GRU神经网络的matlab代码

以下是一个简单的GRU网络的Matlab代码示例。该代码实现了一个单层的GRU网络，包括前向传播和反向传播。

% 初始化参数
n_x = 10; % 输入维度
n_h = 5; % 隐藏层维度
W_xz = rand(n_h, n_x); % 输入门权重
W_hz = rand(n_h, n_h); % 隐藏状态门权重
W_xr = rand(n_h, n_x); % 重置门权重
W_hr = rand(n_h, n_h); % 隐藏状态门权重
W_xh = rand(n_h, n_x); % 新状态权重
W_hh = rand(n_h, n_h); % 隐藏状态门权重
b_z = rand(n_h, 1); % 输入门偏置
b_r = rand(n_h, 1); % 重置门偏置
b_h = rand(n_h, 1); % 新状态偏置
h0 = rand(n_h, 1); % 初始隐藏状态

% 输入和输出数据
X = rand(n_x, 100); % 输入数据
Y = rand(n_h, 100); % 输出数据

% 前向传播
h = h0;
for t = 1:size(X, 2)
    x = X(:, t);
    z = sigmoid(W_xz * x + W_hz * h + b_z);
    r = sigmoid(W_xr * x + W_hr * h + b_r);
    h_tilde = tanh(W_xh * x + W_hh * (r .* h) + b_h);
    h = (1 - z) .* h + z .* h_tilde;
end
Y_pred = h;

% 反向传播
dW_xz = zeros(size(W_xz));
dW_hz = zeros(size(W_hz));
dW_xr = zeros(size(W_xr));
dW_hr = zeros(size(W_hr));
dW_xh = zeros(size(W_xh));
dW_hh = zeros(size(W_hh));
db_z = zeros(size(b_z));
db_r = zeros(size(b_r));
db_h = zeros(size(b_h));
dh = zeros(size(h));
for t = size(X, 2):-1:1
    x = X(:, t);
    y = Y(:, t);
    h_tilde = tanh(W_xh * x + W_hh * (r .* h) + b_h);
    z = sigmoid(W_xz * x + W_hz * h + b_z);
    r = sigmoid(W_xr * x + W_hr * h + b_r);
    h_prev = h0;
    if t > 1
        h_prev = h;
    end
    delta = dh + (W_hh * (r .* (dh .* (1 - z))) + (1 - z) .* (1 - h_tilde .^ 2)) .* (1 - h .^ 2);
    dW_xz = dW_xz + delta * x';
    dW_hz = dW_hz + delta * h_prev';
    db_z = db_z + delta;
    dW_xr = dW_xr + (delta .* (h_prev .* r .* (1 - r))) * x';
    dW_hr = dW_hr + (delta .* (h_prev .* r .* (1 - r))) * h_prev';
    db_r = db_r + delta .* (h_prev .* r .* (1 - r));
    dW_xh = dW_xh + delta * (1 - r) .* (1 - h_tilde .^ 2) * x';
    dW_hh = dW_hh + delta * (1 - r) .* (1 - h_tilde .^ 2) * h_prev';
    db_h = db_h + delta .* (1 - r) .* (1 - h_tilde .^ 2);
    dh = (W_hz * z + W_hr * (r .* h_prev) + W_hh * (1 - z) + W_xh' * (1 - r) .* (1 - h_tilde .^ 2)) * delta;
end

% 更新参数
alpha = 0.01; % 学习率
W_xz = W_xz - alpha * dW_xz;
W_hz = W_hz - alpha * dW_hz;
W_xr = W_xr - alpha * dW_xr;
W_hr = W_hr - alpha * dW_hr;
W_xh = W_xh - alpha * dW_xh;
W_hh = W_hh - alpha * dW_hh;
b_z = b_z - alpha * db_z;
b_r = b_r - alpha * db_r;
b_h = b_h - alpha * db_h;

在上面的代码中，我们首先初始化了GRU网络的参数（权重和偏置），然后使用随机输入数据和输出数据进行前向传播，并计算预测输出。接下来，我们使用反向传播算法计算每个参数的梯度，并使用梯度下降更新参数。