有关于tensorflow的.TFRecords 文件怎么样来生成和读取操作

import os
import tensorflow as tf
from PIL import Image  # 注意Image,后面会用到
import matplotlib as plt


cwd = os.getcwd()
cwd = cwd + '\\17flowers\jpg\\'
classes = {'daffodil', 'snowdrop', 'lilyvalley', 'bluebell', 'crocus', 'iris', 'tigerlily', 'tulip', 'fritiuary',
           'sunflower', 'daisy', 'coltsfoot', 'dandelion', 'cowslip', 'buttercup', 'windflower',
           'pansy'}  # 花为 设定 17 类首先开始要设定好文件的类别

#这里是生成。TFRecords文件
writer = tf.python_io.TFRecordWriter("flower_train.tfrecords")  # 要生成的文件
for index, name in enumerate(classes):
    class_path = cwd + name + '\\'
    for img_name in os.listdir(class_path):
        img_path = class_path + img_name  # 每一个图片的地址路径
        img = Image.open(img_path)
        img = img.resize((224, 224))  # 将图片调整到同意的规格化
        img_raw = img.tobytes()  # 将图片转化为二进制格式
        example = tf.train.Example(features=tf.train.Features(feature={
            "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
            'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
        }))  # example对象对label和image数据进行封装
        writer.write(example.SerializeToString())  # 序列化为字符串
writer.close()

# 读入flower_train.tfrecords的文件
def read_and_decode(filename):  
    filename_queue = tf.train.string_input_producer([filename])  # 生成一个queue队列
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)  # 返回文件名和文件
    features = tf.parse_single_example(serialized_example,
                                       features={
                                           'label': tf.FixedLenFeature([], tf.int64),
                                           'img_raw': tf.FixedLenFeature([], tf.string),
                                       })  # 将image数据和label取出来

    image = tf.decode_raw(features['img_raw'], tf.uint8)
    image = tf.reshape(image, [224, 224, 3])
    label = tf.cast(features['label'], tf.int32)
    label = tf.one_hot(label, 17, 1, 0)
    return image, label


image, label = read_and_decode('flower_train.tfrecords')

with tf.Session() as sess:  # 开始一个会话
    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])  # 在会话中取出image和label
        img = Image.fromarray(example, 'RGB')  # 这里Image是之前提到的
        plt.imshow(img)
        plt.axis('on')  # 不显示坐标轴
        plt.show()
        img.save(cwd + str(i) + '_''Label_' + str(l) + '.jpg')  # 存下图片
        print(example, l)
    coord.request_stop()
    coord.join(threads)