140var data = TextLoader.Create(_mlContext, new TextLoader.Options() 149var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 150.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 151.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 152.Append(_mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel("Output", "Input")) 153.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 154.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 155.AppendCacheCheckpoint(_mlContext) 156.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy()); 162var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 165var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 189var loader = _mlContext.Data.LoadFromEnumerable( 200using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 288var loader = _mlContext.Data.LoadFromEnumerable(data); 292using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 410var loader = _mlContext.Data.LoadFromEnumerable(data); 414using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 506var data = _mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); 507var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 508var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", 32, 32, "ImageReal").Transform(images); 510var pixels = _mlContext.Transforms.ExtractPixels("image_tensor", "ImageCropped", outputAsFloatArray: false).Fit(cropped).Transform(cropped); 511using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 542var reader = _mlContext.Data.CreateTextLoader( 554var images = _mlContext.Transforms.LoadImages("ImageReal", "ImagePath", imageFolder).Fit(data).Transform(data); 555var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", 224, 224, "ImageReal").Fit(images).Transform(images); 556var pixels = _mlContext.Transforms.ExtractPixels(inputName, "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 557using var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 580var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 641schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 655var reader = _mlContext.Data.CreateTextLoader( 669var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 670.Append(_mlContext.Model.LoadTensorFlowModel("mnist_model/frozen_saved_model.pb").ScoreTensorFlowModel(new[] { "Softmax", "dense/Relu" }, new[] { "Placeholder", "reshape_input" })) 671.Append(_mlContext.Transforms.Concatenate("Features", "Softmax", "dense/Relu")) 672.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 676var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 683var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 699var reader = _mlContext.Data.CreateTextLoader(columns: new[] 710var pipe = _mlContext.Transforms.Categorical.OneHotEncoding("OneHotLabel", "Label") 711.Append(_mlContext.Transforms.Normalize(new NormalizingEstimator.MinMaxColumnOptions("Features", "Placeholder"))) 712.Append(_mlContext.Model.RetrainDnnModel( 724.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 725.Append(_mlContext.Transforms.Conversion.MapValueToKey("KeyLabel", "Label", maximumNumberOfKeys: 10)) 726.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("KeyLabel", "Features")); 730var metrics = _mlContext.MulticlassClassification.Evaluate(predicted, labelColumnName: "KeyLabel"); 733var predictionFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 788var reader = _mlContext.Data.CreateTextLoader(new[] 805preprocessedTrainData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 812preprocessedTestData = new RowShufflingTransformer(_mlContext, new RowShufflingTransformer.Options() 824var pipe = _mlContext.Transforms.CopyColumns("Features", "Placeholder") 825.Append(_mlContext.Model.RetrainDnnModel( 838.Append(_mlContext.Transforms.Concatenate("Features", "Prediction")) 839.AppendCacheCheckpoint(_mlContext) 842.Append(_mlContext.MulticlassClassification.Trainers.LightGbm(new Trainers.LightGbm.LightGbmMulticlassTrainer.Options() 852var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 857var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 881var reader = _mlContext.Data.CreateTextLoader(columns: new[] 893var pipe = _mlContext.Transforms.CopyColumns("reshape_input", "Placeholder") 894.Append(_mlContext.Model.LoadTensorFlowModel("mnist_model").ScoreTensorFlowModel(new[] { "Softmax", "dense/Relu" }, new[] { "Placeholder", "reshape_input" })) 895.Append(_mlContext.Transforms.Concatenate("Features", new[] { "Softmax", "dense/Relu" })) 896.Append(_mlContext.MulticlassClassification.Trainers.LightGbm("Label", "Features")); 900var metrics = _mlContext.MulticlassClassification.Evaluate(predicted); 909var predictFunction = _mlContext.Model.CreatePredictionEngine<MNISTData, MNISTPrediction>(trainedModel); 1002_mlContext.Log += (sender, e) => logMessages.Add(e.Message); 1003using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1012var data = _mlContext.Data.LoadFromTextFile(dataFile, 1020var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, 1022.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", 1024.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", 1062using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1071var data = _mlContext.Data.LoadFromTextFile(dataFile, columns: new[] 1077var images = _mlContext.Transforms.LoadImages("ImageReal", imageFolder, "ImagePath").Fit(data).Transform(data); 1078var cropped = _mlContext.Transforms.ResizeImages("ImageCropped", imageWidth, imageHeight, "ImageReal").Fit(images).Transform(images); 1079var pixels = _mlContext.Transforms.ExtractPixels("Input", "ImageCropped", interleavePixelColors: true).Fit(cropped).Transform(cropped); 1106using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1114var dataObjects = InMemoryImage.LoadFromTsv(_mlContext, dataFile, imageFolder); 1116var dataView = _mlContext.Data.LoadFromEnumerable<InMemoryImage>(dataObjects); 1117var pipeline = _mlContext.Transforms.ResizeImages("ResizedImage", imageWidth, imageHeight, nameof(InMemoryImage.LoadedImage)) 1118.Append(_mlContext.Transforms.ExtractPixels("Input", "ResizedImage", interleavePixelColors: true)) 1120.Append(_mlContext.Transforms.Conversion.MapValueToKey("Label")) 1121.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes("Label", "Output")); 1123var cross = _mlContext.MulticlassClassification.CrossValidate(dataView, pipeline, 2); 1134var schema = TensorFlowUtils.GetModelSchema(_mlContext, modelLocation); 1150var data = TextLoader.Create(_mlContext, new TextLoader.Options() 1159var pipeEstimator = new ImageLoadingEstimator(_mlContext, imageFolder, ("ImageReal", "ImagePath")) 1160.Append(new ImageResizingEstimator(_mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) 1161.Append(new ImagePixelExtractingEstimator(_mlContext, "Input", "ImageCropped", interleavePixelColors: true)) 1162.Append(_mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel("Output", "Input")) 1163.Append(new ColumnConcatenatingEstimator(_mlContext, "Features", "Output")) 1164.Append(new ValueToKeyMappingEstimator(_mlContext, "Label")) 1165.AppendCacheCheckpoint(_mlContext) 1166.Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes()); 1173var metrics = _mlContext.MulticlassClassification.Evaluate(transformedData); 1176var predictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(transformer); 1186_mlContext.Model.Save(transformer, data.Schema, mlModelLocation); 1194var testTransformer = _mlContext.Model.Load(mlModelLocation, out loadedInputschema); 1199var testPredictFunction = _mlContext.Model.CreatePredictionEngine<CifarData, CifarPrediction>(testTransformer); 1226var data = _mlContext.Data.LoadFromTextFile(dataFile, 1233var images = new ImageLoadingTransformer(_mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data); 1234var cropped = new ImageResizingTransformer(_mlContext, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images); 1235var pixels = new ImagePixelExtractingTransformer(_mlContext, "Input", "ImageCropped").Transform(cropped); 1237using TensorFlowModel model = _mlContext.Model.LoadTensorFlowModel(modelLocation); 1267var dataView = _mlContext.Data.LoadFromEnumerable(data); 1269var lookupMap = _mlContext.Data.LoadFromTextFile(@"sentiment_model/imdb_word_index.csv", 1283var estimator = _mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text") 1284.Append(_mlContext.Transforms.Conversion.MapValue(lookupMap, lookupMap.Schema["Words"], lookupMap.Schema["Ids"], 1287var dataPipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(model); 1292using var pipelineModel = _mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel(new[] { "Prediction/Softmax" }, new[] { "Features" }) 1293.Append(_mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax")) 1295using var tfEnginePipe = _mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(pipelineModel); 1346using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_string_test"); 1351var dataview = _mlContext.Data.CreateTextLoader<TextInput>().Load(new MultiFileSource(null)); 1354.Append(_mlContext.Transforms.CopyColumns(new[] { new InputOutputColumnPair("AOut", "Original_A"), new InputOutputColumnPair("BOut", "Joined_Splited_Text") })); 1355var transformer = _mlContext.Model.CreatePredictionEngine<TextInput, TextOutput>(pipeline.Fit(dataview)); 1373using var tensorFlowModel = _mlContext.Model.LoadTensorFlowModel(@"model_primitive_input_test"); 1380var dataview = _mlContext.Data.CreateTextLoader<PrimitiveInput>().Load(new MultiFileSource(null)); 1385var transformer = _mlContext.Model.CreatePredictionEngine<PrimitiveInput, PrimitiveOutput>(pipeline.Fit(dataview)); 1406IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1407_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1409shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1415TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1421var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1422.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification("Label", "Image") 1423.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); ; 1427_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1433loadedModel = _mlContext.Model.Load(file, out schema); 1437var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1481IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1482_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1484shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1490TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1495var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1524var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1525.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options) 1526.Append(_mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel", inputColumnName: "PredictedLabel"))); 1530_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1536loadedModel = _mlContext.Model.Load(file, out schema); 1540var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1548using var predictionEngine = _mlContext.Model 1613IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1614_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1616shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1622TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1627var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1682var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1683.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)) 1684.Append(_mlContext.Transforms.Conversion.MapKeyToValue( 1689_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1695loadedModel = _mlContext.Model.Load(file, out schema); 1699var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1707using var predictionEngine = _mlContext.Model 1768IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1769_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1771shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1777TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1784var validationSet = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1816var pipeline = _mlContext.Transforms.LoadRawImageBytes("Image", _fullImagesetFolderPath, "ImagePath") 1817.Append(_mlContext.MulticlassClassification.Trainers.ImageClassification(options)); 1820_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1826loadedModel = _mlContext.Model.Load(file, out schema); 1829var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1857IDataView shuffledFullImagesDataset = _mlContext.Data.ShuffleRows( 1858_mlContext.Data.LoadFromEnumerable(images), seed: 1); 1860shuffledFullImagesDataset = _mlContext.Transforms.Conversion 1862.Append(_mlContext.Transforms.LoadRawImageBytes("Image", fullImagesetFolderPath, "ImagePath")) 1867TrainTestData trainTestData = _mlContext.Data.TrainTestSplit( 1888var pipeline = _mlContext.MulticlassClassification.Trainers.ImageClassification(options); 1891_mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema, 1897loadedModel = _mlContext.Model.Load(file, out schema); 1900var metrics = _mlContext.MulticlassClassification.Evaluate(predictions); 1989using (var ch = (_mlContext as IHostEnvironment).Start("Ensuring image files are present.")) 1991var ensureModel = ResourceManagerUtils.Instance.EnsureResourceAsync(_mlContext, ch, url, destFileName, destDir, timeout); 2056IDataView data = _mlContext.Data.LoadFromTextFile(dataFile, new[] { 2063using (var tfModel = _mlContext.Model.LoadTensorFlowModel(modelLocation)) 2065var pipeline = _mlContext.Transforms.LoadImages("Input", imageFolder, "imagePath") 2066.Append(_mlContext.Transforms.ResizeImages("Input", imageHeight, imageWidth)) 2067.Append(_mlContext.Transforms.ExtractPixels("Input", interleavePixelColors: true))