public void Train()
{
var featurizerModelLocation = inputModelLocation;
ConsoleHelpers.ConsoleWriteHeader("Read model");
Console.WriteLine($"Model location: {featurizerModelLocation}");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Default parameters: image size=({ImageSettings.imageWidth},{ImageSettings.imageHeight}), image mean: {ImageSettings.mean}");
// Get the training data sample images
ConsoleHelpers.ConsoleWriteHeader("Collecting sample training data");
var data = mlContext.Data.LoadFromEnumerable(DataHelper.ReadFromFolder(imagesFolder));
Images.SummarizeTrainingData(imagesFolder);
// Train the model
ConsoleHelpers.ConsoleWriteHeader("Training classification model");
var pipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: LabelTokey, inputColumnName: "Label")
.Append(mlContext.Transforms.LoadImages(outputColumnName: "input", imageFolder: "", inputColumnName: nameof(Images.ImagePath)))
.Append(mlContext.Transforms.ResizeImages(outputColumnName: "input", imageWidth: ImageSettings.imageWidth, imageHeight: ImageSettings.imageHeight, inputColumnName: "input"))
.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input", interleavePixelColors: ImageSettings.channelsLast, offsetImage: ImageSettings.mean))
.Append(mlContext.Model.LoadTensorFlowModel(inputModelLocation).
ScoreTensorFlowModel(outputColumnNames: new[] { "softmax2_pre_activation" }, inputColumnNames: new[] { "input" }, addBatchDimensionInput: true))
.Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(labelColumnName: LabelTokey, featureColumnName: "softmax2_pre_activation"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue(PredictedLabelValue, "PredictedLabel"))
.AppendCacheCheckpoint(mlContext);
ITransformer model = pipeline.Fit(data);
// Save the model to assets/outputs
ConsoleHelpers.ConsoleWriteHeader("Save model to local file");
mlContext.Model.Save(model, data.Schema, outputModelLocation);
Console.WriteLine($"Model saved: {outputModelLocation}");
// Get some performance metrics on the model
var trainData = model.Transform(data);
var classificationContext = mlContext.MulticlassClassification;
ConsoleHelpers.ConsoleWriteHeader("Evaluating classification metrics");
var metrics = classificationContext.Evaluate(trainData, labelColumnName: LabelTokey, predictedLabelColumnName: "PredictedLabel");
Console.WriteLine($"Total Log Loss is: {metrics.LogLoss}");
Console.WriteLine($"Per Class LogLoss is: {String.Join(" , ", metrics.PerClassLogLoss.Select(c => c.ToString()))}");
}
static void Main()
{
string assetsPath = Path.Combine(new FileInfo(typeof(Program).Assembly.Location).Directory.FullName, "assets");
var trainingImages = Path.GetFullPath("../../../assets/inputs/train"); // "C:\\repo\\Image-classification-transfer-learning\\grocery\\train2\\";
var testingImages = Path.GetFullPath("../../../../ImageClassification.Test/assets/test/thor");
var featurizerModel = Path.Combine(assetsPath, "inputs", "inception", "tensorflow_inception_graph.pb");
string trainedModelLocation = Path.GetFullPath("../../../imageClassifier.zip");
//Train model
try
{
var modelBuilder = new ModelBuilder(trainingImages, featurizerModel, trainedModelLocation);
modelBuilder.Train();
}
catch (Exception ex)
{
ConsoleHelpers.ConsoleWriteException(ex.Message);
}
//Test model
ConsoleHelpers.ConsoleWriteHeader("Test model with few sample images");
try
{
ConsoleHelpers.ConsoleWriteHeader("Load saved model");
TrainedModel model = new TrainedModel(trainedModelLocation);
ImagePrediction prediction = new ImagePrediction();
List <PredictionResult> results = new List <PredictionResult>();
List <ImageData> imageList = DataHelper.ReadFromFolder(testingImages);
foreach (ImageData image in imageList)
{
prediction = model.predictor.Predict(image);
PredictionResult result = new PredictionResult(Path.GetFileName(image.ImagePath).ToString(), prediction.PredictedLabelValue, prediction.Score.Max());
results.Add(result);
}
var output = JsonConvert.SerializeObject(results, Formatting.Indented);
Console.WriteLine(output);
}
catch (Exception ex)
{
ConsoleHelpers.ConsoleWriteException(ex.Message);
}
}