Keras: Difference between revisions
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[[Category:Software]][[Category:AI and Machine Learning]] | |||
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"Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano."<ref>https://keras.io/</ref> | |||
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If you are porting a Keras program to one of our clusters, you should follow [[Tutoriel Apprentissage machine/en|our tutorial on the subject]]. | |||
==Installing== | ==Installing== <!--T:2--> | ||
#Install | <!--T:3--> | ||
#Activate the Python virtual environment | #Install [[TensorFlow]], CNTK, or Theano in a Python [[Python#Creating_and_using_a_virtual_environment|virtual environment]]. | ||
#:{{Command2|source $HOME/ | #Activate the Python virtual environment (named <tt>$HOME/tensorflow</tt> in our example). | ||
#Install | #:{{Command2|source $HOME/tensorflow/bin/activate}} | ||
#Install Keras in your virtual environment. | |||
#:{{Command2 | #:{{Command2 | ||
|prompt=( | |prompt=(tensorflow)_[name@server ~]$ | ||
|pip install keras}} | |pip install keras}} | ||
=== R package === | === R package === <!--T:4--> | ||
<!--T:5--> | |||
This section details how to install Keras for R and use TensorFlow as the backend. | This section details how to install Keras for R and use TensorFlow as the backend. | ||
#Install TensorFlow for R by following | <!--T:6--> | ||
#Install TensorFlow for R by following [[Tensorflow#R_package | these instructions]]. | |||
#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. | #:{{Command2|module load gcc/7.3.0 r/3.5.2}} | ||
# Launch R | # Launch R. | ||
#:{{Command2|R}} | #:{{Command2|R}} | ||
#In R, install package | #In R, install the Keras package with <code>devtools</code>. | ||
#:<syntaxhighlight lang='r'> | #:<syntaxhighlight lang='r'> | ||
devtools::install_github('rstudio/keras') | devtools::install_github('rstudio/keras') | ||
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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 | <!--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 <code>pip</code>. To use the Keras package installed in your virtual environment, enter the following commands in R after the environment has been activated. | |||
<syntaxhighlight lang='r'> | <syntaxhighlight lang='r'> | ||
library(keras) | library(keras) | ||
| Line 35: | Line 44: | ||
</syntaxhighlight> | </syntaxhighlight> | ||
== References == | == References == <!--T:8--> | ||
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Latest revision as of 16:11, 27 June 2023
"Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano."[1]
If you are porting a Keras program to one of our clusters, you should follow our tutorial on the subject.
Installing
- Install TensorFlow, CNTK, or Theano in a Python virtual environment.
- Activate the Python virtual environment (named $HOME/tensorflow in our example).
[name@server ~]$ 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 these instructions.
- Follow the instructions from the parent section.
- Load the required modules.
[name@server ~]$ module load gcc/7.3.0 r/3.5.2
- Launch R.
[name@server ~]$ R
- In R, install the Keras package 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 package installed in your virtual environment, enter the following commands in R after the environment has been activated.
library(keras)
use_virtualenv(Sys.getenv('VIRTUAL_ENV'))