---
title: BNNSLossFunctionMeanSquareError
framework: accelerate
role: symbol
role_heading: Global Variable
path: accelerate/bnnslossfunctionmeansquareerror
---

# BNNSLossFunctionMeanSquareError

Mean square error (MSE) computation between input logits and one-hot encoded labels.

## Declaration

```swift
var BNNSLossFunctionMeanSquareError: BNNSLossFunction { get }
```

## Discussion

Discussion BNNSLossFunctionMeanSquareError performs mean square error (MSE) computation between input logits and one hot encoded labels. You can scale the loss with either a scalar value or weight matrix, and reduce the loss according to a reduction function.

## See Also

### Loss Functions

- [init(_:)](accelerate/bnnslossfunction/init(_:).md)
- [init(rawValue:)](accelerate/bnnslossfunction/init(rawvalue:).md)
- [rawValue](accelerate/bnnslossfunction/rawvalue.md)
- [BNNSLossFunctionCategoricalCrossEntropy](accelerate/bnnslossfunctioncategoricalcrossentropy.md)
- [BNNSLossFunctionCosineDistance](accelerate/bnnslossfunctioncosinedistance.md)
- [BNNSLossFunctionHinge](accelerate/bnnslossfunctionhinge.md)
- [BNNSLossFunctionHuber](accelerate/bnnslossfunctionhuber.md)
- [BNNSLossFunctionLog](accelerate/bnnslossfunctionlog.md)
- [BNNSLossFunctionMeanAbsoluteError](accelerate/bnnslossfunctionmeanabsoluteerror.md)
- [BNNSLossFunctionSigmoidCrossEntropy](accelerate/bnnslossfunctionsigmoidcrossentropy.md)
- [BNNSLossFunctionSoftmaxCrossEntropy](accelerate/bnnslossfunctionsoftmaxcrossentropy.md)
- [BNNSLossFunctionYolo](accelerate/bnnslossfunctionyolo.md)