124var data = TextLoader.Create(_mlContext, new TextLoader.Options() 133using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 134var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 135.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 136.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 138.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 139.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 140.AppendCacheCheckpoint(_mlContext) 141.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy()); 147var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 150var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 174var loader = _mlContext.Data.LoadFromEnumerable( 185using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 273var loader = _mlContext.Data.LoadFromEnumerable(data); 277using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 395var loader = _mlContext.Data.LoadFromEnumerable(data); 399using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 491var data = _mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); 492var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 493var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", 32, 32, "ImageReal").Transform(images); 495var pixels = _mlContext.Transforms.ExtractPixels("image_tensor", "ImageCropped", outputAsFloatArray: false).Fit(cropped).Transform(cropped); 496using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 527var reader = _mlContext.Data.CreateTextLoader( 539var images = _mlContext.Transforms.LoadImages("ImageReal", "ImagePath", imageFolder).Fit(data).Transform(data); 540var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", 224, 224, "ImageReal").Fit(images).Transform(images); 541var pixels = _mlContext.Transforms.ExtractPixels(inputName, "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 542using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 565var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 626schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 640var reader = _mlContext.Data.CreateTextLoader( 654using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel("mnist_model/frozen_saved_model.pb"); 655var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 657.Append(_mlContext.Transforms.Concatenate("Features", "Softmax", "dense/Relu")) 658.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 662var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 669var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 685var reader = _mlContext.Data.CreateTextLoader(columns: new[] 696var pipe = _mlContext.Transforms.Categorical.OneHotEncoding("OneHotLabel", "Label") 697.Append(_mlContext.Transforms.Normalize(new NormalizingEstimator.MinMaxColumnOptions("Features", "Placeholder"))) 698.Append(_mlContext.Model.RetrainDnnModel( 710.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 711.Append(_mlContext.Transforms.Conversion.MapValueToKey("KeyLabel", "Label", maximumNumberOfKeys: 10)) 712.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("KeyLabel", "Features")); 716var metrics = _mlContext.MulticlassClassification.Evaluate(predicted, labelColumnName: "KeyLabel"); 719var predictionFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 774var reader = _mlContext.Data.CreateTextLoader(new[] 791preprocessedTrainData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 798preprocessedTestData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 810var pipe = _mlContext.Transforms.CopyColumns("Features", "Placeholder") 811.Append(_mlContext.Model.RetrainDnnModel( 824.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 825.AppendCacheCheckpoint(_mlContext) 828.Append(_mlContext.MulticlassClassification.Trainers.LightGbm(new Trainers.LightGbm.LightGbmMulticlassTrainer.Options() 838var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 843var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 867var reader = _mlContext.Data.CreateTextLoader(columns: new[] 879using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel("mnist_model"); 880var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 882.Append(_mlContext.Transforms.Concatenate("Features", new[] { "Softmax", "dense/Relu" })) 883.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 887var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 896var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 989_mlContext.Log += (sender, e) => logMessages.Add(e.Message); 990using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 999var data = _mlContext.Data.LoadFromTextFile(dataFile, 1007var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, 1009.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", 1011.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", 1049using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1058var data = _mlContext.Data.LoadFromTextFile(dataFile, columns: new[] 1064var images = _mlContext.Transforms.LoadImages("ImageReal", imageFolder, "ImagePath").Fit(data).Transform(data); 1065var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", imageWidth, imageHeight, "ImageReal").Fit(images).Transform(images); 1066var pixels = _mlContext.Transforms.ExtractPixels("Input", "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 1093using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1101var dataObjects = InMemoryImage.LoadFromTsv(_mlContext, dataFile, imageFolder); 1103var dataView = _mlContext.Data.LoadFromEnumerable<InMemoryImage>(dataObjects); 1104var pipeline = _mlContext.Transforms.ResizeImages("ResizedImage", imageWidth, imageHeight, nameof(InMemoryImage.LoadedImage)) 1105.Append(_mlContext.Transforms.ExtractPixels("Input", "ResizedImage", interleavePixelColors: true)) 1107.Append(_mlContext.Transforms.Conversion.MapValueToKey("Label")) 1108.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes("Label", "Output")); 1110var cross = _mlContext.MulticlassClassification.CrossValidate(dataView, pipeline, 2); 1121var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 1137var data = TextLoader.Create(_mlContext, new TextLoader.Options() 1146using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1147var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 1148.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 1149.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 1151.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 1152.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 1153.AppendCacheCheckpoint(_mlContext) 1154.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes()); 1161var metrics = _mlContext.MulticlassClassification.Evaluate(transformedData); 1164var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 1174_mlContext.Model.Save(transformer, data.Schema, mlModelLocation); 1182var testTransformer = _mlContext.Model.Load(mlModelLocation, out loadedInputschema); 1187var testPredictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(testTransformer); 1214var data = _mlContext.Data.LoadFromTextFile(dataFile, 1221var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 1222var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images); 1223var pixels = new ImagePixelExtractingTransformer(_mlContext, "Input", "ImageCropped").Transform(cropped); 1225using TensorFlowModel model = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1255var dataView = _mlContext.Data.LoadFromEnumerable(data); 1257var lookupMap = _mlContext.Data.LoadFromTextFile(@"sentiment_model/imdb_word_index.csv", 1271var estimator = _mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text") 1272.Append(_mlContext.Transforms.Conversion.MapValue(lookupMap, lookupMap.Schema["Words"], lookupMap.Schema["Ids"], 1275var dataPipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(model); 1280using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1282.Append(_mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax")) 1284using var tfEnginePipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(pipelineModel); 1335using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_string_test"); 1340var dataview = _mlContext.Data.CreateTextLoader<TextInput>().Load(new MultiFileSource(null)); 1343.Append(_mlContext.Transforms.CopyColumns(new[] { new InputOutputColumnPair("AOut", "Original_A"), new InputOutputColumnPair("BOut", "Joined_Splited_Text") })); 1344var transformer = _mlContext.Model.CreatePredictionEngine<TextInput, TextOutput>(pipeline.Fit(dataview)); 1362using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_primitive_input_test"); 1369var dataview = _mlContext.Data.CreateTextLoader<PrimitiveInput>().Load(new MultiFileSource(null)); 1374var transformer = _mlContext.Model.CreatePredictionEngine<PrimitiveInput, PrimitiveOutput>(pipeline.Fit(dataview)); 1395IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1396_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1398shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1404TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1410var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1411.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification("Label", "Image") 1412.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); ; 1416_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1422loadedModel = _mlContext.Model.Load(file, out schema); 1426var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1470IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1471_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1473shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1479TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1484var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1513var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1514.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options) 1515.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); 1519_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1525loadedModel = _mlContext.Model.Load(file, out schema); 1529var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1537using var predictionEngine = _mlContext.Model 1602IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1603_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1605shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1611TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1616var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1671var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1672.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)) 1673.Append(_mlContext.Transforms.Conversion.MapKeyToValue( 1678_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1684loadedModel = _mlContext.Model.Load(file, out schema); 1688var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1696using var predictionEngine = _mlContext.Model 1757IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1758_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1760shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1766TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1773var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1805var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1806.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)); 1809_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1815loadedModel = _mlContext.Model.Load(file, out schema); 1818var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1846IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1847_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1849shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1851.Append(_mlContext.Transforms.LoadRawImageBytes("Image", fullImagesetFolderPath, "ImagePath")) 1856TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1877var pipeline = _mlContext.MulticlassClassification.Trainers.ImageClassification(options); 1880_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1886loadedModel = _mlContext.Model.Load(file, out schema); 1889var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 2032IDataView data = _mlContext.Data.LoadFromTextFile(dataFile, new[] { 2039using (var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation)) 2041var pipeline = _mlContext.Transforms.LoadImages("Input", imageFolder, "imagePath") 2042.Append(_mlContext.Transforms.ResizeImages("Input", imageHeight, imageWidth)) 2043.Append(_mlContext.Transforms.ExtractPixels("Input", interleavePixelColors: true))