Initial commit
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Tensorflow/tutoriel6/Figure_1.png
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Tensorflow/tutoriel6/Figure_1.png
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33
Tensorflow/tutoriel6/MNIST_test.py
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33
Tensorflow/tutoriel6/MNIST_test.py
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import cv2
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import numpy as np
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import tensorflow as tf
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from L42Project import ia as LPia
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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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tf.reset_default_graph()
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np.set_printoptions(formatter={'float': '{:0.3f}'.format})
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with tf.Session() as s:
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saver=tf.train.import_meta_graph('./mon_vgg/modele.meta')
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saver.restore(s, tf.train.latest_checkpoint('./mon_vgg/'))
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graph=tf.get_default_graph()
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images=graph.get_tensor_by_name("images:0")
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sortie=graph.get_tensor_by_name("sortie:0")
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is_training=graph.get_tensor_by_name("is_training:0")
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while True:
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image=cv2.imread("/home/laurent/chiffre.png", cv2.IMREAD_GRAYSCALE)
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image=cv2.resize(image, (28, 28))
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image=image.reshape(28, 28, 1)/255
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test_images=[]
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test_images.append(image)
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test_images=np.asarray(test_images)
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#cv2.imshow('image', test_images[0])
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for i in mnist_test_images[0:10]:
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#for i in test_images:
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prediction=s.run(sortie, feed_dict={images: [i], is_training: False})
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print(prediction, np.argmax(prediction))
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#if cv2.waitKey()==ord('q'):
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# break
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break
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cv2.destroyAllWindows()
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50
Tensorflow/tutoriel6/MNIST_vgg.py
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50
Tensorflow/tutoriel6/MNIST_vgg.py
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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 vgg
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taille_batch=100
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nbr_entrainement=200
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train_images=np.fromfile("mnist/train-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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train_labels=np.eye(10)[np.fromfile("mnist/train-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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test_images=np.fromfile("mnist/t10k-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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test_labels=np.eye(10)[np.fromfile("mnist/t10k-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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images, labels, is_training, sortie, train, accuracy, saver=vgg.vggnet()
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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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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: 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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images: test_images[batch:batch+taille_batch],
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labels: test_labels[batch:batch+taille_batch],
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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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saver.save(s, './mon_vgg/modele')
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10
Tensorflow/tutoriel6/README.md
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Tensorflow/tutoriel6/README.md
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# Tutoriel tensorflow
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## Sauvegarde d'un modèle type VGGnet et réutilisation
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La vidéo de ce tutoriel est disponible à l'adresse suivante: https://www.youtube.com/watch?v=vIOdUchtbhQ
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Courbe d'apprentissage sur 200 cycles:
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L'apprentissage prend 6h30 sur une GeForce 1080
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200
Tensorflow/tutoriel6/log
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Tensorflow/tutoriel6/log
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84
Tensorflow/tutoriel6/vgg.py
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84
Tensorflow/tutoriel6/vgg.py
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import tensorflow as tf
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import numpy as np
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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 vggnet(learning_rate=0.01, momentum=0.99):
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ph_images=tf.placeholder(shape=(None, 28, 28, 1), dtype=tf.float32, name='images')
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||||
ph_labels=tf.placeholder(shape=(None, 10), dtype=tf.float32)
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||||
ph_is_training=tf.placeholder_with_default(False, (), name='is_training')
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||||
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||||
result=convolution(ph_images, 3, 64)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 64)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
|
||||
result=convolution(result, 3, 128)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 128)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
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)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 256)
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||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 256)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
|
||||
result=convolution(result, 3, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
|
||||
result=convolution(result, 3, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=convolution(result, 3, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=tf.nn.max_pool(result, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
result=tf.contrib.layers.flatten(result)
|
||||
|
||||
result=fc(result, 512)
|
||||
result=tf.layers.batch_normalization(result, training=ph_is_training, momentum=momentum)
|
||||
result=tf.nn.relu(result)
|
||||
result=fc(result, 10)
|
||||
socs=tf.nn.softmax(result, name="sortie")
|
||||
|
||||
loss=tf.nn.softmax_cross_entropy_with_logits_v2(labels=ph_labels, logits=result)
|
||||
extra_update_ops=tf.get_collection(tf.GraphKeys.UPDATE_OPS)
|
||||
with tf.control_dependencies(extra_update_ops):
|
||||
train=tf.train.AdamOptimizer(learning_rate).minimize(loss)
|
||||
accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(socs, 1), tf.argmax(ph_labels, 1)), tf.float32))
|
||||
|
||||
return ph_images, ph_labels, ph_is_training, socs, train, accuracy, tf.train.Saver()
|
||||
|
||||
30
Tensorflow/tutoriel6/vgg_test_cam.py
Normal file
30
Tensorflow/tutoriel6/vgg_test_cam.py
Normal file
@@ -0,0 +1,30 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
cap=cv2.VideoCapture(0)
|
||||
np.set_printoptions(formatter={'float': '{:0.3f}'.format})
|
||||
with tf.Session() as s:
|
||||
saver=tf.train.import_meta_graph('./mon_vgg/modele.meta')
|
||||
saver.restore(s, tf.train.latest_checkpoint('./mon_vgg/'))
|
||||
graph=tf.get_default_graph()
|
||||
images=graph.get_tensor_by_name("images:0")
|
||||
sortie=graph.get_tensor_by_name("sortie:0")
|
||||
is_training=graph.get_tensor_by_name("is_training:0")
|
||||
while True:
|
||||
ret, frame=cap.read()
|
||||
test=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
test=cv2.resize(test, (28, 28))
|
||||
for x in range(28):
|
||||
for y in range(28):
|
||||
if test[y][x]<110:
|
||||
test[y][x]=1
|
||||
else:
|
||||
test[y][x]=0
|
||||
cv2.imshow('image', cv2.resize(test, (120, 120))*255)
|
||||
prediction=s.run(sortie, feed_dict={images: [test.reshape(28, 28, 1)], is_training: False})
|
||||
print(prediction, np.argmax(prediction))
|
||||
if cv2.waitKey(20)&0xFF==ord('q'):
|
||||
break
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
Reference in New Issue
Block a user