tfrecords读入

#!/usr/bin/env python

import tensorflow as tf
import numpy as np
def read_and_decode(filename): # 读入dog_train.tfrecords
    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
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [4, 4, 1])#reshape为128*128的3通道图片
    #img = tf.cast(img, tf.float32) * (1. / 255) - 0.5 #在流中抛出img张量
    label = tf.cast(features['label'], tf.int32) #在流中抛出label张量
    return img, label
read_and_decode('train.tfrecords')