1D convolution for uni-variate data

I believe that the following image (original link) will be helpful to understand. 1D convolutions work exactly the same way as 2D convolutions, the main difference is how the filter/mask is applied. 1D convolution is intuitively like a sliding window of a certain width.

Many packages like Keras or PyTorch have native 1D convolution function/modules, so I would recommend checking their documentation and perhaps source code for deeper understanding.

Is it possible that you are having problems with specifying the "input_shape" parameter in the first layer properly? Number of channels only take part there so that's probably the issue.

Check this notebook for a 1 dimensional CNN example for univariate data.

Edit: Ok, it is actually a mixed NN with first layer a CNN. But still it should help with the input layer. Let me know if that helps. There must be a CNN somewhere in that folder.