paddle.v2.fluid.layers.beam_search_decode(ids, scores)
-
-paddle.v2.fluid.layers.sequence_expand(x, y, main_program=None, startup_program=None)paddle.v2.fluid.layers.sequence_expand(x, y)
Sequence Expand Layer. This layer will expand the input variable x according to LoD information of y. And the following examples will explain how sequence_expand works:
@@ -1116,8 +1113,6 @@ explain how sequence_expand works:paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None)Lstm unit layer. The equation of a lstm step is:
++++\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]+
The inputs of lstm unit includes \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The implementation separates the linear transformation +and non-linear transformation apart. Here, we take \(i_t\) as an +example. The linear transformation is applied by calling a fc layer and +the equation is:
++++\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i\]+
The non-linear transformation is applied by calling lstm_unit_op and the +equation is:
++++\[i_t = \sigma(L_{i_t})\]+
This layer has two outputs including \(h_t\) and \(o_t\).
+| Parameters: |
|
+
|---|---|
| Returns: | The hidden value and cell value of lstm unit. + |
+
| Return type: | tuple + |
+
| Raises: |
|
+
Examples
+x_t = fluid.layers.fc(input=x_t_data, size=10)
+prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
+prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+ hidden_t_prev=prev_hidden,
+ cell_t_prev=prev_cell)
+IfElse Operator","Design Doc: InferVarType","Intel\u00ae MKL Packed on PaddlePaddle: Design Doc","Intel\u00ae MKL-DNN on PaddlePaddle: Design Doc","Design Doc: Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","RNNOp design","Design: Sequence Decoder Generating LoDTensors","Optimizer Design","Design Doc: NCCL support in Paddle Fluid","Averaging Parameter in PaddlePaddle","Design Doc: The C++ Class Parameters","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design Doc: Supporting new 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\ No newline at end of file
diff --git a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
index 9f3669e11583a4ed6467f1a1bb509481fdf0b9d1..d2bb7a8187ba7f1ae78a5337794a8882c96d2346 100644
--- a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
+++ b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
@@ -188,12 +188,6 @@ beam_search_decode
:noindex:
-lstm
----------
-.. autofunction:: paddle.v2.fluid.layers.lstm
- :noindex:
-
-
lod_rank_table
---------
.. autofunction:: paddle.v2.fluid.layers.lod_rank_table
@@ -300,7 +294,14 @@ conv2d_transpose
.. autofunction:: paddle.v2.fluid.layers.conv2d_transpose
:noindex:
+
sequence_expand
---------
.. autofunction:: paddle.v2.fluid.layers.sequence_expand
:noindex:
+
+
+lstm_unit
+---------
+.. autofunction:: paddle.v2.fluid.layers.lstm_unit
+ :noindex:
diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html
index 427e0763d3b1eac308181264f4500b2458d07d9b..6d349d334f1aff45e7ed40ad440d4571f91f89a4 100644
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -882,9 +882,6 @@ the BatchNorm layer using the configurations from the input parameters.
paddle.v2.fluid.layers.beam_search_decode(ids, scores)
-
-paddle.v2.fluid.layers.sequence_expand(x, y, main_program=None, startup_program=None)paddle.v2.fluid.layers.sequence_expand(x, y)
Sequence Expand Layer. This layer will expand the input variable x according to LoD information of y. And the following examples will explain how sequence_expand works:
@@ -1135,8 +1132,6 @@ explain how sequence_expand works:paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None)Lstm unit layer. The equation of a lstm step is:
++++\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]+
The inputs of lstm unit includes \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The implementation separates the linear transformation +and non-linear transformation apart. Here, we take \(i_t\) as an +example. The linear transformation is applied by calling a fc layer and +the equation is:
++++\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i\]+
The non-linear transformation is applied by calling lstm_unit_op and the +equation is:
++++\[i_t = \sigma(L_{i_t})\]+
This layer has two outputs including \(h_t\) and \(o_t\).
+| 参数: |
|
+
|---|---|
| 返回: | The hidden value and cell value of lstm unit. + |
+
| 返回类型: | tuple + |
+
| Raises: |
|
+
Examples
+x_t = fluid.layers.fc(input=x_t_data, size=10)
+prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
+prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+ hidden_t_prev=prev_hidden,
+ cell_t_prev=prev_cell)
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