61 lines
2.5 KiB
Python
61 lines
2.5 KiB
Python
import tensorflow as tf
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import numpy as np
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from sklearn.utils import shuffle
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import matplotlib.pyplot as plot
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import cv2
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import vgg
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labels=['avion', 'oiseau', 'voiture', 'chat', 'cerf', 'chien', 'cheval', 'singe', 'bateau', 'camion']
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train_images=np.fromfile("stl10_binary/train_X.bin", dtype=np.uint8).reshape(-1, 3, 96, 96).transpose(0, 2, 3, 1)/255
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train_labels=np.eye(10)[np.fromfile("stl10_binary/train_y.bin", dtype=np.uint8)-1]
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test_images=np.fromfile("stl10_binary/test_X.bin", dtype=np.uint8).reshape(-1, 3, 96, 96).transpose(0, 2, 3, 1)/255
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test_labels=np.eye(10)[np.fromfile("stl10_binary/test_y.bin", dtype=np.uint8)-1]
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taille_batch=100
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nbr_entrainement=200
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images, labels, is_training, sortie, train, accuracy, save=vgg.vggnet(nbr_classes=10, learning_rate=0.001)
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#train_images=tf.image.resize_images(train_images, size=[32, 32])
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#test_images=tf.image.resize_images(train_images, size=[32, 32])
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fichier=open("log", "a")
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with tf.Session() as s:
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s.run(tf.global_variables_initializer())
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tab_train=[]
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tab_test=[]
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train_images, train_labels=shuffle(train_images, train_labels)
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for id_entrainement in np.arange(nbr_entrainement):
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print("> Entrainement", id_entrainement)
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for batch in np.arange(0, len(train_images), taille_batch):
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s.run(train, feed_dict={
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images: train_images[batch:batch+taille_batch],
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labels: train_labels[batch:batch+taille_batch],
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is_training: True
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})
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print(" entrainement OK")
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tab_accuracy_train=[]
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for batch in np.arange(0, len(train_images), taille_batch):
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p=s.run(accuracy, feed_dict={
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images: train_images[batch:batch+taille_batch],
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labels: train_labels[batch:batch+taille_batch],
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is_training: False
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})
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tab_accuracy_train.append(p)
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print(" train:", np.mean(tab_accuracy_train))
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tab_accuracy_test=[]
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for batch in np.arange(0, len(test_images), taille_batch):
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p=s.run(accuracy, feed_dict={
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images: test_images[batch:batch+taille_batch],
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labels: test_labels[batch:batch+taille_batch],
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is_training: False
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})
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tab_accuracy_test.append(p)
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print(" test :", np.mean(tab_accuracy_test))
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tab_train.append(1-np.mean(tab_accuracy_train))
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tab_test.append(1-np.mean(tab_accuracy_test))
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fichier.write("{:d}:{:f}:{:f}\n".format(id_entrainement, np.mean(tab_accuracy_train), np.mean(tab_accuracy_test)))
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fichier.close()
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