Abstract
Through a simulation of the tracking method for a neural network weight change on a 2D plane, we noticed that in some cases it was hard for untrained users to observe the neural network weight performance. To overcome this problem, we applied a transformation of the neural network weight trajectories on a 2D plane to the direct controller of a learning-type neural network. The simulation results confirmed that if the trajectory of the neural network weight change on a 2D plane had a simple structure, we could easily determine whether the learning of the neural network had terminated or not. However, if it had a more complex structure, we could not make this determination. The proposed transformation of the neural network weight trajectories to one-dimensional values will be useful for such cases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
