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8c416653
编写于
12月 08, 2022
作者:
L
liu zhengxi
提交者:
GitHub
12月 08, 2022
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refine bsd doc (#48882)
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python/paddle/nn/decode.py
python/paddle/nn/decode.py
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python/paddle/nn/decode.py
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@@ -26,8 +26,6 @@ __all__ = []
class
Decoder
:
"""
:api_attr: Static Graph
Decoder is the base class for any decoder instance used in `dynamic_decode`.
It provides interface for output generation for one time step, which can be
used to generate sequences.
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@@ -146,13 +144,14 @@ class BeamSearchDecoder(Decoder):
Please refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
for more details.
**NOTE** When decoding with beam search, the `inputs` and `states` of cell
would be tiled to `beam_size` (unsqueeze and tile), resulting to shapes like
`[batch_size * beam_size, ...]` , which is built into `BeamSearchDecoder` and
done automatically. Thus any other tensor with shape `[batch_size, ...]` used
in `cell.call` needs to be tiled manually first, which can be completed by using
:code:`BeamSearchDecoder.tile_beam_merge_with_batch` . The most common case
for this is the encoder output in attention mechanism.
Note:
When decoding with beam search, the `inputs` and `states` of cell
would be tiled to `beam_size` (unsqueeze and tile), resulting to shapes like
`[batch_size * beam_size, ...]` , which is built into `BeamSearchDecoder` and
done automatically. Thus any other tensor with shape `[batch_size, ...]` used
in `cell.call` needs to be tiled manually first, which can be completed by using
:code:`BeamSearchDecoder.tile_beam_merge_with_batch` . The most common case
for this is the encoder output in attention mechanism.
Returns:
BeamSearchDecoder: An instance of decoder which can be used in
\
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