Metadata-Version: 1.1
Name: elektronn
Version: 1.0.9
Summary: A highly configurable toolkit for training 3d/2d CNNs and general Neural Networks
Home-page: http://www.elektronn.org/
Author: Marius Killinger, Gregor Urban
Author-email: Marius.Killinger@mailbox.org
License: GPLv3
Description: .. image:: https://badge.fury.io/py/elektronn.svg
            :target: https://badge.fury.io/py/elektronn
        
        .. image:: http://anaconda.org/elektronn/elektronn/badges/version.svg
            :target: http://anaconda.org/elektronn/elektronn
        
        .. image:: http://anaconda.org/elektronn/elektronn/badges/build.svg
            :target: http://anaconda.org/elektronn/elektronn/builds
        
        ELEKTRONN is a highly configurable toolkit for training 3D/2D CNNs and general Neural Networks.
        
        It is written in Python 2 and based on Theano, which allows CUDA-enabled GPUs to significantly accelerate the pipeline.
        
        The package includes a sophisticated training pipeline designed for classification/localisation tasks on 3D/2D images. Additionally, the toolkit offers training routines for tasks on non-image data.
        
        ELEKTRONN was created by Marius Killinger and Gregor Urban at the Max Planck Institute For Medical Research to solve connectomics tasks.
        
        .. image:: http://elektronn.org/downloads/combined_title.jpg
            :width: 1000px
            :alt: Logo+Example
            :target: http://elektronn.org/
        
        Membrane and mitochondria probability maps. Predicted with a CNN with recursive training. Data: zebra finch area X dataset j0126 by Jörgen Kornfeld.
        
        Learn More:
        -----------
        
        `Website <http://www.elektronn.org>`_
        
        `Installation instructions <http://elektronn.org/documentation/Installation.html>`_
        
        `Documentation <http://www.elektronn.org/documentation/>`_ 
        
        `Source code <https://github.com/ELEKTRONN/ELEKTRONN>`_
        
        
        Toy Example
        -----------
        
        ::
        
            $ elektronn-train MNIST_CNN_warp_config.py
        
        This will download the MNIST data set and run a training defined in an example config file. The plots are saved to ``~/CNN_Training/2D/MNIST_example_warp``.
        
        File structure
        --------------
        
        ::
            
            ELEKTRONN
            ├── doc                     # Documentation source files
            ├── elektronn
            │   ├── examples            # Example scripts and config files
            │   ├── net                 #  Neural network library code
            │   ├── scripts             #  Training script and profiling script
            │   ├── training            #  Training library code
            │   └── ... 
            ├── LICENSE.rst
            ├── README.rst
            └── ... 
            
        
Keywords: cnn theano convolutional neural network machine learning classification
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2 :: Only
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
