---
title: "LSTM(_:recurrentWeight:inputWeight:bias:initState:initCell:mask:peephole:descriptor:name:)"
framework: metalperformanceshadersgraph
role: symbol
role_heading: Instance Method
path: "metalperformanceshadersgraph/mpsgraph/lstm(_:recurrentweight:inputweight:bias:initstate:initcell:mask:peephole:descriptor:name:)"
---

# LSTM(_:recurrentWeight:inputWeight:bias:initState:initCell:mask:peephole:descriptor:name:)

Creates an LSTM operation and returns the value tensor and optionally the cell state tensor and  the training state tensor.

## Declaration

```swift
func LSTM(_ source: MPSGraphTensor, recurrentWeight: MPSGraphTensor, inputWeight: MPSGraphTensor?, bias: MPSGraphTensor?, initState: MPSGraphTensor?, initCell: MPSGraphTensor?, mask: MPSGraphTensor?, peephole: 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].
- `mask`: A tensor containing the mask m - optional, if missing the operation assumes ones. Useful for dropout.
- `peephole`: A tensor containing the peephole vector v - optional, if missing the operation assumes zeroes. Shape is [4H], ie. a vector for each gate, or [2,4H] for bidirectional.
- `descriptor`: A descriptor that defines the parameters for the LSTM operation.
- `name`: The name for the operation.

## Return Value

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

Discussion This operation returns tensors h and optionally c and optionally z that are defined recursively as follows: 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 for different activation options for f() and g().