Versioned name: ReduceL2-4
Category: Reduction
Short description: ReduceL2 operation performs reduction with finding the L2 norm (square root of sum of squares) of the 1st input tensor in slices specified by the 2nd input.
Attributes
- keep_dims
- Description: If set to
True
it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
- Range of values: True or False
- Type:
boolean
- Default value: False
- Required: no
Inputs
- 1: Input tensor x of type T1. Required.
- 2: Scalar or 1D tensor of type T_IND with axis indices for the 1st input along which reduction is performed. Accepted range is
[-r, r - 1]
where where r
is the rank of input tensor, all values must be unique, repeats are not allowed. Required.
Outputs
- 1: Tensor of the same type as the 1st input tensor and
shape[i] = shapeOf(input1)[i]
for all i
that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, shape[i] == 1
if keep_dims == True
, or i
-th dimension is removed from the output otherwise.
Types
- T1: floating point type.
- T2:
int64
or int32
.
Detailed Description
Each element in the output is the result of reduction with finding a Lp norm operation along dimensions specified by the 2nd input:
output[i0, i1, ..., iN] = L2[j0,..., jN](x[j0, ..., jN]))
Where indices i0, ..., iN run through all valid indices for the 1st input and finding the Lp norm L2[j0, ..., jN]
have jk = ik
for those dimensions k
that are not in the set of indices specified by the 2nd input of the operation.
Corner cases:
- When the 2nd input is an empty list, then this operation does nothing, it is an identity.
- When the 2nd input contains all dimensions of the 1st input, this means that a single reduction scalar value is calculated for entire input tensor.
Example
<layer id="1" type="ReduceL2" ...>
<data keep_dims="True" />
<input>
<port id="0">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
<port id="1">
<dim>2</dim>
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>12</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="1" type="ReduceL2" ...>
<data keep_dims="False" />
<input>
<port id="0">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
<port id="1">
<dim>2</dim>
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>12</dim>
</port>
</output>
</layer>
<layer id="1" type="ReduceL2" ...>
<data keep_dims="False" />
<input>
<port id="0">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
<port id="1">
<dim>1</dim>
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>10</dim>
<dim>24</dim>
</port>
</output>
</layer>
<layer id="1" type="ReduceL2" ...>
<data keep_dims="False" />
<input>
<port id="0">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
<port id="1">
<dim>1</dim>
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>12</dim>
<dim>24</dim>
</port>
</output>
</layer>