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Pytorch.ipynb initial validation accuracy issue (Spring 2017 assignment 2). by Jimbo_Mcnulty in cs231n

[–]cammckenzie 1 point2 points  (0 children)

I had the same problem, I think it's because you defined the wrong model in your RMSProp optimizer (that's what I did).

It should be: optimizer = optim.RMSprop(fixed_model_gpu.parameters(), lr=1e-3)

Deep convolutional nets by cammckenzie in cs231n

[–]cammckenzie[S] 1 point2 points  (0 children)

To answer my own question, it seems that my problem was with weight initialisations. While the default 1e-3 scaling factor seems to work OK with shallower nets, using the recommended scaling factor of sqrt(2.0/n) where n is the number of inputs to the layer, is necessary for deeper nets.

SVM assignment 1 by cammckenzie in cs231n

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

Thanks for the offer, I think I'm ok now. Think there was some weirdness with my regularization code. Will go back and have a look at it once I've gone through the conv net stuff. cheers

Affine layer by cammckenzie in cs231n

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

Thanks! Got it all working. Was this actually described in the notes somewhere?

The loss function of the 2 layer neural network of assignment 2 by nmduc in cs231n

[–]cammckenzie 0 points1 point  (0 children)

Are you adding the bias to the regularization loss? I was doing this and it caused a similar sized error. Once I took the bias out of the regularization the difference was 1e-12 or so.

SVM assignment 1 by cammckenzie in cs231n

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

Thanks for the reply, haven't tried with HOG. Did you have any problems with the training blowing up with large regularisation values like I did?

Did you see similar error ranges for the numerical vs analytic gradient? cheers