paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')GRU unit layer. The equation of a gru step is:
++++\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\ch_t & = actNode(xc_t + W_c dot(r_t, h_{t-1}) + b_c)\\h_t & = dot((1-u_t), ch_{t-1}) + dot(u_t, h_t)\end{aligned}\end{align} \]+
The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms +of the equation above, the \(z_t\) is split into 3 parts - +\(xu_t\), \(xr_t\) and \(xc_t\). This means that in order to +implement a full GRU unit operator for an input, a fully +connected layer has to be applied, such that \(z_t = W_{fc}x_t\).
+This layer has three outputs \(h_t\), \(dot(r_t, h_{t - 1})\) +and concatenation of \(u_t\), \(r_t\) and \(ch_t\).
+| Parameters: |
|
+
|---|---|
| Returns: | The hidden value, reset-hidden value and gate values. + |
+
| Return type: | tuple + |
+
Examples
+# assuming we have x_t_data and prev_hidden of size=10
+x_t = fluid.layers.fc(input=x_t_data, size=30)
+hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
+ hidden = prev_hidden)
+IfElse Operator","Design Doc: InferVarType","Problem","Intel\u00ae MKL Packed on PaddlePaddle: Design Doc","Intel\u00ae MKL-DNN on PaddlePaddle: Design Doc","Design Doc: Add MKLDNN Kernel in Fluid Operator","Design Doc: Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","Design Doc: The Keys of Operator Kernel Type","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","Introduction","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Execute the Program with Multi CPU","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in 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iOS","Build PaddlePaddle for Raspberry Pi","MOBILE","Cluster bootstrapping tool survey","<no title>","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Model Zoo - ImageNet","Quick 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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 004ee2d8c85ce7661886179570e693d7d61bc6d8..a7c8670f66cc7f319e41155211ead2d89126117f 100644
--- a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
+++ b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
@@ -307,6 +307,12 @@ sequence_expand
:noindex:
+gru_unit
+--------
+.. autofunction:: paddle.v2.fluid.layers.gru_unit
+ :noindex:
+
+
lstm_unit
---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit
diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html
index 2d4884ed4585bdf0c25893e9911e798095891889..592b78ab5cbc0ad5fc1d5a4971e1ff1da4168d1c 100644
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -2024,6 +2024,56 @@ explain how sequence_expand works:
paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')GRU unit layer. The equation of a gru step is:
++++\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\ch_t & = actNode(xc_t + W_c dot(r_t, h_{t-1}) + b_c)\\h_t & = dot((1-u_t), ch_{t-1}) + dot(u_t, h_t)\end{aligned}\end{align} \]+
The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms +of the equation above, the \(z_t\) is split into 3 parts - +\(xu_t\), \(xr_t\) and \(xc_t\). This means that in order to +implement a full GRU unit operator for an input, a fully +connected layer has to be applied, such that \(z_t = W_{fc}x_t\).
+This layer has three outputs \(h_t\), \(dot(r_t, h_{t - 1})\) +and concatenation of \(u_t\), \(r_t\) and \(ch_t\).
+| 参数: |
|
+
|---|---|
| 返回: | The hidden value, reset-hidden value and gate values. + |
+
| 返回类型: | tuple + |
+
Examples
+# assuming we have x_t_data and prev_hidden of size=10
+x_t = fluid.layers.fc(input=x_t_data, size=30)
+hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
+ hidden = prev_hidden)
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