--- a
+++ b/train_and_eval.py
@@ -0,0 +1,71 @@
+import time
+import torch
+from torchmetrics import Accuracy
+
+
+def train_model(model: torch.nn.Module,
+               data_loader: torch.utils.data.DataLoader, 
+               loss_fn: torch.nn.Module, #criterion
+               optimizer: torch.optim.Optimizer,
+               device: torch.device,
+               num_epochs,
+               output_shape):
+    
+    start_time = time.time()
+    
+    accuracy_metric = Accuracy(num_classes= output_shape, task='multiclass').to(device)
+    for epoch in range(num_epochs):
+        print(f'Epoch {epoch + 1}/{num_epochs}')
+
+        model.train() 
+        train_loss = 0
+        for signal, class_label in data_loader: 
+            signal, class_label = signal.to(device), class_label.to(device) #
+            train_pred = model(signal)
+            loss = loss_fn(train_pred, class_label)
+            train_loss += loss.item() 
+
+            accuracy_metric(train_pred, class_label)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        train_acc = accuracy_metric.compute() * 100  
+        print(f"Train loss: {train_loss / len(data_loader):.5f} | Train accuracy: {train_acc:.2f}%")
+        accuracy_metric.reset()
+
+    total_time = (time.time() - start_time)
+    print(f"\nTotal training time: {total_time} seconds")
+    return total_time 
+
+
+def evaluate_model(model: torch.nn.Module,
+                   test_loader: torch.utils.data.DataLoader, 
+                   loss_fn: torch.nn.Module, #criterion
+                   device: torch.device,
+                   output_shape):
+    
+    start_time = time.time()
+
+    test_loss = 0
+    accuracy_metric = Accuracy(num_classes=output_shape, task='multiclass').to(device)
+
+    model.eval()
+    with torch.inference_mode(): 
+        for signal, class_label in test_loader:
+            signal, class_label = signal.to(device), class_label.to(device)
+            test_pred = model(signal)
+            loss = loss_fn(test_pred, class_label)
+            test_loss +=loss.item()
+
+            accuracy_metric(test_pred, class_label)
+
+    test_acc = accuracy_metric.compute() * 100  
+    print(f"\nTest loss: {test_loss/len(test_loader):.5f} | Test accuracy: {test_acc:.2f}%")
+    accuracy_metric.reset() 
+
+    total_time = (time.time() - start_time)
+    print(f"Total evaluation time: {total_time} seconds\n")
+
+    return test_acc.item(), total_time 
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