tensorflow cifar-10文档Read 对tensorflow中cifar-10文档的Read操作详解

时间:2021-04-02 luchi007 人气:0

前言

在tensorflow的官方文档中得卷积神经网络一章，有一个使用cifar-10图片数据集的实验，搭建卷积神经网络倒不难，但是那个cifar10_input文件着实让我费了一番心思。配合着官方文档也算看的七七八八，但是中间还是有一些不太明白，不明白的mark一下，这次记下一些已经明白的。

研究

cifar10_input.py文件的read操作，主要的就是下面的代码：

if not eval_data:
  filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
         for i in xrange(1, 6)]
  num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
 else:
  filenames = [os.path.join(data_dir, 'test_batch.bin')]
  num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
...
filename_queue = tf.train.string_input_producer(filenames)

...

label_bytes = 1 # 2 for CIFAR-100
 result.height = 32
 result.width = 32
 result.depth = 3
 image_bytes = result.height * result.width * result.depth
 # Every record consists of a label followed by the image, with a
 # fixed number of bytes for each.
 record_bytes = label_bytes + image_bytes

 # Read a record, getting filenames from the filename_queue. No
 # header or footer in the CIFAR-10 format, so we leave header_bytes
 # and footer_bytes at their default of 0.
 reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
 result.key, value = reader.read(filename_queue)

 ...

 if shuffle:
  images, label_batch = tf.train.shuffle_batch(
    [image, label],
    batch_size=batch_size,
    num_threads=num_preprocess_threads,
    capacity=min_queue_examples + 3 * batch_size,
    min_after_dequeue=min_queue_examples)
 else:
  images, label_batch = tf.train.batch(
    [image, label],
    batch_size=batch_size,
    num_threads=num_preprocess_threads,
    capacity=min_queue_examples + 3 * batch_size)

开始并不明白这段代码是用来干什么的，越看越糊涂，因为之前使用tensorflow最多也就是使用哪个tf.placeholder()这个操作，并没有使用tensorflow自带的读写方法来读写，所以上面的代码看的很费劲儿。不过我在官方文档的How-To这个document中看到了这个东西：

Batching

def read_my_file_format(filename_queue):
 reader = tf.SomeReader()
 key, record_string = reader.read(filename_queue)
 example, label = tf.some_decoder(record_string)
 processed_example = some_processing(example)
 return processed_example, label

def input_pipeline(filenames, batch_size, num_epochs=None):
 filename_queue = tf.train.string_input_producer(
   filenames, num_epochs=num_epochs, shuffle=True)
 example, label = read_my_file_format(filename_queue)
 # min_after_dequeue defines how big a buffer we will randomly sample
 #  from -- bigger means better shuffling but slower start up and more
 #  memory used.
 # capacity must be larger than min_after_dequeue and the amount larger
 #  determines the maximum we will prefetch. Recommendation:
 #  min_after_dequeue + (num_threads + a small safety margin) * batch_size
 min_after_dequeue = 10000
 capacity = min_after_dequeue + 3 * batch_size
 example_batch, label_batch = tf.train.shuffle_batch(
   [example, label], batch_size=batch_size, capacity=capacity,
   min_after_dequeue=min_after_dequeue)
 return example_batch, label_batch

感觉豁然开朗，再研究一下其官方文档API就能大约明白期间意思。最有代表性的图示官方文档中也给出来了，虽然官方文档给的解释并不多。

API我就不一一解释了，我们下面通过实验来明白。

实验

首先在tensorflow路径下创建两个文件，分别命名为test.txt以及test2.txt，其内容分别是：

test.txt:

test line1
test line2
test line3
test line4
test line5
test line6

test2.txt:

test2 line1
test2 line2
test2 line3
test2 line4
test2 line5
test2 line6

然后再命令行里依次键入下面的命令：

import tensorflow as tf
filenames=['test.txt','test2.txt']
#创建如上图所示的filename_queue
filename_queue=tf.train.string_input_producer(filenames)
#选取的是每次读取一行的TextLineReader
reader=tf.TextLineReader()
init=tf.initialize_all_variables()
#读取文件，也就是创建上图中的Reader
key,value=reader.read(filename_queue)
#读取batch文件，batch_size设置成1，为了方便看
bs=tf.train.batch([value],batch_size=1,num_threads=1,capacity=2)
sess=tf.Session() 
#非常关键，这个是连通各个queue图的关键          
tf.train.start_queue_runners(sess=sess)
#计算有reader的输出
b=reader.num_records_produced()

然后我们执行：

>>> sess.run(bs)
array(['test line1'], dtype=object)
>>> sess.run(b)
4
>>> sess.run(bs)
array(['test line2'], dtype=object)
>>> sess.run(b)
5
>>> sess.run(bs)
array(['test line3'], dtype=object)
>>> sess.run(bs)
array(['test line4'], dtype=object)
>>> sess.run(bs)
array(['test line5'], dtype=object)
>>> sess.run(bs)
array(['test line6'], dtype=object)
>>> sess.run(bs)
array(['test2 line1'], dtype=object)
>>> sess.run(bs)
array(['test2 line2'], dtype=object)
>>> sess.run(bs)
array(['test2 line3'], dtype=object)
>>> sess.run(bs)
array(['test2 line4'], dtype=object)
>>> sess.run(bs)
array(['test2 line5'], dtype=object)
>>> sess.run(bs)
array(['test2 line6'], dtype=object)
>>> sess.run(bs)
array(['test2 line1'], dtype=object)
>>> sess.run(bs)
array(['test2 line2'], dtype=object)
>>> sess.run(bs)
array(['test2 line3'], dtype=object)
>>> sess.run(bs)
array(['test2 line4'], dtype=object)
>>> sess.run(bs)
array(['test2 line5'], dtype=object)
>>> sess.run(bs)
array(['test2 line6'], dtype=object)
>>> sess.run(bs)
array(['test line1'], dtype=object)

我们发现，当batch_size设置成为1的时候，bs的输出是按照文件行数进行逐步打印的，原因是，我们选择的是单个Reader进行操作的，这个Reader先将test.txt文件读取，然后逐行读取并将读取的文本送到example queue(如上图)中，因为这里batch设置的是1，而且用到的是tf.train.batch()方法，中间没有shuffle，所以自然而然是按照顺序输出的，之后Reader再读取test2.txt。但是这里有一个疑惑，为什么reader.num_records_produced的第一个输出不是从1开始的，这点不太清楚。另外，打印出filename_queue的size:

>>> sess.run(filename_queue.size())
32

发现filename_queue的size有32个之多！这点也不明白。。。

我们可以更改实验条件，将batch_size设置成2，会发现也是顺序的输出，而且每次输出为2行文本（和batch_size一样）

我们继续更改实验条件，将tf.train.batch方法换成tf.train.shuffle_batch方法，文本数据不变：

import tensorflow as tf
filenames=['test.txt','test2.txt']
filename_queue=tf.train.string_input_producer(filenames)
reader=tf.TextLineReader()
init=tf.initialize_all_variables()
key,value=reader.read(filename_queue)
bs=tf.train.shuffle_batch([value],batch_size=1,num_threads=1,capacity=4,min_after_dequeue=2)
sess=tf.Session()           
tf.train.start_queue_runners(sess=sess)
b=reader.num_records_produced()

继续刚才的执行：

>>> sess.run(bs)
array(['test2 line2'], dtype=object)
>>> sess.run(bs)
array(['test2 line5'], dtype=object)
>>> sess.run(bs)
array(['test2 line6'], dtype=object)
>>> sess.run(bs)
array(['test2 line4'], dtype=object)
>>> sess.run(bs)
array(['test2 line3'], dtype=object)
>>> sess.run(bs)
array(['test line1'], dtype=object)
>>> sess.run(bs)
array(['test line2'], dtype=object)
>>> sess.run(bs)
array(['test2 line1'], dtype=object)
>>> sess.run(bs)
array(['test line4'], dtype=object)
>>> sess.run(bs)
array(['test line5'], dtype=object)
>>> sess.run(bs)
array(['test2 line1'], dtype=object)
>>> sess.run(bs)
array(['test line3'], dtype=object)

我们发现的是，使用了shuffle操作之后，明显的bs的输出变得不一样了，变得没有规则,然后我们看filename_queue的size：

>>> sess.run(filename_queue.size())
32

发现也是32，由此估计是tensorflow会根据文件大小默认filename_queue的长度。注意这里面的capacity=4,min_after_dequeue=2这些个命令，capacity指的是example queue的最大长度，而min_after_dequeue是指在出队列之后，example queue最少要保留的元素个数，为什么需要这个，其实是为了混合的更显著。也正是有这两个元素，让shuffle变得可能。

到这里基本上大概的思路能明白，但是上面的实验都是对于单个的Reader，和上一节的图不太一致，根据官网教程，为了使用多个Reader，我们可以这样：

import tensorflow as tf
filenames=['test.txt','test2.txt']
filename_queue=tf.train.string_input_producer(filenames)
reader=tf.TextLineReader()
init=tf.initialize_all_variables()
key_list,value_list=[reader.read(filename_queue) for _ in range(2)]
bs2=tf.train.shuffle_batch_join([value_list],batch_size=1,capacity=4,min_after_dequeue=2)
sess=tf.Session()       
sess.run(init)    
tf.train.start_queue_runners(sess=sess)

运行的结果如下：

>>> sess.run(bs2)
[array(['test2.txt:2'], dtype=object), array(['test2 line2'], dtype=object)]
>>> sess.run(bs2)
[array(['test2.txt:5'], dtype=object), array(['test2 line5'], dtype=object)]
>>> sess.run(bs2)
[array(['test2.txt:6'], dtype=object), array(['test2 line6'], dtype=object)]
>>> sess.run(bs2)
[array(['test2.txt:4'], dtype=object), array(['test2 line4'], dtype=object)]
>>> sess.run(bs2)
[array(['test2.txt:3'], dtype=object), array(['test2 line3'], dtype=object)]
>>> sess.run(bs2)
[array(['test2.txt:1'], dtype=object), array(['test2 line1'], dtype=object)]
>>> sess.run(bs2)
[array(['test.txt:4'], dtype=object), array(['test line4'], dtype=object)]
>>> sess.run(bs2)
[array(['test.txt:3'], dtype=object), array(['test line3'], dtype=object)]
>>> sess.run(bs2)
[array(['test.txt:2'], dtype=object), array(['test line2'], dtype=object)]

以上这篇对tensorflow中cifar-10文档的Read操作详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

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