Model Save and Load

When working with neural networks, one of the most important practices is saving and loading models. This not only helps in preserving models after training but also in sharing models and deploying them in different environments. TensorFlow, along with Keras, provides a seamless way to save the architecture, weights, and training configuration of a model, making it easily reusable and shareable.

Why Save and Load Models?

• Long Training Times: Neural networks, especially on large datasets, can take a long time to train. Saving a model enables us to stop and resume training without losing progress.
• Experimentation: Saving multiple versions of a model at different training stages allows for experimentation with different hyperparameters or architectures without retraining from scratch.
• Deployment: For deploying a model in a production environment, it needs to be saved and then reloaded in the deployment environment.

Saving Models in TensorFlow

TensorFlow provides multiple ways to save a model. The most common formats are:

1. SavedModel format
2. HDF5 format

SavedModel Format

The SavedModel format is TensorFlow's recommended format for saving models. It stores:

• The model's architecture
• The model's weight values (learned during training)
• The model's training configuration, if any (what you passed to compile)

HDF5 Format

The HDF5 format is a grid format that is ideal for storing multi-dimensional arrays of numbers. It's useful if you need backward compatibility.

Note

You need to add the .h5 file extension to save it in HDF5 format.

Checkpoint Format

The .ckpt format, short for "checkpoint", is a key file format in TensorFlow for saving the state of a model and its variables at a specific point in time. These files are crucial for continuing training from a stopped point, for model recovery, and for fine-tuning models on new data.

Loading Models

Loading a model is straightforward using the load_model function. This function reconstitutes the model's architecture and weights, ready for use.

Best Practices for Saving and Loading

• Consistency in Environment: Ensure that the environment where you save and load the model is consistent, particularly in terms of TensorFlow and Python version.
• Regular Saving: Save models regularly during training to avoid loss of progress in case of an interruption.
• Document Model Versions: Keep a record of model versions and their corresponding training configurations and performance.