Comments (2)
I think the general consensus is that local response normalization is sort of pointless. To the best of my knowledge people aren't really using this anymore. That said, if you want to reproduce existing models exactly I guess you have no choice but to implement it. It's just a bit unfortunate to be spending valuable development time on stuff like this :)
We have a pure-Theano implementation in Lasagne: https://github.com/Lasagne/Lasagne/blob/master/lasagne/layers/normalization.py#L47
That should translate to CGT fairly well, I assume! It's not the fastest, but maybe it'll save you some time.
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That's interesting to hear that LRN is not considered to be important anymore, and thanks for the pointer to the Lasagne code.
I was also thinking about implementing the cross-channel LRN with a matrix multiplication (after permuting the axes so that channels are last) and the 2D neighborhood version as im2col + matrix multiplication.
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Related Issues (20)
- Product of diagonal elements may be `nan` HOT 1
- cgt.stack only supports stacking scalars HOT 1
- Error on tutorial
- Bug in graph simplification HOT 2
- OSX: Build fails with OpenBLAS dependency HOT 1
- Questions about Installation : cmake and windows HOT 1
- Memory consumption of native backend on Linux
- Random number generation and graph simplification HOT 2
- Negative reshaping check not enforced sometimes HOT 1
- Parameter indexing leads to an assertion error in module definition HOT 1
- Issues with shared scalar HOT 2
- Random number generation and modules (and simplification again)
- CIFAR Demo Error when using GPU
- Installation with anaconda
- Support for square root operation on Node HOT 2
- Simple operation breaks CGT
- Berkely BidMach and CGT
- How to build with python 3?
- Any reference to read? HOT 1
- Links in read-me are dead
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