embeded # of hidden neurons in model save data

added more valves for options.
will update the console every 5 seconds when training now

pairwise_compare.py

@@ -18,7 +18,10 @@ DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_availab
 
 # Valves
 DIMENSIONS = 2
-TRAIN_STEPS = 5000
+HIDDEN_NEURONS = 4
+ADAMW_LR = 5e-3
+ADAMW_WiDECAY = 5e-4 
+TRAIN_STEPS = 2000
 TRAIN_BATCHSZ = 8192
 TRAIN_PROGRESS = 10
 BATCH_LOWER = -100.0
@@ -39,7 +42,6 @@ def parse_training_log(file_path: str) -> pd.DataFrame:
     with lzma.open(file_path, mode='rt') as f:
         text = f.read()
         
-
     pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
     rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
     df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
@@ -107,6 +109,7 @@ def plt_embeddings(model: comp_nn.PairwiseComparator) -> None:
     plt.title("x vs h(x)")
     plt.xlabel("x [input]")
     plt.ylabel("h(x) [embedding]")
+    plt.legend()
     plt.savefig(EMBED_CHART_PATH)
     plt.close()
 
@@ -153,13 +156,15 @@ def training_entry():
     # the seed should initialized normally otherwise
     set_seed(0)
 
-    model = comp_nn.PairwiseComparator(d=DIMENSIONS).to(DEVICE)
+    model = comp_nn.PairwiseComparator(d=DIMENSIONS, hidden=HIDDEN_NEURONS).to(DEVICE)
-    opt = torch.optim.AdamW(model.parameters(), lr=8e-4, weight_decay=1e-3)
+    opt = torch.optim.AdamW(model.parameters(), lr=ADAMW_LR, weight_decay=ADAMW_WiDECAY)
 
     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()
+
         for step in range(TRAIN_STEPS):
             a, b, y = sample_batch(TRAIN_BATCHSZ)
             a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
@@ -178,8 +183,11 @@ 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.
-                if step % 2500 == 0:
+                # 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:
                         log.info(f"still training... step={step} of {TRAIN_STEPS}")
+                        last_ack = datetime.datetime.now()
 
     training_end_time = datetime.datetime.now()
     log.info(f"Training steps complete. Start time: {training_start_time} End time: {training_end_time}")
@@ -195,14 +203,14 @@ def training_entry():
     log.info(f"Final test acc: {acc} errors: {errors}")
 
     # embed model dimensions into the model serialization
-    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS}, MODEL_PATH)
+    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS, "h": HIDDEN_NEURONS}, MODEL_PATH)
     log.info(f"Saved PyTorch Model State to {MODEL_PATH}")
 
 def infer_entry():
     get_torch_info()
 
     model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
-    model = comp_nn.PairwiseComparator(d=model_ckpt["d"]).to(DEVICE)
+    model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
     model.load_state_dict(model_ckpt["state_dict"])
     model.eval()
 
@@ -228,7 +236,7 @@ def graphs_entry():
     get_torch_info()
 
     model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
-    model = comp_nn.PairwiseComparator(d=model_ckpt["d"]).to(DEVICE)
+    model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
     model.load_state_dict(model_ckpt["state_dict"])
     model.eval()