Retrain Custom TensorFlow Model Feature Request

I designed (not trained) a siamese CNN network in Keras (python) and exported it as a SavedModel with the random weights.
_Input = 4 dimensions (left/right image, height, width, RGB).
Output = change of images being equal (sigmoid)

But I don't understand how I can train this model from .NET Core?

When I use:
```csharp
using var tensorFlowModel = mlContext.Model.LoadTensorFlowModel("./model");
var pipeline = tensorFlowModel.ScoreTensorFlowModel(
new[] { inputComparer.Name },
new[] { outputComparer.Name }, addBatchDimensionInput: true);
```

Then I potentially can get the output from the model, but it's untrained, so this makes no sense.
So I need a trainer. I tried with LbfgsLogisticRegression but then I get "Schema mismatch for label column 'StatefulPartitionedCall': expected Boolean, got Vector" which makes sense as the Tensorflow model don't output Booleans, but a Vector. Do I need another trainer?

When I use the [ImageClassification](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/image-classification-api-transfer-learning) from this tutorial, I can only train one of the embedded networks from ImageClassificationTrainer.Architecture. It's a class output instead of binary, but that's OK. One class is also binary. But I want to train my own network.

[This example](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs) was the closest I could find.

How do I train my binary output Tensorflow siamese network?

The reason I want to train this network from .NET Core instead of Python is to test the speed improvements and rollout something independently of Python.