在读取和打印TFRecord格式数据时，必须加入线程机制，否则打印不出任何结果

import tensorflow as tffilename_queue = tf.train.string_input_producer(['train.tfrecords'], shuffle=True)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,features={'image_raw': tf.FixedLenFeature([], tf.string),'label': tf.FixedLenFeature([], tf.int64),'height': tf.FixedLenFeature([], tf.int64),'width': tf.FixedLenFeature([], tf.int64),'depth': tf.FixedLenFeature([], tf.int64)})image = tf.decode_raw(features['image_raw'], tf.float64)
image = tf.reshape(image, [28, 28])
image.set_shape([28, 28])
image = tf.cast(image, tf.float32)
label = tf.cast(features['label'], tf.int32)with tf.Session() as sess:init_op = tf.global_variables_initializer()sess.run(init_op)coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(coord=coord)for i in range(10):example, l = sess.run([image, label])print(example)coord.request_stop()coord.join(threads)