What is the advantage of using the save method on the model itself to save a model in TensorFlow?
Saturday, 05 August 2023
by EITCA Academy
The advantage of using the save method on the model itself to save a model in TensorFlow lies in its simplicity and convenience. By using this method, you can easily save the entire model, including its architecture, weights, and optimizer state, in a single file. This allows you to easily reload the model at a
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Advancing in TensorFlow, Saving and loading models, Examination review
Tagged under:
Artificial Intelligence, Model Loading, Model Persistence, Model Saving, Saving Models, TensorFlow
How can you save a model in TensorFlow using the ModelCheckpoint callback?
Saturday, 05 August 2023
by EITCA Academy
The ModelCheckpoint callback in TensorFlow is a useful tool for saving models during training. It allows you to save the model's weights and other parameters at specified intervals, ensuring that you can resume training from the last saved point if needed. This callback is particularly valuable when training large and complex models that may take