| { |
| "claim": 3, |
| "literal_claim": "PGCM training uses an ELBO-style objective (Equation 2) combining a KL regularization term over prototype selection, task loss, concept loss, and image reconstruction loss (Equation 2).", |
| "native_result": { |
| "all_required_terms_nonzero": true, |
| "all_required_terms_present": true, |
| "all_terms_finite": true, |
| "batch_entropy": 2.523866653442383, |
| "concept_loss_real": 0.5122461318969727, |
| "entropy": 0.03418504819273949, |
| "one_hot_selector_control_kl": 3.4011973816621555, |
| "primary_elbo_derivation_present": true, |
| "prototype_emb_loss": 0.0, |
| "returned_matches_logged_total": true, |
| "returned_training_loss": 0.20000694692134857, |
| "segmentation_loss": 1.7451768030696257e-08, |
| "selector_kl_in_closed_interval": true, |
| "selector_kl_to_uniform": 3.367012333469416, |
| "total_loss": 0.20000694692134857, |
| "train_c_loss": 0.04683137685060501, |
| "train_recons_loss": 0.031370654702186584, |
| "train_y_loss": 0.013922294601798058, |
| "uniform_prior_categories": 30, |
| "uniform_selector_control_kl": 0.0 |
| } |
| } |
|
|
