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SGDR: Stochastic Gradient Descent with Restarts

Lasagne implementation of SGDR on WRNs from "SGDR: Stochastic Gradient Descent with Restarts" by Ilya Loshchilov and Frank Hutter (http://arxiv.org/abs/1608.03983)
This code is based on Lasagne Recipes available at https://github.com/Lasagne/Recipes/blob/master/papers/deep_residual_learning/Deep_Residual_Learning_CIFAR-10.py and on WRNs implementation by Florian Muellerklein available at https://gist.github.com/FlorianMuellerklein/3d9ba175038a3f2e7de3794fa303f1ee

The only input is "iscenario" index used to reproduce the experiments given in the paper
scenario #1 and #2 correspond to the original multi-step learning rate decay on CIFAR-10
scenarios [3-6] are 4 options for our SGDR
scenarios [7-10] are the same options but for 2 times wider WRNs, i.e., WRN-28-20
scenarios [11-20] are the same as [1-10] but for CIFAR-100
scenarios [21-28] are the the original multi-step learning rate decay for 2 times wider WRNs on CIFAR-10 and CIFAR-100

The best reported results in the paper are by SGDR with T0 = 10 and Tmult = 2
3.74% on CIFAR-10 (median of 2 runs of iscenario #10)
18.70% on CIFAR-100 (median of 2 runs of iscenario #20)

Ensembles of WRN-28-10 models trained by SGDR show
3.14% on CIFAR-10
16.21% on CIFAR-100
The latest version of the paper is available at https://openreview.net/pdf?id=Skq89Scxx