用MATLAB写出一个lstm多输入多输出的回归程序
时间: 2023-10-05 21:05:30 浏览: 41
好的,以下是一个简单的 LSTM 多输入多输出回归的 MATLAB 代码示例:
```matlab
% 准备数据
load input_data.mat % 输入数据,大小为 [num_samples, num_features]
load output_data.mat % 输出数据,大小为 [num_samples, num_outputs]
sequence_length = 10; % 序列长度
num_samples = size(input_data, 1) - sequence_length + 1;
input_sequences = zeros(num_samples, sequence_length, size(input_data, 2)); % 输入序列
output_sequences = zeros(num_samples, sequence_length, size(output_data, 2)); % 输出序列
% 生成序列
for i = 1:num_samples
input_sequences(i,:,:) = input_data(i:i+sequence_length-1,:);
output_sequences(i,:,:) = output_data(i:i+sequence_length-1,:);
end
% 划分训练集和测试集
train_ratio = 0.8;
num_train_samples = floor(num_samples * train_ratio);
train_input_sequences = input_sequences(1:num_train_samples,:,:);
train_output_sequences = output_sequences(1:num_train_samples,:,:);
test_input_sequences = input_sequences(num_train_samples+1:end,:,:);
test_output_sequences = output_sequences(num_train_samples+1:end,:,:);
% 构建 LSTM 模型
num_features = size(input_data, 2);
num_outputs = size(output_data, 2);
input_layer = sequenceInputLayer(num_features, 'Name', 'input');
lstm_layer = lstmLayer(64, 'Name', 'lstm');
dropout_layer = dropoutLayer(0.2, 'Name', 'dropout');
output_layer = sequenceFoldingLayer('Name', 'output');
fc_layer = fullyConnectedLayer(num_outputs, 'Name', 'fc');
layers = [input_layer; lstm_layer; dropout_layer; output_layer; fc_layer];
options = trainingOptions('adam', ...
'MaxEpochs', 50, ...
'MiniBatchSize', 64, ...
'SequenceLength', sequence_length, ...
'Shuffle', 'every-epoch', ...
'Plots', 'training-progress');
% 训练模型
net = trainNetwork(train_input_sequences, train_output_sequences, layers, options);
% 测试模型
predicted_output_sequences = predict(net, test_input_sequences);
% 可视化结果
figure;
for i = 1:num_outputs
subplot(num_outputs, 1, i);
plot(test_output_sequences(:,:,i), 'b');
hold on;
plot(predicted_output_sequences(:,:,i), 'r');
xlabel('Time Step');
ylabel('Output');
legend('True', 'Predicted');
end
```
这个代码示例将输入和输出数据加载到 MATLAB 中,然后将输入数据和输出数据转换成序列形式。接下来,它将训练数据和测试数据划分开,并构建一个 LSTM 模型。模型包括一个输入层、一个 LSTM 层、一个 dropout 层、一个 output 层和一个全连接层。最后,它使用训练数据来训练模型,并使用测试数据来测试模型的性能。最后,它将真实的输出序列和预测的输出序列可视化。