时间卷积网络TCN的Matlab代码
时间: 2024-03-26 19:38:47 浏览: 16
以下是一个简单的时间卷积网络(TCN)的Matlab代码示例:
```
% Define hyperparameters
input_channels = 1;
output_channels = 1;
kernel_size = 3;
dilation_factors = [1, 2, 4, 8];
num_layers = length(dilation_factors);
hidden_size = 64;
input_size = 100;
output_size = 50;
learning_rate = 0.001;
num_epochs = 100;
% Define the TCN architecture
layers = [sequenceInputLayer(input_size, input_channels);
fullyConnectedLayer(hidden_size);
reluLayer();
sequenceFoldingLayer('TimeSize','t');
convolution2dLayer([kernel_size, input_channels, hidden_size, output_channels], 'Padding', 'same');
reluLayer();
sequenceUnfoldingLayer('TimeSize','t');
sequenceFoldingLayer('TimeSize','t');
];
for i = 1:num_layers
layers = [layers;
convolution2dLayer([kernel_size, output_channels, output_channels, hidden_size], 'DilationFactor', dilation_factors(i), 'Padding', 'same');
reluLayer();
sequenceUnfoldingLayer('TimeSize','t');
sequenceFoldingLayer('TimeSize','t');
];
end
layers = [layers;
convolution2dLayer([1, output_channels, output_size, output_channels], 'Padding', 'same');
regressionLayer();
];
% Define the options for training
options = trainingOptions('adam', ...
'InitialLearnRate', learning_rate, ...
'MaxEpochs', num_epochs, ...
'MiniBatchSize', 64, ...
'Shuffle', 'every-epoch', ...
'Verbose', true, ...
'Plots', 'training-progress');
% Train the TCN on some input data and target output data
X = randn(input_size, input_channels);
Y = randn(output_size, output_channels);
net = trainNetwork(X, Y, layers, options);
```
在上述代码中,我们首先定义了一些超参数,例如输入通道数、输出通道数、卷积核大小、空洞卷积因子、层数、隐藏层大小、输入大小、输出大小、学习率和迭代次数。然后,我们定义了一个TCN网络,其中包括输入层、全连接层、ReLU层、序列折叠层、卷积层、ReLU层、序列展开层和多个空洞卷积层。最后,我们使用随机的输入数据和输出数据对TCN进行训练。