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Tensorflow/tutoriel9/STL10_vgg.py

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+import tensorflow as tf
+import numpy as np
+from sklearn.utils import shuffle
+import matplotlib.pyplot as plot
+import cv2
+import vgg
+
+labels=['avion', 'oiseau', 'voiture', 'chat', 'cerf', 'chien', 'cheval', 'singe', 'bateau', 'camion']
+train_images=np.fromfile("stl10_binary/train_X.bin", dtype=np.uint8).reshape(-1, 3, 96, 96).transpose(0, 2, 3, 1)/255
+train_labels=np.eye(10)[np.fromfile("stl10_binary/train_y.bin", dtype=np.uint8)-1]
+test_images=np.fromfile("stl10_binary/test_X.bin", dtype=np.uint8).reshape(-1, 3, 96, 96).transpose(0, 2, 3, 1)/255
+test_labels=np.eye(10)[np.fromfile("stl10_binary/test_y.bin", dtype=np.uint8)-1]
+
+taille_batch=100
+nbr_entrainement=200
+
+images, labels, is_training, sortie, train, accuracy, save=vgg.vggnet(nbr_classes=10, learning_rate=0.001)
+
+#train_images=tf.image.resize_images(train_images, size=[32, 32])
+#test_images=tf.image.resize_images(train_images, size=[32, 32])
+
+fichier=open("log", "a")
+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: False
+            })
+            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: False
+            })
+            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))
+        fichier.write("{:d}:{:f}:{:f}\n".format(id_entrainement, np.mean(tab_accuracy_train), np.mean(tab_accuracy_test)))
+    fichier.close()
+     
+