diff --git a/paddle/operators/lstm_op.cc b/paddle/operators/lstm_op.cc
index 3b90b64b4effacf7240fb1bee8c0aa44251ad727..afb095a04e73c2f09b828c01630ef2347ff49613 100644
--- a/paddle/operators/lstm_op.cc
+++ b/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."
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 4e8fd407c9983e2827d3137fa4f49a8425d5dce2..7759ce6af6060fbaa5724763184f39198e6eedac 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -227,6 +227,106 @@ 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 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.
+
+    Set `use_peepholes` 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 $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
+    operations on the input $x_{t}$ are NOT included in this operator.
+    Users can choose to use fully-connect operator before LSTM operator.
+
+    Args:
+def dynamic_lstm(input,
+                 size,
+                 param_attr=None,
+                 bias_attr=None,
+                 use_peepholes=True,
+                 is_reverse=False,
+                 gate_activation='sigmoid',
+                 cell_activation='tanh',
+                 candidate_activation='tanh',
+                 dtype='float32'):
+        input(Variable): The input of dynamic_lstm layer, which support 
+             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): The size of input.
+        param_attr(ParamAttr): The parameter attribute for the learnable 
+             hidden-hidden weights.
+                 - The shape is (D x 4D), where D is the hidden size. 
+                 - param_attr = {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 weight
+            and peephole connections weight if setting `use_peepholes` to True.
+                1. `use_peepholes = False` 
+                  - The shape is (1 x 4D). 
+                  - Bias = {b_c, b_i, b_f, b_o}.
+                2. `use_peepholes = True` 
+                  - The shape is (1 x 7D). 
+                  - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.
+        use_peepholes(bool, defalut: True): whether to enable diagonal/peephole
+                                            connections.
+        is_reverse(bool, defalut: False): whether to compute reversed LSTM.
+        gate_activation(string, choices: "sigmoid", "tanh", "relu", "identity",
+            default: "sigmoid"): The activation for input gate, forget gate and
+                                 output gate.
+        cell_activation(string, choices: "sigmoid", "tanh", "relu", "identity",
+            default: "tanh"): The activation for cell output.
+        candidate_activation(string, choices: "sigmoid", "tanh", "relu", 
+            "identity", default: "tanh"): The activation for candidate hidden 
+            state.
+        dtype(string, )
+
+    Returns:
+        hidden(Variable): the hidden state of LSTM layer. The shape is (T x D),
+            and lod is the same with the `input`.
+        cell(Variable): the cell state of LSTM layer. The shape is (T x D), and 
+            lod is the same with the `input`.
+
+    Example:
+        .. code-block:: python
+
+        hidden_dim = 512
+        forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                                       act='tanh', bias_attr=True)
+        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(