Machine learning newbs: TensorFlow too hard? Kick its ass with Keras
New version 2 integrates better with Google's tough but essential software library
Keras, a popular deep learning library, has been updated with a new API to make it easier for developers to use machine learning in Python.
Artificial intelligence is all the rage right now and techies are keen to explore ways they can use machine learning. But it’s not that easy – especially for coders with little knowledge of neural networks.
Built in 2015 by Google software engineer and AI researcher François Chollet, Keras was designed to be used on top of TensorFlow and Theano – open-source software libraries developed by machine learning researchers at Google and the University of Montreal, Canada.
The update, dubbed Keras 2, has been changed to adapt to TensorFlow API better, allowing developers to mix and match TensorFlow and Keras components together. Since the software runs on TensorFlow and Theano, there is no performance cost to using Keras compared to the other more complex frameworks.
Keras is more specialized for deep learning than TensorFlow or Theano. It’s “higher-level” and “abstracts away a lot of details that most users don’t need to know about,” Chollet explained to The Register.
Instead of dealing with several lines of messy code, developers can directly input deep learning models and customize their own neural nets by clipping together different components or “layers.”
It provides a way for researchers to quickly try out different configurations of their models, reducing the time it takes to set up new experiments.
In the space of two years, the number of people using Keras has grown to a hundred thousand.
“Hundreds of people have contributed to the Keras codebase. Many thousands have contributed to the community. Keras has enabled new startups, made researchers more productive, simplified the workflows of engineers at large companies, and opened up deep learning to thousands of people with no prior machine learning experience,” Chollet wrote in a blog post. ®