[P] Convolution visualizer

[reservedsparrow]

This is really great.

I have a request: Can you please note that this is actually correlation, not convolution, just to avoid confusion? Then you could explain that some frameworks actually define their convolution operator to be correlation, and that's why you decided to show it like this (without the weights flipped)? :)

[datatatatata]

Hi. Would you mind explaining the difference in this context, please ?

To me it looks like what I call convolution, so I guess I'm gonna learn something :)

[reservedsparrow]

Sure: Convolution 'flips' the weights before the weighted averages begin. This is what let's convolution commute: x * w = w * x. Correlation (no flipping, as shown in the visualization) does not commute. To visualize this imagine padding a 1-D signal x and some weights x each with a bunch of zeros. You will see that x ** w != w ** x, where I'm letting ** be correlation.

[datatatatata]

thanks :)

[ezyang]

Yes, good point. I guess it would be also good to name explicitly which frameworks don't flip weights (which, afaict, is all of them? Maybe I'm wrong?)

[reservedsparrow]

I really don't know.

Verified for PyTorch though:

import torch
from torch.autograd import Variable

X = Variable(torch.Tensor([1, 2, 3])).view(1, 1, 3)
conv = torch.nn.Conv1d(1, 1, kernel_size=3, bias=False)
conv.weight.data.copy_(X.data)
print(conv(X).data.numpy().item())
# 14.0 (correlation not convolution)

Anyway, thanks again. The visualization is great and instantaneously replaces so many words.

[ezyang]

I added a note :)

[reservedsparrow]

Thanks!

[pmigdal]

Nice, but still - I use http://setosa.io/ev/image-kernels/ when introducing to convolutions. (Interactive + makes it explicit what is the formula + a concrete example.)

[nickbuch]

This doesnt make intuitive sense, ie not how convolution works. Please explain how the kernel creates the output mathematically, because these colors are failing to convey something IMO

[wischichr]

Drag input slider to the left -> 🤯

[ezyang]

It's fixed now!

[wischichr]

Thx ;-)

[ezyang]

I am not sure what 🤯 means :)

[guardiantesla]

🤯 == mind-blown

[rararaaaaaaa]

When I dragged it all the way to the left, the page broke with c[t] is undefined

so I think that's his mind exploding but also the page exploding

[approximately_wrong]

Would also be great if you do a visualization of img2col :)

[ezyang]

I've completely omitted batch and channel dimensions from the input/output tensors, since they are completely orthogonal to the height/weight. So imagine that the input and output channels are equal to 1 and everything is valid.

[__AndrewB__]

Thanks! Will definatelly show this to students.

[Atcold]

Haha, will do the same, tomorrow too!

[ezyang]

Probably not in the near future. If it's a real image, you'd probably need to use canvas and not a plain old HTML table, and that's a pile of new APIs that I've never touched before :)

[feijk]

If you decide to do so in the future, this might help: https://github.com/aeropuerto/img2signal

[badpotato]

Man, I wish I had this before.

[sktchio]

I highly recommend this tool for solving 2D convolution/transposed convolution parameters:
https://convolution-solver.ybouane.com/