# -*- coding: utf-8 -*-"""Created on Thu Jul 5 21:17:21 2018@author: muli"""import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport numpy as np# 定义函数转化变量类型。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]))# 将数据转化为tf.train.Example格式。def _make_example(pixels, label, image): image_raw = image.tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'pixels': _int64_feature(pixels), 'label': _int64_feature(np.argmax(label)), 'image_raw': _bytes_feature(image_raw) })) return example# 读取mnist训练数据。mnist = input_data.read_data_sets("./datasets/MNIST_data",dtype=tf.uint8, one_hot=True)images = mnist.train.imageslabels = mnist.train.labelspixels = images.shape[1]num_examples = mnist.train.num_examples# 输出包含训练数据的TFRecord文件。with tf.python_io.TFRecordWriter("./TFRecord/output.tfrecords") as writer: for index in range(num_examples): example = _make_example(pixels, labels[index], images[index]) writer.write(example.SerializeToString())print("TFRecord训练文件已保存。")# 读取mnist测试数据。images_test = mnist.test.imageslabels_test = mnist.test.labelspixels_test = images_test.shape[1]num_examples_test = mnist.test.num_examples# 输出包含测试数据的TFRecord文件with tf.python_io.TFRecordWriter("./TFRecord/output_test.tfrecords") as writer: for index in range(num_examples_test): example = _make_example( pixels_test, labels_test[index], images_test[index]) writer.write(example.SerializeToString())print("TFRecord测试文件已保存。")# -*- coding: utf-8 -*-"""Created on Fri Jul 6 10:10:17 2018@author: muli"""import tensorflow as tf# 读取文件reader = tf.TFRecordReader()# 创建一个队列来维护输入文件列表，里面包含多个文件filename_queue = tf.train.string_input_producer( ["./TFRecord/output.tfrecords"])# 读取列表中的文件_,serialized_example = reader.read(filename_queue)# 解析读取的样例features = tf.parse_single_example( serialized_example, features={ 'image_raw':tf.FixedLenFeature([],tf.string), 'pixels':tf.FixedLenFeature([],tf.int64), 'label':tf.FixedLenFeature([],tf.int64) })# 数据格式转换images = tf.decode_raw(features['image_raw'],tf.uint8)labels = tf.cast(features['label'],tf.int32)pixels = tf.cast(features['pixels'],tf.int32)# 创建会话sess = tf.Session()# 声明一个tf.train.Coordinator类来协同多个进程coord = tf.train.Coordinator()# 启动多线程处理输入数据threads = tf.train.start_queue_runners(sess=sess,coord=coord)for i in range(10): image, label, pixel = sess.run([images, labels, pixels]) print("正在处理第"+str(i+1)+"个文件...")
【转载】原文地址： https://blog.csdn.net/mr_muli/article/details/80936793
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