(l-onnx-doc-HardSigmoid)=

# HardSigmoid


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

## HardSigmoid - 22

### Version

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

HardSigmoid takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
HardSigmoid <beta,alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Beta = Constant <value_float: float = @beta> ()
   BetaCast = CastLike (Beta, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   One = Constant <value: tensor = float {1}> ()
   OneCast = CastLike (One, X)
   AlphaMulX = Mul (X, AlphaCast)
   AlphaMulXAddBeta = Add (AlphaMulX, BetaCast)
   MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast)
   Y = Max (MinOneOrAlphaMulXAddBeta, ZeroCast)
}
```

### Attributes

* **alpha - FLOAT** (default is `0.2`):

  Value of alpha.

* **beta - FLOAT** (default is `0.5`):

  Value of beta.

### 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(
    "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6
)

x = np.array([-1, 0, 1]).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * 0.5 + 0.6, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid")
```

#### _hardsigmoid_default

```python
import numpy as np
import onnx

default_alpha = 0.2
default_beta = 0.5
node = onnx.helper.make_node(
    "HardSigmoid",
    inputs=["x"],
    outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x * default_alpha + default_beta, 0, 1)
expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default")
```

```{toctree}
text_diff_HardSigmoid_6_22
```

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

## HardSigmoid - 6

### Version

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

HardSigmoid takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
HardSigmoid <beta,alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Beta = Constant <value_float: float = @beta> ()
   BetaCast = CastLike (Beta, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   One = Constant <value: tensor = float {1}> ()
   OneCast = CastLike (One, X)
   AlphaMulX = Mul (X, AlphaCast)
   AlphaMulXAddBeta = Add (AlphaMulX, BetaCast)
   MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast)
   Y = Max (MinOneOrAlphaMulXAddBeta, ZeroCast)
}
```

### Attributes

* **alpha - FLOAT** (default is `0.2`):

  Value of alpha.

* **beta - FLOAT** (default is `0.5`):

  Value of beta.

### 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_HardSigmoid_1_22
text_diff_HardSigmoid_1_6
```

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

## HardSigmoid - 1

### Version

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

HardSigmoid takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)),
is applied to the tensor elementwise.

### Attributes

* **alpha - FLOAT** (default is `0.2`):

  Value of alpha default to 0.2

* **beta - FLOAT** (default is `0.5`):

  Value of beta default to 0.5

* **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.