Update to 2.0.34

0459a3f1 · Jean-Luc Parouty · a42ab7ca · 0459a3f1 · 0459a3f1 · 0459a3f1
Commit 0459a3f1 authored 3 years ago by Jean-Luc Parouty
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "id": "3f8b217d",
+   "id": "7f8c4a8a",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2022-01-26T14:19:05.708967Z",
+     "iopub.execute_input": "2022-03-08T10:11:20.520072Z",
-     "iopub.status.busy": "2022-01-26T14:19:05.705058Z",
+     "iopub.status.busy": "2022-03-08T10:11:20.519675Z",
-     "iopub.status.idle": "2022-01-26T14:19:05.717400Z",
+     "iopub.status.idle": "2022-03-08T10:11:20.606245Z",
-     "shell.execute_reply": "2022-01-26T14:19:05.717043Z"
+     "shell.execute_reply": "2022-03-08T10:11:20.606632Z"
    },
    "jupyter": {
     "source_hidden": true
@@ -52,7 +52,7 @@
       "[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\n",
       "\n",
       "Current Version : <!-- VERSION_BEGIN -->\n",
-       "**2.0.33**\n",
+       "**2.0.34**\n",
       "<!-- VERSION_END -->\n",
       "\n",
       "\n",
@@ -178,6 +178,8 @@
       "### Generative Adversarial Networks (GANs)\n",
       "- **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)  \n",
       "Episode 1 : Draw me a sheep, revisited with a DCGAN\n",
+       "- **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)  \n",
+       "Episode 2 : Draw me a sheep, revisited with a WGAN-GP\n",
       "\n",
       "### Miscellaneous\n",
       "- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)  \n",

-%% Cell type:code id:3f8b217d tags:
+%% Cell type:code id:7f8c4a8a 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!                    -->
    <!-- --------------------------------------------------- -->
    ## 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, see **https://fidle.cnrs.fr** :
    - **[Fidle site](https://fidle.cnrs.fr)**
    - **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
    - **[Program 2021/2022](https://fidle.cnrs.fr/programme)**
    - [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !
    - [Find us on youtube](https://fidle.cnrs.fr/youtube)
    - [Corrected notebooks](https://fidle.cnrs.fr/done)
    For more information, you can contact us at :
    [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
    Current Version : <!-- VERSION_BEGIN -->
-    **2.0.33**
+    **2.0.34**
    <!-- VERSION_END -->
    ## Course materials
    | | | | |
    |:--:|:--:|:--:|:--:|
    | **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<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://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;<br>&nbsp;|
    Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** 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
    ### Sentiment analysis with transformers
    - **[TRANS1](Transformers/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/01-Distilbert.ipynb)
    Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version
    - **[TRANS2](Transformers/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/02-distilbert_colab.ipynb)
    Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version
    ### 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, using functional API (MNIST dataset)](VAE/01-VAE-with-MNIST.ipynb)
    Construction and training of a VAE, using functional APPI, with a latent space of small dimension.
    - **[VAE2](VAE/02-VAE-with-MNIST.ipynb)** - [VAE, using a custom model class  (MNIST dataset)](VAE/02-VAE-with-MNIST.ipynb)
    Construction and training of a VAE, using model subclass, with a latent space of small dimension.
    - **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb)
    Visualization and analysis of the VAE's latent space of the dataset MNIST
    - **[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/10-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/10-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
    ### Generative Adversarial Networks (GANs)
    - **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)
    Episode 1 : Draw me a sheep, revisited with a DCGAN
+    - **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)
+    Episode 2 : Draw me a sheep, revisited with a WGAN-GP
    ### 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://fidle.cnrs.fr/installation)** 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
@@ -31,7 +31,7 @@ For more information, you can contact us at :
 [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
 Current Version : <!-- VERSION_BEGIN -->
-**2.0.33**
+**2.0.34**
 <!-- VERSION_END -->
@@ -157,6 +157,8 @@ Bash script for SLURM batch submission of VAE8 notebooks
 ### Generative Adversarial Networks (GANs)
 - **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)  
 Episode 1 : Draw me a sheep, revisited with a DCGAN
+- **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)  
+Episode 2 : Draw me a sheep, revisited with a WGAN-GP
 ### Miscellaneous
 - **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)  

--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -437,6 +437,19 @@ Nb_SHEEP1:
    batch_size: default
    num_img: default
    fit_verbosity: default
+Nb_SHEEP2:
+  notebook_id: SHEEP2
+  notebook_dir: DCGAN
+  notebook_src: 02-WGANGP-Draw-me-a-sheep.ipynb
+  notebook_tag: default
+  overrides:
+    run_dir: default
+    scale: default
+    latent_dim: default
+    epochs: default
+    batch_size: default
+    num_img: default
+    fit_verbosity: default
 Nb_ACTF1:
  notebook_id: ACTF1
  notebook_dir: Misc

--- a/fidle/config.py
+++ b/fidle/config.py
@@ -14,7 +14,7 @@
 # ---- Version -----------------------------------------------------
 #
-VERSION = '2.0.33'
+VERSION = '2.0.34'
 # ---- Default notebook name ---------------------------------------
 #

--- a/fidle/logs/catalog.json
+++ b/fidle/logs/catalog.json
@@ -565,6 +565,23 @@
            "run_dir"
        ]
    },
+    "SHEEP2": {
+        "id": "SHEEP2",
+        "dirname": "DCGAN",
+        "basename": "02-WGANGP-Draw-me-a-sheep.ipynb",
+        "title": "A WGAN-GP to Draw a Sheep",
+        "description": "Episode 2 : Draw me a sheep, revisited with a WGAN-GP",
+        "overrides": [
+            "run_dir",
+            "scale",
+            "latent_dim",
+            "epochs",
+            "batch_size",
+            "num_img",
+            "fit_verbosity",
+            "run_dir"
+        ]
+    },
    "ACTF1": {
        "id": "ACTF1",
        "dirname": "Misc",