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

# ai.onnx.ml - Scaler


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

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

### Version

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

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

### Summary

Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.

### Attributes

* **offset - FLOATS** :

  First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.

* **scale - FLOATS** :

  Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset'

### Inputs

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

  Data to be scaled.

### Outputs

- **Y** (heterogeneous) - **tensor(float)**:

  Scaled output data.

### Type Constraints

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

  The input must be a tensor of a numeric type.