99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
import tensorflow as tf
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import numpy as np
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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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from sklearn.utils import shuffle
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def read_cifar_file(file, images, labels):
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shift=0
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f=np.fromfile(file, dtype=np.uint8)
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while shift!=f.shape[0]:
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labels.append(np.eye(10)[f[shift]])
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shift+=1
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images.append(f[shift:shift+3*32*32].reshape(3, 32, 32).transpose(1, 2, 0)/255)
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shift+=3*32*32
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taille_batch=100
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nbr_entrainement=50
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labels=['avion', 'automobile', 'oiseau', 'chat', 'cerf', 'chien', 'grenouille', 'cheval', 'bateau', 'camion']
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train_images=[]
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train_labels=[]
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read_cifar_file("cifar-10-batches-bin/data_batch_1.bin", train_images, train_labels)
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read_cifar_file("cifar-10-batches-bin/data_batch_2.bin", train_images, train_labels)
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read_cifar_file("cifar-10-batches-bin/data_batch_3.bin", train_images, train_labels)
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read_cifar_file("cifar-10-batches-bin/data_batch_4.bin", train_images, train_labels)
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read_cifar_file("cifar-10-batches-bin/data_batch_5.bin", train_images, train_labels)
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test_images=[]
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test_labels=[]
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read_cifar_file("cifar-10-batches-bin/test_batch.bin", test_images, test_labels)
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images, labels, is_training, sortie, train, accuracy, save=vgg.vggnet(nbr_classes=10, learning_rate=0.01)
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def transform_img(img):
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img=tf.image.random_flip_left_right(img)
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img=tf.image.random_hue(img, 0.08)
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img=tf.image.random_saturation(img, 0.6, 1.6)
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img=tf.image.random_brightness(img, 0.05)
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img=tf.image.random_contrast(img, 0.7, 1.3)
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x=int(img.shape[0])
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y=int(img.shape[1])
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z=int(img.shape[2])
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img=tf.image.random_crop(img, [int(x*0.90), int(y*0.90), z])
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img=tf.image.resize_images(img, (x, y))
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return(img)
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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=np.array(train_images, dtype=np.float32)
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train_images2=tf.map_fn(transform_img, train_images)
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train_images3=tf.map_fn(transform_img, train_images)
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train_images4=tf.map_fn(transform_img, train_images)
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train_images=tf.concat([train_images, train_images2, train_images3, train_images4], axis=0)
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train_labels=np.array(train_labels)
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train_labels=tf.concat([train_labels, train_labels, train_labels, train_labels], axis=0)
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train_images=s.run(train_images)
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train_labels=s.run(train_labels)
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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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