golang函数并发控制在机器学习与人工智能中的应用

func myFunction() {
    // 代码
}

// 创建一个 goroutine 来并发执行 myFunction
go myFunction()

// 启动多个 goroutine 并行训练
for i := 0; i < numGoroutines; i++ {
    go trainBatch(i)
}

// trainBatch 函数处理每个批次的训练
func trainBatch(batchNumber int) {
    ...
}

// 并发处理图像识别
results := make(chan string, numImages)

for i := 0; i < numImages; i++ {
    // 创建一个 goroutine 来处理每个图像
    go func(imageIndex int) {
        label := recognizeImage(imageIndex)
        results <- label
    }(i)
}

// 从频道读取识别的标签
for i := 0; i < numImages; i++ {
    ...
}

package main

import (
    "context"
    "fmt"

    tf "<a style='color:#f60; text-decoration:underline;' href="https://www.codesou.cn/" target="_blank">git</a>hub.com/tensorflow/tensorflow/go"
    "github.com/tensorflow/tensorflow/go/core/resourcemanager"
    "github.com/tensorflow/tensorflow/go/op"
    "github.com/tensorflow/tensorflow/go/types"
)

func main() {
    // 创建一个新的 TensorFlow 会话
    sess, err := tf.NewSession(context.Background(), "local", nil)
    if err != nil {
        fmt.Println(err)
        return
    }
    defer sess.Close()

    // 准备输入图片
    var imageData []byte
    ...

    // 使用并发加载多批图像
    numImages := 10 // 修改为实际图像数量
    batchSize := 4

    var blobs [][]byte
    for i := 0; i < numImages; i += batchSize {
        batch := imageData[i : i+batchSize]
        blobs = append(blobs, batch)
    }

    // 创建 TensorFlow 图表
    graph, err := op.NewGraph()
    if err != nil {
        fmt.Println(err)
        return
    }

    placeholder := graph.Placeholder(types.Bool, op.WithName("input_tensors"))
    inTypes := make([]*types.T, len(blobs))
    for i, _ := range inTypes {
        inTypes[i] = types.Bytes
    }

    enqueueOp := op.QueueEnqueue(placeholder).Inputs(inTypes)
    ready, components, queueClose := op.QueueEnqueueMany(placeholder).Args(placeholder, placeholder).Attrs(map[string]interface{}{
        "component_types": types.BytesList,
    }).Output(0).Output(1).Output(2)

    inTensor := op.BuildQueueDequeue(components, op.BuildQueueLen(components[2]), op.BuildQueueSize(components[2]), op.BuildQueueClosed(components[2]))

    modelPath := "path/to/ImageNet_model" // 修改为实际模型路径
    output, err := resourcemanager.LoadModel(modelPath, inTensor, graph)
    if err != nil {
        fmt.Println(err)
        return
    }

    // 运行模型
    for i, blob := range blobs {
        // 并发执行
        go func(i int, blob []byte) {
            sess.Run(op.NewOperation(sess.Graph()).AddInput(placeholder, blob).MustSetAttr("component_type", types.String("string")).Output(enqueueOp),)
        }(i, blob)
    }

    for {
        readyArr, err := sess.Run(ready)
        if err != nil {
            fmt.Println(err)
            break
        }

        // 处理结果
        if readyArr.(bool) == true {
            _, err = sess.Run(op.NewOperation(graph).AddInput(inTensor, 0).Output(output))
            if err != nil {
                fmt.Println(err)
            }
        } else {
            break
        }
    }

    // 处理剩余的图像
    sess.Run(op.NewOperation(sess.Graph()).AddInput(placeholder, []byte(nil)).MustSetAttr("component_type", types.String("string")).Output(queueClose))
}