This article shows how to define, save, and restore models in TensorFlow using tf.Module and the checkpoint and SavedModel utilities
To define a model, a class is created that inherits from tf.Module and includes methods to build layers, variables, and inference operations, leveraging Python's flexibility to organize modular code
Training saving is done with tf.train.Checkpoint, which links the module's variables to paths on disk, allowing the state to be saved at checkpoints and training processes to be resumed
To restore, simply instantiate the same model class, configure the checkpoint, and call the restore method, which loads the trained weights and parameters, ensuring reproducibility
Exporting the model as SavedModel generates a folder with metadata, signatures, and variables, optimal for serving in cloud services like AWS and Azure or integrating into custom applications
At Q2BSTUDIO, we are specialists in custom software development and custom applications, integrating artificial intelligence and cybersecurity for businesses. We also offer cloud services for AWS and Azure, business intelligence services, AI for companies, AI agents, and Power BI solutions
