ML.NET(二) 使用机器学习预测表情分析

 这个例子使用模型进行表情分析：

准备数据： happy,sad 等；

using Common;
using ConsoleApp2;
using Microsoft.ML;
using Microsoft.ML.Data;
using System.Diagnostics;
using static Microsoft.ML.Transforms.ValueToKeyMappingEstimator;/** 训练一个Happy 和Sad 等表情的模型并使用预测  图像分类器（Image Classification） *  ***********************预测不是很准，数据集找对应人种数据可以尝试亚洲，欧美分开*/var projectDirectory = Path.GetFullPath(Path.Combine(AppContext.BaseDirectory, "./"));
var workspaceRelativePath = Path.Combine(projectDirectory, "workspace");
var assetsRelativePath = Path.Combine(projectDirectory, "assets");string outputMlNetModelFilePath = "model.zip";//Path.Combine("", "outputs", "imageClassifier.zip");
string imagesFolderPathForPredictions = Path.Combine("", "inputs", "test-images");
// 设置ML.NET环境
var mlContext = new MLContext();// 加载数据IEnumerable<ImageData> images = LoadImagesFromDirectory(folder: assetsRelativePath, useFolderNameAsLabel: true);
IDataView fullImagesDataset = mlContext.Data.LoadFromEnumerable(images);
IDataView shuffledFullImageFilePathsDataset = mlContext.Data.ShuffleRows(fullImagesDataset);// 3. Load Images with in-memory type within the IDataView and Transform Labels to Keys (Categorical)
IDataView shuffledFullImagesDataset = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: "LabelAsKey", inputColumnName: "Label", keyOrdinality: KeyOrdinality.ByValue).Append(mlContext.Transforms.LoadRawImageBytes(outputColumnName: "Image",imageFolder: assetsRelativePath,inputColumnName: "ImagePath")).Fit(shuffledFullImageFilePathsDataset).Transform(shuffledFullImageFilePathsDataset);// 4. Split the data 80:20 into train and test sets, train and evaluate.
var trainTestData = mlContext.Data.TrainTestSplit(shuffledFullImagesDataset, testFraction: 0.2);
IDataView trainDataView = trainTestData.TrainSet;
IDataView testDataView = trainTestData.TestSet;// 5. Define the model's training pipeline using DNN default values
//
var pipeline = mlContext.MulticlassClassification.Trainers.ImageClassification(featureColumnName: "Image",labelColumnName: "LabelAsKey",validationSet: testDataView).Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel",inputColumnName: "PredictedLabel"));
// Measuring training time
var watch = Stopwatch.StartNew();
Console.WriteLine($"--------------------开始训练-------------------------------");//6. Train
ITransformer trainedModel = pipeline.Fit(trainDataView);watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;Console.WriteLine($"--------------------训练用时: {elapsedMs / 1000} seconds --------------------");// 7. Get the quality metrics (accuracy, etc.)
EvaluateModel(mlContext, testDataView, trainedModel);8. Save the model to assets/outputs (You get ML.NET .zip model file and TensorFlow .pb model file)
mlContext.Model.Save(trainedModel, trainDataView.Schema, outputMlNetModelFilePath); //outputMlNetModelFilePath
Console.WriteLine($"Model saved to: {outputMlNetModelFilePath}");9. Try a single prediction simulating an end-user app
TrySinglePrediction(imagesFolderPathForPredictions, mlContext, trainedModel);static IEnumerable<ImageData> LoadImagesFromDirectory(string folder,bool useFolderNameAsLabel = true)=> FileUtils.LoadImagesFromDirectory(folder, useFolderNameAsLabel).Select(x => new ImageData(x.imagePath, x.label));static void EvaluateModel(MLContext mlContext, IDataView testDataset, ITransformer trainedModel)
{Console.WriteLine("Making predictions in bulk for evaluating model's quality...");// Measuring timevar watch = Stopwatch.StartNew();var predictionsDataView = trainedModel.Transform(testDataset);var metrics = mlContext.MulticlassClassification.Evaluate(predictionsDataView, labelColumnName: "LabelAsKey", predictedLabelColumnName: "PredictedLabel");ConsoleHelper.PrintMultiClassClassificationMetrics("TensorFlow DNN Transfer Learning", metrics);watch.Stop();var elapsed2Ms = watch.ElapsedMilliseconds;Console.WriteLine($"Predicting and Evaluation took: {elapsed2Ms / 1000} seconds");
}
static void TrySinglePrediction(string imagesFolderPathForPredictions, MLContext mlContext, ITransformer trainedModel)
{// Create prediction function to try one predictionvar predictionEngine = mlContext.Model.CreatePredictionEngine<InMemoryImageData, ImagePrediction>(trainedModel);var testImages = FileUtils.LoadInMemoryImagesFromDirectory(imagesFolderPathForPredictions, false);var imageToPredict = testImages.Last();var prediction = predictionEngine.Predict(imageToPredict);Console.WriteLine($"Image Filename : [{imageToPredict.ImageFileName}], " +$"Scores : [{string.Join(",", prediction.Score)}], " +$"Predicted Label : {prediction.PredictedLabel}");
}
// 定义数据结构
class ImageData
{public ImageData(string imagePath, string label){ImagePath = imagePath;Label = label;}public readonly string ImagePath;public readonly string Label;
}class ModelInput
{public byte[] Image { get; set; }public UInt32 LabelAsKey { get; set; }public string ImagePath { get; set; }public string Label { get; set; }
}
class ModelOutput
{public string ImagePath { get; set; }public string Label { get; set; }public string PredictedLabel { get; set; }
}
public class ImagePrediction
{[ColumnName("Score")]public float[] Score;[ColumnName("PredictedLabel")]public string PredictedLabel;
}