Saving model state_dict in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a state_dict in PyTorch?

A state_dict is a Python dictionary object that maps each layer to its parameter tensor. It stores the model's learned weights and biases.

beginner

How do you save a model's state_dict in PyTorch?

Use torch.save(model.state_dict(), 'filename.pth') to save the model's parameters to a file.

intermediate

Why is it better to save state_dict instead of the whole model?

Saving state_dict is more flexible and portable. It avoids issues with code dependencies and allows loading weights into models with the same architecture.

beginner

What PyTorch function do you use to load a saved state_dict into a model?

Use model.load_state_dict(torch.load('filename.pth')) to load the saved parameters back into the model.

intermediate

What should you do before saving the state_dict to ensure consistent results?

Put the model in evaluation mode with model.eval() if you want to save it for inference, or training mode with model.train() if saving during training.

Which PyTorch command saves only the model's parameters?

A torch.save(model.state_dict(), 'model.pth')

B torch.save(model, 'model.pth')

C torch.load('model.pth')

D model.load_state_dict(torch.load('model.pth'))

Saving model.state_dict() saves only the parameters, which is recommended.

What type of object is a state_dict?

A List

B String

C Dictionary

D Tensor

A state_dict is a Python dictionary mapping layer names to tensors.

How do you load saved parameters into a model?

A model.load_state_dict(torch.load('file.pth'))

B torch.save(model.state_dict(), 'file.pth')

C model.eval()

D torch.load_state_dict('file.pth')

Use model.load_state_dict() with torch.load() to load parameters.

Why might you prefer saving state_dict over the entire model?

A It saves the whole code

B It is more portable and flexible

C It saves training history

D It saves the optimizer state

Saving state_dict is more portable and avoids code dependency issues.

Which mode should the model be in before saving for inference?

A model.train()

B model.save()

C model.load()

D model.eval()

Use model.eval() to set the model to evaluation mode before saving for inference.

Explain the steps to save and load a PyTorch model's parameters using state_dict.

Think about saving weights separately from the model code.

Why is saving the state_dict preferred over saving the entire model in PyTorch?

Consider what happens if code changes or you want to share weights only.