54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
import tensorflow as tf
|
|
import numpy as np
|
|
import random
|
|
from sklearn.utils import shuffle
|
|
import common
|
|
|
|
taille_batch=55
|
|
nbr_entrainement=400
|
|
learning_rate=1E-3
|
|
|
|
labels, train_images, train_labels, test_images, test_labels=common.stl10("stl10_binary")
|
|
train_images=train_images/255
|
|
test_images=test_images/255
|
|
|
|
ph_images, ph_labels, ph_is_training, socs, train, accuracy, saver=common.resnet(10, common.b_resnet_3M, learning_rate)
|
|
|
|
fichier=open("log", "a")
|
|
with tf.Session() as s:
|
|
s.run(tf.global_variables_initializer())
|
|
tab_train=[]
|
|
tab_test=[]
|
|
for id_entrainement in np.arange(nbr_entrainement):
|
|
print("> Entrainement", id_entrainement)
|
|
train_images, train_labels=shuffle(train_images, train_labels)
|
|
for batch in np.arange(0, len(train_images), taille_batch):
|
|
s.run(train, feed_dict={
|
|
ph_images: train_images[batch:batch+taille_batch],
|
|
ph_labels: train_labels[batch:batch+taille_batch],
|
|
ph_is_training: True
|
|
})
|
|
print(" entrainement OK")
|
|
tab_accuracy_train=[]
|
|
for batch in np.arange(0, len(train_images), taille_batch):
|
|
p=s.run(accuracy, feed_dict={
|
|
ph_images: train_images[batch:batch+taille_batch],
|
|
ph_labels: train_labels[batch:batch+taille_batch],
|
|
ph_is_training: True
|
|
})
|
|
tab_accuracy_train.append(p)
|
|
print(" train:", np.mean(tab_accuracy_train))
|
|
tab_accuracy_test=[]
|
|
for batch in np.arange(0, len(test_images), taille_batch):
|
|
p=s.run(accuracy, feed_dict={
|
|
ph_images: test_images[batch:batch+taille_batch],
|
|
ph_labels: test_labels[batch:batch+taille_batch],
|
|
ph_is_training: True
|
|
})
|
|
tab_accuracy_test.append(p)
|
|
print(" test :", np.mean(tab_accuracy_test))
|
|
tab_train.append(1-np.mean(tab_accuracy_train))
|
|
tab_test.append(1-np.mean(tab_accuracy_test))
|
|
fichier.write("{:d}:{:f}:{:f}\n".format(id_entrainement, np.mean(tab_accuracy_train), np.mean(tab_accuracy_test)))
|
|
fichier.close()
|