这次要解决的问题是输入一张照片，输出人物的颜值数据。
学习样本来源于华南理工大学发布的SCUT-FBP5500数据集，数据集包括 5500 人，每人按颜值魅力打分，分值在 1 到 5 分之间。其中包括男性、女性、中国人、外国人四个分类。
SCUT-FBP5500_full.csv文件标记了每个图片人物的颜值打分数据。（我把分值一项乘以了20，变成了满分100分，不影响计算结果）
整个程序处理流程和前一篇图片分类的基本一致，唯一的区别，分类用的是多元分类算法，这次采用的是回归算法。
二、源码
下面是全部代码：
namespace TensorFlow_ImageClassification { class Program { //Assets files download from:https://gitee.com/seabluescn/ML_Assets static readonly string AssetsFolder = @"D:\StepByStep\Blogs\ML_Assets"; static readonly string TrainDataFolder = Path.Combine(AssetsFolder, "FaceValueDetection", "SCUT-FBP5500"); static readonly string TrainTagsPath = Path.Combine(AssetsFolder, "FaceValueDetection", "SCUT-FBP5500_asia_full.csv"); static readonly string TestDataFolder = Path.Combine(AssetsFolder, "FaceValueDetection", "testimages"); static readonly string inceptionPb = Path.Combine(AssetsFolder, "TensorFlow", "tensorflow_inception_graph.pb"); static readonly string imageClassifierZip = Path.Combine(Environment.CurrentDirectory, "MLModel", "imageClassifier.zip"); //配置用常量 private struct ImageNetSettings { public const int imageHeight = 224; public const int imageWidth = 224; public const float mean = 117; public const float scale = 1; public const bool channelsLast = true; } static void Main(string[] args) { TrainAndSaveModel(); LoadAndPrediction(); Console.WriteLine("Hit any key to finish the app"); Console.ReadKey(); } public static void TrainAndSaveModel() { MLContext mlContext = new MLContext(seed: 1); // STEP 1: 准备数据 var fulldata = mlContext.Data.LoadFromTextFile<ImageNetData>(path: TrainTagsPath, separatorChar: ',', hasHeader: true); var trainTestData = mlContext.Data.TrainTestSplit(fulldata, testFraction: 0.2); var trainData = trainTestData.TrainSet; var testData = trainTestData.TestSet; // STEP 2：创建学习管道 var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "input", imageFolder: TrainDataFolder, inputColumnName: nameof(ImageNetData.ImagePath)) .Append(mlContext.Transforms.ResizeImages(outputColumnName: "input", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "input")) .Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input", interleavePixelColors: ImageNetSettings.channelsLast, offsetImage: ImageNetSettings.mean)) .Append(mlContext.Model.LoadTensorFlowModel(inceptionPb). ScoreTensorFlowModel(outputColumnNames: new[] { "softmax2_pre_activation" }, inputColumnNames: new[] { "input" }, addBatchDimensionInput: true)) .Append(mlContext.Regression.Trainers.LbfgsPoissonRegression(labelColumnName: "Label", featureColumnName: "softmax2_pre_activation")); // STEP 3：通过训练数据调整模型 ITransformer model = pipeline.Fit(trainData); // STEP 4：评估模型 var predictions = model.Transform(testData); var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score"); PrintRegressionMetrics( metrics); //STEP 5：保存模型 Console.WriteLine("====== Save model to local file ========="); mlContext.Model.Save(model, trainData.Schema, imageClassifierZip); } static void LoadAndPrediction() { MLContext mlContext = new MLContext(seed: 1); // Load the model ITransformer loadedModel = mlContext.Model.Load(imageClassifierZip, out var modelInputSchema); // Make prediction function (input = ImageNetData, output = ImageNetPrediction) var predictor = mlContext.Model.CreatePredictionEngine<ImageNetData, ImageNetPrediction>(loadedModel); DirectoryInfo testdir = new DirectoryInfo(TestDataFolder); foreach (var jpgfile in testdir.GetFiles("*.jpg")) { ImageNetData image = new ImageNetData(); image.ImagePath = jpgfile.FullName; var pred = predictor.Predict(image); Console.WriteLine($"Filename:{jpgfile.Name}:\tPredict Result:{pred.FaceValue}"); } } } public class ImageNetData { [LoadColumn(0)] public string ImagePath; [LoadColumn(1)] public float Label; } public class ImageNetPrediction { [ColumnName("Score")] public float FaceValue; } }