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
%matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.conv_learner import *
from fastai.models.cifar10.wideresnet import wrn_22
torch.backends.cudnn.benchmark = True
PATH = Path("data/cifar10/")
os.makedirs(PATH,exist_ok=True)
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]))
bs=512
sz=32
tfms = tfms_from_stats(stats, sz, aug_tfms=[RandomCrop(sz), RandomFlip()], pad=sz//8)
data = ImageClassifierData.from_paths(PATH, val_name='test', tfms=tfms, bs=bs)
m = wrn_22()
learn = ConvLearner.from_model_data(m, data)
learn.crit = nn.CrossEntropyLoss()
learn.metrics = [accuracy]
wd=1e-4
lr=1.5
%time learn.fit(lr, 1, wds=wd, cycle_len=30, use_clr_beta=(20,20,0.95,0.85))
HBox(children=(IntProgress(value=0, description='Epoch', max=30), HTML(value='')))
epoch trn_loss val_loss accuracy
0 1.456755 1.499619 0.5062
1 1.057333 1.157792 0.6116
2 0.829041 0.783326 0.723
3 0.66619 0.808943 0.7358
4 0.570876 0.748631 0.7361
5 0.495598 1.038086 0.6717
6 0.448354 0.533581 0.8181
7 0.415957 0.546836 0.816
8 0.390528 0.61025 0.7827
9 0.36144 0.751214 0.764
10 0.351138 0.756213 0.7769
11 0.33065 0.872244 0.7549
12 0.323868 0.530568 0.8215
13 0.301522 0.633277 0.8
14 0.281426 0.609825 0.8141
15 0.261843 0.792786 0.7706
16 0.243936 0.727103 0.797
17 0.233351 0.481732 0.8525
18 0.219056 0.522896 0.8375
19 0.196971 0.350686 0.8835
20 0.180855 0.389286 0.8754
21 0.150032 0.372619 0.883
22 0.118364 0.255543 0.9182
23 0.080524 0.22061 0.9311
24 0.051989 0.207242 0.9347
25 0.03802 0.21347 0.9368
26 0.030564 0.211374 0.9381
27 0.023117 0.214783 0.9398
28 0.020133 0.21228 0.9421
29 0.017761 0.212101 0.9423
CPU times: user 34min 14s, sys: 54min 24s, total: 1h 28min 38s
Wall time: 17min 16s
[array([0.2121]), 0.9423000004768372]