I’m thinking cases where we might have more or less dictionary keys, and if the key names do not exactly match.
So far though the model seems to run as expected.
Thank you so much for your help and happy new year!!
EDIT:
@ptrblck I guess the issue I am seeing is losing the trained weights after training the model.
Say for example this is the fully trained model:
model_ft, train_losses, test_losses = train_model(model, criterion, optimizer, exp_lr_scheduler,
                       num_epochs=15)

Saving the model state_dict:
model_dict={'arch': 'vgg19',
            'state_dict': model_ft.state_dict(), 
            'class_to_idx': model_ft.class_to_idx,
            'weights': model_ft.features}

Now say I reload the model:
model_vgg19 = load_model('vgg19.pth')

A quick comparison between model_vgg19 and model_ft shows that the predictions are way off, as if model_vgg19 did not learn anything form the previous one.