（Tensorflow）TFRecord样例程序读写

一：TFRecord样例程序读

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

#生成整数型的属性
def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

#生成字符串型的属性
def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

mnist = input_data.read_data_sets('F:/PycharmProjects/tmp/data', dtype=tf.uint8, one_hot=True)
images = mnist.train.images
labels = mnist.train.labels
pixels = images.shape[1]
num_examples = mnist.train.num_examples

filename = 'F:/PycharmProjects/tmp/output/output.tfrecords'
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
    image_raw = images[index].tostring()
    example = tf.train.Example(features=tf.train.Features(feature={
        'pixels': _int64_feature(pixels),
        'label': _int64_feature(np.argmax(labels[index])),
        'image_raw': _bytes_feature(image_raw)
    }))

    writer.write(example.SerializeToString())

writer.close()

二：TFRecord样例程序写

import tensorflow as tf

reader = tf.TFRecordReader()
filename_queue = tf.train.string_input_producer(['F:/PycharmProjects/tmp/output/output.tfrecords'])

_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
                                   features={
                                       'image_raw': tf.FixedLenFeature([], tf.string),#属性解析方法，返回一个tensor
                                       'pixels': tf.FixedLenFeature([], tf.int64),
                                       'label': tf.FixedLenFeature([], tf.int64)
                                   })

images = tf.decode_raw(features['image_raw'], tf.uint8)
labels = tf.cast(features['label'], tf.int32)
pixels = tf.cast(features['pixels'], tf.int32)

sess = tf.Session()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(10):
    image, label, pixel = sess.run([images, labels, pixels])
    print(image, label, pixel)