How to Install and Uninstall python3-Keras Package on openSUSE Leap
Last updated: December 25,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python3-Keras" package
Please follow the steps below to install python3-Keras on openSUSE Leap
$
sudo zypper refresh
Copied
$
sudo zypper install
python3-Keras
Copied
2. Uninstall "python3-Keras" package
This guide let you learn how to uninstall python3-Keras on openSUSE Leap:
$
sudo zypper remove
python3-Keras
Copied
3. Information about the python3-Keras package on openSUSE Leap
Information for package python3-Keras:
--------------------------------------
Repository : Main Repository
Name : python3-Keras
Version : 2.3.1-bp153.1.13
Arch : x86_64
Vendor : openSUSE
Installed Size : 4,3 MiB
Installed : No
Status : not installed
Source package : python-Keras-2.3.1-bp153.1.13.src
Summary : Deep Learning library
Description :
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. Being able to go from idea to result with the
least possible delay is key to doing good research.
Use Keras if you need a deep learning library that:
Allows for easy and fast prototyping (through user friendliness,
modularity, and extensibility). Supports both convolutional networks and
recurrent networks, as well as combinations of the two. Runs seamlessly on
CPU and GPU.
Read the documentation at Keras.io.
--------------------------------------
Repository : Main Repository
Name : python3-Keras
Version : 2.3.1-bp153.1.13
Arch : x86_64
Vendor : openSUSE
Installed Size : 4,3 MiB
Installed : No
Status : not installed
Source package : python-Keras-2.3.1-bp153.1.13.src
Summary : Deep Learning library
Description :
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. Being able to go from idea to result with the
least possible delay is key to doing good research.
Use Keras if you need a deep learning library that:
Allows for easy and fast prototyping (through user friendliness,
modularity, and extensibility). Supports both convolutional networks and
recurrent networks, as well as combinations of the two. Runs seamlessly on
CPU and GPU.
Read the documentation at Keras.io.