How can I assume that the validation loss properly represents the training progress of my model?
The below code, is how I am uploading the saved models to continue training after restart the Kernel, is it correct?
G = Generator().to(device)
checkpoint = torch.load('generator.pth')
try:
    checkpoint.eval()
except AttributeError as error:
    print (error)


G.load_state_dict(checkpoint['G_state_dict'])
optimizer_G.load_state_dict(checkpoint['optimizer_state_dict_G'])
epoch = checkpoint['epoch']
loss = checkpoint['valid_loss_g']
G.eval()