Neg#

Neg - 13#

Version

  • name: Neg (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

Neg takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where each element flipped sign, y = -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

default

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

x = np.array([-4, 2]).astype(np.float32)
y = np.negative(x)  # expected output [4., -2.],
expect(node, inputs=[x], outputs=[y],
       name='test_neg_example')

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.negative(x)
expect(node, inputs=[x], outputs=[y],
       name='test_neg')

Differences

00Neg takes one input data (Tensor) and produces one output dataNeg takes one input data (Tensor) and produces one output data
11(Tensor) where each element flipped sign, y = -x, is applied to(Tensor) where each element flipped sign, y = -x, is applied to
22the tensor elementwise.the tensor elementwise.
33
44**Inputs****Inputs**
55
66* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
77 Input tensor Input tensor
88
99**Outputs****Outputs**
1010
1111* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1212 Output tensor Output tensor
1313
1414**Type Constraints****Type Constraints**
1515
1616* **T** in (* **T** in (
17 tensor(bfloat16),
1718 tensor(double), tensor(double),
1819 tensor(float), tensor(float),
1920 tensor(float16), tensor(float16),
2021 tensor(int16), tensor(int16),
2122 tensor(int32), tensor(int32),
2223 tensor(int64), tensor(int64),
2324 tensor(int8) tensor(int8)
2425 ): ):
2526 Constrain input and output types to signed numeric tensors. Constrain input and output types to signed numeric tensors.

Neg - 6#

Version

  • name: Neg (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

Neg takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where each element flipped sign, y = -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), tensor(int16), tensor(int32), tensor(int64), tensor(int8) ): Constrain input and output types to signed numeric tensors.

Differences

00Neg takes one input data (Tensor) and produces one output dataNeg takes one input data (Tensor) and produces one output data
11(Tensor) where each element flipped sign, y = -x, is applied to(Tensor) where each element flipped sign, y = -x, is applied to
22the tensor elementwise.the tensor elementwise.
33
4**Attributes**
5
6* **consumed_inputs**:
7 legacy optimization attribute.
8
94**Inputs****Inputs**
105
116* **X** (heterogeneous) - **T**:* **X** (heterogeneous) - **T**:
127 Input tensor Input tensor
138
149**Outputs****Outputs**
1510
1611* **Y** (heterogeneous) - **T**:* **Y** (heterogeneous) - **T**:
1712 Output tensor Output tensor
1813
1914**Type Constraints****Type Constraints**
2015
2116* **T** in (* **T** in (
2217 tensor(double), tensor(double),
2318 tensor(float), tensor(float),
2419 tensor(float16) tensor(float16),
20 tensor(int16),
21 tensor(int32),
22 tensor(int64),
23 tensor(int8)
2524 ): ):
2625 Constrain input and output types to float tensors. Constrain input and output types to signed numeric tensors.

Neg - 1#

Version

  • name: Neg (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

Neg takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where each element flipped sign, y = -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.