Keras: Difference between revisions
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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. It was developed with a focus on enabling fast experimentation."<ref>https://keras.io/</ref> | "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."<ref>https://keras.io/</ref> | ||
==Installing== | ==Installing== <!--T:2--> | ||
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#Install either [[Tensorflow]], [[CTNK]] or [[Theano]]. | #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 <tt>$HOME/tensorflow</tt>), | #Activate the Python virtual environment in which you installed one of the preceding package (assuming you used a virtual environment named <tt>$HOME/tensorflow</tt>), | ||
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|pip install keras}} | |pip install keras}} | ||
=== R package === | === R package === <!--T:4--> | ||
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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. | ||
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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. | ||
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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 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 pip. To use the Keras installed in your virtual environment, enter the following commands in R after the activation of the environment. | ||
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== References == | == References == <!--T:8--> | ||
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Revision as of 16:08, 21 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'))