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e1d074ab
编写于
1月 30, 2017
作者:
Z
zhanghaichao
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updated comments for gru_group and lstm_group in networks.py
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python/paddle/trainer_config_helpers/networks.py
python/paddle/trainer_config_helpers/networks.py
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python/paddle/trainer_config_helpers/networks.py
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@@ -737,12 +737,12 @@ def lstmemory_group(input,
lstm_layer_attr
=
None
,
get_output_layer_attr
=
None
):
"""
lstm_group is a recurrent layer group version Long Short Term Memory. It
lstm_group is a recurrent layer group version
of
Long Short Term Memory. It
does exactly the same calculation as the lstmemory layer (see lstmemory in
layers.py for the maths) does. A promising benefit is that LSTM memory
cell states, or hidden states in every time step are accessible to
for
the
cell states, or hidden states in every time step are accessible to the
user. This is especially useful in attention model. If you do not need to
access t
o t
he internal states of the lstm, but merely use its outputs,
access the internal states of the lstm, but merely use its outputs,
it is recommended to use the lstmemory, which is relatively faster than
lstmemory_group.
...
...
@@ -878,11 +878,11 @@ def gru_group(input,
gate_act
=
None
,
gru_layer_attr
=
None
):
"""
gru_group is a recurrent layer group version Gated Recurrent Unit. It
gru_group is a recurrent layer group version
of
Gated Recurrent Unit. It
does exactly the same calculation as the grumemory layer does. A promising
benefit is that gru hidden s
ates are accessible to for
the user. This is
especially useful in attention model. If you do not need to access
to
any internal state, but merely use the outputs of a GRU, it is recomm
a
nded
benefit is that gru hidden s
tates are accessible to
the user. This is
especially useful in attention model. If you do not need to access
any internal state, but merely use the outputs of a GRU, it is recomm
e
nded
to use the grumemory, which is relatively faster.
Please see grumemory in layers.py for more detail about the maths.
...
...
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