tensorflow tfrecord文件存储

import tensorflow as tf
import numpy as np
import skimage
from skimage import data, io, color
from PIL import Image

path = "1.tfrecords"
img_path = '/data/test/img/1.png'

with tf.python_io.TFRecordWriter(path) as writer:
    # list: int or float
    a = 1024
    b = 10.24

    c = [0.1, 0.2, 0.3]
    c = np.array(c).astype(np.float32).tobytes()

    d = [[1, 2], [3, 4]]
    d = np.array(d).astype(np.int8).tobytes()

    e = "Python"
    e = bytes(e, encoding='utf-8')

    img = io.imread(img_path)
    img = img.astype(np.uint8).tobytes()

    img2 = Image.open(img_path)
    img2 = img2.resize((256, 256))
    img2 = img2.tobytes()

    example = tf.train.Example(features=tf.train.Features(feature={
        'a': tf.train.Feature(int64_list=tf.train.Int64List(value=[a])),
        'b': tf.train.Feature(float_list=tf.train.FloatList(value=[b])),
        'c': tf.train.Feature(bytes_list=tf.train.BytesList(value=[c])),
        'd': tf.train.Feature(bytes_list=tf.train.BytesList(value=[d])),
        'e': tf.train.Feature(bytes_list=tf.train.BytesList(value=[e])),
        'image': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img])),
        'image2': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img2])),

    }))
    writer.write(example.SerializeToString())

# 读取
filename_queue = tf.train.string_input_producer([path])
_, serialized_example = tf.TFRecordReader().read(filename_queue)

features = tf.parse_single_example(serialized_example,
                                   features={
                                       'a': tf.FixedLenFeature([], tf.int64),
                                       'b': tf.FixedLenFeature([], tf.float32),
                                       'c': tf.FixedLenFeature([], tf.string),
                                       'd': tf.FixedLenFeature([], tf.string),
                                       'e': tf.FixedLenFeature([], tf.string),
                                       'image': tf.FixedLenFeature([], tf.string),
                                       'image2': tf.FixedLenFeature([], tf.string),

                                   })

a = features['a'] # 返回是张量
b = features['b']

c = features['c']
c = tf.decode_raw(c, tf.float32)

d = features['d']
d = tf.decode_raw(d, tf.int8)
d = tf.reshape(d, [2, 2])

e = features['e']

img = tf.decode_raw(features['image'], tf.uint8)
img = tf.reshape(img, shape=[256, 256, 3])

img2 = tf.decode_raw(features['image2'], tf.uint8)
img2 = tf.reshape(img2, [256, 256,3])

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    tf.train.start_queue_runners(sess=sess)

    print(sess.run([a, b, c, d, e]))

    e = sess.run(e)
    print(type(e), bytes.decode(e))

    img = sess.run(img)
    io.imshow(img)

    img2 = sess.run(img2)
    io.imshow(img2)