aharley / nn_vis Goto Github PK

I think Web Server for Chrome can solve this problem

Hello,

I am working on a project aimed at developing educational tools to teach high school students about Convolutional Neural Networks (CNNs). I found your nn_vis tool to be a great starting point for this endeavor.

Feature Implementation Request:
I am interested in enhancing the nn_vis tool by adding a feature where clicking on a neuron or feature map box in a higher-level convolutional layer would display edges connecting it to its corresponding neurons or feature maps in the lower layer, continuing down to the first layer. This would help in illustrating the hierarchical structure of CNNs and the connection between different layers.

Purpose:
The purpose of this enhancement is to provide a more interactive and comprehensive understanding of how different layers in a CNN are interconnected, which is crucial for beginners in machine learning.

Request for Insights:
As I am planning to implement this feature myself, I would greatly appreciate any insights or guidance you could provide on how best to approach this modification. Specifically, I am interested in:

• Understanding the current structure of the nn_vis tool, particularly how neurons and feature maps are represented and handled.
• Suggestions on how to efficiently track and display connections between layers in the tool's existing framework.
• Any potential challenges or considerations I should be aware of while implementing this feature.
• It would be very helpful to know what are the functions handling calculating the edges and displaying them.

Vision

Here is an image of how I imagine the fully implemented version would look like.

Conclusion:
Your expertise and insights into the tool's design and functionality would be incredibly valuable for this enhancement. I am eager to learn from your experience and contribute to the development of this educational resource.

Thank you for your time and for creating such a valuable tool for the machine learning community.

Best regards,
Marco Qin
Brandeis University