.gitignore

@@ -2,5 +2,7 @@
 */.ipynb_checkpoints/*
 __pycache__
 */__pycache__/*
-/run/**
+run/
 */data/*
+!/GTSRB/data/dataset.tar.gz
+!/BHPD/data/BostonHousing.csv

BHPD/01-DNN-Regression.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** :
+
+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 the model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## 1/ Init python stuff
+
+%% 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()
+```
+
+%% Output
+
+    IDLE 2020 - Practical Work Module
+      Version            : 0.2.4
+      Run time           : Sunday 2 February 2020, 15:17:29
+      Matplotlib style   : ../fidle/talk.mplstyle
+      TensorFlow version : 2.0.0
+      Keras version      : 2.2.4-tf
+
+%% Cell type:markdown id: tags:
+
+## 2/ Retrieve data
+
+**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:
+
+**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)
+```
+
+%% Cell type:markdown id: tags:
+
+## 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)
+```
+
+%% 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)
+```
+
+%% Cell type:markdown id: tags:
+
+## 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.Dense(64, activation='relu', input_shape=shape))
+    model.add(keras.layers.Dense(64, activation='relu'))
+    model.add(keras.layers.Dense(1))
+
+    model.compile(optimizer = 'rmsprop',
+                  loss      = 'mse',
+                  metrics   = ['mae', 'mse'] )
+    return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ Train the model
+
+%% Cell type:code id: tags:
+
+``` python
+model=get_model_v1( (13,) )
+
+model.summary()
+```
+
+%% Cell type:markdown id: tags:
+
+**Let's go :**
+
+%% 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))
+```
+
+%% Cell type:markdown id: tags:
+
+## 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 $3k.
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 6.2/ Training history
+What was the best result during our training ?
+
+%% Cell type:code id: tags:
+
+``` python
+
+df=pd.DataFrame(data=history.history)
+df.describe()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+print("min( val_mae ) : {:.4f}".format( min(history.history["val_mae"]) ) )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],
+                                'MAE' :['mae', 'val_mae'],
+                                'LOSS':['loss','val_loss']})
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 7 - Make a prediction
+
+%% Cell type:code id: tags:
+
+``` python
+my_data = [ 1.26425925, -0.48522739,  1.0436489 , -0.23112788,  1.37120745,
+       -2.14308942,  1.13489104, -1.06802005,  1.71189006,  1.57042287,
+        0.77859951,  0.14769795,  2.7585581 ]
+real_price = 10.4
+
+my_data=np.array(my_data).reshape(1,13)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+predictions = model.predict( my_data )
+print("Prédiction : {:.2f} K$".format(predictions[0][0]))
+print("Reality    : {:.2f} K$".format(real_price))
+```
+
+%% Cell type:markdown id: tags:
+
+---
+That's all folks !

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:
+
+## 1/ Init python stuff
+
+%% 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()
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ Retrieve data
+
+**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:
+
+**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)
+```
+
+%% Cell type:markdown id: tags:
+
+## 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)
+```
+
+%% 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)
+```
+
+%% Cell type:markdown id: tags:
+
+## 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.Dense(64, activation='relu', input_shape=shape))
+    model.add(keras.layers.Dense(64, activation='relu'))
+    model.add(keras.layers.Dense(1))
+
+    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()
+```
+
+%% 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         = 0,
+                    validation_data = (x_test, y_test),
+                    callbacks       = [savemodel_callback])
+```
+
+%% Cell type:markdown id: tags:
+
+## 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 $3k.
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 6.2/ Training history
+What was the best result during our training ?
+
+%% Cell type:code id: tags:
+
+``` python
+print("min( val_mae ) : {:.4f}".format( min(history.history["val_mae"]) ) )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+reload(ooo)
+ooo.plot_history(history, plot={'MSE' :['mse', 'val_mse'],
+                                'MAE' :['mae', 'val_mae'],
+                                'LOSS':['loss','val_loss']})
+```
+
+%% Cell type:markdown id: tags:
+
+## 7/ Restore a model :
+
+%% Cell type:markdown id: tags:
+
+### 7.1/ Reload model
+
+%% Cell type:code id: tags:
+
+``` python
+loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')
+loaded_model.summary()
+print("Loaded.")
+```
+
+%% Cell type:markdown id: tags:
+
+### 7.2/ Evaluate it :
+
+%% Cell type:code id: tags:
+
+``` python
+score = loaded_model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 7.3/ Make a prediction
+
+%% Cell type:code id: tags:
+
+``` python
+mon_test=[ 1.26425925, -0.48522739,  1.0436489 , -0.23112788,  1.37120745,
+       -2.14308942,  1.13489104, -1.06802005,  1.71189006,  1.57042287,
+        0.77859951,  0.14769795,  2.7585581 ]
+
+mon_test=np.array(mon_test).reshape(1,13)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+predictions = loaded_model.predict( mon_test )
+print("Prédiction : {:.2f} K$   Reality : {:.2f} K$".format(predictions[0][0], y_train[13]))
+```
+
+%% Cell type:markdown id: tags:
+
+-----
+That's all folks !

GTSRB/01-Preparation-of-data.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 1 : Preparation of data
+
+ - Understanding the dataset
+ - Preparing and formatting enhanced data
+ - Save enhanced datasets in h5 file format
+
+%% Cell type:markdown id: tags:
+
+## 1/ Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+import os, time, sys
+import csv
+import math, random
+
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+
+from skimage.morphology import disk
+from skimage.filters import rank
+from skimage import io, color, exposure, transform
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Cell type:markdown id: tags:
+
+## 2/ Read the dataset
+Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
+ - Each directory contains one CSV file with annotations ("GT-<ClassID>.csv") and the training images
+ - First line is fieldnames: Filename;Width;Height;Roi.X1;Roi.Y1;Roi.X2;Roi.Y2;ClassId
+
+### 2.1/ Usefull functions
+
+%% Cell type:code id: tags:
+
+``` python
+def read_dataset_dir(csv_filename):
+    '''Reads traffic sign data from German Traffic Sign Recognition Benchmark dataset.
+
+    Arguments:  csv filename
+                Example /data/GTSRB/Train.csv
+    Returns:   np array of images, np array of corresponding labels'''
+
+    # ---- csv filename and path
+    #
+    name=os.path.basename(csv_filename)
+    path=os.path.dirname(csv_filename)
+
+    # ---- Read csv file
+    #
+    f,x,y = [],[],[]
+    with open(csv_filename) as csv_file:
+        reader = csv.DictReader(csv_file, delimiter=',')
+        for row in reader:
+            f.append( path+'/'+row['Path'] )
+            y.append( int(row['ClassId'])  )
+        csv_file.close()
+    nb_images = len(f)
+
+    # ---- Read images
+    #
+    for filename in f:
+        image=io.imread(filename)
+        x.append(image)
+        ooo.update_progress(name,len(x),nb_images)
+    # ---- Return
+    #
+    return np.array(x),np.array(y)
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.2/ Read the data
+We will read the following datasets:
+ - **x_train, y_train** : Learning data
+ - **x_test, y_test** : Validation or test data
+ - x_meta, y_meta : Illustration data
+
+The learning data will be randomly mixted and the illustration data sorted.
+Will take about 2-3'
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+# ---- Read datasets
+(x_train,y_train) = read_dataset_dir('./data/origine/Train.csv')
+(x_test ,y_test)  = read_dataset_dir('./data/origine/Test.csv')
+(x_meta ,y_meta)  = read_dataset_dir('./data/origine/Meta.csv')
+
+# ---- Shuffle train set
+combined = list(zip(x_train,y_train))
+random.shuffle(combined)
+x_train,y_train = zip(*combined)
+
+# ---- Sort Meta
+combined = list(zip(x_meta,y_meta))
+combined.sort(key=lambda x: x[1])
+x_meta,y_meta = zip(*combined)
+```
+
+%% Cell type:markdown id: tags:
+
+## 3/ Few statistics about train dataset
+We want to know if our images are homogeneous in terms of size, ratio, width or height.
+
+### 3.1/ Do statistics
+
+%% Cell type:code id: tags:
+
+``` python
+train_size  = []
+train_ratio = []
+train_lx    = []
+train_ly    = []
+
+test_size   = []
+test_ratio  = []
+test_lx     = []
+test_ly     = []
+
+for image in x_train:
+    (lx,ly,lz) = image.shape
+    train_size.append(lx*ly/1024)
+    train_ratio.append(lx/ly)
+    train_lx.append(lx)
+    train_ly.append(ly)
+
+for image in x_test:
+    (lx,ly,lz) = image.shape
+    test_size.append(lx*ly/1024)
+    test_ratio.append(lx/ly)
+    test_lx.append(lx)
+    test_ly.append(ly)
+```
+
+%% Cell type:markdown id: tags:
+
+### 3.2/ Show statistics
+
+%% Cell type:code id: tags:
+
+``` python
+# ------ Global stuff
+print("x_train size : ",len(x_train))
+print("y_train size : ",len(y_train))
+print("x_test size  : ",len(x_test))
+print("y_test size  : ",len(y_test))
+
+# ------ Statistics / sizes
+plt.figure(figsize=(16,6))
+plt.hist([train_size,test_size], bins=100)
+plt.gca().set(title='Sizes in Kpixels - Train=[{:5.2f}, {:5.2f}]'.format(min(train_size),max(train_size)),
+              ylabel='Population',
+              xlim=[0,30])
+plt.legend(['Train','Test'])
+plt.show()
+
+# ------ Statistics / ratio lx/ly
+plt.figure(figsize=(16,6))
+plt.hist([train_ratio,test_ratio], bins=100)
+plt.gca().set(title='Ratio lx/ly - Train=[{:5.2f}, {:5.2f}]'.format(min(train_ratio),max(train_ratio)),
+              ylabel='Population',
+              xlim=[0.8,1.2])
+plt.legend(['Train','Test'])
+plt.show()
+
+# ------ Statistics / lx
+plt.figure(figsize=(16,6))
+plt.hist([train_lx,test_lx], bins=100)
+plt.gca().set(title='Images lx - Train=[{:5.2f}, {:5.2f}]'.format(min(train_lx),max(train_lx)),
+              ylabel='Population',
+              xlim=[20,150])
+plt.legend(['Train','Test'])
+plt.show()
+
+# ------ Statistics / ly
+plt.figure(figsize=(16,6))
+plt.hist([train_ly,test_ly], bins=100)
+plt.gca().set(title='Images ly - Train=[{:5.2f}, {:5.2f}]'.format(min(train_ly),max(train_ly)),
+              ylabel='Population',
+              xlim=[20,150])
+plt.legend(['Train','Test'])
+plt.show()
+
+# ------ Statistics / classId
+plt.figure(figsize=(16,6))
+plt.hist([y_train,y_test], bins=43)
+plt.gca().set(title='ClassesId',
+              ylabel='Population',
+              xlim=[0,43])
+plt.legend(['Train','Test'])
+plt.show()
+```
+
+%% Cell type:markdown id: tags:
+
+## 4/ List of classes
+What are the 43 classes of our images...
+
+%% Cell type:code id: tags:
+
+``` python
+ooo.plot_images(x_meta,y_meta, range(43), columns=8, x_size=2, y_size=2,
+                                colorbar=False, y_pred=None, cm='binary')
+```
+
+%% Cell type:markdown id: tags:
+
+## 5/ What does it really look like
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Get and show few images
+
+samples = [ random.randint(0,len(x_train)-1) for i in range(32)]
+ooo.plot_images(x_train,y_train, samples, columns=8, x_size=2, y_size=2, colorbar=False, y_pred=None, cm='binary')
+```
+
+%% Cell type:markdown id: tags:
+
+## 6/ dataset cooking...
+
+Images must have the **same size** to match the size of the network.
+It is possible to work on **rgb** or **monochrome** images and **equalize** the histograms.
+The data must be **normalized**.
+
+See : [Exposure with scikit-image](https://scikit-image.org/docs/dev/api/skimage.exposure.html)
+See : [Local histogram equalization](https://scikit-image.org/docs/dev/api/skimage.filters.rank.html#skimage.filters.rank.equalize)
+See : [Histogram equalization](https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.equalize_hist)
+
+### 6.1/ Enhancement cook
+
+%% Cell type:code id: tags:
+
+``` python
+def images_enhancement(images, width=25, height=25, mode='RGB'):
+    '''
+    Resize and convert images - doesn't change originals.
+    input images must be RGBA or RGB.
+    args:
+        images :         images list
+        width,height :   new images size (25,25)
+        mode :           RGB | RGB-HE | L | L-HE | L-LHE | L-CLAHE
+    return:
+        numpy array of enhanced images
+    '''
+    modes = { 'RGB':3, 'RGB-HE':3, 'L':1, 'L-HE':1, 'L-LHE':1, 'L-CLAHE':1}
+    lz=modes[mode]
+
+    out=[]
+    for img in images:
+
+        # ---- if RGBA, convert to RGB
+        if img.shape[2]==4:
+            img=color.rgba2rgb(img)
+
+        # ---- Resize
+        img = transform.resize(img, (width,height))
+
+        # ---- RGB / Histogram Equalization
+        if mode=='RGB-HE':
+            hsv = color.rgb2hsv(img.reshape(width,height,3))
+            hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
+            img = color.hsv2rgb(hsv)
+
+        # ---- Grayscale
+        if mode=='L':
+            img=color.rgb2gray(img)
+
+        # ---- Grayscale / Histogram Equalization
+        if mode=='L-HE':
+            img=color.rgb2gray(img)
+            img=exposure.equalize_hist(img)
+
+        # ---- Grayscale / Local Histogram Equalization
+        if mode=='L-LHE':
+            img=color.rgb2gray(img)
+            img=rank.equalize(img, disk(10))/255.
+
+        # ---- Grayscale / Contrast Limited Adaptive Histogram Equalization (CLAHE)
+        if mode=='L-CLAHE':
+            img=color.rgb2gray(img)
+            img=exposure.equalize_adapthist(img)
+
+        # ---- Add image in list of list
+        out.append(img)
+        ooo.update_progress('Enhancement: ',len(out),len(images))
+
+    # ---- Reshape images
+    #     (-1, width,height,1) for L
+    #     (-1, width,height,3) for RGB
+    #
+    out = np.array(out,dtype='float64')
+    out = out.reshape(-1,width,height,lz)
+    return out
+```
+
+%% Cell type:markdown id: tags:
+
+### 6.2/ To get an idea of the different recipes
+
+%% Cell type:code id: tags:
+
+``` python
+i=random.randint(0,len(x_train)-16)
+x_samples = x_train[i:i+16]
+y_samples = y_train[i:i+16]
+
+datasets  = {}
+
+datasets['RGB']      = images_enhancement( x_samples, width=25, height=25, mode='RGB'  )
+datasets['RGB-HE']   = images_enhancement( x_samples, width=25, height=25, mode='RGB-HE'  )
+datasets['L']        = images_enhancement( x_samples, width=25, height=25, mode='L'  )
+datasets['L-HE']     = images_enhancement( x_samples, width=25, height=25, mode='L-HE'  )
+datasets['L-LHE']    = images_enhancement( x_samples, width=25, height=25, mode='L-LHE'  )
+datasets['L-CLAHE']  = images_enhancement( x_samples, width=25, height=25, mode='L-CLAHE'  )
+
+print('\nEXPECTED (Meta) :\n')
+x_expected=[ x_meta[i] for i in y_samples]
+ooo.plot_images(x_expected, y_samples, range(16), columns=16, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary')
+
+print('\nORIGINAL IMAGES :\n')
+ooo.plot_images(x_samples,  y_samples, range(16), columns=16, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary')
+
+print('\nENHANCED :\n')
+for k,d in datasets.items():
+    print("dataset : {}  min,max=[{:.3f},{:.3f}]  shape={}".format(k,d.min(),d.max(), d.shape))
+    ooo.plot_images(d, y_samples, range(16), columns=16, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary')
+```
+
+%% Cell type:markdown id: tags:
+
+### 6.3/ Cook and save
+A function to save a dataset
+
+%% Cell type:code id: tags:
+
+``` python
+def save_h5_dataset(x_train, y_train, x_test, y_test, x_meta,y_meta, h5name):
+
+    # ---- Filename
+    filename='./data/'+h5name
+
+    # ---- Create h5 file
+    with h5py.File(filename, "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)
+        f.create_dataset("x_meta",  data=x_meta)
+        f.create_dataset("y_meta",  data=y_meta)
+
+    # ---- done
+    size=os.path.getsize(filename)/(1024*1024)
+    print('Dataset : {:24s}  shape : {:22s} size : {:6.1f} Mo   (saved)\n'.format(filename, str(x_train.shape),size))
+```
+
+%% Cell type:markdown id: tags:
+
+Create enhanced datasets, and save them...
+Will take about 7-8'
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+for s in [24, 48]:
+    for m in ['RGB', 'RGB-HE', 'L', 'L-LHE']:
+        # ---- A nice dataset name
+        name='set-{}x{}-{}.h5'.format(s,s,m)
+        print("\nDataset : ",name)
+        # ---- Enhancement
+        x_train_new = images_enhancement( x_train, width=s, height=s, mode=m )
+        x_test_new  = images_enhancement( x_test,  width=s, height=s, mode=m )
+        x_meta_new  = images_enhancement( x_meta,  width=s, height=s, mode='RGB' )
+        # ---- Save
+        save_h5_dataset( x_train_new, y_train, x_test_new, y_test, x_meta_new,y_meta, name)
+
+x_train_new,x_test_new=0,0
+```
+
+%% Cell type:markdown id: tags:
+
+## 7/ Reload data to be sure ;-)
+
+%% Cell type:code id: tags:
+
+``` python
+%%time
+
+dataset='set-48x48-L'
+samples=range(24)
+
+with  h5py.File('./data/'+dataset+'.h5') as f:
+    x_tmp = f['x_train'][:]
+    y_tmp = f['y_train'][:]
+    print("dataset loaded from h5 file.")
+
+ooo.plot_images(x_tmp,y_tmp, samples, columns=8, x_size=2, y_size=2, colorbar=False, y_pred=None, cm='binary')
+x_tmp,y_tmp=0,0
+```
+
+%% Cell type:markdown id: tags:
+
+----
+That's all folks !

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('Duration : {} s'.format(duration))
+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/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()
+```
+
+%% 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/02-Prediction.ipynb

+%% Cell type:markdown id: tags:
+
+Text Embedding - IMDB dataset
+=============================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Reviews analysis :
+
+The objective is to guess whether our new and personals films reviews are **positive or negative** .
+For this, we will use our previously saved model.
+
+What we're going to do:
+
+ - Preparing the data
+ - Retrieve our saved model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Init python stuff
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+
+import tensorflow as tf
+import tensorflow.keras as keras
+import tensorflow.keras.datasets.imdb as imdb
+
+import matplotlib.pyplot as plt
+import matplotlib
+import seaborn as sns
+import pandas as pd
+
+import os,sys,h5py,json,re
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 2 : Preparing the data
+### 2.1 - Our reviews :
+
+%% Cell type:code id: tags:
+
+``` python
+reviews = [ "This film is particularly nice, a must see.",
+             "Some films are classics and cannot be ignored.",
+             "This movie is just abominable and doesn't deserve to be seen!"]
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Retrieve dictionaries
+
+%% Cell type:code id: tags:
+
+``` python
+with open('./data/word_index.json', 'r') as fp:
+    word_index = json.load(fp)
+    index_word = {index:word for word,index in word_index.items()}
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.3 - Clean, index and padd
+
+%% Cell type:code id: tags:
+
+``` python
+max_len    = 256
+vocab_size = 10000
+
+
+nb_reviews = len(reviews)
+x_data     = []
+
+# ---- For all reviews
+for review in reviews:
+    # ---- First index must be <start>
+    index_review=[1]
+    # ---- For all words
+    for w in review.split(' '):
+        # ---- Clean it
+        w_clean = re.sub(r"[^a-zA-Z0-9]", "", w)
+        # ---- Not empty ?
+        if len(w_clean)>0:
+            # ---- Get the index
+            w_index = word_index.get(w,2)
+            if w_index>vocab_size : w_index=2
+            # ---- Add the index if < vocab_size
+            index_review.append(w_index)
+    # ---- Add the indexed review
+    x_data.append(index_review)
+
+# ---- Padding
+x_data = keras.preprocessing.sequence.pad_sequences(x_data, value   = 0, padding = 'post', maxlen  = max_len)
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.4 - Have a look
+
+%% Cell type:code id: tags:
+
+``` python
+def translate(x):
+    return ' '.join( [index_word.get(i,'?') for i in x] )
+
+for i in range(nb_reviews):
+    imax=np.where(x_data[i]==0)[0][0]+5
+    print(f'\nText review      :',    reviews[i])
+    print(  f'x_train[{i:}]       :', list(x_data[i][:imax]), '(...)')
+    print(  'Translation      :', translate(x_data[i][:imax]), '(...)')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Bring back the model
+
+%% Cell type:code id: tags:
+
+``` python
+model = keras.models.load_model('./run/models/best_model.h5')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Predict
+
+%% Cell type:code id: tags:
+
+``` python
+y_pred   = model.predict(x_data)
+```
+
+%% Cell type:markdown id: tags:
+
+#### And the winner is :
+
+%% Cell type:code id: tags:
+
+``` python
+for i in range(nb_reviews):
+    print(f'\n{reviews[i]:<70} =>',('NEGATIVE' if y_pred[i][0]<0.5 else 'POSITIVE'),f'({y_pred[i][0]:.2f})')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+a=[1]+[i for i in range(3)]
+a
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

README.md

+
+
+FIDLE - Formation Introduction au Deep Learning
+===============================================
+---
+S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  
+
+## 1/ Environment
+To run this examples, you need an environment with the following packages :
+ - Python 3.6
+ - numpy
+ - Tensorflow 2.0
+ - scikit-image
+ - scikit-learn
+ - Matplotlib
+ - seaborn
+ - pyplot
+
+You can install such a predefined environment :
+```
+conda env create -f environment.yml
+```
+
+To manage conda environment see [there](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)  
+
+
+
+## 4/ Misc
+To update an existing environment :  
+```
+conda env update --name=deeplearning2 --file=environment.yml
+```
\ No newline at end of file