diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 57cca597a6c388e04c536e9ae81689f87a31e840..1f2c7adb058124cd579114e0d757340b198fd7e3 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -1,5 +1,33 @@
{
"cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
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d=\"M303.3176,21.7338a2.7048,2.7048,0,0,1,1.9431.7725,2.5792,2.5792,0,0,1,.8,1.911,2.45,2.45,0,0,1-.79,1.87,2.7008,2.7008,0,0,1-1.9535.8031,2.668,2.668,0,0,1-1.9327-.7929,2.5157,2.5157,0,0,1-.81-1.88,2.5982,2.5982,0,0,1,.81-1.911,2.6981,2.6981,0,0,1,1.9327-.7725Zm0,.4879a2.1655,2.1655,0,0,0-1.5794.64,2.13,2.13,0,0,0-.665,1.5552,2.07,2.07,0,0,0,.665,1.5349,2.1682,2.1682,0,0,0,1.5794.6506,2.2422,2.2422,0,0,0,1.6-.6608,1.98,1.98,0,0,0,.6442-1.5349,2.088,2.088,0,0,0-.6546-1.545,2.1891,2.1891,0,0,0-1.59-.64Zm.7378,1.5247v1.0978h-.3118v1.3011h-.852V24.8442H302.58V23.7464a.1665.1665,0,0,1,.0519-.1219.1742.1742,0,0,1,.1247-.0509h1.1222a.174.174,0,0,1,.1247.0509.1662.1662,0,0,1,.052.1219Zm-1.1222-.6912a.3845.3845,0,1,1,.3844.3761.3375.3375,0,0,1-.3844-.3761Z\" style=\"fill-rule:evenodd\"/><path 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d=\"M311.6337,22.5063a2.817,2.817,0,0,0-3.8758,0,2.5983,2.5983,0,0,0-.81,1.911,2.548,2.548,0,0,0,.81,1.88,2.6679,2.6679,0,0,0,1.9327.7929,2.774,2.774,0,0,0,1.9639-.7929,2.5323,2.5323,0,0,0,.7793-1.88,2.5792,2.5792,0,0,0-.8-1.911Zm-.3429,3.4357a2.2248,2.2248,0,0,1-1.6.6608,2.1919,2.1919,0,0,1-1.5794-.6506,2.0756,2.0756,0,0,1-.665-1.5451,2.3219,2.3219,0,0,1,.1143-.7115l.7274.3151h-.052v.3253h.26c0,.0407-.01.0813-.01.1321v.0712h-.2494v.3253h.3013a1.25,1.25,0,0,0,.26.5794,1.3587,1.3587,0,0,0,1.1118.5082,1.6125,1.6125,0,0,0,.717-.1626l-.1039-.4981a1.523,1.523,0,0,1-.53.1118A.8246.8246,0,0,1,309.4,25.18a.8144.8144,0,0,1-.1455-.3151h.9975l1.4131.61a1.7768,1.7768,0,0,1-.374.4675Zm-1.7768-1.4027h0Zm.852-.2033h.0416v-.3253h-.7793l-.3118-.1321a.3816.3816,0,0,1,.0936-.1525.7007.7007,0,0,1,.5611-.244,1.5316,1.5316,0,0,1,.5091.1017l.1351-.5082a1.8313,1.8313,0,0,0-.6962-.1322,1.4183,1.4183,0,0,0-1.06.4574c-.0519.061-.1039.1423-.1558.2135l-.8936-.3863a2.0314,2.0314,0,0,1,.3013-.3659,2.1747,2.1747,0,0,1,1.5794-.6506,2.1982,2.1982,0,0,1,1.59.6506,2.0628,2.0628,0,0,1,.6546,1.5552,2.5876,2.5876,0,0,1-.0623.5692l-1.5067-.6505Z\" style=\"fill-rule:evenodd\"/></g></g></svg>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.SVG object>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import SVG\n",
+ "SVG(\"../fidle/img/00-Fidle-header-01.svg\")"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/README.ipynb b/README.ipynb
index bd577f03ff24920c04832793085be0d8c7c87d89..06431dc766e73c3807c436b24ca6f150fe51db16 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -1,86 +1,136 @@
{
"cells": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# Available notebooks"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<!-- INDEX_BEGIN -->\n",
- "[[NP1] - A short introduction to Numpy](Prerequisites/Numpy.ipynb) \n",
- " Numpy is an essential tool for the Scientific Python. \n",
- "[[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
- " Direct determination of linear regression \n",
- "[[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) \n",
- " An example of gradient descent in the simple case of a linear regression. \n",
- "[[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) \n",
- " Illustration of the problem of complexity with the polynomial regression \n",
- "[[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb) \n",
- " Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. \n",
- "[[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) \n",
- " Example of classification with a fully connected neural network \n",
- "[[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) \n",
- " A Simple regression with a Dense Neural Network (DNN) - BHPD dataset \n",
- "[[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) \n",
- " More advanced example of DNN network code - BHPD dataset \n",
- "[[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) \n",
- " Episode 1: Data analysis and creation of a usable dataset \n",
- "[[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
- " Episode 2 : First convolutions and first results \n",
- "[[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb) \n",
- " Episode 3: Monitoring and analysing training, managing checkpoints \n",
- "[[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb) \n",
- " Episode 4: Improving the results with data augmentation \n",
- "[[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb) \n",
- " Episode 5: A lot of models, a lot of datasets and a lot of results. \n",
- "[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb) \n",
- " Episode 6 : Run Full convolution notebook as a batch \n",
- "[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb) \n",
- " Displaying the reports of the different jobs \n",
- "[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb) \n",
- " 4 ways to use Tensorboard from the Jupyter environment \n",
- "[[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb) \n",
- " A very classical example of word embedding for text classification (sentiment analysis) \n",
- "[[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb) \n",
- " Example of reusing a previously saved model \n",
- "[[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb) \n",
- " Still the same problem, but with a network combining embedding and LSTM \n",
- "[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) \n",
- " First generative network experience with the MNIST dataset \n",
- "[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) \n",
- " Use of the previously trained model, analysis of the results \n",
- "[[VAE3] - About the CelebA dataset](VAE/03-Prepare-CelebA.ipynb) \n",
- " New VAE experience, but with a larger and more fun dataset \n",
- "[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb) \n",
- " Preparation of a clustered dataset, batchable \n",
- "[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb) \n",
- " Verification of prepared data from CelebA dataset \n",
- "[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb) \n",
- " VAE with a more fun and realistic dataset - small resolution and batchable \n",
- "[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb) \n",
- " VAE with a more fun and realistic dataset - medium resolution and batchable \n",
- "[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/12-VAE-withCelebA-post.ipynb) \n",
- " Use of the previously trained model with CelebA, analysis of the results \n",
- "[[BASH1] - OAR batch script](VAE/batch-oar.sh) \n",
- " Bash script for OAR batch submission of a notebook \n",
- "[[BASH2] - SLURM batch script](VAE/batch-slurm.sh) \n",
- " Bash script for SLURM batch submission of a notebook \n",
- "<!-- INDEX_END -->"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "[<img width=\"600px\" src=\"fidle/img/00-Fidle-titre-01.svg\"></img>](#)\n",
+ "\n",
+ "## A propos\n",
+ "\n",
+ "This repository contains all the documents and links of the **Fidle Training**. \n",
+ "\n",
+ "The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, are :\n",
+ " - Understanding the **bases of deep learning** neural networks (Deep Learning)\n",
+ " - Develop a **first experience** through simple and representative examples\n",
+ " - Understand the different types of networks, their **architectures** and their **use cases**.\n",
+ " - Understanding **Tensorflow/Keras and Jupyter lab** technologies on the GPU\n",
+ " - Apprehend the **academic computing environments** Tier-2 (meso) and/or Tier-1 (national)\n",
+ "\n",
+ "## Course materials\n",
+ "**[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img>\n",
+ "Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/z7XZA36xKkMcaTS)** \n",
+ "\n",
+ "\n",
+ "\n",
+ "<!--  -->\n",
+ "Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)\n",
+ "\n",
+ "\n",
+ "## Jupyter notebooks\n",
+ "\n",
+ "[](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Ftalks%2Fdeeplearning.git/master?urlpath=lab/tree/index.ipynb)\n",
+ "\n",
+ "\n",
+ "<!-- DO NOT REMOVE THIS TAG !!! -->\n",
+ "<!-- INDEX -->\n",
+ "<!-- INDEX_BEGIN -->\n",
+ "[[NP1] - A short introduction to Numpy](Prerequisites/Numpy.ipynb) \n",
+ " Numpy is an essential tool for the Scientific Python. \n",
+ "[[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
+ " Direct determination of linear regression \n",
+ "[[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) \n",
+ " An example of gradient descent in the simple case of a linear regression. \n",
+ "[[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) \n",
+ " Illustration of the problem of complexity with the polynomial regression \n",
+ "[[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb) \n",
+ " Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. \n",
+ "[[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) \n",
+ " Example of classification with a fully connected neural network \n",
+ "[[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) \n",
+ " A Simple regression with a Dense Neural Network (DNN) - BHPD dataset \n",
+ "[[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) \n",
+ " More advanced example of DNN network code - BHPD dataset \n",
+ "[[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) \n",
+ " Episode 1: Data analysis and creation of a usable dataset \n",
+ "[[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
+ " Episode 2 : First convolutions and first results \n",
+ "[[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb) \n",
+ " Episode 3: Monitoring and analysing training, managing checkpoints \n",
+ "[[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb) \n",
+ " Episode 4: Improving the results with data augmentation \n",
+ "[[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb) \n",
+ " Episode 5: A lot of models, a lot of datasets and a lot of results. \n",
+ "[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb) \n",
+ " Episode 6 : Run Full convolution notebook as a batch \n",
+ "[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb) \n",
+ " Displaying the reports of the different jobs \n",
+ "[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb) \n",
+ " 4 ways to use Tensorboard from the Jupyter environment \n",
+ "[[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb) \n",
+ " A very classical example of word embedding for text classification (sentiment analysis) \n",
+ "[[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb) \n",
+ " Example of reusing a previously saved model \n",
+ "[[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb) \n",
+ " Still the same problem, but with a network combining embedding and LSTM \n",
+ "[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) \n",
+ " First generative network experience with the MNIST dataset \n",
+ "[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) \n",
+ " Use of the previously trained model, analysis of the results \n",
+ "[[VAE3] - About the CelebA dataset](VAE/03-Prepare-CelebA.ipynb) \n",
+ " New VAE experience, but with a larger and more fun dataset \n",
+ "[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb) \n",
+ " Preparation of a clustered dataset, batchable \n",
+ "[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb) \n",
+ " Verification of prepared data from CelebA dataset \n",
+ "[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb) \n",
+ " VAE with a more fun and realistic dataset - small resolution and batchable \n",
+ "[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb) \n",
+ " VAE with a more fun and realistic dataset - medium resolution and batchable \n",
+ "[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/12-VAE-withCelebA-post.ipynb) \n",
+ " Use of the previously trained model with CelebA, analysis of the results \n",
+ "[[BASH1] - OAR batch script](VAE/batch-oar.sh) \n",
+ " Bash script for OAR batch submission of a notebook \n",
+ "[[BASH2] - SLURM batch script](VAE/batch-slurm.sh) \n",
+ " Bash script for SLURM batch submission of a notebook \n",
+ "<!-- INDEX_END -->\n",
+ "\n",
+ "\n",
+ "## Installation\n",
+ "\n",
+ "A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/howto-jupyter)**.\n",
+ "\n",
+ "## Licence\n",
+ "\n",
+ "[<img width=\"100px\" src=\"fidle/img/00-fidle-CC BY-NC-SA.svg\"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/) \n",
+ "\\[en\\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0) \n",
+ "\\[Fr\\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International \n",
+ "See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). \n",
+ "See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#). \n",
+ "\n",
+ "\n",
+ "----\n",
+ "[<img width=\"80px\" src=\"fidle/img/00-Fidle-logo-01.svg\"></img>](#)"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "---\n",
- "<img width=\"80px\" src=\"fidle/img/00-Fidle-logo-01.svg\"></img>"
+ "from IPython.display import display,Markdown\n",
+ "display(Markdown(open('README.md', 'r').read()))"
]
}
],
diff --git a/fidle/Charte.ipynb b/fidle/Example.ipynb
similarity index 91%
rename from fidle/Charte.ipynb
rename to fidle/Example.ipynb
index fbd6f0fd67366e60fc095edf625720b10cca80c5..c69dff70c5824f044d8ff664b611df24ad79d65c 100644
--- a/fidle/Charte.ipynb
+++ b/fidle/Example.ipynb
@@ -30,13 +30,6 @@
"---\n",
"<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {