Eager Execution: An imperative, define-by-run interface to TensorFlow

Eager execution, introduced by the Google Brain Team, is a new imperative, define-by-run interface for TensorFlow that allows operations to be executed immediately as they are called from Python, enhancing ease of use and intuitiveness for research and development. This approach supports fast debugging, dynamic models with Python control flow, and custom gradients, while maintaining almost all existing TensorFlow operations. Eager execution eliminates the need for Session.run(), enabling direct inspection of intermediate results and facilitating the development of dynamic models. It also allows for the definition of custom gradients, improving efficiency and numerical stability in operations like the computation of cross-entropy. The integration with object-oriented layers and the ability to switch between eager and graph modes provides flexibility in model development and deployment, making it suitable for both interactive development and high-performance production environments. As an experimental feature, it seeks feedback from the community to guide its future development, with support available through documentation and examples on GitHub.

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