\"data row6 col3\" ><a href='../BHPD/02-DNN-Regression-Premium==ci==.ipynb'>02-DNN-Regression-Premium==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row6_col4\" class=\"data row6 col4\" >12/01/21 11:26:55</td>\n",
- " <td id=\"T_a95ec_row6_col5\" class=\"data row6 col5\" >12/01/21 11:27:10</td>\n",
- " <td id=\"T_a95ec_row6_col6\" class=\"data row6 col6\" >0:00:15</td>\n",
- " <td id=\"T_a95ec_row6_col7\" class=\"data row6 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row6_col0\" class=\"data row6 col0\" ><a href='../BHPD/02-DNN-Regression-Premium.ipynb'>BHPD2</a></td>\n",
+ " <td id=\"T_fad5b_row6_col1\" class=\"data row6 col1\" >BHPD</td>\n",
+ " <td id=\"T_fad5b_row6_col2\" class=\"data row6 col2\" ><a href='../BHPD/02-DNN-Regression-Premium.ipynb'>02-DNN-Regression-Premium.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row6_col3\" class=\"data row6 col3\" ><a href='../BHPD/02-DNN-Regression-Premium==done==.ipynb'>02-DNN-Regression-Premium==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row6_col4\" class=\"data row6 col4\" >14/01/21 08:11:28</td>\n",
+ " <td id=\"T_fad5b_row6_col5\" class=\"data row6 col5\" >14/01/21 08:11:40</td>\n",
+ " <td id=\"T_fad5b_row6_col6\" class=\"data row6 col6\" >0:00:11</td>\n",
+ " <td id=\"T_fad5b_row6_col7\" class=\"data row6 col7\" >ok</td>\n",
" </tr>\n",
" <tr>\n",
- " <td id=\"T_a95ec_row7_col0\" class=\"data row7 col0\" ><a href='../MNIST/01-DNN-MNIST.ipynb'>MNIST1</a></td>\n",
- " <td id=\"T_a95ec_row7_col1\" class=\"data row7 col1\" >MNIST</td>\n",
- " <td id=\"T_a95ec_row7_col2\" class=\"data row7 col2\" ><a href='../MNIST/01-DNN-MNIST.ipynb'>01-DNN-MNIST.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row7_col3\" class=\"data row7 col3\" ><a href='../MNIST/01-DNN-MNIST==ci==.ipynb'>01-DNN-MNIST==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row7_col4\" class=\"data row7 col4\" >12/01/21 11:27:10</td>\n",
- " <td id=\"T_a95ec_row7_col5\" class=\"data row7 col5\" >12/01/21 11:27:57</td>\n",
- " <td id=\"T_a95ec_row7_col6\" class=\"data row7 col6\" >0:00:46</td>\n",
- " <td id=\"T_a95ec_row7_col7\" class=\"data row7 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row8_col0\" class=\"data row8 col0\" ><a href='../GTSRB/01-Preparation-of-data.ipynb'>GTSRB1</a></td>\n",
- " <td id=\"T_a95ec_row8_col1\" class=\"data row8 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row8_col2\" class=\"data row8 col2\" ><a href='../GTSRB/01-Preparation-of-data.ipynb'>01-Preparation-of-data.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row8_col3\" class=\"data row8 col3\" ><a href='../GTSRB/01-Preparation-of-data==ci==.ipynb'>01-Preparation-of-data==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row8_col4\" class=\"data row8 col4\" >12/01/21 11:27:57</td>\n",
- " <td id=\"T_a95ec_row8_col5\" class=\"data row8 col5\" >12/01/21 11:29:45</td>\n",
- " <td id=\"T_a95ec_row8_col6\" class=\"data row8 col6\" >0:01:48</td>\n",
- " <td id=\"T_a95ec_row8_col7\" class=\"data row8 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row9_col0\" class=\"data row9 col0\" ><a href='../GTSRB/02-First-convolutions.ipynb'>GTSRB2</a></td>\n",
- " <td id=\"T_a95ec_row9_col1\" class=\"data row9 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row9_col2\" class=\"data row9 col2\" ><a href='../GTSRB/02-First-convolutions.ipynb'>02-First-convolutions.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row9_col3\" class=\"data row9 col3\" ><a href='../GTSRB/02-First-convolutions==ci==.ipynb'>02-First-convolutions==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row9_col4\" class=\"data row9 col4\" >12/01/21 11:29:45</td>\n",
- " <td id=\"T_a95ec_row9_col5\" class=\"data row9 col5\" >12/01/21 11:30:09</td>\n",
- " <td id=\"T_a95ec_row9_col6\" class=\"data row9 col6\" >0:00:23</td>\n",
- " <td id=\"T_a95ec_row9_col7\" class=\"data row9 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row10_col0\" class=\"data row10 col0\" ><a href='../GTSRB/03-Tracking-and-visualizing.ipynb'>GTSRB3</a></td>\n",
- " <td id=\"T_a95ec_row10_col1\" class=\"data row10 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row10_col2\" class=\"data row10 col2\" ><a href='../GTSRB/03-Tracking-and-visualizing.ipynb'>03-Tracking-and-visualizing.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row10_col3\" class=\"data row10 col3\" ><a href='../GTSRB/03-Tracking-and-visualizing==ci==.ipynb'>03-Tracking-and-visualizing==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row10_col4\" class=\"data row10 col4\" >12/01/21 11:30:09</td>\n",
- " <td id=\"T_a95ec_row10_col5\" class=\"data row10 col5\" >12/01/21 11:30:59</td>\n",
- " <td id=\"T_a95ec_row10_col6\" class=\"data row10 col6\" >0:00:49</td>\n",
- " <td id=\"T_a95ec_row10_col7\" class=\"data row10 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row11_col0\" class=\"data row11 col0\" ><a href='../GTSRB/04-Data-augmentation.ipynb'>GTSRB4</a></td>\n",
- " <td id=\"T_a95ec_row11_col1\" class=\"data row11 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row11_col2\" class=\"data row11 col2\" ><a href='../GTSRB/04-Data-augmentation.ipynb'>04-Data-augmentation.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row11_col3\" class=\"data row11 col3\" ><a href='../GTSRB/04-Data-augmentation==ci==.ipynb'>04-Data-augmentation==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row11_col4\" class=\"data row11 col4\" >12/01/21 11:30:59</td>\n",
- " <td id=\"T_a95ec_row11_col5\" class=\"data row11 col5\" >12/01/21 11:31:42</td>\n",
- " <td id=\"T_a95ec_row11_col6\" class=\"data row11 col6\" >0:00:42</td>\n",
- " <td id=\"T_a95ec_row11_col7\" class=\"data row11 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row12_col0\" class=\"data row12 col0\" ><a href='../GTSRB/05-Full-convolutions.ipynb'>GTSRB5</a></td>\n",
- " <td id=\"T_a95ec_row12_col1\" class=\"data row12 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row12_col2\" class=\"data row12 col2\" ><a href='../GTSRB/05-Full-convolutions.ipynb'>05-Full-convolutions.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row12_col3\" class=\"data row12 col3\" ><a href='../GTSRB/05-Full-convolutions==ci==.ipynb'>05-Full-convolutions==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row12_col4\" class=\"data row12 col4\" >12/01/21 11:31:42</td>\n",
- " <td id=\"T_a95ec_row12_col5\" class=\"data row12 col5\" >12/01/21 11:33:00</td>\n",
- " <td id=\"T_a95ec_row12_col6\" class=\"data row12 col6\" >0:01:18</td>\n",
- " <td id=\"T_a95ec_row12_col7\" class=\"data row12 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row13_col0\" class=\"data row13 col0\" ><a href='../GTSRB/06-Notebook-as-a-batch.ipynb'>GTSRB6</a></td>\n",
- " <td id=\"T_a95ec_row13_col1\" class=\"data row13 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row13_col2\" class=\"data row13 col2\" ><a href='../GTSRB/06-Notebook-as-a-batch.ipynb'>06-Notebook-as-a-batch.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row13_col3\" class=\"data row13 col3\" ><a href='../GTSRB/06-Notebook-as-a-batch==ci==.ipynb'>06-Notebook-as-a-batch==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row13_col4\" class=\"data row13 col4\" >12/01/21 11:33:00</td>\n",
- " <td id=\"T_a95ec_row13_col5\" class=\"data row13 col5\" >12/01/21 11:33:04</td>\n",
- " <td id=\"T_a95ec_row13_col6\" class=\"data row13 col6\" >0:00:03</td>\n",
- " <td id=\"T_a95ec_row13_col7\" class=\"data row13 col7\" >ok</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td id=\"T_a95ec_row14_col0\" class=\"data row14 col0\" ><a href='../GTSRB/07-Show-report.ipynb'>GTSRB7</a></td>\n",
- " <td id=\"T_a95ec_row14_col1\" class=\"data row14 col1\" >GTSRB</td>\n",
- " <td id=\"T_a95ec_row14_col2\" class=\"data row14 col2\" ><a href='../GTSRB/07-Show-report.ipynb'>07-Show-report.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row14_col3\" class=\"data row14 col3\" ><a href='../GTSRB/07-Show-report==ci==.ipynb'>07-Show-report==ci==.ipynb</a></td>\n",
- " <td id=\"T_a95ec_row14_col4\" class=\"data row14 col4\" >12/01/21 11:33:04</td>\n",
- " <td id=\"T_a95ec_row14_col5\" class=\"data row14 col5\" >12/01/21 11:33:06</td>\n",
- " <td id=\"T_a95ec_row14_col6\" class=\"data row14 col6\" >0:00:02</td>\n",
- " <td id=\"T_a95ec_row14_col7\" class=\"data row14 col7\" >ok</td>\n",
+ " <td id=\"T_fad5b_row7_col0\" class=\"data row7 col0\" ><a href='../MNIST/01-DNN-MNIST.ipynb'>MNIST1</a></td>\n",
+ " <td id=\"T_fad5b_row7_col1\" class=\"data row7 col1\" >MNIST</td>\n",
+ " <td id=\"T_fad5b_row7_col2\" class=\"data row7 col2\" ><a href='../MNIST/01-DNN-MNIST.ipynb'>01-DNN-MNIST.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row7_col3\" class=\"data row7 col3\" ><a href='../MNIST/01-DNN-MNIST==done==.ipynb'>01-DNN-MNIST==done==.ipynb</a></td>\n",
+ " <td id=\"T_fad5b_row7_col4\" class=\"data row7 col4\" >14/01/21 08:11:40</td>\n",
+ " <td id=\"T_fad5b_row7_col5\" class=\"data row7 col5\" >14/01/21 08:12:13</td>\n",
+ " <td id=\"T_fad5b_row7_col6\" class=\"data row7 col6\" >0:00:33</td>\n",
+ " <td id=\"T_fad5b_row7_col7\" class=\"data row7 col7\" >ok</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7ff3f7a5d6d0>"
+ "<pandas.io.formats.style.Styler at 0x7f1ccdbcc710>"
]
},
"metadata": {},
diff --git a/fidle/ci/full_jdev.yml b/fidle/ci/full_jdev.yml
new file mode 100644
index 0000000..599cdac
--- /dev/null
+++ b/fidle/ci/full_jdev.yml
@@ -0,0 +1,37 @@
+_metadata_:
+ version: '1.0'
+ output_tag: ==done==
+ save_figs: true
+ description: Runned notebooks (done)
+LINR1:
+ notebook_dir: LinearReg
+ notebook_src: 01-Linear-Regression.ipynb
+ notebook_out: default
+GRAD1:
+ notebook_dir: LinearReg
+ notebook_src: 02-Gradient-descent.ipynb
+ notebook_out: default
+POLR1:
+ notebook_dir: LinearReg
+ notebook_src: 03-Polynomial-Regression.ipynb
+ notebook_out: default
+LOGR1:
+ notebook_dir: LinearReg
+ notebook_src: 04-Logistic-Regression.ipynb
+ notebook_out: default
+PER57:
+ notebook_dir: IRIS
+ notebook_src: 01-Simple-Perceptron.ipynb
+ notebook_out: default
+BHPD1:
+ notebook_dir: BHPD
+ notebook_src: 01-DNN-Regression.ipynb
+ notebook_out: default
+BHPD2:
+ notebook_dir: BHPD
+ notebook_src: 02-DNN-Regression-Premium.ipynb
+ notebook_out: default
+MNIST1:
+ notebook_dir: MNIST
+ notebook_src: 01-DNN-MNIST.ipynb
+ notebook_out: default
diff --git a/fidle/config.py b/fidle/config.py
index 341a65c..7413e51 100644
--- a/fidle/config.py
+++ b/fidle/config.py
@@ -14,7 +14,7 @@
# ---- Version -----------------------------------------------------
#
-VERSION = '2.0'
+VERSION = '2.0.1'
# ---- Default notebook name ---------------------------------------
#
diff --git a/fidle/cookindex.py b/fidle/cookindex.py
index 4e71a44..5f8629f 100644
--- a/fidle/cookindex.py
+++ b/fidle/cookindex.py
@@ -70,7 +70,7 @@ def get_notebook_infos(filename, top_dir='..'):
return:
dict : with infos.
'''
-
+ print('Read : ',filename)
about={}
about['id'] = '??'
about['dirname'] = os.path.dirname(filename)
diff --git a/fidle/logs/ci_report.html b/fidle/logs/ci_report.html
index 15a8bc3..b94f323 100644
--- a/fidle/logs/ci_report.html
+++ b/fidle/logs/ci_report.html
@@ -30,176 +30,103 @@
<p>Below is the result of the continuous integration tests of the Fidle project:</p>
<div class='title'>About :</div>
<div class="metadata"><b>Version</b> : 1.0 <br>
-<b>Output_Tag</b> : ==ci== <br>
+<b>Output_Tag</b> : ==done== <br>
<b>Save_Figs</b> : True <br>
-<b>Description</b> : Smart profile, for cpu <br>
+<b>Description</b> : Runned notebooks (done) <br>
<b>Host</b> : Oban <br>
-<b>Profile</b> : ./ci/smart_cpu.yml <br>
-<b>Start</b> : 12/01/21 11:26:19 <br>
-<b>End</b> : 12/01/21 11:33:06 <br>
-<b>Duration</b> : 0:06:47 <br>
+<b>Profile</b> : ./ci/full_jdev.yml <br>
+<b>Start</b> : 14/01/21 08:10:56 <br>
+<b>End</b> : 14/01/21 08:12:13 <br>
+<b>Duration</b> : 0:01:16 <br>
</div>
<div class='title'>Details :</div>
<div class="result"><style type="text/css" >
- #T_6c71a_ td {
+ #T_abe99_ td {
font-size: 110%;
text-align: left;
- } #T_6c71a_ th {
+ } #T_abe99_ th {
font-size: 110%;
text-align: left;
- }#T_6c71a_row1_col7{
- background-color: OrangeRed;
- color: white;
- }</style><table id="T_6c71a_" ><thead> <tr> <th class="col_heading level0 col0" >Id</th> <th class="col_heading level0 col1" >Dir</th> <th class="col_heading level0 col2" >Src</th> <th class="col_heading level0 col3" >Out</th> <th class="col_heading level0 col4" >Start</th> <th class="col_heading level0 col5" >End</th> <th class="col_heading level0 col6" >Duration</th> <th class="col_heading level0 col7" >State</th> </tr></thead><tbody>
+ }</style><table id="T_abe99_" ><thead> <tr> <th class="col_heading level0 col0" >Id</th> <th class="col_heading level0 col1" >Dir</th> <th class="col_heading level0 col2" >Src</th> <th class="col_heading level0 col3" >Out</th> <th class="col_heading level0 col4" >Start</th> <th class="col_heading level0 col5" >End</th> <th class="col_heading level0 col6" >Duration</th> <th class="col_heading level0 col7" >State</th> </tr></thead><tbody>
<tr>
- <td id="T_6c71a_row0_col0" class="data row0 col0" >LINR1</td>
- <td id="T_6c71a_row0_col1" class="data row0 col1" >LinearReg</td>
- <td id="T_6c71a_row0_col2" class="data row0 col2" >01-Linear-Regression.ipynb</td>
- <td id="T_6c71a_row0_col3" class="data row0 col3" >01-Linear-Regression==ci==.ipynb</td>
- <td id="T_6c71a_row0_col4" class="data row0 col4" >12/01/21 11:26:19</td>
- <td id="T_6c71a_row0_col5" class="data row0 col5" >12/01/21 11:26:23</td>
- <td id="T_6c71a_row0_col6" class="data row0 col6" >0:00:03</td>
- <td id="T_6c71a_row0_col7" class="data row0 col7" >ok</td>
+ <td id="T_abe99_row0_col0" class="data row0 col0" >LINR1</td>
+ <td id="T_abe99_row0_col1" class="data row0 col1" >LinearReg</td>
+ <td id="T_abe99_row0_col2" class="data row0 col2" >01-Linear-Regression.ipynb</td>
+ <td id="T_abe99_row0_col3" class="data row0 col3" >01-Linear-Regression==done==.ipynb</td>
+ <td id="T_abe99_row0_col4" class="data row0 col4" >14/01/21 08:10:56</td>
+ <td id="T_abe99_row0_col5" class="data row0 col5" >14/01/21 08:11:01</td>
+ <td id="T_abe99_row0_col6" class="data row0 col6" >0:00:04</td>
+ <td id="T_abe99_row0_col7" class="data row0 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row1_col0" class="data row1 col0" >GRAD1</td>
- <td id="T_6c71a_row1_col1" class="data row1 col1" >LinearReg</td>
- <td id="T_6c71a_row1_col2" class="data row1 col2" >02-Gradient-descent.ipynb</td>
- <td id="T_6c71a_row1_col3" class="data row1 col3" >02-Gradient-descent==ERROR==.ipynb</td>
- <td id="T_6c71a_row1_col4" class="data row1 col4" >12/01/21 11:26:23</td>
- <td id="T_6c71a_row1_col5" class="data row1 col5" >12/01/21 11:26:26</td>
- <td id="T_6c71a_row1_col6" class="data row1 col6" >0:00:02</td>
- <td id="T_6c71a_row1_col7" class="data row1 col7" >ERROR</td>
+ <td id="T_abe99_row1_col0" class="data row1 col0" >GRAD1</td>
+ <td id="T_abe99_row1_col1" class="data row1 col1" >LinearReg</td>
+ <td id="T_abe99_row1_col2" class="data row1 col2" >02-Gradient-descent.ipynb</td>
+ <td id="T_abe99_row1_col3" class="data row1 col3" >02-Gradient-descent==done==.ipynb</td>
+ <td id="T_abe99_row1_col4" class="data row1 col4" >14/01/21 08:11:01</td>
+ <td id="T_abe99_row1_col5" class="data row1 col5" >14/01/21 08:11:07</td>
+ <td id="T_abe99_row1_col6" class="data row1 col6" >0:00:05</td>
+ <td id="T_abe99_row1_col7" class="data row1 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row2_col0" class="data row2 col0" >POLR1</td>
- <td id="T_6c71a_row2_col1" class="data row2 col1" >LinearReg</td>
- <td id="T_6c71a_row2_col2" class="data row2 col2" >03-Polynomial-Regression.ipynb</td>
- <td id="T_6c71a_row2_col3" class="data row2 col3" >03-Polynomial-Regression==ci==.ipynb</td>
- <td id="T_6c71a_row2_col4" class="data row2 col4" >12/01/21 11:26:26</td>
- <td id="T_6c71a_row2_col5" class="data row2 col5" >12/01/21 11:26:31</td>
- <td id="T_6c71a_row2_col6" class="data row2 col6" >0:00:05</td>
- <td id="T_6c71a_row2_col7" class="data row2 col7" >ok</td>
+ <td id="T_abe99_row2_col0" class="data row2 col0" >POLR1</td>
+ <td id="T_abe99_row2_col1" class="data row2 col1" >LinearReg</td>
+ <td id="T_abe99_row2_col2" class="data row2 col2" >03-Polynomial-Regression.ipynb</td>
+ <td id="T_abe99_row2_col3" class="data row2 col3" >03-Polynomial-Regression==done==.ipynb</td>
+ <td id="T_abe99_row2_col4" class="data row2 col4" >14/01/21 08:11:07</td>
+ <td id="T_abe99_row2_col5" class="data row2 col5" >14/01/21 08:11:10</td>
+ <td id="T_abe99_row2_col6" class="data row2 col6" >0:00:03</td>
+ <td id="T_abe99_row2_col7" class="data row2 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row3_col0" class="data row3 col0" >LOGR1</td>
- <td id="T_6c71a_row3_col1" class="data row3 col1" >LinearReg</td>
- <td id="T_6c71a_row3_col2" class="data row3 col2" >04-Logistic-Regression.ipynb</td>
- <td id="T_6c71a_row3_col3" class="data row3 col3" >04-Logistic-Regression==ci==.ipynb</td>
- <td id="T_6c71a_row3_col4" class="data row3 col4" >12/01/21 11:26:32</td>
- <td id="T_6c71a_row3_col5" class="data row3 col5" >12/01/21 11:26:36</td>
- <td id="T_6c71a_row3_col6" class="data row3 col6" >0:00:04</td>
- <td id="T_6c71a_row3_col7" class="data row3 col7" >ok</td>
+ <td id="T_abe99_row3_col0" class="data row3 col0" >LOGR1</td>
+ <td id="T_abe99_row3_col1" class="data row3 col1" >LinearReg</td>
+ <td id="T_abe99_row3_col2" class="data row3 col2" >04-Logistic-Regression.ipynb</td>
+ <td id="T_abe99_row3_col3" class="data row3 col3" >04-Logistic-Regression==done==.ipynb</td>
+ <td id="T_abe99_row3_col4" class="data row3 col4" >14/01/21 08:11:10</td>
+ <td id="T_abe99_row3_col5" class="data row3 col5" >14/01/21 08:11:14</td>
+ <td id="T_abe99_row3_col6" class="data row3 col6" >0:00:03</td>
+ <td id="T_abe99_row3_col7" class="data row3 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row4_col0" class="data row4 col0" >PER57</td>
- <td id="T_6c71a_row4_col1" class="data row4 col1" >IRIS</td>
- <td id="T_6c71a_row4_col2" class="data row4 col2" >01-Simple-Perceptron.ipynb</td>
- <td id="T_6c71a_row4_col3" class="data row4 col3" >01-Simple-Perceptron==ci==.ipynb</td>
- <td id="T_6c71a_row4_col4" class="data row4 col4" >12/01/21 11:26:36</td>
- <td id="T_6c71a_row4_col5" class="data row4 col5" >12/01/21 11:26:40</td>
- <td id="T_6c71a_row4_col6" class="data row4 col6" >0:00:03</td>
- <td id="T_6c71a_row4_col7" class="data row4 col7" >ok</td>
+ <td id="T_abe99_row4_col0" class="data row4 col0" >PER57</td>
+ <td id="T_abe99_row4_col1" class="data row4 col1" >IRIS</td>
+ <td id="T_abe99_row4_col2" class="data row4 col2" >01-Simple-Perceptron.ipynb</td>
+ <td id="T_abe99_row4_col3" class="data row4 col3" >01-Simple-Perceptron==done==.ipynb</td>
+ <td id="T_abe99_row4_col4" class="data row4 col4" >14/01/21 08:11:14</td>
+ <td id="T_abe99_row4_col5" class="data row4 col5" >14/01/21 08:11:17</td>
+ <td id="T_abe99_row4_col6" class="data row4 col6" >0:00:02</td>
+ <td id="T_abe99_row4_col7" class="data row4 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row5_col0" class="data row5 col0" >BHPD1</td>
- <td id="T_6c71a_row5_col1" class="data row5 col1" >BHPD</td>
- <td id="T_6c71a_row5_col2" class="data row5 col2" >01-DNN-Regression.ipynb</td>
- <td id="T_6c71a_row5_col3" class="data row5 col3" >01-DNN-Regression==ci==.ipynb</td>
- <td id="T_6c71a_row5_col4" class="data row5 col4" >12/01/21 11:26:40</td>
- <td id="T_6c71a_row5_col5" class="data row5 col5" >12/01/21 11:26:55</td>
- <td id="T_6c71a_row5_col6" class="data row5 col6" >0:00:14</td>
- <td id="T_6c71a_row5_col7" class="data row5 col7" >ok</td>
+ <td id="T_abe99_row5_col0" class="data row5 col0" >BHPD1</td>
+ <td id="T_abe99_row5_col1" class="data row5 col1" >BHPD</td>
+ <td id="T_abe99_row5_col2" class="data row5 col2" >01-DNN-Regression.ipynb</td>
+ <td id="T_abe99_row5_col3" class="data row5 col3" >01-DNN-Regression==done==.ipynb</td>
+ <td id="T_abe99_row5_col4" class="data row5 col4" >14/01/21 08:11:17</td>
+ <td id="T_abe99_row5_col5" class="data row5 col5" >14/01/21 08:11:28</td>
+ <td id="T_abe99_row5_col6" class="data row5 col6" >0:00:11</td>
+ <td id="T_abe99_row5_col7" class="data row5 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row6_col0" class="data row6 col0" >BHPD2</td>
- <td id="T_6c71a_row6_col1" class="data row6 col1" >BHPD</td>
- <td id="T_6c71a_row6_col2" class="data row6 col2" >02-DNN-Regression-Premium.ipynb</td>
- <td id="T_6c71a_row6_col3" class="data row6 col3" >02-DNN-Regression-Premium==ci==.ipynb</td>
- <td id="T_6c71a_row6_col4" class="data row6 col4" >12/01/21 11:26:55</td>
- <td id="T_6c71a_row6_col5" class="data row6 col5" >12/01/21 11:27:10</td>
- <td id="T_6c71a_row6_col6" class="data row6 col6" >0:00:15</td>
- <td id="T_6c71a_row6_col7" class="data row6 col7" >ok</td>
+ <td id="T_abe99_row6_col0" class="data row6 col0" >BHPD2</td>
+ <td id="T_abe99_row6_col1" class="data row6 col1" >BHPD</td>
+ <td id="T_abe99_row6_col2" class="data row6 col2" >02-DNN-Regression-Premium.ipynb</td>
+ <td id="T_abe99_row6_col3" class="data row6 col3" >02-DNN-Regression-Premium==done==.ipynb</td>
+ <td id="T_abe99_row6_col4" class="data row6 col4" >14/01/21 08:11:28</td>
+ <td id="T_abe99_row6_col5" class="data row6 col5" >14/01/21 08:11:40</td>
+ <td id="T_abe99_row6_col6" class="data row6 col6" >0:00:11</td>
+ <td id="T_abe99_row6_col7" class="data row6 col7" >ok</td>
</tr>
<tr>
- <td id="T_6c71a_row7_col0" class="data row7 col0" >MNIST1</td>
- <td id="T_6c71a_row7_col1" class="data row7 col1" >MNIST</td>
- <td id="T_6c71a_row7_col2" class="data row7 col2" >01-DNN-MNIST.ipynb</td>
- <td id="T_6c71a_row7_col3" class="data row7 col3" >01-DNN-MNIST==ci==.ipynb</td>
- <td id="T_6c71a_row7_col4" class="data row7 col4" >12/01/21 11:27:10</td>
- <td id="T_6c71a_row7_col5" class="data row7 col5" >12/01/21 11:27:57</td>
- <td id="T_6c71a_row7_col6" class="data row7 col6" >0:00:46</td>
- <td id="T_6c71a_row7_col7" class="data row7 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row8_col0" class="data row8 col0" >GTSRB1</td>
- <td id="T_6c71a_row8_col1" class="data row8 col1" >GTSRB</td>
- <td id="T_6c71a_row8_col2" class="data row8 col2" >01-Preparation-of-data.ipynb</td>
- <td id="T_6c71a_row8_col3" class="data row8 col3" >01-Preparation-of-data==ci==.ipynb</td>
- <td id="T_6c71a_row8_col4" class="data row8 col4" >12/01/21 11:27:57</td>
- <td id="T_6c71a_row8_col5" class="data row8 col5" >12/01/21 11:29:45</td>
- <td id="T_6c71a_row8_col6" class="data row8 col6" >0:01:48</td>
- <td id="T_6c71a_row8_col7" class="data row8 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row9_col0" class="data row9 col0" >GTSRB2</td>
- <td id="T_6c71a_row9_col1" class="data row9 col1" >GTSRB</td>
- <td id="T_6c71a_row9_col2" class="data row9 col2" >02-First-convolutions.ipynb</td>
- <td id="T_6c71a_row9_col3" class="data row9 col3" >02-First-convolutions==ci==.ipynb</td>
- <td id="T_6c71a_row9_col4" class="data row9 col4" >12/01/21 11:29:45</td>
- <td id="T_6c71a_row9_col5" class="data row9 col5" >12/01/21 11:30:09</td>
- <td id="T_6c71a_row9_col6" class="data row9 col6" >0:00:23</td>
- <td id="T_6c71a_row9_col7" class="data row9 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row10_col0" class="data row10 col0" >GTSRB3</td>
- <td id="T_6c71a_row10_col1" class="data row10 col1" >GTSRB</td>
- <td id="T_6c71a_row10_col2" class="data row10 col2" >03-Tracking-and-visualizing.ipynb</td>
- <td id="T_6c71a_row10_col3" class="data row10 col3" >03-Tracking-and-visualizing==ci==.ipynb</td>
- <td id="T_6c71a_row10_col4" class="data row10 col4" >12/01/21 11:30:09</td>
- <td id="T_6c71a_row10_col5" class="data row10 col5" >12/01/21 11:30:59</td>
- <td id="T_6c71a_row10_col6" class="data row10 col6" >0:00:49</td>
- <td id="T_6c71a_row10_col7" class="data row10 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row11_col0" class="data row11 col0" >GTSRB4</td>
- <td id="T_6c71a_row11_col1" class="data row11 col1" >GTSRB</td>
- <td id="T_6c71a_row11_col2" class="data row11 col2" >04-Data-augmentation.ipynb</td>
- <td id="T_6c71a_row11_col3" class="data row11 col3" >04-Data-augmentation==ci==.ipynb</td>
- <td id="T_6c71a_row11_col4" class="data row11 col4" >12/01/21 11:30:59</td>
- <td id="T_6c71a_row11_col5" class="data row11 col5" >12/01/21 11:31:42</td>
- <td id="T_6c71a_row11_col6" class="data row11 col6" >0:00:42</td>
- <td id="T_6c71a_row11_col7" class="data row11 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row12_col0" class="data row12 col0" >GTSRB5</td>
- <td id="T_6c71a_row12_col1" class="data row12 col1" >GTSRB</td>
- <td id="T_6c71a_row12_col2" class="data row12 col2" >05-Full-convolutions.ipynb</td>
- <td id="T_6c71a_row12_col3" class="data row12 col3" >05-Full-convolutions==ci==.ipynb</td>
- <td id="T_6c71a_row12_col4" class="data row12 col4" >12/01/21 11:31:42</td>
- <td id="T_6c71a_row12_col5" class="data row12 col5" >12/01/21 11:33:00</td>
- <td id="T_6c71a_row12_col6" class="data row12 col6" >0:01:18</td>
- <td id="T_6c71a_row12_col7" class="data row12 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row13_col0" class="data row13 col0" >GTSRB6</td>
- <td id="T_6c71a_row13_col1" class="data row13 col1" >GTSRB</td>
- <td id="T_6c71a_row13_col2" class="data row13 col2" >06-Notebook-as-a-batch.ipynb</td>
- <td id="T_6c71a_row13_col3" class="data row13 col3" >06-Notebook-as-a-batch==ci==.ipynb</td>
- <td id="T_6c71a_row13_col4" class="data row13 col4" >12/01/21 11:33:00</td>
- <td id="T_6c71a_row13_col5" class="data row13 col5" >12/01/21 11:33:04</td>
- <td id="T_6c71a_row13_col6" class="data row13 col6" >0:00:03</td>
- <td id="T_6c71a_row13_col7" class="data row13 col7" >ok</td>
- </tr>
- <tr>
- <td id="T_6c71a_row14_col0" class="data row14 col0" >GTSRB7</td>
- <td id="T_6c71a_row14_col1" class="data row14 col1" >GTSRB</td>
- <td id="T_6c71a_row14_col2" class="data row14 col2" >07-Show-report.ipynb</td>
- <td id="T_6c71a_row14_col3" class="data row14 col3" >07-Show-report==ci==.ipynb</td>
- <td id="T_6c71a_row14_col4" class="data row14 col4" >12/01/21 11:33:04</td>
- <td id="T_6c71a_row14_col5" class="data row14 col5" >12/01/21 11:33:06</td>
- <td id="T_6c71a_row14_col6" class="data row14 col6" >0:00:02</td>
- <td id="T_6c71a_row14_col7" class="data row14 col7" >ok</td>
+ <td id="T_abe99_row7_col0" class="data row7 col0" >MNIST1</td>
+ <td id="T_abe99_row7_col1" class="data row7 col1" >MNIST</td>
+ <td id="T_abe99_row7_col2" class="data row7 col2" >01-DNN-MNIST.ipynb</td>
+ <td id="T_abe99_row7_col3" class="data row7 col3" >01-DNN-MNIST==done==.ipynb</td>
+ <td id="T_abe99_row7_col4" class="data row7 col4" >14/01/21 08:11:40</td>
+ <td id="T_abe99_row7_col5" class="data row7 col5" >14/01/21 08:12:13</td>
+ <td id="T_abe99_row7_col6" class="data row7 col6" >0:00:33</td>
+ <td id="T_abe99_row7_col7" class="data row7 col7" >ok</td>
</tr>
</tbody></table></div>
diff --git a/fidle/logs/ci_report.json b/fidle/logs/ci_report.json
index f0fc99e..be8e6b5 100644
--- a/fidle/logs/ci_report.json
+++ b/fidle/logs/ci_report.json
@@ -1,148 +1,85 @@
{
"_metadata_": {
"version": "1.0",
- "output_tag": "==ci==",
+ "output_tag": "==done==",
"save_figs": true,
- "description": "Smart profile, for cpu",
+ "description": "Runned notebooks (done)",
"host": "Oban",
- "profile": "./ci/smart_cpu.yml",
- "start": "12/01/21 11:26:19",
- "end": "12/01/21 11:33:06",
- "duration": "0:06:47"
+ "profile": "./ci/full_jdev.yml",
+ "start": "14/01/21 08:10:56",
+ "end": "14/01/21 08:12:13",
+ "duration": "0:01:16"
},
"LINR1": {
"dir": "LinearReg",
"src": "01-Linear-Regression.ipynb",
- "out": "01-Linear-Regression==ci==.ipynb",
- "start": "12/01/21 11:26:19",
- "end": "12/01/21 11:26:23",
- "duration": "0:00:03",
+ "out": "01-Linear-Regression==done==.ipynb",
+ "start": "14/01/21 08:10:56",
+ "end": "14/01/21 08:11:01",
+ "duration": "0:00:04",
"state": "ok"
},
"GRAD1": {
"dir": "LinearReg",
"src": "02-Gradient-descent.ipynb",
- "out": "02-Gradient-descent==ERROR==.ipynb",
- "start": "12/01/21 11:26:23",
- "end": "12/01/21 11:26:26",
- "duration": "0:00:02",
- "state": "ERROR"
+ "out": "02-Gradient-descent==done==.ipynb",
+ "start": "14/01/21 08:11:01",
+ "end": "14/01/21 08:11:07",
+ "duration": "0:00:05",
+ "state": "ok"
},
"POLR1": {
"dir": "LinearReg",
"src": "03-Polynomial-Regression.ipynb",
- "out": "03-Polynomial-Regression==ci==.ipynb",
- "start": "12/01/21 11:26:26",
- "end": "12/01/21 11:26:31",
- "duration": "0:00:05",
+ "out": "03-Polynomial-Regression==done==.ipynb",
+ "start": "14/01/21 08:11:07",
+ "end": "14/01/21 08:11:10",
+ "duration": "0:00:03",
"state": "ok"
},
"LOGR1": {
"dir": "LinearReg",
"src": "04-Logistic-Regression.ipynb",
- "out": "04-Logistic-Regression==ci==.ipynb",
- "start": "12/01/21 11:26:32",
- "end": "12/01/21 11:26:36",
- "duration": "0:00:04",
+ "out": "04-Logistic-Regression==done==.ipynb",
+ "start": "14/01/21 08:11:10",
+ "end": "14/01/21 08:11:14",
+ "duration": "0:00:03",
"state": "ok"
},
"PER57": {
"dir": "IRIS",
"src": "01-Simple-Perceptron.ipynb",
- "out": "01-Simple-Perceptron==ci==.ipynb",
- "start": "12/01/21 11:26:36",
- "end": "12/01/21 11:26:40",
- "duration": "0:00:03",
+ "out": "01-Simple-Perceptron==done==.ipynb",
+ "start": "14/01/21 08:11:14",
+ "end": "14/01/21 08:11:17",
+ "duration": "0:00:02",
"state": "ok"
},
"BHPD1": {
"dir": "BHPD",
"src": "01-DNN-Regression.ipynb",
- "out": "01-DNN-Regression==ci==.ipynb",
- "start": "12/01/21 11:26:40",
- "end": "12/01/21 11:26:55",
- "duration": "0:00:14",
+ "out": "01-DNN-Regression==done==.ipynb",
+ "start": "14/01/21 08:11:17",
+ "end": "14/01/21 08:11:28",
+ "duration": "0:00:11",
"state": "ok"
},
"BHPD2": {
"dir": "BHPD",
"src": "02-DNN-Regression-Premium.ipynb",
- "out": "02-DNN-Regression-Premium==ci==.ipynb",
- "start": "12/01/21 11:26:55",
- "end": "12/01/21 11:27:10",
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\ No newline at end of file
--
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