gru算法matlab代码
时间: 2024-01-24 14:01:01 浏览: 24
GRU(Gated Recurrent Unit)是一种循环神经网络(RNN)的变体,常用于处理序列数据。下面是一个简单的GRU算法的Matlab代码示例:
```matlab
% 设置参数
hidden_units = 64; % 隐藏层单元数
input_size = 10; % 输入维度
output_size = 1; % 输出维度
sequence_length = 100; % 序列长度
learning_rate = 0.01; % 学习率
epochs = 100; % 训练轮数
% 初始化权重
Uz = randn(hidden_units, input_size);
Wr = randn(hidden_units, input_size);
Wh = randn(hidden_units, input_size);
Uz_h = randn(hidden_units, hidden_units);
Wr_h = randn(hidden_units, hidden_units);
Wh_h = randn(hidden_units, hidden_units);
bz = zeros(hidden_units, 1);
br = zeros(hidden_units, 1);
bh = zeros(hidden_units, 1);
Wy = randn(output_size, hidden_units);
by = zeros(output_size, 1);
% 循环训练
for epoch = 1:epochs
% 初始化隐藏状态和损失
h = zeros(hidden_units, 1);
loss = 0;
% 循环遍历序列
for t = 1:sequence_length
% 正向传播
x = randn(input_size, 1); % 输入序列
z = sigmoid(Uz*x + Uz_h*h + bz);
r = sigmoid(Wr*x + Wr_h*h + br);
h_ = tanh(Wh*x + Wh_h*(r.*h) + bh);
h = (1-z).*h + z.*h_;
y = Wy*h + by; % 输出
% 计算损失
target = rand(output_size, 1); % 目标输出
loss = loss + sum((y - target).^2);
% 反向传播
dL_dy = 2*(y - target);
dL_dh = Wy'*dL_dy;
dL_dWy = dL_dy * h';
dL_dby = dL_dy;
dL_dz = dL_dh.*h_.*(1-z).*z;
dL_dh_ = dL_dh.*z.*(1-h_.^2);
dL_dWh = dL_dh_ * (r.*h)';
dL_dWh_h = dL_dh_ * (r.*h)'.*h_;
dL_dr = (Wh'*dL_dh_).*(h.*h).*(1-r).*r;
dL_dr_h = (Wh'*dL_dh_).*(h.*h).*(1-r).*h;
dL_dWr = dL_dr * x';
dL_dWr_h = dL_dr_h * x';
dL_dUz = dL_dz * x';
dL_dUz_h = dL_dz * h';
dL_dWr = dL_dr * x';
dL_dWr_h = dL_dr_h * x';
dL_dWh = dL_dWh * x';
dL_dWh_h = dL_dWh_h * x';
% 更新权重
Uz = Uz - learning_rate * dL_dUz;
Uz_h = Uz_h - learning_rate * dL_dUz_h;
Wr = Wr - learning_rate * dL_dWr;
Wr_h = Wr_h - learning_rate * dL_dWr_h;
Wh = Wh - learning_rate * dL_dWh;
Wh_h = Wh_h - learning_rate * dL_dWh_h;
Wy = Wy - learning_rate * dL_dWy;
by = by - learning_rate * dL_dby;
end
% 每轮训练输出一次损失
fprintf('Epoch %d, Loss: %f\n', epoch, loss);
end
```
注意这只是一个简单的示例代码,实际上GRU算法的实现更复杂,还包含其他细节和技巧。如果需要更准确和完整的GRU算法实现,请参考相关的文献和教材。