---
title: "gru(initialHiddenStates:inputHiddenWeight:hiddenHiddenWeight:bias:inputBias:direction:activation:recurrentActivation:applyResetGateAfterMatMul:outputSequence:)"
framework: accelerate
role: symbol
role_heading: Instance Method
path: "accelerate/bnnsgraph/builder/tensor/gru(initialhiddenstates:inputhiddenweight:hiddenhiddenweight:bias:inputbias:direction:activation:recurrentactivation:applyresetgateaftermatmul:outputsequence:)"
---

# gru(initialHiddenStates:inputHiddenWeight:hiddenHiddenWeight:bias:inputBias:direction:activation:recurrentActivation:applyResetGateAfterMatMul:outputSequence:)

Adds a GRU operation to the current graph.

## Declaration

```swift
func gru(initialHiddenStates: BNNSGraph.Builder.Tensor<T>, inputHiddenWeight: BNNSGraph.Builder.Tensor<T>, hiddenHiddenWeight: BNNSGraph.Builder.Tensor<T>, bias: BNNSGraph.Builder.Tensor<T>, inputBias: BNNSGraph.Builder.Tensor<T>, direction: BNNSGraph.Builder.Direction, activation: BNNSGraph.Builder.Activation, recurrentActivation: BNNSGraph.Builder.Activation, applyResetGateAfterMatMul: Bool, outputSequence: Bool) -> (output: BNNSGraph.Builder.Tensor<T>, hiddenStates: BNNSGraph.Builder.Tensor<T>)
```

## Parameters

- `initialHiddenStates`: The initial hidden states with the shape (N, Hout).
- `inputHiddenWeight`: The input-hidden weight with the shape (3*Hout, Hin).
- `hiddenHiddenWeight`: The hidden-hidden weight with the shape (3*Hout, Hout).
- `bias`: The bias (the sum of input-hidden and hidden-hidden biases) with the shape (3*Hout,).
- `inputBias`: Used when applyResetGateAfterMatMul is true, and is the same shape as bias.
- `direction`: An enumeration that specifies a forward or reverse GRU op. Reverse GRU computes time steps in the reverse direction.
- `activation`: An enumeration that controls the output activation function.
- `recurrentActivation`: An enumeration that controls the recurrent activation function.
- `applyResetGateAfterMatMul`: An enumeration that specifies that the reset gate is applied after the matrix multiply.
- `outputSequence`: When true, output is of shape (L, N, Hout) and contains hidden states from every step, h[:, ...]. When false output is of shape (1, N, Hout) and contains hidden states from the last step, h[-1, ...].

## Discussion

Discussion If applyResetGateAfterMatMul is false, For each time t, from 0 to L-1, computes: Reset gate: r[t, ...] = RA(matmul(W_ir, x[t, ...]) + b_ir + matmul(W_hr, h[t-1, ...]) + b_hr) Update gate: z[t, ...] = RA(matmul(W_iz, x[t, ...]) + b_iz + matmul(W_hz, h[t-1, ...]) + b_hz) New gate: n[t, ...] =  A(matmul(W_in, x[t, ...]) + b_in + matmul(W_hn, h[t-1, ...]) * r[t, ...] + b_hn) Hidden state: h[t, ...] = (1 - z[t, ...]) * n[t, ...] + z[t, ...] * h[t-1, ...] Where: A is the activation function, RA is the recurrentActivation function, inputHiddenWeight = concat(W_ir, W_in, W_iz, axis=-2) hiddenHiddenWeight = concat(W_hr, W_hn, W_hz, axis=-2) bias = concat(b_hr, b_hn, b_hz, axis=-1), inputBias = concat(b_ir, b_in, b_iz, axis=-1 initialHiddenStates is used for h[t-1, ...]at the first step * denotes the Hadamard/elementwise product If applyResetGateAfterMatMul is true, the calculation for the new gate changes to New gate: n[t, ...] =  A(matmul(W_in, x[t, ...]) + b_in + (matmul(W_hn, h[t-1, ...]) + b_hn) * r[t, ...]) The input tensor x is of shape (L, N, Hin) hiddenStates is of shape (N, Hout) and contains hidden states from the last step, h[-1, ...].