---
title: "BNNSFilterCreateLayerNormalization(_:_:_:)"
framework: accelerate
role: symbol
role_heading: Function
path: "accelerate/bnnsfiltercreatelayernormalization(_:_:_:)"
---

# BNNSFilterCreateLayerNormalization(_:_:_:)

Returns a new normalization layer.

## Declaration

```swift
func BNNSFilterCreateLayerNormalization(_ normType: BNNSFilterType, _ layer_params: UnsafePointer<BNNSLayerParametersNormalization>, _ filter_params: UnsafePointer<BNNSFilterParameters>?) -> BNNSFilter?
```

## Parameters

- `normType`: The normalization type.
- `layer_params`: Layer parameters.
- `filter_params`: The filter runtime parameters.

## Discussion

Discussion Use a normalization loss layer to normalize tensor values, that is, calculate new elements to have a zero mean and a unit standard deviation. For example, given a three-channel image that contains the following values: let input: [Float] = [1, 2, 3, 4, 5, 6, 7, 8, 9,                       10, 20, 30, 40, 50, 60, 70, 80, 90,                       100, 200, 300, 400, 500, 600, 700, 800, 900] The following code normalizes each channel and writes the result to the output array: let count = input.count var output = [Float](repeating: 0,                      count: count)

let descriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0),                                        layout: BNNSDataLayoutImageCHW,                                        size: (9, 1, 3, 0, 0, 0, 0, 0),                                        stride: (0, 0, 0, 0, 0, 0, 0, 0),                                        data: nil,                                        data_type: BNNSDataType.float,                                        table_data: nil,                                        table_data_type: BNNSDataType.float,                                        data_scale: 1,                                        data_bias: 0)

var gamma = [Float](repeating: 1,                     count: descriptor.size.2)

gamma.withUnsafeMutableBufferPointer { gammaPtr in          let gammaDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0),                                                 layout: BNNSDataLayoutVector,                                                 size: (descriptor.size.2, 0, 0, 0, 0, 0, 0, 0),                                                 stride: (0, 0, 0, 0, 0, 0, 0, 0),                                                 data: gammaPtr.baseAddress,                                                 data_type: BNNSDataType.float,                                                 table_data: nil,                                                 table_data_type: BNNSDataType.float,                                                 data_scale: 1,                                                 data_bias: 0)          var layerParameters = BNNSLayerParametersNormalization(i_desc: descriptor,                                                            o_desc: descriptor,                                                            beta_desc: BNNSNDArrayDescriptor(),                                                            gamma_desc: gammaDescriptor,                                                            moving_mean_desc: BNNSNDArrayDescriptor(),                                                            moving_variance_desc: BNNSNDArrayDescriptor(),                                                            momentum: 1,                                                            epsilon: 0,                                                            activation: .identity,                                                            num_groups: 1,                                                            normalization_axis: 0)          let filter = BNNSFilterCreateLayerNormalization(BNNSInstanceNorm, &layerParameters, nil)     defer {         BNNSFilterDestroy(filter)     }          BNNSNormalizationFilterApplyBatch(filter, 1,                                       input, count,                                       &output, count,                                       false) } On return, the output contains the following values: [-1.549, -1.162, -0.775, -0.387, 0.0, 0.387, 0.775, 1.162, 1.549,  -1.549, -1.162, -0.775, -0.387, 0.0, 0.387, 0.775, 1.162, 1.549,  -1.549, -1.162, -0.775, -0.387, 0.0, 0.387, 0.775, 1.162, 1.549]

## See Also

### Normalization layers

- [BNNS.NormalizationLayer](accelerate/bnns/normalizationlayer.md)
- [BNNSLayerParametersNormalization](accelerate/bnnslayerparametersnormalization.md)
- [BNNSNormalizationFilterApplyBatch(_:_:_:_:_:_:_:)](accelerate/bnnsnormalizationfilterapplybatch(_:_:_:_:_:_:_:).md)
- [BNNSNormalizationFilterApplyBackwardBatch(_:_:_:_:_:_:_:_:_:_:)](accelerate/bnnsnormalizationfilterapplybackwardbatch(_:_:_:_:_:_:_:_:_:_:).md)