54 lines
2.1 KiB
Python
54 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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import random
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from sklearn.utils import shuffle
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import common
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taille_batch=55
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nbr_entrainement=400
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learning_rate=1E-3
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labels, train_images, train_labels, test_images, test_labels=common.stl10("stl10_binary")
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train_images=train_images/255
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test_images=test_images/255
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ph_images, ph_labels, ph_is_training, socs, train, accuracy, saver=common.resnet(10, common.b_resnet_3M, learning_rate)
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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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for id_entrainement in np.arange(nbr_entrainement):
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print("> Entrainement", id_entrainement)
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train_images, train_labels=shuffle(train_images, train_labels)
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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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ph_images: train_images[batch:batch+taille_batch],
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ph_labels: train_labels[batch:batch+taille_batch],
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ph_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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ph_images: train_images[batch:batch+taille_batch],
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ph_labels: train_labels[batch:batch+taille_batch],
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ph_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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ph_images: test_images[batch:batch+taille_batch],
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ph_labels: test_labels[batch:batch+taille_batch],
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ph_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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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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