Accuracy drops from 96.46% to 58.67%

[oscarriddle]

I tried the project on Python3.6. Here is the log, the accuracy drops significantly, which is different from your blog result: The accuracy dropped from 98.7% to 97.5%.

$ python3 test_pruning.py --prune
CHECK GPU AVAILEBLE: True
/home/web_server/dlpy72/py3.6/lib/python3.6/site-packages/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
  "please use transforms.Resize instead.")
/home/web_server/dlpy72/py3.6/lib/python3.6/site-packages/torchvision/transforms/transforms.py:397: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
  "please use transforms.RandomResizedCrop instead.")
Correct: 845, Failed: 31, Accuracy: 0.9646118721461188
Number of prunning iterations to reduce 67% filters 5
Ranking filters.. 
Layers that will be prunned {28: 130, 17: 56, 26: 71, 21: 53, 0: 5, 19: 60, 10: 20, 12: 20, 7: 9, 2: 4, 24: 62, 14: 13, 5: 9}
Prunning filters.. 
Filters prunned 87.87878787878788%
Correct: 838, Failed: 38, Accuracy: 0.95662100456621
Fine tuning to recover from prunning iteration.
Ranking filters.. 
Layers that will be prunned {28: 110, 26: 69, 14: 17, 24: 80, 21: 60, 10: 23, 17: 64, 7: 7, 19: 52, 12: 18, 5: 5, 0: 4, 2: 3}
Prunning filters.. 
Filters prunned 75.75757575757575%
Correct: 817, Failed: 59, Accuracy: 0.932648401826484
Fine tuning to recover from prunning iteration.
Ranking filters.. 
Layers that will be prunned {24: 80, 21: 47, 17: 75, 14: 22, 26: 92, 2: 4, 12: 23, 19: 64, 10: 21, 28: 67, 5: 8, 7: 8, 0: 1}
Prunning filters.. 
Filters prunned 63.63636363636363%
Correct: 754, Failed: 122, Accuracy: 0.860730593607306
Fine tuning to recover from prunning iteration.
Ranking filters.. 
Layers that will be prunned {26: 103, 19: 98, 14: 19, 17: 54, 21: 88, 24: 63, 12: 17, 10: 16, 28: 42, 7: 2, 2: 1, 0: 6, 5: 3}
Prunning filters.. 
Filters prunned 51.515151515151516%
Correct: 468, Failed: 408, Accuracy: 0.5342465753424658
Fine tuning to recover from prunning iteration.
Ranking filters.. 
Layers that will be prunned {21: 91, 17: 79, 5: 17, 14: 36, 19: 68, 10: 33, 12: 32, 26: 40, 0: 10, 24: 69, 2: 5, 28: 25, 7: 7}
Prunning filters.. 
Filters prunned 39.39393939393939%
Correct: 514, Failed: 362, Accuracy: 0.58675799086758
Fine tuning to recover from prunning iteration.
Finished. Going to fine tune the model a bit more

[oscarriddle]

solved by introducing finetuning