---
title: "bidirectionalLSTM(initialHiddenStates:initialCellStates:inputHiddenWeight:hiddenHiddenWeight:bias:inputHiddenWeightBack:hiddenHiddenWeightBack:biasBack:activation:recurrentActivation:cellActivation:outputSequence:)"
framework: accelerate
role: symbol
role_heading: Instance Method
path: "accelerate/bnnsgraph/builder/tensor/bidirectionallstm(initialhiddenstates:initialcellstates:inputhiddenweight:hiddenhiddenweight:bias:inputhiddenweightback:hiddenhiddenweightback:biasback:activation:recurrentactivation:cellactivation:outputsequence:)"
---

# bidirectionalLSTM(initialHiddenStates:initialCellStates:inputHiddenWeight:hiddenHiddenWeight:bias:inputHiddenWeightBack:hiddenHiddenWeightBack:biasBack:activation:recurrentActivation:cellActivation:outputSequence:)

Adds a bidirectional LSTM operation to the current graph.

## Declaration

```swift
func bidirectionalLSTM(initialHiddenStates: BNNSGraph.Builder.Tensor<T>, initialCellStates: BNNSGraph.Builder.Tensor<T>, inputHiddenWeight: BNNSGraph.Builder.Tensor<T>, hiddenHiddenWeight: BNNSGraph.Builder.Tensor<T>, bias: BNNSGraph.Builder.Tensor<T>, inputHiddenWeightBack: BNNSGraph.Builder.Tensor<T>, hiddenHiddenWeightBack: BNNSGraph.Builder.Tensor<T>, biasBack: BNNSGraph.Builder.Tensor<T>, activation: BNNSGraph.Builder.Activation, recurrentActivation: BNNSGraph.Builder.Activation, cellActivation: BNNSGraph.Builder.Activation, outputSequence: Bool) -> (output: BNNSGraph.Builder.Tensor<T>, hiddenStates: BNNSGraph.Builder.Tensor<T>, memoryStates: BNNSGraph.Builder.Tensor<T>)
```

## Parameters

- `initialHiddenStates`: The initial hidden states with the shape (N, 2*Hout).
- `initialCellStates`: The initial hidden states with the shape (N, 2*Hout).
- `inputHiddenWeight`: The input-hidden weight with the shape (4*Hout, Hin).
- `bias`: The bias (the sum of input-hidden and hidden-hidden biases) with the shape (4*Hout,).
- `inputHiddenWeightBack`: The backward input-hidden weight with the shape (4*Hout, Hin).
- `hiddenHiddenWeightBack`: The backward hidden-hidden weight with the shape (4*Hout, Hout).
- `biasBack`: The backward bias (the sum of input-hidden and hidden-hidden biases) with the shape(4*Hout,).
- `activation`: An enumeration that controls the output activation function.
- `recurrentActivation`: An enumeration that controls the recurrent activation function.
- `cellActivation`: An enumeration that controls the cell activation function.
- `outputSequence`: When true, output is of shape (L, N, 2*Hout) and contains hidden states from every step, h[:, ...]. When false, output is of shape (1, N, 2*Hout) and contains hidden states from the last step, h[-1, ...].

## Discussion

Discussion The input tensor x is of shape (L, N, Hin) Parameter hiddenHiddenWeight The hidden-hidden weight with the shape (4*Hout, Hout). hiddenStates is of shape (N, 2*Hout) and contains hidden states from the last step, h[-1, ...] memoryStates is of shape (N, 2*Hout) and contains memory states from the last step, c[-1, ...] note: lstm(initialHiddenStates:initialCellStates:inputHiddenWeight:hiddenHiddenWeight:bias:direction:activation:recurrentActivation:cellActivation:outputSequence:)