(l-onnx-doc-ThresholdedRelu)=

# ThresholdedRelu


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

## ThresholdedRelu - 22

### Version

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

ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
ThresholdedRelu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   AlphaLessThanX = Less (AlphaCast, X)
   Y = Where (AlphaLessThanX, X, ZeroCast)
}
```

### Attributes

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

  Threshold value

### 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

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

x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32)
y = np.clip(x, alpha, np.inf)  # expected output [0., 0., 0., 0., 2.2]
y[y == alpha] = 0

expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, alpha, np.inf)
y[y == alpha] = 0

expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu")
```

#### _default

```python
import numpy as np
import onnx

default_alpha = 1.0
node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"])
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, default_alpha, np.inf)
y[y == default_alpha] = 0

expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default")
```

```{toctree}
text_diff_ThresholdedRelu_10_22
```

(l-onnx-op-thresholdedrelu-10)=

## ThresholdedRelu - 10

### Version

- **name**: [ThresholdedRelu (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#ThresholdedRelu)
- **domain**: `main`
- **since_version**: `10`
- **function**: `True`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 10**.

### Summary

ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise,
is applied to the tensor elementwise.

#### Function Body

The function definition for this operator.

```
<
  domain: "",
  opset_import: ["" : 18]
>
ThresholdedRelu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   AlphaLessThanX = Less (AlphaCast, X)
   Y = Where (AlphaLessThanX, X, ZeroCast)
}
```

### Attributes

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

  Threshold value

### 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.