Ok finally! I got a small reproducible example that gets this ridiculously low test error:

  
      github.com
  
  
    brando90/my_cifar10_pytorch/blob/master/my_cifar10.py 8
import torch
from torch.autograd import Variable
import torch.optim as optim

import torchvision
import torchvision.transforms as transforms

from math import inf

from pdb import set_trace as st

def error_criterion(outputs,labels):
    max_vals, max_indices = torch.max(outputs,1)
    train_error = (max_indices != labels).sum().data[0]/max_indices.size()[0]
    return train_error

def evalaute_mdl_data_set(loss,error,net,dataloader,enable_cuda,iterations=inf):
    '''
    Evaluate the error of the model under some loss and error with a specific data set.
    '''


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the errors are as follows:
$ python my_cifar10.py
running main
[-1, -1], (train_loss: 2.3172860145568848, train error: 0.0054) , (test loss: 2.317185878753662, test error: 0.0038)
about to start training
[0, 5], (train_loss: 2.22599835395813, train error: 0.015160000000000002) , (test loss: 2.0623881816864014, test error: 0.0066)
[1, 5], (train_loss: 2.014406657218933, train error: 0.00896) , (test loss: 1.9619578123092651, test error: 0.0195)
[2, 5], (train_loss: 1.9428715705871582, train error: 0.01402) , (test loss: 1.918603539466858, test error: 0.0047)
[3, 5], (train_loss: 1.9434458494186402, train error: 0.01192) , (test loss: 1.9194672107696533, test error: 0.0125)
[4, 5], (train_loss: 1.8804980754852294, train error: 0.00794) , (test loss: 1.8549214601516724, test error: 0.004)
[5, 5], (train_loss: 1.8573726177215577, train error: 0.010159999999999999) , (test loss: 1.8625996112823486, test error: 0.0158)
[6, 5], (train_loss: 1.8454653739929199, train error: 0.01524) , (test loss: 1.8155865669250488, test error: 0.0122)
[7, 5], (train_loss: 1.8140610456466675, train error: 0.01066) , (test loss: 1.808283805847168, test error: 0.0101)
[8, 5], (train_loss: 1.8036894083023072, train error: 0.00832) , (test loss: 1.799634575843811, test error: 0.007)
[9, 5], (train_loss: 1.8023016452789307, train error: 0.0077399999999999995) , (test loss: 1.8030155897140503, test error: 0.0114)
Done

this is my terrific breakthrough model @ptrblck. The amazing magnificent 1 conv 2 FCs NN. AlexNet^2. X’’'D