If your encoder shrinks the input down to an element of length 2, you would still have 20 elements of length 2 coming out of the last layer of your encoder, right? So the output of the encoder is a tensor with shape (batch_size, 20,2,1).
If this is correct, then your could plot each of the (20,2,1) elements of your data set by running PCA on the first dimension and specifying a single principal component. This would essentially be picking the most important filter and would give you elements of size (1,2,1) which you could then plot in 2D.