.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

00 - 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)

00 - 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)

BHPD/01-DNN-Regression.ipynb

+%% Cell type:markdown id: tags:
+
+![header1](../fidle/img/00-Fidle-header-01.png)
+
+Deep Neural Network (DNN) - BHPD dataset
+========================================
+
+A very simple and classic example of **regression** :
+
+## Objectives :
+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 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
+0.09378,12.5,7.87,"0",0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7
+0.62976,0,8.14,"0",0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4
+0.63796,0,8.14,"0",0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2
+0.62739,0,8.14,"0",0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9
+1.05393,0,8.14,"0",0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1
+0.7842,0,8.14,"0",0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5
+0.80271,0,8.14,"0",0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2
+0.7258,0,8.14,"0",0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2
+1.25179,0,8.14,"0",0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6
+0.85204,0,8.14,"0",0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6
+1.23247,0,8.14,"0",0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2
+0.98843,0,8.14,"0",0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5
+0.75026,0,8.14,"0",0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6
+0.84054,0,8.14,"0",0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9
+0.67191,0,8.14,"0",0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6
+0.95577,0,8.14,"0",0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8
+0.77299,0,8.14,"0",0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4
+1.00245,0,8.14,"0",0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21
+1.13081,0,8.14,"0",0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7
+1.35472,0,8.14,"0",0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5
+1.38799,0,8.14,"0",0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2
+1.15172,0,8.14,"0",0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1
+1.61282,0,8.14,"0",0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5
+0.06417,0,5.96,"0",0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9
+0.09744,0,5.96,"0",0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20
+0.08014,0,5.96,"0",0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21
+0.17505,0,5.96,"0",0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7
+0.02763,75,2.95,"0",0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
+0.03359,75,2.95,"0",0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
+0.12744,0,6.91,"0",0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6
+0.1415,0,6.91,"0",0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3
+0.15936,0,6.91,"0",0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7
+0.12269,0,6.91,"0",0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2
+0.17142,0,6.91,"0",0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3
+0.18836,0,6.91,"0",0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20
+0.22927,0,6.91,"0",0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6
+0.25387,0,6.91,"0",0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4
+0.21977,0,6.91,"0",0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4
+0.08873,21,5.64,"0",0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7
+0.04337,21,5.64,"0",0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5
+0.0536,21,5.64,"0",0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25
+0.04981,21,5.64,"0",0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43,23.4
+0.0136,75,4,"0",0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8,18.9
+0.01311,90,1.22,"0",0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81,35.4
+0.02055,85,0.74,"0",0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77,24.7
+0.01432,100,1.32,"0",0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6
+0.15445,25,5.13,"0",0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3
+0.10328,25,5.13,"0",0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6
+0.14932,25,5.13,"0",0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7
+0.17171,25,5.13,"0",0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16
+0.11027,25,5.13,"0",0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2
+0.1265,25,5.13,"0",0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5,25
+0.01951,17.5,1.38,"0",0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33
+0.03584,80,3.37,"0",0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5
+0.04379,80,3.37,"0",0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4
+0.05789,12.5,6.07,"0",0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22
+0.13554,12.5,6.07,"0",0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4
+0.12816,12.5,6.07,"0",0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9
+0.08826,0,10.81,"0",0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2
+0.15876,0,10.81,"0",0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7
+0.09164,0,10.81,"0",0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8
+0.19539,0,10.81,"0",0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54,23.4
+0.07896,0,12.83,"0",0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78,24.1
+0.09512,0,12.83,"0",0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4
+0.10153,0,12.83,"0",0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20
+0.08707,0,12.83,"0",0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8
+0.05646,0,12.83,"0",0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2
+0.08387,0,12.83,"0",0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3
+0.04113,25,4.86,"0",0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28
+0.04462,25,4.86,"0",0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9
+0.03659,25,4.86,"0",0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8
+0.03551,25,4.86,"0",0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9
+0.05059,0,4.49,"0",0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9
+0.05735,0,4.49,"0",0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6
+0.05188,0,4.49,"0",0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5
+0.07151,0,4.49,"0",0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44,22.2
+0.0566,0,3.41,"0",0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6
+0.05302,0,3.41,"0",0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7
+0.04684,0,3.41,"0",0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6
+0.03932,0,3.41,"0",0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22
+0.04203,28,15.04,"0",0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9
+0.02875,28,15.04,"0",0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25
+0.04294,28,15.04,"0",0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6
+0.12204,0,2.89,"0",0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4
+0.11504,0,2.89,"0",0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34,21.4
+0.12083,0,2.89,"0",0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7
+0.08187,0,2.89,"0",0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8
+0.0686,0,2.89,"0",0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2
+0.14866,0,8.56,"0",0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5
+0.11432,0,8.56,"0",0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5
+0.22876,0,8.56,"0",0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6
+0.21161,0,8.56,"0",0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44,19.3
+0.1396,0,8.56,"0",0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1
+0.13262,0,8.56,"0",0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5
+0.1712,0,8.56,"0",0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5
+0.13117,0,8.56,"0",0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09,20.4
+0.12802,0,8.56,"0",0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27,19.8
+0.26363,0,8.56,"0",0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4
+0.10793,0,8.56,"0",0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13,21.7
+0.10084,0,10.01,"0",0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16,22.8
+0.12329,0,10.01,"0",0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21,18.8
+0.22212,0,10.01,"0",0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09,18.7
+0.14231,0,10.01,"0",0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5
+0.17134,0,10.01,"0",0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76,18.3
+0.13158,0,10.01,"0",0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2
+0.15098,0,10.01,"0",0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2
+0.13058,0,10.01,"0",0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4
+0.14476,0,10.01,"0",0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61,19.3
+0.06899,0,25.65,"0",0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37,22
+0.07165,0,25.65,"0",0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3
+0.09299,0,25.65,"0",0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5
+0.15038,0,25.65,"0",0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3
+0.09849,0,25.65,"0",0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8
+0.16902,0,25.65,"0",0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4
+0.38735,0,25.65,"0",0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26,15.7
+0.25915,0,21.89,"0",0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2
+0.32543,0,21.89,"0",0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18
+0.88125,0,21.89,"0",0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3
+0.34006,0,21.89,"0",0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2
+1.19294,0,21.89,"0",0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6
+0.59005,0,21.89,"0",0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23
+0.32982,0,21.89,"0",0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4
+0.97617,0,21.89,"0",0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6
+0.55778,0,21.89,"0",0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1
+0.32264,0,21.89,"0",0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4
+0.35233,0,21.89,"0",0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1
+0.2498,0,21.89,"0",0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3
+0.54452,0,21.89,"0",0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8
+0.2909,0,21.89,"0",0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14
+1.62864,0,21.89,"0",0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4
+3.32105,0,19.58,"1",0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4
+4.0974,0,19.58,"0",0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6
+2.77974,0,19.58,"0",0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8
+2.37934,0,19.58,"0",0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8
+2.15505,0,19.58,"0",0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6
+2.36862,0,19.58,"0",0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6
+2.33099,0,19.58,"0",0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8
+2.73397,0,19.58,"0",0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4
+1.6566,0,19.58,"0",0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5
+1.49632,0,19.58,"0",0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6
+1.12658,0,19.58,"1",0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3
+2.14918,0,19.58,"0",0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4
+1.41385,0,19.58,"1",0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17
+3.53501,0,19.58,"1",0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6
+2.44668,0,19.58,"0",0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1
+1.22358,0,19.58,"0",0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3
+1.34284,0,19.58,"0",0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3
+1.42502,0,19.58,"0",0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3
+1.27346,0,19.58,"1",0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27
+1.46336,0,19.58,"0",0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50
+1.83377,0,19.58,"1",0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50
+1.51902,0,19.58,"1",0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50
+2.24236,0,19.58,"0",0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7
+2.924,0,19.58,"0",0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25
+2.01019,0,19.58,"0",0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50
+1.80028,0,19.58,"0",0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8
+2.3004,0,19.58,"0",0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8
+2.44953,0,19.58,"0",0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3
+1.20742,0,19.58,"0",0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4
+2.3139,0,19.58,"0",0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1
+0.13914,0,4.05,"0",0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1
+0.09178,0,4.05,"0",0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6
+0.08447,0,4.05,"0",0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6
+0.06664,0,4.05,"0",0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4
+0.07022,0,4.05,"0",0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2
+0.05425,0,4.05,"0",0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6
+0.06642,0,4.05,"0",0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9
+0.0578,0,2.46,"0",0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2
+0.06588,0,2.46,"0",0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8
+0.06888,0,2.46,"0",0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2
+0.09103,0,2.46,"0",0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9
+0.10008,0,2.46,"0",0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5
+0.08308,0,2.46,"0",0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4
+0.06047,0,2.46,"0",0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6
+0.05602,0,2.46,"0",0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50
+0.07875,45,3.44,"0",0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32
+0.12579,45,3.44,"0",0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8
+0.0837,45,3.44,"0",0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9
+0.09068,45,3.44,"0",0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37
+0.06911,45,3.44,"0",0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5
+0.08664,45,3.44,"0",0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4
+0.02187,60,2.93,"0",0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1
+0.01439,60,2.93,"0",0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1
+0.01381,80,0.46,"0",0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50
+0.04011,80,1.52,"0",0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3
+0.04666,80,1.52,"0",0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3
+0.03768,80,1.52,"0",0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6
+0.0315,95,1.47,"0",0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9
+0.01778,95,1.47,"0",0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9
+0.03445,82.5,2.03,"0",0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1
+0.02177,82.5,2.03,"0",0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3
+0.0351,95,2.68,"0",0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5
+0.02009,95,2.68,"0",0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50
+0.13642,0,10.59,"0",0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6
+0.22969,0,10.59,"0",0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4
+0.25199,0,10.59,"0",0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5
+0.13587,0,10.59,"1",0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4
+0.43571,0,10.59,"1",0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20
+0.17446,0,10.59,"1",0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7
+0.37578,0,10.59,"1",0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3
+0.21719,0,10.59,"1",0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4
+0.14052,0,10.59,"0",0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1
+0.28955,0,10.59,"0",0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7
+0.19802,0,10.59,"0",0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25
+0.0456,0,13.89,"1",0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3
+0.07013,0,13.89,"0",0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7
+0.11069,0,13.89,"1",0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5
+0.11425,0,13.89,"1",0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23
+0.35809,0,6.2,"1",0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7
+0.40771,0,6.2,"1",0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7
+0.62356,0,6.2,"1",0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5
+0.6147,0,6.2,"0",0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1
+0.31533,0,6.2,"0",0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8
+0.52693,0,6.2,"0",0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50
+0.38214,0,6.2,"0",0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6
+0.41238,0,6.2,"0",0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6
+0.29819,0,6.2,"0",0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7
+0.44178,0,6.2,"0",0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5
+0.537,0,6.2,"0",0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3
+0.46296,0,6.2,"0",0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7
+0.57529,0,6.2,"0",0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7
+0.33147,0,6.2,"0",0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3
+0.44791,0,6.2,"1",0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29
+0.33045,0,6.2,"0",0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24
+0.52058,0,6.2,"1",0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1
+0.51183,0,6.2,"0",0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5
+0.08244,30,4.93,"0",0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7
+0.09252,30,4.93,"0",0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3
+0.11329,30,4.93,"0",0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22
+0.10612,30,4.93,"0",0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1
+0.1029,30,4.93,"0",0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2
+0.12757,30,4.93,"0",0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7
+0.20608,22,5.86,"0",0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6
+0.19133,22,5.86,"0",0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5
+0.33983,22,5.86,"0",0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3
+0.19657,22,5.86,"0",0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5
+0.16439,22,5.86,"0",0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5
+0.19073,22,5.86,"0",0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2
+0.1403,22,5.86,"0",0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4
+0.21409,22,5.86,"0",0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8
+0.08221,22,5.86,"0",0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6
+0.36894,22,5.86,"0",0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8
+0.04819,80,3.64,"0",0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9
+0.03548,80,3.64,"0",0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9
+0.01538,90,3.75,"0",0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44
+0.61154,20,3.97,"0",0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50
+0.66351,20,3.97,"0",0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36
+0.65665,20,3.97,"0",0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1
+0.54011,20,3.97,"0",0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8
+0.53412,20,3.97,"0",0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1
+0.52014,20,3.97,"0",0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8
+0.82526,20,3.97,"0",0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31
+0.55007,20,3.97,"0",0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5
+0.76162,20,3.97,"0",0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8
+0.7857,20,3.97,"0",0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7
+0.57834,20,3.97,"0",0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50
+0.5405,20,3.97,"0",0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5
+0.09065,20,6.96,"1",0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7
+0.29916,20,6.96,"0",0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1
+0.16211,20,6.96,"0",0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2
+0.1146,20,6.96,"0",0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4
+0.22188,20,6.96,"1",0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2
+0.05644,40,6.41,"1",0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4
+0.09604,40,6.41,"0",0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32
+0.10469,40,6.41,"1",0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2
+0.06127,40,6.41,"1",0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1
+0.07978,40,6.41,"0",0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1
+0.21038,20,3.33,"0",0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1
+0.03578,20,3.33,"0",0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4
+0.03705,20,3.33,"0",0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4
+0.06129,20,3.33,"1",0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46
+0.01501,90,1.21,"1",0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50
+0.00906,90,2.97,"0",0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2
+0.01096,55,2.25,"0",0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22
+0.01965,80,1.76,"0",0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1
+0.03871,52.5,5.32,"0",0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2
+0.0459,52.5,5.32,"0",0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3
+0.04297,52.5,5.32,"0",0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8
+0.03502,80,4.95,"0",0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5
+0.07886,80,4.95,"0",0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3
+0.03615,80,4.95,"0",0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9
+0.08265,0,13.92,"0",0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9
+0.08199,0,13.92,"0",0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7
+0.12932,0,13.92,"0",0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6
+0.05372,0,13.92,"0",0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1
+0.14103,0,13.92,"0",0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3
+0.06466,70,2.24,"0",0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5
+0.05561,70,2.24,"0",0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29
+0.04417,70,2.24,"0",0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8
+0.03537,34,6.09,"0",0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22
+0.09266,34,6.09,"0",0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4
+0.1,34,6.09,"0",0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1
+0.05515,33,2.18,"0",0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1
+0.05479,33,2.18,"0",0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4
+0.07503,33,2.18,"0",0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4
+0.04932,33,2.18,"0",0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2
+0.49298,0,9.9,"0",0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8
+0.3494,0,9.9,"0",0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3
+2.63548,0,9.9,"0",0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1
+0.79041,0,9.9,"0",0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1
+0.26169,0,9.9,"0",0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4
+0.26938,0,9.9,"0",0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6
+0.3692,0,9.9,"0",0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8
+0.25356,0,9.9,"0",0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2
+0.31827,0,9.9,"0",0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8
+0.24522,0,9.9,"0",0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8
+0.40202,0,9.9,"0",0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1
+0.47547,0,9.9,"0",0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21
+0.1676,0,7.38,"0",0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8
+0.18159,0,7.38,"0",0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1
+0.35114,0,7.38,"0",0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4
+0.28392,0,7.38,"0",0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5
+0.34109,0,7.38,"0",0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25
+0.19186,0,7.38,"0",0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6
+0.30347,0,7.38,"0",0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23
+0.24103,0,7.38,"0",0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2
+0.06617,0,3.24,"0",0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3
+0.06724,0,3.24,"0",0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6
+0.04544,0,3.24,"0",0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8
+0.05023,35,6.06,"0",0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1
+0.03466,35,6.06,"0",0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4
+0.05083,0,5.19,"0",0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2
+0.03738,0,5.19,"0",0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7
+0.03961,0,5.19,"0",0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1
+0.03427,0,5.19,"0",0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5
+0.03041,0,5.19,"0",0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5
+0.03306,0,5.19,"0",0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6
+0.05497,0,5.19,"0",0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19
+0.06151,0,5.19,"0",0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7
+0.01301,35,1.52,"0",0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7
+0.02498,0,1.89,"0",0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5
+0.02543,55,3.78,"0",0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9
+0.03049,55,3.78,"0",0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2
+0.03113,0,4.39,"0",0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5
+0.06162,0,4.39,"0",0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2
+0.0187,85,4.15,"0",0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1
+0.01501,80,2.01,"0",0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5
+0.02899,40,1.25,"0",0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6
+0.06211,40,1.25,"0",0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9
+0.0795,60,1.69,"0",0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1
+0.07244,60,1.69,"0",0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6
+0.01709,90,2.02,"0",0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1
+0.04301,80,1.91,"0",0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2
+0.10659,80,1.91,"0",0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6
+8.98296,0,18.1,"1",0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8
+3.8497,0,18.1,"1",0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7
+5.20177,0,18.1,"1",0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7
+4.26131,0,18.1,"0",0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6
+4.54192,0,18.1,"0",0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25
+3.83684,0,18.1,"0",0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9
+3.67822,0,18.1,"0",0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8
+4.22239,0,18.1,"1",0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8
+3.47428,0,18.1,"1",0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9
+4.55587,0,18.1,"0",0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5
+3.69695,0,18.1,"0",0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9
+13.5222,0,18.1,"0",0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1
+4.89822,0,18.1,"0",0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50
+5.66998,0,18.1,"1",0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50
+6.53876,0,18.1,"1",0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50
+9.2323,0,18.1,"0",0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50
+8.26725,0,18.1,"1",0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50
+11.1081,0,18.1,"0",0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8
+18.4982,0,18.1,"0",0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8
+19.6091,0,18.1,"0",0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15
+15.288,0,18.1,"0",0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9
+9.82349,0,18.1,"0",0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3
+23.6482,0,18.1,"0",0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1
+17.8667,0,18.1,"0",0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2
+88.9762,0,18.1,"0",0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4
+15.8744,0,18.1,"0",0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9
+9.18702,0,18.1,"0",0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3
+7.99248,0,18.1,"0",0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3
+20.0849,0,18.1,"0",0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8
+16.8118,0,18.1,"0",0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2
+24.3938,0,18.1,"0",0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5
+22.5971,0,18.1,"0",0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4
+14.3337,0,18.1,"0",0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2
+8.15174,0,18.1,"0",0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5
+6.96215,0,18.1,"0",0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1
+5.29305,0,18.1,"0",0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2
+11.5779,0,18.1,"0",0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7
+8.64476,0,18.1,"0",0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8
+13.3598,0,18.1,"0",0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7
+8.71675,0,18.1,"0",0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1
+5.87205,0,18.1,"0",0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5
+7.67202,0,18.1,"0",0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5
+38.3518,0,18.1,"0",0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5
+9.91655,0,18.1,"0",0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3
+25.0461,0,18.1,"0",0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6
+14.2362,0,18.1,"0",0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2
+9.59571,0,18.1,"0",0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1
+24.8017,0,18.1,"0",0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3
+41.5292,0,18.1,"0",0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5
+67.9208,0,18.1,"0",0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5
+20.7162,0,18.1,"0",0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9
+11.9511,0,18.1,"0",0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9
+7.40389,0,18.1,"0",0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2
+14.4383,0,18.1,"0",0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5
+51.1358,0,18.1,"0",0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15
+14.0507,0,18.1,"0",0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2
+18.811,0,18.1,"0",0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9
+28.6558,0,18.1,"0",0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3
+45.7461,0,18.1,"0",0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7
+18.0846,0,18.1,"0",0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2
+10.8342,0,18.1,"0",0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5
+25.9406,0,18.1,"0",0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4
+73.5341,0,18.1,"0",0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8
+11.8123,0,18.1,"0",0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4
+11.0874,0,18.1,"0",0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7
+7.02259,0,18.1,"0",0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2
+12.0482,0,18.1,"0",0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8
+7.05042,0,18.1,"0",0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4
+8.79212,0,18.1,"0",0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7
+15.8603,0,18.1,"0",0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3
+12.2472,0,18.1,"0",0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2
+37.6619,0,18.1,"0",0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9
+7.36711,0,18.1,"0",0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11
+9.33889,0,18.1,"0",0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5
+8.49213,0,18.1,"0",0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5
+10.0623,0,18.1,"0",0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1
+6.44405,0,18.1,"0",0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1
+5.58107,0,18.1,"0",0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3
+13.9134,0,18.1,"0",0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7
+11.1604,0,18.1,"0",0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4
+14.4208,0,18.1,"0",0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6
+15.1772,0,18.1,"0",0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7
+13.6781,0,18.1,"0",0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4
+9.39063,0,18.1,"0",0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8
+22.0511,0,18.1,"0",0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5
+9.72418,0,18.1,"0",0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1
+5.66637,0,18.1,"0",0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4
+9.96654,0,18.1,"0",0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4
+12.8023,0,18.1,"0",0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8
+10.6718,0,18.1,"0",0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
+6.28807,0,18.1,"0",0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
+9.92485,0,18.1,"0",0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
+9.32909,0,18.1,"0",0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
+7.52601,0,18.1,"0",0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13
+6.71772,0,18.1,"0",0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4
+5.44114,0,18.1,"0",0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2
+5.09017,0,18.1,"0",0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
+8.24809,0,18.1,"0",0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8
+9.51363,0,18.1,"0",0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
+4.75237,0,18.1,"0",0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
+4.66883,0,18.1,"0",0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
+8.20058,0,18.1,"0",0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
+7.75223,0,18.1,"0",0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9
+6.80117,0,18.1,"0",0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20
+4.81213,0,18.1,"0",0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4
+3.69311,0,18.1,"0",0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
+6.65492,0,18.1,"0",0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5
+5.82115,0,18.1,"0",0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
+7.83932,0,18.1,"0",0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
+3.1636,0,18.1,"0",0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
+3.77498,0,18.1,"0",0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
+4.42228,0,18.1,"0",0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
+15.5757,0,18.1,"0",0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
+13.0751,0,18.1,"0",0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
+4.34879,0,18.1,"0",0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
+4.03841,0,18.1,"0",0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
+3.56868,0,18.1,"0",0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
+4.64689,0,18.1,"0",0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
+8.05579,0,18.1,"0",0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
+6.39312,0,18.1,"0",0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
+4.87141,0,18.1,"0",0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
+15.0234,0,18.1,"0",0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
+10.233,0,18.1,"0",0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
+14.3337,0,18.1,"0",0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
+5.82401,0,18.1,"0",0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
+5.70818,0,18.1,"0",0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
+5.73116,0,18.1,"0",0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
+2.81838,0,18.1,"0",0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
+2.37857,0,18.1,"0",0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
+3.67367,0,18.1,"0",0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
+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
+0.18337,0,27.74,"0",0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
+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
+0.11132,0,27.74,"0",0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
+0.17331,0,9.69,"0",0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
+0.27957,0,9.69,"0",0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
+0.17899,0,9.69,"0",0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
+0.2896,0,9.69,"0",0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
+0.26838,0,9.69,"0",0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
+0.23912,0,9.69,"0",0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
+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
+```

GTSRB/README.ipynb

+%% Cell type:markdown id: tags:
+
+German Traffic Sign Recognition Benchmark (GTSRB)
+=================================================
+
+---
+Introduction au Deep Learning  (IDLE)
+S. Aria, E. Maldonado, JL. Parouty
+CNRS/SARI/DEVLOG - 2020
+
+Objectives of this practical work
+---------------------------------
+
+Traffic sign classification with **CNN**, using Tensorflow and **Keras**
+
+
+About the dataset
+-----------------
+
+Name : [German Traffic Sign Recognition Benchmark (GTSRB)](http://benchmark.ini.rub.de/?section=gtsrb)
+Available [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html)
+or on **[kaggle](https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)**
+
+A nice example from : [Alex Staravoitau](https://navoshta.com/traffic-signs-classification/)
+
+In few words :
+ - Images : Variable dimensions, rgb
+ - Train set : 39209 images
+ - Test set : 12630 images
+ - Classes : 0 to 42
+
+Episodes
+--------
+
+**[01 - Preparation of data](01-Preparation-of-data.ipynb)**
+ - Understanding the dataset
+ - Preparing and formatting data
+ - Organize and backup data
+
+**[02 - First convolutions](02-First-convolutions.ipynb)**
+ - Read dataset
+ - Build a model
+ - Train the model
+ - Model evaluation
+
+
+%% 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
+```