LRN#
LRN - 13#
Version#
name: LRN (GitHub)
domain:
main
since_version:
13
function:
False
support_level:
SupportType.COMMON
shape inference:
True
This version of the operator has been available since version 13.
Summary#
Local Response Normalization proposed in the AlexNet paper.
It normalizes over local input regions.
The local region is defined across the channels. For an element X[n, c, d1, ..., dk]
in a tensor
of shape (N x C x D1 x D2, ..., Dk)
, its region is
{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}
.
square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2)
,
where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))
.
Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta
Attributes#
alpha - FLOAT (default is
'0.0001'
):Scaling parameter.
beta - FLOAT (default is
'0.75'
):The exponent.
bias - FLOAT (default is
'1.0'
):size - INT (required) :
The number of channels to sum over
Inputs#
X (heterogeneous) - T:
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE …].
Outputs#
Y (heterogeneous) - T:
Output tensor, which has the shape and type as input tensor
Type Constraints#
T in (
tensor(bfloat16)
,tensor(double)
,tensor(float)
,tensor(float16)
):Constrain input and output types to float tensors.
LRN - 1#
Version#
name: LRN (GitHub)
domain:
main
since_version:
1
function:
False
support_level:
SupportType.COMMON
shape inference:
True
This version of the operator has been available since version 1.
Summary#
Local Response Normalization proposed in the AlexNet paper. It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, …, dk] in a tensor of shape (N x C x D1 x D2, …, Dk), its region is {X[n, i, d1, …, dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.
square_sum[n, c, d1, …, dk] = sum(X[n, i, d1, …, dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).
Y[n, c, d1, …, dk] = X[n, c, d1, …, dk] / (bias + alpha / size * square_sum[n, c, d1, …, dk] ) ^ beta
Attributes#
alpha - FLOAT (default is
'0.0001'
):Scaling parameter.
beta - FLOAT (default is
'0.75'
):The exponent.
bias - FLOAT (default is
'1.0'
):size - INT (required) :
The number of channels to sum over
Inputs#
X (heterogeneous) - T:
Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE …].
Outputs#
Y (heterogeneous) - T:
Output tensor, which has the shape and type as input tensor
Type Constraints#
T in (
tensor(double)
,tensor(float)
,tensor(float16)
):Constrain input and output types to float tensors.