992 lines
131 KiB
Plaintext
992 lines
131 KiB
Plaintext
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{
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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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"id": "XMMppWbnG3dN"
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},
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"source": [
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"## Auto-encodeurs\n",
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"\n",
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"L'objectif de ce TP est de manipuler des auto-encodeurs sur un exemple simple : la base de données MNIST. L'idée est de pouvoir visualiser les concepts vus en cours, et notamment d'illustrer la notion d'espace latent.\n",
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"\n",
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"Pour rappel, vous avez déjà manipulé les données MNIST en Analyse de Données en première année. Les images MNIST sont des images en niveaux de gris, de taille 28x28 pixels, représentant des chiffres manuscrits de 0 à 9.\n",
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"\n",
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"\n",
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"\n",
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"Pour démarrer, nous vous fournissons un code permettant de créer un premier auto-encodeur simple, de charger les données MNIST et d'entraîner cet auto-encodeur. **L'autoencodeur n'est pas convolutif !** (libre à vous de le transformer pour qu'il le soit, plus tard dans le TP)"
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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": 13,
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"metadata": {
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"id": "nndrdDrlSkho"
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},
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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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"Model: \"model_11\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" input_7 (InputLayer) [(None, 784)] 0 \n",
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" \n",
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" model_9 (Functional) (None, 32) 104608 \n",
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" \n",
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" model_10 (Functional) (None, 784) 105360 \n",
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" \n",
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"=================================================================\n",
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"Total params: 209,968\n",
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"Trainable params: 209,968\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n",
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"Model: \"model_9\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" input_7 (InputLayer) [(None, 784)] 0 \n",
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" \n",
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" dense_12 (Dense) (None, 128) 100480 \n",
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" \n",
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" dense_13 (Dense) (None, 32) 4128 \n",
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" \n",
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"=================================================================\n",
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"Total params: 104,608\n",
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"Trainable params: 104,608\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n",
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"None\n",
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"Model: \"model_10\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" input_8 (InputLayer) [(None, 32)] 0 \n",
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" \n",
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" dense_14 (Dense) (None, 128) 4224 \n",
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" \n",
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" dense_15 (Dense) (None, 784) 101136 \n",
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" \n",
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"=================================================================\n",
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"Total params: 105,360\n",
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"Trainable params: 105,360\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n",
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"None\n"
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]
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}
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],
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"source": [
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"from keras.layers import Input, Dense\n",
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"from keras.models import Model\n",
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"\n",
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"# Dimension de l'entrée\n",
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"input_img = Input(shape=(784,))\n",
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"# Dimension de l'espace latent : PARAMETRE A TESTER !!\n",
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"latent_dim = 32\n",
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"\n",
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"# Définition d'un encodeur\n",
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"x = Dense(128, activation='relu')(input_img)\n",
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"encoded = Dense(latent_dim, activation='linear')(x)\n",
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"\n",
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"# Définition d'un decodeur\n",
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"decoder_input = Input(shape=(latent_dim,))\n",
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"x = Dense(128, activation='relu')(decoder_input)\n",
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"decoded = Dense(784, activation='sigmoid')(x)\n",
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"\n",
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"# Construction d'un modèle séparé pour pouvoir accéder aux décodeur et encodeur\n",
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"encoder = Model(input_img, encoded)\n",
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"decoder = Model(decoder_input, decoded)\n",
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"\n",
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"# Construction du modèle de l'auto-encodeur\n",
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"encoded = encoder(input_img)\n",
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"decoded = decoder(encoded)\n",
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"autoencoder = Model(input_img, decoded)\n",
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"\n",
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"autoencoder.compile(optimizer='Adam', loss='SparseCategoricalCrossentropy')\n",
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"autoencoder.summary()\n",
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"print(encoder.summary())\n",
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"print(decoder.summary())"
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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": 22,
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"metadata": {
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"id": "jwYr0GHCSlIq"
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},
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"outputs": [],
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"source": [
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"from keras.datasets import mnist\n",
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"import numpy as np\n",
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"\n",
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"# Chargement et normalisation (entre 0 et 1) des données de la base de données MNIST\n",
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"(x_train, _), (x_test, _) = mnist.load_data()\n",
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"\n",
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"x_train = x_train.astype('float32') / 255.\n",
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"x_test = x_test.astype('float32') / 255.\n",
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"\n",
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"# Vectorisation des images d'entrée en vecteurs de dimension 784\n",
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"x_train = np.reshape(x_train, (len(x_train), 784))\n",
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"x_test = np.reshape(x_test, (len(x_test), 784))"
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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": 23,
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"metadata": {
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"id": "eKPiA41bSvxC"
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},
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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 1/50\n",
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"469/469 [==============================] - 3s 6ms/step - loss: 0.0784 - val_loss: 0.0781\n",
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"Epoch 2/50\n",
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"469/469 [==============================] - 2s 5ms/step - loss: 0.0783 - val_loss: 0.0780\n",
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"Epoch 3/50\n",
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"469/469 [==============================] - 2s 5ms/step - loss: 0.0783 - val_loss: 0.0781\n",
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"Epoch 4/50\n",
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"469/469 [==============================] - 2s 5ms/step - loss: 0.0783 - val_loss: 0.0780\n",
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"Epoch 5/50\n",
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"469/469 [==============================] - 2s 5ms/step - loss: 0.0782 - val_loss: 0.0779\n",
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"Epoch 6/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0782 - val_loss: 0.0780\n",
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"Epoch 7/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0781 - val_loss: 0.0779\n",
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"Epoch 8/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0781 - val_loss: 0.0778\n",
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"Epoch 9/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0781 - val_loss: 0.0778\n",
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"Epoch 10/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0780 - val_loss: 0.0778\n",
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"Epoch 11/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0780 - val_loss: 0.0777\n",
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"Epoch 12/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0780 - val_loss: 0.0777\n",
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"Epoch 13/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0779 - val_loss: 0.0777\n",
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"Epoch 14/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0779 - val_loss: 0.0777\n",
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"Epoch 15/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0779 - val_loss: 0.0775\n",
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"Epoch 16/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0778 - val_loss: 0.0775\n",
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"Epoch 17/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0778 - val_loss: 0.0776\n",
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"Epoch 18/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0778 - val_loss: 0.0776\n",
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"Epoch 19/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0777 - val_loss: 0.0775\n",
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"Epoch 20/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0777 - val_loss: 0.0775\n",
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"Epoch 21/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0777 - val_loss: 0.0775\n",
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"Epoch 22/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0776 - val_loss: 0.0775\n",
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"Epoch 23/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0776 - val_loss: 0.0774\n",
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"Epoch 24/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0776 - val_loss: 0.0774\n",
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"Epoch 25/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0776 - val_loss: 0.0774\n",
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"Epoch 26/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0775 - val_loss: 0.0774\n",
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"Epoch 27/50\n",
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"469/469 [==============================] - 2s 5ms/step - loss: 0.0775 - val_loss: 0.0774\n",
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"Epoch 28/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0775 - val_loss: 0.0773\n",
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"Epoch 29/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0775 - val_loss: 0.0773\n",
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"Epoch 30/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - val_loss: 0.0773\n",
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"Epoch 31/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - val_loss: 0.0772\n",
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"Epoch 32/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - val_loss: 0.0774\n",
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"Epoch 33/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - val_loss: 0.0773\n",
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"Epoch 34/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - val_loss: 0.0772\n",
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"Epoch 35/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - val_loss: 0.0772\n",
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"Epoch 36/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0774\n",
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"Epoch 37/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0771\n",
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"Epoch 38/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0772\n",
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"Epoch 39/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0772\n",
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"Epoch 40/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0772\n",
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"Epoch 41/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0771\n",
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"Epoch 42/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0773 - val_loss: 0.0772\n",
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"Epoch 43/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0772 - val_loss: 0.0771\n",
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"Epoch 44/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0772 - val_loss: 0.0771\n",
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"Epoch 45/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0772 - val_loss: 0.0770\n",
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"Epoch 46/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0772 - val_loss: 0.0771\n",
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"Epoch 47/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0772 - val_loss: 0.0771\n",
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"Epoch 48/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0771 - val_loss: 0.0771\n",
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"Epoch 49/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0771 - val_loss: 0.0771\n",
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"Epoch 50/50\n",
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"469/469 [==============================] - 2s 4ms/step - loss: 0.0771 - val_loss: 0.0770\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x7f5274368700>"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Entraînement de l'auto-encodeur. On utilise ici les données de test \n",
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"# pour surveiller l'évolution de l'erreur de reconstruction sur des données \n",
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"# non utilisées pendant l'entraînement et ainsi détecter le sur-apprentissage.\n",
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"autoencoder.fit(x_train, x_train,\n",
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" epochs=50,\n",
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" batch_size=128,\n",
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" shuffle=True,\n",
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" validation_data=(x_test, x_test))"
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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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"id": "ohexDvCYrahC"
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},
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"source": [
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"Le code suivant affiche des exemples d'images de la base de test (1e ligne) et de leur reconstruction (2e ligne)."
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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": 16,
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"metadata": {
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"id": "2SC9R1TRTUgN"
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},
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|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x288 with 20 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Prédiction des données de test\n",
|
|||
|
|
"decoded_imgs = autoencoder.predict(x_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"n = 10\n",
|
|||
|
|
"plt.figure(figsize=(20, 4))\n",
|
|||
|
|
"for i in range(n):\n",
|
|||
|
|
" # Affichage de l'image originale\n",
|
|||
|
|
" ax = plt.subplot(2, n, i+1)\n",
|
|||
|
|
" plt.imshow(x_test[i].reshape(28, 28))\n",
|
|||
|
|
" plt.gray()\n",
|
|||
|
|
" ax.get_xaxis().set_visible(False)\n",
|
|||
|
|
" ax.get_yaxis().set_visible(False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Affichage de l'image reconstruite par l'auto-encodeur\n",
|
|||
|
|
" ax = plt.subplot(2, n, i+1 + n)\n",
|
|||
|
|
" plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
|
|||
|
|
" plt.gray()\n",
|
|||
|
|
" ax.get_xaxis().set_visible(False)\n",
|
|||
|
|
" ax.get_yaxis().set_visible(False)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "4oV4eKk4p4Eg"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Travail à faire"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "zjMnHWNtrgwZ"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Travail à faire\n",
|
|||
|
|
"\n",
|
|||
|
|
"1. Pour commencer, observez les résultats obtenus avec le code fourni. Les résultats semblent imparfaits, les images reconstruites sont bruitées. Modifiez le code fourni pour transformer le problème de régression en classification binaire. Les résultats devraient être bien meilleurs ! Conservez cette formulation (même si elle est non standard) pour la suite.\n",
|
|||
|
|
"2. Avec la dimension d'espace latent qui vous est fournie, on observe une (relativement) faible erreur de reconstruction. Tracez une **courbe** (avec seulement quelques points) qui montre l'évolution de l'**erreur de reconstruction en fonction de la dimension de l'espace latent**. Quelle semble être la dimension minimale de l'espace latent qui permet encore d'observer une reconstruction raisonnable des données (avec le réseau qui vous est fourni) ?\n",
|
|||
|
|
"3. Pour diminuer encore plus la dimension de l'espace latent, il est nécessaire d'augmenter la capacité des réseaux encodeur et décodeur. Cherchez à nouveau la dimension minimale de l'espace latent qui permet d'observer une bonne reconstruction des données, mais en augmentant à l'envi la capacité de votre auto-encodeur.\n",
|
|||
|
|
"4. Écrivez une fonction qui, étant donné deux images de votre espace de test $I_1$ et $I_2$, réalise l'interpolation (avec, par exemple, 10 étapes) entre la représentation latente ($z_1 = $encoder($I_1$) et $z_2 = $encoder($I_2$)) de ces deux données, et génère les images $I_i$ correspondant aux représentations latentes intermédiaires $z_i$. En pseudo python, cela donne : \n",
|
|||
|
|
"\n",
|
|||
|
|
"```python\n",
|
|||
|
|
"for i in range(10):\n",
|
|||
|
|
" z_i = z1 + i*(z2-z1)/10\n",
|
|||
|
|
" I_i = decoder(z_i)\n",
|
|||
|
|
"```\n",
|
|||
|
|
"Testez cette fonction avec un auto-encodeur avec une faible erreur de reconstruction, sur deux données présentant le même chiffre écrit différemment, puis deux chiffres différents.\n",
|
|||
|
|
"5. Pour finir, le code qui vous est fourni dans la suite permet de télécharger et de préparer une [base de données de visages](http://vis-www.cs.umass.edu/lfw/). ATTENTION : ici les images sont de taille $32\\times32$, en couleur, et comportent donc 3 canaux (contrairement aux images de MNIST, qui n'en comptent qu'un). Par analogie avec la question précédente, on pourrait grâce à la représentation latente apprise par un auto-encodeur, réaliser un morphing entre deux visages. Essayez d'abord d'entraîner un auto-encodeur à obtenir une erreur de reconstruction faible. Qu'observe-t-on ?\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 3s 6ms/step - loss: 0.2008 - val_loss: 0.1536\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.1478 - val_loss: 0.1415\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.1400 - val_loss: 0.1366\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.1360 - val_loss: 0.1335\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.1333 - val_loss: 0.1311\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1312 - val_loss: 0.1292\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1295 - val_loss: 0.1278\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1282 - val_loss: 0.1269\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1272 - val_loss: 0.1258\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1263 - val_loss: 0.1252\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1883 - val_loss: 0.1332\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1244 - val_loss: 0.1169\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1158 - val_loss: 0.1118\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1117 - val_loss: 0.1087\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1089 - val_loss: 0.1065\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1069 - val_loss: 0.1049\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1053 - val_loss: 0.1036\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1040 - val_loss: 0.1023\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1029 - val_loss: 0.1014\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1020 - val_loss: 0.1008\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1781 - val_loss: 0.1176\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1092 - val_loss: 0.1010\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0992 - val_loss: 0.0951\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0944 - val_loss: 0.0917\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0916 - val_loss: 0.0894\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0895 - val_loss: 0.0878\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0880 - val_loss: 0.0865\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0868 - val_loss: 0.0854\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0858 - val_loss: 0.0845\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0850 - val_loss: 0.0840\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1735 - val_loss: 0.1119\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1029 - val_loss: 0.0943\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0923 - val_loss: 0.0885\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0872 - val_loss: 0.0844\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0839 - val_loss: 0.0817\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0818 - val_loss: 0.0799\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0802 - val_loss: 0.0789\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0789 - val_loss: 0.0774\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 3s 5ms/step - loss: 0.0777 - val_loss: 0.0766\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 3s 5ms/step - loss: 0.0768 - val_loss: 0.0757\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 3s 5ms/step - loss: 0.1649 - val_loss: 0.1054\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0978 - val_loss: 0.0902\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0876 - val_loss: 0.0835\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0829 - val_loss: 0.0801\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0799 - val_loss: 0.0779\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0779 - val_loss: 0.0765\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0763 - val_loss: 0.0751\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0752 - val_loss: 0.0745\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0743 - val_loss: 0.0734\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 5ms/step - loss: 0.0734 - val_loss: 0.0726\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1591 - val_loss: 0.1011\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0938 - val_loss: 0.0874\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0848 - val_loss: 0.0814\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0804 - val_loss: 0.0779\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0777 - val_loss: 0.0759\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0758 - val_loss: 0.0743\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0744 - val_loss: 0.0731\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0732 - val_loss: 0.0721\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0723 - val_loss: 0.0713\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0716 - val_loss: 0.0710\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.1573 - val_loss: 0.0999\n",
|
|||
|
|
"Epoch 2/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0931 - val_loss: 0.0858\n",
|
|||
|
|
"Epoch 3/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0840 - val_loss: 0.0804\n",
|
|||
|
|
"Epoch 4/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0796 - val_loss: 0.0772\n",
|
|||
|
|
"Epoch 5/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0767 - val_loss: 0.0748\n",
|
|||
|
|
"Epoch 6/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0749 - val_loss: 0.0734\n",
|
|||
|
|
"Epoch 7/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0735 - val_loss: 0.0724\n",
|
|||
|
|
"Epoch 8/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0725 - val_loss: 0.0716\n",
|
|||
|
|
"Epoch 9/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0717 - val_loss: 0.0711\n",
|
|||
|
|
"Epoch 10/10\n",
|
|||
|
|
"469/469 [==============================] - 2s 4ms/step - loss: 0.0711 - val_loss: 0.0704\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from keras.layers import Input, Dense\n",
|
|||
|
|
"from keras.models import Model\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Dimension de l'entrée\n",
|
|||
|
|
"input_img = Input(shape=(784,))\n",
|
|||
|
|
"\n",
|
|||
|
|
"errors = []\n",
|
|||
|
|
"latent_dims = [8, 16, 32, 64, 128, 256, 512]\n",
|
|||
|
|
"for latent_dim in latent_dims:\n",
|
|||
|
|
" # Définition d'un encodeur\n",
|
|||
|
|
" x = Dense(128, activation='relu')(input_img)\n",
|
|||
|
|
" encoded = Dense(latent_dim, activation='linear')(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Définition d'un decodeur\n",
|
|||
|
|
" decoder_input = Input(shape=(latent_dim,))\n",
|
|||
|
|
" x = Dense(128, activation='relu')(decoder_input)\n",
|
|||
|
|
" decoded = Dense(784, activation='sigmoid')(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Construction d'un modèle séparé pour pouvoir accéder aux décodeur et encodeur\n",
|
|||
|
|
" encoder = Model(input_img, encoded)\n",
|
|||
|
|
" decoder = Model(decoder_input, decoded)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Construction du modèle de l'auto-encodeur\n",
|
|||
|
|
" encoded = encoder(input_img)\n",
|
|||
|
|
" decoded = decoder(encoded)\n",
|
|||
|
|
" autoencoder = Model(input_img, decoded)\n",
|
|||
|
|
"\n",
|
|||
|
|
" autoencoder.compile(optimizer='Adam', loss='bce')\n",
|
|||
|
|
"\n",
|
|||
|
|
" autoencoder.fit(\n",
|
|||
|
|
" x_train, x_train,\n",
|
|||
|
|
" epochs=10,\n",
|
|||
|
|
" batch_size=128,\n",
|
|||
|
|
" shuffle=True,\n",
|
|||
|
|
" validation_data=(x_test, x_test)\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" errors.append(autoencoder.history.history[\"val_loss\"][-1])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[<matplotlib.lines.Line2D at 0x7f521837f280>]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x720 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(20, 10))\n",
|
|||
|
|
"plt.plot(latent_dims, errors)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 52,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from keras.datasets import mnist\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Chargement et normalisation (entre 0 et 1) des données de la base de données MNIST\n",
|
|||
|
|
"(x_train, _), (x_test, _) = mnist.load_data()\n",
|
|||
|
|
"\n",
|
|||
|
|
"x_train = x_train.astype('float32') / 255.0\n",
|
|||
|
|
"x_test = x_test.astype('float32') / 255.0"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 58,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: \"model_83\"\n",
|
|||
|
|
"_________________________________________________________________\n",
|
|||
|
|
" Layer (type) Output Shape Param # \n",
|
|||
|
|
"=================================================================\n",
|
|||
|
|
" input_53 (InputLayer) [(None, 28, 28, 1)] 0 \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_37 (Conv2D) (None, 28, 28, 64) 640 \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_38 (Conv2D) (None, 28, 28, 64) 36928 \n",
|
|||
|
|
" \n",
|
|||
|
|
" max_pooling2d_8 (MaxPooling (None, 14, 14, 64) 0 \n",
|
|||
|
|
" 2D) \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_39 (Conv2D) (None, 14, 14, 128) 73856 \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_40 (Conv2D) (None, 14, 14, 128) 147584 \n",
|
|||
|
|
" \n",
|
|||
|
|
" max_pooling2d_9 (MaxPooling (None, 7, 7, 128) 0 \n",
|
|||
|
|
" 2D) \n",
|
|||
|
|
" \n",
|
|||
|
|
" flatten_2 (Flatten) (None, 6272) 0 \n",
|
|||
|
|
" \n",
|
|||
|
|
" dense_114 (Dense) (None, 5) 31365 \n",
|
|||
|
|
" \n",
|
|||
|
|
"=================================================================\n",
|
|||
|
|
"Total params: 290,373\n",
|
|||
|
|
"Trainable params: 290,373\n",
|
|||
|
|
"Non-trainable params: 0\n",
|
|||
|
|
"_________________________________________________________________\n",
|
|||
|
|
"None\n",
|
|||
|
|
"Model: \"model_84\"\n",
|
|||
|
|
"_________________________________________________________________\n",
|
|||
|
|
" Layer (type) Output Shape Param # \n",
|
|||
|
|
"=================================================================\n",
|
|||
|
|
" input_54 (InputLayer) [(None, 5)] 0 \n",
|
|||
|
|
" \n",
|
|||
|
|
" dense_115 (Dense) (None, 6272) 37632 \n",
|
|||
|
|
" \n",
|
|||
|
|
" reshape_2 (Reshape) (None, 7, 7, 128) 0 \n",
|
|||
|
|
" \n",
|
|||
|
|
" up_sampling2d_5 (UpSampling (None, 14, 14, 128) 0 \n",
|
|||
|
|
" 2D) \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_41 (Conv2D) (None, 14, 14, 128) 147584 \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_42 (Conv2D) (None, 14, 14, 128) 147584 \n",
|
|||
|
|
" \n",
|
|||
|
|
" up_sampling2d_6 (UpSampling (None, 28, 28, 128) 0 \n",
|
|||
|
|
" 2D) \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_43 (Conv2D) (None, 28, 28, 64) 32832 \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_44 (Conv2D) (None, 28, 28, 1) 577 \n",
|
|||
|
|
" \n",
|
|||
|
|
"=================================================================\n",
|
|||
|
|
"Total params: 366,209\n",
|
|||
|
|
"Trainable params: 366,209\n",
|
|||
|
|
"Non-trainable params: 0\n",
|
|||
|
|
"_________________________________________________________________\n",
|
|||
|
|
"None\n",
|
|||
|
|
"Model: \"model_85\"\n",
|
|||
|
|
"_________________________________________________________________\n",
|
|||
|
|
" Layer (type) Output Shape Param # \n",
|
|||
|
|
"=================================================================\n",
|
|||
|
|
" input_53 (InputLayer) [(None, 28, 28, 1)] 0 \n",
|
|||
|
|
" \n",
|
|||
|
|
" model_83 (Functional) (None, 5) 290373 \n",
|
|||
|
|
" \n",
|
|||
|
|
" model_84 (Functional) (None, 28, 28, 1) 366209 \n",
|
|||
|
|
" \n",
|
|||
|
|
"=================================================================\n",
|
|||
|
|
"Total params: 656,582\n",
|
|||
|
|
"Trainable params: 656,582\n",
|
|||
|
|
"Non-trainable params: 0\n",
|
|||
|
|
"_________________________________________________________________\n",
|
|||
|
|
"Epoch 1/10\n",
|
|||
|
|
"116/469 [======>.......................] - ETA: 3:19 - loss: 0.2379"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"ename": "KeyboardInterrupt",
|
|||
|
|
"evalue": "",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[1;32m/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb Cell 12\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=50'>51</a>\u001b[0m autoencoder\u001b[39m.\u001b[39msummary()\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=52'>53</a>\u001b[0m \u001b[39m# Entraînement de l'auto-encodeur. On utilise ici les données de test \u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=53'>54</a>\u001b[0m \u001b[39m# pour surveiller l'évolution de l'erreur de reconstruction sur des données \u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=54'>55</a>\u001b[0m \u001b[39m# non utilisées pendant l'entraînement et ainsi détecter le sur-apprentissage.\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=55'>56</a>\u001b[0m autoencoder\u001b[39m.\u001b[39;49mfit(x_train, x_train,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m epochs\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=57'>58</a>\u001b[0m batch_size\u001b[39m=\u001b[39;49m\u001b[39m128\u001b[39;49m,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=58'>59</a>\u001b[0m shuffle\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S9%20-%20IAM/IAM2022_TP_Autoencodeurs_Sujet.ipynb#X16sZmlsZQ%3D%3D?line=59'>60</a>\u001b[0m validation_data\u001b[39m=\u001b[39;49m(x_test, x_test))\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 66\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/engine/training.py:1384\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1377\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[1;32m 1378\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m 1379\u001b[0m epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[1;32m 1380\u001b[0m step_num\u001b[39m=\u001b[39mstep,\n\u001b[1;32m 1381\u001b[0m batch_size\u001b[39m=\u001b[39mbatch_size,\n\u001b[1;32m 1382\u001b[0m _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[1;32m 1383\u001b[0m callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1384\u001b[0m tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[1;32m 1385\u001b[0m \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m 1386\u001b[0m context\u001b[39m.\u001b[39masync_wait()\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 912\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 914\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 917\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 918\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m 945\u001b[0m \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 946\u001b[0m \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_stateless_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds) \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 948\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateful_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 949\u001b[0m \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 950\u001b[0m \u001b[39m# in parallel.\u001b[39;00m\n\u001b[1;32m 951\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/function.py:2956\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2953\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m 2954\u001b[0m (graph_function,\n\u001b[1;32m 2955\u001b[0m filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2956\u001b[0m \u001b[39mreturn\u001b[39;00m graph_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[1;32m 2957\u001b[0m filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mgraph_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/function.py:1853\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1849\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1850\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1851\u001b[0m \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1852\u001b[0m \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1853\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[1;32m 1854\u001b[0m ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[1;32m 1855\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1856\u001b[0m args,\n\u001b[1;32m 1857\u001b[0m possible_gradient_type,\n\u001b[1;32m 1858\u001b[0m executing_eagerly)\n\u001b[1;32m 1859\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[1;32m 498\u001b[0m \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m 500\u001b[0m \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[1;32m 501\u001b[0m num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[1;32m 502\u001b[0m inputs\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m 503\u001b[0m attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[1;32m 504\u001b[0m ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[1;32m 505\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 506\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 507\u001b[0m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[1;32m 508\u001b[0m num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 511\u001b[0m ctx\u001b[39m=\u001b[39mctx,\n\u001b[1;32m 512\u001b[0m cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
|
|||
|
|
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[1;32m 55\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 56\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 57\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from keras.layers import Input, Dense\n",
|
|||
|
|
"from keras.models import Model\n",
|
|||
|
|
"\n",
|
|||
|
|
"import keras\n",
|
|||
|
|
"from keras.layers import *\n",
|
|||
|
|
"from keras import *\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Dimension de l'entrée\n",
|
|||
|
|
"input_img = Input(shape=(28, 28, 1))\n",
|
|||
|
|
"latent_dim = 5\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Définition d'un encodeur\n",
|
|||
|
|
"x = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_img)\n",
|
|||
|
|
"x = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"x = MaxPooling2D(pool_size=(2, 2))(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
"x = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"x = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"x = MaxPooling2D(pool_size=(2, 2))(x)\n",
|
|||
|
|
"x = Flatten()(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
"encoded = Dense(latent_dim, activation='linear')(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Définition d'un decodeur\n",
|
|||
|
|
"decoder_input = Input(shape=(latent_dim,))\n",
|
|||
|
|
"\n",
|
|||
|
|
"x = Dense(7*7*128, activation='linear')(decoder_input)\n",
|
|||
|
|
"x = Reshape((7,7,128))(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
"x = UpSampling2D(size = (2,2))(x)\n",
|
|||
|
|
"x = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"x = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
"x = UpSampling2D(size = (2,2))(x)\n",
|
|||
|
|
"x = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"decoded = Conv2D(1, 3, activation = 'sigmoid', padding = 'same', kernel_initializer = 'he_normal')(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Construction d'un modèle séparé pour pouvoir accéder aux décodeur et encodeur\n",
|
|||
|
|
"encoder = Model(input_img, encoded)\n",
|
|||
|
|
"print(encoder.summary())\n",
|
|||
|
|
"\n",
|
|||
|
|
"decoder = Model(decoder_input, decoded)\n",
|
|||
|
|
"print(decoder.summary())\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Construction du modèle de l'auto-encodeur\n",
|
|||
|
|
"encoded = encoder(input_img)\n",
|
|||
|
|
"decoded = decoder(encoded)\n",
|
|||
|
|
"autoencoder = Model(input_img, decoded)\n",
|
|||
|
|
"\n",
|
|||
|
|
"autoencoder.compile(optimizer='Adam', loss='bce')\n",
|
|||
|
|
"autoencoder.summary()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Entraînement de l'auto-encodeur. On utilise ici les données de test \n",
|
|||
|
|
"# pour surveiller l'évolution de l'erreur de reconstruction sur des données \n",
|
|||
|
|
"# non utilisées pendant l'entraînement et ainsi détecter le sur-apprentissage.\n",
|
|||
|
|
"autoencoder.fit(x_train, x_train,\n",
|
|||
|
|
" epochs=10,\n",
|
|||
|
|
" batch_size=128,\n",
|
|||
|
|
" shuffle=True,\n",
|
|||
|
|
" validation_data=(x_test, x_test))\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[<matplotlib.lines.Line2D at 0x7f521b374df0>]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x720 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(20, 10))\n",
|
|||
|
|
"plt.plot(factors, errors)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "B6dM--9H_wEM"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Visages"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Ot-zkfDBQUkl"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"import tarfile, tqdm, cv2, os\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Télécharger les données de la base de données \"Labelled Faces in the Wild\"\n",
|
|||
|
|
"!wget http://www.cs.columbia.edu/CAVE/databases/pubfig/download/lfw_attributes.txt\n",
|
|||
|
|
"!wget http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz\n",
|
|||
|
|
"!wget http://vis-www.cs.umass.edu/lfw/lfw.tgz\n",
|
|||
|
|
" \n",
|
|||
|
|
"ATTRS_NAME = \"lfw_attributes.txt\"\n",
|
|||
|
|
"IMAGES_NAME = \"lfw-deepfunneled.tgz\"\n",
|
|||
|
|
"RAW_IMAGES_NAME = \"lfw.tgz\"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def decode_image_from_raw_bytes(raw_bytes):\n",
|
|||
|
|
" img = cv2.imdecode(np.asarray(bytearray(raw_bytes), dtype=np.uint8), 1)\n",
|
|||
|
|
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
|
|||
|
|
" return img\n",
|
|||
|
|
"\n",
|
|||
|
|
"def load_lfw_dataset(\n",
|
|||
|
|
" use_raw=False,\n",
|
|||
|
|
" dx=80, dy=80,\n",
|
|||
|
|
" dimx=45, dimy=45):\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Read attrs\n",
|
|||
|
|
" df_attrs = pd.read_csv(ATTRS_NAME, sep='\\t', skiprows=1)\n",
|
|||
|
|
" df_attrs = pd.DataFrame(df_attrs.iloc[:, :-1].values, columns=df_attrs.columns[1:])\n",
|
|||
|
|
" imgs_with_attrs = set(map(tuple, df_attrs[[\"person\", \"imagenum\"]].values))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Read photos\n",
|
|||
|
|
" all_photos = []\n",
|
|||
|
|
" photo_ids = []\n",
|
|||
|
|
"\n",
|
|||
|
|
" # tqdm in used to show progress bar while reading the data in a notebook here, you can change\n",
|
|||
|
|
" # tqdm_notebook to use it outside a notebook\n",
|
|||
|
|
" with tarfile.open(RAW_IMAGES_NAME if use_raw else IMAGES_NAME) as f:\n",
|
|||
|
|
" for m in tqdm.tqdm_notebook(f.getmembers()):\n",
|
|||
|
|
" # Only process image files from the compressed data\n",
|
|||
|
|
" if m.isfile() and m.name.endswith(\".jpg\"):\n",
|
|||
|
|
" # Prepare image\n",
|
|||
|
|
" img = decode_image_from_raw_bytes(f.extractfile(m).read())\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Crop only faces and resize it\n",
|
|||
|
|
" img = img[dy:-dy, dx:-dx]\n",
|
|||
|
|
" img = cv2.resize(img, (dimx, dimy))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Parse person and append it to the collected data\n",
|
|||
|
|
" fname = os.path.split(m.name)[-1]\n",
|
|||
|
|
" fname_splitted = fname[:-4].replace('_', ' ').split()\n",
|
|||
|
|
" person_id = ' '.join(fname_splitted[:-1])\n",
|
|||
|
|
" photo_number = int(fname_splitted[-1])\n",
|
|||
|
|
" if (person_id, photo_number) in imgs_with_attrs:\n",
|
|||
|
|
" all_photos.append(img)\n",
|
|||
|
|
" photo_ids.append({'person': person_id, 'imagenum': photo_number})\n",
|
|||
|
|
"\n",
|
|||
|
|
" photo_ids = pd.DataFrame(photo_ids)\n",
|
|||
|
|
" all_photos = np.stack(all_photos).astype('uint8')\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Preserve photo_ids order!\n",
|
|||
|
|
" all_attrs = photo_ids.merge(df_attrs, on=('person', 'imagenum')).drop([\"person\", \"imagenum\"], axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return all_photos, all_attrs\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Prépare le dataset et le charge dans la variable X\n",
|
|||
|
|
"X, attr = load_lfw_dataset(use_raw=True, dimx=32, dimy=32)\n",
|
|||
|
|
"# Normalise les images\n",
|
|||
|
|
"X = X/255\n",
|
|||
|
|
"# Sépare les images en données d'entraînement et de test\n",
|
|||
|
|
"X_train, X_test = train_test_split(X, test_size=0.1, random_state=42)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kt5l_VB7pI5i"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"n = 10\n",
|
|||
|
|
"plt.figure(figsize=(20, 4))\n",
|
|||
|
|
"for i in range(n):\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
" ax = plt.subplot(1, n, i+1)\n",
|
|||
|
|
" plt.imshow(X_train[i].reshape(32, 32, 3))\n",
|
|||
|
|
" plt.gray()\n",
|
|||
|
|
" ax.get_xaxis().set_visible(False)\n",
|
|||
|
|
" ax.get_yaxis().set_visible(False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"accelerator": "GPU",
|
|||
|
|
"colab": {
|
|||
|
|
"collapsed_sections": [
|
|||
|
|
"B6dM--9H_wEM"
|
|||
|
|
],
|
|||
|
|
"machine_shape": "hm",
|
|||
|
|
"provenance": []
|
|||
|
|
},
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3.10.7 64-bit",
|
|||
|
|
"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": "3.10.8"
|
|||
|
|
},
|
|||
|
|
"vscode": {
|
|||
|
|
"interpreter": {
|
|||
|
|
"hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 0
|
|||
|
|
}
|