Installing kerasR

Python packages

Create a virtualenv:

$ virtualenv pydata --python=/usr/bin/python3
Running virtualenv with interpreter /usr/bin/python3
Using base prefix '/usr'
New python executable in /home/brian/pydata/bin/python3
Also creating executable in /home/brian/pydata/bin/python
Installing setuptools, pip, wheel...done.
$ source pydata/bin/activate
(pydata) $

Install keras. This will also install the other prerequisites for doing any sort of datasciency stuff in Python (numpy, pandas) as well as Theano. Tensorflow will be installed in the next step.

(pydata) $ pip install keras
Collecting keras
Collecting six (from keras)
Using cached six-1.10.0-py2.py3-none-any.whl
Collecting theano (from keras)
Collecting pyyaml (from keras)
Collecting scipy>=0.14 (from theano->keras)
  Downloading scipy-0.19.0-cp35-cp35m-manylinux1_x86_64.whl (47.9MB)
    100% |████████████████████████████████| 47.9MB 27kB/s
Collecting numpy>=1.9.1 (from theano->keras)
  Downloading numpy-1.13.0-cp35-cp35m-manylinux1_x86_64.whl (16.9MB)
    100% |████████████████████████████████| 16.9MB 66kB/s
Installing collected packages: six, numpy, scipy, theano, pyyaml, keras
Successfully installed keras-2.0.4 numpy-1.13.0 pyyaml-3.12 scipy-0.19.0 six-1.10.0 theano-0.9.0

Install Tensorflow:

(pydata) $ pip install tensorflow

kerasR

In R, install the kerasR package:

> install.packages("kerasR")
Installing package into ‘/home/brian/R/x86_64-pc-linux-gnu-library/3.4’
...
** testing if installed package can be loaded
successfully loaded keras
* DONE (kerasR)

This may also install the reticulate package, which is an interface to Python objects and methods.

A guide to using kerasR is provided as a vignette.

Troubleshooting

If you get an error message when executing library(kerasR) saying:

> library(kerasR)

keras not available
See reticulate::use_python() to set python path,
then use kerasR::keras_init() to retry

this means kerasR (or more specifically, reticulate) can't find the keras python package, you need to start R after loading your virtualenv:

$ source pydata/bin/activate
(pydata) $ R
> library(kerasR)
Using TensorFlow backend.
successfully loaded keras
>