ML.NET验证码识别 多分类 StochasticDualCoordinateAscent 算法 样本量增加，识别率急剧下降

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  图片（20*25）数据特征为每个像素点用0或者1表示，形成500位的数据，类别为数字2-9和英文a-z共33类。

  装载数据后采用：mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features");进行模型训练。

  每一类500个样本时，测试集正确率75%。

  每一类1000个样本时，测试集正确率70%.

  每一类3000个样本时，测试集正确率26%

  为什么数据越多正确率急剧下降呢？

  代码如下：

  //训练模型：
   MLContext mlContext = new MLContext(seed: 1);
  
              TextLoader textLoader = mlContext.Data.CreateTextReader(Host.columns,
                                                  hasHeader: true,
                                                  separatorChar: ',');
  
              IDataView fullData = textLoader.Read(AppDomain.CurrentDomain.BaseDirectory + "\\trainData.txt");
  
              (IDataView trainingDataView, IDataView testingDataView) = mlContext.Clustering.TrainTestSplit(fullData, testFraction: 0.2);
  
              var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", Host.featuresColumnNames);
  
              var transformer = dataProcessPipeline.Fit(trainingDataView);
              var transformedData = transformer.Transform(trainingDataView);
  
              var transformer1 = dataProcessPipeline.Fit(trainingDataView);
              var transformedData1 = transformer.Transform(trainingDataView);
  
              SdcaMultiClassTrainer sdcaMultiClassTrainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features");            
  
              var trainingPipeline = dataProcessPipeline.Append(sdcaMultiClassTrainer);
  
              ITransformer trainedModel = trainingPipeline.Fit(trainingDataView);
  
              var predictions = trainedModel.Transform(testingDataView);
              var metrics = mlContext.MulticlassClassification.Evaluate(predictions, "Label", "Score");
  
              using (var fs = new FileStream(AppDomain.CurrentDomain.BaseDirectory + "\\imageModel.zip", FileMode.Create, FileAccess.Write, FileShare.Write))
              {
                  mlContext.Model.Save(trainedModel, fs);
              }
  
  //测试集校验
  MLContext mlContext = new MLContext(seed: 1);
  
              ITransformer trainedModel;
              using (var stream = new FileStream(AppDomain.CurrentDomain.BaseDirectory + "\\imageModel.zip", FileMode.Open, FileAccess.Read, FileShare.Read))
              {
                  trainedModel = mlContext.Model.Load(stream);
              }
  
              var predEngine = trainedModel.CreatePredictionEngine<ImageData, ImageResult>(mlContext);
  
              float minScore = 0.5f;
  
              float totalCount = 0;
              float rightCount = 0;
              foreach (ImageData id in imageDatas)
              {
                  ImageResult ir = predEngine.Predict(id);
                  List<float> results = ir.Score.ToList();
                  float max = results.Max();
  
                  int resultIndex = results.IndexOf(max);
                  if (resultIndex == id.Label)
                  {
                      if (max < minScore)
                      {
                          minScore = max;
                      }
  
                      rightCount++;
                  }
                  totalCount++;
              }
              Console.WriteLine("正确率：" + Convert.ToSingle(rightCount / totalCount));
              Console.ReadKey();  
  

  
  

  2019年2月18日 10:10

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