95 lines
4.0 KiB
Python
95 lines
4.0 KiB
Python
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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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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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taille_batch=100
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nbr_entrainement=3
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learning_rate=0.001
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mnist_train_images=np.fromfile("mnist/train-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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mnist_train_labels=np.eye(10)[np.fromfile("mnist/train-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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mnist_test_images=np.fromfile("mnist/t10k-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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mnist_test_labels=np.eye(10)[np.fromfile("mnist/t10k-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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ph_images=tf.placeholder(shape=(None, 28, 28, 1), 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, 5, 32)
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result=convolution(result, 5, 32)
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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, 5, 128)
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result=convolution(result, 5, 128)
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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=tf.nn.sigmoid(result)
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result=fc(result, 10)
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scso=tf.nn.softmax(result)
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loss=tf.nn.softmax_cross_entropy_with_logits_v2(labels=ph_labels, logits=result)
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train=tf.train.AdamOptimizer(learning_rate).minimize(loss)
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accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(scso, 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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for id_entrainement in np.arange(nbr_entrainement):
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tab_accuracy_train=[]
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tab_accuracy_test=[]
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for batch in np.arange(0, len(mnist_train_images), taille_batch):
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s.run(train, feed_dict={
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ph_images: mnist_train_images[batch:batch+taille_batch],
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ph_labels: mnist_train_labels[batch:batch+taille_batch]
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})
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for batch in np.arange(0, len(mnist_train_images), taille_batch):
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precision=s.run(accuracy, feed_dict={
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ph_images: mnist_train_images[batch:batch+taille_batch],
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ph_labels: mnist_train_labels[batch:batch+taille_batch]
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})
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tab_accuracy_train.append(precision)
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for batch in np.arange(0, len(mnist_test_images), taille_batch):
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precision=s.run(accuracy, feed_dict={
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ph_images: mnist_test_images[batch:batch+taille_batch],
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ph_labels: mnist_test_labels[batch:batch+taille_batch]
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})
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tab_accuracy_test.append(precision)
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print("> Entrainement", id_entrainement)
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print(" train:", np.mean(tab_accuracy_train))
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tab_train.append(1-np.mean(tab_accuracy_train))
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print(" test :", np.mean(tab_accuracy_test))
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tab_test.append(1-np.mean(tab_accuracy_test))
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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(scso, feed_dict={ph_images: mnist_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]))
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print("sortie attendue :", mnist_test_labels[image], np.argmax(mnist_test_labels[image]))
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cv2.imshow('image', mnist_test_images[image])
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if cv2.waitKey()&0xFF==ord('q'):
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break
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