Initial commit

Tensorflow/tutoriel16/STL10_resnet.py

@@ -0,0 +1,53 @@
+import tensorflow as tf
+import numpy as np
+import random
+from sklearn.utils import shuffle
+import common
+
+taille_batch=55
+nbr_entrainement=400
+learning_rate=1E-3
+
+labels, train_images, train_labels, test_images, test_labels=common.stl10("stl10_binary")
+train_images=train_images/255
+test_images=test_images/255
+
+ph_images, ph_labels, ph_is_training, socs, train, accuracy, saver=common.resnet(10, common.b_resnet_3M, learning_rate)
+
+fichier=open("log", "a")
+with tf.Session() as s:
+    s.run(tf.global_variables_initializer())
+    tab_train=[]
+    tab_test=[]
+    for id_entrainement in np.arange(nbr_entrainement):
+        print("> Entrainement", id_entrainement)
+        train_images, train_labels=shuffle(train_images, train_labels)
+        for batch in np.arange(0, len(train_images), taille_batch):
+            s.run(train, feed_dict={
+                ph_images: train_images[batch:batch+taille_batch],
+                ph_labels: train_labels[batch:batch+taille_batch],
+                ph_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={
+                ph_images: train_images[batch:batch+taille_batch],
+                ph_labels: train_labels[batch:batch+taille_batch],
+                ph_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={
+                ph_images: test_images[batch:batch+taille_batch],
+                ph_labels: test_labels[batch:batch+taille_batch],
+                ph_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))
+        fichier.write("{:d}:{:f}:{:f}\n".format(id_entrainement, np.mean(tab_accuracy_train), np.mean(tab_accuracy_test)))
+    fichier.close()