51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
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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 vgg
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taille_batch=100
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nbr_entrainement=200
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train_images=np.fromfile("mnist/train-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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train_labels=np.eye(10)[np.fromfile("mnist/train-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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test_images=np.fromfile("mnist/t10k-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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test_labels=np.eye(10)[np.fromfile("mnist/t10k-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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images, labels, is_training, sortie, train, accuracy, saver=vgg.vggnet()
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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: True
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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: True
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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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saver.save(s, './mon_vgg/modele')
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