Switch to 2.0

9cdd8333 · Jean-Luc Parouty · d8d85974 · 9cdd8333 · 9cdd8333 · 9cdd8333
Commit 9cdd8333 authored 4 years ago by Jean-Luc Parouty
--- a/README.ipynb
+++ b/README.ipynb
@@ -5,10 +5,10 @@
   "execution_count": 1,
   "metadata": {
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-     "iopub.status.busy": "2021-01-12T10:16:04.977754Z",
-     "iopub.status.idle": "2021-01-12T10:16:04.982210Z",
-     "shell.execute_reply": "2021-01-12T10:16:04.981710Z"
+     "iopub.execute_input": "2021-01-12T10:47:42.374568Z",
+     "iopub.status.busy": "2021-01-12T10:47:42.374015Z",
+     "iopub.status.idle": "2021-01-12T10:47:42.382550Z",
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@@ -43,7 +43,7 @@
       "For more information, you can contact us at : \n",
       "[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)  \n",
       "Current Version : <!-- VERSION_BEGIN -->\n",
-       "1.2b1 DEV\n",
+       "2.0\n",
       "<!-- VERSION_END -->\n",
       "\n",
       "\n",

 %% Cell type:code id: 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!                    -->
    <!-- --------------------------------------------------- -->
    
    
    ## A propos
    
    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 SARI and DEVLOG 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 us at :
    [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
    Current Version : <!-- VERSION_BEGIN -->
-    1.2b1 DEV
+    2.0
    <!-- 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>(10 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/Install-Fidle)** 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
    
    ### 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-Embedding-Keras.ipynb)** - [Sentiment alalysis with text embedding](IMDB/01-Embedding-Keras.ipynb)
    A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
    - **[IMDB2](IMDB/02-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/02-Prediction.ipynb)
    Retrieving a saved model to perform a sentiment analysis (movie review)
    - **[IMDB3](IMDB/03-LSTM-Keras.ipynb)** - [Sentiment analysis with a LSTM network](IMDB/03-LSTM-Keras.ipynb)
    Still the same problem, but with a network combining embedding and LSTM
    
    ### Time series with Recurrent Neural Network (RNN)
    - **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb)
    Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)
    - **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb)
    Episode 2 : Learning session and weather prediction attempt at 3h
    - **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb)
    Episode 3: Attempt to predict in a more longer term
    
    ### Unsupervised learning with an autoencoder neural network (AE)
    - **[AE1](AE/01-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/01-AE-with-MNIST.ipynb)
    Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.
    - **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [Exploring our denoiser model](AE/02-AE-with-MNIST-post.ipynb)
    Episode 2 : Using the previously trained autoencoder to denoise data
    
    ### 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-withCelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-withCelebA-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.
    - **[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
    
    A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle)**.
    
    ## 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
@@ -23,7 +23,7 @@ The objectives of this training are :
 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 -->
-1.2b1 DEV
+2.0
 <!-- VERSION_END -->



--- a/fidle/config.py
+++ b/fidle/config.py
@@ -14,7 +14,7 @@

 # ---- Version -----------------------------------------------------
 #
-VERSION = '1.2b1 DEV'
+VERSION = '2.0'

 # ---- Default notebook name ---------------------------------------
 #