---
title: "GRU(_:recurrentWeight:inputWeight:bias:descriptor:name:)"
framework: metalperformanceshadersgraph
role: symbol
role_heading: Instance Method
path: "metalperformanceshadersgraph/mpsgraph/gru(_:recurrentweight:inputweight:bias:descriptor:name:)"
---

# GRU(_:recurrentWeight:inputWeight:bias:descriptor:name:)

Creates a GRU operation and returns the value and optionally the training state tensor.

## Declaration

```swift
func GRU(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, descriptor: MPSGraphGRUDescriptor, name: String?) -> [MPSGraphTensor]
```

## Parameters

- `source`: A tensor containing the source data x[t] with the data layout [T,N,I]. In case inputWeight = nil and bidirectional = NO then the layout is [T,N,3H] and for inputWeight = nil and bidirectional = YES the layout is [T,N,6H].
- `recurrentWeight`: A tensor containing the recurrent weights R. For bidirectional the layout is [2,3H,H] and otherwise it is [3H,H].
- `inputWeight`: A tensor containing the input weights matrix W - optional, if missing the operation assumes a diagonal unit-matrix. For bidirectional the layout is [6H,I] and otherwise it is [3H,I].
- `bias`: A tensor containing the bias b - optional, if missing the operation assumes zeroes. For bidirectional the layout is [6H] and otherwise it is [3H].
- `descriptor`: A descriptor that defines the parameters for the GRU operation.
- `name`: The name for the operation.

## Return Value

Return Value A valid MPSGraphTensor array of size 1 or 2 depending on value of  descriptor.training. The layout of the state output is [T,N,H] or [T,N,2H] for bidirectional, and the layout of the trainingState output is [T,N,3H] or [T,N,6H] for bidirectional.

## Discussion

Discussion This operation returns tensors h and optionally z that are defined recursively as follows: for t = 0 to T-1   z[t] = fz( (h[t-1] m) R^T + x[t] W^T + b ),   r[t] = fr( (h[t-1] m) R^T + x[t] W^T + b ),   c[t] = (h[t-1] r[t] m) R^T   o[t] = fo( c[t] + x[t] W^T + b )   h[t] = z[t]h[t-1] + (1-z[t])o[t] If resetAfter = YES then c[t] is replaced by   c[t] = ( (h[t-1] m) R^T + b2 ) r[t] If flipZ = YES then h[t] is replaced by   h[t] = (1-z[t])h[t-1] + z[t]o[t]. W is optional inputWeight, R is recurrentWeight, b is optional  bias, m is optional mask, x[t] is source h[t] is the first output, z[t] is the second output (optional) and h[-1] is initState. b2 is an optional resetBias vector, only used when resetAfter = YES. See MPSGraphGRUDescriptor for different activation options for f().