人工知能開発に Go 言語を使用するにはどうすればよいですか?

import (
    "math"
    "gonum.org/v1/gonum/floats"
)

func Standardize(data *mat.Dense) {
    _, c := data.Dims()
    means := make([]float64, c)
    stddevs := make([]float64, c)

    for i := 0; i < c; i++ {
        col := mat.Col(nil, i, data)
        means[i] = floats.Sum(col) / float64(len(col))
        stddevs[i] = floats.StdDev(col, means[i])
        floats.AddConst(-means[i], col)
        floats.Scale(1/stddevs[i], col)
        data.SetCol(i, col)
    }
}

import (
    "gorgonia.org/gorgonia"
    "gorgonia.org/tensor"
)

func BuildCNN() {
    g := gorgonia.NewGraph()

    // 定义输入层
    x := gorgonia.NodeFromAny(g, tensor.New(tensor.WithShape(1, 28, 28, 1), tensor.WithBacking(tensor.Random(tensor.Float64, tensor.Shape{1, 28, 28, 1}))))

    // 定义卷积层
    conv := gorgonia.Conv2d(x, tensor.New(tensor.WithShape(32, 3, 3, 1), tensor.WithBacking(tensor.Random(tensor.Float64, tensor.Shape{32, 3, 3, 1}))), tensor.Shape{1, 1}, tensor.Shape{1, 1}, tensor.Shape{0, 0})

    // 定义激活函数和池化层
    relu := gorgonia.Must(gorgonia.Rectify(conv))
    maxpool := gorgonia.Must(gorgonia.MaxPool2D(relu, tensor.Shape{2, 2}, tensor.Shape{0, 0}, tensor.Shape{2, 2}))

    // 定义全连接层
    fc := gorgonia.Must(gorgonia.Mul(maxpool, tensor.New(tensor.WithShape(1152, 10), tensor.WithBacking(tensor.Random(tensor.Float64, tensor.Shape{1152, 10})))))
    output := gorgonia.Must(gorgonia.SoftMax(fc))

    // 创建计算图
    machine := gorgonia.NewTapeMachine(g)

    // 运行计算图
    if err := machine.RunAll(); err != nil {
        panic(err)
    }
}

func TrainAndEvaluateCNN() {
    // 加载数据集
    xTrain, yTrain, xTest, yTest := loadData()

    // 构建 CNN
    g := gorgonia.NewGraph()
    // ...

    // 创建计算图
    machine := gorgonia.NewTapeMachine(g)

    // 训练模型
    for i := 0; i < 1000; i++ {
        // ...
        // 更新参数
        if err := machine.RunAll(); err != nil {
            panic(err)
        }
    }

    // 评估模型
    errRate := 0.0
    for i := range xTest {
        // ...
        // 预测结果
        if err := machine.RunAll(); err != nil {
            panic(err)
        }
        // 计算错误率
        if !floats.EqualApprox(outputValue, yTest[i], 1e-5) {
            errRate++
        }
    }
    errRate /= float64(len(xTest))
    fmt.Printf("Test Error Rate: %v
", errRate)
}