Saving weights only in TensorFlow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does 'saving weights only' mean in TensorFlow?

It means saving just the learned parameters (weights and biases) of a model, without saving the model's architecture or optimizer state.

beginner

How do you save only the weights of a TensorFlow model?

Use the method model.save_weights('path') to save only the weights to the specified path.

beginner

How do you load saved weights back into a TensorFlow model?

Use model.load_weights('path') to load the saved weights into the model with the same architecture.

intermediate

Why might you want to save only weights instead of the full model?

Saving only weights is smaller in size and useful when you want to recreate the model architecture separately or share just the learned parameters.

intermediate

What file formats can TensorFlow use to save weights?

TensorFlow can save weights in TensorFlow checkpoint format (default) or in HDF5 format if the filename ends with .h5.

Which TensorFlow method saves only the weights of a model?

A model.save_weights()

B model.save()

C model.load_weights()

D model.compile()

model.save_weights() saves only the weights. model.save() saves the full model.

What must you do before loading saved weights into a model?

A Save the model again

B Compile the model with a new optimizer

C Call model.fit() first

D Define the model architecture exactly the same

The model architecture must match to load weights correctly.

What file extension saves weights in HDF5 format in TensorFlow?

A .h5

B .ckpt

C .json

D .pb

Using .h5 extension saves weights in HDF5 format.

Why might saving only weights be preferred over saving the full model?

A Weights save the model architecture

B Weights include optimizer state

C Weights files are smaller and easier to share

D Weights save training history

Weights files are smaller because they exclude architecture and optimizer info.

Which of these is NOT saved when using model.save_weights()?

A Weights and biases

B Model architecture

C Learned parameters

D TensorFlow checkpoint files

model.save_weights() does not save the model architecture.

Explain the process and benefits of saving only weights in TensorFlow models.

Think about what is saved and why it might be useful.

Describe how to load saved weights into a TensorFlow model and what conditions must be met.

Consider what is needed before loading weights.