CNTK_MNIST_Training.py

'''
-----------------------------------------
Training the Convolutional Neural Network
-----------------------------------------
'''
num_training_epoch = 1
training_progress_output_freq = 10

for epoch in range(num_training_epoch):

    sample_count = 0
    num_minibatch = 0

    # loop over minibatches in the epoch
    while sample_count < num_train_samples:

        minibatch = train_minibatch_source.next_minibatch(min(train_minibatch_size, num_train_samples - sample_count))

        # Specify the mapping of input variables in the model to actual minibatch data to be trained with
        data = {input_vars: minibatch[training_features],
                labels: minibatch[training_labels]}
        trainer.train_minibatch(data)

        sample_count += data[labels].num_samples
        num_minibatch += 1

        # Print the training progress data
        if num_minibatch % training_progress_output_freq == 0:
            training_loss = cntk.get_train_loss(trainer)
            eval_error = cntk.get_train_eval_criterion(trainer)
            print("Epoch %d  |  # of Samples: %6d  |  Loss: %.6f  |  Error: %.6f" % (epoch, sample_count, training_loss, eval_error))

print("Training Completed.", end="\n\n")