ReduceMean¶

General¶

ReduceMean operation performs the reduction with finding the arithmetic mean on a given src data along dimensions specified by axes.

Take channel axis = 0 and keep_dims = True as an example:

\[{dst}_{0,\cdots,\cdots} =\frac{ {\sum\limits_{i}{src_{i,\cdots,\cdots}}}}{channelNum} ,i \in [0,channelNum-1]\]

Operation attributes¶

Attribute Name	Description	Value Type	Supported Values	Required or Optional
axes	Specify indices of src tensor, along which the reduction is performed. If axes is a list, reduce over all of them. If axes is empty, corresponds to the identity operation. If axes contains all dimensions of src tensor, a single reduction value is calculated for the entire src tensor. Exactly one of attribute `axes` and the second input tensor `axes` should be available.	s64	A s64 list values which is in the range of [-r, r-1] where r = rank(src). Empty list(default)	Optional
keep_dims	If set to `true` it holds axes that are used for reduction. For each such axes, dst dimension is equal to 1.	bool	`true` , `false` (default)	Optional

Execution arguments¶

The inputs and outputs must be provided according to below index order when constructing an operation.

Inputs¶

Index	Argument Name	Required or Optional
0	`src`	Required
1	`axes`	Optional

Note

axes is an 1-D tensor specifying the axis along which the reduction is performed. 1D tensor of unique elements. The range of elements is [-r, r-1], where r is the rank of src tensor. Exactly one of attribute axes and the second input tensor axes should be available.

Outputs¶

Index	Argument Name	Required or Optional
0	`dst`	Required

Note

The result of ReduceMean function applied to src tensor. shape[i] = shapeOf[data](i) for all i that is not in the list of axes from the second input. For dimensions from axes, shape[i] == 1 if keep_dims == True, or i-th dimension is removed from the dst otherwise.

Supported data types¶

ReduceMean operation supports the following data type combinations.

Src	Dst	Axes
f32	f32	s32
bf16	bf16	s32
f16	f16	s32

ReduceMax ReduceMin