GRU神经网络matlab代码

以下是一个简单的GRU神经网络的MATLAB代码示例：

% 定义超参数
input_size = 10; % 输入向量的大小
hidden_size = 20; % 隐藏层向量的大小
output_size = 5; % 输出向量的大小
sequence_length = 100; % 序列的长度
learning_rate = 0.01; % 学习率

% 初始化权重和偏置
W_z = randn(hidden_size, input_size + hidden_size);
W_r = randn(hidden_size, input_size + hidden_size);
W = randn(hidden_size, input_size + hidden_size);
U_z = randn(hidden_size, hidden_size);
U_r = randn(hidden_size, hidden_size);
U = randn(hidden_size, hidden_size);
b_z = zeros(hidden_size, 1);
b_r = zeros(hidden_size, 1);
b = zeros(hidden_size, 1);
V = randn(output_size, hidden_size);
c = zeros(output_size, 1);

% 初始化梯度
dW_z = zeros(size(W_z));
dW_r = zeros(size(W_r));
dW = zeros(size(W));
dU_z = zeros(size(U_z));
dU_r = zeros(size(U_r));
dU = zeros(size(U));
db_z = zeros(size(b_z));
db_r = zeros(size(b_r));
db = zeros(size(b));
dV = zeros(size(V));
dc = zeros(size(c));

% 定义输入和输出数据
X = randn(input_size, sequence_length);
Y = randn(output_size, sequence_length);

% 定义前向传播函数
h = zeros(hidden_size, sequence_length);
z = zeros(hidden_size, sequence_length);
r = zeros(hidden_size, sequence_length);
y_hat = zeros(output_size, sequence_length);
for t = 2:sequence_length
    z(:,t) = sigmoid(W_z * [X(:,t); h(:,t-1)] + U_z * h(:,t-1) + b_z);
    r(:,t) = sigmoid(W_r * [X(:,t); h(:,t-1)] + U_r * h(:,t-1) + b_r);
    h_tilda = tanh(W * [X(:,t); r(:,t) .* h(:,t-1)] + U * (r(:,t) .* h(:,t-1)) + b);
    h(:,t) = (1 - z(:,t)) .* h(:,t-1) + z(:,t) .* h_tilda;
    y_hat(:,t) = softmax(V * h(:,t) + c);
end

% 定义损失函数和反向传播函数
loss = -sum(sum(Y .* log(y_hat))) / sequence_length;
dy_hat = y_hat - Y;
dh = zeros(hidden_size, sequence_length);
dz = zeros(hidden_size, sequence_length);
dr = zeros(hidden_size, sequence_length);
dX = zeros(input_size, sequence_length);
for t = sequence_length:-1:2
    dV = dV + dy_hat(:,t) * h(:,t)';
    dc = dc + dy_hat(:,t);
    dh(:,t) = dh(:,t) + V' * dy_hat(:,t);
    dH_tilda = dh(:,t) .* z(:,t) .* (1 - tanh(W * [X(:,t); r(:,t) .* h(:,t-1)] + U * (r(:,t) .* h(:,t-1)) + b).^2);
    dH_tilda = dH_tilda + dh(:,t) .* (1 - z(:,t));
    dR = (W(:,input_size+1:end)' * dH_tilda) .* h(:,t-1);
    dX(:,t) = W(:,1:input_size)' * dH_tilda;
    dz(:,t) = z(:,t) .* (1 - z(:,t)) .* (dh(:,t) .* (h_tilda - h(:,t-1)));
    dr(:,t) = r(:,t) .* (1 - r(:,t)) .* (dR + dh(:,t) .* (r(:,t) .* (1 - h(:,t-1).^2)));
    dh(:,t-1) = dh(:,t-1) + W(:,input_size+1:end)' * dH_tilda .* r(:,t);
    dW_z = dW_z + dz(:,t) * [X(:,t); h(:,t-1)]';
    dW_r = dW_r + dr(:,t) * [X(:,t); h(:,t-1)]';
    dW = dW + dH_tilda * [X(:,t); r(:,t) .* h(:,t-1)]';
    dU_z = dU_z + dz(:,t) * h(:,t-1)';
    dU_r = dU_r + dr(:,t) * h(:,t-1)';
    dU = dU + dH_tilda * (r(:,t) .* h(:,t-1))';
    db_z = db_z + dz(:,t);
    db_r = db_r + dr(:,t);
    db = db + dH_tilda;
end

% 更新参数
W_z = W_z - learning_rate * dW_z;
W_r = W_r - learning_rate * dW_r;
W = W - learning_rate * dW;
U_z = U_z - learning_rate * dU_z;
U_r = U_r - learning_rate * dU_r;
U = U - learning_rate * dU;
b_z = b_z - learning_rate * db_z;
b_r = b_r - learning_rate * db_r;
b = b - learning_rate * db;
V = V - learning_rate * dV;
c = c - learning_rate * dc;

请注意，此代码示例仅用于说明GRU神经网络的概念，不适用于生产环境。在实践中，您应该使用现有的深度学习框架（如TensorFlow或PyTorch）来实现GRU神经网络。