(l-onnx-docai-onnx-ml-Binarizer)=

# ai.onnx.ml - Binarizer


(l-onnx-opai-onnx-ml-binarizer-1)=

## Binarizer - 1 (ai.onnx.ml)

### Version

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

This version of the operator has been available
**since version 1 of domain ai.onnx.ml**.

### Summary

Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value.

### Attributes

* **threshold - FLOAT** (default is `'0.0'`):

  Values greater than this are mapped to 1, others to 0.

### Inputs

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

  Data to be binarized

### Outputs

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

  Binarized output data

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(int32)`, `tensor(int64)` ):

  The input must be a tensor of a numeric type. The output will be of the same tensor type.

### Examples

#### default

```python
import numpy as np
import onnx

threshold = 1.0
node = onnx.helper.make_node(
    "Binarizer",
    inputs=["X"],
    outputs=["Y"],
    threshold=threshold,
    domain="ai.onnx.ml",
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = compute_binarizer(x, threshold)[0]

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