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¶
%matplotlib inline
%reload_ext autoreload
%autoreload 2
from pathlib import Path
from fastai.conv_learner import *
PATH = Path("data/cifar10/")
bs=64
sz=32
tfms = tfms_from_model(resnet18, sz, aug_tfms=[RandomFlip()], pad=sz//8)
data = ImageClassifierData.from_csv(PATH, 'train', PATH/'train.csv', tfms=tfms, bs=bs)
learn = ConvLearner.pretrained(resnet18, data)
lr=1e-2; wd=1e-5
learn.lr_find()
learn.sched.plot()
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learn.fit(lr, 1, cycle_len=1)
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epoch trn_loss val_loss accuracy
0 1.249359 1.116181 0.604056
1 1.215158 1.07421 0.613115
[1.0742103, 0.6131150265957447]
lrs = np.array([lr/9,lr/3,lr])
learn.unfreeze()
learn.lr_find(lrs/1000)
learn.sched.plot()
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learn.fit(lrs, 1, cycle_len=1, wds=wd)
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epoch trn_loss val_loss accuracy
0 0.863994 0.792711 0.726479
[0.7927107, 0.7264793882978723]
Simplenet¶
stats = (np.array([ 0.4914 , 0.48216, 0.44653]), np.array([ 0.24703, 0.24349, 0.26159]))
tfms = tfms_from_stats(stats, sz, aug_tfms=[RandomFlip()], pad=sz//8)
data = ImageClassifierData.from_csv(PATH, 'train', PATH/'train.csv', tfms=tfms, bs=bs)
class SimpleConv(nn.Module):
def __init__(self, ic, oc, ks=3, drop=0.2, bn=True):
super().__init__()
self.conv = nn.Conv2d(ic, oc, ks, padding=(ks-1)//2)
self.bn = nn.BatchNorm2d(oc, momentum=0.05) if bn else None
self.drop = nn.Dropout(drop, inplace=True)
self.act = nn.ReLU(True)
def forward(self, x):
x = self.conv(x)
if self.bn: x = self.bn(x)
return self.drop(self.act(x))
net = nn.Sequential(
SimpleConv(3, 64),
SimpleConv(64, 128),
SimpleConv(128, 128),
SimpleConv(128, 128),
nn.MaxPool2d(2),
SimpleConv(128, 128),
SimpleConv(128, 128),
SimpleConv(128, 256),
nn.MaxPool2d(2),
SimpleConv(256, 256),
SimpleConv(256, 256),
nn.MaxPool2d(2),
SimpleConv(256, 512),
SimpleConv(512, 2048, ks=1, bn=False),
SimpleConv(2048, 256, ks=1, bn=False),
nn.MaxPool2d(2),
SimpleConv(256, 256, bn=False, drop=0),
nn.MaxPool2d(2),
Flatten(),
nn.Linear(256, 10)
)
bm = BasicModel(net.cuda(), name='simplenet')
learn = ConvLearner(data, bm)
learn.crit = nn.CrossEntropyLoss()
learn.opt_fn = optim.Adam
learn.unfreeze()
learn.metrics=[accuracy]
lr = 1e-3
wd = 5e-3
#sgd mom
learn.lr_find()
learn.sched.plot()
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#adam
learn.lr_find()
learn.sched.plot()
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learn.fit(lr, 1, wds=wd, cycle_len=20, use_clr=(32,10))
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epoch trn_loss val_loss accuracy
0 1.464019 1.812134 0.324219
1 1.299797 1.872144 0.301779
2 1.152769 1.641428 0.405336
3 1.06013 1.531731 0.46875
4 1.001071 1.344982 0.546875
5 0.957563 1.159598 0.629405
6 0.895986 1.152674 0.619265
7 0.852257 1.277312 0.607713
8 0.844254 1.373495 0.538813
9 0.784301 0.972733 0.717586
10 0.751162 0.859369 0.741606
11 0.735842 0.921104 0.729555
12 0.690585 0.966144 0.706034
13 0.662635 0.824769 0.759142
14 0.626122 0.784435 0.775598
15 0.61732 0.772561 0.772689
16 0.570246 0.727107 0.785322
17 0.526993 0.718699 0.786652
18 0.499946 0.645241 0.812916
19 0.499634 0.630276 0.816572
[0.630276, 0.8165724734042553]
learn.fit(lr, 1, wds=wd, cycle_len=5, use_clr=(32,10))
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epoch trn_loss val_loss accuracy
0 1.603266 2.02473 0.271941
1 1.326654 1.682021 0.391955
2 1.124686 1.564738 0.427776
3 0.963391 1.164936 0.603225
4 0.82219 1.19409 0.578291
[1.1940901, 0.5782912234042553]
learn.save('0')
learn.fit(lr, 3, cycle_len=1, cycle_mult=2, wds=wd)
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epoch trn_loss val_loss accuracy
0 0.819311 1.080679 0.636386
1 0.90712 1.294629 0.547457
2 0.717722 0.938504 0.700881
3 0.898441 1.263396 0.586187
4 0.803364 1.037912 0.666888
5 0.668088 0.855235 0.737616
6 0.616654 0.754756 0.770778
[0.75475585, 0.7707779255319149]
learn.save('1')
learn.fit(lr, 1, wds=wd, cycle_len=10, use_clr=(32,10))
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epoch trn_loss val_loss accuracy
0 0.833685 1.148864 0.620928
1 0.819332 1.212562 0.608627
2 0.803363 0.984564 0.697224
3 0.790965 1.016013 0.702045
4 0.733683 0.902306 0.735622
5 0.698549 0.878661 0.732131
6 0.648197 0.783731 0.758311
7 0.597658 0.738099 0.782912
8 0.557584 0.646611 0.80768
9 0.507423 0.603345 0.822058
[0.60334516, 0.8220578457446809]
learn.save('2')
Fin¶