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Project Description

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.

Membrane and mitochondria probability maps. Predicted with a CNN with recursive training. Data: zebra finch area X dataset j0126 by Jörgen Kornfeld.

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
└── ...
Release History

Release History

1.0.12

This version

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1.0.11

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1.0.10

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1.0.9

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1.0.8

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1.0.7

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1.0.6

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1.0.5

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Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
elektronn-1.0.12-cp27-cp27m-manylinux1_x86_64.whl (474.3 kB) Copy SHA256 Checksum SHA256 cp27 Wheel Jul 16, 2016
elektronn-1.0.12.tar.gz (103.9 kB) Copy SHA256 Checksum SHA256 Source May 15, 2016

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