(l-onnx-doc-LeakyRelu)=

# LeakyRelu


(l-onnx-op-leakyrelu-16)=

## LeakyRelu - 16

### Version

- **name**: [LeakyRelu (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#LeakyRelu)
- **domain**: `main`
- **since_version**: `16`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 16**.

### Summary

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

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 16]
>
LeakyRelu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   XLessThanZero = Less (X, ZeroCast)
   AlphaMulX = Mul (AlphaCast, X)
   Y = Where (XLessThanZero, AlphaMulX, X)
}
```

### Attributes

* **alpha - FLOAT** (default is `'0.01'`):

  Coefficient of leakage.

### 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.

```{toctree}
text_diff_LeakyRelu_6_16
```

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

## LeakyRelu - 6

### Version

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

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

### Attributes

* **alpha - FLOAT** (default is `'0.01'`):

  Coefficient of leakage.

### 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_LeakyRelu_1_16
text_diff_LeakyRelu_1_6
```

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

## LeakyRelu - 1

### Version

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

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

### Attributes

* **alpha - FLOAT** (default is `'0.01'`):

  Coefficient of leakage default to 0.01.

* **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.