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 ResNet34(c, **kargs):
return n.Seq(
n.ResNet([3, 4, 6, 3], bias=True, ibp_init=True),
n.FFNN([512, 512, c],
bias=True,
last_lin=True,
last_zono=True,
ibp_init=True,
**kargs))
def SkipNet18(c, **kargs):
return n.Seq(
n.ResNet([2, 2, 2, 2], 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 resnet34(c, **kargs):
return n.Seq(n.ResNet([3, 4, 6, 3]),
n.FFNN([512, 512, c], bias=False, last_lin=True, **kargs))
def resnet18(c, **kargs):
return n.Seq(n.ResNet([2, 2, 2, 2]),
n.FFNN([512, 512, c], bias=False, last_lin=True, **kargs))
def resnet18small(c, **kargs):
return n.Seq(n.ResNet([2, 2, 2]),
n.FFNN([100, c], bias=True, last_lin=False, **kargs))
