protected PredictionModel <ImageNetData, ImageNetPrediction> Train(LearningPipeline pipeline) { // Initialize TensorFlow engine TensorFlowUtils.Initialize(); var model = pipeline.Train <ImageNetData, ImageNetPrediction>(); return(model); }
private async Task <PredictionModel <ImageNetData, ImageNetPrediction> > LoadModel() { ConsoleWriteHeader("Read model"); Console.WriteLine($"Model location: {modelLocation}"); // Initialize TensorFlow engine TensorFlowUtils.Initialize(); return(await PredictionModel.ReadAsync <ImageNetData, ImageNetPrediction>(modelLocation)); }
protected PredictionModel <ImageNetData, ImageNetPrediction> TrainModel(LearningPipeline pipeline) { ConsoleWriteHeader("Training classification model"); // Initialize TensorFlow engine TensorFlowUtils.Initialize(); var model = pipeline.Train <ImageNetData, ImageNetPrediction>(); return(model); }
public async Task Evaluate() { var model = await PredictionModel.ReadAsync <ImageNetData, ImageNetPrediction>(modelLocation); // Initialize TensorFlow engine TensorFlowUtils.Initialize(); var predictions = GetPredictions(dataLocation, imagesFolder, model).ToArray(); ShowMetrics(dataLocation, model); }
public async Task Evaluate() { // Initialize TensorFlow engine (Needed before loading the ML.NET related to TensorFlow model. This won't be needed when using the new API in v0.6 with Estimators, etc.) TensorFlowUtils.Initialize(); var model = await PredictionModel.ReadAsync <ImageNetData, ImageNetPrediction>(modelLocation); // Get Predictions var predictions = GetPredictions(dataLocation, imagesFolder, model).ToArray(); ShowMetrics(dataLocation, model); }
public void TensorFlowTransformMNISTConvPipelineTest() { var model_location = "mnist_model/frozen_saved_model.pb"; var dataPath = GetDataPath("Train-Tiny-28x28.txt"); var pipeline = new Legacy.LearningPipeline(seed: 1); pipeline.Add(new Microsoft.ML.Legacy.Data.TextLoader(dataPath).CreateFrom <MNISTData>(useHeader: false)); pipeline.Add(new Legacy.Transforms.ColumnCopier() { Column = new[] { new CopyColumnsTransformColumn() { Name = "reshape_input", Source = "Placeholder" } } }); pipeline.Add(new TensorFlowScorer() { ModelFile = model_location, OutputColumns = new[] { "Softmax", "dense/Relu" }, InputColumns = new[] { "Placeholder", "reshape_input" } }); pipeline.Add(new Legacy.Transforms.ColumnConcatenator() { Column = new[] { new ConcatTransformColumn() { Name = "Features", Source = new[] { "Placeholder", "dense/Relu" } } } }); pipeline.Add(new Legacy.Trainers.LogisticRegressionClassifier()); TensorFlowUtils.Initialize(); var model = pipeline.Train <MNISTData, MNISTPrediction>(); var sample1 = new MNISTData() { Placeholder = new float[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 } }; MNISTPrediction prediction = model.Predict(sample1); }