www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/PredictContinuousMeasurementsExample.m
%% Predict Continuous Measurements Using Trained Autoencoder %% % Load the training data. X = iris_dataset; %% % The training data contains measurements on four attributes of iris flowers: % Sepal length, sepal width, petal length, petal width. %% % Train an autoencoder on the training data using the positive saturating % linear transfer function in the encoder and linear transfer function in % the decoder. autoenc = trainAutoencoder(X,'EncoderTransferFunction',... 'satlin','DecoderTransferFunction','purelin'); %% % Reconstruct the measurements using the trained network, |autoenc|. xReconstructed = predict(autoenc,X); %% % Plot the predicted measurement values along with the actual values in % the training dataset. for i = 1:4 h(i) = subplot(1,4,i); plot(X(i,:),'r.'); hold on plot(xReconstructed(i,:),'go'); hold off; end title(h(1),{'Sepal';'Length'}); title(h(2),{'Sepal';'Width'}); title(h(3),{'Petal';'Length'}); title(h(4),{'Petal';'Width'}); %% % The red dots represent the training data and the green circles represent % the reconstructed data.