paddle.v2.fluid.layers.lod_rank_table(x, level=0)This function creates an operator for creating a LOD_RANK_TABLE -using the input x.
+LoD Rank Table Operator. Given an input variable x and a level number +of LoD, this layer creates a LodRankTable object. A LoDRankTable object +contains a list of bi-element tuples. Each tuple consists of an index and +a length, both of which are int type. Reffering to specified level of LoD, +the index is the sequence index number and the length representes the +sequence length. Please note that the list is ranked in descending order by +the length. The following is an example:
+++++x is a LoDTensor: + x.lod = [[0, 2, 3], + [0, 5, 6, 7]] + x.data = [a, b, c, d, e, f, g] + +1. set level to 0: + Create lod rank table: + lod_rank_table_obj = lod_rank_table(x, level=0) + + Get: + lod_rank_table_obj.items() = [(0, 2), (1, 1)] + +2. set level to 1: + Create lod rank table: + lod_rank_table_obj = lod_rank_table(x, level=1) + + Get: + lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)] +
| Parameters: |
|
+
|---|---|
| Returns: | The created LoDRankTable object. + |
+
| Return type: | Variable + |
+
Examples
+x = fluid.layers.data(name='x', shape=[10],
+ dtype='float32', lod_level=1)
+out = layers.lod_rank_table(x=x, level=0)
+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","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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diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html
index dd0be547a1b0fff9b403e298f2ae32e70caffaf1..11695f2f243a0fd1bd509a83fb6ca6db57617e90 100644
--- a/develop/doc_cn/api/v2/fluid/layers.html
+++ b/develop/doc_cn/api/v2/fluid/layers.html
@@ -1238,8 +1238,61 @@ the BatchNorm layer using the configurations from the input parameters.
paddle.v2.fluid.layers.lod_rank_table(x, level=0)This function creates an operator for creating a LOD_RANK_TABLE -using the input x.
+LoD Rank Table Operator. Given an input variable x and a level number +of LoD, this layer creates a LodRankTable object. A LoDRankTable object +contains a list of bi-element tuples. Each tuple consists of an index and +a length, both of which are int type. Reffering to specified level of LoD, +the index is the sequence index number and the length representes the +sequence length. Please note that the list is ranked in descending order by +the length. The following is an example:
+++++x is a LoDTensor: + x.lod = [[0, 2, 3], + [0, 5, 6, 7]] + x.data = [a, b, c, d, e, f, g] + +1. set level to 0: + Create lod rank table: + lod_rank_table_obj = lod_rank_table(x, level=0) + + Get: + lod_rank_table_obj.items() = [(0, 2), (1, 1)] + +2. set level to 1: + Create lod rank table: + lod_rank_table_obj = lod_rank_table(x, level=1) + + Get: + lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)] +
| 参数: |
|
+
|---|---|
| 返回: | The created LoDRankTable object. + |
+
| 返回类型: | Variable + |
+
Examples
+x = fluid.layers.data(name='x', shape=[10],
+ dtype='float32', lod_level=1)
+out = layers.lod_rank_table(x=x, level=0)
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