Simplify input scaling on LeNet network #976
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@gheinrich any issue with this? The reasons for a separate power layer aren't particularly compelling (see #733). |
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@mpbrigham maybe also reflect this in the torch's implementation by changing https://github.com/NVIDIA/DIGITS/blob/master/digits/standard-networks/torch/lenet.lua#L36. I recall the learning rate should also be updated to yield identical results, not sure. |
Update scaling factor in Torch implementation to 0.0125.
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@TimZaman I've synced the scaling factor in torch's implementation (commit |
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That looks OK to me thanks. |
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Thanks for the improvement @mpbrigham! |
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Mar 30, 2017
Simplify input scaling on LeNet network
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Removed scaling from data layers and kept single unified power layer. Previous definition was faster due to multi-threaded data loader, but potentially less straightforward to new users.
The scaling factor 0.0125 corresponds to 1/(standard deviation on MNIST dataset), which is ~80 per pixel (from a range of [0-255] per pixel). Scaling factor comment updated to reflect this.