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nmi-lab / decolle-public

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accuracy #8

Open meltingCat opened 3 years ago

meltingCat commented 3 years ago

Hi, I ran the scripts train_lenet_decolle.py with default setting and get the test_acc.npy in logs folder. The result is down below, am I doing right? What are the meaing in each column?

Python 3.8.3 (default, Jul  2 2020, 16:21:59) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.load('test_acc.npy')
array([[0.67275785, 0.96008969, 0.97578475, 0.97421525],
       [0.71950673, 0.9706278 , 0.98307175, 0.98273543],
       [0.74204036, 0.97253363, 0.98430493, 0.97443946],
       [0.75257848, 0.97443946, 0.98598655, 0.98363229],
       [0.76345291, 0.97600897, 0.98688341, 0.98542601],
       [0.77399103, 0.97780269, 0.98643498, 0.9853139 ],
       [0.78195067, 0.97959641, 0.98800448, 0.98508969],
       [0.78396861, 0.97847534, 0.98553812, 0.97970852],
       [0.78251121, 0.97959641, 0.98789238, 0.98508969],
       [0.78026906, 0.97982063, 0.98766816, 0.9793722 ],
       [0.78621076, 0.97982063, 0.98665919, 0.97701794],
       [0.79091928, 0.98038117, 0.98867713, 0.98441704],
       [0.79147982, 0.98038117, 0.98834081, 0.98408072],
       [0.79248879, 0.98127803, 0.9896861 , 0.97096413],
       [0.79809417, 0.98161435, 0.98878924, 0.98206278],
       [0.79002242, 0.97959641, 0.98834081, 0.982287  ],
       [0.7970852 , 0.98049327, 0.98946188, 0.97825112],
       [0.79585202, 0.98004484, 0.98811659, 0.9808296 ],
       [0.79730942, 0.9809417 , 0.98778027, 0.9808296 ],
       [0.7941704 , 0.9809417 , 0.98856502, 0.98520179],
       [0.79495516, 0.98161435, 0.9882287 , 0.98307175],
       [0.8014574 , 0.98139013, 0.98923767, 0.97679372],
       [0.79977578, 0.9794843 , 0.98744395, 0.98026906],
       [0.79977578, 0.98139013, 0.98800448, 0.98183857],
       [0.79742152, 0.98150224, 0.98834081, 0.97780269],
       [0.8       , 0.98150224, 0.98834081, 0.98172646],
       [0.80190583, 0.98195067, 0.98912556, 0.98195067],
       [0.80403587, 0.98116592, 0.98845291, 0.98127803],
       [0.80807175, 0.98060538, 0.98811659, 0.97993274],
       [0.80257848, 0.98150224, 0.98800448, 0.97813901],
       [0.80235426, 0.98206278, 0.99058296, 0.98475336],
       [0.80493274, 0.98172646, 0.99103139, 0.98497758],
       [0.80639013, 0.98217489, 0.9896861 , 0.98452915],
       [0.80672646, 0.98273543, 0.99024664, 0.9853139 ],
       [0.80325112, 0.98172646, 0.98957399, 0.98497758],
       [0.80560538, 0.98251121, 0.99013453, 0.98598655],
       [0.80526906, 0.98172646, 0.98991031, 0.98733184],
       [0.80257848, 0.98206278, 0.99002242, 0.98396861],
       [0.80504484, 0.982287  , 0.99002242, 0.98452915],
       [0.80515695, 0.98217489, 0.99069507, 0.9867713 ],
       [0.80818386, 0.98206278, 0.99035874, 0.98553812],
       [0.80493274, 0.9823991 , 0.99035874, 0.98699552],
       [0.80661435, 0.98262332, 0.99058296, 0.98542601],
       [0.80751121, 0.98195067, 0.99002242, 0.98710762],
       [0.80795964, 0.98206278, 0.99013453, 0.98621076],
       [0.80616592, 0.98262332, 0.99024664, 0.98654709],
       [0.8059417 , 0.98217489, 0.9896861 , 0.98497758],
       [0.8058296 , 0.982287  , 0.99024664, 0.98508969],
       [0.80538117, 0.98273543, 0.99002242, 0.98452915],
       [0.807287  , 0.98217489, 0.99024664, 0.98721973],
       [0.80695067, 0.98150224, 0.98912556, 0.98587444],
       [0.80773543, 0.98295964, 0.98979821, 0.98699552],
       [0.80807175, 0.98206278, 0.98991031, 0.98587444],
       [0.8088565 , 0.98139013, 0.98991031, 0.9853139 ],
       [0.80840807, 0.98206278, 0.99035874, 0.98396861],
       [0.80695067, 0.98262332, 0.99035874, 0.98609865],
       [0.80840807, 0.98251121, 0.99013453, 0.98542601],
       [0.80426009, 0.98262332, 0.9896861 , 0.98609865],
       [0.80784753, 0.9823991 , 0.99013453, 0.98396861],
       [0.807287  , 0.982287  , 0.99024664, 0.98553812],
       [0.80695067, 0.9823991 , 0.99058296, 0.98609865],
       [0.80616592, 0.98273543, 0.99047085, 0.98587444],
       [0.80852018, 0.9823991 , 0.99069507, 0.98643498],
       [0.80639013, 0.98273543, 0.98979821, 0.98699552],
       [0.8088565 , 0.982287  , 0.99058296, 0.98699552],
       [0.80762332, 0.98195067, 0.98991031, 0.98699552],
       [0.80807175, 0.98206278, 0.99013453, 0.98553812],
       [0.80639013, 0.98262332, 0.98991031, 0.98665919],
       [0.80616592, 0.98273543, 0.99047085, 0.98699552],
       [0.80773543, 0.9823991 , 0.99080717, 0.98654709],
       [0.80639013, 0.98195067, 0.99035874, 0.98710762],
       [0.80807175, 0.98251121, 0.99047085, 0.98778027],
       [0.80997758, 0.98195067, 0.99013453, 0.98621076],
       [0.80605381, 0.982287  , 0.99047085, 0.98621076],
       [0.80952915, 0.98251121, 0.99013453, 0.98733184],
       [0.80930493, 0.98206278, 0.98979821, 0.98699552],
       [0.8073991 , 0.98262332, 0.99024664, 0.98654709],
       [0.81053812, 0.98206278, 0.99024664, 0.98665919],
       [0.80930493, 0.98251121, 0.99002242, 0.98834081],
       [0.8088565 , 0.9823991 , 0.99047085, 0.98699552],
       [0.80896861, 0.98295964, 0.98991031, 0.98688341],
       [0.80919283, 0.98251121, 0.99002242, 0.98699552],
       [0.80930493, 0.98295964, 0.99002242, 0.98665919],
       [0.80840807, 0.9823991 , 0.99024664, 0.98699552],
       [0.8117713 , 0.98217489, 0.98946188, 0.9867713 ],
       [0.80964126, 0.98307175, 0.99058296, 0.98598655],
       [0.80852018, 0.98195067, 0.98991031, 0.98665919],
       [0.8103139 , 0.982287  , 0.99002242, 0.98688341],
       [0.81143498, 0.98251121, 0.99047085, 0.98755605],
       [0.80997758, 0.98284753, 0.99024664, 0.98721973],
       [0.81087444, 0.98217489, 0.9896861 , 0.98665919],
       [0.80762332, 0.98195067, 0.99024664, 0.98688341],
       [0.81121076, 0.98262332, 0.9896861 , 0.98699552],
       [0.80930493, 0.982287  , 0.98991031, 0.98654709],
       [0.80795964, 0.98251121, 0.99002242, 0.98665919],
       [0.80840807, 0.98295964, 0.99002242, 0.9867713 ],
       [0.8103139 , 0.98273543, 0.99002242, 0.9867713 ],
       [0.81076233, 0.98284753, 0.98991031, 0.98665919],
       [0.81053812, 0.9823991 , 0.99013453, 0.98654709]])
eneftci commented 3 years ago

Yes, this looks correct. Each column is the accuracy in the given layer. So .99013453 is the accuracy of the third convolutional layer. The last column is the accuracy of a non-spiking softmax layer, assuming you didn't change the model.