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 (Tensor<T>) and slope tensor as input, and produces one output data (Tensor<T>) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise.
History - Version 16 adds bfloat16 to the types allowed. 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 then 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.
Examples
default
import numpy as np
import onnx
node = onnx.helper.make_node(
"PRelu",
inputs=["x", "slope"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example")
_prelu_broadcast
import numpy as np
import onnx
node = onnx.helper.make_node(
"PRelu",
inputs=["x", "slope"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast")
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 (Tensor<T>) and slope tensor as input, and produces one output data (Tensor<T>) where the function f(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 then 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 (Tensor<T>) and slope tensor as input, and produces one output data (Tensor<T>) where the function f(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 then 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 (Tensor<T>) and slope tensor as input, and produces one output data (Tensor<T>) where the function f(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 (Tensor<T>) and slope tensor as input, and produces one output data (Tensor<T>) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise.
Attributes
consumed_inputs: 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.