Matlab实现循环神经网络

循环神经网络（Recurrent Neural Network, RNN）是一种特殊类型的神经网络，非常适合处理序列数据，如时间序列分析、自然语言处理等。在MATLAB中，可以使用Deep Learning Toolbox来构建和训练RNN。

孺子牛 for world · 2024-08-25 21:49:14 发布

首先，需要准备或生成一些序列数据。为了简单起见，我们将生成一些随机的正弦波数据作为训练集和测试集。

	`% 生成数据`
	`numTimeStepsTrain = floor(0.9*1000);`
	`data = sin(1:0.01:10pi) + 0.1randn(size(1:0.01:10*pi));`

	`% 划分数据为训练和测试集`
	`XTrain = data(1:numTimeStepsTrain+10);`
	`XTest = data(numTimeStepsTrain+11:end);`

	`% 准备RNN的输入数据格式: [numSequences, numTimeSteps, numFeatures]`
	`numTimeStepsTrain = floor(length(XTrain)/10); % 假设每个序列包含10个时间步`
	`numFeatures = 1;`

	`XTrain = reshape(XTrain(1:numTimeStepsTrain*10), numTimeStepsTrain, 10, numFeatures);`
	`XTest = reshape(XTest(1:floor(length(XTest)/10)*10), floor(length(XTest)/10), 10, numFeatures);`

	`% 预测目标：下一个时间步的值`
	`YTrain = XTrain(:,2:end,:);`
	`YTest = XTest(:,2:end,:);`

在MATLAB中，你可以使用layerGraph或layerArray来定义网络结构。

	`layers = [`
	`sequenceInputLayer(numFeatures)`
	`lstmLayer(50,'OutputMode','sequence') % LSTM层，50个隐藏单元`
	`fullyConnectedLayer(numFeatures)`
	`regressionLayer`
	`];`

	`options = trainingOptions('adam', ...`
	`'MaxEpochs',100, ...`
	`'GradientThreshold',1, ...`
	`'InitialLearnRate',0.005, ...`
	`'LearnRateSchedule','piecewise', ...`
	`'LearnRateDropPeriod',125, ...`
	`'LearnRateDropFactor',0.2, ...`
	`'Verbose',false, ...`
	`'Plots','training-progress');`

net = trainNetwork(XTrain,YTrain,layers,options);

	`YPred = predict(net,XTest);`

	`% 计算一些性能指标（例如，均方误差）`
	`YTest = YTest(:); % Flatten YTest`
	`YPred = YPred(:); % Flatten YPred`
	`mse = mean((YTest-YPred).^2);`
	`disp(['Mean Squared Error: ', num2str(mse)]);`