> That's the problem I always had, you may get them into a trained state, but good luck figuring out any reason 'why' they ended up in that state (or even what that state really is).
Can you give a specific example of what you mean? I ask because I see this sentiment often, but primarily from people who are very new to deep learning.
You can definitely debug a neural network. You mostly want to look at metrics from training such as gradient norms or try adjusting parameters to see if you can get a gain in overall performance as measured by cross validation performance.
You can definitely analyze a neural network. You do so by forming hypotheses, preparing datasets that reflect those hypotheses, and running them through your trained models, noting cross validation performance. It's also possible to visualize the weights in various ways, there are many papers written about it.
So what do you mean exactly when you say no one has figured out how to debug or analyze DNNs?
You cannot say: "This image was classified as a stop sign, because this part recognized the shape and this part recognized the color, and this part the text", which you could do with other approaches.
When it doesn't discover that it's a stop sign, how do you debug it? Did it recognize the shape.. who knows?
> When it doesn't discover that it's a stop sign, how do you debug it? Did it recognize the shape.. who knows?
Barring other analytic tools (like looking at which parts contribute the most to the wrong result), the same way you test other things when you have a (somewhat) black box:
Form hypotheses and test them.
Maybe it didn't recognise the shape, so try adjusting the image to clean it up, and once you have one it recognises, try to reduce and alter the difference between them. Maybe it turns out the image e.g. has the stop sign slightly covered, making the shape look wrong, and there's nothing in the training set like that.
Maybe the hue or brightness is off and the training set is mostly all lit a certain way. Test it by adjusting hue and brightness of the test image and see if it gets recognised.
And so on.
There are plenty of other areas where we are similarly constrained from taking apart that which we're observing, so it's not like this isn't something scientists are dealing with all the time.
Within comp.sci. we're just spoiled in that so much of what we do can be easily instrumented, isolated and tested in ways that often lets us determine clear, specific root causes through analysis.
Can you give a specific example of what you mean? I ask because I see this sentiment often, but primarily from people who are very new to deep learning.
You can definitely debug a neural network. You mostly want to look at metrics from training such as gradient norms or try adjusting parameters to see if you can get a gain in overall performance as measured by cross validation performance.
You can definitely analyze a neural network. You do so by forming hypotheses, preparing datasets that reflect those hypotheses, and running them through your trained models, noting cross validation performance. It's also possible to visualize the weights in various ways, there are many papers written about it.
So what do you mean exactly when you say no one has figured out how to debug or analyze DNNs?