add the notation of input array and incoming signal
@@ -209,6 +209,9 @@ def lstm(c_prev, x):
`Long Short-Term Memory in Recurrent Neural Networks \
<http://www.felixgers.de/papers/phd.pdf>`_.
+ .. seealso::
+ :class:`~chainer.links.LSTM`
+
.. admonition:: Example
Assuming ``y`` is the current incoming signal, ``c`` is the previous
@@ -230,5 +233,19 @@ def lstm(c_prev, x):
the previous outgoing signal ``h``. Different parameters are used for
different kind of input sources.
+ .. note::
+
+ We use the naming rule below.
+
+ - incoming signal
+ The formal input of the formulation of LSTM (e.g. in NLP, word
+ vector or output of previous RNN). The input of
+ :class:`chainer.links.LSTM` is the *incoming signal*.
+ - input array
+ The array which is linear transformed from *incoming signal*. The
+ *input array* contsins four sources, the sources of cell input,
+ input gate, forget gate and output gate. The input of
+ :class:`chainer.functions.LSTM` is the *input array*.
+
"""
return LSTM()(c_prev, x)
