namespace MulticlassClassification_Mnist
{class Program{//Assets files download from:https://gitee.com/seabluescn/ML_Assetsstatic readonly string AssetsFolder = @"D:\StepByStep\Blogs\ML_Assets\MNIST";static readonly string TrainTagsPath = Path.Combine(AssetsFolder, "train_tags.tsv");static readonly string TrainDataFolder = Path.Combine(AssetsFolder, "train");static readonly string ModelPath = Path.Combine(Environment.CurrentDirectory, "Data", "SDCA-Model.zip");static void Main(string[] args){MLContext mlContext = new MLContext(seed: 1);TrainAndSaveModel(mlContext);TestSomePredictions(mlContext);Console.WriteLine("Hit any key to finish the app");Console.ReadKey();}public static void TrainAndSaveModel(MLContext mlContext){// STEP 1: 准备数据var fulldata = mlContext.Data.LoadFromTextFile<InputData>(path: TrainTagsPath, separatorChar: '\t', hasHeader: false);var trainTestData = mlContext.Data.TrainTestSplit(fulldata, testFraction: 0.1);var trainData = trainTestData.TrainSet;var testData = trainTestData.TestSet;// STEP 2: 配置数据处理管道        var dataProcessPipeline = mlContext.Transforms.CustomMapping(new LoadImageConversion().GetMapping(), contractName: "LoadImageConversionAction").Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "Number", keyOrdinality: ValueToKeyMappingEstimator.KeyOrdinality.ByValue)).Append(mlContext.Transforms.NormalizeMeanVariance( outputColumnName: "FeaturesNormalizedByMeanVar", inputColumnName: "ImagePixels"));// STEP 3: 配置训练算法 (using a maximum entropy classification model trained with the L-BFGS method)var trainer = mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(labelColumnName: "Label", featureColumnName: "FeaturesNormalizedByMeanVar");var trainingPipeline = dataProcessPipeline.Append(trainer).Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictNumber", "Label"));// STEP 4: 训练模型使其与数据集拟合           ITransformer trainedModel = trainingPipeline.Fit(trainData);          // STEP 5:评估模型的准确性           var predictions = trainedModel.Transform(testData);var metrics = mlContext.MulticlassClassification.Evaluate(data: predictions, labelColumnName: "Label", scoreColumnName: "Score");PrintMultiClassClassificationMetrics(trainer.ToString(), metrics);// STEP 6:保存模型
            mlContext.Model.Save(trainedModel, trainData.Schema, ModelPath);           }private static void TestSomePredictions(MLContext mlContext){// Load Model           ITransformer trainedModel = mlContext.Model.Load(ModelPath, out var modelInputSchema);// Create prediction engine var predEngine = mlContext.Model.CreatePredictionEngine<InputData, OutPutData>(trainedModel);DirectoryInfo TestFolder = new DirectoryInfo(Path.Combine(AssetsFolder, "test"));           foreach(var image in TestFolder.GetFiles()){count++;InputData img = new InputData(){FileName = image.Name};var result = predEngine.Predict(img);Console.WriteLine($"Current Source={img.FileName},PredictResult={result.GetPredictResult()}");                }}       }class InputData{[LoadColumn(0)]public string FileName;[LoadColumn(1)]public string Number;[LoadColumn(1)]public float Serial;       }class OutPutData : InputData{public float[] Score;public int GetPredictResult(){float max = 0;int index = 0;for (int i = 0; i < Score.Length; i++){if (Score[i] > max){max = Score[i];index = i;}}return index;}       }
}