资源算法gonn

gonn

2019-12-06 | |  53 |   0 |   0

GoNN GoDoc

Neural Network in GoLang

Feature

  • BackPropagation Network / RBF Network / Perceptron Network

  • Parallel BackPropagation Network (each neural has its own go-routine)

Benchmark

  • Dataset: MNIST Acurrency Rate : 98.2% (800 hidden nodes)

  • Actually, you can get 96.9% using 100 hidden nodes in just three minutes of training

TODO

  • currently, the parallel version is much slower than the tranditional one, maybe caused by the cost of context switch of threads


上一篇:nengo

下一篇:vlfeat

用户评价
全部评价

热门资源

  • seetafaceJNI

    项目介绍 基于中科院seetaface2进行封装的JAVA...

  • spark-corenlp

    This package wraps Stanford CoreNLP annotators ...

  • Keras-ResNeXt

    Keras ResNeXt Implementation of ResNeXt models...

  • capsnet-with-caps...

    CapsNet with capsule-wise convolution Project ...

  • inferno-boilerplate

    This is a very basic boilerplate example for pe...