yelp.py 文件源码

def training_inputs(self):
        fps, labels = self._load_training_labelmap()
        filepaths = tf.constant(fps)
        labels = tf.constant(labels, dtype=tf.int32)

        min_num_examples_in_queue = int(FLAGS.min_frac_examples_in_queue * len(fps))

        filename_queue = tf.RandomShuffleQueue(len(fps), min_num_examples_in_queue, [tf.string, tf.int32],
                                               name='training_filename_queue')
        enqueue_op = filename_queue.enqueue_many([filepaths, labels])
        qr = tf.train.QueueRunner(filename_queue, [enqueue_op])
        tf.train.add_queue_runner(qr)

        example_list = [self._read_and_preprocess_image_for_training(filename_queue) for _ in
                        xrange(FLAGS.num_consuming_threads)]

        image_batch, label_batch = tf.train.shuffle_batch_join(
                example_list,
                batch_size=FLAGS.batch_size,
                capacity=min_num_examples_in_queue + (FLAGS.num_consuming_threads + 2) * FLAGS.batch_size,
                min_after_dequeue=min_num_examples_in_queue,
                shapes=[[224, 224, 3], []],
                name='training_example_queue'
        )

        return image_batch, util.encode_one_hot(label_batch, self.num_classes)