GRUmatlab代码

以下是一个简单的 Matlab 代码示例，用于实现 Gated Recurrent Unit (GRU) 模型：

% GRU implementation in MATLAB

% Hyperparameters
hidden_size = 100;  % 隐藏层大小
seq_length = 10;   % 输入序列长度
learning_rate = 0.01;

% 初始化权重参数
Wz = randn(hidden_size, hidden_size + seq_length);
Wr = randn(hidden_size, hidden_size + seq_length);
Wh = randn(hidden_size, hidden_size + seq_length);
Uz = randn(hidden_size, hidden_size);
Ur = randn(hidden_size, hidden_size);
Uh = randn(hidden_size, hidden_size);
bz = zeros(hidden_size, 1);
br = zeros(hidden_size, 1);
bh = zeros(hidden_size, 1);

% 训练数据和标签
data = ... % 输入序列数据
labels = ... % 目标标签

% 前向传播和反向传播
for iteration = 1:1000
    % 初始化隐藏状态和损失
    h = zeros(hidden_size, 1);
    loss = 0;
    
    for t = 1:seq_length
        % 前向传播
        x = data(:, t);
        z = sigmoid(Wz * [h; x] + Uz * h + bz);
        r = sigmoid(Wr * [h; x] + Ur * h + br);
        h_tilde = tanh(Wh * [r .* h; x] + bh);
        h = (1 - z) .* h + z .* h_tilde;
        
        % 计算损失
        loss = loss + norm(h - labels(:, t))^2;
    end
    
    % 反向传播
    dWz = zeros(size(Wz));
    dWr = zeros(size(Wr));
    dWh = zeros(size(Wh));
    dUz = zeros(size(Uz));
    dUr = zeros(size(Ur));
    dUh = zeros(size(Uh));
    dbz = zeros(size(bz));
    dbr = zeros(size(br));
    dbh = zeros(size(bh));
    dh_next = zeros(size(h));
    
    for t = seq_length:-1:1
        x = data(:, t);
        z = sigmoid(Wz * [h; x] + Uz * h + bz);
        r = sigmoid(Wr * [h; x] + Ur * h + br);
        h_tilde = tanh(Wh * [r .* h; x] + bh);
        
        dh = 2 * (h_tilde - labels(:, t));
        dz = dh .* (h_tilde - h) .* z .* (1 - z);
        dr = (Wh(:, 1:hidden_size)' * dz) .* h .* r .* (1 - r);
        dh_tilde = (Wh(:, hidden_size+1:end)' * dz) .* (1 - h_tilde.^2);
        dh_combined = dh_next + (Wh(:, hidden_size+1:end)' * dz) .* r;
        
        dWz = dWz + dz * [h; x]';
        dWr = dWr + dr * [h; x]';
        dWh = dWh + dh_combined * [r .* h; x]';
        dUz = dUz + dz * h';
        dUr = dUr + dr * h';
        dUh = dUh + dh_combined * (r .* h)';
        dbz = dbz + dz;
        dbr = dbr + dr;
        dbh = dbh + dh_combined;
        
        dh_next = (Wz(:, 1:hidden_size)' * dz) + (Wr(:, 1:hidden_size)' * dr) + (Wh(:, 1:hidden_size)' * dh_combined);
        
        % 更新权重
        Wz = Wz - learning_rate * dWz;
        Wr = Wr - learning_rate * dWr;
        Wh = Wh - learning_rate * dWh;
        Uz = Uz - learning_rate * dUz;
        Ur = Ur - learning_rate * dUr;
        Uh = Uh - learning_rate * dUh;
        bz = bz - learning_rate * dbz;
        br = br - learning_rate * dbr;
        bh = bh - learning_rate * dbh;
    end
    
    % 打印损失
    fprintf('Iteration %d, Loss: %f\n', iteration, loss);
end

这只是一个简单的 GRU 模型实现示例，实际应用中可能需要根据具体问题进行调整和改进。希望对你有所帮助！