Delete baseline.py

baseline.py

@@ -1,41 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn.utils import clip_grad_norm_
-from torch.utils.data import DataLoader
-
-
-def update_baseline(actor, baseline, validation_set, record_scores, batch_size=100, threshold=0.95):
-
-    val_dataloader = DataLoader(dataset=validation_set,
-                                batch_size=batch_size,
-                                collate_fn=validation_set.collate)
-
-    actor.greedy_search()
-    actor.eval()
-
-    actor_scores = []
-    for batch in val_dataloader:
-        with torch.no_grad():
-            actor_output = actor(batch)
-            actor_cost = actor_output['total_time']
-            actor_cost.reshape(-1)
-        actor_scores.append(actor_cost)
-    actor_scores = torch.cat(actor_scores, dim=0)
-
-
-    if record_scores is None:
-        baseline.load_state_dict(actor.state_dict())
-        record_scores = actor_scores
-        return record_scores
-    else:
-
-        if actor_scores.mean().item() < record_scores.mean().item():
-            print('\n', flush=True)
-            print('baseline updated', flush=True)
-            print('\n', flush=True)
-
-            baseline.load_state_dict(actor.state_dict())
-            record_scores = actor_scores
-            return record_scores
-        else:
-            return record_scores