PRelu#
PRelu - 16#
Version#
name: PRelu (GitHub)
domain:
main
since_version:
16
function:
True
support_level:
SupportType.COMMON
shape inference:
True
This version of the operator has been available since version 16.
Summary#
PRelu takes input data (Tensorf(x) = slope * x for x < 0
,
f(x) = x for x >= 0
., is applied to the data tensor elementwise.
This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check Broadcasting in ONNX.
Function Body#
The function definition for this operator.
<
domain: "",
opset_import: ["" : 16]
>
PRelu (X, slope) => (Y)
{
Zero = Constant <value: tensor = float {0}> ()
ZeroCast = CastLike (Zero, X)
XLessThanZero = Less (X, ZeroCast)
SlopeMulX = Mul (slope, X)
Y = Where (XLessThanZero, SlopeMulX, X)
}
Inputs#
X (heterogeneous) - T:
Input tensor
slope (heterogeneous) - T:
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
Outputs#
Y (heterogeneous) - T:
Output tensor (same size as X)
Type Constraints#
T in (
tensor(bfloat16)
,tensor(double)
,tensor(float)
,tensor(float16)
,tensor(int32)
,tensor(int64)
,tensor(uint32)
,tensor(uint64)
):Constrain input and output types to float/int tensors.
PRelu - 9#
Version#
name: PRelu (GitHub)
domain:
main
since_version:
9
function:
False
support_level:
SupportType.COMMON
shape inference:
True
This version of the operator has been available since version 9.
Summary#
PRelu takes input data (Tensorf(x) = slope * x for x < 0
,
f(x) = x for x >= 0
., is applied to the data tensor elementwise.
This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check Broadcasting in ONNX.
Inputs#
X (heterogeneous) - T:
Input tensor
slope (heterogeneous) - T:
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
Outputs#
Y (heterogeneous) - T:
Output tensor (same size as X)
Type Constraints#
T in (
tensor(double)
,tensor(float)
,tensor(float16)
,tensor(int32)
,tensor(int64)
,tensor(uint32)
,tensor(uint64)
):Constrain input and output types to float/int tensors.
PRelu - 7#
Version#
name: PRelu (GitHub)
domain:
main
since_version:
7
function:
False
support_level:
SupportType.COMMON
shape inference:
True
This version of the operator has been available since version 7.
Summary#
PRelu takes input data (Tensorf(x) = slope * x for x < 0
,
f(x) = x for x >= 0
., is applied to the data tensor elementwise.
This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check Broadcasting in ONNX.
Inputs#
X (heterogeneous) - T:
Input tensor
slope (heterogeneous) - T:
Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X
Outputs#
Y (heterogeneous) - T:
Output tensor (same size as X)
Type Constraints#
T in (
tensor(double)
,tensor(float)
,tensor(float16)
):Constrain input and output types to float tensors.
PRelu - 6#
Version#
name: PRelu (GitHub)
domain:
main
since_version:
6
function:
False
support_level:
SupportType.COMMON
shape inference:
True
This version of the operator has been available since version 6.
Summary#
PRelu takes input data (Tensorf(x) = slope * x for x < 0
,
f(x) = x for x >= 0
., is applied to the data tensor elementwise.
Inputs#
X (heterogeneous) - T:
Input tensor
slope (heterogeneous) - T:
Slope tensor. If
Slope
is of size 1, the value is sharedacross different channels
Outputs#
Y (heterogeneous) - T:
Output tensor
Type Constraints#
T in (
tensor(double)
,tensor(float)
,tensor(float16)
):Constrain input and output types to float tensors.
PRelu - 1#
Version#
name: PRelu (GitHub)
domain:
main
since_version:
1
function:
False
support_level:
SupportType.COMMON
shape inference:
False
This version of the operator has been available since version 1.
Summary#
PRelu takes input data (Tensorf(x) = slope * x for x < 0
,
f(x) = x for x >= 0
., is applied to the data tensor elementwise.
Attributes#
consumed_inputs - INTS :
legacy optimization attribute.
Inputs#
X (heterogeneous) - T:
Input tensor
slope (heterogeneous) - T:
Slope tensor. If
Slope
is of size 1, the value is sharedacross different channels
Outputs#
Y (heterogeneous) - T:
Output tensor
Type Constraints#
T in (
tensor(double)
,tensor(float)
,tensor(float16)
):Constrain input and output types to float tensors.