Relu

Relu - 14

Version

• name: Relu (GitHub)

• domain: main

• since_version: 14

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 14.

Summary

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

Inputs

• X (heterogeneous) - T: Input tensor

Outputs

• Y (heterogeneous) - T: Output tensor

Type Constraints

• T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8) ): Constrain input and output types to signed numeric tensors.

Examples

Differences

0	0	Relu takes one input data (Tensor) and produces one output data	Relu takes one input data (Tensor) and produces one output data
1	1	(Tensor) where the rectified linear function, y = max(0, x), is applied to	(Tensor) where the rectified linear function, y = max(0, x), is applied to
2	2	the tensor elementwise.	the tensor elementwise.
3	3
4	4	**Inputs**	**Inputs**
5	5
6	6	* **X** (heterogeneous) - **T**:	* **X** (heterogeneous) - **T**:
7	7	Input tensor	Input tensor
8	8
9	9	**Outputs**	**Outputs**
10	10
11	11	* **Y** (heterogeneous) - **T**:	* **Y** (heterogeneous) - **T**:
12	12	Output tensor	Output tensor
13	13
14	14	**Type Constraints**	**Type Constraints**
15	15
16	16	* **T** in (	* **T** in (
17	17	tensor(bfloat16),	tensor(bfloat16),
18	18	tensor(double),	tensor(double),
19	19	tensor(float),	tensor(float),
20	20	tensor(float16)	tensor(float16),
	21		tensor(int16),
	22		tensor(int32),
	23		tensor(int64),
	24		tensor(int8)
21	25	):	):
22	26	Constrain input and output types to float tensors.	Constrain input and output types to signed numeric tensors.

Relu - 13

Version

• name: Relu (GitHub)

• domain: main

• since_version: 13

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 13.

Summary

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

Inputs

• X (heterogeneous) - T: Input tensor

Outputs

• Y (heterogeneous) - T: Output tensor

Type Constraints

• T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Differences

0	0	Relu takes one input data (Tensor) and produces one output data	Relu takes one input data (Tensor) and produces one output data
1	1	(Tensor) where the rectified linear function, y = max(0, x), is applied to	(Tensor) where the rectified linear function, y = max(0, x), is applied to
2	2	the tensor elementwise.	the tensor elementwise.
3	3
4	4	**Inputs**	**Inputs**
5	5
6	6	* **X** (heterogeneous) - **T**:	* **X** (heterogeneous) - **T**:
7	7	Input tensor	Input tensor
8	8
9	9	**Outputs**	**Outputs**
10	10
11	11	* **Y** (heterogeneous) - **T**:	* **Y** (heterogeneous) - **T**:
12	12	Output tensor	Output tensor
13	13
14	14	**Type Constraints**	**Type Constraints**
15	15
16	16	* **T** in (	* **T** in (
	17		tensor(bfloat16),
17	18	tensor(double),	tensor(double),
18	19	tensor(float),	tensor(float),
19	20	tensor(float16)	tensor(float16)
20	21	):	):
21	22	Constrain input and output types to float tensors.	Constrain input and output types to float tensors.

Relu - 6

Version

• name: Relu (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

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

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.

Differences

0	0	Relu takes one input data (Tensor) and produces one output data	Relu takes one input data (Tensor) and produces one output data
1	1	(Tensor) where the rectified linear function, y = max(0, x), is applied to	(Tensor) where the rectified linear function, y = max(0, x), is applied to
2	2	the tensor elementwise.	the tensor elementwise.
3	3
4		**Attributes**
5
6		* **consumed_inputs**:
7		legacy optimization attribute.
8
9	4	**Inputs**	**Inputs**
10	5
11	6	* **X** (heterogeneous) - **T**:	* **X** (heterogeneous) - **T**:
12	7	Input tensor	Input tensor
13	8
14	9	**Outputs**	**Outputs**
15	10
16	11	* **Y** (heterogeneous) - **T**:	* **Y** (heterogeneous) - **T**:
17	12	Output tensor	Output tensor
18	13
19	14	**Type Constraints**	**Type Constraints**
20	15
21	16	* **T** in (	* **T** in (
22	17	tensor(double),	tensor(double),
23	18	tensor(float),	tensor(float),
24	19	tensor(float16)	tensor(float16)
25	20	):	):
26	21	Constrain input and output types to float tensors.	Constrain input and output types to float tensors.

Relu - 1

Version

• name: Relu (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

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

Attributes

• 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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