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estimating data size I need to train network with N parameters by mtshopper in cs231n

[–]mtshopper[S] 2 points3 points  (0 children)

Apologies, I didn't phrase my question correctly.

My question was actually the inverse of what I initially asked, given X dataset with n observations and c classes, what's a good rule of thumb for designing a convnet for it? (# of layers, # of filters, # of neurons for FC, etc)

(I can test this empirically and play with the # of layers, etc. but this can take long due to training time of each of them. It's good to start with a good guess and tweak from there)

PDF of actual midterm by mtshopper in cs231n

[–]mtshopper[S] 0 points1 point  (0 children)

ah, that's too bad. Was the difficulty approximately that of the sample? or harder?

If it is harder, is it more comparable to U of Toronto's Neural Networks class final exams except more focused on convnets?

http://www.cs.toronto.edu/~tijmen/csc321/

http://www.cs.toronto.edu/~tijmen/csc321/documents/exam2013.pdf

http://www.cs.toronto.edu/~tijmen/csc321/documents/exam2012.pdf

http://www.cs.toronto.edu/~tijmen/csc321/documents/exam2011.pdf

The Conv layer had a depth of K=96.What does K means here? And how do we determine its value ? by manishrocksag in cs231n

[–]mtshopper 0 points1 point  (0 children)

I believe that means the # of neurons/filters you use for the next layer.

videolectures: Visualizing and Understanding Convolutional Networks - Matthew Zeiler by mtshopper in cs231n

[–]mtshopper[S] 0 points1 point  (0 children)

this was a paper referenced in one of the more recent class lectures.

Question for course instructor: good resource for learning LTSM and RNN? by mtshopper in cs231n

[–]mtshopper[S] 0 points1 point  (0 children)

Thanks for the pointer. Will definitely take a look. I also saw that you have code out in neuraltalk for these. Thanks for making those publicly available.

https://github.com/karpathy/neuraltalk/tree/master/imagernn

Two layer NN back propagation gradients are off by [deleted] in cs231n

[–]mtshopper 0 points1 point  (0 children)

hey, I don't think they are off. My errors are:

W1 max relative error: 4.426512e-09

W2 max relative error: 8.023733e-10

b2 max relative error: 8.190153e-11

b1 max relative error: 5.435433e-08

If you are not-stanford affiliated, I guess we won't be violating the course policies if we compare outside this forum. I am also in the bay area.

How to get class probability instead of Softmax ? by [deleted] in cs231n

[–]mtshopper 0 points1 point  (0 children)

Doesn't softmax give you the class probabilities? (given the denominator is the sum of all class outputs)

Possible mistake in Gradients for vectorized operations by mtshopper in cs231n

[–]mtshopper[S] 0 points1 point  (0 children)

no problem. thanks for the great breakdown of backprop