Introduction

TensorFlow is a popular open-source machine learning library developed by the Google Brain team. One of its key features is its ability to save and restore models. In TensorFlow 1.x, models can be saved and loaded in two main formats: as frozen graphs and as checkpoint models. While a frozen graph is a serialized model format that contains both the model's computational graph and its parameters, a checkpoint contains only the parameters and must be used in conjunction with the original model definition to fully recreate a model. Sometimes, it's necessary to convert a frozen graph back into a checkpoint model for further training or fine-tuning. This article explores whether and how this can be done.

Understanding Frozen Graphs

A frozen graph is a serializable format that combines the network architecture and the model weights into a single file. It is highly suitable for deployment because it aggregates all necessary components and is optimized for inference, stripping away unneeded operations.

Creating a Frozen Graph

To create a frozen graph, you'll typically export a model's computational graph and freeze the parameters using the `freeze_graph` tool provided by TensorFlow. The output is a `.pb` (Protocol Buffer) file.

Limitations of Frozen Graphs

• They cannot be used directly for further training.
• Optimizations applied during the freezing process may hinder retraining.
• They lack the optimizer states and other non-variable states crucial for resuming training.

Understanding Checkpoints

A checkpoint consists of a set of binary files that store:

• Weights
• Biases
• State information for optimizers

Unlike a frozen graph, a checkpoint does not include the computational graph. You need the original architecture to recreate the full model.

Creating a Checkpoint

Checkpoint files often come in pairs: an index file and data files. TensorFlow's `tf.train.Checkpoint` object or `tf.compat.v1.train.Saver` class are typically used for saving and restoring checkpoints.

Converting a Frozen Graph to a Checkpoint Model

Converting a frozen graph back into its checkpoint format is generally not straightforward. Frozen graphs are primarily meant for inference; they strip away all non-essential parts, including optimizer states. However, under certain conditions and with specialized scripts, you can extract weights from a frozen graph and save them back into a checkpoint format.

Process Overview

1. Load and Examine the Frozen Graph:
   • Use TensorFlow functions like `tf.GraphDef()` to load the frozen graph.
2. Reconstruct the Model:
   • You will need to know the original architecture of the model. This involves replicating the model setup in code.
3. Extract and Map Variables:
   • Iterate through the layers and extract weights from the graph.
   • Map these extracted weights back to the corresponding positions in your reconstructed model.
4. Save as a Checkpoint:
   • Use TensorFlow's checkpoint saving utilities to save the model after assigning the extracted weights.

Example Code Snippet

Below is a simplified example demonstrating how to load a frozen graph and extract weights: