tf.train.shuffle_batch函数解析
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tf.train.shuffle_batch
(tensor_list, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, name=None)
Creates batches by randomly shuffling tensors. 通过随机打乱张量的顺序创建批次.
简单来说就是读取一个文件并且加载一个张量中的batch_size行
This function adds the following to the current Graph
:
这个函数将以下内容加入到现有的图中.
A shuffling queue into which tensors from
tensor_list
are enqueued.
一个由传入张量组成的随机乱序队列A
dequeue_many
operation to create batches from the queue.
从张量队列中取出张量的出队操作A
QueueRunner
toQUEUE_RUNNER
collection, to enqueue the tensors
fromtensor_list
.
一个队列运行器管理出队操作.
Ifenqueue_many
isFalse
,tensor_list
is assumed to represent a single example. An input tensor with shape[x, y, z]
will be output as a tensor with shape[batch_size, x, y, z]
.If
enqueue_many
isTrue
,tensor_list
is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members oftensor_list
should have the same size in the first dimension. If an input tensor has shape[*, x, y, z]
, the output will have shape[batch_size, x, y, z]
.
enqueue_many主要是设置tensor中的数据是否能重复,如果想要实现同一个样本多次出现可以将其设置为:"True",如果只想要其出现一次,也就是保持数据的唯一性,这时候我们将其设置为默认值:"False"
The
capacity
argument controls the how long the prefetching is allowed to grow the queues. capacity控制了预抓取操作对于增加队列长度操作的长度.For example:
# Creates batches of 32 images and 32 labels.
image_batch, label_batch = tf.train.shuffle_batch( [single_image, single_label], batch_size=32, num_threads=4,capacity=50000,min_after_dequeue=10000)
这段代码写的是从[single_image, single_label]利用4个线程读取32个数据作为一个batch
Args:
-
tensor_list
: The list of tensors to enqueue.
入队的张量列表 -
batch_size
: The new batch size pulled from the queue.
表示进行一次批处理的tensors数量. -
capacity
: An integer. The maximum number of elements in the queue.
容量:一个整数,队列中的最大的元素数.
这个参数一定要比min_after_dequeue参数的值大,并且决定了我们可以进行预处理操作元素的最大值.
推荐其值为:
\]
-
min_after_dequeue
: Minimum number elements in the queue after a
dequeue(出列), used to ensure a level of mixing of elements. - 当一次出列操作完成后,队列中元素的最小数量,往往用于定义元素的混合级别.
- 定义了随机取样的缓冲区大小,此参数越大表示更大级别的混合但是会导致启动更加缓慢,并且会占用更多的内存
-
num_threads
: The number of threads enqueuingtensor_list
. - 设置num_threads的值大于1,使用多个线程在tensor_list中读取文件,这样保证了同一时刻只在一个文件中进行读取操作(但是读取速度依然优于单线程),而不是之前的同时读取多个文件,这种方案的优点是:
- 避免了两个不同的线程从同一文件中读取用一个样本
- 避免了过多的磁盘操作
-
seed
: Seed for the random shuffling within the queue.
打乱tensor队列的随机数种子 -
enqueue_many
: Whether each tensor intensor_list
is a single example.
定义tensor_list中的tensor是否冗余. -
shapes
: (Optional) The shapes for each example. Defaults to the
inferred shapes fortensor_list
.
用于改变读取tensor的形状,默认情况下和直接读取的tensor的形状一致. -
name
: (Optional) A name for the operations.
Returns:
- A list of tensors with the same number and types as
tensor_list
.
默认返回一个和读取tensor_list数据和类型一个tensor列表.