How to use Go language for deep learning development?

In recent years, with the rapid development of the field of artificial intelligence, deep learning has become one of the technologies that has received extremely high attention and application value. However, deep learning development usually requires powerful computing power and complex algorithm implementation, which poses considerable challenges to developers. Fortunately, Go language, as a fast, efficient, compilable and executable programming language, provides some powerful libraries and tools to help developers carry out simpler and more efficient deep learning development. This article will introduce how to use Go language for deep learning development.

Introduction to Deep Learning

Deep learning is a subset of the field of machine learning that focuses on building large neural networks to solve more complex problems. It can not only perform tasks such as classification, regression, and clustering, but also automatically extract features and patterns in data. Deep learning has a wide range of applications, including image processing, natural language processing, voice recognition and data mining.

Deep Learning in Go Language

As a language for modern computer systems, Go language’s system programming ideas and efficient performance provide many advantages for the implementation of deep learning. Go language supports high concurrency, good scalability, conciseness and easy readability, etc., so it has great potential in deep learning development.

Deep learning in Go language is mainly implemented through the use of deep learning libraries. Here are some common deep learning libraries.

1. Gorgonia

Gorgonia is a deep learning framework based on the Go language, which can help us build and train neural networks. At its core, Gorgonia is a symbolic computational graph. This means we can define variables, tensors, and operations in a computational graph and then use automatic differentiation to calculate gradients. Gorgonia also provides many useful features such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.

The following is a simple example program for building, training, and testing a fully connected neural network on the MNIST dataset.

package main

import (
    "fmt"
    "log"

    "github.com/gonum/matrix/mat64"
    "gorgonia.org/gorgonia"
    "gorgonia.org/tensor"
)

func main() {
    // 1. Load data
    data, labels, err := loadData()
    if err != nil {
        log.Fatal(err)
    }

    // 2. Create neural network
    g := gorgonia.NewGraph()
    x := gorgonia.NewMatrix(g, tensor.Float64, gorgonia.WithShape(len(data), len(data[0])), gorgonia.WithName("x"))
    y := gorgonia.NewMatrix(g, tensor.Float64, gorgonia.WithShape(len(labels), 1), gorgonia.WithName("y"))
    w := gorgonia.NewMatrix(g, tensor.Float64, gorgonia.WithShape(len(data[0]), 10), gorgonia.WithName("w"))
    b := gorgonia.NewVector(g, tensor.Float64, gorgonia.WithShape(10), gorgonia.WithName("b"))
    pred := gorgonia.Must(gorgonia.Mul(x, w))
    pred = gorgonia.Must(gorgonia.Add(pred, b))
    loss := gorgonia.Must(gorgonia.Mean(gorgonia.Must(gorgonia.SoftMax(pred)), gorgonia.Must(gorgonia.ArgMax(y, 1))))
    if _, err := gorgonia.Grad(loss, w, b); err != nil {
        log.Fatal(err)
    }

    // 3. Train neural network
    machine := gorgonia.NewTapeMachine(g)
    solver := gorgonia.NewAdamSolver()
    for i := 0; i < 100; i++ {
        if err := machine.RunAll(); err != nil {
            log.Fatal(err)
        }
        if err := solver.Step(gorgonia.Nodes{w, b}, gorgonia.Nodes{loss}); err != nil {
            log.Fatal(err)
        }
        machine.Reset()
    }

    // 4. Test neural network
    test, testLabels, err := loadTest()
    if err != nil {
        log.Fatal(err)
    }
    testPred := gorgonia.Must(gorgonia.Mul(gorgonia.NewMatrix(g, tensor.Float64, gorgonia.WithShape(len(test), len(test[0])), test, gorgonia.WithName("test")), w))
    testPred = gorgonia.Must(gorgonia.Add(testPred, b))
    testLoss, err := gorgonia.SoftMax(gorgonia.Must(gorgonia.Mul(gorgonia.OnesLike(testPred), testPred)), 1)
    if err != nil {
        log.Fatal(err)
    }
    fmt.Println("Accuracy:", accuracy(testPred.Value().Data().([]float64), testLabels))
}

func accuracy(preds mat64.Matrix, labels []float64) float64 {
    correct := 0
    for i := 0; i < preds.Rows(); i++ {
        if preds.At(i, int(labels[i])) == mat64.Max(preds.RowView(i)) {
            correct++
        }
    }
    return float64(correct) / float64(preds.Rows())
}

func loadData() (data *mat64.Dense, labels *mat64.Dense, err error) {
    // ...
}

func loadTest() (test *mat64.Dense, labels []float64, err error) {
    // ...
}

2. Golearn

Golearn is a machine learning library written in Go language. The library contains many classic machine learning algorithms, such as decision trees, support vector machines and K-nearest neighbor algorithm. In addition to classic machine learning algorithms, Golearn also includes some deep learning algorithms, such as neurons, convolutional neural networks, and recurrent neural networks.

The following is an example program for building, training, and testing a multilayer perceptron on the XOR dataset.

package main

import (
    "fmt"

    "github.com/sjwhitworth/golearn/base"
    "github.com/sjwhitworth/golearn/linear_models"
    "github.com/sjwhitworth/golearn/neural"
)

func main() {
    // 1. Load data
    data, err := base.ParseCSVToInstances("xor.csv", false)
    if err != nil {
        panic(err)
    }

    // 2. Create neural network
    net := neural.NewMultiLayerPerceptron([]int{2, 2, 1}, []string{"relu", "sigmoid"})
    net.Initialize()

    // 3. Train neural network
    trainer := neural.NewBackpropTrainer(net, 0.1, 0.5)
    for i := 0; i < 5000; i++ {
        trainer.Train(data)
    }

    // 4. Test neural network
    meta := base.NewLazilyFilteredInstances(data, func(r base.FixedDataGridRow) bool {
        return r.RowString(0) != "0" && r.RowString(1) != "0"
    })
    preds, err := net.Predict(meta)
    if err != nil {
        panic(err)
    }
    fmt.Println(preds)
}

3. Gorgonia/XGBoost

XGBoost is a well-known gradient boosting library that can be used for various machine learning tasks, such as classification, regression, and ranking. In the Go language, we can use Gorgonia/XGBoost as the Go language interface of XGBoost. This library provides some functions that facilitate deep learning development using XGBoost.

The following is an example program for building, training, and testing an XGBoost classifier on the XOR dataset.

package main

import (
    "fmt"

    "gorgonia.org/xgboost"
)

func main() {
    // 1. Load data
    train, err := xgboost.ReadCSVFile("xor.csv")
    if err != nil {
        panic(err)
    }

    // 2. Create XGBoost classifier
    param := xgboost.NewClassificationParams()
    param.MaxDepth = 2
    model, err := xgboost.Train(train, param)
    if err != nil {
        panic(err)
    }

    // 3. Test XGBoost classifier
    test, err := xgboost.ReadCSVFile("xor.csv")
    if err != nil {
        panic(err)
    }
    preds, err := model.Predict(test)
    if err != nil {
        panic(err)
    }
    fmt.Println(preds)
}

Conclusion

This article introduces how to use Go language for deep learning development and introduces several common deep learning libraries. As a fast, efficient, compilable and executable programming language, Go language has shown considerable advantages in deep learning development. If you're looking for an efficient way to develop for deep learning, Go is worth a try.

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