MLNetTensorFlow.cs

MLNetTensorFlow.cs

using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;

namespace MLNetConsole10
{
    class Program
    {
        static void Main(string[] args)
        {
            const int imageHeight = 32;
            const int imageWidth = 32;
            const string modelLocation = "cifar_model/frozen_model.pb";
            const string dataFile = "images/images.tsv";
            var imageFolder = Path.GetDirectoryName(dataFile);

            var pipeline = new LearningPipeline();
            pipeline.Add(new Microsoft.ML.Data.TextLoader(dataFile).CreateFrom<CifarData>());
            pipeline.Add(new ImageLoader(("ImagePath", "ImageReal"))
            {
                ImageFolder = imageFolder
            });

            pipeline.Add(new ImageResizer(("ImageReal", "ImageCropped"))
            {
                ImageHeight = imageHeight,
                ImageWidth = imageWidth,
                Resizing = ImageResizerTransformResizingKind.IsoCrop
            });

            pipeline.Add(new ImagePixelExtractor(("ImageCropped", "Input"))
            {
                UseAlpha = false,
                InterleaveArgb = true
            });

            pipeline.Add(new TensorFlowScorer()
            {
                ModelFile = modelLocation,
                InputColumns = new[] { "Input" },
                OutputColumn = "Output"
            });

            pipeline.Add(new ColumnConcatenator("Features", "Output"));
            pipeline.Add(new TextToKeyConverter("Label"));
            pipeline.Add(new StochasticDualCoordinateAscentClassifier());

            var model = pipeline.Train<CifarData, CifarPrediction>();
            model.TryGetScoreLabelNames(out var scoreLabels);

            Console.WriteLine($"ScoreLabels.Length {scoreLabels.Length}");
            Console.WriteLine($"banana {scoreLabels[0]}");
            Console.WriteLine($"hotdog {scoreLabels[1]}");
            Console.WriteLine($"tomato {scoreLabels[2]}");

            var prediction = model.Predict(new CifarData()
            {
                ImagePath = "images/banana.jpg"
            });
            Console.WriteLine($"{prediction.PredictedLabels[0]}");
            Console.WriteLine($"{prediction.PredictedLabels[1]}");
            Console.WriteLine($"{prediction.PredictedLabels[2]}");

        }
    }

    public class CifarData
    {
        [Column("0")]
        public string ImagePath;

        [Column("1")]
        public string Label;
    }

    public class CifarPrediction
    {
        [ColumnName("Score")]
        public float[] PredictedLabels;
    }
}