人工智能 (AI) 进阶【C#】版

使用C#和ML.NET进行图像分类任务。这个示例将展示如何加载图像数据、构建和训练模型，以及进行预测。

进阶版：图像分类

我们将使用ML.NET和预训练的TensorFlow模型进行图像分类。首先，确保你已经安装了以下NuGet包：

dotnet add package Microsoft.ML
dotnet add package Microsoft.ML.ImageAnalytics
dotnet add package Microsoft.ML.TensorFlow

代码示例：图像分类

1.创建数据模型：

using Microsoft.ML.Data;public class ImageData
{[LoadColumn(0)]public string ImagePath;[LoadColumn(1)]public string Label;
}public class ImagePrediction : ImageData
{[ColumnName("Score")]public float[] Score;
}

2.创建和训练模型：

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Image;
using System.Drawing;class Program
{static void Main(string[] args){// 创建ML上下文var mlContext = new MLContext();// 加载数据var data = new List<ImageData>{new ImageData { ImagePath = "path/to/image1.jpg", Label = "Label1" },new ImageData { ImagePath = "path/to/image2.jpg", Label = "Label2" }};var dataView = mlContext.Data.LoadFromEnumerable(data);// 定义管道var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "input", imageFolder: "", inputColumnName: nameof(ImageData.ImagePath)).Append(mlContext.Transforms.ResizeImages(outputColumnName: "input", imageWidth: 224, imageHeight: 224)).Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input")).Append(mlContext.Model.LoadTensorFlowModel("path/to/model")).Append(mlContext.Transforms.CopyColumns("Score", "softmax2"));// 训练模型var model = pipeline.Fit(dataView);// 创建预测引擎var predictionEngine = mlContext.Model.CreatePredictionEngine<ImageData, ImagePrediction>(model);// 进行预测var image = new ImageData { ImagePath = "path/to/image3.jpg" };var prediction = predictionEngine.Predict(image);// 打印预测结果for (int i = 0; i < prediction.Score.Length; i++){Console.WriteLine($"Label {i}: {prediction.Score[i]:0.####}");}}
}