ReduceLogSumExp - version 11#

This page documents version 11 of operator ReduceLogSumExp. See ReduceLogSumExp for the latest version (since version 18).

  • Domain: ai.onnx

  • Since version: 11

Computes the log sum exponent of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. Negative axes are supported in the axes attribute.

Inputs

  • data (T): An input tensor.

Outputs

  • reduced (T): Reduced output tensor.

Attributes

  • axes (int[]): A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).

  • keepdims (int): Keep the reduced dimension or not, default 1 means keep reduced dimension.

Type Constraints

  • T: Constrain input and output types to high-precision numeric tensors. Allowed types: tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64).

Differences with previous version (1)#

SchemaDiff: ReduceLogSumExp (domain 'ai.onnx')

  • old version: 1

  • new version: 11

  • breaking: no

Documentation:

  • line similarity: 0.86 (+1/-0 lines)

--- ReduceLogSumExp v1
+++ ReduceLogSumExp v11
@@ -1,3 +1,4 @@
 Computes the log sum exponent of the input tensor's element along the provided axes.
 The resulting tensor has the same rank as the input if keepdims equals 1.
 If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
+Negative axes are supported in the axes attribute.