tensorflow-TFRecord 文件详解
TFRecord 是 tensorflow 内置的文件格式，它是一种二进制文件，具有以下优点：

  1. 统一各种输入文件的操作

  2. 更好的利用内存，方便复制和移动

  3. 将二进制数据和标签(label)存储在同一个文件中

import os 
import numpy as np
import tempfile
import tensorflow as tf

# example_path = os.path.join(tempfile.gettempdir(), "example.tfrecords")

example_path='./temp.tfrecords'
np.random.seed(0)

# Write the records to a file.
with tf.io.TFRecordWriter(example_path) as file_writer:
  for _ in range(4):
    #产生随机数
    x, y = np.random.random(), np.random.random()
    
    print(x,y,'--->')
    x1=tf.train.Feature(float_list=tf.train.FloatList(value=[x]))
    y1=tf.train.Feature(float_list=tf.train.FloatList(value=[y]))
    
    print('x1=',x1)
    print('y1=',y1)
    
    feature0={
        "x": x1,
        "y":y1 ,
    }
    
    print('feature0=',feature0)
    
    features0=tf.train.Features(feature=feature0)
    
    print('features=',feature0)
    record_bytes = tf.train.Example(features=features0).SerializeToString()
    file_writer.write(record_bytes)
    
    
# Read the data back out.
def decode_fn(record_bytes):
  return tf.io.parse_single_example(
      # Data
      record_bytes,

      # Schema
      {"x": tf.io.FixedLenFeature([], dtype=tf.float32),
       "y": tf.io.FixedLenFeature([], dtype=tf.float32)}
  )



for batch in tf.data.TFRecordDataset([example_path]).map(decode_fn):
  print("x = {x:.4f},  y = {y:.4f}".format(**batch))