def print_test_accuracy(show_example_errors=False,
show_confusion_matrix=False):
# For all the images in the test-set,
# calculate the predicted classes and whether they are correct.
correct, cls_pred = predict_cls_test()
#----------------------------------------
# save cls_pred and cls_true
save_cls_pred(images_name[:-4], time_stamp, cls_pred)
save_cls_true(images_name[:-4], time_stamp, data.test.cls)
#----------------------------------------
# Classification accuracy and the number of correct classifications.
acc, num_correct = cls_accuracy(correct)
# Number of images being classified.
num_images = len(correct)
# Print the accuracy.
msg = "Accuracy on Test-Set: {0:.1%} ({1} / {2})"
print(msg.format(acc, num_correct, num_images))
# Plot the confusion matrix, if desired.
if show_confusion_matrix:
print("Confusion Matrix:")
plot_confusion_matrix(cls_pred=cls_pred)
# Plot some examples of mis-classifications, if desired.
if show_example_errors:
print("Example errors:")
plot_example_errors(cls_pred=cls_pred, correct=correct)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#-----------------------------------
# Saver
saver = tf.train.Saver()
print ("AI:{0}".format(AI_saved_dir))
if not os.path.exists(AI_saved_dir):
print ("No AI can be restore, please check folder ./checkpoints")
save_path = os.path.join(AI_saved_dir, 'best_validation')
#-----------------------------------
# Tensorflow run
session = tf.Session()
# restore previous weight
saver.restore(sess=session, save_path=save_path)
batch_size = 512
print ("batch_size = {0}".format(batch_size))
# test the restored AI, show confusion matrix and example_errors
# and save the cls of prediction
print_test_accuracy(show_confusion_matrix=True)
# save labels of prediction
label_pred = predict_label(data.test.images, data.test.labels)
save_label_pred(images_name[:-4], directory, label_pred)
# save cls_pred and cls_true
save_cls_pred(images_name[:-4], directory, cls_pred)
save_cls_true(images_name[:-4], directory, data.test.cls)
session.close()
#-----------------------------------
# measuring time
elapsed_time = time.time() - start_time
print ("Exiting Main Program, spending ", elapsed_time, "seconds.")