Golangと人工知能に注目：テクノロジー統合の可能性を探る

package main

import (
    "fmt"
    "github.com/sudhakar-mns/mygograd/common"
    "github.com/sudhakar-mns/mygograd/nn"
)

func main() {
    // 创建一个神经网络
    n := nn.NewNetwork([]int{2, 2, 1}, "tanh")

    // 创建训练集
    trainingData := []common.TrainingData{
        {Input: []float64{0, 0}, Output: []float64{0}},
        {Input: []float64{0, 1}, Output: []float64{1}},
        {Input: []float64{1, 0}, Output: []float64{1}},
        {Input: []float64{1, 1}, Output: []float64{0}},
    }

    // 训练神经网络
    n.Train(trainingData, 10000, 0.1)

    // 测试神经网络
    fmt.Println("0 XOR 0 =", n.Predict([]float64{0, 0}))
    fmt.Println("0 XOR 1 =", n.Predict([]float64{0, 1}))
    fmt.Println("1 XOR 0 =", n.Predict([]float64{1, 0}))
    fmt.Println("1 XOR 1 =", n.Predict([]float64{1, 1}))
}

package main

import (
    "fmt"
    "gocv.io/x/gocv"
)

func main() {
    // 打开摄像头
    webcam, err := gocv.OpenVideoCapture(0)
    if err != nil {
        fmt.Println("Error opening video capture device: ", err)
        return
    }
    defer webcam.Close()

    window := gocv.NewWindow("Face Detect")
    defer window.Close()

    img := gocv.NewMat()
    defer img.Close()

    classifier := gocv.NewCascadeClassifier()
    defer classifier.Close()

    if !classifier.Load("haarcascade_frontalface_default.xml") {
        fmt.Println("Error reading cascade file: haarcascade_frontalface_default.xml")
        return
    }

    for {
        if webcam.Read(&img) {
            if img.Empty() {
                continue
            }

            rects := classifier.DetectMultiScale(img)
            for _, r := range rects {
                gocv.Rectangle(&img, r, color, 2)
            }

            window.IMShow(img)
            if window.WaitKey(1) >= 0 {
                break
            }
        } else {
            break
        }
    }
}