Initial commit

Tensorflow/tutoriel6/MNIST_vgg.py

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+import tensorflow as tf
+import numpy as np
+from sklearn.utils import shuffle
+import vgg
+
+taille_batch=100
+nbr_entrainement=200
+
+train_images=np.fromfile("mnist/train-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
+train_labels=np.eye(10)[np.fromfile("mnist/train-labels-idx1-ubyte", dtype=np.uint8)[8:]]
+test_images=np.fromfile("mnist/t10k-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
+test_labels=np.eye(10)[np.fromfile("mnist/t10k-labels-idx1-ubyte", dtype=np.uint8)[8:]]
+
+images, labels, is_training, sortie, train, accuracy, saver=vgg.vggnet()
+
+with tf.Session() as s:
+    s.run(tf.global_variables_initializer())
+    tab_train=[]
+    tab_test=[]
+    train_images, train_labels=shuffle(train_images, train_labels)
+    for id_entrainement in np.arange(nbr_entrainement):
+        print("> Entrainement", id_entrainement)
+        for batch in np.arange(0, len(train_images), taille_batch):
+            s.run(train, feed_dict={
+                images: train_images[batch:batch+taille_batch],
+                labels: train_labels[batch:batch+taille_batch],
+                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={
+                images: train_images[batch:batch+taille_batch],
+                labels: train_labels[batch:batch+taille_batch],
+                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={
+                images: test_images[batch:batch+taille_batch],
+                labels: test_labels[batch:batch+taille_batch],
+                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))
+    saver.save(s, './mon_vgg/modele')