tensorflow学习——tfreader格式，队列读取数据tf.train.shuffle_batch()

1、说明
tf.train.shuffle_batch()
这个函数的功能是：Creates batches by randomly shuffling tensors.
但需要注意的是它是一种图运算，要跑在sess.run()里
This function adds the following to the current Graph:
在运行这个函数时它会在当前图上创建如下的东西：
A shuffling queue into which tensors from tensors are enqueued.
一个乱序的队列，进队的正是传入的tensors
A dequeue_many operation to create batches from the queue.
一个dequeue_many的操作从队列中推出成batch的tensor
A QueueRunner to QUEUE_RUNNER collection, to enqueue the tensors from tensors.
一个QueueRunner的线程，正是这个线程将传入的数据推进队列中．

创建一个队列之后，最好手动关闭。形式如下：

with tf.Session() as sess:    coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(coord=coord)    // do your things    coord.request_stop()    coord.join(threads)

2、例子：将图片存为tfreader格式，然后读出并恢复图片。
tensorflow数据读取机制：tensorflow数据读取机制

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Tue Aug  1 23:07:29 2017@author: no1"""import tensorflow as tf   import scipy.misc as miscimport osdef write_binary():      cwd = os.getcwd()#    all_files = os.listdir(cwd)    classes=['a','b','c']    writer = tf.python_io.TFRecordWriter('data.tfrecord')      for index, name in enumerate(classes):        class_path = os.path.join(cwd,name)#        if tf.gfile.Exists(class_path):#            tf.gfile.DeleteRecursively(class_path)#        tf.gfile.MakeDirs(class_path)        for img_name in os.listdir(class_path):            img_path = os.path.join(class_path , img_name)            img = misc.imread(img_path)            img1 = misc.imresize(img,[250,250,3])            img_raw = img1.tobytes()              #将图片转化为原生bytes            example = tf.train.Example(features=tf.train.Features(feature={                    'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),                "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index]))}                ))  #  将数据整理成 TFRecord 需要的数据结构     #序列化              serialized = example.SerializeToString()      #写入文件              writer.write(serialized)      writer.close()  def read_and_decode(filename):      #创建文件队列,不限读取的数量      filename_queue = tf.train.string_input_producer([filename],shuffle=False)      # create a reader from file queue      reader = tf.TFRecordReader()      #reader从 TFRecord 读取内容并保存到 serialized_example 中     _, serialized_example = reader.read(filename_queue)      features = tf.parse_single_example(     # 读取 serialized_example 的格式         serialized_example,          features={              'label': tf.FixedLenFeature([], tf.int64),              'img_raw': tf.FixedLenFeature([], tf.string)              }      )  # 解析从 serialized_example 读取到的内容      img=tf.decode_raw(features['img_raw'],tf.uint8)    img = tf.reshape(img, [250, 250, 3])    label = tf.cast(features['label'], tf.int32)    return img,label  #write_binary()  img,label = read_and_decode('data.tfrecord')  img_batch, label_batch = tf.train.shuffle_batch([img,label], batch_size=18, capacity=2000, min_after_dequeue=100, num_threads=2)  ##  # sess  init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)      coord = tf.train.Coordinator()  #创建一个协调器，管理线程    #启动QueueRunner, 此时文件名队列已经进队。    threads=tf.train.start_queue_runners(sess=sess,coord=coord)      img, label = sess.run([img_batch, label_batch])      #for i in range(18):    #    cv2.imwrite('%d_%d_p.jpg'%(i,label[i]),img[i])    coord.request_stop()    coord.join(threads)