Resnet

This repository contains tensorflow implementation of Residual Neural Network. For comparison, three network architectures are implemented:

• PlainNet: Conventional stacking of Convolutional layers
• ResNet: Stacking of Residual "blocks"
• ResNetV2: Stacking Residual blocks with Identity mappings

Usage

To run sample training on sin function dataset, run

$ python sin_function.py

This generates the comparison graphs between PlainNet and ResNet.

To run the training on CIFAR-10 with a particular architecture, run

$ python train.py <Network> -n <nblocks>

Eg: python train.py ResNetV2 -n 4

Apart from the defaults, you can also specify command line parameters to tweak the whole network:

$ python train.py --help

to see all the options.

Results on CIFAR 10

The results obtained on CIFAR 10 dataset are on this tensorboard log. To replicate the experiment, run in shell:

for net in PlainNet ResNet ResNetV2; do
	for size in 2 4 6; do
		python train.py $net -n $size
	done
done