Keras: Difference between revisions
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#Install TensorFlow for R by following the instructions provided [[Tensorflow#R_package | here]]. | #Install TensorFlow for R by following the instructions provided [[Tensorflow#R_package | here]]. | ||
#Follow the instructions from the parent section. | #Follow the instructions from the parent section. | ||
#Load the required modules | #Load the required modules. | ||
#:{{Command2|module load gcc r/3.5.0}} | #:{{Command2|module load gcc r/3.5.0}} | ||
# Launch R | # Launch R. | ||
#:{{Command2|R}} | #:{{Command2|R}} | ||
#In R, install package keras with devtools | #In R, install package keras with <code>devtools</code>. | ||
#:<syntaxhighlight lang='r'> | #:<syntaxhighlight lang='r'> | ||
devtools::install_github('rstudio/keras') | devtools::install_github('rstudio/keras') | ||
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<!--T:7--> | <!--T:7--> | ||
You are then good to go. Do not call <code>install_keras()</code> in R, as Keras and TensorFlow have already been installed in your virtual environment with pip. To use the Keras installed in your virtual environment, enter the following commands in R after the activation of the environment. | You are then good to go. Do not call <code>install_keras()</code> in R, as Keras and TensorFlow have already been installed in your virtual environment with <code>pip</code>. To use the Keras installed in your virtual environment, enter the following commands in R after the activation of the environment. | ||
<syntaxhighlight lang='r'> | <syntaxhighlight lang='r'> | ||
library(keras) | library(keras) | ||
Revision as of 20:38, 28 January 2019
"Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation."[1]
Installing
- Install either Tensorflow, CTNK or Theano.
- Activate the Python virtual environment in which you installed one of the preceding package (assuming you used a virtual environment named $HOME/tensorflow),
[name@server ~]$ source $HOME/tensorflow/bin/activate
- Install keras in your virtual environment.
(tensorflow)_[name@server ~]$ pip install keras
R package
This section details how to install Keras for R and use TensorFlow as the backend.
- Install TensorFlow for R by following the instructions provided here.
- Follow the instructions from the parent section.
- Load the required modules.
[name@server ~]$ module load gcc r/3.5.0
- Launch R.
[name@server ~]$ R
- In R, install package keras with
devtools.devtools::install_github('rstudio/keras')
You are then good to go. Do not call install_keras() in R, as Keras and TensorFlow have already been installed in your virtual environment with pip. To use the Keras installed in your virtual environment, enter the following commands in R after the activation of the environment.
library(keras)
use_virtualenv(Sys.getenv('VIRTUAL_ENV'))