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https://github.com/ArthurDanjou/ArtStudies.git
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285 lines
112 KiB
Plaintext
285 lines
112 KiB
Plaintext
{
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"cells": [
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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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"# Séance 1 - Bonus : Première régularisation d'un réseau de neurones\n",
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"\n",
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"Pour poursuivre le travail du TP, on se propose d'explorer une première manière de régulariser un réseau de neurones. Nous avons vu que nous pouvions atteindre de bonne performances, mais que cela entraînait fréquemment un sur-apprentissage. \n",
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"Une des premières manières de régulariser que l'on apprend en Machine Learning est de pénaliser une régression linéaire : c'est la régression Ridge. Le principe est de modifier la fonction de perte pour contraindre les poids appris à être *petit*. Il est possible de le faire couche par couche dans un réseau de neurones. Essayons !\n",
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"\n",
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"Commençons par importer et traiter les données."
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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": 1,
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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 pandas as pd\n",
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"\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"sns.set(style='whitegrid')\n",
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"\n",
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"import tensorflow as tf\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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"from tensorflow import keras\n",
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"\n",
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"(X_train_full, y_train_full), (X_test, y_test) = (keras.datasets.mnist.load_data())\n",
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"X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, train_size=0.8)\n",
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"\n",
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"scaler = StandardScaler()\n",
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"X_train = scaler.fit_transform(X_train.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28)\n",
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"X_valid = scaler.transform(X_valid.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28)\n",
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"X_test = scaler.transform(X_test.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28)"
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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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"## Pénalisation $L_2$\n",
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"\n",
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"La pénalisation à laquelle on s'intéresse ici est la pénalisation $L_2$. Si l'on considère un problème d'optimisation d'une fonction de perte $\\mathcal{L}$\n",
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"\n",
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"$$w^* = \\arg\\min_{w\\in\\mathbb{R}^d} \\mathcal{L}(w)$$\n",
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"\n",
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"Alors sa version pénalisée est la suivante avec $\\lambda > 0$ :\n",
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"\n",
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"$$w^* = \\arg\\min_{w\\in\\mathbb{R}^d} \\mathcal{L}(w) + \\lambda\\|w\\|^2$$\n",
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"\n",
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"Ce n'est plus exactement le même objectif puisqu'on force ici le vecteur solution $w^*$ a prendre de plus petite valeurs. Nous pouvons faire cela couche par couche.\n",
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"\n",
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"**Consigne** : Définir un modèle en ajoutant une pénalisation [$L_2$](https://keras.io/api/layers/regularizers/#l2-class) aux couches [`Dense`](https://keras.io/api/layers/core_layers/dense/)."
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = keras.models.Sequential(\n",
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" [\n",
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" keras.layers.Input(shape=[28, 28]),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(256, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(0.001)),\n",
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" keras.layers.Dense(128, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(0.001)),\n",
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" keras.layers.Dense(10, activation=\"softmax\"),\n",
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" ]\n",
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")\n"
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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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"**Consigne** : Lancer sur quelques époques le modèle pour valider qu'il fonctionne correctement."
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/5\n",
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"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.6333 - loss: 1.8730 - val_accuracy: 0.8069 - val_loss: 1.3328\n",
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"Epoch 2/5\n",
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"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8519 - loss: 1.1420 - val_accuracy: 0.8643 - val_loss: 1.0756\n",
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"Epoch 3/5\n",
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"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8861 - loss: 0.9830 - val_accuracy: 0.8863 - val_loss: 0.9850\n",
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"Epoch 4/5\n",
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"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9014 - loss: 0.9122 - val_accuracy: 0.8977 - val_loss: 0.9364\n",
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"Epoch 5/5\n",
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"\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 999us/step - accuracy: 0.9112 - loss: 0.8692 - val_accuracy: 0.9056 - val_loss: 0.9040\n"
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]
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}
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],
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"source": [
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"model.compile(\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" optimizer=keras.optimizers.SGD(learning_rate=1e-3),\n",
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" metrics=[\"accuracy\"],\n",
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")\n",
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"\n",
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"epochs = 5\n",
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"batch_size = 32\n",
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"\n",
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"history = model.fit(\n",
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" X_train,\n",
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" y_train,\n",
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" epochs=epochs,\n",
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" batch_size=batch_size,\n",
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" validation_data=(X_valid, y_valid),\n",
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")"
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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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"De la même manière que l'on souhaitais observer l'impact du learning rate sur l'entraînement, on souhaite mesurer l'apport de la régularisation au modèle. \n",
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"\n",
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"## Mesure de l'impact de la régularisation\n",
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"\n",
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"**Consigne** : Définir une fonction `get_model` qui prend en paramètre :\n",
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"* *lambda_l2* : float correspondant à la magnitude de la pénalisation $L_2$\n",
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"* *learning_rate* : float par défaut à $0.001$ correspond au learning rate de l'optimizer\n",
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"\n",
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"La fonction renvoie le modèle compilé."
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_model(lambda_l2: float, learning_rate: float) -> keras.Model:\n",
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" model = keras.models.Sequential(\n",
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" [\n",
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" keras.layers.Input(shape=[28, 28]),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(256, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(lambda_l2)),\n",
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" keras.layers.Dense(128, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(lambda_l2)),\n",
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" keras.layers.Dense(10, activation=\"softmax\"),\n",
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" ]\n",
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" )\n",
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" model.compile(\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" optimizer=keras.optimizers.SGD(learning_rate=learning_rate),\n",
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" metrics=[\"accuracy\"],\n",
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" )\n",
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" return model"
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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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"**Consigne** : En s'inspirant du travail réalisé pour le learning rate, comparer différente valeur de régularisation. Commenter."
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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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"n_epochs = 30\n",
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"batch_size = 256\n",
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"learning_rates = [10 ** (-power) for power in range(1, 4)]\n",
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"lambda_l2_values = [0.001, 0.01, 0.1]\n",
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"\n",
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"results = []\n",
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"for learning_rate in learning_rates:\n",
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" for lambda_l2 in lambda_l2_values:\n",
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" model = get_model(lambda_l2, learning_rate)\n",
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" history = model.fit(\n",
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" X_train,\n",
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" y_train,\n",
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" epochs=n_epochs,\n",
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" batch_size=batch_size,\n",
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" validation_data=(X_valid, y_valid),\n",
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" verbose=0,\n",
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" )\n",
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" results.append(\n",
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" {\n",
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" \"learning_rate\": learning_rate,\n",
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" \"lambda_l2\": lambda_l2,\n",
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" \"history\": pd.DataFrame(history.history),\n",
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" \"n_epochs\": n_epochs,\n",
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" }\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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"def show_results(results: list) -> None:\n",
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" plt.figure(figsize=(12, 4))\n",
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"\n",
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" for _, result in enumerate(results):\n",
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" history_df = result[\"history\"]\n",
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" learning_rate = result[\"learning_rate\"]\n",
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" lambda_l2 = result[\"lambda_l2\"]\n",
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"\n",
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" plt.subplot(1, 2, 1)\n",
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" plt.plot(history_df[\"val_loss\"], label=f\"LR={learning_rate}, L2={lambda_l2}\")\n",
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" plt.xlabel(\"Epochs\")\n",
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" plt.ylabel(\"Validation Loss\")\n",
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" plt.legend()\n",
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"\n",
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" plt.subplot(1, 2, 2)\n",
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" plt.plot(history_df[\"val_accuracy\"], label=f\"LR={learning_rate}, L2={lambda_l2}\")\n",
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" plt.xlabel(\"Epochs\")\n",
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" plt.ylabel(\"Validation Accuracy\")\n",
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" plt.legend()\n",
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"\n",
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" plt.show()\n"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x400 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"show_results(results)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "studies",
|
|
"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": "3.13.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|