Hi Isabelle Museck,
The error you're encountering is due to a mismatch between the number of channels in your predictions and the number of channels in your targets. Since your target is a single continuous output (a 1x500 double), you need to ensure that your network's final output layer produces a single output channel.
To resolve this, you can add a fully connected layer with a single output neuron after your convolutional layers. This fully connected layer will map the multi-channel output of the convolutional layers to a single continuous output.
Here's a revised version of your code to include this adjustment:
numFilters = 64;
filterSize = 5;
dropoutFactor = 0.005;
numBlocks = 4;
dilationFactor = 1;
net = dlnetwork;
% Define the layers
layers = [
sequenceInputLayer(3, Normalization="rescale-symmetric", Name="inputaccelerometers")
convolution1dLayer(filterSize, numFilters, DilationFactor=dilationFactor, Padding="causal", Name="conv1_")
reluLayer(Name="relu1_")
fullyConnectedLayer(1, Name="fc")
];
net = addLayers(net,layers)
% Define training options
options = trainingOptions("adam", ...
MaxEpochs=60, ...
MiniBatchSize=1, ...
Plots="training-progress", ...
Metrics="rmse", ...
Verbose=0);
% Train the network
net = trainnet(Xtrain', Ytrain', net,"mse",options );
Hope this helps!
