def load_checkpoint(filepath):
    checkpoint = torch.load(filepath)
    model = fc_model.Network(checkpoint['input_size'],
                             checkpoint['output_size'],
                             checkpoint['hidden_layers'])
    model.load_state_dict(checkpoint['state_dict'])
    
    return model

Thank you for the reply.
So basically , we can re-use the weights but we need to change the architecture based on this function
What’s the difference if we re-train again when we define the network? In what way we’re re-using
checkpoint['state_dict'] and how it differs with transfer learning?
side track,
I’ve been going from coursera deeplearning.ai , fast.ai and now udacity’s pytorch. It seems like I prefer pytorch ( or fast.ai  ) and I love the community here.
My question is how do I get used to the syntax
( i.e turn off gradient and dropout during inferencing ).
Thank you for the reply.
What I’m trying to say is there is a lot of steps that are consistent I think, so how does one remember these steps OR usually we do not need to remember ( just keep it in your notepad) and understand what it is trying to do. Because DL is definitely not as straight forward as sklearn’s .fit and .predict.
Thank you.