Update README for sequence 6

24266241 · Jean-Luc Parouty · bb7aa4e4 · 24266241 · 24266241 · 24266241
Commit 24266241 authored 4 years ago by Jean-Luc Parouty
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "id": "metallic-symphony",
+   "id": "regional-acrobat",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2021-03-14T20:44:10.127899Z",
+     "iopub.execute_input": "2021-03-19T14:01:11.950357Z",
-     "iopub.status.busy": "2021-03-14T20:44:10.127457Z",
+     "iopub.status.busy": "2021-03-19T14:01:11.944861Z",
-     "iopub.status.idle": "2021-03-14T20:44:10.131176Z",
+     "iopub.status.idle": "2021-03-19T14:01:11.953351Z",
-     "shell.execute_reply": "2021-03-14T20:44:10.130783Z"
+     "shell.execute_reply": "2021-03-19T14:01:11.953719Z"
    },
    "jupyter": {
     "source_hidden": true
@@ -40,9 +40,9 @@
       "**- Prochain rendez-vous -**  \n",
       "\n",
       "\n",
-       "|**Jeudi 18 mars, 14h, Séquence 5 : <br>Les réseaux autoencodeurs (AE), un exemple d'apprentissage non supervisé.**|\n",
+       "|**Jeudi 25 mars, 14h, Séquence 6 : <br>Du réseau autoencodeur (AE) au Variational Autoencoder (VAE)<br>ou comment parcourir l'imagination de nos réseaux.**|\n",
       "|--|\n",
-       "|Principes et architecture des réseaux autoencodeurs.<br> Convolutions classiques et transposées - Espaces latents.<br> Programmation procédurale avec Keras - GPU et batch.<br>Exemple proposé :<br>Débruitage d'images fortement bruitées.<br>Où, comment à partir de ces image :<br> ![AE2-07-test-noisy-xs](./fidle/img/AE2-noisy.png)<br> Il est possible de retrouver  celles-ci !<br>![AE2-08-test-predict-xs](./fidle/img/AE2-predict.png) |\n",
+       "|Principes et architecture d'un Variational Autoencoder (VAE).<br>Problématiques liées à la gestion de \"gros\" datasets.<br>Projection gaussienne - Génération de données - Morphing dans l'espace latent.<br>Programmation avancée avec Keras - Datasets clusterisés.<br>Exemple proposé :<br>Mise en œuvre d'un VAE, génération de données et morphing dans l'espace latent.<br>![VAE](./fidle/img/VAE.jpg )|\n",
       "|Durée : 2h - Les paramètres de diffusion seront [précisés la veille dans le wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref) |\n",
       "\n",
       "A propos de **[Fidle à distance](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)**\\\n",

-%% Cell type:code id:metallic-symphony tags:
+%% Cell type:code id:regional-acrobat tags:
 ``` python
 from IPython.display import display,Markdown
 display(Markdown(open('README.md', 'r').read()))
 #
 # This README is visible under Jupiter LAb ! :-)
 ```
 %% Output
    <a name="top"></a>
    [<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#top)
    <!-- --------------------------------------------------- -->
    <!-- To correctly view this README under Jupyter Lab     -->
    <!-- Open the notebook: README.ipynb!                    -->
    <!-- --------------------------------------------------- -->
    ## Session Fidle à distance (NEW !)
    Faute de pouvoir organiser des sessions en présentiel,\
    nous vous proposons une version **Fidle à distance** :-)
    **- Voir ou revoir en ligne -**
    https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ
    **- Prochain rendez-vous -**
-    |**Jeudi 18 mars, 14h, Séquence 5 : <br>Les réseaux autoencodeurs (AE), un exemple d'apprentissage non supervisé.**|
+    |**Jeudi 25 mars, 14h, Séquence 6 : <br>Du réseau autoencodeur (AE) au Variational Autoencoder (VAE)<br>ou comment parcourir l'imagination de nos réseaux.**|
    |--|
-    |Principes et architecture des réseaux autoencodeurs.<br> Convolutions classiques et transposées - Espaces latents.<br> Programmation procédurale avec Keras - GPU et batch.<br>Exemple proposé :<br>Débruitage d'images fortement bruitées.<br>Où, comment à partir de ces image :<br> ![AE2-07-test-noisy-xs](./fidle/img/AE2-noisy.png)<br> Il est possible de retrouver  celles-ci !<br>![AE2-08-test-predict-xs](./fidle/img/AE2-predict.png) |
+    |Principes et architecture d'un Variational Autoencoder (VAE).<br>Problématiques liées à la gestion de "gros" datasets.<br>Projection gaussienne - Génération de données - Morphing dans l'espace latent.<br>Programmation avancée avec Keras - Datasets clusterisés.<br>Exemple proposé :<br>Mise en œuvre d'un VAE, génération de données et morphing dans l'espace latent.<br>![VAE](./fidle/img/VAE.jpg )|
    |Durée : 2h - Les paramètres de diffusion seront [précisés la veille dans le wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref) |
    A propos de **[Fidle à distance](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)**\
    Voir le [programme](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/Pr%C3%A9sentation#programme-)\
    Voir ou revoir les [vidéos](https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ)
    ## About Fidle
    This repository contains all the documents and links of the **Fidle Training** .
    Fidle (for Formation Introduction au Deep Learning) is a 2-day training session
    co-organized by the Formation Permanente CNRS and the Resinfo/SARI and DevLOG CNRS networks.
    The objectives of this training are :
     - Understanding the **bases of Deep Learning** neural networks
     - Develop a **first experience** through simple and representative examples
     - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
     - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
    For more information, you can contact [Fidle team](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20team) at :
    [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)\
    Don't forget to look at the [Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
    Current Version : <!-- VERSION_BEGIN -->
    **2.0.20**
    <!-- VERSION_END -->
    ## Course materials
    | | | |
    |:--:|:--:|:--:|
    | **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/archive/master/fidle-master.zip)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>All the needed datasets<br>(1.2 Go)|
    Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/install%20fidle)** these notebooks and datasets.
    ## Jupyter notebooks
    <!-- INDEX_BEGIN -->
    ### Linear and logistic regression
    - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
    Low-level implementation, using numpy, of a direct resolution for a linear regression
    - **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
    Low level implementation of a solution by gradient descent. Basic and stochastic approach.
    - **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
    Illustration of the problem of complexity with the polynomial regression
    - **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb)
    Simple example of logistic regression with a sklearn solution
    ### Perceptron Model 1957
    - **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)
    Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
    ### Basic regression using DNN
    - **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
    Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
    - **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
    A more advanced implementation of the precedent example
    ### Basic classification using a DNN
    - **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
    An example of classification using a dense neural network for the famous MNIST dataset
    - **[MNIST2](MNIST/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST/02-CNN-MNIST.ipynb)
    An example of classification using a convolutional neural network for the famous MNIST dataset
    ### Images classification with Convolutional Neural Networks (CNN)
    - **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
    Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
    - **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb)
    Episode 2 : First convolutions and first classification of our traffic signs
    - **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb)
    Episode 3 : Monitoring, analysis and check points during a training session
    - **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
    Episode 4 : Adding data by data augmentation when we lack it, to improve our results
    - **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb)
    Episode 5 : A lot of models, a lot of datasets and a lot of results.
    - **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)
    Episode 6 : To compute bigger, use your notebook in batch mode
    - **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb)
    Episode 7 : Displaying our jobs report, and the winner is...
    - **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh)
    Bash script for an OAR batch submission of an ipython code
    - **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh)
    Bash script for a Slurm batch submission of an ipython code
    ### Sentiment analysis with word embedding
    - **[IMDB1](IMDB/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](IMDB/01-One-hot-encoding.ipynb)
    A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)
    - **[IMDB2](IMDB/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](IMDB/02-Keras-embedding.ipynb)
    A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
    - **[IMDB3](IMDB/03-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/03-Prediction.ipynb)
    Retrieving a saved model to perform a sentiment analysis (movie review)
    - **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)
    Retrieving embedded vectors from our trained model
    - **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)
    Still the same problem, but with a network combining embedding and RNN
    ### Time series with Recurrent Neural Network (RNN)
    - **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)
    Artificial dataset generation and prediction attempt via a recurrent network
    - **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)
    Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)
    - **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)
    Episode 2 : RNN training session for weather prediction attempt at 3h
    - **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)
    Episode 3: Attempt to predict in a more longer term
    ### Unsupervised learning with an autoencoder neural network (AE)
    - **[AE1](AE/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE/01-Prepare-MNIST-dataset.ipynb)
    Episode 1: Preparation of a noisy MNIST dataset
    - **[AE2](AE/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/02-AE-with-MNIST.ipynb)
    Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
    - **[AE3](AE/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE/03-AE-with-MNIST-post.ipynb)
    Episode 2 : Using the previously trained autoencoder to denoise data
    - **[AE4](AE/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE/04-ExtAE-with-MNIST.ipynb)
    Episode 4 : Construction of a denoiser and classifier model
    - **[AE5](AE/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE/05-ExtAE-with-MNIST.ipynb)
    Episode 5 : Construction of an advanced denoiser and classifier model
    ### Generative network with Variational Autoencoder (VAE)
    - **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, with a small dataset (MNIST)](VAE/01-VAE-with-MNIST.ipynb)
    Construction and training of a VAE with a latent space of small dimension.
    - **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Analysis of the associated latent space](VAE/02-VAE-with-MNIST-post.ipynb)
    Visualization and analysis of the VAE's latent space
    - **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb)
    Episode 1 : Presentation of the CelebA dataset and problems related to its size
    - **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb)
    Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset
    - **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb)
    Episode : 3 Clustered dataset verification and testing of our datagenerator
    - **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb)
    Episode 4 : Training with our clustered datasets in notebook or batch mode
    - **[VAE9](VAE/09-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-with-CelebA-post.ipynb)
    Episode 5 : Exploring latent space to generate new data
    - **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh)
    Bash script for SLURM batch submission of VAE8 notebooks
    ### Miscellaneous
    - **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)
    Some activation functions, with their derivatives.
    - **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb)
    Numpy is an essential tool for the Scientific Python.
    - **[SCRATCH1](Misc/Scratchbook.ipynb)** - [Scratchbook](Misc/Scratchbook.ipynb)
    A scratchbook for small examples
    - **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)
    4 ways to use Tensorboard from the Jupyter environment
    <!-- INDEX_END -->
    ## Installation
    Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Using%20Fidle/install%20fidle)** these notebooks and datasets.
    ## Licence
    [<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/)
    \[en\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
    \[Fr\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
    See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
    See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
    ----
    [<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img>](#top)

--- a/README.md
+++ b/README.md
@@ -19,9 +19,9 @@ https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ
 **- Prochain rendez-vous -**  
-|**Jeudi 18 mars, 14h, Séquence 5 : <br>Les réseaux autoencodeurs (AE), un exemple d'apprentissage non supervisé.**|
+|**Jeudi 25 mars, 14h, Séquence 6 : <br>Du réseau autoencodeur (AE) au Variational Autoencoder (VAE)<br>ou comment parcourir l'imagination de nos réseaux.**|
 |--|
-|Principes et architecture des réseaux autoencodeurs.<br> Convolutions classiques et transposées - Espaces latents.<br> Programmation procédurale avec Keras - GPU et batch.<br>Exemple proposé :<br>Débruitage d'images fortement bruitées.<br>Où, comment à partir de ces image :<br> ![AE2-07-test-noisy-xs](./fidle/img/AE2-noisy.png)<br> Il est possible de retrouver  celles-ci !<br>![AE2-08-test-predict-xs](./fidle/img/AE2-predict.png) |
+|Principes et architecture d'un Variational Autoencoder (VAE).<br>Problématiques liées à la gestion de "gros" datasets.<br>Projection gaussienne - Génération de données - Morphing dans l'espace latent.<br>Programmation avancée avec Keras - Datasets clusterisés.<br>Exemple proposé :<br>Mise en œuvre d'un VAE, génération de données et morphing dans l'espace latent.<br>![VAE](./fidle/img/VAE.jpg )|
 |Durée : 2h - Les paramètres de diffusion seront [précisés la veille dans le wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref) |
 A propos de **[Fidle à distance](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)**\

--- a/fidle/01-update-index.ipynb
+++ b/fidle/01-update-index.ipynb
@@ -475,7 +475,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Completed on :  Sunday 14 March 2021, 21:44:10\n"
+      "Completed on :  Friday 19 March 2021, 15:01:12\n"
     ]
    }
   ],

 %% Cell type:markdown id: tags:
 <img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img>
 ## Mise a jour du catalog des notebooks et des READMEs
 - Génération du **catalog des notebooks** : [./logs/catalog.json](./logs/catalog.json)
   Ce fichier comporte une liste détaillée de tous les notebooks et scripts.
 - Génération automatique de la table des matières et mise à jour des **README**
     - [README.md](../README.md)
     - [README.ipynb](../README.ipynb)
 %% Cell type:markdown id: tags:
 ## Step 1 - Load modules and init
 %% Cell type:code id: tags:
 ``` python
 import nbformat
 from nbconvert.preprocessors import ExecutePreprocessor
 from IPython.display import display,Image,Markdown,HTML
 import re
 import sys, os, glob
 import datetime, time
 import json
 from collections import OrderedDict
 from importlib import reload
 sys.path.append('..')
 import fidle.pwk as pwk
 import fidle.config as config
 import fidle.cookindex as cookindex
 ```
 %% Cell type:markdown id: tags:
 ## Step 2 - List of folders containing notebooks to be indexed :
 Order wil be index order
 %% Cell type:code id: tags:
 ``` python
 directories_to_index = {'LinearReg':'Linear and logistic regression',
                        'IRIS':'Perceptron Model 1957',
                        'BHPD':'Basic regression using DNN',
                        'MNIST':'Basic classification using a DNN',
                        'GTSRB':'Images classification with Convolutional Neural Networks (CNN)',
                        'IMDB':'Sentiment analysis with word embedding',
                        'SYNOP':'Time series with Recurrent Neural Network (RNN)',
                        'AE':'Unsupervised learning with an autoencoder neural network (AE)',
                        'VAE':'Generative network with Variational Autoencoder (VAE)',
                        'Misc':'Miscellaneous'
                        }
 ```
 %% Cell type:markdown id: tags:
 ## Step 3 - Catalog of notebooks
 ### 3.1 - Build catalog
 %% Cell type:code id: tags:
 ``` python
 # ---- Get the notebook list
 #
 files_list = cookindex.get_files(directories_to_index.keys())
 # ---- Get a detailled catalog for this list
 #
 catalog = cookindex.get_catalog(files_list)
 with open(config.CATALOG_FILE,'wt') as fp:
    json.dump(catalog,fp,indent=4)
    print(f'Catalog saved as {config.CATALOG_FILE}')
    print('Entries : ',len(catalog))
 ```
 %% Output
    Read :  LinearReg/01-Linear-Regression.ipynb
    Read :  LinearReg/02-Gradient-descent.ipynb
    Read :  LinearReg/03-Polynomial-Regression.ipynb
    Read :  LinearReg/04-Logistic-Regression.ipynb
    Read :  IRIS/01-Simple-Perceptron.ipynb
    Read :  BHPD/01-DNN-Regression.ipynb
    Read :  BHPD/02-DNN-Regression-Premium.ipynb
    Read :  MNIST/01-DNN-MNIST.ipynb
    Read :  MNIST/02-CNN-MNIST.ipynb
    Read :  GTSRB/01-Preparation-of-data.ipynb
    Read :  GTSRB/02-First-convolutions.ipynb
    Read :  GTSRB/03-Tracking-and-visualizing.ipynb
    Read :  GTSRB/04-Data-augmentation.ipynb
    Read :  GTSRB/05-Full-convolutions.ipynb
    Read :  GTSRB/06-Notebook-as-a-batch.ipynb
    Read :  GTSRB/07-Show-report.ipynb
    Read :  IMDB/01-One-hot-encoding.ipynb
    Read :  IMDB/02-Keras-embedding.ipynb
    Read :  IMDB/03-Prediction.ipynb
    Read :  IMDB/04-Show-vectors.ipynb
    Read :  IMDB/05-LSTM-Keras.ipynb
    Read :  SYNOP/LADYB1-Ladybug.ipynb
    Read :  SYNOP/SYNOP1-Preparation-of-data.ipynb
    Read :  SYNOP/SYNOP2-First-predictions.ipynb
    Read :  SYNOP/SYNOP3-12h-predictions.ipynb
    Read :  AE/01-Prepare-MNIST-dataset.ipynb
    Read :  AE/02-AE-with-MNIST.ipynb
    Read :  AE/03-AE-with-MNIST-post.ipynb
    Read :  AE/04-ExtAE-with-MNIST.ipynb
    Read :  AE/05-ExtAE-with-MNIST.ipynb
    Read :  VAE/01-VAE-with-MNIST.ipynb
    Read :  VAE/02-VAE-with-MNIST-post.ipynb
    Read :  VAE/05-About-CelebA.ipynb
    Read :  VAE/06-Prepare-CelebA-datasets.ipynb
    Read :  VAE/07-Check-CelebA.ipynb
    Read :  VAE/08-VAE-with-CelebA.ipynb
    Read :  VAE/09-VAE-with-CelebA-post.ipynb
    Read :  Misc/Activation-Functions.ipynb
    Read :  Misc/Numpy.ipynb
    Read :  Misc/Scratchbook.ipynb
    Read :  Misc/Using-Tensorboard.ipynb
    Catalog saved as ../fidle/logs/catalog.json
    Entries :  44
 %% Cell type:markdown id: tags:
 ### 3.2 build index
 %% Cell type:code id: tags:
 ``` python
 styles = open('css/readme.css', "r").read()
 lines_md=[]
 lines_html=[styles]
 for directory,title in directories_to_index.items():
    lines_md.append(f'\n### {title}')
    lines_html.append( f'<div class="fid_section">{title}</div>')
    entries = { k:v for k,v in catalog.items() if v['dirname']==directory }
    for id, about in entries.items():
        id          = about['id']
        dirname     = about['dirname']
        basename    = about['basename']
        title       = about['title']
        description = about['description']
        link=f'{dirname}/{basename}'.replace(' ','%20')
        md   = f'- **[{id}]({link})** - [{title}]({link})  \n'
        md  += f'{description}'
        html = f"""<div class="fid_line">
                       <span class="fid_id">
                           <a href="{link}">{id}</a>
                       </span> <a href="{link}">{title}</a><br>
                       <span class="fid_desc">{description}</span>
                  </div>
                """
        lines_md.append(md)
        lines_html.append(html)
 index_md   = '\n'.join(lines_md)
 index_html = '\n'.join(lines_html)
 display(Markdown('**Index is :**'))
 display(Markdown(index_md))
 # display(HTML(index_html))
 ```
 %% Output
    **Index is :**
    ### Linear and logistic regression
    - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
    Low-level implementation, using numpy, of a direct resolution for a linear regression
    - **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
    Low level implementation of a solution by gradient descent. Basic and stochastic approach.
    - **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
    Illustration of the problem of complexity with the polynomial regression
    - **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb)
    Simple example of logistic regression with a sklearn solution
    ### Perceptron Model 1957
    - **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)
    Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
    ### Basic regression using DNN
    - **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
    Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
    - **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
    A more advanced implementation of the precedent example
    ### Basic classification using a DNN
    - **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
    An example of classification using a dense neural network for the famous MNIST dataset
    - **[MNIST2](MNIST/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST/02-CNN-MNIST.ipynb)
    An example of classification using a convolutional neural network for the famous MNIST dataset
    ### Images classification with Convolutional Neural Networks (CNN)
    - **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
    Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
    - **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb)
    Episode 2 : First convolutions and first classification of our traffic signs
    - **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb)
    Episode 3 : Monitoring, analysis and check points during a training session
    - **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
    Episode 4 : Adding data by data augmentation when we lack it, to improve our results
    - **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb)
    Episode 5 : A lot of models, a lot of datasets and a lot of results.
    - **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)
    Episode 6 : To compute bigger, use your notebook in batch mode
    - **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb)
    Episode 7 : Displaying our jobs report, and the winner is...
    - **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh)
    Bash script for an OAR batch submission of an ipython code
    - **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh)
    Bash script for a Slurm batch submission of an ipython code
    ### Sentiment analysis with word embedding
    - **[IMDB1](IMDB/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](IMDB/01-One-hot-encoding.ipynb)
    A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)
    - **[IMDB2](IMDB/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](IMDB/02-Keras-embedding.ipynb)
    A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
    - **[IMDB3](IMDB/03-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/03-Prediction.ipynb)
    Retrieving a saved model to perform a sentiment analysis (movie review)
    - **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)
    Retrieving embedded vectors from our trained model
    - **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)
    Still the same problem, but with a network combining embedding and RNN
    ### Time series with Recurrent Neural Network (RNN)
    - **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)
    Artificial dataset generation and prediction attempt via a recurrent network
    - **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)
    Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)
    - **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)
    Episode 2 : RNN training session for weather prediction attempt at 3h
    - **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)
    Episode 3: Attempt to predict in a more longer term
    ### Unsupervised learning with an autoencoder neural network (AE)
    - **[AE1](AE/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE/01-Prepare-MNIST-dataset.ipynb)
    Episode 1: Preparation of a noisy MNIST dataset
    - **[AE2](AE/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/02-AE-with-MNIST.ipynb)
    Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
    - **[AE3](AE/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE/03-AE-with-MNIST-post.ipynb)
    Episode 2 : Using the previously trained autoencoder to denoise data
    - **[AE4](AE/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE/04-ExtAE-with-MNIST.ipynb)
    Episode 4 : Construction of a denoiser and classifier model
    - **[AE5](AE/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE/05-ExtAE-with-MNIST.ipynb)
    Episode 5 : Construction of an advanced denoiser and classifier model
    ### Generative network with Variational Autoencoder (VAE)
    - **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, with a small dataset (MNIST)](VAE/01-VAE-with-MNIST.ipynb)
    Construction and training of a VAE with a latent space of small dimension.
    - **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Analysis of the associated latent space](VAE/02-VAE-with-MNIST-post.ipynb)
    Visualization and analysis of the VAE's latent space
    - **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb)
    Episode 1 : Presentation of the CelebA dataset and problems related to its size
    - **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb)
    Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset
    - **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb)
    Episode : 3 Clustered dataset verification and testing of our datagenerator
    - **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb)
    Episode 4 : Training with our clustered datasets in notebook or batch mode
    - **[VAE9](VAE/09-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-with-CelebA-post.ipynb)
    Episode 5 : Exploring latent space to generate new data
    - **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh)
    Bash script for SLURM batch submission of VAE8 notebooks
    ### Miscellaneous
    - **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)
    Some activation functions, with their derivatives.
    - **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb)
    Numpy is an essential tool for the Scientific Python.
    - **[SCRATCH1](Misc/Scratchbook.ipynb)** - [Scratchbook](Misc/Scratchbook.ipynb)
    A scratchbook for small examples
    - **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)
    4 ways to use Tensorboard from the Jupyter environment
 %% Cell type:markdown id: tags:
 ## Step 4 - Update README.md
 %% Cell type:code id: tags:
 ``` python
 # ---- Load README.md
 #
 with open('../README.md','r') as fp:
    readme=fp.read()
 # ---- Update index, version
 #
 readme = cookindex.tag('INDEX',   index_md,                readme)
 readme = cookindex.tag('VERSION', f'**{config.VERSION}**', readme)
 # ---- Save it
 #
 with open('../README.md','wt') as fp:
    fp.write(readme)
 print('README.md is updated.')
 ```
 %% Output
    README.md is updated.
 %% Cell type:markdown id: tags:
 ## Step 5 - README.ipynb
 Just execute README.ipynb
 %% Cell type:raw id: tags:
 # ---- Load notebook
 #
 notebook = nbformat.read('../README.ipynb', nbformat.NO_CONVERT)
 # new_cell = nbformat.v4.new_markdown_cell(source=readme)
 # notebook.cells.append(new_cell)
 # ---- Execute it
 #
 ep = ExecutePreprocessor(timeout=600, kernel_name="python3")
 ep.preprocess(notebook,  {'metadata': {'path': '..'}})
 # ---- Save it
 with open('../READMEv2.ipynb', mode="w", encoding='utf-8') as fp:
    nbformat.write(notebook)
 %% Cell type:markdown id: tags:
 ## Step 6 - More fun : Create and execute it :-)
 %% Cell type:markdown id: tags:
 Plus rigolo, on va fabriquer le README.ipynb et l'executer :-)
 %% Cell type:code id: tags:
 ``` python
 # ---- Create Notebook from scratch
 #
 notebook = nbformat.v4.new_notebook()
 # ---- Add a code cell
 #
 code = "from IPython.display import display,Markdown\n"
 code+= "display(Markdown(open('README.md', 'r').read()))\n"
 code+= "#\n"
 code+= "# This README is visible under Jupiter LAb ! :-)"
 new_cell = nbformat.v4.new_code_cell(source=code)
 new_cell['metadata']= { "jupyter": { "source_hidden": True} }
 notebook.cells.append(new_cell)
 # --- Pour éviter une modification lors de l'ouverture du notebook
 #     pas génante, mais nécessite de resauvegarder le document à la fermeture...
 notebook['metadata']["kernelspec"] = {"display_name": "Python 3", "language": "python", "name": "python3" }
 # ---- Run it
 #
 ep = ExecutePreprocessor(timeout=600, kernel_name="python3")
 ep.preprocess(notebook,  {'metadata': {'path': '..'}})
 # ---- Save it
 #
 with open('../README.ipynb', mode="w", encoding='utf-8') as fp:
    nbformat.write(notebook, fp)
 print('README.ipynb built and saved')
 ```
 %% Output
    README.ipynb built and saved
 %% Cell type:code id: tags:
 ``` python
 now = datetime.datetime.now()
 print('Completed on : ', now.strftime("%A %d %B %Y, %H:%M:%S"))
 ```
 %% Output
-    Completed on :  Sunday 14 March 2021, 21:44:10
+    Completed on :  Friday 19 March 2021, 15:01:12
 %% Cell type:markdown id: tags:
 ---
 <img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img>

--- a/fidle/img/VAE.jpg
+++ b/fidle/img/VAE.jpg