Celu#

Celu - 12#

Version

  • name: Celu (GitHub)

  • domain: main

  • since_version: 12

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 12.

Summary

Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula:

max(0,x) + min(0,alpha*(exp(x/alpha)-1))

Attributes

  • alpha: The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. Default value is 1.0.

Inputs

  • X (heterogeneous) - T: Input tensor

Outputs

  • Y (heterogeneous) - T: Output tensor

Type Constraints

  • T in ( tensor(float) ): Constrain input and output types to float32 tensors.

Examples

default

alpha = 2.0
node = onnx.helper.make_node(
    'Celu',
    inputs=['X'],
    outputs=['Y'],
    alpha=alpha,
)

input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
    [[0.02916367], [0.12964272], [0.5060197]],
    [[0.79538304], [0.9411346], [0.9546573]]],
    [[[0.17730942], [0.46192095], [0.26480448]],
    [[0.6746842], [0.01665257], [0.62473077]],
    [[0.9240844], [0.9722341], [0.11965699]]],
    [[[0.41356155], [0.9129373], [0.59330076]],
    [[0.81929934], [0.7862604], [0.11799799]],
    [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)

# Calculate expected output data
positive_input = np.maximum(0, input_data)
negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
expected_output = positive_input + negative_input

expect(node, inputs=[input_data], outputs=[expected_output],
       name='test_celu')