lstm with scheduler tested

config.yml

@@ -43,7 +43,7 @@ Optimizer: Adam # in {Adam}
 
 #Training parameters
 Training:
-  Epochs: 20
+  Epochs: 60
   
 #Model selection
 Model:

main.py

@@ -114,6 +114,14 @@ if __name__ == "__main__":
 
     optimizer = optimizer(cfg, network)
 
+    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+        optimizer, 
+        'min', 
+        patience = 5, 
+        threshold = 0.2,
+        factor = 0.5
+    )
+
     logdir, raw_run_name = utils.create_unique_logpath(rootDir, cfg["Model"]["Name"])
     network_checkpoint = model.ModelCheckpoint(logdir + "/best_model.pt", network)
 
@@ -121,14 +129,16 @@ if __name__ == "__main__":
         wandb.run.name = raw_run_name
         wandb.watch(network, log_freq = log_freq)
 
-    torch.autograd.set_detect_anomaly(True)
-    
+    #torch.autograd.set_detect_anomaly(True)
+
     for t in range(cfg["Training"]["Epochs"]):
         print("Epoch {}".format(t))
         train(args, network, train_loader, f_loss, optimizer, device, log_interval)
 
         val_loss = test.test(network, valid_loader, f_loss, device)
 
+        scheduler.step(val_loss)
+
         network_checkpoint.update(val_loss)
 
         print(" Validation : Loss : {:.4f}".format(val_loss))

train.py

@@ -39,7 +39,6 @@ def train(args, model, loader, f_loss, optimizer, device, log_interval = 100):
         optimizer.zero_grad()
         loss.backward()
 
-
         #torch.nn.utils.clip_grad_norm(model.parameters(), 50)
         
         Y = list(model.parameters())[0].grad.cpu().tolist()