@radek No magic here
from fastai.models.cifar10.senet import SENet18
m = SENet18()
bm = BasicModel(m.cuda(), name=‘cifar10_SENet18’)
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(m, sz))
#data = ImageClassifierData.from_paths(PATH)
learn = ConvLearner(data, bm)
learn.unfreeze()
learn.load(‘sen_32x32_8’)
learn.fit(0.01, 5)
@zpnc No num_classes enable on the SENet18()
This is predicting 10 classes
Out[24]:
array([[ -0.03931, -3.25638, -11.64748, -13.02063, -13.74167, -14.32404, -13.12414, -16.31706, -15.34966,
-13.79075],
[ -0.00171, -6.37512, -17.67231, -21.87936, -24.85791, -24.22245, -22.4937 , -26.0106 , -24.5726 ,
-23.42298],
[ -0.00004, -10.18968, -25.22412, -33.11356, -37.17604, -35.85092, -32.23149, -38.18449, -36.37085,
-34.89988],
[ -0.00043, -7.74531, -16.0502 , -23.37728, -22.80679, -24.57661, -22.05182, -25.2618 , -23.55353,
-23.559 ],
[ -0.18095, -1.79912, -9.98963, -12.32735, -12.66292, -13.37598, -10.96291, -14.3664 , -13.65081,
-11.73629],
[ -0.00154, -6.4734 , -17.16404, -25.36037, -26.31315, -26.37898, -23.26433, -26.81647, -23.59966,
-24.0616 ],
[ -0.00007, -9.62542, -25.56021, -32.92747, -35.47162, -34.49596, -31.37358, -36.26967, -33.80552,
-32.72007],
[ -0.00002, -10.69174, -23.49642, -33.88793, -34.70346, -35.1624 , -31.05718, -36.33319, -32.74025,
-33.47608],
[ -0.00613, -5.09705, -13.74302, -17.84868, -18.88378, -19.37073, -16.35867, -20.6087 , -19.64924,
-18.3053 ],
[ -0.08595, -2.49756, -10.14199, -12.39996, -12.31104, -13.47758, -12.54866, -13.82268, -12.55679,
-11.21001]], dtype=float32)