Hello,
I understand that you are facing an error while preparing your data for a convolutional 1D layer in MATLAB for a regression problem.
The error occurred because the input layer is not compatible with the structure of your data. Convolutional 1D layers in MATLAB expect data with one spatial dimension, one temporal dimension, or one of each. However, your data doesn't have any of these dimensions when it is being processed through the "featureInputLayer".
Use "sequenceInputLayer" when dealing with sequential data, such as time series or text data, and the order of observations or features matters for the task at hand. Use "featureInputLayer" when working with non-sequential data, like images, audio, or tabular data, and the order of features is not significant for the task.
Since your data consists of spectra with 242 data points each, using a "featureInputLayer" is more appropriate. This layer is suitable for non-timeseries data, such as your intensity values at different wavelengths. Ensure that your input data is in the correct format. For a "featureInputLayer", you should have a 3D array with dimensions [numFeatures, numObservations, 1]. In your case, "numFeatures" is 242, and "numObservations" is the number of spectra, 656.
To correctly convert your data for use with a featureInputLayer, you can reshape your data as follows:
xTrain = reshape(xTrain, [numFeatures, numObservations, 1]);
You can refer to the documentation below to understand more about the "sequenceInputLayer" and the "featureInputLayer" layers in MATLAB.
