Introduction

Keras is a powerful and commonly used high-level neural network library that is often paired with TensorFlow, which provides the underlying operations and computations. However, several issues arise in setting TensorFlow as the session for Keras models. This article dives into these complications, their root causes, and potential solutions.

Background

In earlier versions of Keras and TensorFlow, TensorFlow's Session object was a critical component, controlling the management of resources for computational graphs. With the release of TensorFlow 2.0, the architecture underwent significant changes, with eager execution set as the default mode. Eager execution evaluates operations immediately without building computational graphs, thus abstracting away the need for manual Session management in most cases. However, understanding and controlling a TensorFlow session remains vital when dealing with legacy code or specific advanced applications.

The Problem with TensorFlow Session in Keras

When using Keras with TensorFlow as a backend in graph mode (as was common pre-TensorFlow 2.0), you often had to manage TensorFlow sessions manually, especially for custom operation execution or when intertwining with existing graph-based code. This section details the issues associated with TensorFlow's session management within Keras:

1. Incompatibility with Eager Execution:
   Eager execution, while simplifying operation execution, can create compatibility issues with codebases relying on graph-based execution. Session management becomes obsolete under eager execution, causing problems when mixing legacy and new code.
2. Complexity of Graph Life Cycle:
   Managing the graph life cycle (creation, execution, destruction) manually is error-prone and complex. Memory leaks, resource deadlocks, or unintended persistent states are common pitfalls in TensorFlow session management.
3. Limited Interoperability:
   Using a custom TensorFlow session in Keras often limits interoperability with higher-level TensorFlow APIs and functionalities that assume control over session management. This limits the integration of Keras models with other TensorFlow features.
4. Lack of Flexibility:
   The manual session management requirement hinders dynamic model creation and training, an advantage offered by eager execution. Manual session handling restricts creative programming patterns like dynamic input shapes or real-time data augmentation.

Example Problems

Example 1: Custom Session Setup

Consider a scenario where you have to set up a custom TensorFlow session for a Keras model. This can become cumbersome with added complexity:

• Embrace Eager Execution:
• Legacy Code Conversion:
• Using tf.data API:
• Graph and Session Encapsulation: