.gitignore

@@ -2,5 +2,10 @@
 */.ipynb_checkpoints/*
 __pycache__
 */__pycache__/*
-/run/**
-*/data/*
+run/
+GTSRB/data
+IMDB/data
+MNIST/data
+VAE/data
+BHPD/data/*
+!BHPD/data/BostonHousing.csv

BHPD/01-DNN-Regression.ipynb

+%% Cell type:markdown id: tags:
+
+![Fidle](../fidle/img/00-Fidle-header-01.png)
+![Fidle](00-Fidle-header-01.png)
+
+# Deep Neural Network (DNN) - BHPD dataset
+
+<!-- INDEX : Simple regression with a Dense Neural Network (DNN) - BHPD dataset -->
+
+A very simple and classic example of **regression** :
+
+## Objectives :
+ - Predicts **housing prices** from a set of house features.
+ - Understanding the principle and the architecture of a regression with a dense neural network
+
+
+The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston.
+Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town,
+age of people who own the house and many other attributes...
+
+What we're going to do:
+
+ - Retrieve data
+ - Preparing the data
+ - Build a model
+ - Train the model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import os,sys
+
+from IPython.display import display, Markdown
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+os.makedirs('./run/models',   mode=0o750, exist_ok=True)
+```
+
+%% Output
+
+
+    
+    FIDLE 2020 - Practical Work Module
+    Version              : 0.2.9
+    Run time             : Tuesday 18 February 2020, 14:42:02
+    TensorFlow version   : 2.0.0
+    Keras version        : 2.2.4-tf
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Retrieve data
+
+### 2.1 - Option 1  : From Keras
+Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
+
+%% Cell type:raw id: tags:
+
+(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Option 2 : From a csv file
+More fun !
+
+%% Cell type:code id: tags:
+
+``` python
+data = pd.read_csv('./data/BostonHousing.csv', header=0)
+
+display(data.head(5).style.format("{0:.2f}"))
+print('Données manquantes : ',data.isna().sum().sum(), '  Shape is : ', data.shape)
+```
+
+%% Output
+
+
+    Données manquantes :  0   Shape is :  (506, 14)
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Preparing the data
+### 3.1 - Split data
+We will use 70% of the data for training and 30% for validation.
+x will be input data and y the expected output
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Split => train, test
+#
+data_train = data.sample(frac=0.7, axis=0)
+data_test  = data.drop(data_train.index)
+
+# ---- Split => x,y (medv is price)
+#
+x_train = data_train.drop('medv',  axis=1)
+y_train = data_train['medv']
+x_test  = data_test.drop('medv',   axis=1)
+y_test  = data_test['medv']
+
+print('Original data shape was : ',data.shape)
+print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)
+print('x_test  : ',x_test.shape,  'y_test  : ',y_test.shape)
+```
+
+%% Output
+
+    Original data shape was :  (506, 14)
+    x_train :  (354, 13) y_train :  (354,)
+    x_test  :  (152, 13) y_test  :  (152,)
+
+%% Cell type:markdown id: tags:
+
+### 3.2 - Data normalization
+**Note :**
+ - All input data must be normalized, train and test.
+ - To do this we will **subtract the mean** and **divide by the standard deviation**.
+ - But test data should not be used in any way, even for normalization.
+ - The mean and the standard deviation will therefore only be calculated with the train data.
+
+%% Cell type:code id: tags:
+
+``` python
+display(x_train.describe().style.format("{0:.2f}").set_caption("Before normalization :"))
+
+mean = x_train.mean()
+std  = x_train.std()
+x_train = (x_train - mean) / std
+x_test  = (x_test  - mean) / std
+
+display(x_train.describe().style.format("{0:.2f}").set_caption("After normalization :"))
+
+x_train, y_train = np.array(x_train), np.array(y_train)
+x_test,  y_test  = np.array(x_test),  np.array(y_test)
+```
+
+%% Output
+
+
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Build a model
+About informations about :
+ - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)
+ - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)
+ - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)
+ - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)
+
+%% Cell type:code id: tags:
+
+``` python
+  def get_model_v1(shape):
+
+    model = keras.models.Sequential()
+    model.add(keras.layers.Input(shape, name="InputLayer"))
+    model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))
+    model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))
+    model.add(keras.layers.Dense(1, name='Output'))
+
+    model.compile(optimizer = 'rmsprop',
+                  loss      = 'mse',
+                  metrics   = ['mae', 'mse'] )
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 5 - Train the model
+### 5.1 - Get it
+
+%% Cell type:code id: tags:
+
+``` python
+model=get_model_v1( (13,) )
+
+model.summary()
+keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)
+```
+
+%% Output
+
+    Model: "sequential"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    Dense_n1 (Dense)             (None, 64)                896
+    _________________________________________________________________
+    Dense_n2 (Dense)             (None, 64)                4160
+    _________________________________________________________________
+    Output (Dense)               (None, 1)                 65
+    =================================================================
+    Total params: 5,121
+    Trainable params: 5,121
+    Non-trainable params: 0
+    _________________________________________________________________
+
+
+    <IPython.core.display.Image object>
+
+%% Cell type:markdown id: tags:
+
+### 5.2 - Train it
+
+%% Cell type:code id: tags:
+
+``` python
+history = model.fit(x_train,
+                    y_train,
+                    epochs          = 100,
+                    batch_size      = 10,
+                    verbose         = 1,
+                    validation_data = (x_test, y_test))
+```
+
+%% Output
+
+    Train on 354 samples, validate on 152 samples
+    Epoch 1/100
+    354/354 [==============================] - 1s 2ms/sample - loss: 414.5603 - mae: 18.2577 - mse: 414.5602 - val_loss: 266.3728 - val_mae: 13.9913 - val_mse: 266.3728
+    Epoch 2/100
+    354/354 [==============================] - 0s 190us/sample - loss: 165.4507 - mae: 10.4618 - mse: 165.4507 - val_loss: 74.4125 - val_mae: 6.0372 - val_mse: 74.4125
+    Epoch 3/100
+    354/354 [==============================] - 0s 187us/sample - loss: 54.2313 - mae: 5.3763 - mse: 54.2313 - val_loss: 47.0203 - val_mae: 4.7399 - val_mse: 47.0203
+    Epoch 4/100
+    354/354 [==============================] - 0s 166us/sample - loss: 32.3303 - mae: 4.2632 - mse: 32.3303 - val_loss: 38.0120 - val_mae: 4.2484 - val_mse: 38.0120
+    Epoch 5/100
+    354/354 [==============================] - 0s 153us/sample - loss: 25.3763 - mae: 3.7745 - mse: 25.3763 - val_loss: 32.4707 - val_mae: 3.8465 - val_mse: 32.4707
+    Epoch 6/100
+    354/354 [==============================] - 0s 153us/sample - loss: 22.2331 - mae: 3.4720 - mse: 22.2331 - val_loss: 29.6142 - val_mae: 3.4844 - val_mse: 29.6142
+    Epoch 7/100
+    354/354 [==============================] - 0s 154us/sample - loss: 19.7834 - mae: 3.2245 - mse: 19.7834 - val_loss: 27.1649 - val_mae: 3.5465 - val_mse: 27.1649
+    Epoch 8/100
+    354/354 [==============================] - 0s 155us/sample - loss: 18.0991 - mae: 3.0669 - mse: 18.0991 - val_loss: 26.0093 - val_mae: 3.5617 - val_mse: 26.0093
+    Epoch 9/100
+    354/354 [==============================] - 0s 161us/sample - loss: 16.9247 - mae: 2.9184 - mse: 16.9247 - val_loss: 23.2549 - val_mae: 3.3243 - val_mse: 23.2549
+    Epoch 10/100
+    354/354 [==============================] - 0s 150us/sample - loss: 16.0827 - mae: 2.8116 - mse: 16.0827 - val_loss: 21.1365 - val_mae: 3.0248 - val_mse: 21.1365
+    Epoch 11/100
+    354/354 [==============================] - 0s 170us/sample - loss: 15.0334 - mae: 2.7214 - mse: 15.0334 - val_loss: 20.0163 - val_mae: 2.9800 - val_mse: 20.0163
+    Epoch 12/100
+    354/354 [==============================] - 0s 180us/sample - loss: 14.4011 - mae: 2.6949 - mse: 14.4011 - val_loss: 19.8958 - val_mae: 2.9262 - val_mse: 19.8958
+    Epoch 13/100
+    354/354 [==============================] - 0s 184us/sample - loss: 13.9168 - mae: 2.5674 - mse: 13.9168 - val_loss: 18.5729 - val_mae: 2.7302 - val_mse: 18.5729
+    Epoch 14/100
+    354/354 [==============================] - 0s 161us/sample - loss: 13.5575 - mae: 2.5442 - mse: 13.5575 - val_loss: 17.8812 - val_mae: 2.6748 - val_mse: 17.8812
+    Epoch 15/100
+    354/354 [==============================] - 0s 166us/sample - loss: 12.8689 - mae: 2.4779 - mse: 12.8689 - val_loss: 18.9649 - val_mae: 2.7560 - val_mse: 18.9649
+    Epoch 16/100
+    354/354 [==============================] - 0s 159us/sample - loss: 12.6470 - mae: 2.4670 - mse: 12.6470 - val_loss: 16.5834 - val_mae: 2.6016 - val_mse: 16.5834
+    Epoch 17/100
+    354/354 [==============================] - 0s 159us/sample - loss: 12.3566 - mae: 2.4280 - mse: 12.3566 - val_loss: 16.7371 - val_mae: 2.6670 - val_mse: 16.7371
+    Epoch 18/100
+    354/354 [==============================] - 0s 158us/sample - loss: 12.3328 - mae: 2.4060 - mse: 12.3328 - val_loss: 16.3754 - val_mae: 2.6027 - val_mse: 16.3754
+    Epoch 19/100
+    354/354 [==============================] - 0s 152us/sample - loss: 11.8357 - mae: 2.3106 - mse: 11.8357 - val_loss: 16.1015 - val_mae: 2.6255 - val_mse: 16.1015
+    Epoch 20/100
+    354/354 [==============================] - 0s 163us/sample - loss: 11.6722 - mae: 2.3482 - mse: 11.6722 - val_loss: 16.1405 - val_mae: 2.6889 - val_mse: 16.1405
+    Epoch 21/100
+    354/354 [==============================] - 0s 175us/sample - loss: 11.2774 - mae: 2.3344 - mse: 11.2774 - val_loss: 15.2110 - val_mae: 2.5038 - val_mse: 15.2110
+    Epoch 22/100
+    354/354 [==============================] - 0s 180us/sample - loss: 11.2491 - mae: 2.3055 - mse: 11.2491 - val_loss: 15.4745 - val_mae: 2.4494 - val_mse: 15.4744
+    Epoch 23/100
+    354/354 [==============================] - 0s 187us/sample - loss: 10.9102 - mae: 2.2171 - mse: 10.9102 - val_loss: 15.1145 - val_mae: 2.4282 - val_mse: 15.1145
+    Epoch 24/100
+    354/354 [==============================] - 0s 168us/sample - loss: 10.7952 - mae: 2.2533 - mse: 10.7952 - val_loss: 14.3789 - val_mae: 2.3683 - val_mse: 14.3789
+    Epoch 25/100
+    354/354 [==============================] - 0s 171us/sample - loss: 10.7250 - mae: 2.2489 - mse: 10.7250 - val_loss: 15.1102 - val_mae: 2.3422 - val_mse: 15.1102
+    Epoch 26/100
+    354/354 [==============================] - 0s 158us/sample - loss: 10.4010 - mae: 2.1702 - mse: 10.4010 - val_loss: 14.3260 - val_mae: 2.3176 - val_mse: 14.3260
+    Epoch 27/100
+    354/354 [==============================] - 0s 149us/sample - loss: 10.1442 - mae: 2.1797 - mse: 10.1442 - val_loss: 13.6694 - val_mae: 2.3864 - val_mse: 13.6694
+    Epoch 28/100
+    354/354 [==============================] - 0s 168us/sample - loss: 10.1391 - mae: 2.1809 - mse: 10.1391 - val_loss: 14.0177 - val_mae: 2.3467 - val_mse: 14.0177
+    Epoch 29/100
+    354/354 [==============================] - 0s 149us/sample - loss: 9.9119 - mae: 2.1267 - mse: 9.9119 - val_loss: 14.0739 - val_mae: 2.4617 - val_mse: 14.0739
+    Epoch 30/100
+    354/354 [==============================] - 0s 164us/sample - loss: 10.0176 - mae: 2.1669 - mse: 10.0176 - val_loss: 13.5116 - val_mae: 2.3158 - val_mse: 13.5116
+    Epoch 31/100
+    354/354 [==============================] - 0s 189us/sample - loss: 9.8259 - mae: 2.1407 - mse: 9.8259 - val_loss: 13.7364 - val_mae: 2.3531 - val_mse: 13.7364
+    Epoch 32/100
+    354/354 [==============================] - 0s 178us/sample - loss: 9.4495 - mae: 2.0922 - mse: 9.4495 - val_loss: 14.1936 - val_mae: 2.3887 - val_mse: 14.1936
+    Epoch 33/100
+    354/354 [==============================] - 0s 164us/sample - loss: 9.6721 - mae: 2.0870 - mse: 9.6721 - val_loss: 13.4267 - val_mae: 2.3508 - val_mse: 13.4267
+    Epoch 34/100
+    354/354 [==============================] - 0s 167us/sample - loss: 9.1042 - mae: 2.0644 - mse: 9.1042 - val_loss: 13.3821 - val_mae: 2.4709 - val_mse: 13.3821
+    Epoch 35/100
+    354/354 [==============================] - 0s 155us/sample - loss: 9.0129 - mae: 2.0482 - mse: 9.0129 - val_loss: 14.2184 - val_mae: 2.2754 - val_mse: 14.2184
+    Epoch 36/100
+    354/354 [==============================] - 0s 160us/sample - loss: 9.2470 - mae: 2.0661 - mse: 9.2470 - val_loss: 14.3466 - val_mae: 2.5561 - val_mse: 14.3466
+    Epoch 37/100
+    354/354 [==============================] - 0s 169us/sample - loss: 9.1695 - mae: 2.0766 - mse: 9.1695 - val_loss: 13.3818 - val_mae: 2.2373 - val_mse: 13.3818
+    Epoch 38/100
+    354/354 [==============================] - 0s 165us/sample - loss: 9.1663 - mae: 2.0617 - mse: 9.1663 - val_loss: 14.7461 - val_mae: 2.5061 - val_mse: 14.7461
+    Epoch 39/100
+    354/354 [==============================] - 0s 159us/sample - loss: 8.7273 - mae: 2.0208 - mse: 8.7273 - val_loss: 12.5890 - val_mae: 2.3037 - val_mse: 12.5890
+    Epoch 40/100
+    354/354 [==============================] - 0s 166us/sample - loss: 8.9038 - mae: 2.0352 - mse: 8.9038 - val_loss: 12.9754 - val_mae: 2.2079 - val_mse: 12.9754
+    Epoch 41/100
+    354/354 [==============================] - 0s 153us/sample - loss: 8.6155 - mae: 2.0267 - mse: 8.6155 - val_loss: 13.9239 - val_mae: 2.3525 - val_mse: 13.9239
+    Epoch 42/100
+    354/354 [==============================] - 0s 163us/sample - loss: 8.5479 - mae: 2.0170 - mse: 8.5479 - val_loss: 13.6362 - val_mae: 2.2694 - val_mse: 13.6362
+    Epoch 43/100
+    354/354 [==============================] - 0s 165us/sample - loss: 8.7087 - mae: 2.0062 - mse: 8.7087 - val_loss: 13.1138 - val_mae: 2.2386 - val_mse: 13.1138
+    Epoch 44/100
+    354/354 [==============================] - 0s 160us/sample - loss: 8.3942 - mae: 1.9622 - mse: 8.3942 - val_loss: 12.3461 - val_mae: 2.2337 - val_mse: 12.3461
+    Epoch 45/100
+    354/354 [==============================] - 0s 168us/sample - loss: 8.4101 - mae: 2.0098 - mse: 8.4101 - val_loss: 13.2116 - val_mae: 2.2682 - val_mse: 13.2116
+    Epoch 46/100
+    354/354 [==============================] - 0s 156us/sample - loss: 8.3264 - mae: 1.9483 - mse: 8.3264 - val_loss: 12.5519 - val_mae: 2.4063 - val_mse: 12.5519
+    Epoch 47/100
+    354/354 [==============================] - 0s 158us/sample - loss: 8.1445 - mae: 1.9549 - mse: 8.1445 - val_loss: 12.1838 - val_mae: 2.2591 - val_mse: 12.1838
+    Epoch 48/100
+    354/354 [==============================] - 0s 156us/sample - loss: 8.0389 - mae: 1.9304 - mse: 8.0389 - val_loss: 12.6978 - val_mae: 2.1907 - val_mse: 12.6978
+    Epoch 49/100
+    354/354 [==============================] - 0s 164us/sample - loss: 8.0705 - mae: 1.9493 - mse: 8.0705 - val_loss: 12.4833 - val_mae: 2.4720 - val_mse: 12.4833
+    Epoch 50/100
+    354/354 [==============================] - 0s 158us/sample - loss: 8.1872 - mae: 1.9630 - mse: 8.1872 - val_loss: 12.0043 - val_mae: 2.2610 - val_mse: 12.0043
+    Epoch 51/100
+    354/354 [==============================] - 0s 158us/sample - loss: 8.0357 - mae: 1.8946 - mse: 8.0357 - val_loss: 11.3982 - val_mae: 2.1770 - val_mse: 11.3982
+    Epoch 52/100
+    354/354 [==============================] - 0s 162us/sample - loss: 7.6882 - mae: 1.8951 - mse: 7.6882 - val_loss: 13.0714 - val_mae: 2.4109 - val_mse: 13.0714
+    Epoch 53/100
+    354/354 [==============================] - 0s 162us/sample - loss: 7.9639 - mae: 1.9103 - mse: 7.9639 - val_loss: 12.4297 - val_mae: 2.2996 - val_mse: 12.4297
+    Epoch 54/100
+    354/354 [==============================] - 0s 183us/sample - loss: 7.7929 - mae: 1.8971 - mse: 7.7929 - val_loss: 11.9751 - val_mae: 2.2491 - val_mse: 11.9751
+    Epoch 55/100
+    354/354 [==============================] - 0s 185us/sample - loss: 7.4411 - mae: 1.8631 - mse: 7.4411 - val_loss: 11.3761 - val_mae: 2.3416 - val_mse: 11.3761
+    Epoch 56/100
+    354/354 [==============================] - 0s 186us/sample - loss: 7.6105 - mae: 1.9111 - mse: 7.6105 - val_loss: 12.4939 - val_mae: 2.4095 - val_mse: 12.4939
+    Epoch 57/100
+    354/354 [==============================] - 0s 190us/sample - loss: 7.5013 - mae: 1.9146 - mse: 7.5013 - val_loss: 11.6668 - val_mae: 2.1468 - val_mse: 11.6668
+    Epoch 58/100
+    354/354 [==============================] - 0s 195us/sample - loss: 7.4096 - mae: 1.8515 - mse: 7.4096 - val_loss: 13.8000 - val_mae: 2.5222 - val_mse: 13.8000
+    Epoch 59/100
+    354/354 [==============================] - 0s 180us/sample - loss: 7.2263 - mae: 1.8241 - mse: 7.2263 - val_loss: 10.8964 - val_mae: 2.2130 - val_mse: 10.8964
+    Epoch 60/100
+    354/354 [==============================] - 0s 161us/sample - loss: 7.1773 - mae: 1.8526 - mse: 7.1773 - val_loss: 10.7862 - val_mae: 2.1088 - val_mse: 10.7862
+    Epoch 61/100
+    354/354 [==============================] - 0s 165us/sample - loss: 7.0812 - mae: 1.8308 - mse: 7.0812 - val_loss: 10.8147 - val_mae: 2.3209 - val_mse: 10.8147
+    Epoch 62/100
+    354/354 [==============================] - 0s 155us/sample - loss: 7.2235 - mae: 1.8367 - mse: 7.2235 - val_loss: 11.0399 - val_mae: 2.2583 - val_mse: 11.0399
+    Epoch 63/100
+    354/354 [==============================] - 0s 155us/sample - loss: 7.0341 - mae: 1.8172 - mse: 7.0341 - val_loss: 10.9894 - val_mae: 2.1429 - val_mse: 10.9894
+    Epoch 64/100
+    354/354 [==============================] - 0s 157us/sample - loss: 6.8729 - mae: 1.7492 - mse: 6.8729 - val_loss: 10.5465 - val_mae: 2.1532 - val_mse: 10.5465
+    Epoch 65/100
+    354/354 [==============================] - 0s 164us/sample - loss: 6.9345 - mae: 1.7837 - mse: 6.9345 - val_loss: 11.5379 - val_mae: 2.1963 - val_mse: 11.5379
+    Epoch 66/100
+    354/354 [==============================] - 0s 166us/sample - loss: 6.8218 - mae: 1.7714 - mse: 6.8218 - val_loss: 10.1486 - val_mae: 2.1617 - val_mse: 10.1486
+    Epoch 67/100
+    354/354 [==============================] - 0s 157us/sample - loss: 6.8711 - mae: 1.8045 - mse: 6.8711 - val_loss: 10.3196 - val_mae: 2.2297 - val_mse: 10.3196
+    Epoch 68/100
+    354/354 [==============================] - 0s 162us/sample - loss: 6.7281 - mae: 1.7762 - mse: 6.7281 - val_loss: 11.2361 - val_mae: 2.2046 - val_mse: 11.2361
+    Epoch 69/100
+    354/354 [==============================] - 0s 158us/sample - loss: 6.5518 - mae: 1.7292 - mse: 6.5518 - val_loss: 10.2378 - val_mae: 2.1494 - val_mse: 10.2378
+    Epoch 70/100
+    354/354 [==============================] - 0s 161us/sample - loss: 6.6489 - mae: 1.7383 - mse: 6.6489 - val_loss: 11.1613 - val_mae: 2.2212 - val_mse: 11.1613
+    Epoch 71/100
+    354/354 [==============================] - 0s 176us/sample - loss: 6.5827 - mae: 1.7564 - mse: 6.5827 - val_loss: 10.0177 - val_mae: 2.2440 - val_mse: 10.0177
+    Epoch 72/100
+    354/354 [==============================] - 0s 168us/sample - loss: 6.3411 - mae: 1.7463 - mse: 6.3411 - val_loss: 10.7929 - val_mae: 2.1946 - val_mse: 10.7929
+    Epoch 73/100
+    354/354 [==============================] - 0s 163us/sample - loss: 6.3621 - mae: 1.7466 - mse: 6.3621 - val_loss: 9.7344 - val_mae: 2.1441 - val_mse: 9.7344
+    Epoch 74/100
+    354/354 [==============================] - 0s 158us/sample - loss: 6.2298 - mae: 1.7411 - mse: 6.2298 - val_loss: 11.2495 - val_mae: 2.1948 - val_mse: 11.2495
+    Epoch 75/100
+    354/354 [==============================] - 0s 159us/sample - loss: 6.3037 - mae: 1.7169 - mse: 6.3037 - val_loss: 10.1339 - val_mae: 2.1716 - val_mse: 10.1339
+    Epoch 76/100
+    354/354 [==============================] - 0s 158us/sample - loss: 6.0780 - mae: 1.6686 - mse: 6.0780 - val_loss: 11.9975 - val_mae: 2.3317 - val_mse: 11.9975
+    Epoch 77/100
+    354/354 [==============================] - 0s 165us/sample - loss: 6.3311 - mae: 1.7082 - mse: 6.3311 - val_loss: 11.6433 - val_mae: 2.2756 - val_mse: 11.6433
+    Epoch 78/100
+    354/354 [==============================] - 0s 155us/sample - loss: 6.0620 - mae: 1.6765 - mse: 6.0620 - val_loss: 13.0159 - val_mae: 2.5073 - val_mse: 13.0159
+    Epoch 79/100
+    354/354 [==============================] - 0s 167us/sample - loss: 6.1819 - mae: 1.7157 - mse: 6.1819 - val_loss: 10.1000 - val_mae: 2.1462 - val_mse: 10.1000
+    Epoch 80/100
+    354/354 [==============================] - 0s 158us/sample - loss: 5.9085 - mae: 1.6720 - mse: 5.9085 - val_loss: 11.7867 - val_mae: 2.5045 - val_mse: 11.7866
+    Epoch 81/100
+    354/354 [==============================] - 0s 168us/sample - loss: 6.0201 - mae: 1.6678 - mse: 6.0201 - val_loss: 10.8789 - val_mae: 2.3031 - val_mse: 10.8789
+    Epoch 82/100
+    354/354 [==============================] - 0s 159us/sample - loss: 6.1278 - mae: 1.6799 - mse: 6.1278 - val_loss: 9.8114 - val_mae: 2.1048 - val_mse: 9.8114
+    Epoch 83/100
+    354/354 [==============================] - 0s 150us/sample - loss: 5.6372 - mae: 1.6280 - mse: 5.6372 - val_loss: 10.0971 - val_mae: 2.1464 - val_mse: 10.0971
+    Epoch 84/100
+    354/354 [==============================] - 0s 153us/sample - loss: 5.9587 - mae: 1.6421 - mse: 5.9587 - val_loss: 9.4731 - val_mae: 2.1915 - val_mse: 9.4731
+    Epoch 85/100
+    354/354 [==============================] - 0s 158us/sample - loss: 5.6189 - mae: 1.6223 - mse: 5.6189 - val_loss: 9.9788 - val_mae: 2.3332 - val_mse: 9.9788
+    Epoch 86/100
+    354/354 [==============================] - 0s 158us/sample - loss: 5.8193 - mae: 1.6930 - mse: 5.8193 - val_loss: 10.4070 - val_mae: 2.1490 - val_mse: 10.4070
+    Epoch 87/100
+    354/354 [==============================] - 0s 155us/sample - loss: 5.5919 - mae: 1.6152 - mse: 5.5919 - val_loss: 9.9985 - val_mae: 2.2546 - val_mse: 9.9985
+    Epoch 88/100
+    354/354 [==============================] - 0s 160us/sample - loss: 5.6652 - mae: 1.6246 - mse: 5.6652 - val_loss: 9.1506 - val_mae: 2.0642 - val_mse: 9.1506
+    Epoch 89/100
+    354/354 [==============================] - 0s 157us/sample - loss: 5.6349 - mae: 1.6108 - mse: 5.6349 - val_loss: 9.8522 - val_mae: 2.0813 - val_mse: 9.8522
+    Epoch 90/100
+    354/354 [==============================] - 0s 159us/sample - loss: 5.6165 - mae: 1.6449 - mse: 5.6165 - val_loss: 9.1553 - val_mae: 2.0421 - val_mse: 9.1553
+    Epoch 91/100
+    354/354 [==============================] - 0s 161us/sample - loss: 5.5416 - mae: 1.6153 - mse: 5.5416 - val_loss: 10.4231 - val_mae: 2.2880 - val_mse: 10.4231
+    Epoch 92/100
+    354/354 [==============================] - 0s 158us/sample - loss: 5.3909 - mae: 1.5863 - mse: 5.3909 - val_loss: 8.8087 - val_mae: 2.1022 - val_mse: 8.8087
+    Epoch 93/100
+    354/354 [==============================] - 0s 155us/sample - loss: 5.3540 - mae: 1.5986 - mse: 5.3540 - val_loss: 9.6963 - val_mae: 2.1931 - val_mse: 9.6963
+    Epoch 94/100
+    354/354 [==============================] - 0s 161us/sample - loss: 5.3198 - mae: 1.6074 - mse: 5.3198 - val_loss: 9.1875 - val_mae: 2.1917 - val_mse: 9.1875
+    Epoch 95/100
+    354/354 [==============================] - 0s 165us/sample - loss: 5.2299 - mae: 1.5638 - mse: 5.2299 - val_loss: 8.8746 - val_mae: 2.1273 - val_mse: 8.8746
+    Epoch 96/100
+    354/354 [==============================] - 0s 163us/sample - loss: 5.2789 - mae: 1.5651 - mse: 5.2789 - val_loss: 9.7351 - val_mae: 2.2359 - val_mse: 9.7351
+    Epoch 97/100
+    354/354 [==============================] - 0s 153us/sample - loss: 5.3399 - mae: 1.6002 - mse: 5.3399 - val_loss: 9.7185 - val_mae: 2.1080 - val_mse: 9.7185
+    Epoch 98/100
+    354/354 [==============================] - 0s 159us/sample - loss: 5.0072 - mae: 1.5055 - mse: 5.0072 - val_loss: 8.3621 - val_mae: 2.0586 - val_mse: 8.3621
+    Epoch 99/100
+    354/354 [==============================] - 0s 156us/sample - loss: 5.2596 - mae: 1.5557 - mse: 5.2596 - val_loss: 8.6406 - val_mae: 2.0527 - val_mse: 8.6406
+    Epoch 100/100
+    354/354 [==============================] - 0s 159us/sample - loss: 5.0983 - mae: 1.5543 - mse: 5.0983 - val_loss: 8.4836 - val_mae: 2.0234 - val_mse: 8.4836
+
+%% Cell type:markdown id: tags:
+
+## Step 6 - Evaluate
+### 6.1 - Model evaluation
+MAE =  Mean Absolute Error (between the labels and predictions)
+A mae equal to 3 represents an average error in prediction of $3k.
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Output
+
+    x_test / loss      : 8.4836
+    x_test / mae       : 2.0234
+    x_test / mse       : 8.4836
+
+%% Cell type:markdown id: tags:
+
+### 6.2 - Training history
+What was the best result during our training ?
+
+%% Cell type:code id: tags:
+
+``` python
+
+df=pd.DataFrame(data=history.history)
+df.describe()
+```
+
+%% Output
+
+                 loss         mae         mse    val_loss     val_mae     val_mse
+    count  100.000000  100.000000  100.000000  100.000000  100.000000  100.000000
+    mean    15.144930    2.312168   15.144930   17.019036    2.582618   17.019036
+    std     43.707091    1.906713   43.707090   26.587745    1.288267   26.587746
+    min      5.007155    1.505515    5.007155    8.362053    2.023406    8.362053
+    25%      6.285225    1.716563    6.285225   10.419040    2.192718   10.419040
+    50%      8.037316    1.922454    8.037317   12.488579    2.301342   12.488580
+    75%     10.482029    2.189933   10.482029   14.470699    2.503943   14.470701
+    max    414.560260   18.257650  414.560242  266.372801   13.991282  266.372803
+
+%% Cell type:code id: tags:
+
+``` python
+print("min( val_mae ) : {:.4f}".format( min(history.history["val_mae"]) ) )
+```
+
+%% Output
+
+    min( val_mae ) : 2.0234
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],
+                                'MAE' :['mae', 'val_mae'],
+                                'LOSS':['loss','val_loss']})
+```
+
+%% Output
+
+
+
+
+
+
+
+%% Cell type:markdown id: tags:
+
+## Step 7 - Make a prediction
+
+%% Cell type:code id: tags:
+
+``` python
+my_data = [ 1.26425925, -0.48522739,  1.0436489 , -0.23112788,  1.37120745,
+       -2.14308942,  1.13489104, -1.06802005,  1.71189006,  1.57042287,
+        0.77859951,  0.14769795,  2.7585581 ]
+real_price = 10.4
+
+my_data=np.array(my_data).reshape(1,13)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+predictions = model.predict( my_data )
+print("Prédiction : {:.2f} K$".format(predictions[0][0]))
+print("Reality    : {:.2f} K$".format(real_price))
+```
+
+%% Output
+
+    Prédiction : 11.59 K$
+    Reality    : 10.40 K$
+
+%% Cell type:markdown id: tags:
+
+---
+![](../fidle/img/00-Fidle-logo-01_s.png)

BHPD/02-DNN-Regression Premium.ipynb

+%% Cell type:markdown id: tags:
+
+Deep Neural Network (DNN) - BHPD dataset
+========================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## A very simple example of **regression** (Premium edition):
+
+Objective is to predicts **housing prices** from a set of house features.
+
+The **[Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html)** consists of price of houses in various places in Boston.
+Alongside with price, the dataset also provide information such as Crime, areas of non-retail business in the town,
+age of people who own the house and many other attributes...
+
+What we're going to do:
+
+ - (Retrieve data)
+ - (Preparing the data)
+ - (Build a model)
+ - Train and save the model
+ - Restore saved model
+ - Evaluate the model
+ - Make some predictions
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import os,sys
+
+from IPython.display import display, Markdown
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+os.makedirs('./run/models',   mode=0o750, exist_ok=True)
+```
+
+%% Output
+
+
+    
+    FIDLE 2020 - Practical Work Module
+    Version              : 0.2.8
+    Run time             : Saturday 15 February 2020, 12:32:05
+    TensorFlow version   : 2.0.0
+    Keras version        : 2.2.4-tf
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Retrieve data
+
+### 2.1 - Option 1  : From Keras
+Boston housing is a famous historic dataset, so we can get it directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
+
+%% Cell type:raw id: tags:
+
+(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data(test_split=0.2, seed=113)
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Option 2 : From a csv file
+More fun !
+
+%% Cell type:code id: tags:
+
+``` python
+data = pd.read_csv('./data/BostonHousing.csv', header=0)
+
+display(data.head(5).style.format("{0:.2f}"))
+print('Données manquantes : ',data.isna().sum().sum(), '  Shape is : ', data.shape)
+```
+
+%% Output
+
+
+    Données manquantes :  0   Shape is :  (506, 14)
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Preparing the data
+### 3.1 - Split data
+We will use 80% of the data for training and 20% for validation.
+x will be input data and y the expected output
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Split => train, test
+#
+data_train = data.sample(frac=0.7, axis=0)
+data_test  = data.drop(data_train.index)
+
+# ---- Split => x,y (medv is price)
+#
+x_train = data_train.drop('medv',  axis=1)
+y_train = data_train['medv']
+x_test  = data_test.drop('medv',   axis=1)
+y_test  = data_test['medv']
+
+print('Original data shape was : ',data.shape)
+print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)
+print('x_test  : ',x_test.shape,  'y_test  : ',y_test.shape)
+```
+
+%% Output
+
+    Original data shape was :  (506, 14)
+    x_train :  (354, 13) y_train :  (354,)
+    x_test  :  (152, 13) y_test  :  (152,)
+
+%% Cell type:markdown id: tags:
+
+### 3.2 - Data normalization
+**Note :**
+ - All input data must be normalized, train and test.
+ - To do this we will subtract the mean and divide by the standard deviation.
+ - But test data should not be used in any way, even for normalization.
+ - The mean and the standard deviation will therefore only be calculated with the train data.
+
+%% Cell type:code id: tags:
+
+``` python
+display(x_train.describe().style.format("{0:.2f}").set_caption("Before normalization :"))
+
+mean = x_train.mean()
+std  = x_train.std()
+x_train = (x_train - mean) / std
+x_test  = (x_test  - mean) / std
+
+display(x_train.describe().style.format("{0:.2f}").set_caption("After normalization :"))
+
+x_train, y_train = np.array(x_train), np.array(y_train)
+x_test,  y_test  = np.array(x_test),  np.array(y_test)
+```
+
+%% Output
+
+
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Build a model
+More informations about :
+ - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)
+ - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)
+ - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)
+ - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)
+
+%% Cell type:code id: tags:
+
+``` python
+  def get_model_v1(shape):
+
+    model = keras.models.Sequential()
+    model.add(keras.layers.Input(shape, name="InputLayer"))
+    model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))
+    model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))
+    model.add(keras.layers.Dense(1, name='Output'))
+
+    model.compile(optimizer = 'rmsprop',
+                  loss      = 'mse',
+                  metrics   = ['mae', 'mse'] )
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 5 - Train the model
+### 5.1 - Get it
+
+%% Cell type:code id: tags:
+
+``` python
+model=get_model_v1( (13,) )
+
+model.summary()
+keras.utils.plot_model( model, to_file='./run/model.png', show_shapes=True, show_layer_names=True, dpi=96)
+```
+
+%% Output
+
+    Model: "sequential"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    Dense_n1 (Dense)             (None, 64)                896
+    _________________________________________________________________
+    Dense_n2 (Dense)             (None, 64)                4160
+    _________________________________________________________________
+    Output (Dense)               (None, 1)                 65
+    =================================================================
+    Total params: 5,121
+    Trainable params: 5,121
+    Non-trainable params: 0
+    _________________________________________________________________
+
+
+    <IPython.core.display.Image object>
+
+%% Cell type:markdown id: tags:
+
+### 5.2 - Add callback
+
+%% Cell type:code id: tags:
+
+``` python
+os.makedirs('./run/models',   mode=0o750, exist_ok=True)
+save_dir = "./run/models/best_model.h5"
+
+savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.3 - Train it
+
+%% Cell type:code id: tags:
+
+``` python
+history = model.fit(x_train,
+                    y_train,
+                    epochs          = 100,
+                    batch_size      = 10,
+                    verbose         = 1,
+                    validation_data = (x_test, y_test),
+                    callbacks       = [savemodel_callback])
+```
+
+%% Output
+
+    Train on 354 samples, validate on 152 samples
+    Epoch 1/100
+    354/354 [==============================] - 1s 3ms/sample - loss: 446.5069 - mae: 19.1690 - mse: 446.5069 - val_loss: 328.7387 - val_mae: 16.4455 - val_mse: 328.7387
+    Epoch 2/100
+    354/354 [==============================] - 0s 301us/sample - loss: 206.7491 - mae: 12.2281 - mse: 206.7491 - val_loss: 102.8150 - val_mae: 8.6449 - val_mse: 102.8150
+    Epoch 3/100
+    354/354 [==============================] - 0s 302us/sample - loss: 65.8724 - mae: 6.2331 - mse: 65.8724 - val_loss: 33.7508 - val_mae: 4.5848 - val_mse: 33.7508
+    Epoch 4/100
+    354/354 [==============================] - 0s 318us/sample - loss: 33.4179 - mae: 4.2331 - mse: 33.4179 - val_loss: 27.0058 - val_mae: 3.9154 - val_mse: 27.0058
+    Epoch 5/100
+    354/354 [==============================] - 0s 312us/sample - loss: 24.9602 - mae: 3.5624 - mse: 24.9602 - val_loss: 23.2470 - val_mae: 3.5429 - val_mse: 23.2470
+    Epoch 6/100
+    354/354 [==============================] - 0s 316us/sample - loss: 21.4080 - mae: 3.2530 - mse: 21.4080 - val_loss: 22.1707 - val_mae: 3.4498 - val_mse: 22.1707
+    Epoch 7/100
+    354/354 [==============================] - 0s 262us/sample - loss: 18.3586 - mae: 3.0399 - mse: 18.3586 - val_loss: 24.4102 - val_mae: 3.4754 - val_mse: 24.4102
+    Epoch 8/100
+    354/354 [==============================] - 0s 307us/sample - loss: 16.9126 - mae: 2.8925 - mse: 16.9126 - val_loss: 20.1919 - val_mae: 3.2138 - val_mse: 20.1919
+    Epoch 9/100
+    354/354 [==============================] - 0s 312us/sample - loss: 15.5047 - mae: 2.7532 - mse: 15.5047 - val_loss: 19.0378 - val_mae: 3.0763 - val_mse: 19.0378
+    Epoch 10/100
+    354/354 [==============================] - 0s 273us/sample - loss: 14.5763 - mae: 2.6404 - mse: 14.5763 - val_loss: 19.9752 - val_mae: 3.0986 - val_mse: 19.9752
+    Epoch 11/100
+    354/354 [==============================] - 0s 310us/sample - loss: 13.5901 - mae: 2.5801 - mse: 13.5901 - val_loss: 18.9675 - val_mae: 3.0192 - val_mse: 18.9675
+    Epoch 12/100
+    354/354 [==============================] - 0s 270us/sample - loss: 12.9341 - mae: 2.5158 - mse: 12.9341 - val_loss: 20.6757 - val_mae: 3.1029 - val_mse: 20.6757
+    Epoch 13/100
+    354/354 [==============================] - 0s 311us/sample - loss: 12.4520 - mae: 2.5061 - mse: 12.4520 - val_loss: 17.6596 - val_mae: 2.8839 - val_mse: 17.6596
+    Epoch 14/100
+    354/354 [==============================] - 0s 311us/sample - loss: 11.9484 - mae: 2.4710 - mse: 11.9484 - val_loss: 16.7645 - val_mae: 2.8083 - val_mse: 16.7645
+    Epoch 15/100
+    354/354 [==============================] - 0s 269us/sample - loss: 11.6260 - mae: 2.3959 - mse: 11.6260 - val_loss: 17.5048 - val_mae: 2.8007 - val_mse: 17.5048
+    Epoch 16/100
+    354/354 [==============================] - 0s 267us/sample - loss: 11.2504 - mae: 2.3567 - mse: 11.2504 - val_loss: 18.6748 - val_mae: 2.8771 - val_mse: 18.6748
+    Epoch 17/100
+    354/354 [==============================] - 0s 269us/sample - loss: 10.8352 - mae: 2.3051 - mse: 10.8352 - val_loss: 19.4796 - val_mae: 3.0041 - val_mse: 19.4796
+    Epoch 18/100
+    354/354 [==============================] - 0s 267us/sample - loss: 10.6488 - mae: 2.3377 - mse: 10.6488 - val_loss: 17.0329 - val_mae: 2.7640 - val_mse: 17.0329
+    Epoch 19/100
+    354/354 [==============================] - 0s 273us/sample - loss: 10.2134 - mae: 2.2439 - mse: 10.2134 - val_loss: 18.0589 - val_mae: 2.8565 - val_mse: 18.0589
+    Epoch 20/100
+    354/354 [==============================] - 0s 315us/sample - loss: 10.1024 - mae: 2.2432 - mse: 10.1024 - val_loss: 16.5968 - val_mae: 2.7402 - val_mse: 16.5968
+    Epoch 21/100
+    354/354 [==============================] - 0s 277us/sample - loss: 10.0576 - mae: 2.2401 - mse: 10.0576 - val_loss: 18.4496 - val_mae: 2.8156 - val_mse: 18.4496
+    Epoch 22/100
+    354/354 [==============================] - 0s 269us/sample - loss: 9.6590 - mae: 2.1500 - mse: 9.6590 - val_loss: 18.7084 - val_mae: 2.8309 - val_mse: 18.7084
+    Epoch 23/100
+    354/354 [==============================] - 0s 277us/sample - loss: 9.4596 - mae: 2.1967 - mse: 9.4596 - val_loss: 18.0308 - val_mae: 2.7595 - val_mse: 18.0308
+    Epoch 24/100
+    354/354 [==============================] - 0s 272us/sample - loss: 9.2778 - mae: 2.1680 - mse: 9.2778 - val_loss: 18.9343 - val_mae: 2.9152 - val_mse: 18.9343
+    Epoch 25/100
+    354/354 [==============================] - 0s 267us/sample - loss: 9.1075 - mae: 2.1451 - mse: 9.1076 - val_loss: 18.0646 - val_mae: 2.8202 - val_mse: 18.0646
+    Epoch 26/100
+    354/354 [==============================] - 0s 273us/sample - loss: 9.2196 - mae: 2.1282 - mse: 9.2196 - val_loss: 18.7244 - val_mae: 2.8288 - val_mse: 18.7244
+    Epoch 27/100
+    354/354 [==============================] - 0s 267us/sample - loss: 8.5733 - mae: 2.0703 - mse: 8.5733 - val_loss: 16.9568 - val_mae: 2.8123 - val_mse: 16.9568
+    Epoch 28/100
+    354/354 [==============================] - 0s 309us/sample - loss: 8.6252 - mae: 2.0821 - mse: 8.6252 - val_loss: 16.4984 - val_mae: 2.7069 - val_mse: 16.4984
+    Epoch 29/100
+    354/354 [==============================] - 0s 307us/sample - loss: 8.6336 - mae: 2.0822 - mse: 8.6336 - val_loss: 16.0498 - val_mae: 2.6532 - val_mse: 16.0498
+    Epoch 30/100
+    354/354 [==============================] - 0s 321us/sample - loss: 8.5071 - mae: 2.0379 - mse: 8.5071 - val_loss: 15.1042 - val_mae: 2.6004 - val_mse: 15.1042
+    Epoch 31/100
+    354/354 [==============================] - 0s 273us/sample - loss: 8.2888 - mae: 2.0627 - mse: 8.2888 - val_loss: 16.2730 - val_mae: 2.7019 - val_mse: 16.2730
+    Epoch 32/100
+    354/354 [==============================] - 0s 271us/sample - loss: 8.2021 - mae: 2.0000 - mse: 8.2021 - val_loss: 17.2852 - val_mae: 2.7962 - val_mse: 17.2852
+    Epoch 33/100
+    354/354 [==============================] - 0s 272us/sample - loss: 8.2973 - mae: 2.0336 - mse: 8.2973 - val_loss: 16.8973 - val_mae: 2.7318 - val_mse: 16.8973
+    Epoch 34/100
+    354/354 [==============================] - 0s 257us/sample - loss: 8.1033 - mae: 2.0105 - mse: 8.1033 - val_loss: 16.6509 - val_mae: 2.8218 - val_mse: 16.6509
+    Epoch 35/100
+    354/354 [==============================] - 0s 272us/sample - loss: 8.0724 - mae: 2.0170 - mse: 8.0724 - val_loss: 16.0802 - val_mae: 2.6733 - val_mse: 16.0802
+    Epoch 36/100
+    354/354 [==============================] - 0s 257us/sample - loss: 7.7939 - mae: 1.9606 - mse: 7.7939 - val_loss: 17.1008 - val_mae: 2.7384 - val_mse: 17.1008
+    Epoch 37/100
+    354/354 [==============================] - 0s 269us/sample - loss: 7.7812 - mae: 1.9719 - mse: 7.7812 - val_loss: 16.3472 - val_mae: 2.6939 - val_mse: 16.3472
+    Epoch 38/100
+    354/354 [==============================] - 0s 276us/sample - loss: 7.4494 - mae: 1.9224 - mse: 7.4494 - val_loss: 19.3916 - val_mae: 2.9414 - val_mse: 19.3916
+    Epoch 39/100
+    354/354 [==============================] - 0s 271us/sample - loss: 7.8023 - mae: 1.9978 - mse: 7.8023 - val_loss: 16.3499 - val_mae: 2.7018 - val_mse: 16.3499
+    Epoch 40/100
+    354/354 [==============================] - 0s 270us/sample - loss: 7.3681 - mae: 1.9293 - mse: 7.3681 - val_loss: 16.0445 - val_mae: 2.6872 - val_mse: 16.0445
+    Epoch 41/100
+    354/354 [==============================] - 0s 267us/sample - loss: 7.3013 - mae: 1.8820 - mse: 7.3013 - val_loss: 16.5657 - val_mae: 2.7222 - val_mse: 16.5657
+    Epoch 42/100
+    354/354 [==============================] - 0s 274us/sample - loss: 7.3978 - mae: 1.9154 - mse: 7.3978 - val_loss: 15.9821 - val_mae: 2.6576 - val_mse: 15.9821
+    Epoch 43/100
+    354/354 [==============================] - 0s 319us/sample - loss: 6.9832 - mae: 1.9037 - mse: 6.9832 - val_loss: 14.4977 - val_mae: 2.5418 - val_mse: 14.4977
+    Epoch 44/100
+    354/354 [==============================] - 0s 269us/sample - loss: 7.2307 - mae: 1.8968 - mse: 7.2307 - val_loss: 15.0962 - val_mae: 2.6188 - val_mse: 15.0962
+    Epoch 45/100
+    354/354 [==============================] - 0s 256us/sample - loss: 7.0289 - mae: 1.8685 - mse: 7.0289 - val_loss: 17.0531 - val_mae: 2.8123 - val_mse: 17.0531
+    Epoch 46/100
+    354/354 [==============================] - 0s 270us/sample - loss: 6.9010 - mae: 1.8537 - mse: 6.9010 - val_loss: 16.7469 - val_mae: 2.7081 - val_mse: 16.7469
+    Epoch 47/100
+    354/354 [==============================] - 0s 268us/sample - loss: 6.9256 - mae: 1.8664 - mse: 6.9256 - val_loss: 16.1227 - val_mae: 2.7760 - val_mse: 16.1227
+    Epoch 48/100
+    354/354 [==============================] - 0s 273us/sample - loss: 6.8333 - mae: 1.8552 - mse: 6.8333 - val_loss: 14.9262 - val_mae: 2.6213 - val_mse: 14.9262
+    Epoch 49/100
+    354/354 [==============================] - 0s 313us/sample - loss: 6.7351 - mae: 1.8375 - mse: 6.7351 - val_loss: 14.2252 - val_mae: 2.5309 - val_mse: 14.2252
+    Epoch 50/100
+    354/354 [==============================] - 0s 276us/sample - loss: 6.6672 - mae: 1.7913 - mse: 6.6672 - val_loss: 16.5652 - val_mae: 2.7693 - val_mse: 16.5652
+    Epoch 51/100
+    354/354 [==============================] - 0s 271us/sample - loss: 6.6222 - mae: 1.8325 - mse: 6.6222 - val_loss: 14.8928 - val_mae: 2.5921 - val_mse: 14.8928
+    Epoch 52/100
+    354/354 [==============================] - 0s 271us/sample - loss: 6.5606 - mae: 1.8150 - mse: 6.5606 - val_loss: 14.7382 - val_mae: 2.6124 - val_mse: 14.7382
+    Epoch 53/100
+    354/354 [==============================] - 0s 273us/sample - loss: 6.5737 - mae: 1.7757 - mse: 6.5737 - val_loss: 14.8866 - val_mae: 2.6357 - val_mse: 14.8866
+    Epoch 54/100
+    354/354 [==============================] - 0s 264us/sample - loss: 6.3009 - mae: 1.7569 - mse: 6.3009 - val_loss: 14.6100 - val_mae: 2.6115 - val_mse: 14.6100
+    Epoch 55/100
+    354/354 [==============================] - 0s 272us/sample - loss: 6.2524 - mae: 1.7679 - mse: 6.2524 - val_loss: 17.4939 - val_mae: 2.8652 - val_mse: 17.4939
+    Epoch 56/100
+    354/354 [==============================] - 0s 319us/sample - loss: 6.2461 - mae: 1.7830 - mse: 6.2461 - val_loss: 14.0397 - val_mae: 2.5829 - val_mse: 14.0397
+    Epoch 57/100
+    354/354 [==============================] - 0s 267us/sample - loss: 6.3124 - mae: 1.7788 - mse: 6.3124 - val_loss: 15.4946 - val_mae: 2.7133 - val_mse: 15.4946
+    Epoch 58/100
+    354/354 [==============================] - 0s 269us/sample - loss: 6.1133 - mae: 1.7282 - mse: 6.1133 - val_loss: 14.5244 - val_mae: 2.5982 - val_mse: 14.5244
+    Epoch 59/100
+    354/354 [==============================] - 0s 259us/sample - loss: 6.2866 - mae: 1.7860 - mse: 6.2866 - val_loss: 15.8915 - val_mae: 2.7331 - val_mse: 15.8915
+    Epoch 60/100
+    354/354 [==============================] - 0s 311us/sample - loss: 5.9945 - mae: 1.7178 - mse: 5.9945 - val_loss: 13.2656 - val_mae: 2.5189 - val_mse: 13.2656
+    Epoch 61/100
+    354/354 [==============================] - 0s 263us/sample - loss: 6.0649 - mae: 1.7064 - mse: 6.0649 - val_loss: 15.4134 - val_mae: 2.7351 - val_mse: 15.4134
+    Epoch 62/100
+    354/354 [==============================] - 0s 268us/sample - loss: 5.9954 - mae: 1.6767 - mse: 5.9954 - val_loss: 13.8741 - val_mae: 2.5721 - val_mse: 13.8741
+    Epoch 63/100
+    354/354 [==============================] - 0s 254us/sample - loss: 5.9648 - mae: 1.7023 - mse: 5.9648 - val_loss: 15.1974 - val_mae: 2.6602 - val_mse: 15.1974
+    Epoch 64/100
+    354/354 [==============================] - 0s 272us/sample - loss: 5.7276 - mae: 1.7202 - mse: 5.7276 - val_loss: 14.5766 - val_mae: 2.6508 - val_mse: 14.5766
+    Epoch 65/100
+    354/354 [==============================] - 0s 266us/sample - loss: 5.8443 - mae: 1.6907 - mse: 5.8443 - val_loss: 15.5797 - val_mae: 2.6848 - val_mse: 15.5797
+    Epoch 66/100
+    354/354 [==============================] - 0s 273us/sample - loss: 5.8195 - mae: 1.7295 - mse: 5.8195 - val_loss: 14.5484 - val_mae: 2.6527 - val_mse: 14.5484
+    Epoch 67/100
+    354/354 [==============================] - 0s 266us/sample - loss: 5.8216 - mae: 1.6966 - mse: 5.8216 - val_loss: 14.3616 - val_mae: 2.5733 - val_mse: 14.3616
+    Epoch 68/100
+    354/354 [==============================] - 0s 271us/sample - loss: 5.6572 - mae: 1.6543 - mse: 5.6572 - val_loss: 16.1438 - val_mae: 2.8151 - val_mse: 16.1438
+    Epoch 69/100
+    354/354 [==============================] - 0s 259us/sample - loss: 5.5142 - mae: 1.6657 - mse: 5.5142 - val_loss: 14.2295 - val_mae: 2.5796 - val_mse: 14.2295
+    Epoch 70/100
+    354/354 [==============================] - 0s 273us/sample - loss: 5.4965 - mae: 1.6313 - mse: 5.4965 - val_loss: 15.2662 - val_mae: 2.6980 - val_mse: 15.2662
+    Epoch 71/100
+    354/354 [==============================] - 0s 270us/sample - loss: 5.4534 - mae: 1.6717 - mse: 5.4534 - val_loss: 14.5025 - val_mae: 2.6441 - val_mse: 14.5025
+    Epoch 72/100
+    354/354 [==============================] - 0s 253us/sample - loss: 5.5146 - mae: 1.6526 - mse: 5.5146 - val_loss: 13.7906 - val_mae: 2.5753 - val_mse: 13.7906
+    Epoch 73/100
+    354/354 [==============================] - 0s 272us/sample - loss: 5.4499 - mae: 1.6130 - mse: 5.4499 - val_loss: 15.1649 - val_mae: 2.7624 - val_mse: 15.1649
+    Epoch 74/100
+    354/354 [==============================] - 0s 309us/sample - loss: 5.3808 - mae: 1.6297 - mse: 5.3808 - val_loss: 12.9326 - val_mae: 2.5007 - val_mse: 12.9326
+    Epoch 75/100
+    354/354 [==============================] - 0s 258us/sample - loss: 5.3546 - mae: 1.6313 - mse: 5.3546 - val_loss: 13.6397 - val_mae: 2.5810 - val_mse: 13.6397
+    Epoch 76/100
+    354/354 [==============================] - 0s 265us/sample - loss: 5.1666 - mae: 1.5998 - mse: 5.1666 - val_loss: 15.6069 - val_mae: 2.7630 - val_mse: 15.6069
+    Epoch 77/100
+    354/354 [==============================] - 0s 272us/sample - loss: 5.2465 - mae: 1.6192 - mse: 5.2465 - val_loss: 14.8084 - val_mae: 2.6388 - val_mse: 14.8084
+    Epoch 78/100
+    354/354 [==============================] - 0s 265us/sample - loss: 5.1107 - mae: 1.5772 - mse: 5.1107 - val_loss: 13.6319 - val_mae: 2.5756 - val_mse: 13.6319
+    Epoch 79/100
+    354/354 [==============================] - 0s 272us/sample - loss: 5.2677 - mae: 1.5989 - mse: 5.2677 - val_loss: 15.0306 - val_mae: 2.7715 - val_mse: 15.0306
+    Epoch 80/100
+    354/354 [==============================] - 0s 274us/sample - loss: 5.0534 - mae: 1.5504 - mse: 5.0534 - val_loss: 13.3917 - val_mae: 2.5352 - val_mse: 13.3917
+    Epoch 81/100
+    354/354 [==============================] - 0s 272us/sample - loss: 5.1013 - mae: 1.5826 - mse: 5.1013 - val_loss: 14.6761 - val_mae: 2.7158 - val_mse: 14.6761
+    Epoch 82/100
+    354/354 [==============================] - 0s 258us/sample - loss: 5.1137 - mae: 1.5984 - mse: 5.1137 - val_loss: 14.7063 - val_mae: 2.6576 - val_mse: 14.7063
+    Epoch 83/100
+    354/354 [==============================] - 0s 269us/sample - loss: 4.9343 - mae: 1.5545 - mse: 4.9343 - val_loss: 13.6205 - val_mae: 2.5494 - val_mse: 13.6205
+    Epoch 84/100
+    354/354 [==============================] - 0s 277us/sample - loss: 4.9839 - mae: 1.5815 - mse: 4.9839 - val_loss: 13.3857 - val_mae: 2.6047 - val_mse: 13.3857
+    Epoch 85/100
+    354/354 [==============================] - 0s 277us/sample - loss: 4.9946 - mae: 1.5818 - mse: 4.9946 - val_loss: 14.1012 - val_mae: 2.6176 - val_mse: 14.1012
+    Epoch 86/100
+    354/354 [==============================] - 0s 273us/sample - loss: 4.7884 - mae: 1.5321 - mse: 4.7884 - val_loss: 14.5182 - val_mae: 2.6687 - val_mse: 14.5182
+    Epoch 87/100
+    354/354 [==============================] - 0s 311us/sample - loss: 4.8134 - mae: 1.5660 - mse: 4.8134 - val_loss: 12.7966 - val_mae: 2.5734 - val_mse: 12.7966
+    Epoch 88/100
+    354/354 [==============================] - 0s 273us/sample - loss: 4.7923 - mae: 1.5483 - mse: 4.7923 - val_loss: 14.4001 - val_mae: 2.6707 - val_mse: 14.4001
+    Epoch 89/100
+    354/354 [==============================] - 0s 274us/sample - loss: 4.6705 - mae: 1.5086 - mse: 4.6705 - val_loss: 15.3677 - val_mae: 2.7359 - val_mse: 15.3677
+    Epoch 90/100
+    354/354 [==============================] - 0s 280us/sample - loss: 4.8776 - mae: 1.5806 - mse: 4.8776 - val_loss: 14.4442 - val_mae: 2.6343 - val_mse: 14.4442
+    Epoch 91/100
+    354/354 [==============================] - 0s 260us/sample - loss: 4.6349 - mae: 1.5300 - mse: 4.6349 - val_loss: 14.2969 - val_mae: 2.7718 - val_mse: 14.2969
+    Epoch 92/100
+    354/354 [==============================] - 0s 273us/sample - loss: 4.7835 - mae: 1.5637 - mse: 4.7835 - val_loss: 13.1123 - val_mae: 2.5578 - val_mse: 13.1123
+    Epoch 93/100
+    354/354 [==============================] - 0s 277us/sample - loss: 4.6759 - mae: 1.5259 - mse: 4.6759 - val_loss: 14.3508 - val_mae: 2.6888 - val_mse: 14.3507
+    Epoch 94/100
+    354/354 [==============================] - 0s 273us/sample - loss: 4.7856 - mae: 1.5560 - mse: 4.7856 - val_loss: 14.5237 - val_mae: 2.6956 - val_mse: 14.5237
+    Epoch 95/100
+    354/354 [==============================] - 0s 313us/sample - loss: 4.7038 - mae: 1.5331 - mse: 4.7038 - val_loss: 12.7707 - val_mae: 2.5393 - val_mse: 12.7707
+    Epoch 96/100
+    354/354 [==============================] - 0s 277us/sample - loss: 4.6006 - mae: 1.5331 - mse: 4.6006 - val_loss: 13.8540 - val_mae: 2.6720 - val_mse: 13.8540
+    Epoch 97/100
+    354/354 [==============================] - 0s 269us/sample - loss: 4.4720 - mae: 1.4912 - mse: 4.4720 - val_loss: 13.1524 - val_mae: 2.6311 - val_mse: 13.1524
+    Epoch 98/100
+    354/354 [==============================] - 0s 309us/sample - loss: 4.4242 - mae: 1.4854 - mse: 4.4242 - val_loss: 11.7020 - val_mae: 2.4886 - val_mse: 11.7020
+    Epoch 99/100
+    354/354 [==============================] - 0s 280us/sample - loss: 4.5642 - mae: 1.4920 - mse: 4.5642 - val_loss: 12.6523 - val_mae: 2.5232 - val_mse: 12.6523
+    Epoch 100/100
+    354/354 [==============================] - 0s 274us/sample - loss: 4.1971 - mae: 1.4564 - mse: 4.1971 - val_loss: 18.7164 - val_mae: 3.0774 - val_mse: 18.7164
+
+%% Cell type:markdown id: tags:
+
+## Step 6 - Evaluate
+### 6.1 - Model evaluation
+MAE =  Mean Absolute Error (between the labels and predictions)
+A mae equal to 3 represents an average error in prediction of $3k.
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Output
+
+    x_test / loss      : 18.7164
+    x_test / mae       : 3.0774
+    x_test / mse       : 18.7164
+
+%% Cell type:markdown id: tags:
+
+### 6.2 - Training history
+What was the best result during our training ?
+
+%% Cell type:code id: tags:
+
+``` python
+print("min( val_mae ) : {:.4f}".format( min(history.history["val_mae"]) ) )
+```
+
+%% Output
+
+    min( val_mae ) : 2.4886
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],
+                                'MAE' :['mae', 'val_mae'],
+                                'LOSS':['loss','val_loss']})
+```
+
+%% Output
+
+
+
+
+
+
+
+%% Cell type:markdown id: tags:
+
+## Step 7 - Restore a model :
+
+%% Cell type:markdown id: tags:
+
+### 7.1 - Reload model
+
+%% Cell type:code id: tags:
+
+``` python
+loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')
+loaded_model.summary()
+print("Loaded.")
+```
+
+%% Output
+
+    Model: "sequential"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    Dense_n1 (Dense)             (None, 64)                896
+    _________________________________________________________________
+    Dense_n2 (Dense)             (None, 64)                4160
+    _________________________________________________________________
+    Output (Dense)               (None, 1)                 65
+    =================================================================
+    Total params: 5,121
+    Trainable params: 5,121
+    Non-trainable params: 0
+    _________________________________________________________________
+    Loaded.
+
+%% Cell type:markdown id: tags:
+
+### 7.2 - Evaluate it :
+
+%% Cell type:code id: tags:
+
+``` python
+score = loaded_model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Output
+
+    x_test / loss      : 11.7020
+    x_test / mae       : 2.4886
+    x_test / mse       : 11.7020
+
+%% Cell type:markdown id: tags:
+
+### 7.3 - Make a prediction
+
+%% Cell type:code id: tags:
+
+``` python
+mon_test=[-0.20113196, -0.48631663,  1.23572348, -0.26929877,  2.67879106,
+       -0.89623587,  1.09961251, -1.05826704, -0.55823117, -0.06159088,
+       -1.76085159, -1.97039608,  0.52775666]
+
+mon_test=np.array(mon_test).reshape(1,13)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+predictions = loaded_model.predict( mon_test )
+print("Prédiction : {:.2f} K$   Reality : {:.2f} K$".format(predictions[0][0], y_train[13]))
+```
+
+%% Output
+
+    Prédiction : 16.20 K$   Reality : 21.70 K$
+
+%% Cell type:markdown id: tags:
+
+-----
+That's all folks !

BHPD/data/BostonHousing.csv

+"crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","b","lstat","medv"
+0.00632,18,2.31,"0",0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
+0.02731,0,7.07,"0",0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
+0.02729,0,7.07,"0",0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
+0.03237,0,2.18,"0",0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
+0.06905,0,2.18,"0",0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
+0.02985,0,2.18,"0",0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
+0.08829,12.5,7.87,"0",0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
+0.14455,12.5,7.87,"0",0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
+0.21124,12.5,7.87,"0",0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
+0.17004,12.5,7.87,"0",0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
+0.22489,12.5,7.87,"0",0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
+0.11747,12.5,7.87,"0",0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
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+5.69175,0,18.1,"0",0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
+4.83567,0,18.1,"0",0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
+0.15086,0,27.74,"0",0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
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+0.20746,0,27.74,"0",0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
+0.10574,0,27.74,"0",0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
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+0.17899,0,9.69,"0",0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
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+0.17783,0,9.69,"0",0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
+0.22438,0,9.69,"0",0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
+0.06263,0,11.93,"0",0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
+0.04527,0,11.93,"0",0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
+0.06076,0,11.93,"0",0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
+0.10959,0,11.93,"0",0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
+0.04741,0,11.93,"0",0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9

GTSRB/02-First-convolutions.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Episode 2 : First Convolutions
+
+Our main steps:
+ - Read H5 dataset
+ - Build a model
+ - Train the model
+ - Evaluate the model
+
+## 1/ Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.callbacks import TensorBoard
+
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+import os,time,sys
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ Load dataset
+We're going to retrieve a previously recorded dataset.
+For example: set-24x24-L
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+def read_dataset(name):
+    '''Reads h5 dataset from ./data
+
+    Arguments:  dataset name, without .h5
+    Returns:    x_train,y_train,x_test,y_test data'''
+    # ---- Read dataset
+    filename='./data/'+name+'.h5'
+    with  h5py.File(filename) as f:
+        x_train = f['x_train'][:]
+        y_train = f['y_train'][:]
+        x_test  = f['x_test'][:]
+        y_test  = f['y_test'][:]
+
+    # ---- done
+    print('Dataset "{}" is loaded. ({:.1f} Mo)\n'.format(name,os.path.getsize(filename)/(1024*1024)))
+    return x_train,y_train,x_test,y_test
+
+x_train,y_train,x_test,y_test = read_dataset('set-24x24-L')
+```
+
+%% Cell type:markdown id: tags:
+
+## 3/ Have a look to the dataset
+We take a quick look as we go by...
+
+%% Cell type:code id: tags:
+
+``` python
+print("x_train : ", x_train.shape)
+print("y_train : ", y_train.shape)
+print("x_test  : ", x_test.shape)
+print("y_test  : ", y_test.shape)
+
+ooo.plot_images(x_train, y_train, range(12), columns=6,  x_size=2, y_size=2)
+ooo.plot_images(x_train, y_train, range(36), columns=12, x_size=1, y_size=1)
+```
+
+%% Cell type:markdown id: tags:
+
+## 4/ Create model
+We will now build a model and train it...
+
+Some models :
+
+%% Cell type:code id: tags:
+
+``` python
+
+# A basic model
+#
+def get_model_v1(lx,ly,lz):
+
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(1500, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+
+# A more sophisticated model
+#
+def get_model_v2(lx,ly,lz):
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))
+    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
+    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))
+    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(512, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+
+# My sphisticated model, but small and fast
+#
+def get_model_v3(lx,ly,lz):
+    model = keras.models.Sequential()
+    model.add( keras.layers.Conv2D(32, (3,3),   activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(1152, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ Train the model
+**Get the shape of my data :**
+
+%% Cell type:code id: tags:
+
+``` python
+(n,lx,ly,lz) = x_train.shape
+print("Images of the dataset have this folowing shape : ",(lx,ly,lz))
+```
+
+%% Cell type:markdown id: tags:
+
+**Get and compile a model, with the data shape :**
+
+%% Cell type:code id: tags:
+
+``` python
+model = get_model_v1(lx,ly,lz)
+
+model.summary()
+
+model.compile(optimizer = 'adam',
+              loss      = 'sparse_categorical_crossentropy',
+              metrics   = ['accuracy'])
+```
+
+%% Cell type:markdown id: tags:
+
+**Train it :**
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+batch_size = 64
+epochs     = 5
+
+# ---- Shuffle train data
+x_train,y_train=ooo.shuffle_np_dataset(x_train,y_train)
+
+# ---- Train
+history = model.fit(  x_train, y_train,
+                      batch_size      = batch_size,
+                      epochs          = epochs,
+                      verbose         = 1,
+                      validation_data = (x_test, y_test))
+```
+
+%% Cell type:markdown id: tags:
+
+**Evaluate it :**
+
+%% Cell type:code id: tags:
+
+``` python
+max_val_accuracy = max(history.history["val_accuracy"])
+print("Max validation accuracy is : {:.4f}".format(max_val_accuracy))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('Test loss      : {:5.4f}'.format(score[0]))
+print('Test accuracy  : {:5.4f}'.format(score[1]))
+```

GTSRB/03-Tracking-and-visualizing.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Episode 3 : Tracking, visualizing and save models
+
+Our main steps:
+ - Monitoring and understanding our model training
+ - Add recovery points
+ - Analyze the results
+ - Restore and run recovery pont
+
+## 1/ Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.callbacks import TensorBoard
+
+import numpy as np
+import h5py
+
+from sklearn.metrics import confusion_matrix
+
+import matplotlib.pyplot as plt
+import seaborn as sn
+import os, sys, time, random
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ Load dataset
+Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc.
+First of all, we're going to use a smart dataset : **set-24x24-L**
+(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+def read_dataset(name):
+    '''Reads h5 dataset from ./data
+
+    Arguments:  dataset name, without .h5
+    Returns:    x_train,y_train,x_test,y_test data'''
+    # ---- Read dataset
+    filename='./data/'+name+'.h5'
+    with  h5py.File(filename) as f:
+        x_train = f['x_train'][:]
+        y_train = f['y_train'][:]
+        x_test  = f['x_test'][:]
+        y_test  = f['y_test'][:]
+        x_meta  = f['x_meta'][:]
+        y_meta  = f['y_meta'][:]
+
+    # ---- done
+    print('Dataset "{}" is loaded. ({:.1f} Mo)\n'.format(name,os.path.getsize(filename)/(1024*1024)))
+    return x_train,y_train,x_test,y_test,x_meta,y_meta
+
+x_train,y_train,x_test,y_test,x_meta,y_meta = read_dataset('set-24x24-L')
+```
+
+%% Cell type:markdown id: tags:
+
+## 3/ Have a look to the dataset
+Note: Data must be reshape for matplotlib
+
+%% Cell type:code id: tags:
+
+``` python
+print("x_train : ", x_train.shape)
+print("y_train : ", y_train.shape)
+print("x_test  : ", x_test.shape)
+print("y_test  : ", y_test.shape)
+
+ooo.plot_images(x_train, y_train, range(12), columns=6,  x_size=2, y_size=2)
+ooo.plot_images(x_train, y_train, range(36), columns=12, x_size=1, y_size=1)
+```
+
+%% Cell type:markdown id: tags:
+
+## 4/ Create model
+We will now build a model and train it...
+
+Some models...
+
+%% Cell type:code id: tags:
+
+``` python
+# A basic model
+#
+def get_model_v1(lx,ly,lz):
+
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(1500, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ Prepare callbacks
+We will add 2 callbacks :
+ - **TensorBoard**
+Training logs, which can be visualised with Tensorboard.
+`#tensorboard --logdir ./run/logs`
+IMPORTANT : Relancer tensorboard à chaque run
+ - **Model backup**
+ It is possible to save the model each xx epoch or at each improvement.
+ The model can be saved completely or partially (weight).
+ For full format, we can use HDF5 format.
+
+%% Cell type:raw id: tags:
+
+%%bash
+# To clean old logs and saved model, run this cell
+#
+/bin/rm -r ./run/logs   2>/dev/null
+/bin/rm -r ./run/models 2>/dev/null
+/bin/mkdir -p -m 755 ./run/logs
+/bin/mkdir -p -m 755 ./run/models
+echo -e "Reset directories : ./run/logs and ./run/models ."
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.mkdir('./run/models')
+ooo.mkdir('./run/logs')
+
+# ---- Callback tensorboard
+log_dir = "./run/logs/tb_" + ooo.tag_now()
+tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+# ---- Callback ModelCheckpoint - Save best model
+save_dir = "./run/models/best-model.h5"
+bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)
+
+# ---- Callback ModelCheckpoint - Save model each epochs
+save_dir = "./run/models/model-{epoch:04d}.h5"
+savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_freq=2000*5)
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ Train the model
+**Get the shape of my data :**
+
+%% Cell type:code id: tags:
+
+``` python
+(n,lx,ly,lz) = x_train.shape
+print("Images of the dataset have this folowing shape : ",(lx,ly,lz))
+```
+
+%% Cell type:markdown id: tags:
+
+**Get and compile a model, with the data shape :**
+
+%% Cell type:code id: tags:
+
+``` python
+model = get_model_v1(lx,ly,lz)
+
+# model.summary()
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+```
+
+%% Cell type:markdown id: tags:
+
+**Train it :**
+Note: The training curve is visible in real time with Tensorboard :
+`#tensorboard --logdir ./run/logs`
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+batch_size = 64
+epochs     = 30
+
+# ---- Shuffle train data
+x_train,y_train=ooo.shuffle_np_dataset(x_train,y_train)
+
+# ---- Train
+# Note: To be faster in our example, we can take only 2000 values
+#
+history = model.fit(  x_train, y_train,
+                      batch_size=batch_size,
+                      epochs=epochs,
+                      verbose=1,
+                      validation_data=(x_test, y_test),
+                      callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )
+
+model.save('./run/models/last-model.h5')
+```
+
+%% Cell type:markdown id: tags:
+
+**Evaluate it :**
+
+%% Cell type:code id: tags:
+
+``` python
+max_val_accuracy = max(history.history["val_accuracy"])
+print("Max validation accuracy is : {:.4f}".format(max_val_accuracy))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('Test loss      : {:5.4f}'.format(score[0]))
+print('Test accuracy  : {:5.4f}'.format(score[1]))
+```
+
+%% Cell type:markdown id: tags:
+
+## 6/ History
+The return of model.fit() returns us the learning history
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history)
+```
+
+%% Cell type:markdown id: tags:
+
+## 7/ Evaluation and confusion
+
+%% Cell type:code id: tags:
+
+``` python
+y_pred   = model.predict_classes(x_test)
+conf_mat = confusion_matrix(y_test,y_pred, normalize="true", labels=range(43))
+
+ooo.plot_confusion_matrix(conf_mat)
+```
+
+%% Cell type:markdown id: tags:
+
+## 8/ Restore and evaluate
+### 8.1/ List saved models :
+
+%% Cell type:code id: tags:
+
+``` python
+!find ./run/models/
+```
+
+%% Cell type:markdown id: tags:
+
+### 8.2/ Restore a model :
+
+%% Cell type:code id: tags:
+
+``` python
+loaded_model = tf.keras.models.load_model('./run/models/best-model.h5')
+# loaded_model.summary()
+print("Loaded.")
+```
+
+%% Cell type:markdown id: tags:
+
+### 8.3/ Evaluate it :
+
+%% Cell type:code id: tags:
+
+``` python
+score = loaded_model.evaluate(x_test, y_test, verbose=0)
+
+print('Test loss      : {:5.4f}'.format(score[0]))
+print('Test accuracy  : {:5.4f}'.format(score[1]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 8.4/ Make a prediction :
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Get a random image
+#
+i   = random.randint(1,len(x_test))
+x,y = x_test[i], y_test[i]
+
+# ---- Do prediction
+#
+predictions = loaded_model.predict( np.array([x]) )
+
+# ---- A prediction is just the output layer
+#
+print("\nOutput layer from model is (x100) :\n")
+with np.printoptions(precision=2, suppress=True, linewidth=95):
+    print(predictions*100)
+
+# ---- Graphic visualisation
+#
+print("\nGraphically :\n")
+plt.figure(figsize=(12,2))
+plt.bar(range(43), predictions[0], align='center', alpha=0.5)
+plt.ylabel('Probability')
+plt.ylim((0,1))
+plt.xlabel('Class')
+plt.title('Trafic Sign prediction')
+plt.show()
+
+# ---- Predict class
+#
+p = np.argmax(predictions)
+
+# ---- Show result
+#
+print("\nPrediction on the left, real stuff on the right :\n")
+ooo.plot_images([x,x_meta[y]], [p,y], range(2),  columns=3,  x_size=3, y_size=2)
+
+if p==y:
+    print("YEEES ! that's right!")
+else:
+    print("oups, that's wrong ;-(")
+```
+
+%% Cell type:markdown id: tags:
+
+---
+That's all folks !
+
+%% Cell type:code id: tags:
+
+``` python
+```

GTSRB/04-Data-augmentation.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Episode 4 : Data augmentation
+
+Our main steps:
+ - Increase and improve the learning dataset
+
+## 1/ Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.callbacks import TensorBoard
+
+import numpy as np
+import h5py
+
+from sklearn.metrics import confusion_matrix
+
+import matplotlib.pyplot as plt
+import seaborn as sn
+import os, sys, time, random
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ Dataset loader
+Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc.
+First of all, we're going to use a smart dataset : **set-24x24-L**
+(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+def read_dataset(name):
+    '''Reads h5 dataset from ./data
+
+    Arguments:  dataset name, without .h5
+    Returns:    x_train,y_train,x_test,y_test data'''
+    # ---- Read dataset
+    filename='./data/'+name+'.h5'
+    with  h5py.File(filename) as f:
+        x_train = f['x_train'][:]
+        y_train = f['y_train'][:]
+        x_test  = f['x_test'][:]
+        y_test  = f['y_test'][:]
+
+    # ---- done
+    print('Dataset "{}" is loaded. ({:.1f} Mo)\n'.format(name,os.path.getsize(filename)/(1024*1024)))
+    return x_train,y_train,x_test,y_test
+```
+
+%% Cell type:markdown id: tags:
+
+## 3/ Models
+We will now build a model and train it...
+
+This is my model ;-)
+
+%% Cell type:code id: tags:
+
+``` python
+# A basic model
+#
+def get_model_v1(lx,ly,lz):
+
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(1500, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 4/ Callbacks
+We prepare 2 kind callbacks :  TensorBoard and Model backup
+
+%% Cell type:code id: tags:
+
+``` python
+%%bash
+# To clean old logs and saved model, run this cell
+#
+/bin/rm -r ./run/logs   2>/dev/null
+/bin/rm -r ./run/models 2>/dev/null
+/bin/mkdir -p -m 755 ./run/logs
+/bin/mkdir -p -m 755 ./run/models
+echo -e "Reset directories : ./run/logs and ./run/models ."
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Callback tensorboard
+log_dir = "./run/logs/tb_" + ooo.tag_now()
+tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+# ---- Callback ModelCheckpoint - Save best model
+save_dir = "./run/models/best-model.h5"
+bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)
+
+# ---- Callback ModelCheckpoint - Save model each epochs
+save_dir = "./run/models/model-{epoch:04d}.h5"
+savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_freq=2000*5)
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ Load and prepare dataset
+### 5.1/ Load
+
+%% Cell type:code id: tags:
+
+``` python
+x_train,y_train,x_test,y_test = read_dataset('set-48x48-L-LHE')
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.2/ Data augmentation
+
+%% Cell type:code id: tags:
+
+``` python
+datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,
+                             featurewise_std_normalization=False,
+                             width_shift_range=0.1,
+                             height_shift_range=0.1,
+                             zoom_range=0.2,
+                             shear_range=0.1,
+                             rotation_range=10.)
+datagen.fit(x_train)
+```
+
+%% Cell type:markdown id: tags:
+
+## 6/ Train the model
+**Get the shape of my data :**
+
+%% Cell type:code id: tags:
+
+``` python
+(n,lx,ly,lz) = x_train.shape
+print("Images of the dataset have this folowing shape : ",(lx,ly,lz))
+```
+
+%% Cell type:markdown id: tags:
+
+**Get and compile a model, with the data shape :**
+
+%% Cell type:code id: tags:
+
+``` python
+model = get_model_v3(lx,ly,lz)
+
+# model.summary()
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+```
+
+%% Cell type:markdown id: tags:
+
+**Train it :**
+Note : La courbe d'apprentissage est visible en temps réel avec Tensorboard :
+`#tensorboard --logdir ./run/logs`
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+batch_size = 64
+epochs     = 30
+
+# ---- Shuffle train data
+#x_train,y_train=ooo.shuffle_np_dataset(x_train,y_train)
+
+# ---- Train
+#
+history = model.fit(  datagen.flow(x_train, y_train, batch_size=batch_size),
+                      steps_per_epoch = int(x_train.shape[0]/batch_size),
+                      epochs=epochs,
+                      verbose=1,
+                      validation_data=(x_test, y_test),
+                      callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )
+
+model.save('./run/models/last-model.h5')
+```
+
+%% Cell type:markdown id: tags:
+
+**Evaluate it :**
+
+%% Cell type:code id: tags:
+
+``` python
+max_val_accuracy = max(history.history["val_accuracy"])
+print("Max validation accuracy is : {:.4f}".format(max_val_accuracy))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('Test loss      : {:5.4f}'.format(score[0]))
+print('Test accuracy  : {:5.4f}'.format(score[1]))
+```
+
+%% Cell type:markdown id: tags:
+
+## 7/ History
+The return of model.fit() returns us the learning history
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history)
+```
+
+%% Cell type:markdown id: tags:
+
+## 8/ Evaluate best model
+
+%% Cell type:markdown id: tags:
+
+### 8.1/ Restore best model :
+
+%% Cell type:code id: tags:
+
+``` python
+loaded_model = tf.keras.models.load_model('./run/models/best-model.h5')
+# best_model.summary()
+print("Loaded.")
+```
+
+%% Cell type:markdown id: tags:
+
+### 8.2/ Evaluate it :
+
+%% Cell type:code id: tags:
+
+``` python
+score = loaded_model.evaluate(x_test, y_test, verbose=0)
+
+print('Test loss      : {:5.4f}'.format(score[0]))
+print('Test accuracy  : {:5.4f}'.format(score[1]))
+```
+
+%% Cell type:markdown id: tags:
+
+**Plot confusion matrix**
+
+%% Cell type:code id: tags:
+
+``` python
+y_pred   = model.predict_classes(x_test)
+conf_mat = confusion_matrix(y_test,y_pred, normalize="true", labels=range(43))
+
+ooo.plot_confusion_matrix(conf_mat)
+```
+
+%% Cell type:markdown id: tags:
+
+---
+That's all folks !
+
+%% Cell type:code id: tags:
+
+``` python
+```

GTSRB/05-Full-convolutions.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Episode 5 : Full Convolutions
+
+Our main steps:
+ - Try n models with n datasets
+ - Save a Pandas/h5 report
+ - Write to be run in batch mode
+
+## 1/ Import
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import h5py
+import os,time,json
+import random
+
+from IPython.display import display
+
+VERSION='1.6'
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ Init and start
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Where I am ?
+now    = time.strftime("%A %d %B %Y - %Hh%Mm%Ss")
+here   = os.getcwd()
+random.seed(time.time())
+tag_id = '{:06}'.format(random.randint(0,99999))
+
+# ---- Who I am ?
+if 'OAR_JOB_ID' in os.environ:
+    oar_id=os.environ['OAR_JOB_ID']
+else:
+    oar_id='???'
+
+print('\nFull Convolutions Notebook')
+print('  Version            : {}'.format(VERSION))
+print('  Now is             : {}'.format(now))
+print('  OAR id             : {}'.format(oar_id))
+print('  Tag id             : {}'.format(tag_id))
+print('  Working directory  : {}'.format(here))
+print('  TensorFlow version :',tf.__version__)
+print('  Keras version      :',tf.keras.__version__)
+print('  for tensorboard    : --logdir {}/run/logs_{}'.format(here,tag_id))
+```
+
+%% Cell type:markdown id: tags:
+
+## 3/ Dataset loading
+
+%% Cell type:code id: tags:
+
+``` python
+def read_dataset(name):
+    '''Reads h5 dataset from ./data
+
+    Arguments:  dataset name, without .h5
+    Returns:    x_train,y_train,x_test,y_test data'''
+    # ---- Read dataset
+    filename='./data/'+name+'.h5'
+    with  h5py.File(filename,'r') as f:
+        x_train = f['x_train'][:]
+        y_train = f['y_train'][:]
+        x_test  = f['x_test'][:]
+        y_test  = f['y_test'][:]
+
+    return x_train,y_train,x_test,y_test
+```
+
+%% Cell type:markdown id: tags:
+
+## 4/ Models collection
+
+%% Cell type:code id: tags:
+
+``` python
+
+# A basic model
+#
+def get_model_v1(lx,ly,lz):
+
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(1500, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+
+# A more sophisticated model
+#
+def get_model_v2(lx,ly,lz):
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))
+    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
+    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))
+    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(512, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+
+def get_model_v3(lx,ly,lz):
+    model = keras.models.Sequential()
+    model.add(tf.keras.layers.Conv2D(32, (5, 5), padding='same',  activation='relu', input_shape=(lx,ly,lz)))
+    model.add(tf.keras.layers.BatchNormalization(axis=-1))
+    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(tf.keras.layers.Dropout(0.2))
+
+    model.add(tf.keras.layers.Conv2D(64, (5, 5), padding='same',  activation='relu'))
+    model.add(tf.keras.layers.BatchNormalization(axis=-1))
+    model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', activation='relu'))
+    model.add(tf.keras.layers.BatchNormalization(axis=-1))
+    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(tf.keras.layers.Dropout(0.2))
+
+    model.add(tf.keras.layers.Flatten())
+    model.add(tf.keras.layers.Dense(512, activation='relu'))
+    model.add(tf.keras.layers.BatchNormalization())
+    model.add(tf.keras.layers.Dropout(0.4))
+
+    model.add(tf.keras.layers.Dense(43, activation='softmax'))
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ Multiple datasets, multiple models ;-)
+
+%% Cell type:code id: tags:
+
+``` python
+def multi_run(datasets, models, datagen=None,
+              train_size=1, test_size=1, batch_size=64, epochs=16,
+              verbose=0, extension_dir='last'):
+
+    # ---- Logs and models dir
+    #
+    os.makedirs('./run/logs_{}'.format(extension_dir),   mode=0o750, exist_ok=True)
+    os.makedirs('./run/models_{}'.format(extension_dir), mode=0o750, exist_ok=True)
+
+    # ---- Columns of output
+    #
+    output={}
+    output['Dataset']=[]
+    output['Size']   =[]
+    for m in models:
+        output[m+'_Accuracy'] = []
+        output[m+'_Duration'] = []
+
+    # ---- Let's go
+    #
+    for d_name in datasets:
+        print("\nDataset : ",d_name)
+
+        # ---- Read dataset
+        x_train,y_train,x_test,y_test = read_dataset(d_name)
+        d_size=os.path.getsize('./data/'+d_name+'.h5')/(1024*1024)
+        output['Dataset'].append(d_name)
+        output['Size'].append(d_size)
+
+        # ---- Get the shape
+        (n,lx,ly,lz) = x_train.shape
+        n_train = int(x_train.shape[0]*train_size)
+        n_test  = int(x_test.shape[0]*test_size)
+
+        # ---- For each model
+        for m_name,m_function in models.items():
+            print("    Run model {}  : ".format(m_name), end='')
+            # ---- get model
+            try:
+                model=m_function(lx,ly,lz)
+                # ---- Compile it
+                model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+                # ---- Callbacks tensorboard
+                log_dir = "./run/logs_{}/tb_{}_{}".format(extension_dir, d_name, m_name)
+                tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+                # ---- Callbacks bestmodel
+                save_dir = "./run/models_{}/model_{}_{}.h5".format(extension_dir, d_name, m_name)
+                bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)
+                # ---- Train
+                start_time = time.time()
+                if datagen==None:
+                    # ---- No data augmentation (datagen=None) --------------------------------------
+                    history = model.fit(x_train[:n_train], y_train[:n_train],
+                                        batch_size      = batch_size,
+                                        epochs          = epochs,
+                                        verbose         = verbose,
+                                        validation_data = (x_test[:n_test], y_test[:n_test]),
+                                        callbacks       = [tensorboard_callback, bestmodel_callback])
+                else:
+                    # ---- Data augmentation (datagen given) ----------------------------------------
+                    datagen.fit(x_train)
+                    history = model.fit(datagen.flow(x_train, y_train, batch_size=batch_size),
+                                        steps_per_epoch = int(n_train/batch_size),
+                                        epochs          = epochs,
+                                        verbose         = verbose,
+                                        validation_data = (x_test[:n_test], y_test[:n_test]),
+                                        callbacks       = [tensorboard_callback, bestmodel_callback])
+
+                # ---- Result
+                end_time = time.time()
+                duration = end_time-start_time
+                accuracy = max(history.history["val_accuracy"])*100
+                #
+                output[m_name+'_Accuracy'].append(accuracy)
+                output[m_name+'_Duration'].append(duration)
+                print("Accuracy={:.2f} and Duration={:.2f})".format(accuracy,duration))
+            except:
+                output[m_name+'_Accuracy'].append('0')
+                output[m_name+'_Duration'].append('999')
+                print('-')
+    return output
+```
+
+%% Cell type:markdown id: tags:
+
+## 6/ Run !
+
+%% Cell type:code id: tags:
+
+``` python
+start_time = time.time()
+
+print('\n---- Run','-'*50)
+
+# --------- Datasets, models, and more.. -----------------------------------
+#
+# ---- For tests
+# datasets   = ['set-24x24-L', 'set-24x24-RGB']
+# models     = {'v1':get_model_v1, 'v4':get_model_v2}
+# batch_size = 64
+# epochs     = 2
+# train_size = 0.1
+# test_size  = 0.1
+# with_datagen = False
+# verbose      = 0
+#
+# ---- All possibilities -> Run A
+# datasets     = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']
+# models       = {'v1':get_model_v1, 'v2':get_model_v2, 'v3':get_model_v3}
+# batch_size   = 64
+# epochs       = 16
+# train_size   = 1
+# test_size    = 1
+# with_datagen = False
+# verbose      = 0
+#
+# ---- Data augmentation -> Run B
+datasets     = ['set-48x48-RGB']
+models       = {'v2':get_model_v2}
+batch_size   = 64
+epochs       = 20
+train_size   = 1
+test_size    = 1
+with_datagen = True
+verbose      = 0
+#
+# ---------------------------------------------------------------------------
+
+# ---- Data augmentation
+#
+if with_datagen :
+    datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,
+                                                           featurewise_std_normalization=False,
+                                                           width_shift_range=0.1,
+                                                           height_shift_range=0.1,
+                                                           zoom_range=0.2,
+                                                           shear_range=0.1,
+                                                           rotation_range=10.)
+else:
+    datagen=None
+
+# ---- Run
+#
+output = multi_run(datasets, models,
+                   datagen=datagen,
+                   train_size=train_size, test_size=test_size,
+                   batch_size=batch_size, epochs=epochs,
+                   verbose=verbose,
+                   extension_dir=tag_id)
+
+# ---- Save report
+#
+report={}
+report['output']=output
+report['description']='train_size={} test_size={} batch_size={} epochs={} data_aug={}'.format(train_size,test_size,batch_size,epochs,with_datagen)
+
+report_name='./run/report_{}.json'.format(tag_id)
+
+with open(report_name, 'w') as file:
+    json.dump(report, file)
+
+print('\nReport saved as ',report_name)
+end_time = time.time()
+duration = end_time-start_time
+print(f'Duration : {duration:.2f} s')
+print('-'*59)
+```
+
+%% Cell type:markdown id: tags:
+
+## 7/ That's all folks..
+
+%% Cell type:code id: tags:
+
+``` python
+print('\n{}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
+print("The work is done.\n")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

GTSRB/05.1-Full-convolutions-batch.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Episode 5.1 : Full Convolutions / run
+
+Our main steps:
+ - Run Full-convolution.ipynb as a batch :
+    - Notebook mode
+    - Script mode
+ - Tensorboard follow up
+
+## 1/ Run a notebook as a batch
+To run a notebook :
+```jupyter nbconvert --to notebook --execute <notebook>```
+
+%% Cell type:raw id: tags:
+
+%%bash
+
+# ---- This will execute and save a notebook
+#
+jupyter nbconvert --ExecutePreprocessor.timeout=-1 --to notebook --output='./run/full_convolutions' --execute '05-Full-convolutions.ipynb'
+
+%% Cell type:markdown id: tags:
+
+## 2/ Export as a script (better choice)
+To export a notebook as a script :
+```jupyter nbconvert --to script <notebook>```
+To run the script :
+```ipython <script>```
+
+%% Cell type:code id: tags:
+
+``` python
+%%bash
+
+# ---- This will convert a notebook to a notebook.py script
+#
+jupyter nbconvert --to script --output='./run/full_convolutions_B' '05-Full-convolutions.ipynb'
+```
+
+%% Output
+
+    [NbConvertApp] Converting notebook 05-Full-convolutions.ipynb to script
+    [NbConvertApp] Writing 11305 bytes to ./run/full_convolutions_B.py
+
+%% Cell type:code id: tags:
+
+``` python
+!ls -l ./run/*.py
+```
+
+%% Output
+
+    -rw-r--r-- 1 pjluc pjluc 11305 Jan 21 00:13 ./run/full_convolutions_B.py
+
+%% Cell type:markdown id: tags:
+
+## 3/ Batch submission
+Create batch script :
+
+%% Cell type:code id: tags:
+
+``` python
+%%writefile "./run/batch_full_convolutions_B.sh"
+#!/bin/bash
+#OAR -n Full convolutions
+#OAR -t gpu
+#OAR -l /nodes=1/gpudevice=1,walltime=01:00:00
+#OAR --stdout _batch/full_convolutions_%jobid%.out
+#OAR --stderr _batch/full_convolutions_%jobid%.err
+#OAR --project deeplearningshs
+
+#---- For cpu
+# use :
+# OAR -l /nodes=1/core=32,walltime=01:00:00
+# and add a 2>/dev/null to ipython xxx
+
+# ----------------------------------
+#   _           _       _
+#  | |__   __ _| |_ ___| |__
+#  | '_ \ / _` | __/ __| '_ \
+#  | |_) | (_| | || (__| | | |
+#  |_.__/ \__,_|\__\___|_| |_|
+#                  Full convolutions
+# ----------------------------------
+#
+
+CONDA_ENV=deeplearning2
+RUN_DIR=~/fidle/GTSRB
+RUN_SCRIPT=./run/full_convolutions_B.py
+
+# ---- Cuda Conda initialization
+#
+echo '------------------------------------------------------------'
+echo "Start : $0"
+echo '------------------------------------------------------------'
+#
+source /applis/environments/cuda_env.sh dahu 10.0
+source /applis/environments/conda.sh
+#
+conda activate "$CONDA_ENV"
+
+# ---- Run it...
+#
+cd $RUN_DIR
+ipython $RUN_SCRIPT
+```
+
+%% Output
+
+    Writing ./run/batch_full_convolutions_B.sh
+
+%% Cell type:code id: tags:
+
+``` python
+%%bash
+chmod 755 ./run/*.sh
+chmod 755 ./run/*.py
+ls -l ./run/*full_convolutions*
+```
+
+%% Output
+
+    -rwxr-xr-x 1 pjluc pjluc  1045 Jan 21 00:15 ./run/batch_full_convolutions_B.sh
+    -rwxr-xr-x 1 pjluc pjluc   611 Jan 19 15:53 ./run/batch_full_convolutions.sh
+    -rwxr-xr-x 1 pjluc pjluc 11305 Jan 21 00:13 ./run/full_convolutions_B.py
+
+%% Cell type:raw id: tags:
+
+%%bash
+./run/batch_full_convolutions.sh
+
+%% Cell type:code id: tags:
+
+``` python
+```

GTSRB/05.2-Full-convolutions-reports.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Episode 5.2 : Full Convolutions Reports
+
+Ou main steps :
+ - Show reports
+
+## 1/ Import
+
+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+import os,glob,json
+from pathlib import Path
+from IPython.display import display, Markdown
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ A nice function
+
+%% Cell type:code id: tags:
+
+``` python
+def highlight_max(s):
+    is_max = (s == s.max())
+    return ['background-color: yellow' if v else '' for v in is_max]
+
+def show_report(file):
+    # ---- Read json file
+    with open(file) as infile:
+        dict_report = json.load( infile )
+    output      = dict_report['output']
+    description = dict_report['description']
+    # ---- about
+    print("\n\n\nReport : ",Path(file).stem)
+    print(    "Desc.  : ",description,'\n')
+    # ---- Create a pandas
+    report       = pd.DataFrame (output)
+    col_accuracy = [ c for c in output.keys() if c.endswith('Accuracy')]
+    col_duration = [ c for c in output.keys() if c.endswith('Duration')]
+    # ---- Build formats
+    lambda_acc = lambda x : '{:.2f} %'.format(x) if (isinstance(x, float)) else '{:}'.format(x)
+    lambda_dur = lambda x : '{:.1f} s'.format(x) if (isinstance(x, float)) else '{:}'.format(x)
+    formats = {'Size':'{:.2f} Mo'}
+    for c in col_accuracy:
+        formats[c]=lambda_acc
+    for c in col_duration:
+        formats[c]=lambda_dur
+    t=report.style.highlight_max(subset=col_accuracy).format(formats).hide_index()
+    display(t)
+```
+
+%% Cell type:markdown id: tags:
+
+## 3/ Reports display
+
+%% Cell type:code id: tags:
+
+``` python
+for file in glob.glob("./run/*.json"):
+    show_report(file)
+```
+
+%% Output
+
+    
+    
+    
+    Report :  report_009557
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False
+    
+
+
+    
+    
+    
+    Report :  report_020341
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False
+    
+
+
+    
+    
+    
+    Report :  report_041040
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True
+    
+
+
+    
+    
+    
+    Report :  report_088809
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True
+    
+
+
+    
+    
+    
+    Report :  report_093384
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False
+    
+
+
+    
+    
+    
+    Report :  report_094801
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=20 data_aug=True
+    
+
+
+    
+    
+    
+    Report :  report_2020_01_20_17h22m23s
+    Desc.  :  train_size=1 test_size=1 batch_size=64 epochs=16 data_aug=False
+    
+
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

GTSRB/99 Scripts-Tensorboard.ipynb

+%% Cell type:markdown id: tags:
+
+Running Tensorboard from Jupyter lab
+====================================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+Vesion : 1.0
+
+%% Cell type:markdown id: tags:
+
+## 1/ Méthode 1 : Shell command
+
+%% Cell type:code id: tags:
+
+``` python
+%%bash
+tensorboard_start --logdir ./run/logs
+```
+
+%% Cell type:code id: tags:
+
+``` python
+%%bash
+tensorboard_status
+```
+
+%% Cell type:code id: tags:
+
+``` python
+%%bash
+tensorboard_stop
+```
+
+%% Cell type:markdown id: tags:
+
+## Méthode 2 : Magic command
+**Start**
+
+%% Cell type:code id: tags:
+
+``` python
+%load_ext tensorboard
+```
+
+%% Cell type:code id: tags:
+
+``` python
+%tensorboard --port 21277 --host 0.0.0.0 --logdir ./run/logs
+```
+
+%% Cell type:markdown id: tags:
+
+**Stop**
+No way... use bash method
+
+## Methode 3  : Tensorboard module
+
+**Start**
+
+%% Cell type:code id: tags:
+
+``` python
+import tensorboard.notebook as tsb
+```
+
+%% Cell type:code id: tags:
+
+``` python
+tsb.start('--port 21277 --host 0.0.0.0 --logdir ./run/logs')
+```
+
+%% Cell type:markdown id: tags:
+
+**Check**
+
+%% Cell type:code id: tags:
+
+``` python
+a=tsb.list()
+```
+
+%% Output
+
+    No known TensorBoard instances running.
+
+%% Cell type:markdown id: tags:
+
+**Stop**
+No way... use bash method
+
+%% Cell type:code id: tags:
+
+``` python
+!kill 214798
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

IMDB/01-Embedding-Keras.ipynb

+%% Cell type:markdown id: tags:
+
+Text Embedding - IMDB dataset
+=============================
+---
+Formation Introduction au Deep Learning  (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Text classification using **Text embedding** :
+
+The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text.
+
+Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)**
+Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/)
+For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
+
+What we're going to do:
+
+ - Retrieve data
+ - Preparing the data
+ - Build a model
+ - Train the model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Init python stuff
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+
+import tensorflow as tf
+import tensorflow.keras as keras
+import tensorflow.keras.datasets.imdb as imdb
+
+import matplotlib.pyplot as plt
+import matplotlib
+# import seaborn as sns
+
+import os,sys,h5py,json
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Output
+
+
+    
+    FIDLE 2020 - Practical Work Module
+    Version              : 0.2.8
+    Run time             : Friday 14 February 2020, 17:09:29
+    TensorFlow version   : 2.0.0
+    Keras version        : 2.2.4-tf
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Retrieve data
+
+**From Keras :**
+This IMDb dataset can bet get directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
+
+Due to their nature, textual data can be somewhat complex.
+
+### 2.1 - Data structure :
+The dataset is composed of 2 parts: **reviews** and **opinions** (positive/negative),  with a **dictionary**
+
+  - dataset = (reviews, opinions)
+    - reviews = \[ review_0, review_1, ...\]
+      - review_i = [ int1, int2, ...] where int_i is the index of the word in the dictionary.
+    - opinions = \[ int0, int1, ...\] where int_j == 0 if opinion is negative or 1 if opinion is positive.
+  - dictionary = \[ mot1:int1, mot2:int2, ... ]
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Get dataset
+For simplicity, we will use a pre-formatted dataset.
+See : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data
+
+However, Keras offers some usefull tools for formatting textual data.
+See : https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text
+
+%% Cell type:code id: tags:
+
+``` python
+vocab_size = 10000
+
+# ----- Retrieve x,y
+#
+(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words  = vocab_size,
+                                                       skip_top   = 0,
+                                                       maxlen     = None,
+                                                       seed       = 42,
+                                                       start_char = 1,
+                                                       oov_char   = 2,
+                                                       index_from = 3, )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print("  Max(x_train,x_test)  : ", ooo.rmax([x_train,x_test]) )
+print("  x_train : {}  y_train : {}".format(x_train.shape, y_train.shape))
+print("  x_test  : {}  y_test  : {}".format(x_test.shape,  y_test.shape))
+
+print('\nReview example (x_train[12]) :\n\n',x_train[12])
+```
+
+%% Output
+
+      Max(x_train,x_test)  :  9999
+      x_train : (25000,)  y_train : (25000,)
+      x_test  : (25000,)  y_test  : (25000,)
+    
+    Review example (x_train[12]) :
+    
+     [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]
+
+%% Cell type:markdown id: tags:
+
+### 2.3 - Have a look for humans (optional)
+When we loaded the dataset, we asked for using \<start\> as 1, \<unknown word\> as 2
+So, we shifted the dataset by 3 with the parameter index_from=3
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Retrieve dictionary {word:index}, and encode it in ascii
+
+word_index = imdb.get_word_index()
+
+# ---- Shift the dictionary from +3
+
+word_index = {w:(i+3) for w,i in word_index.items()}
+
+# ---- Add <pad>, <start> and unknown tags
+
+word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )
+
+# ---- Create a reverse dictionary : {index:word}
+
+index_word = {index:word for word,index in word_index.items()}
+
+# ---- Add a nice function to transpose :
+#
+def dataset2text(review):
+    return ' '.join([index_word.get(i, '?') for i in review])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print('\nDictionary size     : ', len(word_index))
+print('\nReview example (x_train[12]) :\n\n',x_train[12])
+print('\nIn real words :\n\n', dataset2text(x_train[12]))
+```
+
+%% Output
+
+    
+    Dictionary size     :  88587
+    
+    Review example (x_train[12]) :
+    
+     [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]
+    
+    In real words :
+    
+     <start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint
+
+%% Cell type:markdown id: tags:
+
+### 2.4 - Have a look for neurons
+
+%% Cell type:code id: tags:
+
+``` python
+plt.figure(figsize=(12, 6))
+ax=sns.distplot([len(i) for i in x_train],bins=60)
+ax.set_title('Distribution of reviews by size')
+plt.xlabel("Review's sizes")
+plt.ylabel('Density')
+ax.set_xlim(0, 1500)
+plt.show()
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    NameError                                 Traceback (most recent call last)
+    <ipython-input-10-b7cb31218cd1> in <module>
+          1 plt.figure(figsize=(12, 6))
+    ----> 2 ax=sns.distplot([len(i) for i in x_train],bins=60)
+          3 ax.set_title('Distribution of reviews by size')
+          4 plt.xlabel("Review's sizes")
+          5 plt.ylabel('Density')
+    NameError: name 'sns' is not defined
+
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Preprocess the data
+In order to be processed by an NN, all entries must have the same length.
+We chose a review length of **review_len**
+We will therefore complete them with a padding (of \<pad\>\)
+
+%% Cell type:code id: tags:
+
+``` python
+review_len = 256
+
+x_train = keras.preprocessing.sequence.pad_sequences(x_train,
+                                                     value   = 0,
+                                                     padding = 'post',
+                                                     maxlen  = review_len)
+
+x_test  = keras.preprocessing.sequence.pad_sequences(x_test,
+                                                     value   = 0 ,
+                                                     padding = 'post',
+                                                     maxlen  = review_len)
+
+print('\nReview example (x_train[12]) :\n\n',x_train[12])
+print('\nIn real words :\n\n', dataset2text(x_train[12]))
+```
+
+%% Output
+
+    
+    Review example (x_train[12]) :
+    
+     [   1   14   22 1367   53  206  159    4  636  898   74   26   11  436
+      363  108    7   14  432   14   22    9 1055   34 8599    2    5  381
+     3705 4509   14  768   47  839   25  111 1517 2579 1991  438 2663  587
+        4  280  725    6   58   11 2714  201    4  206   16  702    5 5176
+       19  480 5920  157   13   64  219    4    2   11  107  665 1212   39
+        4  206    4   65  410   16  565    5   24   43  343   17 5602    8
+      169  101   85  206  108    8 3008   14   25  215  168   18    6 2579
+     1991  438    2   11  129 1609   36   26   66  290 3303   46    5  633
+      115 4363    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0    0    0    0    0    0    0    0    0    0    0
+        0    0    0    0]
+    
+    In real words :
+    
+     <start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>
+
+%% Cell type:markdown id: tags:
+
+### Save dataset and dictionary (can be usefull)
+
+%% Cell type:code id: tags:
+
+``` python
+os.makedirs('./data',   mode=0o750, exist_ok=True)
+
+with h5py.File('./data/dataset_imdb.h5', 'w') as f:
+    f.create_dataset("x_train",    data=x_train)
+    f.create_dataset("y_train",    data=y_train)
+    f.create_dataset("x_test",     data=x_test)
+    f.create_dataset("y_test",     data=y_test)
+
+with open('./data/word_index.json', 'w') as fp:
+    json.dump(word_index, fp)
+
+with open('./data/index_word.json', 'w') as fp:
+    json.dump(index_word, fp)
+
+print('Saved.')
+```
+
+%% Output
+
+    Saved.
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Build the model
+Few remarks :
+1. We'll choose a dense vector size for the embedding output with **dense_vector_size**
+2. **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly)
+In other words: we average the set of vectors/words of a sentence
+3. L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling)
+Voir : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer
+
+A SUIVRE : https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks
+### 4.1 - Build
+More documentation about :
+ - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)
+ - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)
+
+%% Cell type:code id: tags:
+
+``` python
+def get_model(dense_vector_size=32):
+
+    model = keras.Sequential()
+    model.add(keras.layers.Embedding(input_dim    = vocab_size,
+                                     output_dim   = dense_vector_size,
+                                     input_length = review_len))
+    model.add(keras.layers.GlobalAveragePooling1D())
+    model.add(keras.layers.Dense(dense_vector_size, activation='relu'))
+    model.add(keras.layers.Dense(1,                 activation='sigmoid'))
+
+    model.compile(optimizer = 'adam',
+                  loss      = 'binary_crossentropy',
+                  metrics   = ['accuracy'])
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 5 - Train the model
+### 5.1 - Get it
+
+%% Cell type:code id: tags:
+
+``` python
+model = get_model(32)
+
+model.summary()
+```
+
+%% Output
+
+    Model: "sequential"
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    embedding (Embedding)        (None, 256, 32)           320000
+    _________________________________________________________________
+    global_average_pooling1d (Gl (None, 32)                0
+    _________________________________________________________________
+    dense (Dense)                (None, 32)                1056
+    _________________________________________________________________
+    dense_1 (Dense)              (None, 1)                 33
+    =================================================================
+    Total params: 321,089
+    Trainable params: 321,089
+    Non-trainable params: 0
+    _________________________________________________________________
+
+%% Cell type:markdown id: tags:
+
+### 5.2 - Add callback
+
+%% Cell type:code id: tags:
+
+``` python
+os.makedirs('./run/models',   mode=0o750, exist_ok=True)
+save_dir = "./run/models/best_model.h5"
+savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.1 - Train it
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+n_epochs   = 30
+batch_size = 512
+
+history = model.fit(x_train,
+                    y_train,
+                    epochs          = n_epochs,
+                    batch_size      = batch_size,
+                    validation_data = (x_test, y_test),
+                    verbose         = 1,
+                    callbacks       = [savemodel_callback])
+```
+
+%% Output
+
+    Train on 25000 samples, validate on 25000 samples
+    Epoch 1/30
+    25000/25000 [==============================] - 2s 78us/sample - loss: 0.6901 - accuracy: 0.5984 - val_loss: 0.6838 - val_accuracy: 0.7333
+    Epoch 2/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.6646 - accuracy: 0.7538 - val_loss: 0.6378 - val_accuracy: 0.7683
+    Epoch 3/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.5878 - accuracy: 0.7973 - val_loss: 0.5433 - val_accuracy: 0.7966
+    Epoch 4/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.4778 - accuracy: 0.8386 - val_loss: 0.4481 - val_accuracy: 0.8300
+    Epoch 5/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.3860 - accuracy: 0.8691 - val_loss: 0.3815 - val_accuracy: 0.8550
+    Epoch 6/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.3264 - accuracy: 0.8856 - val_loss: 0.3439 - val_accuracy: 0.8643
+    Epoch 7/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.2883 - accuracy: 0.8956 - val_loss: 0.3223 - val_accuracy: 0.8695
+    Epoch 8/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.2602 - accuracy: 0.9052 - val_loss: 0.3083 - val_accuracy: 0.8748
+    Epoch 9/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.2388 - accuracy: 0.9122 - val_loss: 0.2981 - val_accuracy: 0.8770
+    Epoch 10/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.2201 - accuracy: 0.9197 - val_loss: 0.2928 - val_accuracy: 0.8791
+    Epoch 11/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.2050 - accuracy: 0.9261 - val_loss: 0.2898 - val_accuracy: 0.8809
+    Epoch 12/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.1917 - accuracy: 0.9308 - val_loss: 0.2872 - val_accuracy: 0.8823
+    Epoch 13/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1799 - accuracy: 0.9356 - val_loss: 0.2903 - val_accuracy: 0.8799
+    Epoch 14/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1699 - accuracy: 0.9405 - val_loss: 0.2890 - val_accuracy: 0.8824
+    Epoch 15/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.1599 - accuracy: 0.9455 - val_loss: 0.2925 - val_accuracy: 0.8820
+    Epoch 16/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1517 - accuracy: 0.9484 - val_loss: 0.2953 - val_accuracy: 0.8816
+    Epoch 17/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1436 - accuracy: 0.9517 - val_loss: 0.2996 - val_accuracy: 0.8817
+    Epoch 18/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1366 - accuracy: 0.9546 - val_loss: 0.3053 - val_accuracy: 0.8790
+    Epoch 19/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1299 - accuracy: 0.9575 - val_loss: 0.3104 - val_accuracy: 0.8798
+    Epoch 20/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1246 - accuracy: 0.9589 - val_loss: 0.3181 - val_accuracy: 0.8756
+    Epoch 21/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1180 - accuracy: 0.9617 - val_loss: 0.3233 - val_accuracy: 0.8760
+    Epoch 22/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1131 - accuracy: 0.9644 - val_loss: 0.3305 - val_accuracy: 0.8750
+    Epoch 23/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1075 - accuracy: 0.9668 - val_loss: 0.3379 - val_accuracy: 0.8745
+    Epoch 24/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.1029 - accuracy: 0.9684 - val_loss: 0.3462 - val_accuracy: 0.8723
+    Epoch 25/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.0976 - accuracy: 0.9711 - val_loss: 0.3564 - val_accuracy: 0.8726
+    Epoch 26/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.0940 - accuracy: 0.9728 - val_loss: 0.3627 - val_accuracy: 0.8716
+    Epoch 27/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.0893 - accuracy: 0.9750 - val_loss: 0.3719 - val_accuracy: 0.8695
+    Epoch 28/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.0852 - accuracy: 0.9763 - val_loss: 0.3814 - val_accuracy: 0.8692
+    Epoch 29/30
+    25000/25000 [==============================] - 1s 35us/sample - loss: 0.0816 - accuracy: 0.9778 - val_loss: 0.3910 - val_accuracy: 0.8682
+    Epoch 30/30
+    25000/25000 [==============================] - 1s 34us/sample - loss: 0.0783 - accuracy: 0.9795 - val_loss: 0.4004 - val_accuracy: 0.8673
+    CPU times: user 45.5 s, sys: 2.38 s, total: 47.8 s
+    Wall time: 27.2 s
+
+%% Cell type:markdown id: tags:
+
+## Step 6 - Evaluate
+### 6.1 - Training history
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history)
+```
+
+%% Output
+
+
+
+
+
+%% Cell type:markdown id: tags:
+
+### 6.2 - Reload and evaluate best model
+
+%% Cell type:code id: tags:
+
+``` python
+model = keras.models.load_model('./run/models/best_model.h5')
+
+# ---- Evaluate
+reload(ooo)
+score  = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / accuracy  : {:5.4f}'.format(score[1]))
+
+values=[score[1], 1-score[1]]
+ooo.plot_donut(values,["Accuracy","Errors"], title="#### Accuracy donut is :")
+
+# ---- Confusion matrix
+
+y_pred   = model.predict_classes(x_test)
+
+# ooo.display_confusion_matrix(y_test,y_pred,labels=range(2),color='orange',font_size='20pt')
+ooo.display_confusion_matrix(y_test,y_pred,labels=range(2))
+```
+
+%% Output
+
+    x_test / loss      : 0.2872
+    x_test / accuracy  : 0.8823
+
+    #### Accuracy donut is :
+
+
+
+    #### Confusion matrix is :
+
+    ---------------------------------------------------------------------------
+    TypeError                                 Traceback (most recent call last)
+    <ipython-input-20-362e872d02fc> in <module>
+         16
+         17 # ooo.display_confusion_matrix(y_test,y_pred,labels=range(2),color='orange',font_size='20pt')
+    ---> 18 ooo.display_confusion_matrix(y_test,y_pred,labels=range(2))
+
+    /gpfsdswork/projects/rech/mlh/uja62cb/fidle/fidle/pwk.py in display_confusion_matrix(y_true, y_pred, labels, color, font_size, title)
+        323     if title != None :  display(Markdown(title))
+        324
+    --> 325     cm = confusion_matrix( y_true,y_pred, normalize="true", labels=labels)
+        326     df=pd.DataFrame(cm)
+        327
+    TypeError: confusion_matrix() got an unexpected keyword argument 'normalize'
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

IMDB/02-Prediction.ipynb

+%% Cell type:markdown id: tags:
+
+Text Embedding - IMDB dataset
+=============================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Reviews analysis :
+
+The objective is to guess whether our new and personals films reviews are **positive or negative** .
+For this, we will use our previously saved model.
+
+What we're going to do:
+
+ - Preparing the data
+ - Retrieve our saved model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Init python stuff
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+
+import tensorflow as tf
+import tensorflow.keras as keras
+import tensorflow.keras.datasets.imdb as imdb
+
+import matplotlib.pyplot as plt
+import matplotlib
+import seaborn as sns
+import pandas as pd
+
+import os,sys,h5py,json,re
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    ModuleNotFoundError                       Traceback (most recent call last)
+    <ipython-input-1-94e372328354> in <module>
+          7 import matplotlib.pyplot as plt
+          8 import matplotlib
+    ----> 9 import seaborn as sns
+         10 import pandas as pd
+         11
+    ModuleNotFoundError: No module named 'seaborn'
+
+%% Cell type:markdown id: tags:
+
+## Step 2 : Preparing the data
+### 2.1 - Our reviews :
+
+%% Cell type:code id: tags:
+
+``` python
+reviews = [ "This film is particularly nice, a must see.",
+             "Some films are classics and cannot be ignored.",
+             "This movie is just abominable and doesn't deserve to be seen!"]
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Retrieve dictionaries
+
+%% Cell type:code id: tags:
+
+``` python
+with open('./data/word_index.json', 'r') as fp:
+    word_index = json.load(fp)
+    index_word = {index:word for word,index in word_index.items()}
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.3 - Clean, index and padd
+
+%% Cell type:code id: tags:
+
+``` python
+max_len    = 256
+vocab_size = 10000
+
+
+nb_reviews = len(reviews)
+x_data     = []
+
+# ---- For all reviews
+for review in reviews:
+    # ---- First index must be <start>
+    index_review=[1]
+    # ---- For all words
+    for w in review.split(' '):
+        # ---- Clean it
+        w_clean = re.sub(r"[^a-zA-Z0-9]", "", w)
+        # ---- Not empty ?
+        if len(w_clean)>0:
+            # ---- Get the index
+            w_index = word_index.get(w,2)
+            if w_index>vocab_size : w_index=2
+            # ---- Add the index if < vocab_size
+            index_review.append(w_index)
+    # ---- Add the indexed review
+    x_data.append(index_review)
+
+# ---- Padding
+x_data = keras.preprocessing.sequence.pad_sequences(x_data, value   = 0, padding = 'post', maxlen  = max_len)
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.4 - Have a look
+
+%% Cell type:code id: tags:
+
+``` python
+def translate(x):
+    return ' '.join( [index_word.get(i,'?') for i in x] )
+
+for i in range(nb_reviews):
+    imax=np.where(x_data[i]==0)[0][0]+5
+    print(f'\nText review      :',    reviews[i])
+    print(  f'x_train[{i:}]       :', list(x_data[i][:imax]), '(...)')
+    print(  'Translation      :', translate(x_data[i][:imax]), '(...)')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Bring back the model
+
+%% Cell type:code id: tags:
+
+``` python
+model = keras.models.load_model('./run/models/best_model.h5')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Predict
+
+%% Cell type:code id: tags:
+
+``` python
+y_pred   = model.predict(x_data)
+```
+
+%% Cell type:markdown id: tags:
+
+#### And the winner is :
+
+%% Cell type:code id: tags:
+
+``` python
+for i in range(nb_reviews):
+    print(f'\n{reviews[i]:<70} =>',('NEGATIVE' if y_pred[i][0]<0.5 else 'POSITIVE'),f'({y_pred[i][0]:.2f})')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+a=[1]+[i for i in range(3)]
+a
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

IMDB/03-LSTM-Keras.ipynb

+%% Cell type:markdown id: tags:
+
+Text Embedding - IMDB dataset
+=============================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Text classification using **Text embedding** :
+
+The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text.
+
+Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)**
+Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/)
+For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
+
+What we're going to do:
+
+ - Retrieve data
+ - Preparing the data
+ - Build a model
+ - Train the model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Init python stuff
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+
+import tensorflow as tf
+import tensorflow.keras as keras
+import tensorflow.keras.datasets.imdb as imdb
+
+import matplotlib.pyplot as plt
+import matplotlib
+import seaborn as sns
+
+import os,sys,h5py,json
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Retrieve data
+
+**From Keras :**
+This IMDb dataset can bet get directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)
+
+Due to their nature, textual data can be somewhat complex.
+
+### 2.1 - Data structure :
+The dataset is composed of 2 parts: **reviews** and **opinions** (positive/negative),  with a **dictionary**
+
+  - dataset = (reviews, opinions)
+    - reviews = \[ review_0, review_1, ...\]
+      - review_i = [ int1, int2, ...] where int_i is the index of the word in the dictionary.
+    - opinions = \[ int0, int1, ...\] where int_j == 0 if opinion is negative or 1 if opinion is positive.
+  - dictionary = \[ mot1:int1, mot2:int2, ... ]
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Get dataset
+For simplicity, we will use a pre-formatted dataset.
+See : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data
+
+However, Keras offers some usefull tools for formatting textual data.
+See : https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text
+
+%% Cell type:code id: tags:
+
+``` python
+vocab_size = 10000
+
+# ----- Retrieve x,y
+#
+(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words  = vocab_size,
+                                                       skip_top   = 0,
+                                                       maxlen     = None,
+                                                       seed       = 42,
+                                                       start_char = 1,
+                                                       oov_char   = 2,
+                                                       index_from = 3, )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print("  Max(x_train,x_test)  : ", ooo.rmax([x_train,x_test]) )
+print("  x_train : {}  y_train : {}".format(x_train.shape, y_train.shape))
+print("  x_test  : {}  y_test  : {}".format(x_test.shape,  y_test.shape))
+
+print('\nReview example (x_train[12]) :\n\n',x_train[12])
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.3 - Have a look for humans (optional)
+When we loaded the dataset, we asked for using \<start\> as 1, \<unknown word\> as 2
+So, we shifted the dataset by 3 with the parameter index_from=3
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Retrieve dictionary {word:index}, and encode it in ascii
+
+word_index = imdb.get_word_index()
+
+# ---- Shift the dictionary from +3
+
+word_index = {w:(i+3) for w,i in word_index.items()}
+
+# ---- Add <pad>, <start> and unknown tags
+
+word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )
+
+# ---- Create a reverse dictionary : {index:word}
+
+index_word = {index:word for word,index in word_index.items()}
+
+# ---- Add a nice function to transpose :
+#
+def dataset2text(review):
+    return ' '.join([index_word.get(i, '?') for i in review])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print('\nDictionary size     : ', len(word_index))
+print('\nReview example (x_train[12]) :\n\n',x_train[12])
+print('\nIn real words :\n\n', dataset2text(x_train[12]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.4 - Have a look for neurons
+
+%% Cell type:code id: tags:
+
+``` python
+plt.figure(figsize=(12, 6))
+ax=sns.distplot([len(i) for i in x_train],bins=60)
+ax.set_title('Distribution of reviews by size')
+plt.xlabel("Review's sizes")
+plt.ylabel('Density')
+ax.set_xlim(0, 1500)
+plt.show()
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Preprocess the data
+In order to be processed by an NN, all entries must have the same length.
+We chose a review length of **review_len**
+We will therefore complete them with a padding (of \<pad\>\)
+
+%% Cell type:code id: tags:
+
+``` python
+review_len = 256
+
+x_train = keras.preprocessing.sequence.pad_sequences(x_train,
+                                                     value   = 0,
+                                                     padding = 'post',
+                                                     maxlen  = review_len)
+
+x_test  = keras.preprocessing.sequence.pad_sequences(x_test,
+                                                     value   = 0 ,
+                                                     padding = 'post',
+                                                     maxlen  = review_len)
+
+print('\nReview example (x_train[12]) :\n\n',x_train[12])
+print('\nIn real words :\n\n', dataset2text(x_train[12]))
+```
+
+%% Cell type:markdown id: tags:
+
+### Save dataset and dictionary (can be usefull)
+
+%% Cell type:code id: tags:
+
+``` python
+os.makedirs('./data',   mode=0o750, exist_ok=True)
+
+with h5py.File('./data/dataset_imdb.h5', 'w') as f:
+    f.create_dataset("x_train",    data=x_train)
+    f.create_dataset("y_train",    data=y_train)
+    f.create_dataset("x_test",     data=x_test)
+    f.create_dataset("y_test",     data=y_test)
+
+with open('./data/word_index.json', 'w') as fp:
+    json.dump(word_index, fp)
+
+with open('./data/index_word.json', 'w') as fp:
+    json.dump(index_word, fp)
+
+print('Saved.')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Build the model
+Few remarks :
+1. We'll choose a dense vector size for the embedding output with **dense_vector_size**
+2. **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly)
+In other words: we average the set of vectors/words of a sentence
+3. L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling)
+Voir : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer
+
+A SUIVRE : https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks
+### 4.1 - Build
+More documentation about :
+ - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)
+ - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)
+
+%% Cell type:code id: tags:
+
+``` python
+def get_model(dense_vector_size=128):
+
+    model = keras.Sequential()
+    model.add(keras.layers.Embedding(input_dim    = vocab_size,
+                                     output_dim   = dense_vector_size,
+                                     input_length = review_len))
+    model.add(keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2))
+    model.add(keras.layers.Dense(1,                 activation='sigmoid'))
+
+    model.compile(optimizer = 'adam',
+                  loss      = 'binary_crossentropy',
+                  metrics   = ['accuracy'])
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 5 - Train the model
+### 5.1 - Get it
+
+%% Cell type:code id: tags:
+
+``` python
+model = get_model()
+
+model.summary()
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.2 - Add callback
+
+%% Cell type:code id: tags:
+
+``` python
+os.makedirs('./run/models',   mode=0o750, exist_ok=True)
+save_dir = "./run/models/best_model.h5"
+savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.1 - Train it
+GPU : batch_size=512 : 305s
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+n_epochs   = 10
+batch_size = 32
+
+history = model.fit(x_train,
+                    y_train,
+                    epochs          = n_epochs,
+                    batch_size      = batch_size,
+                    validation_data = (x_test, y_test),
+                    verbose         = 1,
+                    callbacks       = [savemodel_callback])
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 6 - Evaluate
+### 6.1 - Training history
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history)
+```
+
+%% Cell type:markdown id: tags:
+
+### 6.2 - Reload and evaluate best model
+
+%% Cell type:code id: tags:
+
+``` python
+model = keras.models.load_model('./run/models/best_model.h5')
+
+# ---- Evaluate
+reload(ooo)
+score  = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / accuracy  : {:5.4f}'.format(score[1]))
+
+values=[score[1], 1-score[1]]
+ooo.plot_donut(values,["Accuracy","Errors"], title="#### Accuracy donut is :")
+
+# ---- Confusion matrix
+
+y_pred   = model.predict_classes(x_test)
+
+ooo.display_confusion_matrix(y_test,y_pred,labels=range(2),color='orange',font_size='20pt')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

LinearReg/01-Linear-Regression.ipynb

+%% Cell type:markdown id: tags:
+
+![header1](../fidle/img/00-Fidle-header-01.png)
+
+Linear regression with direct resolution
+========================================
+An example of direct linear regression.
+
+## Objectives :
+ - Just one, the illustration of a direct resolution :-)
+
+Equation : $ Y = X.\theta + N$
+Where N is a noise vector
+and $\theta = (a,b)$ a vector as y = a.x + b
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import math
+import matplotlib
+import matplotlib.pyplot as plt
+import sys
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Output
+
+
+    
+    FIDLE 2020 - Practical Work Module
+    Version              : 0.2.9
+    Run time             : Tuesday 18 February 2020, 16:02:39
+    TensorFlow version   : 2.0.0
+    Keras version        : 2.2.4-tf
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Retrieve a set of points
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Paramètres
+nb    = 100     # Nombre de points
+xmin  = 0       # Distribution / x
+xmax  = 10
+a     = 4       # Distribution / y
+b     = 2       # y= a.x + b (+ bruit)
+noise = 7       # bruit
+
+theta = np.array([[a],[b]])
+
+# ---- Vecteur X  (1,x) x nb
+#      la premiere colonne est a 1 afin que X.theta <=> 1.b + x.a
+
+Xc1 = np.ones((nb,1))
+Xc2 = np.random.uniform(xmin,xmax,(nb,1))
+X = np.c_[ Xc1, Xc2 ]
+
+# ---- Noise
+# N = np.random.uniform(-noise,noise,(nb,1))
+N = noise * np.random.normal(0,1,(nb,1))
+
+# ---- Vecteur Y
+Y = (X @ theta) + N
+
+# print("X:\n",X,"\nY:\n ",Y)
+```
+
+%% Cell type:markdown id: tags:
+
+### Show it
+
+%% Cell type:code id: tags:
+
+``` python
+width = 12
+height = 6
+
+fig, ax = plt.subplots()
+fig.set_size_inches(width,height)
+ax.plot(X[:,1], Y, ".")
+ax.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
+ax.set_xlabel('x axis')
+ax.set_ylabel('y axis')
+plt.show()
+```
+
+%% Output
+
+
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Direct calculation of the normal equation
+
+
+We'll try to find an optimal value of $\theta$, minimizing a cost function.
+The cost function, classically used in the case of linear regressions, is the **root mean square error** (racine carré de l'erreur quadratique moyenne):
+
+$RMSE(X,h_\theta)=\sqrt{\frac1n\sum_{i=1}^n\left[h_\theta(X^{(i)})-Y^{(i)}\right]^2}$
+
+With the simplified variant : $MSE(X,h_\theta)=\frac1n\sum_{i=1}^n\left[h_\theta(X^{(i)})-Y^{(i)}\right]^2$
+
+The optimal value of regression is : $ \hat{ \theta } =( X^{-T} .X)^{-1}.X^{-T}.Y$
+
+Démontstration : https://eli.thegreenplace.net/2014/derivation-of-the-normal-equation-for-linear-regression
+
+%% Cell type:code id: tags:
+
+``` python
+theta_hat = np.linalg.inv(X.T @ X) @ X.T @ Y
+
+print("Theta :\n",theta,"\n\ntheta hat :\n",theta_hat)
+```
+
+%% Output
+
+    Theta :
+     [[4]
+     [2]]
+    
+    theta hat :
+     [[6.81242007]
+     [1.56836316]]
+
+%% Cell type:markdown id: tags:
+
+### Show it
+
+%% Cell type:code id: tags:
+
+``` python
+Xd = np.array([[1,xmin], [1,xmax]])
+Yd = Xd @ theta_hat
+
+fig, ax = plt.subplots()
+fig.set_size_inches(width,height)
+ax.plot(X[:,1], Y, ".")
+ax.plot(Xd[:,1], Yd, "-")
+ax.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
+ax.set_xlabel('x axis')
+ax.set_ylabel('y axis')
+plt.show()
+```
+
+%% Output
+
+
+
+%% Cell type:markdown id: tags:
+
+---
+![](../fidle/img/00-Fidle-logo-01_s.png)

LinearReg/03-Polynomial-Regression.ipynb

+%% Cell type:markdown id: tags:
+
+![header1](../fidle/img/00-Fidle-header-01.png)
+
+# Polynomial regression
+An example of polynomial regression.
+
+## Objectives :
+ - Just one, the illustration of a polynomial regression :-)
+
+
+We are looking for a polynomial function to approximate the observed series :
+$ y = a_n\cdot x^n + \dots + a_i\cdot x^i + \dots + a_1\cdot x + b $
+
+
+## Step 1 - Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+import math
+import random
+import matplotlib
+import matplotlib.pyplot as plt
+import sys
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Output
+
+
+    
+    FIDLE 2020 - Practical Work Module
+    Version              : 0.2.9
+    Run time             : Tuesday 18 February 2020, 17:23:05
+    TensorFlow version   : 2.0.0
+    Keras version        : 2.2.4-tf
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Preparation of learning data :
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Parameters
+
+n         = 100
+
+xob_min   = -5
+xob_max   = 5
+
+deg       =  7
+a_min     = -2
+a_max     =  2
+
+noise     =  2000
+
+# ---- Train data
+#      X,Y              : data
+#      X_norm,Y_norm    : normalized data
+
+X = np.random.uniform(xob_min,xob_max,(n,1))
+# N = np.random.uniform(-noise,noise,(n,1))
+N = noise * np.random.normal(0,1,(n,1))
+
+a = np.random.uniform(a_min,a_max, (deg,))
+fy = np.poly1d( a )
+
+Y = fy(X) + N
+
+# ---- Data normalization
+#
+X_norm = (X - X.mean(axis=0)) / X.std(axis=0)
+Y_norm = (Y - Y.mean(axis=0)) / Y.std(axis=0)
+
+# ---- Data visualization
+
+width = 12
+height = 6
+nb_viz = min(2000,n)
+
+def vector_infos(name,V):
+    m=V.mean(axis=0).item()
+    s=V.std(axis=0).item()
+    print("{:8} :      mean={:+12.4f}  std={:+12.4f}    min={:+12.4f}    max={:+12.4f}".format(name,m,s,V.min(),V.max()))
+
+
+print("Nombre de points : {}  a={} deg={} bruit={}".format(n,a,deg,noise))
+
+ooo.display_md('#### Before normalization :')
+print("\nDonnées d'aprentissage brute :")
+print("({} points visibles sur {})".format(nb_viz,n))
+plt.figure(figsize=(width, height))
+plt.plot(X[:nb_viz], Y[:nb_viz], '.')
+plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
+plt.xlabel('x axis')
+plt.ylabel('y axis')
+plt.show()
+vector_infos('X',X)
+vector_infos('Y',Y)
+
+ooo.display_md('#### After normalization :')
+print("\nDonnées d'aprentissage normalisées :")
+print("({} points visibles sur {})".format(nb_viz,n))
+plt.figure(figsize=(width, height))
+plt.plot(X_norm[:nb_viz], Y_norm[:nb_viz], '.')
+plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
+plt.xlabel('x axis')
+plt.ylabel('y axis')
+plt.show()
+vector_infos('X_norm',X_norm)
+vector_infos('Y_norm',Y_norm)
+```
+
+%% Output
+
+    Nombre de points : 100  a=[-1.40023862  1.64009905  1.89987647  1.24972783  1.17765272  1.90935391
+      1.11259327] deg=7 bruit=2000
+
+    #### Before normalization :
+
+    
+    Données d'aprentissage brute :
+    (100 points visibles sur 100)
+
+
+
+    X        :      mean=     +0.2539  std=     +2.9283    min=     -4.9332    max=     +4.9177
+    Y        :      mean=  -2914.8537  std=  +5532.3607    min= -23848.6013    max=  +5139.0627
+
+    #### After normalization :
+
+    
+    Données d'aprentissage normalisées :
+    (100 points visibles sur 100)
+
+
+
+    X_norm   :      mean=     +0.0000  std=     +1.0000    min=     -1.7714    max=     +1.5927
+    Y_norm   :      mean=     -0.0000  std=     +1.0000    min=     -3.7839    max=     +1.4558
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Polynomial regression with NumPy
+### 3.1 - Underfitting
+
+%% Cell type:code id: tags:
+
+``` python
+def draw_reg(X_norm, Y_norm, x_hat,fy_hat, size):
+    plt.figure(figsize=size)
+    plt.plot(X_norm, Y_norm, '.')
+
+    x_hat = np.linspace(X_norm.min(), X_norm.max(), 100)
+
+    plt.plot(x_hat, fy_hat(x_hat))
+    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
+    plt.xlabel('x axis')
+    plt.ylabel('y axis')
+    plt.show()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+reg_deg=1
+
+a_hat   = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)
+fy_hat  = np.poly1d( a_hat )
+
+print("Nombre de degrés : {} a_hat={}".format(reg_deg, a_hat))
+draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], x_hat,fy_hat, (width,height))
+```
+
+%% Output
+
+    Nombre de degrés : 1 a_hat=[ 2.15635737e-01 -1.26046371e-16]
+
+
+
+%% Cell type:markdown id: tags:
+
+### 3.2 - Good fitting
+
+%% Cell type:code id: tags:
+
+``` python
+reg_deg=5
+
+a_hat   = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)
+fy_hat  = np.poly1d( a_hat )
+
+print("Nombre de degrés : {} a_hat={}".format(reg_deg, a_hat))
+draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], x_hat,fy_hat, (width,height))
+```
+
+%% Output
+
+    Nombre de degrés : 5 a_hat=[ 0.09676506 -0.49102546 -0.19674074  0.41539174 -0.00888844  0.51187173]
+
+
+
+%% Cell type:markdown id: tags:
+
+### 3.3 - Overfitting
+
+%% Cell type:code id: tags:
+
+``` python
+reg_deg=24
+
+a_hat   = np.polyfit(X_norm.reshape(-1,), Y_norm.reshape(-1,), reg_deg)
+fy_hat  = np.poly1d( a_hat )
+
+print("Nombre de degrés : {} a_hat={}".format(reg_deg, a_hat))
+draw_reg(X_norm[:nb_viz],Y_norm[:nb_viz], x_hat,fy_hat, (width,height))
+```
+
+%% Output
+
+    Nombre de degrés : 24 a_hat=[-3.39583761e-01  3.66524802e+00  1.35968152e+01 -5.33709389e+01
+     -1.54708597e+02  3.43661072e+02  8.76139324e+02 -1.29075459e+03
+     -2.93637308e+03  3.13537269e+03  6.25956959e+03 -5.14495063e+03
+     -8.72124478e+03  5.75688179e+03  7.93008239e+03 -4.30734159e+03
+     -4.57884398e+03  2.04404969e+03  1.58025869e+03 -5.55090657e+02
+     -2.89374022e+02  7.05282858e+01  2.12619645e+01 -2.67799255e+00
+      3.95718335e-01]
+
+
+
+%% Cell type:markdown id: tags:
+
+---
+![](../fidle/img/00-Fidle-logo-01_s.png)