Merge pull request #7640 from kuke/add_lstm_doc

Add python doc for dynamic_lstm

paddle/operators/lstm_op.cc

@@ -117,7 +117,7 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("C0",
              "(Tensor, optional) the initial cell state is an optional "
              "input. This is a tensor with shape (N x D), where N is the "
-             "batch size. `H0` and `C0` can be NULL but only at the same time")
+             "batch size. `H0` and `C0` can be NULL but only at the same time.")
         .AsDispensable();
     AddInput("Weight",
              "(Tensor) the learnable hidden-hidden weights."

python/paddle/v2/fluid/layers/nn.py

@@ -206,6 +206,102 @@ def dynamic_lstm(input,
                  cell_activation='tanh',
                  candidate_activation='tanh',
                  dtype='float32'):
+    """
+    **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-line 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.
+
+    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): The parameter attribute for the learnable 
+                               hidden-hidden weights. 
+
+                               - The shape is (D x 4D), where D is the hidden 
+                                 size. 
+                               - Weights = {:math:`W_{ch}, W_{ih}, \
+                                                W_{fh}, W_{oh}`}
+        bias_attr(ParamAttr): 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`. 
+
+                              1. `use_peepholes = False` 
+                                - The shape is (1 x 4D). 
+                                - Biases = {:math:`b_c, b_i, b_f, b_o`}.
+                              2. `use_peepholes = True` 
+                                - The shape is (1 x 7D). 
+                                - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
+                                                 W_{fc}, W_{oc}`}.
+        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".
+
+    Returns:
+        tuple: The hidden state, and cell state of LSTM. The shape of both \
+        is (T x D), and lod is the same with the `input`.
+
+    Examples:
+        .. code-block:: python
+
+            hidden_dim = 512
+            forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                                           act=None, bias_attr=None)
+            forward, _ = fluid.layers.dynamic_lstm(
+                input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
+    """
     helper = LayerHelper('lstm', **locals())
     size = size / 4
     weight = helper.create_parameter(