paddle.v2.fluid.layers.sequence_softmax(**kwargs)Sequence Softmax Operator.
+SequenceSoftmaxOp computes the softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of the length +of all sequences.
+For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7.
+| Parameters: | x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. +Duplicable: False Optional: False | +
|---|---|
| Returns: | (LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1. | +
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 d2bb7a8187ba7f1ae78a5337794a8882c96d2346..92ca1cf0f836a376387f3e6f2b5a24c78109323d 100644
--- a/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
+++ b/develop/doc_cn/_sources/api/v2/fluid/layers.rst.txt
@@ -305,3 +305,10 @@ lstm_unit
---------
.. autofunction:: paddle.v2.fluid.layers.lstm_unit
:noindex:
+
+
+sequence_softmax
+---------
+.. autofunction:: paddle.v2.fluid.layers.sequence_softmax
+ :noindex:
+
diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html
index 6d349d334f1aff45e7ed40ad440d4571f91f89a4..6dfd1f6cfc9331196e52a462912cc946d6d19fcf 100644
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -1217,6 +1217,47 @@ bias weights will be created and be set to default value.
+
+paddle.v2.fluid.layers.sequence_softmax(**kwargs)Sequence Softmax Operator.
+SequenceSoftmaxOp computes the softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of the length +of all sequences.
+For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7.
+| εζ°: | x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. +Duplicable: False Optional: False | +
|---|---|
| θΏε: | (LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1. | +
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