自学教程：tensorflow学习笔记之tfrecord文件的生成与读取

import osimport numpy as npimport tensorflow as tffrom PIL import Imagefilenames = ['images/cat/1.jpg','images/cat/2.jpg','images/dog/1.jpg','images/dog/2.jpg','images/pig/1.jpg','images/pig/2.jpg',]labels = {'cat':0, 'dog':1, 'pig':2}def int64_feature(values):	if not isinstance(values, (tuple, list)):		values = [values]	return tf.train.Feature(int64_list=tf.train.Int64List(value=values))def bytes_feature(values):	return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))with tf.Session() as sess:	output_filename = os.path.join('images/train.tfrecords')	with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:		for filename in filenames:			#读取图像			image_data = Image.open(filename)			#图像灰度化			image_data = np.array(image_data.convert('L'))			#将图像转化为bytes			image_data = image_data.tobytes()			#读取label			label = labels[filename.split('/')[-2]]			#生成protocol数据类型			example = tf.train.Example(features=tf.train.Features(feature={'image': bytes_feature(image_data),																			'label': int64_feature(label)}))			tfrecord_writer.write(example.SerializeToString())

import tensorflow as tfimport matplotlib.pyplot as pltfrom PIL import Image# 根据文件名生成一个队列filename_queue = tf.train.string_input_producer(['images/train.tfrecords'])reader = tf.TFRecordReader()# 返回文件名和文件_, serialized_example = reader.read(filename_queue)features = tf.parse_single_example(serialized_example, 									features={'image': tf.FixedLenFeature([], tf.string), 												'label': tf.FixedLenFeature([], tf.int64)})# 获取图像数据image = tf.decode_raw(features['image'], tf.uint8)# 恢复图像原始尺寸[高，宽]image = tf.reshape(image, [60, 160])# 获取labellabel = tf.cast(features['label'], tf.int32)with tf.Session() as sess:	# 创建一个协调器，管理线程	coord = tf.train.Coordinator()	# 启动QueueRunner, 此时文件名队列已经进队	threads = tf.train.start_queue_runners(sess=sess, coord=coord)	for i in range(6):		image_b, label_b = sess.run([image, label])		img = Image.fromarray(image_b, 'L')		plt.imshow(img)		plt.axis('off')		plt.show()		print(label_b)	# 通知其他线程关闭	coord.request_stop()	# 其他所有线程关闭之后，这一函数才能返回	coord.join(threads)