鲸鱼优化GRU神经网络的matlab代码

以下是使用鲸鱼优化算法优化GRU神经网络的Matlab代码：

% 加载数据集
load dataset.mat

% 初始化神经网络参数
input_size = size(X_train, 1);
hidden_size = 128;
output_size = size(Y_train, 1);
Wz = randn(hidden_size, input_size);
Wr = randn(hidden_size, input_size);
Wh = randn(hidden_size, input_size);
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);
Wy = randn(output_size, hidden_size);
by = zeros(output_size, 1);

% 定义损失函数
loss = @(Wz, Wr, Wh, Uz, Ur, Uh, bz, br, bh, Wy, by) gru_loss(Wz, Wr, Wh, Uz, Ur, Uh, bz, br, bh, Wy, by, X_train, Y_train);

% 定义鲸鱼优化器参数
options.MaxIterations = 100;
options.PopulationSize = 20;
options.CrossoverRate = 0.8;
options.MutationFcn = @mutationadaptfeasible;
options.Display = 'iter';

% 使用鲸鱼优化算法优化神经网络参数
[x, fval] = whale_optimization(loss, [Wz(:); Wr(:); Wh(:); Uz(:); Ur(:); Uh(:); bz(:); br(:); bh(:); Wy(:); by(:)], options);

% 恢复最优参数
Wz = reshape(x(1:hidden_size*input_size), hidden_size, input_size);
Wr = reshape(x(hidden_size*input_size+1:2*hidden_size*input_size), hidden_size, input_size);
Wh = reshape(x(2*hidden_size*input_size+1:3*hidden_size*input_size), hidden_size, input_size);
Uz = reshape(x(3*hidden_size*input_size+1:3*hidden_size*input_size+hidden_size*hidden_size), hidden_size, hidden_size);
Ur = reshape(x(3*hidden_size*input_size+hidden_size*hidden_size+1:3*hidden_size*input_size+2*hidden_size*hidden_size), hidden_size, hidden_size);
Uh = reshape(x(3*hidden_size*input_size+2*hidden_size*hidden_size+1:3*hidden_size*input_size+3*hidden_size*hidden_size), hidden_size, hidden_size);
bz = x(3*hidden_size*input_size+3*hidden_size*hidden_size+1:3*hidden_size*input_size+3*hidden_size*hidden_size+hidden_size);
br = x(3*hidden_size*input_size+3*hidden_size*hidden_size+hidden_size+1:3*hidden_size*input_size+3*hidden_size*hidden_size+2*hidden_size);
bh = x(3*hidden_size*input_size+3*hidden_size*hidden_size+2*hidden_size+1:3*hidden_size*input_size+3*hidden_size*hidden_size+3*hidden_size);
Wy = reshape(x(3*hidden_size*input_size+3*hidden_size*hidden_size+3*hidden_size+1:3*hidden_size*input_size+3*hidden_size*hidden_size+3*hidden_size+output_size*hidden_size), output_size, hidden_size);
by = x(end-output_size+1:end);

% 预测测试集
Y_pred = gru_predict(Wz, Wr, Wh, Uz, Ur, Uh, bz, br, bh, Wy, by, X_test);

% 计算准确率
accuracy = sum(Y_pred == Y_test) / length(Y_test);
disp(['Test Accuracy: ' num2str(accuracy)]);

其中，`gru_loss` 函数用于计算GRU神经网络的损失函数，`whale_optimization` 函数则使用鲸鱼优化算法优化神经网络的参数。最后，使用 `gru_predict` 函数对测试集进行预测，并计算准确率。