(l-onnx-doc-Elu)=

# Elu


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

## Elu - 22

### Version

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

Elu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
Elu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   One = Constant <value: tensor = float {1}> ()
   OneCast = CastLike (One, X)
   XLessThanZero = Less (X, ZeroCast)
   ExpX = Exp (X)
   ExpXSubOne = Sub (ExpX, OneCast)
   AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne)
   Y = Where (XLessThanZero, AlphaMulExpXSubOne, X)
}
```

### Attributes

* **alpha - FLOAT** (default is `1.0`):

  Coefficient of ELU.

### 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("Elu", inputs=["x"], outputs=["y"], alpha=2.0)

x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-1.2642411, 0., 1.]
y = np.clip(x, 0, np.inf) + np.expm1(np.clip(x, -np.inf, 0)) * 2.0
expect(node, inputs=[x], outputs=[y], name="test_elu_example")

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

#### _elu_default

```python
import numpy as np
import onnx

default_alpha = 1.0
node = onnx.helper.make_node(
    "Elu",
    inputs=["x"],
    outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.expm1(np.clip(x, -np.inf, 0)) * default_alpha
expect(node, inputs=[x], outputs=[y], name="test_elu_default")
```

```{toctree}
text_diff_Elu_6_22
```

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

## Elu - 6

### Version

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

Elu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
Elu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   One = Constant <value: tensor = float {1}> ()
   OneCast = CastLike (One, X)
   XLessThanZero = Less (X, ZeroCast)
   ExpX = Exp (X)
   ExpXSubOne = Sub (ExpX, OneCast)
   AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne)
   Y = Where (XLessThanZero, AlphaMulExpXSubOne, X)
}
```

### Attributes

* **alpha - FLOAT** (default is `1.0`):

  Coefficient of ELU.

### 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_Elu_1_22
text_diff_Elu_1_6
```

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

## Elu - 1

### Version

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

Elu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the function `f(x) = alpha * (exp(x) - 1.) for x <
0`, `f(x) = x for x >= 0`., is applied to the tensor elementwise.

### Attributes

* **alpha - FLOAT** (default is `1.0`):

  Coefficient of ELU default to 1.0.

* **consumed_inputs - INTS** :

  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.