Buenas!
Hoy post rápido que el código se explica por si solo.
En primer lugar, basado en los posts anteriores, el proceso de definir y entrenar el modelo. Lo importante sucede en líneas 15 y 16, donde se graba el modelo.
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| class Program | |
| { | |
| static void Main(string[] args) | |
| { | |
| var agesRangesCsv = "AgeRangeData.csv"; | |
| var pipeline = new LearningPipeline(); | |
| pipeline.Add(new TextLoader<AgeRangeData>(agesRangesCsv, separator: ",")); | |
| pipeline.Add(new Dictionarizer("Label")); | |
| pipeline.Add(new ColumnConcatenator("Features", "AgeStart", "AgeEnd")); | |
| pipeline.Add(new StochasticDualCoordinateAscentClassifier()); | |
| pipeline.Add(new PredictedLabelColumnOriginalValueConverter {PredictedLabelColumn = "PredictedLabel"}); | |
| var model = pipeline.Train<AgeRangeData, AgeRangePrediction>(); | |
| var modelFilePath = "AgeRangeDataModel.zip"; | |
| model.WriteAsync(modelFilePath); | |
| Console.ReadLine(); | |
| } | |
| } |
Y luego otro proyecto, donde se utiliza el modelo grabado. Atención a las líneas 18 a 21, a partir de allí la forma de utilizar el modelo es el mismo.
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| class Program | |
| { | |
| private static string _modelFilePath = "AgeRangeDataModel.zip"; | |
| private static PredictionModel<AgeRangeData, AgeRangePrediction> _model; | |
| static void Main(string[] args) | |
| { | |
| LoadModel(); | |
| var prediction = _model.Predict(new AgeRangeData() | |
| { | |
| AgeStart = 1, | |
| AgeEnd = 2 | |
| }); | |
| Console.WriteLine($"Predicted age range is: {prediction.PredictedLabels}"); | |
| Console.ReadLine(); | |
| } | |
| private static async void LoadModel() | |
| { | |
| _model = await PredictionModel.ReadAsync<AgeRangeData, AgeRangePrediction>(_modelFilePath); | |
| } | |
| } |
Happy Coding!
Greetings @ Toronto
El Bruno
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