While Keras makes it simple for us to switch backends (all we need to do is install our respective backends and edit a simple JSON configuration file), we still need to be mindful of what the trends are telling us: that TensorFlow will continue to be the preferred Keras backend in the (near) future. Keras follows at #2 with Theano all the way at #9.Īs we can see, TensorFlow is topping the charts by a mile (#1) with Theano at #9. If you need further proof all you need to do is take a look at this deep learning GitHub activity analysis from François Chollet (creator and maintainer of Keras): Figure 2: TensorFlow tops the charts as the deep learning library with most GitHub activity. Regardless of the exact reasoning, there is one thing you cannot dispute: TensorFlow is here to stay. This 67% of respondents might be swayed since TensorFlow is now the default backend when installing Keras…or it could be because many of my followers are finding TensorFlow a better, more efficient backend (and using more TensorFlow specific features). How, as a long-time Keras user, could I possibly be in the minority? My eyes started to open when I ran this recent poll on Twitter asking my followers which backend they preferred when using Keras: Figure 1: I polled my Twitter followers ( to determine whether they preferred using Theano or TensorFlow as their Keras backend.Ħ7% of respondents said they were using TensorFlow as their backend. Theano was working well for what I needed it for, so why bother switching? I haven’t given much thought to whether Theano or TensorFlow should be my “go to” backend. To be totally honest with you, I started using Keras well before TensorFlow was released (or even rumored to exist) - this was back when Theano was the only possible choice of backend. This makes TensorFlow an excellent choice for training distributed deep learning networks in an architecture agnostic way, something that Theano does not (currently) provide. The short version is that TensorFlow is extremely flexible, allowing you to deploy network computation to multiple CPUs, GPUs, servers, or even mobile systems without having to change a single line of code. So why might you want to use TensorFlow over a different backend (such as the no-longer-being-developed Theano)? Theano is older than TensorFlow and was originally the only choice when selecting a backend for Keras. When it comes to Keras you have two choices for a backend engine - either TensorFlow or Theano. Keras abstracts away much of the complexity of building a deep neural network, leaving us with a very simple, nice, and easy to use interface to rapidly build, test, and deploy deep learning architectures.
It’s important to start this discussion by saying that Keras is simply a wrapper around more complex numerical computation engines such as TensorFlow and Theano. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using.įrom there I provide detailed instructions that you can use to install Keras with a TensorFlow backend for machine learning on your own system.
I’ll also (optionally) demonstrate how you can integrate OpenCV into this setup for a full-fledged computer vision + deep learning development environment.
In today’s blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team.
INSTALL THEANO FOR MAC HOW TO
Click here to download the source code to this postĪ few months ago I demonstrated how to install the Keras deep learning library with a Theano backend.
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