124var data = TextLoader.Create(_mlContext, new TextLoader.Options() 133var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 134.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 135.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 136.Append(_mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel("Output", "Input")) 137.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 138.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 139.AppendCacheCheckpoint(_mlContext) 140.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy()); 146var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 149var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 173var loader = _mlContext.Data.LoadFromEnumerable( 184using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 272var loader = _mlContext.Data.LoadFromEnumerable(data); 276using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 394var loader = _mlContext.Data.LoadFromEnumerable(data); 398using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 490var data = _mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); 491var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 492var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", 32, 32, "ImageReal").Transform(images); 494var pixels = _mlContext.Transforms.ExtractPixels("image_tensor", "ImageCropped", outputAsFloatArray: false).Fit(cropped).Transform(cropped); 495using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 526var reader = _mlContext.Data.CreateTextLoader( 538var images = _mlContext.Transforms.LoadImages("ImageReal", "ImagePath", imageFolder).Fit(data).Transform(data); 539var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", 224, 224, "ImageReal").Fit(images).Transform(images); 540var pixels = _mlContext.Transforms.ExtractPixels(inputName, "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 541using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 564var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 625schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 639var reader = _mlContext.Data.CreateTextLoader( 653var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 654.Append(_mlContext.Model.LoadTensorFlowModel("mnist_model/frozen_saved_model.pb").ScoreTensorFlowModel(new[] { "Softmax", "dense/Relu" }, new[] { "Placeholder", "reshape_input" })) 655.Append(_mlContext.Transforms.Concatenate("Features", "Softmax", "dense/Relu")) 656.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 660var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 667var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 683var reader = _mlContext.Data.CreateTextLoader(columns: new[] 694var pipe = _mlContext.Transforms.Categorical.OneHotEncoding("OneHotLabel", "Label") 695.Append(_mlContext.Transforms.Normalize(new NormalizingEstimator.MinMaxColumnOptions("Features", "Placeholder"))) 696.Append(_mlContext.Model.RetrainDnnModel( 708.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 709.Append(_mlContext.Transforms.Conversion.MapValueToKey("KeyLabel", "Label", maximumNumberOfKeys: 10)) 710.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("KeyLabel", "Features")); 714var metrics = _mlContext.MulticlassClassification.Evaluate(predicted, labelColumnName: "KeyLabel"); 717var predictionFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 772var reader = _mlContext.Data.CreateTextLoader(new[] 789preprocessedTrainData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 796preprocessedTestData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 808var pipe = _mlContext.Transforms.CopyColumns("Features", "Placeholder") 809.Append(_mlContext.Model.RetrainDnnModel( 822.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 823.AppendCacheCheckpoint(_mlContext) 826.Append(_mlContext.MulticlassClassification.Trainers.LightGbm(new Trainers.LightGbm.LightGbmMulticlassTrainer.Options() 836var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 841var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 865var reader = _mlContext.Data.CreateTextLoader(columns: new[] 877var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 878.Append(_mlContext.Model.LoadTensorFlowModel("mnist_model").ScoreTensorFlowModel(new[] { "Softmax", "dense/Relu" }, new[] { "Placeholder", "reshape_input" })) 879.Append(_mlContext.Transforms.Concatenate("Features", new[] { "Softmax", "dense/Relu" })) 880.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 884var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 893var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 986_mlContext.Log += (sender, e) => logMessages.Add(e.Message); 987using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 996var data = _mlContext.Data.LoadFromTextFile(dataFile, 1004var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, 1006.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", 1008.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", 1046using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1055var data = _mlContext.Data.LoadFromTextFile(dataFile, columns: new[] 1061var images = _mlContext.Transforms.LoadImages("ImageReal", imageFolder, "ImagePath").Fit(data).Transform(data); 1062var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", imageWidth, imageHeight, "ImageReal").Fit(images).Transform(images); 1063var pixels = _mlContext.Transforms.ExtractPixels("Input", "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 1090using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1098var dataObjects = InMemoryImage.LoadFromTsv(_mlContext, dataFile, imageFolder); 1100var dataView = _mlContext.Data.LoadFromEnumerable<InMemoryImage>(dataObjects); 1101var pipeline = _mlContext.Transforms.ResizeImages("ResizedImage", imageWidth, imageHeight, nameof(InMemoryImage.LoadedImage)) 1102.Append(_mlContext.Transforms.ExtractPixels("Input", "ResizedImage", interleavePixelColors: true)) 1104.Append(_mlContext.Transforms.Conversion.MapValueToKey("Label")) 1105.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes("Label", "Output")); 1107var cross = _mlContext.MulticlassClassification.CrossValidate(dataView, pipeline, 2); 1118var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 1134var data = TextLoader.Create(_mlContext, new TextLoader.Options() 1143var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 1144.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 1145.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 1146.Append(_mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel("Output", "Input")) 1147.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 1148.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 1149.AppendCacheCheckpoint(_mlContext) 1150.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes()); 1157var metrics = _mlContext.MulticlassClassification.Evaluate(transformedData); 1160var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 1170_mlContext.Model.Save(transformer, data.Schema, mlModelLocation); 1178var testTransformer = _mlContext.Model.Load(mlModelLocation, out loadedInputschema); 1183var testPredictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(testTransformer); 1210var data = _mlContext.Data.LoadFromTextFile(dataFile, 1217var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 1218var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images); 1219var pixels = new ImagePixelExtractingTransformer(_mlContext, "Input", "ImageCropped").Transform(cropped); 1221using TensorFlowModel model = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1251var dataView = _mlContext.Data.LoadFromEnumerable(data); 1253var lookupMap = _mlContext.Data.LoadFromTextFile(@"sentiment_model/imdb_word_index.csv", 1267var estimator = _mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text") 1268.Append(_mlContext.Transforms.Conversion.MapValue(lookupMap, lookupMap.Schema["Words"], lookupMap.Schema["Ids"], 1271var dataPipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(model); 1276using var pipelineModel = _mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel(new[] { "Prediction/Softmax" }, new[] { "Features" }) 1277.Append(_mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax")) 1279using var tfEnginePipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(pipelineModel); 1330using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_string_test"); 1335var dataview = _mlContext.Data.CreateTextLoader<TextInput>().Load(new MultiFileSource(null)); 1338.Append(_mlContext.Transforms.CopyColumns(new[] { new InputOutputColumnPair("AOut", "Original_A"), new InputOutputColumnPair("BOut", "Joined_Splited_Text") })); 1339var transformer = _mlContext.Model.CreatePredictionEngine<TextInput, TextOutput>(pipeline.Fit(dataview)); 1357using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_primitive_input_test"); 1364var dataview = _mlContext.Data.CreateTextLoader<PrimitiveInput>().Load(new MultiFileSource(null)); 1369var transformer = _mlContext.Model.CreatePredictionEngine<PrimitiveInput, PrimitiveOutput>(pipeline.Fit(dataview)); 1390IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1391_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1393shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1399TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1405var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1406.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification("Label", "Image") 1407.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); ; 1411_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1417loadedModel = _mlContext.Model.Load(file, out schema); 1421var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1465IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1466_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1468shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1474TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1479var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1508var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1509.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options) 1510.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); 1514_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1520loadedModel = _mlContext.Model.Load(file, out schema); 1524var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1532using var predictionEngine = _mlContext.Model 1597IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1598_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1600shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1606TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1611var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1666var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1667.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)) 1668.Append(_mlContext.Transforms.Conversion.MapKeyToValue( 1673_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1679loadedModel = _mlContext.Model.Load(file, out schema); 1683var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1691using var predictionEngine = _mlContext.Model 1752IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1753_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1755shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1761TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1768var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1800var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1801.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)); 1804_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1810loadedModel = _mlContext.Model.Load(file, out schema); 1813var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1841IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1842_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1844shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1846.Append(_mlContext.Transforms.LoadRawImageBytes("Image", fullImagesetFolderPath, "ImagePath")) 1851TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1872var pipeline = _mlContext.MulticlassClassification.Trainers.ImageClassification(options); 1875_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1881loadedModel = _mlContext.Model.Load(file, out schema); 1884var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 2027IDataView data = _mlContext.Data.LoadFromTextFile(dataFile, new[] { 2034using (var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation)) 2036var pipeline = _mlContext.Transforms.LoadImages("Input", imageFolder, "imagePath") 2037.Append(_mlContext.Transforms.ResizeImages("Input", imageHeight, imageWidth)) 2038.Append(_mlContext.Transforms.ExtractPixels("Input", interleavePixelColors: true))