Saving Model Checkpoint vs Saving Entire Model in Keras

When training deep learning models using Keras, it's crucial to save your model's state to avoid losing your work due to interruptions, to resume training, or deploy a model after training. Keras offers two primary options: saving model checkpoints during training and saving the entire model. In this article, we'll explore the distinctions between these methods, their advantages and disadvantages, and when it's suitable to use each.

Saving Model Checkpoints

Saving model checkpoints refers to the process of saving the model's weights at certain points during training. This is done to provide an opportunity to resume training from the last saved state if training is interrupted. It's especially useful for long training processes that are susceptible to interruptions or when working with limited computational resources.

How to Save Model Checkpoints

Keras provides the `ModelCheckpoint` callback to achieve this functionality. The callback saves the weights to a file at a specified frequency, which can be determined by epochs or by monitoring a specific performance metric.

• `save_weights_only`: If set to `True`, only weights are saved. If `False`, the entire model (architecture + weights) is saved.
• `monitor`: Determines which metric to monitor (e.g., `val_loss`, `accuracy`).
• `mode`: Can be set to `min`, `max`, or `auto`. Determines whether the model should save weights when the monitored metric is minimized or maximized.
• `save_best_only`: If `True`, only the model with the best metric is saved.
• Efficient: Saves only weights, thus consuming less disk space.
• Flexibility in Training: Enables resuming training anytime from the last checkpoint.
• Focus on Best Performance: By setting `save_best_only`, can ensure that only the best-performing weights are saved.
• Requires Model Architecture: To restore a model from checkpoints, you need to have access to the model's architecture code.
• Comprehensive: Includes both model architecture and weights.
• Versatile: Ideal for deployment as the whole model is standalone.
• Self-contained: No need for model architecture definition code when loading the model.
• Ease of Use: Directly loadable for both continued training or inference.
• Storage: Takes more storage space compared to saving only weights.
• Slower Save/Load: Potentially slower I/O operations due to larger file sizes.
• Long Training Sessions: Use model checkpoints to safeguard against interruptions.
• Deployment: Save the entire model when deploying to production environments.
• Collaboration/Sharing: The entire model is preferable for sharing with others.
• Storage Constraints: Checkpoints are more efficient in terms of storage.