--- a
+++ b/src/fit.py
@@ -0,0 +1,82 @@
+import torch
+from src.utils import train_metric
+
+def fit(epochs,model,train_loader,val_loader, icdtype, opt_fn,loss_fn, learning_rate, device):
+  
+  optimizer = opt_fn(model.parameters(), lr=learning_rate)
+  print('-'*10 + icdtype + '-'*10)
+  for epoch in range(1,epochs+1):
+
+    model.train()
+
+    train_epoch_loss=0
+    train_epoch_accuracy=0
+    train_epoch_hammingloss=0
+    train_epoch_f1score=0
+
+    val_epoch_loss=0
+    val_epoch_accuracy=0
+    val_epoch_hammingloss=0
+    val_epoch_f1score=0
+
+    
+    for x, y_dict in train_loader:
+
+      x = x.to(device)
+
+      y = y_dict[icdtype]
+      y = y.to(device)
+
+      
+      preds=model(x)
+
+      optimizer.zero_grad()
+      loss=loss_fn(preds,y)
+      loss.backward()
+      optimizer.step()
+      
+      accuracy, hammingloss, f1score = train_metric(preds,y)
+
+      train_epoch_loss+=loss.item()
+      train_epoch_accuracy+=accuracy.item()
+      train_epoch_hammingloss+=hammingloss
+      train_epoch_f1score+=f1score
+    
+    model.eval()
+    with torch.no_grad():
+      for x,y_dict in val_loader:
+        
+        x=x.to(device)
+
+        y = y_dict[icdtype]
+        y = y.to(device)
+
+        
+        preds=model(x)
+
+        loss=loss_fn(preds,y)
+        accuracy, hammingloss, f1score  = train_metric(preds,y)
+
+        val_epoch_loss+=loss.item()
+        val_epoch_accuracy+=accuracy.item()
+        val_epoch_hammingloss+=hammingloss
+        val_epoch_f1score+=f1score
+
+    
+  
+    print("\n")
+    print('-'*100)
+    print('Epoch = {}/{}:\n train_loss = {:.4f}, train_accuracy = {:.4f}, train_hammingloss = {:.4f}, train_f1score = {:.4f}\n val_loss = {:.4f}, val_accuracy = {:.4f}, val_hammmingloss = {:.4f}, val_f1score = {:.4f}'.format(epoch
+                                                              ,epochs
+                                                              ,train_epoch_loss/len(train_loader)
+                                                              ,train_epoch_accuracy/len(train_loader)
+                                                              ,train_epoch_hammingloss/len(train_loader)
+                                                              ,train_epoch_f1score/len(train_loader)
+                                                              ,val_epoch_loss/len(val_loader)
+                                                              ,val_epoch_accuracy/len(val_loader)
+                                                              ,val_epoch_hammingloss/len(val_loader)
+                                                              ,val_epoch_f1score/len(val_loader)
+                                                              ))
+    print('-'*100)
+    print("\n")
+  
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