(l-onnx-doc-Softplus)=

# Softplus


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

## Softplus - 22

### Version

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

Softplus takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to
the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
Softplus (X) => (Y)
{
   exp_x = Exp (X)
   one = Constant <value: tensor = float {1}> ()
   one_cast = CastLike (one, X)
   exp_x_add_one = Add (exp_x, one_cast)
   Y = Log (exp_x_add_one)
}
```

### Inputs

- **X** (heterogeneous) - **T**:

  1D input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  1D input tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_Softplus_1_22
```

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

## Softplus - 1

### Version

- **name**: [Softplus (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softplus)
- **domain**: `main`
- **since_version**: `1`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 1**.

### Summary

Softplus takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the softplus function, y = ln(exp(x) + 1), is applied to
the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
Softplus (X) => (Y)
{
   exp_x = Exp (X)
   one = Constant <value: tensor = float {1}> ()
   one_cast = CastLike (one, X)
   exp_x_add_one = Add (exp_x, one_cast)
   Y = Log (exp_x_add_one)
}
```

### Inputs

- **X** (heterogeneous) - **T**:

  1D input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  1D input tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.