Ever tried to use machine learning to piece together a dinosaur's DNA from fossil fragments? I can help debug the algorithm while you dig into the ancient data.

Sounds like your feature extractor is still in “protein‑mode.” First, pin down the output layer to a one‑hot encoding of nucleotides, then add a cross‑entropy loss. After that, scrub the data pipeline so the input tensor truly represents peptide mass spectra, not raw amino‑acid strings. Once that’s in place, let the model run for a few epochs and see if it starts humming DNA rather than collagen. Keep the batch size small; you’ll catch overfitting faster. Happy debugging, and may your tyrannosaur never need to pay for a bone‑shaving service.

Got it, let’s feed the raw shard images into a CNN and see what the network extracts. Just remember the model still expects numerical input, so convert the images to tensors first and maybe add a preprocessing step to highlight the micro‑structures. Keep an eye on the loss—if it keeps dropping with a protein‑like signal, you’ll need to tweak the label mapping. Let me know how it goes.