tensorflow TFRecords文件的生成和读取的方法

message Example {   Features features = 1; }; message Features {   map<string, Feature> feature = 1; }; message Feature {   oneof kind {   BytesList bytes_list = 1;   FloatList float_list = 2;   Int64List int64_list = 3; } }; 

from random import shuffle import numpy as np import glob import tensorflow as tf import cv2 import sys import os  # 因为我装的是CPU版本的，运行起来会有'warning'，解决方法入下，眼不见为净~ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  shuffle_data = True image_path = '/path/to/image/*.jpg'  # 取得该路径下所有图片的路径，type（addrs）= list addrs = glob.glob(image_path) # 标签数据的获得具体情况具体分析，type（labels）= list labels = ...  # 这里是打乱数据的顺序 if shuffle_data:   c = list(zip(addrs, labels))   shuffle(c)   addrs, labels = zip(*c)  # 按需分割数据集 train_addrs = addrs[0:int(0.7*len(addrs))] train_labels = labels[0:int(0.7*len(labels))]  val_addrs = addrs[int(0.7*len(addrs)):int(0.9*len(addrs))] val_labels = labels[int(0.7*len(labels)):int(0.9*len(labels))]  test_addrs = addrs[int(0.9*len(addrs)):] test_labels = labels[int(0.9*len(labels)):]  # 上面不是获得了image的地址么，下面这个函数就是根据地址获取图片 def load_image(addr): # A function to Load image   img = cv2.imread(addr)   img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC)   img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)   # 这里/255是为了将像素值归一化到[0，1]   img = img / 255.   img = img.astype(np.float32)   return img  # 将数据转化成对应的属性 def _int64_feature(value):    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))   def _bytes_feature(value):   return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))   def _float_feature(value):   return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))  # 下面这段就开始把数据写入TFRecods文件  train_filename = '/path/to/train.tfrecords' # 输出文件地址  # 创建一个writer来写 TFRecords 文件 writer = tf.python_io.TFRecordWriter(train_filename)  for i in range(len(train_addrs)):   # 这是写入操作可视化处理   if not i % 1000:     print('Train data: {}/{}'.format(i, len(train_addrs)))     sys.stdout.flush()   # 加载图片   img = load_image(train_addrs[i])    label = train_labels[i]    # 创建一个属性（feature）   feature = {'train/label': _int64_feature(label),         'train/image': _bytes_feature(tf.compat.as_bytes(img.tostring()))}    # 创建一个 example protocol buffer   example = tf.train.Example(features=tf.train.Features(feature=feature))    # 将上面的example protocol buffer写入文件   writer.write(example.SerializeToString())  writer.close() sys.stdout.flush() 

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import os  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' data_path = 'train.tfrecords' # tfrecords 文件的地址  with tf.Session() as sess:   # 先定义feature，这里要和之前创建的时候保持一致   feature = {     'train/image': tf.FixedLenFeature([], tf.string),     'train/label': tf.FixedLenFeature([], tf.int64)   }   # 创建一个队列来维护输入文件列表   filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)    # 定义一个 reader ，读取下一个 record   reader = tf.TFRecordReader()   _, serialized_example = reader.read(filename_queue)    # 解析读入的一个record   features = tf.parse_single_example(serialized_example, features=feature)    # 将字符串解析成图像对应的像素组   image = tf.decode_raw(features['train/image'], tf.float32)    # 将标签转化成int32   label = tf.cast(features['train/label'], tf.int32)    # 这里将图片还原成原来的维度   image = tf.reshape(image, [224, 224, 3])    # 你还可以进行其他一些预处理....    # 这里是创建顺序随机 batches(函数不懂的自行百度)   images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, min_after_dequeue=10)    # 初始化   init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())   sess.run(init_op)    # 启动多线程处理输入数据   coord = tf.train.Coordinator()   threads = tf.train.start_queue_runners(coord=coord)    ....    #关闭线程   coord.request_stop()   coord.join(threads)   sess.close()