lstm神经网络matlab代码

以下是一个简单的LSTM神经网络的MATLAB代码示例：

% 准备数据
input_data = randn(100, 50);
target_data = randn(100, 10);

% 设置LSTM网络参数
input_size = 50;
hidden_size = 100;
output_size = 10;
learning_rate = 0.01;

% 初始化权重和偏置
Wf = randn(hidden_size, input_size + hidden_size);
Wi = randn(hidden_size, input_size + hidden_size);
Wc = randn(hidden_size, input_size + hidden_size);
Wo = randn(hidden_size, input_size + hidden_size);
Wy = randn(output_size, hidden_size);
bf = zeros(hidden_size, 1);
bi = zeros(hidden_size, 1);
bc = zeros(hidden_size, 1);
bo = zeros(hidden_size, 1);
by = zeros(output_size, 1);

% 初始化状态变量
h_prev = zeros(hidden_size, 1);
c_prev = zeros(hidden_size, 1);

% 训练网络
for t = 1:size(input_data, 1)
    % 前向传播
    x = input_data(t, :)';
    z = [h_prev; x];
    f = sigmoid(Wf * z + bf);
    i = sigmoid(Wi * z + bi);
    c_bar = tanh(Wc * z + bc);
    c = f .* c_prev + i .* c_bar;
    o = sigmoid(Wo * z + bo);
    h = o .* tanh(c);
    y = Wy * h + by;
    
    % 计算误差
    target = target_data(t, :)';
    error = y - target;
    
    % 反向传播
    dWy = error * h';
    dby = error;
    dh = (Wy' * error) .* (1 - h.^2);
    do = dh .* tanh(c);
    dc = (dh .* o) .* (1 - tanh(c).^2) + (c - c_prev) .* f;
    dc_prev = dc .* f;
    df = dc .* c_prev;
    di = dc .* c_bar;
    dc_bar = dc .* i;
    dzf = df .* f .* (1 - f);
    dzi = di .* i .* (1 - i);
    dzc = dc_bar .* (1 - c_bar.^2);
    dzo = do .* o .* (1 - o);
    dz = [dzf; dzi; dzc; dzo];
    dWf = dzf * z';
    dWi = dzi * z';
    dWc = dzc * z';
    dWo = dzo * z';
    dbf = dzf;
    dbi = dzi;
    dbc = dzc;
    dbo = dzo;
    dh_prev = dz(1:hidden_size);
    dx = dz(hidden_size+1:end);
    
    % 更新权重和偏置
    Wy = Wy - learning_rate * dWy;
    by = by - learning_rate * dby;
    Wf = Wf - learning_rate * dWf;
    Wi = Wi - learning_rate * dWi;
    Wc = Wc - learning_rate * dWc;
    Wo = Wo - learning_rate * dWo;
    bf = bf - learning_rate * dbf;
    bi = bi - learning_rate * dbi;
    bc = bc - learning_rate * dbc;
    bo = bo - learning_rate * dbo;
    
    % 更新状态
    h_prev = h;
    c_prev = c;
end

这里的LSTM网络有一个输入层，一个隐藏层和一个输出层。输入数据是一个100x50的矩阵，目标数据是一个100x10的矩阵。网络的隐藏层大小为100，学习率为0.01。权重和偏置是随机初始化的。在每个时间步长上，网络接收一个50维的输入向量，并产生一个10维的输出向量。在训练期间，网络逐步更新权重和偏置，以最小化输出与目标之间的误差。