follow comments

paddle/fluid/operators/linear_chain_crf_op.cc

@@ -84,6 +84,7 @@ CRF. Please refer to http://www.cs.columbia.edu/~mcollins/fb.pdf and
 http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for details.
 
 Equation:
+
 1. Denote Input(Emission) to this operator as $x$ here.
 2. The first D values of Input(Transition) to this operator are for starting
 weights, denoted as $a$ here.
@@ -106,6 +107,7 @@ Finally, the linear chain CRF operator outputs the logarithm of the conditional
 likelihood of each training sample in a mini-batch.
 
 NOTE:
+
 1. The feature function for a CRF is made up of the emission features and the
 transition features. The emission feature weights are NOT computed in
 this operator. They MUST be computed first before this operator is called.

paddle/fluid/operators/lstm_op.cc

@@ -198,20 +198,20 @@ c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
 h_t = o_t \odot act_h(c_t)
 $$
 
-where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
-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,
-we use vectors to reprenset these diagonal weight matrices. The b terms
-denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
-is the non-line activations, such as logistic sigmoid function, and
-$i, f, o$ and $c$ are the input gate, forget gate, output gate,
-and cell activation vectors, respectively, all of which have the same size as
-the cell output activation vector $h$.
-
-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
-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.
+- W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
+  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,
+  we use vectors to reprenset these diagonal weight matrices.
+- The b terms denote bias vectors ($b_i$ is the input gate bias vector).
+- $\sigma$ is the non-line activations, such as logistic sigmoid function.
+- $i, f, o$ and $c$ are the input gate, forget gate, output gate,
+  and cell activation vectors, respectively, all of which have the same size as
+  the cell output activation vector $h$.
+- 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 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.
 
 Set `use_peepholes` False to disable peephole connection. The formula
 is omitted here, please refer to the paper

python/paddle/fluid/layers/nn.py

@@ -262,6 +262,7 @@ def embedding(input,
 
 
 # TODO(qijun): expose H0 and C0
+@templatedoc(op_type="lstm")
 def dynamic_lstm(input,
                  size,
                  param_attr=None,
@@ -274,64 +275,19 @@ def dynamic_lstm(input,
                  dtype='float32',
                  name=None):
     """
-    **Dynamic LSTM Layer**
-
-    The defalut implementation is diagonal/peephole connection
-    (https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:
-
-    .. math::
-
-        i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)
-
-        f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)
-
-        \\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)
-
-        o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)
-
-        c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
-
-        h_t & = o_t \odot act_h(c_t)
-
-    where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is
-    the matrix of weights from the input gate to the input), :math:`W_{ic}, \
-    W_{fc}, W_{oc}` are diagonal weight matrices for peephole connections. In
-    our implementation, we use vectors to reprenset these diagonal weight
-    matrices. The :math:`b` terms denote bias vectors (:math:`b_i` is the input
-    gate bias vector), :math:`\sigma` is the non-linear activations, such as
-    logistic sigmoid function, and :math:`i, f, o` and :math:`c` are the input
-    gate, forget gate, output gate, and cell activation vectors, respectively,
-    all of which have the same size as the cell output activation vector :math:`h`.
-
-    The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
-    and :math:`act_h` are the cell input and cell output activation functions
-    and `tanh` is usually used for them. :math:`\\tilde{c_t}` is also called
-    candidate hidden state, which is computed based on the current input and
-    the previous hidden state.
-
-    Set `use_peepholes` to `False` to disable peephole connection. The formula
-    is omitted here, please refer to the paper
-    http://www.bioinf.jku.at/publications/older/2604.pdf for details.
-
-    Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
-    operations on the input :math:`x_{t}` are NOT included in this operator.
-    Users can choose to use fully-connect layer before LSTM layer.
+    ${comment}
 
     Args:
-        input(Variable): The input of dynamic_lstm layer, which supports
-                         variable-time length input sequence. The underlying
-                         tensor in this Variable is a matrix with shape
-                         (T X 4D), where T is the total time steps in this
-                         mini-batch, D is the hidden size.
-        size(int): 4 * hidden size.
-        param_attr(ParamAttr|None): The parameter attribute for the learnable
+        input (Variable): ${input_comment}
+        size (int): 4 * hidden size.
+        param_attr (ParamAttr|None): The parameter attribute for the learnable
                                hidden-hidden weights.
 
                                - Weights = {:math:`W_{ch}, W_{ih}, \
                                                 W_{fh}, W_{oh}`}
                                - The shape is (D x 4D), where D is the hidden
                                  size.
-        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
+        bias_attr (ParamAttr|None): The bias attribute for the learnable bias
                               weights, which contains two parts, input-hidden
                               bias weights and peephole connections weights if
                               setting `use_peepholes` to `True`.
@@ -343,20 +299,13 @@ def dynamic_lstm(input,
                                  - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                  W_{fc}, W_{oc}`}.
                                  - The shape is (1 x 7D).
-        use_peepholes(bool): Whether to enable diagonal/peephole connections,
-                             default `True`.
-        is_reverse(bool): Whether to compute reversed LSTM, default `False`.
-        gate_activation(str): The activation for input gate, forget gate and
-                              output gate. Choices = ["sigmoid", "tanh", "relu",
-                              "identity"], default "sigmoid".
-        cell_activation(str): The activation for cell output. Choices = ["sigmoid",
-                              "tanh", "relu", "identity"], default "tanh".
-        candidate_activation(str): The activation for candidate hidden state.
-                              Choices = ["sigmoid", "tanh",
-                                  "relu", "identity"],
-                              default "tanh".
-        dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
-        name(str|None): A name for this layer(optional). If set None, the layer
+        use_peepholes (bool): ${use_peepholes_comment}
+        is_reverse (bool): ${is_reverse_comment}
+        gate_activation (str): ${gate_activation_comment}
+        cell_activation (str): ${cell_activation_comment}
+        candidate_activation (str): ${candidate_activation_comment}
+        dtype (str): Data type. Choices = ["float32", "float64"], default "float32".
+        name (str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
 
     Returns: