renamed ADAMW_WiDECAY

pairwise_compare.py

@@ -20,7 +20,7 @@ DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_availab
 DIMENSIONS = 2
 HIDDEN_NEURONS = 4
 ADAMW_LR = 5e-3
-ADAMW_WiDECAY = 5e-4 
+ADAMW_DECAY = 5e-4
 TRAIN_STEPS = 2000
 TRAIN_BATCHSZ = 8192
 TRAIN_PROGRESS = 10
@@ -157,13 +157,13 @@ def training_entry():
     set_seed(0)
 
     model = comp_nn.PairwiseComparator(d=DIMENSIONS, hidden=HIDDEN_NEURONS).to(DEVICE)
-    opt = torch.optim.AdamW(model.parameters(), lr=ADAMW_LR, weight_decay=ADAMW_WiDECAY)
+    opt = torch.optim.AdamW(model.parameters(), lr=ADAMW_LR, weight_decay=ADAMW_DECAY)
 
     log.info(f"Using {TRAINING_LOG_PATH} as the logging destination for training...")
     with lzma.open(TRAINING_LOG_PATH, mode='wt') as tlog:
         # training loop
         training_start_time = datetime.datetime.now()
-        last_ack = datetime.datetime.now()
+        last_ack = datetime.datetime.now(datetime.timezone.utc)
 
         for step in range(TRAIN_STEPS):
             a, b, y = sample_batch(TRAIN_BATCHSZ)
@@ -182,9 +182,9 @@ def training_entry():
             tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")
 
             # also print to normal text log occasionally to show some activity.
-            # every 100 steps check if its been longer than 5 seconds since we've updated the user
-            if step % 100 == 0:
-                if (datetime.datetime.now() - last_ack).total_seconds() > 5:
+            # every 10 steps check if its been longer than 5 seconds since we've updated the user
+            if step % 10 == 0:
+                if (datetime.datetime.now(datetime.timezone.utc) - last_ack).total_seconds() > 5:
                     log.info(f"still training... step={step} of {TRAIN_STEPS}")
                     last_ack = datetime.datetime.now()