ltfn_9_3.py

with tf.Session() as session:
  #Restore Model
  saver = tf.train.Saver()                                        #Create a saver (object to save/restore sessions)
  saver.restore(session, "/tmp/vggnet/vgg_net.ckpt")              #Restore the session from a previously saved checkpoint

  #Now we test our restored model exactly as before 
  batch_size = 100
  num_test_batches = int(len(test_images) / 100)
  total_accuracy = 0
  total_cost = 0
  for step in range(num_test_batches):
      offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
      batch_images = test_images[offset:(offset + batch_size)]
      batch_labels = test_labels[offset:(offset + batch_size)]
      feed_dict = {input: batch_images, labels: batch_labels}

      c, acc = session.run([cost, accuracy], feed_dict=feed_dict)
      total_cost = total_cost + c
      total_accuracy = total_accuracy + acc

  print("Test Cost: ", total_cost / num_test_batches)
  print("Test accuracy: ", total_accuracy * 100.0 / num_test_batches, "%")