Skip to content
implement Artificial Neural Network on different languages
Branch: master
Clone or download
Latest commit 0af85b2 Mar 4, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
c++ add c++ Feb 27, 2018
go add go rnn Mar 4, 2018
javascript add javascript Feb 26, 2018
julia add julia Feb 28, 2018
php
python add go rnn Mar 4, 2018
ruby add ruby Feb 26, 2018
LICENSE Initial commit Feb 26, 2018
README.md add go rnn Mar 4, 2018
iris.csv add golang Feb 27, 2018

README.md

Neural-Network-Multilanguages

implement Gradient Descent Feed-forward and Recurrent Neural Network on different languages, only use vector / linear algebra library.

Artificial Neural Network is relatively easy if you really understand it!

Support

Ruby

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

Python

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

Javascript

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

Go

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

C++

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

Julia

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

PHP

  • feed-forward iris
  • recurrent generator
  • recurrent forecasting

Instructions

  1. Go to any language folder.
  2. run install.sh
  3. run the program.

Neural Network Architectures

  1. Feed-forward Neural Network to predict Iris dataset.
  • 3 layers included input and output layer
  • first 2 layers squashed into sigmoid function
  • last layer squashed into softmax function
  • loss function is cross-entropy
  1. Vanilla Recurrent Neural Network to generate text.
  • 1 hidden layer
  • tanh as activation function
  • softmax and cross entropy combination for derivative
  • sequence length = 15
  1. Vanilla Recurrent Neural Network to predict TESLA market.
  • 1 hidden layer
  • tanh as activation function
  • mean square error for derivative
  • sequence length = 5

All implemention like max(), mean(), softmax(), cross_entropy(), sigmoid() are hand-coded, no other libraries.

Status

Will update overtime.

Warning

You would not see high accuracy for other languages that natively are not using float64. During backpropagation, the changes are very small, float32 ignored it.

Authors

You can’t perform that action at this time.