提交 d4fcd2a5 编写于 作者: D dangqingqing

Fix the doc of LSTM operator.

上级 44e39144
...@@ -181,7 +181,7 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -181,7 +181,7 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC( AddComment(R"DOC(
Long-Short Term Memory (LSTM) Operator. Long-Short Term Memory (LSTM) Operator.
The defalut implementation is diagonal/peephole connection The defalut implementation is diagonal/peephole connection
(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows: (https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:
$$ $$
...@@ -198,27 +198,27 @@ c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\ ...@@ -198,27 +198,27 @@ c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
h_t = o_t \odot act_h(c_t) h_t = o_t \odot act_h(c_t)
$$ $$
where the W terms denote weight matrices (e.g. \f$W_{xi}\f$ is the matrix where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
of weights from the input gate to the input), \f$W_{ic}, W_{fc}, W_{oc}\f$ of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation, are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices. The b terms we use vectors to reprenset these diagonal weight matrices. The b terms
denote bias vectors (\f$b_i\f$ is the input gate bias vector), \f$\sigma\f$ denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
is the non-line activations, such as logistic sigmoid function, and is the non-line activations, such as logistic sigmoid function, and
\f$i, f, o\f$ and \f$c\f$ are the input gate, forget gate, output gate, $i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector \f$h\f$. the cell output activation vector $h$.
The \f$\odot\f$ is the element-wise product of the vectors. \f$act_g\f$ and \f$act_h\f$ The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
are the cell input and cell output activation functions and `tanh` is usually are the cell input and cell output activation functions and `tanh` is usually
used for them. \f$\tilde{c_t}\f$ is also called candidate hidden state, used for them. $\tilde{c_t}$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state. which is computed based on the current input and the previous hidden state.
Set `use_peepholes` False to disable peephole connection Set `use_peepholes` False to disable peephole connection. The formula
(http://www.bioinf.jku.at/publications/older/2604.pdf). The formula is omitted here, please refer to the paper
is omitted here. http://www.bioinf.jku.at/publications/older/2604.pdf for details.
Note that these \f$W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\f$ Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
operations on the input \f$x_{t}\f$ are NOT included in this operator. operations on the input $x_{t}$ are NOT included in this operator.
Users can choose to use fully-connect operator before LSTM operator. Users can choose to use fully-connect operator before LSTM operator.
)DOC"); )DOC");
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