# GRU(_:recurrentWeight:inputWeight:bias:initState:mask:secondaryBias:descriptor:name:) Creates a GRU operation and returns the value and optionally the training state tensor. ``` func GRU(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, initState: MPSGraphTensor?, mask: MPSGraphTensor?, secondaryBias: 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]. `initState` The initial internal state of the LSTM `h[-1]` - optional, if missing the operation assumes zeroes. For `bidirectional` the layout is [N,2H] and otherwise it is [N,H]. `mask` A tensor containing the mask `m` - optional, if missing the operation assumes ones. Useful for dropout. `secondaryBias` A tensor containing the secondary bias vector `b2` - optional, if missing the operation assumes zeroes. Only used with `reset_after = YES`. Shape is [H], ie. a vector for each gate, or [2H] for bidirectional. `descriptor` A descriptor that defines the parameters for the GRU operation. `name` The name for the operation. ## 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 This operation returns tensors `h` and optionally `z` that are defined recursively as follows: ```md 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 ```md c[t] = ( (h[t-1] m) R^T + b2 ) r[t] ``` If `flipZ = YES` then `h[t]` is replaced by ```md 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`](/documentation/MetalPerformanceShadersGraph/MPSGraphGRUDescriptor) for different `activation` options for `f()`. --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)