Delete train.py

train.py

@@ -1,54 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn.utils import clip_grad_norm_
-from torch.utils.data import DataLoader
-from just_time_windows.Actor.actor import Actor
-
-
-
-
-def train_batch(actor, baseline, batch, optimizer, gradient_clipping=True, comparison_model=None, compute_cost_ratio=True):
-
-    device = actor.device
-
-    actor.train_mode()
-    actor.train()
-    actor_output = actor(batch)
-    actor_cost, log_probs = actor_output['total_time'], actor_output['log_probs']
-
-
-    with torch.no_grad():
-        baseline.greedy_search()
-        baseline_output = baseline(batch)
-        baseline_cost = baseline_output['total_time']
-
-    loss = ((actor_cost - baseline_cost).detach() * log_probs).mean()
-
-    optimizer.zero_grad()
-    loss.backward()
-
-    if gradient_clipping:
-        for group in optimizer.param_groups:
-            clip_grad_norm_(
-                group['params'],
-                1,
-                norm_type=2
-            )
-
-    optimizer.step()
-
-    if compute_cost_ratio and (comparison_model is None):
-        normalize = actor.apply_normalization
-        comparison_model = Actor(model=None, num_neighbors_action=1, normalize=normalize, device=device)
-
-    if compute_cost_ratio:
-        with torch.no_grad():
-            comp_output = comparison_model(batch)
-            comp_cost = comp_output['total_time']
-
-        a = comp_cost.sum().item()
-        b = actor_cost.sum().item()
-        return b / a
-    else:
-        return None
-