131var data = TextLoader.Create(_mlContext, new TextLoader.Options() 140using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 141var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 142.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 143.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 145.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 146.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 147.AppendCacheCheckpoint(_mlContext) 148.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy()); 154var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 157var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 181var loader = _mlContext.Data.LoadFromEnumerable( 192using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 280var loader = _mlContext.Data.LoadFromEnumerable(data); 284using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 402var loader = _mlContext.Data.LoadFromEnumerable(data); 406using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 498var data = _mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); 499var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 500var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", 32, 32, "ImageReal").Transform(images); 502var pixels = _mlContext.Transforms.ExtractPixels("image_tensor", "ImageCropped", outputAsFloatArray: false).Fit(cropped).Transform(cropped); 503using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 534var reader = _mlContext.Data.CreateTextLoader( 546var images = _mlContext.Transforms.LoadImages("ImageReal", "ImagePath", imageFolder).Fit(data).Transform(data); 547var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", 224, 224, "ImageReal").Fit(images).Transform(images); 548var pixels = _mlContext.Transforms.ExtractPixels(inputName, "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 549using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 572var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 633schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 647var reader = _mlContext.Data.CreateTextLoader( 661using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel("mnist_model/frozen_saved_model.pb"); 662var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 664.Append(_mlContext.Transforms.Concatenate("Features", "Softmax", "dense/Relu")) 665.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 669var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 676var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 692var reader = _mlContext.Data.CreateTextLoader(columns: new[] 703var pipe = _mlContext.Transforms.Categorical.OneHotEncoding("OneHotLabel", "Label") 704.Append(_mlContext.Transforms.Normalize(new NormalizingEstimator.MinMaxColumnOptions("Features", "Placeholder"))) 705.Append(_mlContext.Model.RetrainDnnModel( 717.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 718.Append(_mlContext.Transforms.Conversion.MapValueToKey("KeyLabel", "Label", maximumNumberOfKeys: 10)) 719.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("KeyLabel", "Features")); 723var metrics = _mlContext.MulticlassClassification.Evaluate(predicted, labelColumnName: "KeyLabel"); 726var predictionFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 781var reader = _mlContext.Data.CreateTextLoader(new[] 798preprocessedTrainData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 805preprocessedTestData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 817var pipe = _mlContext.Transforms.CopyColumns("Features", "Placeholder") 818.Append(_mlContext.Model.RetrainDnnModel( 831.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 832.AppendCacheCheckpoint(_mlContext) 835.Append(_mlContext.MulticlassClassification.Trainers.LightGbm(new Trainers.LightGbm.LightGbmMulticlassTrainer.Options() 845var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 850var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 874var reader = _mlContext.Data.CreateTextLoader(columns: new[] 886using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel("mnist_model"); 887var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 889.Append(_mlContext.Transforms.Concatenate("Features", new[] { "Softmax", "dense/Relu" })) 890.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 894var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 903var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 996_mlContext.Log += (sender, e) => logMessages.Add(e.Message); 997using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1006var data = _mlContext.Data.LoadFromTextFile(dataFile, 1014var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, 1016.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", 1018.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", 1056using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1065var data = _mlContext.Data.LoadFromTextFile(dataFile, columns: new[] 1071var images = _mlContext.Transforms.LoadImages("ImageReal", imageFolder, "ImagePath").Fit(data).Transform(data); 1072var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", imageWidth, imageHeight, "ImageReal").Fit(images).Transform(images); 1073var pixels = _mlContext.Transforms.ExtractPixels("Input", "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 1100using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1108var dataObjects = InMemoryImage.LoadFromTsv(_mlContext, dataFile, imageFolder); 1110var dataView = _mlContext.Data.LoadFromEnumerable<InMemoryImage>(dataObjects); 1111var pipeline = _mlContext.Transforms.ResizeImages("ResizedImage", imageWidth, imageHeight, nameof(InMemoryImage.LoadedImage)) 1112.Append(_mlContext.Transforms.ExtractPixels("Input", "ResizedImage", interleavePixelColors: true)) 1114.Append(_mlContext.Transforms.Conversion.MapValueToKey("Label")) 1115.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes("Label", "Output")); 1117var cross = _mlContext.MulticlassClassification.CrossValidate(dataView, pipeline, 2); 1128var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 1144var data = TextLoader.Create(_mlContext, new TextLoader.Options() 1153using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1154var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 1155.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 1156.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 1158.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 1159.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 1160.AppendCacheCheckpoint(_mlContext) 1161.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes()); 1168var metrics = _mlContext.MulticlassClassification.Evaluate(transformedData); 1171var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 1181_mlContext.Model.Save(transformer, data.Schema, mlModelLocation); 1189var testTransformer = _mlContext.Model.Load(mlModelLocation, out loadedInputschema); 1194var testPredictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(testTransformer); 1221var data = _mlContext.Data.LoadFromTextFile(dataFile, 1228var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 1229var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images); 1230var pixels = new ImagePixelExtractingTransformer(_mlContext, "Input", "ImageCropped").Transform(cropped); 1232using TensorFlowModel model = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1262var dataView = _mlContext.Data.LoadFromEnumerable(data); 1264var lookupMap = _mlContext.Data.LoadFromTextFile(@"sentiment_model/imdb_word_index.csv", 1278var estimator = _mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text") 1279.Append(_mlContext.Transforms.Conversion.MapValue(lookupMap, lookupMap.Schema["Words"], lookupMap.Schema["Ids"], 1282var dataPipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(model); 1287using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1289.Append(_mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax")) 1291using var tfEnginePipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(pipelineModel); 1342using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_string_test"); 1347var dataview = _mlContext.Data.CreateTextLoader<TextInput>().Load(new MultiFileSource(null)); 1350.Append(_mlContext.Transforms.CopyColumns(new[] { new InputOutputColumnPair("AOut", "Original_A"), new InputOutputColumnPair("BOut", "Joined_Splited_Text") })); 1351var transformer = _mlContext.Model.CreatePredictionEngine<TextInput, TextOutput>(pipeline.Fit(dataview)); 1369using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_primitive_input_test"); 1376var dataview = _mlContext.Data.CreateTextLoader<PrimitiveInput>().Load(new MultiFileSource(null)); 1381var transformer = _mlContext.Model.CreatePredictionEngine<PrimitiveInput, PrimitiveOutput>(pipeline.Fit(dataview)); 1402IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1403_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1405shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1411TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1417var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1418.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification("Label", "Image") 1419.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); ; 1423_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1429loadedModel = _mlContext.Model.Load(file, out schema); 1433var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1477IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1478_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1480shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1486TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1491var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1520var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1521.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options) 1522.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); 1526_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1532loadedModel = _mlContext.Model.Load(file, out schema); 1536var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1544using var predictionEngine = _mlContext.Model 1609IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1610_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1612shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1618TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1623var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1678var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1679.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)) 1680.Append(_mlContext.Transforms.Conversion.MapKeyToValue( 1685_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1691loadedModel = _mlContext.Model.Load(file, out schema); 1695var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1703using var predictionEngine = _mlContext.Model 1764IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1765_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1767shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1773TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1780var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1812var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1813.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)); 1816_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1822loadedModel = _mlContext.Model.Load(file, out schema); 1825var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1853IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1854_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1856shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1858.Append(_mlContext.Transforms.LoadRawImageBytes("Image", fullImagesetFolderPath, "ImagePath")) 1863TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1884var pipeline = _mlContext.MulticlassClassification.Trainers.ImageClassification(options); 1887_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1893loadedModel = _mlContext.Model.Load(file, out schema); 1896var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 2039IDataView data = _mlContext.Data.LoadFromTextFile(dataFile, new[] { 2046using (var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation)) 2048var pipeline = _mlContext.Transforms.LoadImages("Input", imageFolder, "imagePath") 2049.Append(_mlContext.Transforms.ResizeImages("Input", imageHeight, imageWidth)) 2050.Append(_mlContext.Transforms.ExtractPixels("Input", interleavePixelColors: true))