# LSTM(_:recurrentWeight:inputWeight:bias:initState:initCell:descriptor:name:) Creates an LSTM operation and returns the value tensor and optionally the cell state tensor and the training state tensor. ``` func LSTM(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, initState: MPSGraphTensor?, initCell: MPSGraphTensor?, descriptor: MPSGraphLSTMDescriptor, 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,4H] and for `inputWeight = nil` and `bidirectional = YES` the layout is [T,N,8H]. `recurrentWeight` A tensor containing the recurrent weights `R`. For `bidirectional` the layout is [2,4H,H] and otherwise it is [4H,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 [8H,I] and otherwise it is [4H,I]. `bias` A tensor containing the bias `b` - optional, if missing the operation assumes zeroes. For `bidirectional` the layout is [8H] and otherwise it is [4H]. `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]. `initCell` The initial internal cell 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]. `descriptor` A descriptor that defines the parameters for the LSTM operation. `name` The name for the operation. ## Return Value A valid `MPSGraphTensor` array of size 1 or 2 or 3, depending on values of `descriptor.produceCell` and `descriptor.training`. The layout of the both state and cell outputs are [T,N,H] or [T,N,2H] for bidirectional, and the layout of the trainingState output is [T,N,4H] or [T,N,8H] for bidirectional. ## Discussion This operation returns tensors `h` and optionally `c` and optionally `z` that are defined recursively as follows: ```md for t = 0 to T-1 z[t] = [i, f, z, o][t] = f( (h[t-1] m) R^T + x'[t] + p c[t-1] ) x'[t] = x[t] W^T + b c[t] = f[t]c[t-1] + i[t]z[t] h[t] = o[t]g(c[t]), where ``` `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, `c[t]` is the second output (optional), `z[t]` is either the second or third output (optional), `h[-1]` is `initCell`. and `h[-1]` is `initState`. `p` is an optional peephole vector. See [`MPSGraphLSTMDescriptor`](/documentation/MetalPerformanceShadersGraph/MPSGraphLSTMDescriptor) for different `activation` options for `f()` and `g()`. --- 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)