Deep Neural Network package written in Go programming Language
go get github.com/xigh/godnn
This package is very simple:
- Import the package
import "github.com/xigh/godnn"- Create a network instance
func Create(topology []uint) (*Net, error)where parameter to dnn.Create is the topology of your neural network layer (ie the number of neuron per layer). First layer is the input, last layer is the output.
- Train your network:
func (net *Net) Train(input, target []float64, rate float64) (float64, error)where input is the input vector, target is the expected result to converge to, rate is learning rate. It returns the average error.
- Ask you network to predict and answer:
func (net *Net) Predict(input []float64) ([]float64, error)It returns the output ...
I'm not a AI researcher. I mean I've not studied AI at school, but I often use it at http://mediawen.com. This is the reason why I wrote this small IBM Watson SDK in Go.
I watched the Prof Patrick Henry Winston course at MIT Open Courseware along with Yann Lecun videos here and there (especially the course at Collège de France).
With this DNN package, I want to learn more how DNN works. My goal is to use it inside our tools we develop for STVHub, our subtitling platform...
Testing.
Better doc.
Make it more configurable (threshold function, ...).
Make it more scalable. Use OpenCL/CUDA.
Make some benchmarks.
Add more examples (train it with MNIST DATASET)
Try RNN (Recurrent Neural Network) with LSTM (Long short-term memory) architecture.
Learn, learn, study and learn...
As funny example, I trained this DNN to learn Rock-Paper-Scissors-Lezard-Spock. You can find the rules in Big Bang Theory serie Episode 8, Season 2. Here is the result:
Copyright (c) 2016, Philippe Anel All rights reserved.
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