Deep Learning with R + Keras
Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
By Rajiv Shah, Data Scientist, Professor.
For R users, there hasn’t been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to perform image classification. The post ends by providing some code snippets that show Keras is intuitive and powerful.
Last January, Tensorflow for R was released, which provided access to the Tensorflow API from R. This was signficant, as Tensorflow is the most popular library for deep learning. However, for most R users, the Tensorflow for R interface was not very R like. Take a look at this code chunk for training a model:
Unless you are familiar with Tensorflow, it’s not readily apparent what is going on. A quick search on Github finds less than a 100 code results using Tensorflow for R. 😔
All this is going to change with Keras and R! ☺️
For background, Keras is a high-level neural network API that is designed for experimentation and can run on top of Tensorflow. Keras is what data scientists like to use. 🤓 Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
As a simple example, here is the code to train a model in Keras:
Image Classification with Keras
So if you are still with me, let me show you how to build deep learning models using R, Keras, and Tensorflow together. You will find a Github repo at https://github.com/rajshah4/image_keras/ that contains the code and data you will need. Included is an R notebook (and Python notebooks) that walk through buiding an image classifier (telling 🐱 from 🐶), but can easily be generalized to other images. The walk through includes advanced methods that are commonly used for production deep learning work including:
- augmenting data
- using the bottleneck features of a pre-trained network
- fine-tuning the top layers of a pre-trained network
- saving weights of models
Code Snippets of Keras
The R interface to Keras truly makes it easy to build deep learning models in R. Here are some code snippets to illustrate how intuitive and useful Keras for R is:
To load 🖼 from a folder:
To define a simple convolutional neural network:
To augment data:
To load a pretrained network:
To save model weights:
I believe the Keras for R interface will make it much easier for R users and the R community to build and refine deep learning models with R. This means you don’t have to force everyone to use Python to build, refine, and test your models. I really think this will open up deep learning to a wider audience that was a bit apprehensive on using python. So for now, give it a spin!
Grab my repo, fire up RStudio (or your IDE of choice), and go build a simple classifier using Keras.
Bio: Rajiv Shah has a passion for understanding the dynamics between technology and people. He was educated as an electrical engineer, went to law school, but found in communications a home for his research. Rajiv left academia a few years ago and now works as a data scientist. He is always looking for new projects that engage me.
Original. Reposted with permission.
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