System.MissingMethodException: 'Method not found: 'Tensorflow.Tensor Tensorflow.tensorflow.truncated_normal' #4678
Description
System information
- OS version/distro:
Window 10 build-17134 - .NET Version (eg., dotnet --info):
NetCore 3.1
Issue
- What did you do?
That example Tutorial: Automated visual inspection using transfer learning with the ML.NET Image Classification API - What happened?
Exception on step ITransformer trainedModel = trainingPipeline.Fit(trainSet);:
Method not found: 'Tensorflow.Tensor Tensorflow.tensorflow.truncated_normal(Int32[], Single, Single, Tensorflow.TF_DataType, System.Nullable`1, System.String)'.
static void Main(string[] args)
{
var workspaceRelativePath = @"C:\Test_dev\ML_Main_Test\workspace";
var assetsRelativePath = @"C:\Test_dev\ML_Main_Test\assets";
MLContext mlContext = new MLContext();
IEnumerable<ImageData> images = LoadImagesFromDirectory(folder: assetsRelativePath, useFolderNameAsLabel: true);
var test = images.First();
IDataView imageData = mlContext.Data.LoadFromEnumerable(images);
IDataView shuffledData = mlContext.Data.ShuffleRows(imageData);
var preprocessingPipeline = mlContext.Transforms.Conversion.MapValueToKey(
inputColumnName: "Label",
outputColumnName: "LabelAsKey")
.Append(mlContext.Transforms.LoadRawImageBytes(
outputColumnName: "Image",
imageFolder: assetsRelativePath,
inputColumnName: "ImagePath"));
IDataView preProcessedData = preprocessingPipeline.Fit(shuffledData)
.Transform(shuffledData);
TrainTestData trainSplit = mlContext.Data.TrainTestSplit(data: preProcessedData, testFraction: 0.2);
TrainTestData validationTestSplit = mlContext.Data.TrainTestSplit(trainSplit.TestSet);
IDataView trainSet = trainSplit.TrainSet;
IDataView validationSet = validationTestSplit.TrainSet;
IDataView testSet = validationTestSplit.TestSet;
var classifierOptions = new ImageClassificationTrainer.Options()
{
FeatureColumnName = "Image",
LabelColumnName = "LabelAsKey",
ValidationSet = validationSet,
Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
MetricsCallback = Console.WriteLine,
TestOnTrainSet = false,
ReuseTrainSetBottleneckCachedValues = true,
ReuseValidationSetBottleneckCachedValues = true,
WorkspacePath = workspaceRelativePath
};
var trainingPipeline = mlContext.MulticlassClassification.Trainers.ImageClassification(classifierOptions)
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
ITransformer trainedModel = trainingPipeline.Fit(trainSet);
mlContext.Model.Save(trainedModel, preProcessedData.Schema, "model.zip");
ClassifySingleImage(mlContext, testSet, trainedModel);
}