165 lines
6.3 KiB
Python
165 lines
6.3 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 matplotlib.pyplot as plot
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import cv2
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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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def convolution(couche_prec, taille_noyau, nbr_noyau):
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w=tf.Variable(tf.random.truncated_normal(shape=(taille_noyau, taille_noyau, int(couche_prec.get_shape()[-1]), nbr_noyau)))
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b=np.zeros(nbr_noyau)
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result=tf.nn.conv2d(couche_prec, w, strides=[1, 1, 1, 1], padding='SAME')+b
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return result
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def fc(couche_prec, nbr_neurone):
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w=tf.Variable(tf.random.truncated_normal(shape=(int(couche_prec.get_shape()[-1]), nbr_neurone), dtype=tf.float32))
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b=tf.Variable(np.zeros(shape=(nbr_neurone)), dtype=tf.float32)
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result=tf.matmul(couche_prec, w)+b
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return result
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def normalisation(couche_prec):
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mean, var=tf.nn.moments(couche_prec, [0])
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scale=tf.Variable(tf.ones(shape=(np.shape(couche_prec)[-1])))
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beta=tf.Variable(tf.zeros(shape=(np.shape(couche_prec)[-1])))
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result=tf.nn.batch_normalization(couche_prec, mean, var, beta, scale, 0.001)
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return result
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taille_batch=100
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nbr_entrainement=200
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learning_rate=0.01
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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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ph_images=tf.placeholder(shape=(None, 32, 32, 3), dtype=tf.float32)
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ph_labels=tf.placeholder(shape=(None, 10), dtype=tf.float32)
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result=convolution(ph_images, 3, 64)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 64)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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result=convolution(result, 3, 128)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 128)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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result=convolution(result, 3, 256)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 256)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 256)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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result=convolution(result, 3, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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result=convolution(result, 3, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=convolution(result, 3, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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result=tf.contrib.layers.flatten(result)
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result=fc(result, 512)
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result=normalisation(result)
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result=tf.nn.relu(result)
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result=fc(result, 10)
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socs=tf.nn.softmax(result)
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erreur=tf.nn.softmax_cross_entropy_with_logits_v2(labels=ph_labels, logits=result)
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train=tf.train.AdamOptimizer(learning_rate).minimize(erreur)
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precision=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(socs, 1), tf.argmax(ph_labels, 1)), tf.float32))
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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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ph_images: train_images[batch:batch+taille_batch],
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ph_labels: train_labels[batch:batch+taille_batch],
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})
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print(" entrainement OK")
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tab_precision_train=[]
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for batch in np.arange(0, len(train_images), taille_batch):
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p=s.run(precision, 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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})
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tab_precision_train.append(p)
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print(" train:", np.mean(tab_precision_train))
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tab_precision_test=[]
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for batch in np.arange(0, len(test_images), taille_batch):
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p=s.run(precision, 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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})
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tab_precision_test.append(p)
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print(" test :", np.mean(tab_precision_test))
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tab_train.append(1-np.mean(tab_precision_train))
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tab_test.append(1-np.mean(tab_precision_test))
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quit()
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plot.ylim(0, 1)
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plot.grid()
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plot.plot(tab_train, label="Train error")
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plot.plot(tab_test, label="Test error")
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plot.legend(loc="upper right")
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plot.show()
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resulat=s.run(socs, feed_dict={ph_images: test_images[0:taille_batch]})
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np.set_printoptions(formatter={'float': '{:0.3f}'.format})
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for image in range(taille_batch):
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print("image", image)
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print("sortie du réseau:", resulat[image], np.argmax(resulat[image]), labels[np.argmax(resulat[image])])
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print("sortie attendue :", test_labels[image], np.argmax(test_labels[image]), labels[np.argmax(test_labels[image])])
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cv2.imshow('image', test_images[image])
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if cv2.waitKey()&0xFF==ord('q'):
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break
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