I am training a machine learning model to classify Alzheimer's disease into four categories. After running the training epochs, I used code to save the model in a .pth file. However, after downloading the saved model and connecting it with my interface, it did not yield any results. Upon further inspection, it seems that the model is empty.

import torch
import torch.nn as nn
import torch.optim as optim
from tqdm.auto import tqdm
from torchvision import models

# Set device (GPU if available)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Define the ResNet18 model
class ResNet18(nn.Module):
    def __init__(self, num_classes):
        super(ResNet18, self).__init__()
        self.model = models.resnet18(pretrained=False)  # Set to True to use pre-trained weights
        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)  # Modify the final layer

    def forward(self, x):
        return self.model(x)

# Initialize the model with the number of classes in your dataset
num_classes = len(train_data_simple.classes)  # Adjust this based on your augmented dataset
model_resnet = ResNet18(num_classes=num_classes).to(device)

# Loss function and optimizer (SGD and CrossEntropy)
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(model_resnet.parameters(), lr=0.1, momentum=0.9)

# Training loop (with progress bar using tqdm)
def train(model, train_dataloader, test_dataloader, optimizer, loss_fn, epochs):
    for epoch in tqdm(range(epochs)):
        model.train()  # Set the model to training mode
        running_loss = 0.0
        correct_train, total_train = 0, 0

        for batch, (X, y) in enumerate(train_dataloader):
            X, y = X.to(device), y.to(device)

            optimizer.zero_grad()  # Zero the gradients

            outputs = model(X)  # Forward pass
            loss = loss_fn(outputs, y)  # Compute loss

            loss.backward()  # Backpropagation
            optimizer.step()  # Update the weights

            running_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total_train += y.size(0)
            correct_train += (predicted == y).sum().item()

        # Print training loss and accuracy
        print(f"Epoch [{epoch+1}/{epochs}], Loss: {running_loss/len(train_dataloader):.4f}")
        print(f"Training Accuracy: {100 * correct_train / total_train:.2f}%")

        # Validation
        model.eval()  # Switch model to evaluation mode for validation
        correct_val, total_val = 0, 0
        with torch.no_grad():
            for X_val, y_val in test_dataloader:
                X_val, y_val = X_val.to(device), y_val.to(device)
                val_outputs = model(X_val)
                _, predicted = torch.max(val_outputs.data, 1)
                total_val += y_val.size(0)
                correct_val += (predicted == y_val).sum().item()

        # Print validation accuracy
        print(f"Validation Accuracy: {100 * correct_val / total_val:.2f}%")

    return model

# Example training
NUM_EPOCHS = 30  # Adjust epochs as per your requirement
# Use the augmented training data loader
trained_model_resnet = train(model_resnet, train_dataloader_simple, test_dataloader_simple, optimizer, loss_fn, NUM_EPOCHS)

this is the code i ran in different cell to save the model

# Save the trained model
model_path = "trained_resnet18_model.pth"
torch.save(model_resnet.state_dict(), model_path)
print(f"Model saved to {model_path}")

I'm training a machine learning model to classify Alzheimer's disease into four categories using PyTorch. After running the training over several epochs, I want to save the trained model using code like model.save('my_model.h5') or torch.save(model.state_dict(), 'model.pth').

Should I add the code to save the trained model in the same cell where the training epochs are defined and the training loop runs, or is it better to place it in a different cell? Does the placement affect how the model is saved or how I can load it later?