在Golang的Tensorflow中使用嵌入层打开Keras模型

package main

import (
    "fmt"
    "github.com/tensorflow/tensorflow/tensorflow/go"
    "github.com/tensorflow/tensorflow/tensorflow/go/op"
)

func main() {
    // 加载Keras模型
    modelPath := "path/to/keras/model"
    model, err := tf.LoadSavedModel(modelPath, []string{"serve"}, nil)
    if err != nil {
        fmt.Println("Failed to load model:", err)
        return
    }

    // 创建输入张量
    inputShape := []int64{1, 10} // 根据模型的输入层定义输入张量的形状
    inputTensor, _ := tf.NewTensor([1][10]int32{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}}) // 根据模型的输入层定义输入张量的数据类型和值

    // 创建嵌入层
    embedDim := 5 // 嵌入维度
    embedMatrix := op.Const(model.Graph(), [10][5]int32{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}, {16, 17, 18, 19, 20}, {21, 22, 23, 24, 25}, {26, 27, 28, 29, 30}, {31, 32, 33, 34, 35}, {36, 37, 38, 39, 40}, {41, 42, 43, 44, 45}, {46, 47, 48, 49, 50}})
    embedLayer := op.ApplyEmbeddingLookup(model.Graph(), inputTensor, embedMatrix, embedDim)

    // 创建会话并运行模型
    session, _ := tf.NewSession(model.Graph(), nil)
    defer session.Close()

    output, err := session.Run(nil, []tf.Output{embedLayer}, nil)
    if err != nil {
        fmt.Println("Failed to run model:", err)
        return
    }

    // 输出结果
    result := output[0].Value().([][]float32)
    fmt.Println("Embedded input:", result)
}