CNTK_MNIST_Testing.py

'''
-------------------
Classification Test
--------------------
'''
test_minibatch_size = 1000

sample_count = 0
test_results = []

while sample_count < num_test_samples:

    minibatch = test_minibatch_source.next_minibatch(min(test_minibatch_size, num_test_samples - sample_count))

    # Specify the mapping of input variables in the model to actual minibatch data to be tested with
    data = {input_vars: minibatch[test_features],
            labels: minibatch[test_labels]}
    eval_error = trainer.test_minibatch(data)
    test_results.append(eval_error)

    sample_count += data[labels].num_samples

# Printing the average of evaluation errors of all test minibatches
print("Average errors of all test minibatches: %.3f%%" % (float(np.mean(test_results, dtype=float))*100))