An Interactive Node-Link Visualization of Convolutional Neural Networks

Abstract

Convolutional neural networks are at the core of state-of-the-art approaches to a variety of computer vision tasks. Visualizations of neural networks typically take the form of static node-link diagrams, which illustrate only the structure of a network, rather than the behavior. Motivated by this observation, this project presents a new interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real time.

Paper PDF

Live demos

Live demos for all models are available at https://adamharley.com/nn_vis:

1. 3d visualization of a multi-layer perceptron:

   

2. 3d visualization of a convolutional network:

   

3. 2d visualization of a multi-layer perceptron:

   

4. 2d visualization of a convolutional network:

   

FAQ

• Can I use this in my course/textbook/presentation?

  • Yes! Please just cite the work appropriately.

• How do I cite you?

  • Here is a plaintext citation:

    A. W. Harley, "An Interactive Node-Link Visualization of Convolutional Neural Networks," in ISVC, pages 867-877, 2015

    Here is a bibtex snippet:

    @inproceedings{harley2015isvc,
    title = {An Interactive Node-Link Visualization of Convolutional Neural Networks},
    author = {Adam W Harley},
    booktitle = {ISVC},
    pages = {867--877},
    year = {2015}
    }
    

• I tried to run the code locally, and I see classifications, but I do not see the network visualization. What's wrong?

  • This is probably related to json requests being blocked by something called CORS policy. The solution is to upload the code to a web address and run it from there, instead of running locally.

Contact: aharley@cs.stanford.edu