提交 650f7791 编写于 作者: H helinwang 提交者: GitHub

Merge pull request #1243 from Haichao-Zhang/updated_comments_for_gru_and_lstm_group

updated comments for gru_group and lstm_group in networks.py
......@@ -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 to the 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 sates 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 recommanded
benefit is that gru hidden states 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 recommended
to use the grumemory, which is relatively faster.
Please see grumemory in layers.py for more detail about the maths.
......
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