---
title: "init(embeddingCount:embeddingDimension:paddingIndex:maximumNorm:pNorm:scalesGradientByFrequency:)"
framework: ML Compute
role: symbol
role_heading: Initializer
platforms: [iOS 14.0+, iPadOS 14.0+, Mac Catalyst 14.0+, macOS 11.0+, tvOS 14.0+]
path: "mlcompute/mlcembeddingdescriptor/init(embeddingcount:embeddingdimension:paddingindex:maximumnorm:pnorm:scalesgradientbyfrequency:)"
---

# init(embeddingCount:embeddingDimension:paddingIndex:maximumNorm:pNorm:scalesGradientByFrequency:)

Creates an embedding descriptor with the size and dimension of embedding vectors, padding index, and norm and scaling options that you specify.

## Declaration

```swift
convenience init?(embeddingCount: Int, embeddingDimension: Int, paddingIndex: Int?, maximumNorm: Float?, pNorm: Float?, scalesGradientByFrequency: Bool)
```

## Parameters

- `embeddingCount`: The size of the dictionary.
- `embeddingDimension`: The dimension of embedding vectors.
- `paddingIndex`: An unsigned integer value. If set, the layer initializes the embedding vector at that index to zero, and won’t update the vector in the gradient pass. The default value is nil.
- `maximumNorm`: A float value. If set, the layer renormalizes the selected embedding vectors in the forward pass only to have an Lp norm less than this value. The default value is nil.
- `pNorm`: A float value that specifies the p of the Lp norm. The default value is 2.0.
- `scalesGradientByFrequency`: A Boolean value that indicates whether you scale the gradients by the inverse of the frequency of words in batch before the weight update. The default value is false.

## See Also

### Creating Embedding Descriptors

- [init(embeddingCount:embeddingDimension:)](mlcompute/mlcembeddingdescriptor/init(embeddingcount:embeddingdimension:).md)