双向门控循环单元 matlab代码

Sure! Here's an example of bidirectional gated recurrent unit (BiGRU) implementation in MATLAB:

function [output_sequence] = bidirectional_gru(input_sequence, hidden_size)
    sequence_length = size(input_sequence, 2);
    output_sequence = zeros(hidden_size, sequence_length);
    
    % Forward GRU
    forward_hidden_state = zeros(hidden_size, 1);
    forward_update_gate = zeros(hidden_size, 1);
    forward_reset_gate = zeros(hidden_size, 1);
    forward_candidate = zeros(hidden_size, 1);
    
    % Backward GRU
    backward_hidden_state = zeros(hidden_size, 1);
    backward_update_gate = zeros(hidden_size, 1);
    backward_reset_gate = zeros(hidden_size, 1);
    backward_candidate = zeros(hidden_size, 1);
    
    % Initialize weights
    W_zf_forward = rand(hidden_size, size(input_sequence, 1));
    W_zb_backward = rand(hidden_size, size(input_sequence, 1));
    U_zf_forward = rand(hidden_size, hidden_size);
    U_zb_backward = rand(hidden_size, hidden_size);
    b_z_forward = rand(hidden_size, 1);
    b_z_backward = rand(hidden_size, 1);
    
    W_rf_forward = rand(hidden_size, size(input_sequence, 1));
    W_rb_backward = rand(hidden_size, size(input_sequence, 1));
    U_rf_forward = rand(hidden_size, hidden_size);
    U_rb_backward = rand(hidden_size, hidden_size);
    b_r_forward = rand(hidden_size, 1);
    b_r_backward = rand(hidden_size, 1);
    
    W_cf_forward = rand(hidden_size, size(input_sequence, 1));
    W_cb_backward = rand(hidden_size, size(input_sequence, 1));
    U_cf_forward = rand(hidden_size, hidden_size);
    U_cb_backward = rand(hidden_size, hidden_size);
    b_c_forward = rand(hidden_size, 1);
    b_c_backward = rand(hidden_size, 1);
    
    % Forward pass
    for t = 1:sequence_length
        x = input_sequence(:, t);
        
        forward_update_gate = sigmoid(W_zf_forward * x + U_zf_forward * forward_hidden_state + b_z_forward);
        forward_reset_gate = sigmoid(W_rf_forward * x + U_rf_forward * forward_hidden_state + b_r_forward);
        forward_candidate = tanh(W_cf_forward * x + U_cf_forward * (forward_reset_gate .* forward_hidden_state) + b_c_forward);
        
        forward_hidden_state = (1 - forward_update_gate) .* forward_hidden_state + forward_update_gate .* forward_candidate;
        
        output_sequence(:, t) = forward_hidden_state;
    end
    
    % Backward pass
    for t = sequence_length:-1:1
        x = input_sequence(:, t);
        
        backward_update_gate = sigmoid(W_zb_backward * x + U_zb_backward * backward_hidden_state + b_z_backward);
        backward_reset_gate = sigmoid(W_rb_backward * x + U_rb_backward * backward_hidden_state + b_r_backward);
        backward_candidate = tanh(W_cb_backward * x + U_cb_backward * (backward_reset_gate .* backward_hidden_state) + b_c_backward);
        
        backward_hidden_state = (1 - backward_update_gate) .* backward_hidden_state + backward_update_gate .* backward_candidate;
        
        output_sequence(:, t) = output_sequence(:, t) + backward_hidden_state;
    end
end

function [output] = sigmoid(x)
    output = 1 ./ (1 + exp(-x));
end

You can use the `bidirectional_gru` function to compute the output sequence from an input sequence. The `input_sequence` is a matrix of size `(input_size, sequence_length)`, where `input_size` is the size of each input vector and `sequence_length` is the length of the input sequence. The `hidden_size` parameter determines the size of the hidden state of the GRU.

Please note that this is a basic implementation and you may need to modify it based on your specific requirements.