utilities.py 文件源码

def input_pipeline_dis(filenames, batch_size, read_threads=4, num_epochs=None, is_training=True):
    filename_queue = tf.train.string_input_producer(
                        filenames, num_epochs=FLAGS.num_epochs, shuffle=is_training)
    # initialize local variables if num_epochs is not None or it'll raise uninitialized problem
    tf.get_default_session().run(tf.local_variables_initializer())

    example_list = [read_my_file_format_dis(filename_queue, is_training) \
                        for _ in range(read_threads)]

    min_after_dequeue = 300 if is_training else 10
    capacity = min_after_dequeue + 3 * batch_size
    clip_batch, label_batch, text_batch = tf.train.shuffle_batch_join(
        example_list, batch_size=batch_size, capacity=capacity,
        min_after_dequeue=min_after_dequeue)

    return clip_batch, label_batch, text_batch