HardSigmoid

HardSigmoid - 6

Version

• name: HardSigmoid (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

HardSigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise.

Attributes

• alpha: Value of alpha. Default value is 0.20000000298023224.

• beta: Value of beta. Default value is 0.5.

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

hardsigmoid_default

default_alpha = 0.2
default_beta = 0.5
node = onnx.helper.make_node(
    'HardSigmoid',
    inputs=['x'],
    outputs=['y'],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * default_alpha + default_beta, 0, 1)
expect(node, inputs=[x], outputs=[y],
       name='test_hardsigmoid_default')

Differences

0	0	HardSigmoid takes one input data (Tensor) and produces one output data	HardSigmoid takes one input data (Tensor) and produces one output data
1	1	(Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),	(Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
2	2	is applied to the tensor elementwise.	is applied to the tensor elementwise.
3	3
4	4	**Attributes**	**Attributes**
5	5
6	6	* **alpha**:	* **alpha**:
7	7	Value of alpha default to 0.2 Default value is 0.20000000298023224.	Value of alpha. Default value is 0.20000000298023224.
8	8	* **beta**:	* **beta**:
9	9	Value of beta default to 0.5 Default value is 0.5.	Value of beta. Default value is 0.5.
10		* **consumed_inputs**:
11		legacy optimization attribute.
12	10
13	11	**Inputs**	**Inputs**
14	12
15	13	* **X** (heterogeneous) - **T**:	* **X** (heterogeneous) - **T**:
16	14	Input tensor	Input tensor
17	15
18	16	**Outputs**	**Outputs**
19	17
20	18	* **Y** (heterogeneous) - **T**:	* **Y** (heterogeneous) - **T**:
21	19	Output tensor	Output tensor
22	20
23	21	**Type Constraints**	**Type Constraints**
24	22
25	23	* **T** in (	* **T** in (
26	24	tensor(double),	tensor(double),
27	25	tensor(float),	tensor(float),
28	26	tensor(float16)	tensor(float16)
29	27	):	):
30	28	Constrain input and output types to float tensors.	Constrain input and output types to float tensors.

HardSigmoid - 1

Version

• name: HardSigmoid (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

HardSigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise.

Attributes

• alpha: Value of alpha default to 0.2 Default value is 0.20000000298023224.

• beta: Value of beta default to 0.5 Default value is 0.5.

• consumed_inputs: legacy optimization attribute.

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.

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