def SkipNet18_Combo(c, **kargs):
dl = n.DeepLoss
cmk = n.CorrFix
dec = lambda x: n.DecorrMin(x, num_to_keep = True)
return n.Seq(n.ResNet([2,2,2,2], extra = [ (cmk(20),2),(dec(10),2)
,(cmk(10),3),(dec(5),3),(dl(S.Until(90, S.Lin(0, 0.2, 50, 40), 0)), 3)
,(cmk(5),4),(dec(2),4)], bias = True, ibp_init=True, skip_net = True), n.FFNN([512, 512, c], bias=True, last_lin=True, last_zono = True, ibp_init=True, **kargs))
def ResNetTiny_FewCombo(
c, **kargs
): # resnetWide also used by mixtrain and scaling provable adversarial defenses
def wb(c, bias=True, **kargs):
return n.WideBlock(c,
False,
bias=bias,
ibp_init=True,
batch_norm=False,
**kargs)
dl = n.DeepLoss
cmk = n.CorrMaxK
cm2d = n.CorrMaxPool2D
cm3d = n.CorrMaxPool3D
dec = lambda x: n.DecorrMin(x, num_to_keep=True)
return n.Seq(
cmk(32), n.Conv(16, 3, padding=1, bias=True, ibp_init=True), dec(8),
wb(16), dec(4), wb(32), n.Concretize(), wb(32), wb(32), wb(32),
cmk(10),
n.FFNN([500, c],
bias=True,
last_lin=True,
ibp_init=True,
last_zono=True,
**kargs))
def ResNetLarge_LargeCombo(c, **kargs): # resnetWide also used by mixtrain and scaling provable adversarial defenses
def wb(c, bias = True, **kargs):
return n.WideBlock(c, False, bias=bias, ibp_init=True, batch_norm = False, **kargs)
dl = n.DeepLoss
cmk = n.CorrMaxK
cm2d = n.CorrMaxPool2D
cm3d = n.CorrMaxPool3D
dec = lambda x: n.DecorrMin(x, num_to_keep = True)
return n.Seq(n.Conv(16, 3, padding=1, bias=True, ibp_init = True), cmk(4),
wb(16), cmk(4), dec(4),
wb(32), cmk(4), dec(4),
wb(32), dl(S.Until(1, 0, S.Lin(0.5, 0, 50, 3))),
wb(32), cmk(4), dec(4),
wb(64), cmk(4), dec(2),
wb(64), dl(S.Until(24, S.Lin(0, 0.1, 20, 4), S.Lin(0.1, 0, 50))),
wb(64),
n.FFNN([1000, c], bias=True, last_lin=True, ibp_init = True, **kargs))
