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I'm working with data scientists who would like to gain insight and understanding of the neural network models that they train using the visual interfaces in Azure Machine Learning Studio/Service. Is it possible to dump out and inspect the internal representation of a neural network model? Is there a way that I could write code that accesses the nodes and weights of a trained neural network in order to visualize the network as a graph structure? Or if Azure Machine Learning Studio/Service doesn't support this I'd appreciate advice on a different machine learning framework that might be more appropriate for this kind of analysis.

Things I have tried:

  • Train Model outputs an ILearnerDotNet (AML Studio) or Model (AML Service). I looked for items to drag into the workspace where I could write custom code such as Execute Python Script. They seem to accept datasets, but not ILearnerDotNet/Model as input.
  • I wasn't able to locate documentation about the ILearnerDotNet/Model interfaces.
  • Selecting the Train Model output offers the option to Save as Trained Model. This creates a trained model object and that would help me reference the trained model in other places, but I didn't find a way to use this to get at its internals.

I'm new to the Azure Machine Learning landscape and could use some help with how to get started on how to access this data.

1 Answer

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This document can help: click here

Here is another article on Neural network in Azure ML Studio.

You can use CNTK which will allow you to define your computational graph. You will be able to create a fully customizable model.

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