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如何将intro ML.Net演示翻译为F#?

ml.net f# .net

red-swan · 技术社区 · 6 年前

我在这里查看cs文件: https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet/get-started/windows 在我尝试将其转换为F#时,它编译得很好,但抛出 System.Reflection.TargetInvocationException 运行时: FormatException: One of the identified items was in an invalid format .我错过了什么?

编辑:以前使用过记录

open Microsoft.ML
open Microsoft.ML.Runtime.Api
open Microsoft.ML.Trainers
open Microsoft.ML.Transforms
open System

type IrisData = 
    [<Column("0")>] val mutable SepalLength : float
    [<Column("1")>] val mutable SepalWidth : float
    [<Column("2")>] val mutable PetalLength : float
    [<Column("3")>] val mutable PetalWidth : float
    [<Column("4");ColumnName("Label")>] val mutable Label : string

    new(sepLen, sepWid, petLen, petWid, label) = 
        { SepalLength = sepLen
          SepalWidth = sepWid
          PetalLength = petLen
          PetalWidth =  petWid
          Label = label }

type IrisPrediction = 
    [<ColumnName("PredictedLabel")>] val mutable PredictedLabels : string
    new() = { PredictedLabels = "Iris-setosa" }


[<EntryPoint>]
let main argv = 
    let pipeline = new LearningPipeline()
    let dataPath = "iris.data.txt"
    pipeline.Add(new TextLoader<IrisData>(dataPath,separator = ","))
    pipeline.Add(new Dictionarizer("Label"))
    pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
    pipeline.Add(new StochasticDualCoordinateAscentClassifier())
    pipeline.Add(new PredictedLabelColumnOriginalValueConverter(PredictedLabelColumn = "PredictedLabel") )    
    let model = pipeline.Train<IrisData, IrisPrediction>()


    let prediction = model.Predict(IrisData(3.3, 1.6, 0.2, 5.1,""))

    Console.WriteLine("Predicted flower type is: {prediction.PredictedLabels}")

    0 // return an integer exit code

1 回复 | 直到 6 年前

Davide Fiocco 6 年前

您可以在下面找到 ML tutorial ,使用Microsoft。ML 0.1.0(可能与较新版本中断)。使示例工作的代码的两个主要区别都在 IrisData 和 IrisPrediction 类型定义:

C#POCO在F中的精确表示,具有无参数构造函数和字段的公共访问权限
C的正确移植# float 至F#,即 float32

这是代码

open Microsoft.ML
open Microsoft.ML.Runtime.Api
open Microsoft.ML.Trainers
open Microsoft.ML.Transforms
open System

type IrisData() =
    [<Column("0")>]
    [<DefaultValue>]
    val mutable public SepalLength: float32
    [<DefaultValue>]
    [<Column("1")>]
    val mutable public SepalWidth: float32
    [<DefaultValue>]
    [<Column("2")>]
    val mutable public PetalLength:float32
    [<DefaultValue>]
    [<Column("3")>]
    val mutable public PetalWidth:float32
    [<DefaultValue>]
    [<Column("4")>]
    [<ColumnName("Label")>]
    val mutable public Label:string

type IrisPrediction() =
    [<ColumnName("PredictedLabel")>]
    [<DefaultValue>]
    val mutable public PredictedLabel : string

[<EntryPoint>]
let main argv =
    let pipeline = new LearningPipeline()
    let dataPath = "iris.data.txt"
    let a = IrisPrediction()
    pipeline.Add(new TextLoader<IrisData>(dataPath,separator = ","))
    pipeline.Add(new Dictionarizer("Label"))
    pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
    pipeline.Add(new StochasticDualCoordinateAscentClassifier())
    pipeline.Add(new PredictedLabelColumnOriginalValueConverter(PredictedLabelColumn = "PredictedLabel") )    
    let model = pipeline.Train<IrisData, IrisPrediction>()

    let x = IrisData()
    x.SepalLength <- 3.3f
    x.SepalWidth <- 1.6f
    x.PetalLength <- 0.2f
    x.PetalWidth <- 5.1f
    let prediction = model.Predict(x)

    printfn "Predicted flower type is: %s"  prediction.PredictedLabel

    0

以及它产生的输出:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Using 4 threads to train.
Automatically choosing a check frequency of 4.
Auto-tuning parameters: maxIterations = 9996.
Auto-tuning parameters: L2 = 2.668802E-05.
Auto-tuning parameters: L1Threshold (L1/L2) = 0.
Using best model from iteration 892.
Not training a calibrator because it is not needed.
Predicted flower type is: Iris-virginica
Press any key to continue . . .