Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x

CIFAR 10



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%matplotlib inline
%reload_ext autoreload
%autoreload 2



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from fastai.conv_learner import *
PATH = "data/cifar10/"
os.makedirs(PATH,exist_ok=True)



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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
stats = (np.array([ 0.4914 ,  0.48216,  0.44653]), np.array([ 0.24703,  0.24349,  0.26159]))



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def get_data(sz,bs):
    tfms = tfms_from_stats(stats, sz, aug_tfms=[RandomFlip()], pad=sz//8)
    return ImageClassifierData.from_paths(PATH, val_name='test', tfms=tfms, bs=bs)



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bs=128

Look at data



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data = get_data(32,4)



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x,y=next(iter(data.trn_dl))



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plt.imshow(data.trn_ds.denorm(x)[0]);



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plt.imshow(data.trn_ds.denorm(x)[1]);

Initial model



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from fastai.models.cifar10.resnext import resnext29_8_64

m = resnext29_8_64()
bm = BasicModel(m.cuda(), name='cifar10_rn29_8_64')



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data = get_data(8,bs*4)



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learn = ConvLearner(data, bm)
learn.unfreeze()



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lr=1e-2; wd=5e-4



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learn.lr_find()









    





 
 










    



[        0.              2.41622  23356282.              0.09889]



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learn.sched.plot()



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%time learn.fit(lr, 1)









    





 
 










    



[ 0.       1.69719  1.58988  0.43418]                       

CPU times: user 33.3 s, sys: 7.2 s, total: 40.5 s
Wall time: 40.8 s



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learn.fit(lr, 2, cycle_len=1)









    





 
 










    



[ 0.       1.43256  1.39725  0.49746]                       
[ 1.       1.35969  1.3411   0.51602]



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learn.fit(lr, 3, cycle_len=1, cycle_mult=2, wds=wd)









    





 
 










    



[ 0.       1.31041  1.29344  0.53174]                       
[ 1.       1.30313  1.31292  0.53418]                       
[ 2.       1.15682  1.22019  0.5668 ]                       
[ 3.       1.26632  1.34606  0.54121]                       
[ 4.       1.14698  1.18958  0.57598]                       
[ 5.       1.02205  1.13905  0.60254]                       
[ 6.       0.93291  1.13761  0.60596]



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learn.save('8x8_8')

16x16



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learn.load('8x8_8')



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learn.set_data(get_data(16,bs*2))



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%time learn.fit(1e-3, 1, wds=wd)









    





 
 










    



[ 0.       5.14421  5.90379  0.28985]



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learn.unfreeze()



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learn.lr_find()









    





 
 










    



[   0.         2.28283  966.72705    0.09523]



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learn.sched.plot()



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lr=1e-2



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learn.fit(lr, 2, cycle_len=1, wds=wd)









    





 
 










    



[ 0.       1.38133  1.36374  0.51137]                       
[ 1.       1.13313  1.14011  0.60384]



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learn.fit(lr, 3, cycle_len=1, cycle_mult=2, wds=wd)









    





 
 










    



[ 0.       0.98926  0.98239  0.6605 ]                        
[ 1.       0.9254   0.93705  0.67257]                        
[ 2.       0.75597  0.785    0.72646]                        
[ 3.       0.84544  0.90955  0.68958]                        
[ 4.       0.66637  0.71065  0.75752]                        
[ 5.       0.56253  0.63     0.78066]                        
[ 6.       0.4769   0.60606  0.79114]



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learn.save('16x16_8')

24x24



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learn.load('16x16_8')



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learn.set_data(get_data(24,bs))



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learn.fit(1e-2, 1, wds=wd)









    





 
 










    



[ 0.       0.61302  0.67112  0.77321]



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learn.unfreeze()



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learn.fit(lr, 1, cycle_len=1, wds=wd)









    





 
 










    



[ 0.       0.49323  0.51107  0.82698]



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learn.fit(lr, 3, cycle_len=1, cycle_mult=2, wds=wd)









    





 
 










    



[ 0.       0.43563  0.47423  0.8373 ]                        
[ 1.       0.46499  0.51238  0.82282]                        
[ 2.       0.32453  0.40952  0.86141]                        
[ 3.       0.47472  0.62542  0.78919]                        
[ 4.       0.34746  0.43835  0.85566]                        
[ 5.       0.24327  0.37241  0.87728]                        
[ 6.       0.19959  0.35643  0.88214]



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learn.save('24x24_8')



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log_preds,y = learn.TTA()
preds = np.mean(np.exp(log_preds),0), metrics.log_loss(y,preds), accuracy_np(preds,y)









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(0.32011880736219389, 0.89410000000000001)

32x32



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learn.load('24x24_8')



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learn.set_data(get_data(32,bs))



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learn.fit(1e-2, 1, wds=wd)









    





 
 










    



[ 0.       0.28159  0.37857  0.87114]



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learn.unfreeze()



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learn.fit(lr, 3, cycle_len=1, cycle_mult=2, wds=wd)









    





 
 










    



[ 0.       0.24112  0.3168   0.89587]                        
[ 1.       0.28276  0.38893  0.87342]                        
[ 2.       0.16777  0.29451  0.90378]                        
[ 3.       0.27507  0.57156  0.81695]                        
[ 4.       0.20369  0.35616  0.88786]                        
[ 5.       0.12198  0.2949   0.90437]                        
[ 6.       0.08032  0.27401  0.91169]



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learn.fit(lr, 3, cycle_len=4, wds=wd)









    





 
 










    



[ 0.       0.23548  0.43751  0.86086]                         
[ 1.       0.15057  0.32182  0.89616]                        
[ 2.       0.08559  0.28051  0.91218]                         
[ 3.       0.05464  0.26494  0.91555]                         
[ 4.       0.18577  0.4403   0.86541]                        
[ 5.       0.11875  0.32632  0.89943]                        
[ 6.       0.06624  0.26536  0.91911]                         
[ 7.       0.03661  0.26202  0.92059]                         
[ 8.       0.16668  0.39448  0.87718]                        
[ 9.       0.09441  0.31163  0.9019 ]                         
[ 10.        0.0395    0.26366   0.92   ]                     
[ 11.        0.02982   0.25696   0.92375]



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log_preds,y = learn.TTA()
metrics.log_loss(y,np.exp(log_preds)), accuracy_np(log_preds,y)









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(0.22727449907067793, 0.93049999999999999)



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learn.save('32x32_8')



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