LSTM的matlab代码
时间: 2023-07-22 12:24:26 浏览: 83
以下是一个简单的LSTM的MATLAB实现代码:
```matlab
% LSTM implementation in MATLAB
% Define input sequence
seq = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0];
% Define parameters
input_size = 1; % Input size
hidden_size = 2; % Hidden size
output_size = 1; % Output size
% Define weight matrices
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);
% Define initial hidden state and cell state
h = zeros(hidden_size, 1);
c = zeros(hidden_size, 1);
% Define learning rate
learning_rate = 0.1;
% Train LSTM
for i = 1:length(seq)
% Forward pass
x = seq(i);
f = sigmoid(Wf * [h; x] + bf);
i = sigmoid(Wi * [h; x] + bi);
c_ = tanh(Wc * [h; x] + bc);
c = f .* c + i .* c_;
o = sigmoid(Wo * [h; x] + bo);
h = o .* tanh(c);
y = Wy * h + by;
% Compute loss
loss = (y - x)^2;
% Backward pass
dWy = (y - x) * h';
dby = y - x;
dh = Wy' * (y - x) + do .* tanh(c);
do = dh .* tanh(c);
dc = dh .* o .* (1 - tanh(c).^2);
dc_ = dc .* i;
di = dc .* c_;
df = dc .* c;
dWf = df * [h; x]';
dWi = di * [h; x]';
dWc = dc_ * [h; x]';
dWo = do * [h; x]';
dbf = df;
dbi = di;
dbc = dc_;
dbo = do;
% Update weights
Wy = Wy - learning_rate * dWy;
by = by - learning_rate * dby;
Wf = Wf - learning_rate * dWf;
bf = bf - learning_rate * dbf;
Wi = Wi - learning_rate * dWi;
bi = bi - learning_rate * dbi;
Wc = Wc - learning_rate * dWc;
bc = bc - learning_rate * dbc;
Wo = Wo - learning_rate * dWo;
bo = bo - learning_rate * dbo;
end
```
请注意,这只是一个基本的实现,并且可能不适用于更复杂的问题。此外,这个实现可能会出现梯度爆炸或消失问题,需要使用一些技巧来解决这些问题。