(l-onnx-doc-Selu)=

# Selu


(l-onnx-op-selu-22)=

## Selu - 22

### Version

- **name**: [Selu (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Selu)
- **domain**: `main`
- **since_version**: `22`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 22**.

### Summary

Selu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the scaled exponential linear unit function,
`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
Selu <gamma,alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Gamma = Constant <value_float: float = @gamma> ()
   GammaCast = CastLike (Gamma, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   ExpX = Exp (X)
   AlphaMulExpX = Mul (AlphaCast, ExpX)
   AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast)
   Neg = Mul (GammaCast, AlphaMulExpXSubAlpha)
   Pos = Mul (GammaCast, X)
   XLessThanZero = Less (X, ZeroCast)
   Y = Where (XLessThanZero, Neg, Pos)
}
```

### Attributes

* **alpha - FLOAT** (default is `1.67326`):

  Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).

* **gamma - FLOAT** (default is `1.0507`):

  Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).

### 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

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "Selu", inputs=["x"], outputs=["y"], alpha=2.0, gamma=3.0
)

x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-3.79272318, 0., 3.]
y = np.clip(x, 0, np.inf) * 3.0 + np.expm1(np.clip(x, -np.inf, 0)) * 2.0 * 3.0
expect(node, inputs=[x], outputs=[y], name="test_selu_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) * 3.0 + np.expm1(np.clip(x, -np.inf, 0)) * 2.0 * 3.0
expect(node, inputs=[x], outputs=[y], name="test_selu")
```

#### _selu_default

```python
import numpy as np
import onnx

default_alpha = 1.67326319217681884765625
default_gamma = 1.05070102214813232421875
node = onnx.helper.make_node(
    "Selu",
    inputs=["x"],
    outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = (
    np.clip(x, 0, np.inf) * default_gamma
    + np.expm1(np.clip(x, -np.inf, 0)) * default_alpha * default_gamma
)
expect(node, inputs=[x], outputs=[y], name="test_selu_default")
```

```{toctree}
text_diff_Selu_6_22
```

(l-onnx-op-selu-6)=

## Selu - 6

### Version

- **name**: [Selu (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Selu)
- **domain**: `main`
- **since_version**: `6`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 6**.

### Summary

Selu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the scaled exponential linear unit function,
`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
Selu <gamma,alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Gamma = Constant <value_float: float = @gamma> ()
   GammaCast = CastLike (Gamma, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   ExpX = Exp (X)
   AlphaMulExpX = Mul (AlphaCast, ExpX)
   AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast)
   Neg = Mul (GammaCast, AlphaMulExpXSubAlpha)
   Pos = Mul (GammaCast, X)
   XLessThanZero = Less (X, ZeroCast)
   Y = Where (XLessThanZero, Neg, Pos)
}
```

### Attributes

* **alpha - FLOAT** (default is `1.67326`):

  Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).

* **gamma - FLOAT** (default is `1.0507`):

  Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).

### 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.

```{toctree}
text_diff_Selu_1_22
text_diff_Selu_1_6
```

(l-onnx-op-selu-1)=

## Selu - 1

### Version

- **name**: [Selu (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Selu)
- **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

Selu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the scaled exponential linear unit function,
`y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`,
is applied to the tensor elementwise.

### Attributes

* **alpha - FLOAT** (default is `1.6732`):

  Coefficient of SELU default to 1.6732.

* **consumed_inputs - INTS** :

  legacy optimization attribute.

* **gamma - FLOAT** (default is `1.0507`):

  Coefficient of SELU default to 1.0507.

### 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.