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TCN model to predict continuous variable

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Isabelle Museck
Isabelle Museck 2024-5-8
评论： Isabelle Museck 2024-5-14
Hello there I am trying to build a TCN machine learning model for regression purposes (to predict a continuous variable). Simialar to the example here: https://www.mathworks.com/help/deeplearning/ug/sequence-to-sequence-classification-using-1-d-convolutions.html#SeqToSeqClassificationUsing1DConvAndModelFunctionExample-11. I have time series data in which I am using 3 input features (accelrometer measuments in x,y,z directions), but instead of classifying an acitivity, I am trying to estimate/predict a continuous variable. My data is stored in a table with: Time, Accel_x, Accel_Y, Accel_Z, and Response Variable. How would I modify the code here:
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
net = dlnetwork;
layer = sequenceInputLayer(NumFeatures,Normalization="rescale-symmetric",Name="input");
net = addLayers(net,layer);
outputName = layer.Name;
for i = 1:numBlocks
dilationFactor = 2^(i-1);
layers = [
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_"+i)
layerNormalizationLayer
spatialDropoutLayer(droupoutFactor)
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal")
layerNormalizationLayer
reluLayer
spatialDropoutLayer(dropoutFactor)
additionLayer(2,Name="add_"+i)];
% Add and connect layers.
net = addLayers(net,layers);
net = connectLayers(net,outputName,"conv1_"+i);
% Skip connection.
if i == 1
% Include convolution in first skip connection.
layer = convolution1dLayer(1,numFilters,Name="convSkip");
net = addLayers(net,layer);
net = connectLayers(net,outputName,"convSkip");
net = connectLayers(net,"convSkip","add_" + i + "/in2");
else
net = connectLayers(net,outputName,"add_" + i + "/in2");
end
% Update layer output name.
outputName = "add_" + i;
end
layers = [
fullyConnectedLayer(numClasses,Name="fc")
softmaxLayer];
net = addLayers(net,layers);
net = connectLayers(net,outputName,"fc");
options = trainingOptions("adam", ...
MaxEpochs=60, ...
miniBatchSize=1, ...
InputDataFormats="CTB", ...
Plots="training-progress", ...
Metrics="accuracy", ...
Verbose=0);
net = trainnet(DataTrain,DataTrain.ResonseVariable,net,"crossentropy",options);

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回答（1 个）

Ayush Aniket
Ayush Aniket 2024-5-13
编辑：Ayush Aniket 2024-5-13
Hi Isabelle,
For a regression task you will need to make the following changes to the architecture and training process of your Temporal Convolutional Network (TCN):
  1. Since you are performing regression, you should replace the final 'fullyConnectedLayer' with a number of outputs corresponding to your prediction task (usually 1 for a single continuous variable) and remove the 'softmaxLayer' which is specific to classification tasks.
  2. For regression, a common loss function is 'Mean Squared Error (MSE)' instead of 'cross-entropy' loss used in classification. You would need to specify this in the 'trainnet' function.
  3. 'Accuracy' as a metric is not relevant for this task. Instead, you might track the loss or another regression-specific metric like 'RMSE (Root Mean Squared Error)' in the 'trainingOptions' function.
Refer to the documentation links below to read more about the loss functions and various metrics:
  1 个评论
Isabelle Museck
Isabelle Museck 2024-5-14
Thank you so much for your response. It was very helpful. I made your suggested changes and ran the code and it worked; however the RMSE value is very large.
The data used to train and test looks like this:
Xtrain= [DataTrain.Accelerometer_X,DataTrain.Accelerometer_Y,DataTrain.Accelerometer_Z_Sacrum]; % this is 433x3 double with accel x,y,z accel measurments in the colums for each 433 seconds
Ytrain = [DataTrain.ResponseVar];
%this is a 433x1 double with true values for the response variable we are trying to predict
Xtest = [DataTest.Accelerometer_X_Sacrum,DataTest.Accelerometer_Y_Sacrum,DataTest.Accelerometer_Z_Sacrum];
Ytest = [DataTest.("CoP Velocity")];
The code used is here:
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
net = dlnetwork;
layer = sequenceInputLayer(NumFeatures,Normalization="rescale-symmetric",Name="input");
net = addLayers(net,layer);
outputName = layer.Name;
for i = 1:numBlocks
dilationFactor = 2^(i-1);
layers = [
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_"+i)
layerNormalizationLayer
spatialDropoutLayer(Name= "spat_drop_"+i,Probability=droupoutFactor)
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal")
layerNormalizationLayer
reluLayer
spatialDropoutLayer(Name="spat_drop2_"+i,Probability=droupoutFactor)
additionLayer(2,Name="add_"+i)];
% Add and connect layers.
net = addLayers(net,layers);
net = connectLayers(net,outputName,"conv1_"+i);
% Skip connection.
if i == 1
% Include convolution in first skip connection.
layer = convolution1dLayer(1,numFilters,Name="convSkip");
net = addLayers(net,layer);
net = connectLayers(net,outputName,"convSkip");
net = connectLayers(net,"convSkip","add_" + i + "/in2");
else
net = connectLayers(net,outputName,"add_" + i + "/in2");
end
% Update layer output name.
outputName = "add_" + i;
end
layers = [
fullyConnectedLayer(1)];
net = addLayers(net,layers);
net = connectLayers(net,outputName,"fc");
options = trainingOptions("adam", ...
MaxEpochs=60, ...
miniBatchSize=1, ...
InputDataFormats="CTB", ...
Plots="training-progress", ...
Metrics="rmse", ...
Verbose=0);
net = trainnet(Xtrain,TTrain,net,"mse",options)
Test Model
Test =minibatchpredict(net,Xtest,InputDataFormats="CTB")
True = Ytest
RMSE = rmse(Test,True)
Is possibly becsue my Imput Data format is incorrect? Or is it an issue with my model and the layers? Or the training options need to be adjusted?

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