If I have a 1D data set of size 1 by D and want to apply a 1D convolution of kernel size K and the number of filters is F, how does one do it?
import torch
D = 10 # size of the data
x=torch.nn.FloatTensor(1,D)

essentially what I want to do is apply the same function (represented by the weight sharing) to each data point of x(d) (meaning it has kernel size K=1 for this example of course). So at the end of the convolution, I want to add all the values of the filters for a specific point in x to produce f(x(d)). So each convolution will have F filters and those F filters are linearly combined to produce the output of the function applied at each x(d).
Example:

Screen Shot 2018-01-12 at 6.37.41 PM.png814×513 519 KB