---
title: "normalizationGammaGradient(withIncomingGradientTensor:sourceTensor:mean:varianceTensor:reductionAxes:epsilon:name:)"
framework: metalperformanceshadersgraph
role: symbol
role_heading: Instance Method
path: "metalperformanceshadersgraph/mpsgraph/normalizationgammagradient(withincominggradienttensor:sourcetensor:mean:variancetensor:reductionaxes:epsilon:name:)"
---

# normalizationGammaGradient(withIncomingGradientTensor:sourceTensor:mean:varianceTensor:reductionAxes:epsilon:name:)

Creates a normalization gamma-gradient operation and returns the result tensor.

## Declaration

```swift
func normalizationGammaGradient(withIncomingGradientTensor incomingGradientTensor: MPSGraphTensor, sourceTensor: MPSGraphTensor, mean meanTensor: MPSGraphTensor, varianceTensor: MPSGraphTensor, reductionAxes axes: [NSNumber], epsilon: Float, name: String?) -> MPSGraphTensor
```

## Parameters

- `incomingGradientTensor`: The incoming original resultTensor gradient.
- `sourceTensor`: The original input source in forward direction.
- `meanTensor`: The mean tensor.
- `varianceTensor`: The variance tensor.
- `axes`: The axes of normalization.
- `epsilon`: A small value to add to the variance when normalizing the inputs.
- `name`: An optional name for the operation.

## Return Value

Return Value A valid MPSGraphTensor object.

## Discussion

Discussion The mean and variance tensors should be outputs of meanWithTensor:axes:name and varianceWithTensor:meanTensor:axes:name. Use the axes parameter to achieve different types of normalizations. For example (assuming your data is in NxHxWxC format) Batch normalization: axes = [0, 1, 2] Instance normalization: axes = [1, 2]