cnn_stuff.py

epoch = 0

def next_batch(images, labels, start, batch_size):
    global epoch
    end = start + batch_size

    if end > len(images):
        # After each epoch we update this
        epoch += 1
        start = 0
        end = batch_size
        assert batch_size <= len(images)

    return end, images[start:end], labels[start:end]

total_iterations = 0

def train(num_iteration):
    global total_iterations
    train_start = 0
    test_start = 0

    for i in range(total_iterations,
                   total_iterations + num_iteration):

        train_start, x_batch, y_true_batch = next_batch(train_data, train_labels, train_start, batch_size)
        test_start, x_valid_batch, y_valid_batch = next_batch(test_data, test_labels, test_start, batch_size)

        feed_dict_tr = {x: x_batch,
                           y_true: y_true_batch}
        feed_dict_val = {x: x_valid_batch,
                              y_true: y_valid_batch}

        session.run(optimizer, feed_dict=feed_dict_tr)

        if i % int(len(train_data)/batch_size) == 0:
            val_loss = session.run(cost, feed_dict=feed_dict_val)
            epoch = int(i / int(len(train_data)/batch_size))
            if len(sys.argv) > 2:
                show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)
        if i == total_iterations + num_iteration - 1:
            print(session.run(accuracy, feed_dict=feed_dict_val))

    total_iterations += num_iteration