From 853282dd0101bfa45241c774e46aaf48f272b2be Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@simap.grenoble-inp.fr>
Date: Mon, 6 Jan 2025 16:33:44 +0100
Subject: [PATCH] Update to 3.0.14
---
GTSRB.Keras3/04-Keras-cv.ipynb | 172 ---------------------------------
README.ipynb | 26 ++---
README.md | 12 +--
fidle/about.yml | 3 +-
fidle/ci/default.yml | 13 +--
5 files changed, 14 insertions(+), 212 deletions(-)
delete mode 100644 GTSRB.Keras3/04-Keras-cv.ipynb
diff --git a/GTSRB.Keras3/04-Keras-cv.ipynb b/GTSRB.Keras3/04-Keras-cv.ipynb
deleted file mode 100644
index 105d485..0000000
--- a/GTSRB.Keras3/04-Keras-cv.ipynb
+++ /dev/null
@@ -1,172 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [K3GTSRB4] - Hight level example (Keras-cv)\n",
- "<!-- DESC --> An example of using a pre-trained model with Keras-cv\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - Using a pre-trained model\n",
- " \n",
- "## What we're going to do :\n",
- "\n",
- " - Load and use a pre-trained model\n",
- "\n",
- " See : https://keras.io/guides/keras_cv/classification_with_keras_cv/ \n",
- " Imagenet classes can be found at : https://gist.githubusercontent.com/LukeWood/62eebcd5c5c4a4d0e0b7845780f76d55/raw/fde63e5e4c09e2fa0a3436680f436bdcb8325aac/ImagenetClassnames.json\n",
- "\n",
- "## ATTENTION : A specific environment is required for this example !\n",
- "This python environment required for this notebook is :\n",
- "```\n",
- "python3 -m venv fidle-kcv\n",
- "pip install --upgrade keras-cv tensorflow torch torchvision torchaudio Matplotlib Jupyterlab\n",
- "pip install --upgrade keras jupyterlab\n",
- "```\n",
- "Note: Tensorflow is not used for interference, and will no longer be required in later versions of Keras 3.\n",
- "\n",
- "## Step 1 - Import and init\n",
- "\n",
- "### 1.1 - Python stuffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "os.environ[\"KERAS_BACKEND\"] = \"torch\" # @param [\"tensorflow\", \"jax\", \"torch\"]\n",
- "\n",
- "import json\n",
- "import numpy as np\n",
- "\n",
- "import keras\n",
- "import keras_cv\n",
- "\n",
- "from modules.ImagenetClassnames import ImagenetClassnames"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Get some images"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "images_url=['https://i.imgur.com/2eOWImx.jpeg', 'https://i.imgur.com/YB8sG8R.jpeg', 'https://i.imgur.com/orZEMlv.jpeg']\n",
- "\n",
- "images=[]\n",
- "for img_url in images_url:\n",
- " \n",
- " # Get images from urls in ~/.keras cache\n",
- " img_path = keras.utils.get_file(origin=img_url)\n",
- "\n",
- " # Get image\n",
- " img = keras.utils.load_img(img_path, target_size=(256,256))\n",
- " images.append(img)\n",
- "images=np.array(images)\n",
- "\n",
- "keras_cv.visualization.plot_image_gallery( images, rows=1, cols=3, value_range=(0, 255), show=True, scale=2)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Get a nice pretrained classifier (and classes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "classifier = keras_cv.models.ImageClassifier.from_preset( \"efficientnetv2_b0_imagenet_classifier\" )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Try some predictions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "predictions = classifier.predict(images)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Show result"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get classes name \n",
- "imc = ImagenetClassnames()\n",
- "\n",
- "for i,img in enumerate(images):\n",
- " # Get classes id instead classes probabilities\n",
- " classes_id = predictions[i].argsort(axis=-1)\n",
- " # Get classes name instead classes id\n",
- " classes_name = imc.get(classes_id, top_n=2)\n",
- " # Plot it\n",
- " keras_cv.visualization.plot_image_gallery( np.array([img]), rows=1, cols=1, value_range=(0, 255), show=True, scale=2)\n",
- " print(classes_name)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/logo-paysage.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "fidle-kcv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/README.ipynb b/README.ipynb
index 1ac248a..0c2e410 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "632b4ee8",
+ "id": "f31b5632",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-12-22T17:56:42.379486Z",
- "iopub.status.busy": "2024-12-22T17:56:42.379188Z",
- "iopub.status.idle": "2024-12-22T17:56:42.384886Z",
- "shell.execute_reply": "2024-12-22T17:56:42.384583Z"
+ "iopub.execute_input": "2025-01-06T15:33:06.393183Z",
+ "iopub.status.busy": "2025-01-06T15:33:06.392990Z",
+ "iopub.status.idle": "2025-01-06T15:33:06.401877Z",
+ "shell.execute_reply": "2025-01-06T15:33:06.401059Z"
},
"jupyter": {
"source_hidden": true
@@ -53,7 +53,7 @@
"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",
"\n",
- "Current Version : <!-- VERSION_BEGIN -->3.0.12<!-- VERSION_END -->\n",
+ "Current Version : <!-- VERSION_BEGIN -->3.0.14<!-- VERSION_END -->\n",
"\n",
"\n",
"## Course materials\n",
@@ -68,7 +68,7 @@
"## Jupyter notebooks\n",
"\n",
"<!-- TOC_BEGIN -->\n",
- "<!-- Automatically generated on : 22/12/24 18:56:39 -->\n",
+ "<!-- Automatically generated on : 06/01/25 16:33:05 -->\n",
"\n",
"### Linear and logistic regression\n",
"- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
@@ -125,8 +125,6 @@
"Episode 2 : First convolutions and first classification of our traffic signs, using Keras3\n",
"- **[K3GTSRB3](GTSRB.Keras3/03-Better-convolutions.ipynb)** - [Training monitoring](GTSRB.Keras3/03-Better-convolutions.ipynb) \n",
"Episode 3 : Monitoring, analysis and check points during a training session, using Keras3\n",
- "- **[K3GTSRB4](GTSRB.Keras3/04-Keras-cv.ipynb)** - [Hight level example (Keras-cv)](GTSRB.Keras3/04-Keras-cv.ipynb) \n",
- "An example of using a pre-trained model with Keras-cv\n",
"- **[K3GTSRB10](GTSRB.Keras3/batch_oar.sh)** - [OAR batch script submission](GTSRB.Keras3/batch_oar.sh) \n",
"Bash script for an OAR batch submission of an ipython code\n",
"- **[K3GTSRB11](GTSRB.Keras3/batch_slurm.sh)** - [SLURM batch script](GTSRB.Keras3/batch_slurm.sh) \n",
@@ -148,12 +146,6 @@
"- **[K3LADYB1](RNN.Keras3/01-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](RNN.Keras3/01-Ladybug.ipynb) \n",
"Artificial dataset generation and prediction attempt via a recurrent network, using Keras 3 and PyTorch\n",
"\n",
- "### Sentiment analysis with transformer, using PyTorch\n",
- "- **[TRANS1](Transformers.PyTorch/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers.PyTorch/01-Distilbert.ipynb) \n",
- "Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version\n",
- "- **[TRANS2](Transformers.PyTorch/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers.PyTorch/02-distilbert_colab.ipynb) \n",
- "Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version\n",
- "\n",
"### Graph Neural Networks\n",
"\n",
"### Unsupervised learning with an autoencoder neural network (AE), using Keras3\n",
@@ -243,7 +235,7 @@
"from IPython.display import display,Markdown\n",
"display(Markdown(open('README.md', 'r').read()))\n",
"#\n",
- "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 22/12/24 18:56:40"
+ "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 06/01/25 16:33:05"
]
}
],
@@ -263,7 +255,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.7"
+ "version": "3.11.2"
}
},
"nbformat": 4,
diff --git a/README.md b/README.md
index 0e4f31b..add0f1b 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@ For more information, see **https://fidle.cnrs.fr** :
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 -->3.0.12<!-- VERSION_END -->
+Current Version : <!-- VERSION_BEGIN -->3.0.14<!-- VERSION_END -->
## Course materials
@@ -47,7 +47,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)
## Jupyter notebooks
<!-- TOC_BEGIN -->
-<!-- Automatically generated on : 22/12/24 18:56:39 -->
+<!-- Automatically generated on : 06/01/25 16:33:05 -->
### Linear and logistic regression
- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
@@ -104,8 +104,6 @@ Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
Episode 2 : First convolutions and first classification of our traffic signs, using Keras3
- **[K3GTSRB3](GTSRB.Keras3/03-Better-convolutions.ipynb)** - [Training monitoring](GTSRB.Keras3/03-Better-convolutions.ipynb)
Episode 3 : Monitoring, analysis and check points during a training session, using Keras3
-- **[K3GTSRB4](GTSRB.Keras3/04-Keras-cv.ipynb)** - [Hight level example (Keras-cv)](GTSRB.Keras3/04-Keras-cv.ipynb)
-An example of using a pre-trained model with Keras-cv
- **[K3GTSRB10](GTSRB.Keras3/batch_oar.sh)** - [OAR batch script submission](GTSRB.Keras3/batch_oar.sh)
Bash script for an OAR batch submission of an ipython code
- **[K3GTSRB11](GTSRB.Keras3/batch_slurm.sh)** - [SLURM batch script](GTSRB.Keras3/batch_slurm.sh)
@@ -127,12 +125,6 @@ Still the same problem, but with a network combining embedding and RNN, using Ke
- **[K3LADYB1](RNN.Keras3/01-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](RNN.Keras3/01-Ladybug.ipynb)
Artificial dataset generation and prediction attempt via a recurrent network, using Keras 3 and PyTorch
-### Sentiment analysis with transformer, using PyTorch
-- **[TRANS1](Transformers.PyTorch/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers.PyTorch/01-Distilbert.ipynb)
-Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version
-- **[TRANS2](Transformers.PyTorch/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers.PyTorch/02-distilbert_colab.ipynb)
-Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version
-
### Graph Neural Networks
### Unsupervised learning with an autoencoder neural network (AE), using Keras3
diff --git a/fidle/about.yml b/fidle/about.yml
index 366f0bd..ab119fe 100644
--- a/fidle/about.yml
+++ b/fidle/about.yml
@@ -13,7 +13,7 @@
#
# This file describes the notebooks used by the Fidle training.
-version: 3.0.12
+version: 3.0.14
content: notebooks
name: Notebooks Fidle
description: All notebooks used by the Fidle training
@@ -36,7 +36,6 @@ toc:
GTSRB.Keras3: Images classification GTSRB with Convolutional Neural Networks (CNN), using Keras3/PyTorch
Embedding.Keras3: Sentiment analysis with word embedding, using Keras3/PyTorch
RNN.Keras3: Time series with Recurrent Neural Network (RNN), using Keras3/PyTorch
- Transformers.PyTorch: Sentiment analysis with transformer, using PyTorch
GNN.PyTorch: Graph Neural Networks
AE.Keras3: Unsupervised learning with an autoencoder neural network (AE), using Keras3
VAE.Keras3: Generative network with Variational Autoencoder (VAE), using Keras3
diff --git a/fidle/ci/default.yml b/fidle/ci/default.yml
index b70e0a7..bc46050 100644
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -1,6 +1,6 @@
campain:
version: '1.0'
- description: Automatically generated ci profile (22/12/24 18:56:39)
+ description: Automatically generated ci profile (06/01/25 16:33:05)
directory: ./campains/default
existing_notebook: 'remove # remove|skip'
report_template: 'fidle # fidle|default'
@@ -114,8 +114,6 @@ K3GTSRB3:
epochs: default
scale: default
fit_verbosity: default
-K3GTSRB4:
- notebook: GTSRB.Keras3/04-Keras-cv.ipynb
#
# ------------ Embedding.Keras3
@@ -177,14 +175,7 @@ K3LADYB1:
predict_len: default
batch_size: default
epochs: default
-
-#
-# ------------ Transformers.PyTorch
-#
-TRANS1:
- notebook: Transformers.PyTorch/01-Distilbert.ipynb
-TRANS2:
- notebook: Transformers.PyTorch/02-distilbert_colab.ipynb
+ fit_verbosity: default
#
# ------------ GNN.PyTorch
--
GitLab