In [3]:

%reload_ext autoreload
%autoreload 2

from fastai import *
from fastai.vision import *
from fastai.vision.models.wrn import wrn_22
from fastai.docs import *
from fastai.docs import CIFAR_PATH

torch.backends.cudnn.benchmark = True

In [4]:

untar_data(CIFAR_PATH)

In [3]:

ds_tfms = ([pad(padding=4), crop(size=32, row_pct=(0,1), col_pct=(0,1)), flip_lr(p=0.5)], [])
data = image_data_from_folder(CIFAR_PATH, valid='test', ds_tfms=ds_tfms, tfms=cifar_norm, bs=512)

In [4]:

learn = Learner(data, wrn_22(), metrics=accuracy).to_fp16()
learn.fit_one_cycle(30, 3e-3, wd=0.4, div_factor=10, pct_start=0.5)

VBox(children=(HBox(children=(IntProgress(value=0, max=1), HTML(value='0.00% [0/1 00:00<00:00]'))), HTML(value…

Total time: 00:40
epoch  train loss  valid loss  accuracy
0      1.295876    1.038308    0.631300  (00:40)

With mixup

In [ ]:

learn = Learner(data, wrn_22(), metrics=accuracy).to_fp16().mixup()
learn.fit_one_cycle(24, 3e-3, wd=0.2, div_factor=10, pct_start=0.5)