用GRU神经网络预测流量的matlab代码
时间: 2023-08-25 07:07:44 浏览: 52
以下是用 MATLAB 实现的 GRU 神经网络预测流量的代码示例:
```matlab
% 加载数据
data = load('traffic_data.mat');
X = data.X;
y = data.y;
% 划分训练集和测试集
train_ratio = 0.8;
train_size = floor(train_ratio * size(X, 1));
X_train = X(1:train_size, :);
y_train = y(1:train_size, :);
X_test = X(train_size+1:end, :);
y_test = y(train_size+1:end, :);
% 设置神经网络参数
input_size = size(X_train, 2);
output_size = size(y_train, 2);
hidden_size = 100;
num_epochs = 100;
learning_rate = 0.01;
% 初始化权重和偏置
W_z = randn(input_size, hidden_size);
W_r = randn(input_size, hidden_size);
W_h = randn(input_size, hidden_size);
U_z = randn(hidden_size, hidden_size);
U_r = randn(hidden_size, hidden_size);
U_h = randn(hidden_size, hidden_size);
b_z = zeros(1, hidden_size);
b_r = zeros(1, hidden_size);
b_h = zeros(1, hidden_size);
V = randn(hidden_size, output_size);
c = zeros(1, output_size);
% 训练神经网络
for epoch = 1:num_epochs
% 前向传播
h_prev = zeros(1, hidden_size);
for t = 1:size(X_train, 1)
x_t = X_train(t, :);
z_t = sigmoid(x_t * W_z + h_prev * U_z + b_z);
r_t = sigmoid(x_t * W_r + h_prev * U_r + b_r);
h_tilde_t = tanh(x_t * W_h + (r_t .* h_prev) * U_h + b_h);
h_t = (1 - z_t) .* h_prev + z_t .* h_tilde_t;
h_prev = h_t;
end
y_pred = h_t * V + c;
% 计算损失和梯度
loss = mean((y_train - y_pred).^2);
dL_dV = h_t' * (y_pred - y_train) / size(X_train, 1);
dL_dc = mean(y_pred - y_train);
dL_dh_t = (y_pred - y_train) * V';
dL_dz_t = zeros(1, hidden_size);
dL_dr_t = zeros(1, hidden_size);
dL_dh_prev = zeros(1, hidden_size);
for t = size(X_train, 1):-1:1
x_t = X_train(t, :);
z_t = sigmoid(x_t * W_z + h_prev * U_z + b_z);
r_t = sigmoid(x_t * W_r + h_prev * U_r + b_r);
h_tilde_t = tanh(x_t * W_h + (r_t .* h_prev) * U_h + b_h);
h_t = (1 - z_t) .* h_prev + z_t .* h_tilde_t;
if t == size(X_train, 1)
dL_dh_tilde_t = dL_dh_t;
else
dL_dh_tilde_t = dL_dh_t .* (1 - z_t) .* (1 - h_tilde_t.^2);
end
dL_dz_t = dL_dz_t + dL_dh_tilde_t * h_prev * z_t .* (1 - z_t);
dL_dr_t = dL_dr_t + dL_dh_tilde_t .* (h_prev .* U_h) .* r_t .* (1 - r_t);
dL_dh_prev = dL_dh_prev + dL_dh_tilde_t .* z_t .* (1 - h_tilde_t.^2) .* U_h .* r_t;
dL_dW_z = x_t' * (dL_dh_tilde_t .* z_t .* (1 - z_t));
dL_dW_r = x_t' * (dL_dh_tilde_t .* r_t .* (1 - r_t));
dL_dW_h = x_t' * (dL_dh_tilde_t .* z_t .* (1 - h_tilde_t.^2));
dL_dU_z = h_prev' * (dL_dh_tilde_t .* z_t .* (1 - z_t));
dL_dU_r = h_prev' * (dL_dh_tilde_t .* r_t .* (1 - r_t));
dL_dU_h = (r_t .* h_prev)' * (dL_dh_tilde_t .* z_t .* (1 - h_tilde_t.^2));
dL_db_z = sum(dL_dh_tilde_t .* z_t .* (1 - z_t), 1);
dL_db_r = sum(dL_dh_tilde_t .* r_t .* (1 - r_t), 1);
dL_db_h = sum(dL_dh_tilde_t .* z_t .* (1 - h_tilde_t.^2), 1);
dL_dh_t = dL_dh_prev;
end
% 更新权重和偏置
V = V - learning_rate * dL_dV;
c = c - learning_rate * dL_dc;
W_z = W_z - learning_rate * dL_dW_z;
W_r = W_r - learning_rate * dL_dW_r;
W_h = W_h - learning_rate * dL_dW_h;
U_z = U_z - learning_rate * dL_dU_z;
U_r = U_r - learning_rate * dL_dU_r;
U_h = U_h - learning_rate * dL_dU_h;
b_z = b_z - learning_rate * dL_db_z;
b_r = b_r - learning_rate * dL_db_r;
b_h = b_h - learning_rate * dL_db_h;
% 输出训练进度
fprintf('Epoch %d, Loss: %f\n', epoch, loss);
end
% 在测试集上进行预测
h_prev = zeros(1, hidden_size);
for t = 1:size(X_test, 1)
x_t = X_test(t, :);
z_t = sigmoid(x_t * W_z + h_prev * U_z + b_z);
r_t = sigmoid(x_t * W_r + h_prev * U_r + b_r);
h_tilde_t = tanh(x_t * W_h + (r_t .* h_prev) * U_h + b_h);
h_t = (1 - z_t) .* h_prev + z_t .* h_tilde_t;
h_prev = h_t;
end
y_pred = h_t * V + c;
% 计算测试集上的均方误差
mse = mean((y_test - y_pred).^2);
fprintf('Test MSE: %f\n', mse);
```
需要注意的是,这只是一个简单的 GRU 神经网络的示例代码,并且可能需要根据具体的数据集和问题进行修改和调整。