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
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%reload_ext autoreload
%autoreload 2
%matplotlib inline
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from fastai.conv_learner import *
PATH = "data/dogscats/"
sz=224; bs=48
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def nasnet(pre): return nasnetalarge(pretrained = 'imagenet' if pre else None)
model_features[nasnet]=4032*2
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stats = ([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
tfms = tfms_from_stats(stats, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs)
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learn = ConvLearner.pretrained(nasnet, data, precompute=True, xtra_fc=[], ps=0.5)
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%time learn.fit(1e-2, 2)
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learn.precompute=False
learn.bn_freeze=True
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%time learn.fit(1e-2, 1, cycle_len=1)
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learn.save('nas_pre')
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def freeze_to(m, n):
c=children(m[0])
for l in c: set_trainable(l, False)
for l in c[n:]: set_trainable(l, True)
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freeze_to(learn.model, 17)
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learn.fit([1e-5,1e-4,1e-2], 1, cycle_len=1)
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learn.save('nas')
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