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Update function comment of tf.train.batch

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Change: 132224446
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A. Unique TensorFlower 2016-09-05 01:09:10 -08:00 committed by TensorFlower Gardener
parent f4002dd3cb
commit 79fd8f205d
1 changed files with 4 additions and 4 deletions
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tensorflow/python/training/input.py
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@ -519,7 +519,7 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
examples, where the first dimension is indexed by example, and all members of
`tensor_list` should have the same size in the first dimension. If an input
`tensors` 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]`. The `capacity` argument controls the how long the prefetching is
allowed to grow the queues.
@ -553,11 +553,11 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
Args:
tensors: The list or dictionary of tensors to enqueue.
batch_size: The new batch size pulled from the queue.
num_threads: The number of threads enqueuing `tensor_list`.
num_threads: The number of threads enqueuing `tensors`.
capacity: An integer. The maximum number of elements in the queue.
enqueue_many: Whether each tensor in `tensor_list` is a single example.
enqueue_many: Whether each tensor in `tensors` is a single example.
shapes: (Optional) The shapes for each example. Defaults to the
inferred shapes for `tensor_list`.
inferred shapes for `tensors`.
dynamic_pad: Boolean. Allow variable dimensions in input shapes.
The given dimensions are padded upon dequeue so that tensors within a
batch have the same shapes.