# Sigmoid#

## Sigmoid - 13#

Version

• name: Sigmoid (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

Sigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-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.

Examples

default

```node = onnx.helper.make_node(
"Sigmoid",
inputs=["x"],
outputs=["y"],
)

x = np.array([-1, 0, 1]).astype(np.float32)
y = 1.0 / (
1.0 + np.exp(np.negative(x))
)  # expected output [0.26894143, 0.5, 0.7310586]
expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = 1.0 / (1.0 + np.exp(np.negative(x)))
expect(node, inputs=[x], outputs=[y], name="test_sigmoid")
```

Differences

 `0` `0` `Sigmoid takes one input data (Tensor) and produces one output data` `Sigmoid takes one input data (Tensor) and produces one output data` `1` `1` `(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the` `(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the` `2` `2` `tensor elementwise.` `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.`

## Sigmoid - 6#

Version

• name: Sigmoid (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

Sigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-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` `Sigmoid takes one input data (Tensor) and produces one output data` `Sigmoid takes one input data (Tensor) and produces one output data` `1` `1` `(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the` `(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the` `2` `2` `tensor elementwise.` `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.`

## Sigmoid - 1#

Version

• name: Sigmoid (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

Sigmoid takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the sigmoid function, y = 1 / (1 + exp(-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.