VAE/modules/vae.py

+
+# ------------------------------------------------------------------
+#     _____ _     _ _
+#    |  ___(_) __| | | ___
+#    | |_  | |/ _` | |/ _ \
+#    |  _| | | (_| | |  __/
+#    |_|   |_|\__,_|_|\___|
+# ------------------------------------------------------------------
+# Formation Introduction au Deep Learning  (FIDLE)
+# CNRS/SARI/DEVLOG 2020 - S. Arias, E. Maldonado, JL. Parouty
+# ------------------------------------------------------------------
+# by JL Parouty (feb 2020), based on David Foster examples.
+
+import numpy as np
+import math
+
+import tensorflow as tf
+import tensorflow.keras as keras
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Reshape, Lambda
+from tensorflow.keras.layers import Activation, BatchNormalization, LeakyReLU, Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.utils import plot_model
+
+from modules.callbacks      import ImagesCallback
+from modules.data_generator import DataGenerator
+
+
+import os, json, time, datetime
+
+
+
+class VariationalAutoencoder():
+
+    version = '1.28'
+    
+    def __init__(self, input_shape=None, encoder_layers=None, decoder_layers=None, z_dim=None, run_tag='000', verbose=0):
+               
+        self.name           = 'Variational AutoEncoder'
+        self.input_shape    = list(input_shape)
+        self.encoder_layers = encoder_layers
+        self.decoder_layers = decoder_layers
+        self.z_dim          = z_dim
+        self.run_tag        = str(run_tag)
+        self.verbose        = verbose
+        self.run_directory  = f'./run/{run_tag}'
+        
+        # ---- Create run directories
+        for d in ('','/models','/figs','/logs','/images'):
+            os.makedirs(self.run_directory+d, mode=0o750, exist_ok=True)
+        
+        # ==== Encoder ================================================================
+        
+        # ---- Input layer
+        encoder_input = Input(shape=self.input_shape, name='encoder_input')
+        x = encoder_input
+        
+        # ---- Add next layers
+        i=1
+        for l_config in encoder_layers:
+            l_type   = l_config['type']
+            l_params = l_config.copy()
+            l_params.pop('type')
+            if l_type=='Conv2D':
+                layer = Conv2D(**l_params)
+            if l_type=='Dropout':
+                layer = Dropout(**l_params)
+            x = layer(x)
+            i+=1
+            
+        # ---- Flatten
+        shape_before_flattening = K.int_shape(x)[1:]
+        x = Flatten()(x)
+        
+        # ---- mu <-> log_var
+        self.mu      = Dense(self.z_dim, name='mu')(x)
+        self.log_var = Dense(self.z_dim, name='log_var')(x)
+
+        self.encoder_mu_log_var = Model(encoder_input, (self.mu, self.log_var))
+
+        # ---- output layer
+        def sampling(args):
+            mu, log_var = args
+            epsilon = K.random_normal(shape=K.shape(mu), mean=0., stddev=1.)
+            return mu + K.exp(log_var / 2) * epsilon
+
+        encoder_output = Lambda(sampling, name='encoder_output')([self.mu, self.log_var])
+
+        self.encoder = Model(encoder_input, encoder_output)
+
+        # ==== Decoder ================================================================
+
+        # ---- Input layer
+        decoder_input = Input(shape=(self.z_dim,), name='decoder_input')
+        
+        # ---- First dense layer
+        x = Dense(np.prod(shape_before_flattening))(decoder_input)
+        x = Reshape(shape_before_flattening)(x)
+
+        # ---- Add next layers
+        i=1
+        for l_config in decoder_layers:
+            l_type   = l_config['type']
+            l_params = l_config.copy()
+            l_params.pop('type')
+            if l_type=='Conv2DTranspose':
+                layer = Conv2DTranspose(**l_params)
+            if l_type=='Dropout':
+                layer = Dropout(**l_params)
+            x = layer(x)
+            i+=1
+
+        decoder_output = x
+
+        self.decoder = Model(decoder_input, decoder_output)
+        
+        # ==== Encoder-Decoder ========================================================
+        
+        model_input = encoder_input
+        model_output = self.decoder(encoder_output)
+
+        self.model = Model(model_input, model_output)
+
+        # ==== Verbosity ==============================================================
+
+        print('Model initialized.')
+        print(f'Outputs will be in  : {self.run_directory}')
+        
+        if verbose>0 :
+            print('\n','-'*10,'Encoder','-'*50,'\n')
+            self.encoder.summary()
+            print('\n','-'*10,'Encoder','-'*50,'\n')
+            self.decoder.summary()
+            self.plot_model()
+        
+        
+        
+    def compile(self, optimizer='adam', r_loss_factor='1000'):
+        self.r_loss_factor = r_loss_factor
+
+        def vae_r_loss(y_true, y_pred):
+            r_loss = K.mean(K.square(y_true - y_pred), axis = [1,2,3])
+            return r_loss_factor * r_loss
+
+        def vae_kl_loss(y_true, y_pred):
+            kl_loss =  -0.5 * K.sum(1 + self.log_var - K.square(self.mu) - K.exp(self.log_var), axis = 1)
+            return kl_loss
+
+        def vae_loss(y_true, y_pred):
+            r_loss = vae_r_loss(y_true, y_pred)
+            kl_loss = vae_kl_loss(y_true, y_pred)
+            return  r_loss + kl_loss
+
+        self.model.compile(optimizer=optimizer, 
+                           loss = vae_loss,
+                           metrics = [vae_r_loss, vae_kl_loss], 
+                           experimental_run_tf_function=False)
+        print('Compiled.')
+    
+    
+    
+    def train(self, 
+              x_train=None,
+              x_test=None,
+              data_generator=None,
+              batch_size=32, 
+              epochs=20,
+              initial_epoch=0,
+              k_size=1
+             ):
+
+        # ---- Data given or via generator
+        mode_data = (data_generator is None)
+        
+        # ---- Size of the dataset we are going to use
+        #      k_size ==1 : mean 100%
+        #      Unused with data generator
+        #
+        if mode_data:
+            n_train = int(x_train.shape[0] * k_size)
+            n_test  = int(x_test.shape[0]  * k_size)
+                   
+        # ---- Callback : Images
+        filename = self.run_directory+"/images/image-{epoch:03d}-{i:02d}.jpg"
+        callbacks_images = ImagesCallback(filename, z_dim=self.z_dim, decoder=self.decoder)
+        
+        # ---- Callback : Checkpoint
+        filename = self.run_directory+"/models/model-{epoch:03d}.h5"
+        callback_chkpts = ModelCheckpoint(filename, save_freq='epoch' ,verbose=0)
+
+        # ---- Callback : Best model
+        filename = self.run_directory+"/models/best_model.h5"
+        callback_bestmodel = ModelCheckpoint(filename, save_best_only=True, mode='min',monitor='val_loss',verbose=0)
+
+        # ---- Callback tensorboard
+        dirname = self.run_directory+"/logs"
+        callback_tensorboard = TensorBoard(log_dir=dirname, histogram_freq=1)
+
+        callbacks_list = [callbacks_images, callback_chkpts, callback_bestmodel, callback_tensorboard]
+#         callbacks_list = [callback_chkpts, callback_bestmodel, callback_tensorboard]
+
+        # ---- Let's go...
+        start_time   = time.time()
+        
+        if mode_data:
+            #
+            # ---- With pure data (x_train) -----------------------------------------
+            #                             
+            self.history = self.model.fit(x_train[:n_train], x_train[:n_train],
+                                          batch_size = batch_size,
+                                          shuffle = True,
+                                          epochs = epochs,
+                                          initial_epoch = initial_epoch,
+                                          callbacks = callbacks_list,
+                                          validation_data = (x_test[:n_test], x_test[:n_test])
+                                          )
+            #
+        else:
+            # ---- With Data Generator ----------------------------------------------
+            #
+            self.history = self.model.fit(data_generator,
+                                          shuffle = True,
+                                          epochs = epochs,
+                                          initial_epoch = initial_epoch,
+                                          callbacks = callbacks_list,
+                                          validation_data = (x_test, x_test)
+                                         )
+        
+        end_time  = time.time()
+        dt  = end_time-start_time
+        dth = str(datetime.timedelta(seconds=int(dt)))
+        self.duration = dt
+        print(f'\nTrain duration : {dt:.2f} sec. - {dth:}')
+
+
+        
+        
+    def plot_model(self):
+        d=self.run_directory+'/figs'
+        plot_model(self.model,   to_file=f'{d}/model.png',   show_shapes = True, show_layer_names = True, expand_nested=True)
+        plot_model(self.encoder, to_file=f'{d}/encoder.png', show_shapes = True, show_layer_names = True)
+        plot_model(self.decoder, to_file=f'{d}/decoder.png', show_shapes = True, show_layer_names = True)
+
+        
+        
+        
+    def save(self,config='vae_config.json', model='model.h5', force=False):
+        
+        # ---- Check if the place is still used
+        
+        if os.path.isfile(self.run_directory+'/models/best_model.h5') and not force:
+            print('\n*** Oops. There are already stuff in the target folder !\n')
+            assert False, f'Tag directory {self.run_directory} is not empty...'
+           
+        # ---- Save config in json
+        
+        if config!=None:
+            to_save  = ['input_shape', 'encoder_layers', 'decoder_layers', 'z_dim', 'run_tag', 'verbose']
+            data     = { i:self.__dict__[i] for i in to_save }
+            filename = self.run_directory+'/models/'+config
+            with open(filename, 'w') as outfile:
+                json.dump(data, outfile)
+            print(f'Config saved in     : {filename}')
+        
+        # ---- Save model
+        
+        if model!=None:
+            filename = self.run_directory+'/models/'+model
+            self.model.save(filename)
+            print(f'Model saved in      : {filename}')
+
+        
+        
+    def load_weights(self,model='model.h5'):
+        filename = self.run_directory+'/models/'+model
+        self.model.load_weights(filename)
+        print(f'Weights loaded from : {filename}')
+    
+            
+    @classmethod
+    def load(cls, run_tag='000', config='vae_config.json', weights='model.h5'):
+        # ---- Instantiate a new vae
+        filename = f'./run/{run_tag}/models/{config}'
+        with open(filename, 'r') as infile:
+            params=json.load(infile)
+            vae=cls( **params)
+        # ---- weights==None, just return it
+        if weights==None: return vae
+        # ---- weights!=None, get weights
+        vae.load_weights(weights)
+        return vae
+    
+    @classmethod
+    def about(cls):
+        print('\nFIDLE 2020 - Variational AutoEncoder (VAE)')
+        print('TensorFlow version   :',tf.__version__)
+        print('VAE version          :', cls.version)
\ No newline at end of file

environment.yml

+name: fidle
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _tflow_select=2.1.0=gpu
+  - absl-py=0.9.0=py37_0
+  - astor=0.8.0=py37_0
+  - attrs=19.3.0=py_0
+  - backcall=0.1.0=py37_0
+  - blas=1.0=mkl
+  - bleach=3.1.0=py37_0
+  - blosc=1.16.3=hd408876_0
+  - bzip2=1.0.8=h7b6447c_0
+  - c-ares=1.15.0=h7b6447c_1001
+  - ca-certificates=2020.1.1=0
+  - cairo=1.14.12=h8948797_3
+  - certifi=2019.11.28=py37_0
+  - cloudpickle=1.3.0=py_0
+  - cudatoolkit=10.0.130=0
+  - cudnn=7.6.5=cuda10.0_0
+  - cupti=10.0.130=0
+  - cycler=0.10.0=py37_0
+  - cytoolz=0.10.1=py37h7b6447c_0
+  - dask-core=2.10.1=py_0
+  - dbus=1.13.12=h746ee38_0
+  - decorator=4.4.1=py_0
+  - defusedxml=0.6.0=py_0
+  - entrypoints=0.3=py37_0
+  - expat=2.2.6=he6710b0_0
+  - fontconfig=2.13.0=h9420a91_0
+  - freetype=2.9.1=h8a8886c_1
+  - fribidi=1.0.5=h7b6447c_0
+  - gast=0.2.2=py37_0
+  - glib=2.63.1=h5a9c865_0
+  - gmp=6.1.2=h6c8ec71_1
+  - google-pasta=0.1.8=py_0
+  - graphite2=1.3.13=h23475e2_0
+  - graphviz=2.40.1=h21bd128_2
+  - grpcio=1.16.1=py37hf8bcb03_1
+  - gst-plugins-base=1.14.0=hbbd80ab_1
+  - gstreamer=1.14.0=hb453b48_1
+  - h5py=2.10.0=py37h7918eee_0
+  - harfbuzz=1.8.8=hffaf4a1_0
+  - hdf5=1.10.4=hb1b8bf9_0
+  - icu=58.2=h9c2bf20_1
+  - imageio=2.6.1=py37_0
+  - importlib_metadata=1.5.0=py37_0
+  - intel-openmp=2020.0=166
+  - ipykernel=5.1.4=py37h39e3cac_0
+  - ipython=7.12.0=py37h5ca1d4c_0
+  - ipython_genutils=0.2.0=py37_0
+  - jedi=0.16.0=py37_0
+  - jinja2=2.11.1=py_0
+  - joblib=0.14.1=py_0
+  - jpeg=9b=h024ee3a_2
+  - json5=0.9.1=py_0
+  - jsonschema=3.2.0=py37_0
+  - jupyter_client=5.3.4=py37_0
+  - jupyter_core=4.6.1=py37_0
+  - jupyterlab=1.2.6=pyhf63ae98_0
+  - jupyterlab_server=1.0.6=py_0
+  - keras-applications=1.0.8=py_0
+  - keras-preprocessing=1.1.0=py_1
+  - kiwisolver=1.1.0=py37he6710b0_0
+  - ld_impl_linux-64=2.33.1=h53a641e_7
+  - libedit=3.1.20181209=hc058e9b_0
+  - libffi=3.2.1=hd88cf55_4
+  - libgcc-ng=9.1.0=hdf63c60_0
+  - libgfortran-ng=7.3.0=hdf63c60_0
+  - libpng=1.6.37=hbc83047_0
+  - libprotobuf=3.11.3=hd408876_0
+  - libsodium=1.0.16=h1bed415_0
+  - libstdcxx-ng=9.1.0=hdf63c60_0
+  - libtiff=4.1.0=h2733197_0
+  - libuuid=1.0.3=h1bed415_2
+  - libxcb=1.13=h1bed415_1
+  - libxml2=2.9.9=hea5a465_1
+  - lz4-c=1.8.1.2=h14c3975_0
+  - lzo=2.10=h49e0be7_2
+  - markdown=3.1.1=py37_0
+  - markupsafe=1.1.1=py37h7b6447c_0
+  - matplotlib=3.1.3=py37_0
+  - matplotlib-base=3.1.3=py37hef1b27d_0
+  - mistune=0.8.4=py37h7b6447c_0
+  - mkl=2020.0=166
+  - mkl-service=2.3.0=py37he904b0f_0
+  - mkl_fft=1.0.15=py37ha843d7b_0
+  - mkl_random=1.1.0=py37hd6b4f25_0
+  - mock=4.0.1=py_0
+  - more-itertools=8.2.0=py_0
+  - nbconvert=5.6.1=py37_0
+  - nbformat=5.0.4=py_0
+  - ncurses=6.1=he6710b0_1
+  - networkx=2.4=py_0
+  - notebook=6.0.3=py37_0
+  - numexpr=2.7.1=py37h423224d_0
+  - numpy=1.18.1=py37h4f9e942_0
+  - numpy-base=1.18.1=py37hde5b4d6_1
+  - olefile=0.46=py37_0
+  - openssl=1.1.1d=h7b6447c_4
+  - opt_einsum=3.1.0=py_0
+  - pandas=1.0.1=py37h0573a6f_0
+  - pandoc=2.2.3.2=0
+  - pandocfilters=1.4.2=py37_1
+  - pango=1.42.4=h049681c_0
+  - parso=0.6.1=py_0
+  - patsy=0.5.1=py37_0
+  - pcre=8.43=he6710b0_0
+  - pexpect=4.8.0=py37_0
+  - pickleshare=0.7.5=py37_0
+  - pillow=7.0.0=py37hb39fc2d_0
+  - pip=20.0.2=py37_1
+  - pixman=0.38.0=h7b6447c_0
+  - prometheus_client=0.7.1=py_0
+  - prompt_toolkit=3.0.3=py_0
+  - protobuf=3.11.3=py37he6710b0_0
+  - ptyprocess=0.6.0=py37_0
+  - pydot=1.4.1=py37_0
+  - pygments=2.5.2=py_0
+  - pyparsing=2.4.6=py_0
+  - pyqt=5.9.2=py37h05f1152_2
+  - pyrsistent=0.15.7=py37h7b6447c_0
+  - pytables=3.6.1=py37h71ec239_0
+  - python=3.7.6=h0371630_2
+  - python-dateutil=2.8.1=py_0
+  - pytz=2019.3=py_0
+  - pywavelets=1.1.1=py37h7b6447c_0
+  - pyzmq=18.1.1=py37he6710b0_0
+  - qt=5.9.7=h5867ecd_1
+  - readline=7.0=h7b6447c_5
+  - scikit-image=0.16.2=py37h0573a6f_0
+  - scikit-learn=0.22.1=py37hd81dba3_0
+  - scipy=1.4.1=py37h0b6359f_0
+  - seaborn=0.10.0=py_0
+  - send2trash=1.5.0=py37_0
+  - setuptools=45.2.0=py37_0
+  - sip=4.19.8=py37hf484d3e_0
+  - six=1.14.0=py37_0
+  - snappy=1.1.7=hbae5bb6_3
+  - sqlite=3.31.1=h7b6447c_0
+  - statsmodels=0.11.0=py37h7b6447c_0
+  - tensorboard=2.0.0=pyhb38c66f_1
+  - tensorflow=2.0.0=gpu_py37h768510d_0
+  - tensorflow-base=2.0.0=gpu_py37h0ec5d1f_0
+  - tensorflow-estimator=2.0.0=pyh2649769_0
+  - tensorflow-gpu=2.0.0=h0d30ee6_0
+  - termcolor=1.1.0=py37_1
+  - terminado=0.8.3=py37_0
+  - testpath=0.4.4=py_0
+  - tk=8.6.8=hbc83047_0
+  - toolz=0.10.0=py_0
+  - tornado=6.0.3=py37h7b6447c_3
+  - traitlets=4.3.3=py37_0
+  - wcwidth=0.1.8=py_0
+  - webencodings=0.5.1=py37_1
+  - werkzeug=0.16.0=py_0
+  - wheel=0.34.2=py37_0
+  - wrapt=1.11.2=py37h7b6447c_0
+  - xz=5.2.4=h14c3975_4
+  - zeromq=4.3.1=he6710b0_3
+  - zipp=2.2.0=py_0
+  - zlib=1.2.11=h7b6447c_3
+  - zstd=1.3.7=h0b5b093_0

fidle/__init__.py

+
+VERSION='0.1a'
\ No newline at end of file

fidle/css/custom.css

+<style>
+
+div.warn {    
+    background-color: #fcf2f2;
+    border-color: #dFb5b4;
+    border-left: 5px solid #dfb5b4;
+    padding: 0.5em;
+    font-weight: bold;
+    font-size: 1.1em;;
+    }
+
+
+
+div.nota {    
+    background-color: #DAFFDE;
+    border-left: 5px solid #92CC99;
+    padding: 0.5em;
+    }
+
+
+
+</style>
+

fidle/img/00-Fidle-header-01.svg

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\ No newline at end of file

fidle/mplstyles/custom.mplstyle

+
+# See : https://matplotlib.org/users/customizing.html
+
+axes.titlesize : 24
+axes.labelsize : 20
+axes.edgecolor      : dimgrey
+axes.labelcolor     : dimgrey
+axes.linewidth      : 2
+axes.grid           : False
+
+axes.prop_cycle    : cycler('color', ['steelblue', 'tomato', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
+
+lines.linewidth     : 3
+lines.markersize    : 10
+
+xtick.color         : black
+xtick.labelsize     : 18
+ytick.color         : black
+ytick.labelsize     : 18
+
+axes.spines.left   : True
+axes.spines.bottom : True
+axes.spines.top    : False
+axes.spines.right  : False
+
+savefig.dpi         : 300      # figure dots per inch or 'figure'
+savefig.facecolor   : white    # figure facecolor when saving
+savefig.edgecolor   : white    # figure edgecolor when saving
+savefig.format      : svg
+savefig.bbox        : tight
+savefig.pad_inches  : 0.1
+savefig.transparent : True
+savefig.jpeg_quality: 95

fidle/pwk.py

+
+# ==================================================================
+#  ____                 _   _           _  __        __         _
+# |  _ \ _ __ __ _  ___| |_(_) ___ __ _| | \ \      / /__  _ __| | __
+# | |_) | '__/ _` |/ __| __| |/ __/ _` | |  \ \ /\ / / _ \| '__| |/ /
+# |  __/| | | (_| | (__| |_| | (_| (_| | |   \ V  V / (_) | |  |   <
+# |_|   |_|  \__,_|\___|\__|_|\___\__,_|_|    \_/\_/ \___/|_|  |_|\_\
+#                                                        module pwk                                   
+# ==================================================================
+# A simple module to host some common functions for practical work
+# Jean-Luc Parouty 2020
+
+import os
+import glob
+from datetime import datetime
+import itertools
+import datetime, time
+
+import math
+import numpy as np
+from collections.abc import Iterable
+
+import tensorflow as tf
+from tensorflow import keras
+from sklearn.metrics import confusion_matrix
+
+import pandas as pd
+import matplotlib
+import matplotlib.pyplot as plt
+# import seaborn as sn     #IDRIS : module en cours d'installation
+
+from IPython.display import display,Markdown,HTML
+
+VERSION='0.2.9'
+
+
+# -------------------------------------------------------------
+# init_all
+# -------------------------------------------------------------
+#
+def init(mplstyle='../fidle/mplstyles/custom.mplstyle', cssfile='../fidle/css/custom.css'):
+    global VERSION
+    # ---- matplotlib and css
+    matplotlib.style.use(mplstyle)
+    load_cssfile(cssfile)
+    # ---- Hello world
+#     now = datetime.datetime.now()
+    print('\nFIDLE 2020 - Practical Work Module')
+    print('Version              :', VERSION)
+    print('Run time             : {}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
+    print('TensorFlow version   :',tf.__version__)
+    print('Keras version        :',tf.keras.__version__)
+          
+# -------------------------------------------------------------
+# Folder cooking
+# -------------------------------------------------------------
+#
+def tag_now():
+    return datetime.datetime.now().strftime("%Y-%m-%d_%Hh%Mm%Ss")
+
+def mkdir(path):
+    os.makedirs(path, mode=0o750, exist_ok=True)
+      
+def get_directory_size(path):
+    """
+    Return the directory size, but only 1 level
+    args:
+        path : directory path
+    return:
+        size in Mo
+    """
+    size=0
+    for f in os.listdir(path):
+        if os.path.isfile(path+'/'+f):
+            size+=os.path.getsize(path+'/'+f)
+    return size/(1024*1024)
+
+# -------------------------------------------------------------
+# shuffle_dataset
+# -------------------------------------------------------------
+#
+def shuffle_np_dataset(x, y):
+    """
+    Shuffle a dataset (x,y)
+    args:
+        x,y : dataset
+    return:
+        x,y mixed
+    """
+    assert (len(x) == len(y)), "x and y must have same size"
+    p = np.random.permutation(len(x))
+    return x[p], y[p]
+
+
+def update_progress(what,i,imax, redraw=False):
+    """
+    Display a text progress bar, as :
+    My progress bar : ############# 34%
+    args:
+        what  : Progress bas name
+        i     : Current progress
+        imax  : Max value for i
+    return:
+        nothing
+    """
+    bar_length = min(40,imax)
+    if (i%int(imax/bar_length))!=0 and i<imax and not redraw:
+        return
+    progress  = float(i/imax)
+    block     = int(round(bar_length * progress))
+    endofline = '\r' if progress<1 else '\n'
+    text = "{:16s} [{}] {:>5.1f}% of {}".format( what, "#"*block+"-"*(bar_length-block), progress*100, imax)
+    print(text, end=endofline)
+
+    
+def rmax(l):
+    """
+    Recursive max() for a given iterable of iterables
+    Should be np.array of np.array or list of list, etc.
+    args:
+        l : Iterable of iterables
+    return: 
+        max value
+    """
+    maxi = float('-inf')
+    for item in l:
+        if isinstance(item, Iterable):
+            t = rmax(item)
+        else:
+            t = item
+        if t > maxi:
+            maxi = t
+    return maxi
+
+def rmin(l):
+    """
+    Recursive min() for a given iterable of iterables
+    Should be np.array of np.array or list of list, etc.
+    args:
+        l : Iterable of iterables
+    return: 
+        min value
+    """
+    mini = float('inf')
+    for item in l:
+        if isinstance(item, Iterable):
+            t = rmin(item)
+        else:
+            t = item
+        if t < mini:
+            mini = t
+    return mini
+
+# -------------------------------------------------------------
+# show_images
+# -------------------------------------------------------------
+#
+def plot_images(x,y=None, indices='all', columns=12, x_size=1, y_size=1,
+                colorbar=False, y_pred=None, cm='binary',y_padding=0.35, spines_alpha=1,
+                fontsize=20):
+    """
+    Show some images in a grid, with legends
+    args:
+        x: images - Shapes must be (-1,lx,ly) (-1,lx,ly,1) or (-1,lx,ly,3)
+        y: real classes or labels or None (None)
+        indices: indices of images to show or None for all (None)
+        columns: number of columns (12)
+        x_size,y_size: figure size (1), (1)
+        colorbar: show colorbar (False)
+        y_pred: predicted classes (None)
+        cm: Matplotlib color map (binary)
+    returns: 
+        nothing
+    """
+    if indices=='all': indices=range(len(x))
+    draw_labels = (y is not None)
+    draw_pred   = (y_pred is not None)
+    rows        = math.ceil(len(indices)/columns)
+    fig=plt.figure(figsize=(columns*x_size, rows*(y_size+y_padding)))
+    n=1
+    for i in indices:
+        axs=fig.add_subplot(rows, columns, n)
+        n+=1
+        # ---- Shape is (lx,ly)
+        if len(x[i].shape)==2:
+            xx=x[i]
+        # ---- Shape is (lx,ly,n)
+        if len(x[i].shape)==3:
+            (lx,ly,lz)=x[i].shape
+            if lz==1: 
+                xx=x[i].reshape(lx,ly)
+            else:
+                xx=x[i]
+        img=axs.imshow(xx,   cmap = cm, interpolation='lanczos')
+        axs.spines['right'].set_visible(True)
+        axs.spines['left'].set_visible(True)
+        axs.spines['top'].set_visible(True)
+        axs.spines['bottom'].set_visible(True)
+        axs.spines['right'].set_alpha(spines_alpha)
+        axs.spines['left'].set_alpha(spines_alpha)
+        axs.spines['top'].set_alpha(spines_alpha)
+        axs.spines['bottom'].set_alpha(spines_alpha)
+        axs.set_yticks([])
+        axs.set_xticks([])
+        if draw_labels and not draw_pred:
+            axs.set_xlabel(y[i],fontsize=fontsize)
+        if draw_labels and draw_pred:
+            if y[i]!=y_pred[i]:
+                axs.set_xlabel(f'{y_pred[i]} ({y[i]})',fontsize=fontsize)
+                axs.xaxis.label.set_color('red')
+            else:
+                axs.set_xlabel(y[i],fontsize=fontsize)
+        if colorbar:
+            fig.colorbar(img,orientation="vertical", shrink=0.65)
+    plt.show()
+
+    
+def plot_image(x,cm='binary', figsize=(4,4)):
+    """
+    Draw a single image.
+    Image shape can be (lx,ly), (lx,ly,1) or (lx,ly,n)
+    args:
+        x       : image as np array
+        cm      : color map ('binary')
+        figsize : fig size (4,4)
+    """
+    # ---- Shape is (lx,ly)
+    if len(x.shape)==2:
+        xx=x
+    # ---- Shape is (lx,ly,n)
+    if len(x.shape)==3:
+        (lx,ly,lz)=x.shape
+        if lz==1: 
+            xx=x.reshape(lx,ly)
+        else:
+            xx=x
+    # ---- Draw it
+    plt.figure(figsize=figsize)
+    plt.imshow(xx,   cmap = cm, interpolation='lanczos')
+    plt.show()
+
+
+# -------------------------------------------------------------
+# show_history
+# -------------------------------------------------------------
+#
+def plot_history(history, figsize=(8,6), 
+                  plot={"Accuracy":['accuracy','val_accuracy'], 'Loss':['loss', 'val_loss']}):
+    """
+    Show history
+    args:
+        history: history
+        figsize: fig size
+        plot: list of data to plot : {<title>:[<metrics>,...], ...}
+    """
+    for title,curves in plot.items():
+        plt.figure(figsize=figsize)
+        plt.title(title)
+        plt.ylabel(title)
+        plt.xlabel('Epoch')
+        for c in curves:
+            plt.plot(history.history[c])
+        plt.legend(curves, loc='upper left')
+        plt.show()
+
+    
+    
+# -------------------------------------------------------------
+# plot_confusion_matrix
+# -------------------------------------------------------------
+# Bug in Matplotlib 3.1.1
+#
+def plot_confusion_matrix(cm,
+                          title='Confusion matrix',
+                          figsize=(12,8),
+                          cmap="gist_heat_r",
+                          vmin=0,
+                          vmax=1,
+                          xticks=5,yticks=5):
+    """
+    given a sklearn confusion matrix (cm), make a nice plot
+    Note:bug in matplotlib 3.1.1
+
+    Args:
+        cm:           confusion matrix from sklearn.metrics.confusion_matrix
+        title:        the text to display at the top of the matrix
+        figsize:      Figure size (12,8)
+        cmap:         color map (gist_heat_r)
+        vmi,vmax:     Min/max 0 and 1
+        
+    """
+ 
+    accuracy = np.trace(cm) / float(np.sum(cm))
+    misclass = 1 - accuracy
+
+    plt.figure(figsize=figsize)
+    sn.heatmap(cm, linewidths=1, linecolor="#ffffff",square=True, 
+               cmap=cmap, xticklabels=xticks, yticklabels=yticks,
+               vmin=vmin,vmax=vmax,annot=True)
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
+
+    plt.show()
+
+
+    
+def display_confusion_matrix(y_true,y_pred,labels=None,color='green',
+                             font_size='12pt', title="#### Confusion matrix is :"):
+    """
+    Show a confusion matrix for a predictions.
+    see : sklearn.metrics.confusion_matrix
+
+    Args:
+        y_true        Real classes
+        y_pred        Predicted classes
+        labels        List of classes to show in the cm
+        color:        Color for the palette (green)
+        font_size:    Values font size 
+        title:        the text to display at the top of the matrix        
+    """
+    assert (labels!=None),"Label must be set"
+    
+    if title != None :  display(Markdown(title)) 
+    
+    cm = confusion_matrix( y_true,y_pred, normalize="true", labels=labels)
+    df=pd.DataFrame(cm)
+
+    cmap = sn.light_palette(color, as_cmap=True)
+    df.style.set_properties(**{'font-size': '20pt'})
+    display(df.style.format('{:.2f}') \
+            .background_gradient(cmap=cmap)
+            .set_properties(**{'font-size': font_size}))
+    
+    
+def plot_donut(values, labels, colors=["lightsteelblue","coral"], figsize=(6,6), title=None):
+    """
+    Draw a donut
+    args:
+        values   : list of values
+        labels   : list of labels
+        colors   : list of color (["lightsteelblue","coral"])
+        figsize  : size of figure ( (6,6) )
+    return:
+        nothing
+    """
+    # ---- Title or not
+    if title != None :  display(Markdown(title))
+    # ---- Donut
+    plt.figure(figsize=figsize)
+    # ---- Draw a pie  chart..
+    plt.pie(values, labels=labels, 
+            colors = colors, autopct='%1.1f%%', startangle=70, pctdistance=0.85,
+            textprops={'fontsize': 18},
+            wedgeprops={"edgecolor":"w",'linewidth': 5, 'linestyle': 'solid', 'antialiased': True})
+    # ---- ..with a white circle
+    circle = plt.Circle((0,0),0.70,fc='white')
+    ax = plt.gca()
+    ax.add_artist(circle)
+    # Equal aspect ratio ensures that pie is drawn as a circle
+    plt.axis('equal')  
+    plt.tight_layout()
+    plt.show()
+    
+def display_md(md_text):
+    display(Markdown(md_text))
+    
+def hdelay(sec):
+    return str(datetime.timedelta(seconds=int(sec)))
+
+def hsize(num, suffix='o'):
+    for unit in ['','K','M','G','T','P','E','Z']:
+        if abs(num) < 1024.0:
+            return f'{num:3.1f} {unit}{suffix}'
+        num /= 1024.0
+    return f'{num:.1f} Y{suffix}'
+
+def load_cssfile(cssfile):
+    if cssfile is None: return
+    styles = open(cssfile, "r").read()
+    display(HTML(styles))
+    
+    
+def good_place( places={'SOMEWHERE':'/tmp'} ):
+    for place_name, place_dir in places.items():
+        if os.path.isdir(place_dir):
+            print(f'Well, we should be at {place_name} !')
+            print(f'We are going to use: {place_dir}')
+            return place_name,place_dir
+
+    print('** Attention : No expected folder exists in this environment..')
+    assert False, 'No expected folder exists in this environment..'
+     
+     
+     
+     
+     
+     
+     
+