Neural Networks written in go

Overview

gobrain

Neural Networks written in go

GoDoc Build Status

Getting Started

The version 1.0.0 includes just basic Neural Network functions such as Feed Forward and Elman Recurrent Neural Network. A simple Feed Forward Neural Network can be constructed and trained as follows:

package main

import (
	"github.com/goml/gobrain"
	"math/rand"
)

func main() {
	// set the random seed to 0
	rand.Seed(0)

	// create the XOR representation patter to train the network
	patterns := [][][]float64{
		{{0, 0}, {0}},
		{{0, 1}, {1}},
		{{1, 0}, {1}},
		{{1, 1}, {0}},
	}

	// instantiate the Feed Forward
	ff := &gobrain.FeedForward{}

	// initialize the Neural Network;
	// the networks structure will contain:
	// 2 inputs, 2 hidden nodes and 1 output.
	ff.Init(2, 2, 1)

	// train the network using the XOR patterns
	// the training will run for 1000 epochs
	// the learning rate is set to 0.6 and the momentum factor to 0.4
	// use true in the last parameter to receive reports about the learning error
	ff.Train(patterns, 1000, 0.6, 0.4, true)
}

After running this code the network will be trained and ready to be used.

The network can be tested running using the Test method, for instance:

ff.Test(patterns)

The test operation will print in the console something like:

[0 0] -> [0.057503945708445]  :  [0]
[0 1] -> [0.930100635071210]  :  [1]
[1 0] -> [0.927809966227284]  :  [1]
[1 1] -> [0.097408795324620]  :  [0]

Where the first values are the inputs, the values after the arrow -> are the output values from the network and the values after : are the expected outputs.

The method Update can be used to predict the output given an input, for example:

inputs := []float64{1, 1}
ff.Update(inputs)

the output will be a vector with values ranging from 0 to 1.

In the example folder there are runnable examples with persistence of the trained network on file.

In example/02 the network is saved on file and in example/03 the network is loaded from file.

To run the example cd in the folder and run

go run main.go

Recurrent Neural Network

This library implements Elman's Simple Recurrent Network.

To take advantage of this, one can use the SetContexts function.

ff.SetContexts(1, nil)

In the example above, a single context will be created initialized with 0.5. It is also possible to create custom initialized contexts, for instance:

contexts := [][]float64{
	{0.5, 0.8, 0.1}
}

Note that custom contexts must have the same size of hidden nodes + 1 (bias node), in the example above the size of hidden nodes is 2, thus the context has 3 values.

Changelog

  • 1.0.0 - Added Feed Forward Neural Network with contexts from Elman RNN
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Comments
  • gobrain

    gobrain

    Hello Jonas,

    Thanks for creating gobrain.

    it would be nice to do the training on the gpu using https://code.google.com/p/cuda-convnet2/ ... there is already a golang wrapper for cuda available: https://github.com/barnex/cuda5

    If you want to do a task like image recognition using neural networks: https://www.kaggle.com/c/cifar-10 ... it would be really slow on the cpu?

    Any thoughts about that?

    Have you also looked into? https://github.com/sjwhitworth/golearn

    Thanks, Gerald

    opened by geraldstanje 6
  • Context weights

    Context weights

    Should not the contexts have a separate set of weights?

    	for i := 0; i < nn.NHiddens-1; i++ {
    		var sum float64
    
    		for j := 0; j < nn.NInputs; j++ {
    			sum += nn.InputActivations[j] * nn.InputWeights[j][i]
    		}
    
    		// compute contexts sum
    		for k := 0; k < len(nn.Contexts); k++ {
    			for j := 0; j < nn.NHiddens-1; j++ {
    				sum += nn.Contexts[k][j]
    			}
    		}
    
    		nn.HiddenActivations[i] = sigmoid(sum)
    	}
    

    Without ContextWeights, the contexts sum is equal for every hidden node and there's no reason to recalculate it for each one. Am I missing something?

    opened by thallingstad 4
  • Add weights for Elman SRN contexts

    Add weights for Elman SRN contexts

    As mentioned by me in issue #10.

    This adds ContextWeights and ContextChanges: 3-dimensional arrays of weights and changes, one matrix for each context layer. The weights are calculated just as if they were extra input layers.

    The contexts with weights are initialized in SetContexts just as before, so this should not break backward compatibility.

    opened by thallingstad 1
  • Fix broken headings in Markdown files

    Fix broken headings in Markdown files

    GitHub changed the way Markdown headings are parsed, so this change fixes it.

    See bryant1410/readmesfix for more information.

    Tackles bryant1410/readmesfix#1

    opened by bryant1410 0
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