ThresholdedRelu#

ThresholdedRelu - 10#

Version

  • name: ThresholdedRelu (GitHub)

  • domain: main

  • since_version: 10

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 10.

Summary

ThresholdedRelu takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise.

Attributes

  • alpha: Threshold value Default value is 1.0.

Inputs

  • X (heterogeneous) - T: Input tensor

Outputs

  • Y (heterogeneous) - T: Output tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Examples

default

alpha = 2.0
node = onnx.helper.make_node(
    'ThresholdedRelu',
    inputs=['x'],
    outputs=['y'],
    alpha=alpha
)

x = np.array([-1.5, 0., 1.2, 2.0, 2.2]).astype(np.float32)
y = np.clip(x, alpha, np.inf)  # expected output [0., 0., 0., 0., 2.2]
y[y == alpha] = 0

expect(node, inputs=[x], outputs=[y],
       name='test_thresholdedrelu_example')

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, alpha, np.inf)
y[y == alpha] = 0

expect(node, inputs=[x], outputs=[y],
       name='test_thresholdedrelu')

_default

default_alpha = 1.0
node = onnx.helper.make_node(
    'ThresholdedRelu',
    inputs=['x'],
    outputs=['y']
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, default_alpha, np.inf)
y[y == default_alpha] = 0

expect(node, inputs=[x], outputs=[y],
       name='test_thresholdedrelu_default')