Update function comment of tf.train.batch
Change: 132224446
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@ -519,7 +519,7 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
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If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
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If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
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examples, where the first dimension is indexed by example, and all members of
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examples, where the first dimension is indexed by example, and all members of
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`tensor_list` should have the same size in the first dimension. If an input
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`tensors` should have the same size in the first dimension. If an input
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tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,
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tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,
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y, z]`. The `capacity` argument controls the how long the prefetching is
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y, z]`. The `capacity` argument controls the how long the prefetching is
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allowed to grow the queues.
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allowed to grow the queues.
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@ -553,11 +553,11 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
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Args:
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Args:
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tensors: The list or dictionary of tensors to enqueue.
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tensors: The list or dictionary of tensors to enqueue.
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batch_size: The new batch size pulled from the queue.
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batch_size: The new batch size pulled from the queue.
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num_threads: The number of threads enqueuing `tensor_list`.
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num_threads: The number of threads enqueuing `tensors`.
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capacity: An integer. The maximum number of elements in the queue.
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capacity: An integer. The maximum number of elements in the queue.
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enqueue_many: Whether each tensor in `tensor_list` is a single example.
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enqueue_many: Whether each tensor in `tensors` is a single example.
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shapes: (Optional) The shapes for each example. Defaults to the
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shapes: (Optional) The shapes for each example. Defaults to the
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inferred shapes for `tensor_list`.
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inferred shapes for `tensors`.
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dynamic_pad: Boolean. Allow variable dimensions in input shapes.
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dynamic_pad: Boolean. Allow variable dimensions in input shapes.
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The given dimensions are padded upon dequeue so that tensors within a
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The given dimensions are padded upon dequeue so that tensors within a
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batch have the same shapes.
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batch have the same shapes.
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