ResNet and DenseNet with Bipolar Rectified Linear Units

This repo is copied from: https://github.com/facebook/fb.resnet.torch

Then, the very deep 1K layer model (resnet-pre-act.lua) is copied from here: https://github.com/KaimingHe/resnet-1k-layers

Then, the DenseNet model (densenet.lua) is copied from here: https://github.com/liuzhuang13/DenseNet

Then, the code is modified in the following ways:

• Added command line option for replacing ReLU with BReLU (BReLU)
• Added command line options for DenseNet (growthRate, dropRate and bottleneck)
• Train Cifar-10 and Cifar-100 networks for 300 epochs
• Train Cifar-10 and Cifar-100 networks with learning rate decay at epoch 150 and 225 (as prescribed for densenet)
• Added logging of training and test error to log.txt every epoch
• Added logging of command line options to file opt.txt at startup
• Added logging of model structure as text to model.txt at startup
• Commented out debug printout every batch of training and validation

The current implementation of BReLU does not support training on multiple GPU's.

Results with BReLU compared to ReLU on Cifar-10 with data augmentation:

Results with BReLU compared to ReLU on Cifar-100 with data augmentation:

To train a pre-activation ResNet-1001 with BReLU with 1 GPU:

th main.lua -netType resnet-pre-act -depth 1001 -batchSize 64 -nGPU 1 -dataset cifar10 -nEpochs 200 -BReLU true

To train a ResNet-44 with BReLU with 1 GPU:

th main.lua -netType resnet -depth 44 -batchSize 128 -nGPU 1 -dataset cifar10 -BReLU true

To train a ResNet-110 with BReLU with 1 GPU:

th main.lua -netType resnet -depth 110 -batchSize 128 -nGPU 1 -dataset cifar10 -BReLU true

To train a DenseNet-100 with BReLU with 1 GPU:

th main.lua -netType densenet -depth 100 -batchSize 128 -growthRate 12 -nGPU 1 -dataset cifar10 -optnet true -BReLU true

To train a DenseNet-190 with BReLU with 1 GPU:

th main.lua -netType densenet -depth 190 -batchSize 64 -growthRate 40 -nGPU 1 -dataset cifar10 -optnet true -BReLU true