Can have seqlet embeddings based on filter activations

Corresponds to PR #61. Instead of deriving an embedding for coarse-grained similarity embedding using gapped k-mers, can derive the embedding from a neural network model (e.g. a by averaging the conv filter activations). Example notebook in https://github.com/kundajelab/tfmodisco/blob/36972870853e6631b2d32f1e489676a8241b385c/examples/simulated_TAL_GATA_deeplearning/TF_MoDISco_TAL_GATA_With_Filter_Embeddings.ipynb.