Initial commit

Tensorflow/tutoriel6/MNIST_test.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+from L42Project import ia as LPia
+
+mnist_test_images=np.fromfile("mnist/t10k-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
+mnist_test_labels=np.eye(10)[np.fromfile("mnist/t10k-labels-idx1-ubyte", dtype=np.uint8)[8:]]
+
+tf.reset_default_graph()
+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:
+        image=cv2.imread("/home/laurent/chiffre.png", cv2.IMREAD_GRAYSCALE)
+        image=cv2.resize(image, (28, 28))
+        image=image.reshape(28, 28, 1)/255
+        test_images=[]
+        test_images.append(image)
+        test_images=np.asarray(test_images)
+        #cv2.imshow('image', test_images[0])
+        for i in mnist_test_images[0:10]:
+        #for i in test_images:
+            prediction=s.run(sortie, feed_dict={images: [i], is_training: False})
+            print(prediction, np.argmax(prediction))
+        #if cv2.waitKey()==ord('q'):
+        #    break
+        break
+cv2.destroyAllWindows()