After appropriately patching Keras, I proceeded with PReLUs. Unfortunately, I’ve not matched plain ReLUs for this task, albeight I came close. At Epoch 61, I scored a low of 1.72% training and 2.84% validation error, passably good but not as good as the 2.48% validation error my plain ReLU network achieved. The training curves can … Continue reading Close, but no cigar: PReLUs give ~2.84%| Olexa Bilaniuk's IFT6266H16 Course Blog
In my previous post, I had brought up as a possible research direction that the SqueezeNet authors had used sparsification, which broadly speaking involves: Train the neural network until it stops …| Olexa Bilaniuk's IFT6266H16 Course Blog
After removing dropout from the input, and training for 193 epochs with three manual lowerings of the learning rate, I’ve now arrived at 2.06% training error and 3.32% validation error, using a full-sized SqueezeNet with CUBAN-style input layers. This is in contrast to the same network but with dropout applied on the input, which got … Continue reading New Success with SqueezeNet: 3.32% Validation Error| Olexa Bilaniuk's IFT6266H16 Course Blog
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First, an idea that didn’t work. I hypothesized that images shouldn’t need to be rotated as far as 60 degrees and that 30 degrees might be enough. Wrong. This caused training to plateau…| Olexa Bilaniuk's IFT6266H16 Course Blog
I’ve just created a new SqueezeNet-based model with Keras, which reuses some of my ideas from CUBAN, my IFT6390 project. In that project I had been using: “Fine” filters, run on t…| Olexa Bilaniuk's IFT6266H16 Course Blog