Programming language: Go
License: MIT License
Tags: Machine Learning    

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Feed forward/backpropagation neural network implementation. Currently supports:

  • Activation functions: sigmoid, hyperbolic, ReLU
  • Solvers: SGD, SGD with momentum/nesterov, Adam
  • Classification modes: regression, multi-class, multi-label, binary
  • Supports batch training in parallel
  • Bias nodes

Networks are modeled as a set of neurons connected through synapses. No GPU computations - don't use this for any large scale applications.


  • Dropout
  • Batch normalization


go get -u github.com/patrikeh/go-deep


Import the go-deep package

import (
    deep "github.com/patrikeh/go-deep"

Define some data...

var data = training.Examples{
    {[]float64{2.7810836, 2.550537003}, []float64{0}},
    {[]float64{1.465489372, 2.362125076}, []float64{0}},
    {[]float64{3.396561688, 4.400293529}, []float64{0}},
    {[]float64{1.38807019, 1.850220317}, []float64{0}},
    {[]float64{7.627531214, 2.759262235}, []float64{1}},
    {[]float64{5.332441248, 2.088626775}, []float64{1}},
    {[]float64{6.922596716, 1.77106367}, []float64{1}},
    {[]float64{8.675418651, -0.242068655}, []float64{1}},

Create a network with two hidden layers of size 2 and 2 respectively:

n := deep.NewNeural(&deep.Config{
    /* Input dimensionality */
    Inputs: 2,
    /* Two hidden layers consisting of two neurons each, and a single output */
    Layout: []int{2, 2, 1},
    /* Activation functions: Sigmoid, Tanh, ReLU, Linear */
    Activation: deep.ActivationSigmoid,
    /* Determines output layer activation & loss function: 
    ModeRegression: linear outputs with MSE loss
    ModeMultiClass: softmax output with Cross Entropy loss
    ModeMultiLabel: sigmoid output with Cross Entropy loss
    ModeBinary: sigmoid output with binary CE loss */
    Mode: deep.ModeBinary,
    /* Weight initializers: {deep.NewNormal(μ, σ), deep.NewUniform(μ, σ)} */
    Weight: deep.NewNormal(1.0, 0.0),
    /* Apply bias */
    Bias: true,


// params: learning rate, momentum, alpha decay, nesterov
optimizer := training.NewSGD(0.05, 0.1, 1e-6, true)
// params: optimizer, verbosity (print stats at every 50th iteration)
trainer := training.NewTrainer(optimizer, 50)

training, heldout := data.Split(0.5)
trainer.Train(n, training, heldout, 1000) // training, validation, iterations

resulting in:

Epochs        Elapsed       Error         
---           ---           ---           
5             12.938µs      0.36438       
10            125.691µs     0.02261       
15            177.194µs     0.00404       
1000          10.703839ms   0.00000       

Finally, make some predictions:

fmt.Println(data[0].Input, "=>", n.Predict(data[0].Input))
fmt.Println(data[5].Input, "=>", n.Predict(data[5].Input))

Alternatively, batch training can be performed in parallell:

optimizer := NewAdam(0.001, 0.9, 0.999, 1e-8)
// params: optimizer, verbosity (print info at every n:th iteration), batch-size, number of workers
trainer := training.NewBatchTrainer(optimizer, 1, 200, 4)

training, heldout := data.Split(0.75)
trainer.Train(n, training, heldout, 1000) // training, validation, iterations


See training/trainer_test.go for a variety of toy examples of regression, multi-class classification, binary classification, etc.

See examples/ for more realistic examples:

Dataset Topology Epochs Accuracy
wines [5 5] 10000 ~98%
mnist [50] 25 ~97%