Converting a TensorFlow model into a static computation graph stored in a Protocol Buffer (PB) format is a critical step for deployment across various platforms and devices. This process, commonly referred to as "freezing" a graph, combines both the model architecture and weights into a single file. In TensorFlow 2.x, the process differs slightly from TensorFlow 1.x due to the dynamic execution environment of Eager Execution. Here, we'll explore the technical steps and components involved in freezing a TensorFlow 2.x graph into a `.pb` file.

Overview of Freezing a Graph in TensorFlow 2.x

The freezing process in TensorFlow 2.0 involves converting a Keras model or a `SavedModel` into a static computation graph, suitable for inference. This includes:

• Serializing the model's computation graph and parameters.
• Converting variables to constants.
• Saving the complete model to a `.pb` file.

Process Steps

1. Train and Save the Model: Before freezing, ensure you have a saved model. TensorFlow 2.x simplifies model saving using the `tf.keras.Model` API.
2. Convert Variables to Constants: Using TensorFlow's conversion utilities, transform the trained weights into constants and incorporate them into the graph.
3. Serialize the Graph to PB: Export the converted graph to a Protocol Buffer format that can be deployed.

Key Steps with Example Code

Step 1: Train and Save Your Model

First, ensure your Keras model is trained and saved. You can save the entire model using:

• Portability: Easy deployment on various platforms, including mobile devices with TensorFlow Lite.
• Performance: Optimization opportunities, such as graph transformations and constant folding.
• Consistency: Ensures that your training graph and inference graph are identical.
• Graph Optimizations: Explore TensorFlow's Graph Transform Tool for further optimizations post-freeze.
• Custom Layers: Ensure compatibility of custom layers/functions during the freezing process.
• Backward Compatibility: Frozen graphs generated are compatible with versions of TensorFlow that support binary Protocol Buffers.