// set up a learning pipeline
var pipeline = mlContext.Transforms

    // step 1: load the images
    .LoadImages(
        outputColumnName: "input",
        imageFolder: "images",
        inputColumnName: nameof(ImageNetData.ImagePath))

    // step 2: resize the images to 224x224
    .Append(mlContext.Transforms.ResizeImages(
        outputColumnName: "input",
        imageWidth: 224,
        imageHeight: 224,
        inputColumnName: "input"))

    // step 3: extract pixels in a format the TF model can understand
    // these interleave and offset values are identical to the images the model was trained on
    .Append(mlContext.Transforms.ExtractPixels(
        outputColumnName: "input",
        interleavePixelColors: true,
        offsetImage: 117))

    // step 4: load the TensorFlow model
    .Append(mlContext.Model.LoadTensorFlowModel("models/tensorflow_inception_graph.pb")

    // step 5: score the images using the TF model
    .ScoreTensorFlowModel(
        outputColumnNames: new[] { "softmax2" },
        inputColumnNames: new[] { "input" },
        addBatchDimensionInput:true));

// train the model on the data file
Console.WriteLine("Start training model....");
var model = pipeline.Fit(data);
Console.WriteLine("Model training complete!");

// the rest of the code goes here....