Ajout du lab de workflows reproductibles avec documentation complète
- Implémentation du notebook lab.ipynb avec code complet pour créer des workflows d'IA reproductibles - Ajout d'un README.md pédagogique de 600+ lignes en français - Configuration des graines aléatoires pour la reproductibilité - Implémentation de la génération, division, normalisation et sauvegarde des données - Création et entraînement d'un réseau de neurones avec TensorFlow/Keras - Démonstration du rechargement et de la vérification de la reproductibilité 🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.instructions/INSTRUCTIONS.mdoc
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.instructions/INSTRUCTIONS.mdoc
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{% steps %}
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{% step title="Introduction to Reproducible Workflows" %}
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### Introduction
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Welcome to the "Introduction to Reproducible Workflows" lab!
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This lab is designed to give you a foundational understanding of creating reproducible workflows for training an AI model,
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its importance, and examples of key parts in model training to define fixed seeds.
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### Learning Objectives
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- Display key areas within AI model workflows to define fixed seeds
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- Review saving datasets after train/test splits
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- Practice recovering models and training datasets to repeat results
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### Prerequisites
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- A high level understanding of AI neural networks
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- Experience in training models.
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{% /step %}
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{% step title="Creating Reproducible Datasets" %}
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### Creating Reproducible Datasets
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For you to create a repeatable workflow you will need to start by defining the random seeds. When you define the random seed, it
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will determine how data is split, generated, and how these train/test datasets are fed into the model. Your starting seed
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helps ensure that all aspects of the workflow are repeatable. The provided code below sets the random seed for several
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different libraries used in model development.
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```python
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random.seed(42)
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np.random.seed(42)
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tf.random.set_seed(42)
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```
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From here you can define the synthetic data generation similar to how it is defined in other labs.
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```python
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X, y = make_classification(
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n_samples=1000,
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n_features=20,
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n_informative=15,
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n_redundant=5,
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n_classes=2,
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random_state=42
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)
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```
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{% /step %}
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{% step title="Training Reproducible Model" %}
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### Training Reproducible Models
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For the next step of model creation you will need to split your data into training and test datasets. The code provided below
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allows for the initial dataset to be split into training and test sets. The key parameter for you to focus on here is the
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```random_state``` which defines the random seed for the split. When you define the random seed, you are
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ensuring future instances of the models training will result in the same training dataset being used, and therefore the
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same model being created.
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```python
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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```
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You can then also save the `train` and `test` data splits to allow for reproducibility in instances where the dataset you are
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using may be a subset of a larger dataset.
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```python
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joblib.dump((X_train, y_train), 'train_data.pkl')
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joblib.dump((X_test, y_test), 'test_data.pkl')
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```
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With the datasets saved and reproducible you can move forward with defining and training your model. You will define and train
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your model in this lab so you can evaluate it and compare it to a model trained on the same training dataset to ensure it's
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properly reproducible. Within the lab all code is provided for
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training and evaluating the model.
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{% /step %}
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{% step title="Saving Model and Dataset" %}
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### Saving Model and Dataset
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Now that your model has been trained and evaluated, you can save it with the following code
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```python
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model.save('my_model.keras')
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```
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From there you can move onto reproducing models which is meant to mimic an entirely new environment of loading a model into.
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You can load the previously saved model and datasets using the following code below, including required imports.
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```python
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from tensorflow.keras.models import load_model
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import joblib
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modelReloaded = load_model('my_model.keras')
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X_train_reloaded, y_train_reloaded = joblib.load('train_data.pkl')
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X_test_reloaded, y_test_reloaded = joblib.load('test_data.pkl')
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```
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As you revaluate the model on the exact same training and test sets using the previously defined evaluation code, you should
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see results that are exactly the same as the previously evaluted model.
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```python
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loss, accuracy = modelReloaded.evaluate(X_test, y_test)
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print(f"Test accuracy: {accuracy:.2f}")
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```
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{% /step %}
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{% /steps %}
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22
.vscode/settings.json
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.vscode/settings.json
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{
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"terminal.integrated.fontSize": 15,
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"editor.fontSize": 15,
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"terminal.integrated.defaultProfile.linux": "bash",
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"workbench.colorTheme": "Default Dark Modern",
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"workbench.startupEditor": "none",
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"files.associations": {
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"*.md": "markdoc"
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},
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"workspace": {
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"view": "readme",
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"terminals": [
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{
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"name": "Terminal",
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"active": false
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}
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],
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"files": [
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"./lab.ipynb"
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],
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}
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}
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166
lab.ipynb
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lab.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"from sklearn.datasets import make_classification\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"import random\n",
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"import joblib"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Creating Reproducible Datasets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# 1. Set random seeds for reproducibility\n# Définir les graines aléatoires pour la reproductibilité\nrandom.seed(42) # Pour le module random\nnp.random.seed(42) # Pour NumPy\ntf.random.set_seed(42) # Pour TensorFlow\n\nprint(\"✓ Graines aléatoires définies avec succès !\")"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# 2. Generate synthetic data\n# Générer des données synthétiques pour la classification\nX, y = make_classification(\n n_samples=1000, # 1000 exemples\n n_features=20, # 20 caractéristiques\n n_informative=15, # 15 caractéristiques utiles\n n_redundant=5, # 5 caractéristiques redondantes\n n_classes=2, # 2 classes (classification binaire)\n random_state=42 # Graine aléatoire pour reproductibilité\n)\n\nprint(f\"✓ Données générées : {X.shape[0]} exemples, {X.shape[1]} caractéristiques\")"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Training Reproducible Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# 3. Split and scale the data\n# Diviser les données en ensembles d'entraînement (80%) et de test (20%)\nX_train, X_test, y_train, y_test = train_test_split(\n X, y, \n test_size=0.2, # 20% pour le test\n random_state=42 # Important pour la reproductibilité !\n)\n\n# Normaliser les données (StandardScaler centre les données autour de 0)\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train) # Apprendre et transformer les données d'entraînement\nX_test = scaler.transform(X_test) # Transformer les données de test\n\nprint(f\"✓ Données divisées :\")\nprint(f\" - Entraînement : {X_train.shape[0]} exemples\")\nprint(f\" - Test : {X_test.shape[0]} exemples\")"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Sauvegarder les données d'entraînement et de test pour la reproductibilité\njoblib.dump((X_train, y_train), 'train_data.pkl')\njoblib.dump((X_test, y_test), 'test_data.pkl')\n\nprint(\"✓ Données sauvegardées :\")\nprint(\" - train_data.pkl\")\nprint(\" - test_data.pkl\")"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Model Initalization and Training"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 4. Build a neural network\n",
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"model = keras.Sequential([\n",
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" keras.layers.Dense(32, activation='relu', input_shape=(X_train.shape[1],)),\n",
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" keras.layers.Dense(16, activation='relu'),\n",
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" keras.layers.Dense(1, activation='sigmoid')\n",
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"])\n",
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"\n",
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"model.compile(\n",
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" optimizer='adam',\n",
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" loss='binary_crossentropy',\n",
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" metrics=['accuracy']\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 5. Train the model\n",
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"model.fit(X_train, y_train, epochs=20, batch_size=32, validation_split=0.1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 6. Evaluate the model\n",
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"loss, accuracy = model.evaluate(X_test, y_test)\n",
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"print(f\"Test accuracy: {accuracy:.2f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Saving Models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# 7. Save the model and scaler\n# Sauvegarder le modèle entraîné\nmodel.save('my_model.keras')\n\nprint(\"✓ Modèle sauvegardé : my_model.keras\")"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Reproducing Models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "#8. Reloading model later\n# Recharger le modèle et les données sauvegardées\nfrom tensorflow.keras.models import load_model\n\nmodelReloaded = load_model('my_model.keras')\nX_train_reloaded, y_train_reloaded = joblib.load('train_data.pkl')\nX_test_reloaded, y_test_reloaded = joblib.load('test_data.pkl')\n\nprint(\"✓ Modèle et données rechargés avec succès !\")"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "# Vérifier que le modèle rechargé donne les mêmes résultats\nloss_reloaded, accuracy_reloaded = modelReloaded.evaluate(X_test_reloaded, y_test_reloaded)\nprint(f\"\\n🎯 Précision du modèle rechargé : {accuracy_reloaded:.2f}\")\nprint(\"\\n💡 Si la précision est identique à celle obtenue plus haut,\")\nprint(\" votre workflow est reproductible ! ✓\")"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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