The Genesis and Evolution of Keras: From Niche RNN Tool to TensorFlow's Core API

François Chollet recounts the inception of Keras in February 2015, driven by a personal need for better tools for Recurrent Neural Networks (RNNs) and LSTMs, particularly in Python. At a time when Caffe dominated computer vision and most deep learning libraries relied on static configuration files like YAML, Keras made the then-unconventional choice to define models directly in Python code. This decision, coupled with its focus on usability inspired by scikit-learn, allowed Keras to offer a "magical" abstraction for complex training loops, making deep learning more accessible. It was also notable for being one of the first open-source implementations of LSTMs and for enabling the combination of RNNs and Convolutional Neural Networks (CNNs) within a single framework.\n\nKeras quickly gained traction following its release in March 2015, coinciding with a growing interest in RNNs and Natural Language Processing (NLP). Chollet's subsequent move to Google, initially for computer vision research, exposed him to the nascent TensorFlow. Recognizing its potential as an improved successor to Theano, he independently undertook the significant task of refactoring Keras to support multiple backends, including TensorFlow and Theano, by December 2015. This architectural decision to abstract backend functionality proved crucial, allowing Keras to remain relevant and adaptable as TensorFlow rapidly evolved and eventually surpassed Theano in the deep learning ecosystem.\n\nThe project transitioned from a side endeavor to a core component of Google's deep learning strategy in late 2016, when Rajat, the lead of TensorFlow, invited Chollet to integrate Keras more tightly into TensorFlow. This led to Keras's inclusion in `tf.contrib` and eventually its full integration into TensorFlow core, marking a pivotal moment in its development. This shift solidified Keras's role as the high-level API for TensorFlow, significantly broadening its adoption and impact across the machine learning community, and allowing Chollet to dedicate his full attention to its advancement.\n\nChollet highlights TensorFlow 2.0 as a major achievement, addressing the historical trade-off between usability and flexibility in deep learning frameworks. With features like eager execution, TensorFlow 2.0, through Keras, now offers a comprehensive spectrum of workflows. This allows both data scientists and machine learning engineers to leverage Keras's intuitive, scikit-learn-like high-level API, while also providing researchers with the flexibility to subclass models and write custom training loops. This unified approach caters to a diverse range of user profiles, from those seeking simplicity to those requiring deep customization, further democratizing access to advanced deep learning capabilities.

I started working on chaos to the name chaos at the time I actually picked the name like just today I was gonna release it so I started working on it in February 2015

cafe was vastly more popular than ya know in in late 2014 early 2015 cafe was the one library that everyone was using for computer vision

I decided to build my own and that first the pitch for that was it was going to be mostly around LSTA memory on your networks it was going to be in Python an important decision at the time

it was kind of like going against the mainstream at the time because cafe thailand who wants on like all the big libraries were actually going with you approach sharing static configuration files in yamen to define models

I drew a lot of inspiration from cycling when I meant Cara's it's almost like psychically and for neural networks yeah the fit function exactly the v function like reducing a complex training loop to a single function core right and of course you know some people will say this is hiding a lot of details but that's exactly the point all right the magic is the point right so it's magical but in a good way it's magical in the sense that it's delightful

I immediately knew I had to Port Charles to this new tensorflow thing

it was more accurate factoring where I was abstracting away all the backend functionality into one module then the same codebase could run on top of multiple backends right so on top of danceBlue orthia no

now you can have the same framework offering the same set of api's that enable a spectrum of workflows that are more or less Louisville more less high level that are suitable for you know profiles ranging from researchers to data scientists and everything in between