TensorFlow Integration and Theano’s Retirement (2017)

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TensorFlow Becomes the Primary Backend: In 2017, TensorFlow emerged as the most popular deep learning framework, and its relationship with Keras deepened. TensorFlow’s low-level flexibility complemented Keras’ high-level abstractions. In TensorFlow 1.2, Keras was officially integrated into TensorFlow, allowing users to seamlessly switch between high-level Keras APIs and low-level TensorFlow code.
Theano’s Discontinuation: In September 2017, the Montreal Institute for Learning Algorithms (MILA) announced that they would cease development of Theano, marking the end of an era for one of the earliest deep learning frameworks. This shift encouraged more Keras users to adopt TensorFlow as their backend of choice.
Key Contributions by Google: As Keras became a core part of the TensorFlow ecosystem, Google contributed significantly to its development, improving scalability, performance, and deployment tools. TensorFlow’s distributed training, TensorFlow Serving, and TensorFlow Lite enabled Keras to move from research experiments to real-world production systems.

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