https://github.com/ezyang/convolution-visualizer
Convolution visualizations
https://github.com/ezyang/convolution-visualizer
Last synced: 5 days ago
JSON representation
Convolution visualizations
- Host: GitHub
- URL: https://github.com/ezyang/convolution-visualizer
- Owner: ezyang
- Created: 2018-02-12T09:37:22.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-04-22T23:23:34.000Z (10 months ago)
- Last Synced: 2024-10-14T21:45:17.809Z (4 months ago)
- Language: JavaScript
- Size: 1.32 MB
- Stars: 353
- Watchers: 9
- Forks: 108
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Convolution Visualizer
Live at [Convolution Visualizer](https://ezyang.github.io/convolution-visualizer/index.html).
Made with the help of our fine friends at [React](https://reactjs.org/)
and [D3.js](https://d3js.org/).
### Things to do
Want to play around with the code? Clone this repository and run `yarn
start` to start a development instance. The main code lives in
`src/index.js`. This [React manual](https://github.com/facebookincubator/create-react-app/blob/master/packages/react-scripts/template/README.md) may be of interest.
Here are some project ideas:
* Tweak the CSS so that the weight and output matrices
are displayed to the right of the input if there is space.
* Add a slider for adjusting speed of the animation.
* Add a slider which specifies the animation timestep you
are on; this way, you can run the animation forward and
backward by dragging the slider.
* Add output size and output padding sliders. When these
sliders are adjusted, you recompute the input size using
the transposed convolution formula.
* Add an onClick handler, which pins your selection at
the current mouse collection until another click
occurs (disabling the hover behavior.)
* Add a mode which, when enabled, labels cells with variables and
renders the mathematical formula to compute the output
cell you are moused over.
* Render code for PyTorch (or your favorite framework) which performs the
selected convolution.
* Add more exotic convolution types like circular convolution.
* Add a "true" convolution mode, where the weights are flipped
before multiplication.
* Support bigger input sizes than 16 (decreasing the size of
the squares when inputs are large), and optimize the code so that it
still runs quickly in these cases.
* Support assymmetric inputs/kernels/strides/dilations.
Bigger projects:
* Create an in-browser canvas application, which convolves
an input image against a displayed filter. Bonus points
if your canvas supports painting capabilities.
* Design a visualization which demonstrates the principles
of group convolution, allowing you to slide from standard
to depthwise convolution.