How to Install and Uninstall python2-Theano Package on openSUSE Leap
Last updated: November 22,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python2-Theano" package
Please follow the instructions below to install python2-Theano on openSUSE Leap
$
sudo zypper refresh
Copied
$
sudo zypper install
python2-Theano
Copied
2. Uninstall "python2-Theano" package
This is a short guide on how to uninstall python2-Theano on openSUSE Leap:
$
sudo zypper remove
python2-Theano
Copied
3. Information about the python2-Theano package on openSUSE Leap
Information for package python2-Theano:
---------------------------------------
Repository : Main Repository
Name : python2-Theano
Version : 1.0.4-bp153.1.15
Arch : noarch
Vendor : openSUSE
Installed Size : 23,5 MiB
Installed : No
Status : not installed
Source package : python-Theano-1.0.4-bp153.1.15.src
Summary : A scientific python library
Description :
Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional arrays.
Theano features:
* tight integration with numpy - Use numpy.ndarray in Theano-compiled
functions.
* transparent use of a GPU
* symbolic differentiation - Let Theano do your derivatives.
* speed and stability optimizations – Get the right answer for log(1+x)
even when x is really tiny.
* dynamic C code generation - Evaluate expressions faster.
* extensive unit-testing and self-verification – Detect and diagnose
many types of mistake.
---------------------------------------
Repository : Main Repository
Name : python2-Theano
Version : 1.0.4-bp153.1.15
Arch : noarch
Vendor : openSUSE
Installed Size : 23,5 MiB
Installed : No
Status : not installed
Source package : python-Theano-1.0.4-bp153.1.15.src
Summary : A scientific python library
Description :
Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional arrays.
Theano features:
* tight integration with numpy - Use numpy.ndarray in Theano-compiled
functions.
* transparent use of a GPU
* symbolic differentiation - Let Theano do your derivatives.
* speed and stability optimizations – Get the right answer for log(1+x)
even when x is really tiny.
* dynamic C code generation - Evaluate expressions faster.
* extensive unit-testing and self-verification – Detect and diagnose
many types of mistake.