Introduction

TensorFlow's Protocol Buffer (".pb") format and Keras' HDF5 (".h5") format are two prevalent ways to store and manage deep learning models. The need to convert between these two formats arises from their differing use-cases and advantages. This article provides a comprehensive overview of the conversion process from TensorFlow ".pb" to Keras ".h5", alongside technical explanations and examples.

Understanding the Formats

TensorFlow Protocol Buffer (.pb)

• Purpose: Primarily used in deployment contexts.
• Structure: Stores the model's computation graph, encompassing both operations (ops) and nodes.
• Flexibility: Platform-independent which allows easy export and import across different environments.
• Usage: Suitable for scenarios where low-level control of computation graphs is necessary.

Keras HDF5 (.h5)

• Purpose: Used for model saving and transfer in experimental and development workflows.
• Structure: Contains model architecture (in JSON), weights, and optimizer states.
• Ease of Use: Facilitates loading and alterations due to its popularity within Keras' high-level API.
• Usage: Ideal for research and development where rapid iteration and modification are common.

Conversion Process

Converting from TensorFlow ".pb" files to Keras ".h5" format often requires a multi-step approach, involving loading the model using TensorFlow, acquiring its weights, and re-saving using Keras:

Step 1: Load the .pb Model

• Model Architecture: Accurate reconstruction of the Keras model architecture is critical. Review `.pb` graph nodes to understand op connections and parameters.
• Layer Correspondence: Ensure layers in the Keras model correspond precisely with those in the TensorFlow graph for consistent weight mapping.
• Environment Compatibility: Verify TensorFlow and Keras versions during both loading and saving, as API differences can impede the conversion process.