I wonder if someone could give some hint on constructing a simple demo program for class identification, more concretely a network which can identify a so-called "two phase flow regime". I believe the problem is not complicated, but I am a complete novice of using Wolfram codes for such a purpose, this is the only hinder. The various types of two phase flow of water in a heated channel are usually divided into four classes depending on the topology of the flow, called "bubbly", "slug", "churn-turbulent" and "annular". See an illustration below.

My idea, following a webinar by Jon McLoone recently, was to start with downloading an already existing code for image identification from the Wolfram Neural Network Repository (Wolfram ImageIdentify Net V1), then re-train the network with my data. Here is a screenshot of the attempt I made: However, in this form it does not work, because the repository network has an output vector of size 4315, with labels which do not contain mine, i.e. "bubbly" etc. What I would need is to re-define the size of the output vector, and define the four classes which I want to use. Is this possible? If so, any hint on how? Or, is there a network in the repository, which is more suited to such a task? I would need this to make a small demonstration of pattern recognition with neural networks in a course which is about how to solve inverse tasks in diagnostics of industrial processes. Any input would be highly appreciated.