Error load Model (Generated from 64 bit ) From 32 bit

DevLob-zz commented 5 years ago

System information

OS version/distro: W10
.NET Version (eg., dotnet --4.6.1):

Issue

What did you do? i generated a model From 64 bit application and saved the .zip file then try load this Model from 32 bit application
What happened? it throw an exception while loading the model "'Could not load type 'Microsoft.ML.Transforms.Categorical.CategoricalTransform' from assembly 'Microsoft.ML.Transforms, Version=1.0.0.0, Culture=neutral"
What did you expect? my believe is there is a different in transform information between 32 , 64
Source code / logs

Please paste or attach the code or logs or traces that would be helpful to diagnose the issue you are reporting.

Generating model from 64 bit process

` public void BuildNormalTrainEvaluateAndSaveModel() { //Set a random seed for repeatable/deterministic results across multiple trainings. var mlContext = new MLContext(seed: 1);

        #region "STEP 1: Common data loading configuration"
        IDataView trainingDataView = GetNormalDataSet(mlContext);
        #endregion
        #region "STEP 2: Common data process configuration with pipeline data transformations"
        var dataProcessPipeline1 = mlContext.Transforms.Text.FeaturizeText(new List<string> { "tagText", "firstWord", "fontColor" }, "Features");

        var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", "fontSize",
                                                                                   "isBold",
                                                                                   "isItalic",
                                                                                   "isUnderLine",
                                                                                   "containsDot",
                                                                                   "containsQuestionMark",
                                                                                   "isAllCaps"
                                                                                    );
        //var dataProcessPipeline2 = mlContext.Transforms.Text.FeaturizeText(new List<string> { "Label" }, "Features");
        dataProcessPipeline.Append(dataProcessPipeline1);
        //mlContext.Transforms.CustomMapping()
        #endregion
        #region  "STEP 3: Set the training algorithm, then create and config the modelBuilder"                            
        var modelBuilder = new ModelBuilder<NormalTagsModelFeatures, NormalTagsPrediction>(mlContext, dataProcessPipeline);
        // We apply our selected Trainer 
        //modelBuilder.AddEstimator(dataProcessPipeline1);
        //modelBuilder.AddEstimator(new Dictionarizer("Label"));
        //modelBuilder.AddEstimator(dataProcessPipeline2);

        var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
            .Append(dataProcessPipeline1)
            .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features"))
            .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

        //var trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features");
        //modelBuilder.AddTrainer(trainer);
        modelBuilder.AddTrainer(pipeline);
        #endregion
        #region "STEP 4: Train the model fitting to the DataSet"
        //The pipeline is trained on the dataset that has been loaded and transformed.
        //Console.WriteLine("=============== Training the model ===============");

        modelBuilder.Train(trainingDataView);
        #endregion
        #region "STEP 5: Evaluate the model and show accuracy stats"
        //Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
        //var metrics = modelBuilder.EvaluateMultiClassClassificationModel(trainingDataView, "Label");
        //Common.ConsoleHelper.PrintMultiClassClassificationMetrics(trainer.ToString(), metrics);
        #endregion
        #region "STEP 6: Save/persist the trained model to a .ZIP file
        //Console.WriteLine("=============== Saving the model to a file ===============");
        string fullFilePath = GetModelPath("Norm");

Norm_131931638095612677_544677f3-3124-4979-aae8-0a6142ab4c67.Zip

        modelBuilder.SaveModelAsFile(fullFilePath);
        #endregion
    }

`

Load Model From 32 bit proccess

public void LoadNormalTagsModel(string modelPath)
        {
            var mlContext = new MLContext(seed: 0);
            ITransformer trainedModel;
//Here is the Exception line
            using (var stream = new FileStream(modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
            {
                trainedModel = mlContext.Model.Load(stream);
            }
            var predEngine = trainedModel.CreatePredictionEngine<NormalTagsModelFeatures, NormalTagsPrediction>(mlContext);

        }

i attached the generated model ,

how can i overcome this scenario please advice

History 👍 https://github.com/dotnet/machinelearning-samples/issues/216

DevLob-zz commented 5 years ago

Norm_131931638095612677_544677f3-3124-4979-aae8-0a6142ab4c67.Zip