In dfalbel/keras: R Interface to 'Keras'

knitr::opts_chunk$set(echo = TRUE)

There are countless deep learning frameworks available today. Why use Keras rather than any other? Here are some of the areas in which Keras compares favorably to existing alternatives.

Keras prioritizes developer experience

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster -- which in turn helps you win machine learning competitions.
This ease of use does not come at the cost of reduced flexibility: because Keras integrates with lower-level deep learning languages (in particular TensorFlow), it enables you to implement anything you could have built in the base language. The Keras API integrates seamlessly with your TensorFlow workflows.

Keras has broad adoption in the industry and the research community

Deep learning frameworks ranking computed by Jeff Hale, based on 11 data sources across 7 categories

With over 250,000 individual users as of mid-2018, Keras has stronger adoption in both the industry and the research community than any other deep learning framework except TensorFlow itself (and the Keras API is now the official frontend of TensorFlow).

You are already constantly interacting with features built with Keras -- it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products.

Keras is also a favorite among deep learning researchers, coming in #2 in terms of mentions in scientific papers uploaded to the preprint server arXiv.org:

Keras has also been adopted by researchers at large scientific organizations, in particular CERN and NASA.

Keras makes it easy to turn models into products

Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework:

On iOS, via Apple’s CoreML (Keras support officially provided by Apple)
On Android, via the TensorFlow Android runtime. Example: Not Hotdog app
In the browser, via GPU-accelerated JavaScript runtimes such as Keras.js and WebDNN
On Google Cloud, via TensorFlow-Serving
In an R or Python webapp backend (such as a Shiny or Flask app)

Keras supports multiple backend engines and does not lock you into one ecosystem

Your Keras models can be developed with a range of different deep learning backends. Importantly, any Keras model that only leverages built-in layers will be portable across all these backends: you can train a model with one backend, and load it with another (e.g. for deployment). Available backends include:

The TensorFlow backend (from Google)
The CNTK backend (from Microsoft)
The Theano backend

Amazon is also currently working on developing a MXNet backend for Keras.

As such, your Keras model can be trained on a number of different hardware platforms beyond CPUs:

NVIDIA GPUs
Google TPUs, via the TensorFlow backend and Google Cloud
OpenCL-enabled GPUs, such as those from AMD, via the PlaidML Keras backend

Keras has strong multi-GPU support and distributed training support

Keras has built-in support for multi-GPU data parallelism
Horovod, from Uber, has first-class support for Keras models
Keras models can be turned into TensorFlow Estimators and trained on clusters of GPUs on Google Cloud
Keras can be run on Spark via Dist-Keras (from CERN) and Elephas

Keras development is backed by key companies in the deep learning ecosystem

Keras development is backed primarily by Google, and the Keras API comes packaged in TensorFlow as tf.keras. Additionally, Microsoft maintains the CNTK Keras backend. Amazon AWS is developing MXNet support. Other contributing companies include NVIDIA, Uber, and Apple (with CoreML).