How to predict with 1Dconvolution layers: get 3 channels to produce one output?
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Isabelle Museck
il 7 Giu 2024
Risposto: Milan Bansal
il 7 Giu 2024
Hello there I am tying to build a model to test what a 1D convolution layer will do to my data. I have 3 input accelerometer mesurements in the x,y,z directions and am trying to predict a continuous output. The Xtrain is a 3x500 double with the 3 input accelerometer mesurements over 500 time steps and the Ytrain is a 1x500 double of the target output variable. The code I have developed is here:
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
dilationFactor = 1;
net = dlnetwork;
layers = sequenceInputLayer(NumFeatures,Normalization="rescale-symmetric",Name="inputaccelerometers")
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_")
net = addLayers(net,layers)
options = trainingOptions("adam", ...
MaxEpochs=60, ...
miniBatchSize=1, ...
InputDataFormats="CTB", ...
Plots="training-progress", ...
Metrics="rmse", ...
Verbose=0);
net = trainnet(Xtrain',Ytrain',net,"mse",options)
But when I try and train the model I get and error that says: Number of channels in predictions (3) must match the number of channels in the targets (1). Do I need to add another layer to convert the inputs into one predicted output and which layer should I use to do this? Any suggestions on how to remove this error and get the network to run accurately? Thanks so much!
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Milan Bansal
il 7 Giu 2024
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!
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