def creluBoxy(self):
if self.errors is None:
if self.beta is None:
return self.new(F.relu(self.head), None, None)
er = self.beta
mx = F.relu(self.head + er)
mn = F.relu(self.head - er)
return self.new((mn + mx) / 2, (mx - mn) / 2, None)
aber = torch.abs(self.errors)
sm = torch.sum(aber, 0)
if not self.beta is None:
sm += self.beta
mx = self.head + sm
mn = self.head - sm
should_box = mn.lt(0) * mx.gt(0)
gtz = self.head.gt(0).float()
mx /= 2
newhead = h.ifThenElse(should_box, mx, gtz * self.head)
newbeta = h.ifThenElse(
should_box, mx, gtz * (self.beta if not self.beta is None else 0))
newerr = (1 - should_box.float()) * gtz * self.errors
return self.new(newhead, newbeta, newerr)
def creluSwitch(dom):
if dom.errors is None:
if dom.beta is None:
return dom.new(F.relu(dom.head), None, None)
er = dom.beta
mx = F.relu(dom.head + er)
mn = F.relu(dom.head - er)
return dom.new((mn + mx) / 2, (mx - mn) / 2 , None)
aber = torch.abs(dom.errors)
sm = torch.sum(aber, 0)
if not dom.beta is None:
sm += dom.beta
mn = dom.head - sm
mx = sm
mx += dom.head
should_box = mn.lt(0) * mx.gt(0)
gtz = dom.head.gt(0)
mn.neg_()
should_boxer = mn.gt(mx)
mn /= 2
newhead = h.ifThenElse(should_box, h.ifThenElse(should_boxer, mx / 2, dom.head + mn ), gtz.float() * dom.head)
zbet = dom.beta if not dom.beta is None else 0
newbeta = h.ifThenElse(should_box, h.ifThenElse(should_boxer, mx / 2, mn + zbet), gtz.float() * zbet)
newerr = h.ifThenElseL(should_box, 1 - should_boxer, gtz).float() * dom.errors
return dom.new(newhead, newbeta , newerr)
def creluBoxySound(dom):
if dom.errors is None:
if dom.beta is None:
return dom.new(F.relu(dom.head), None, None)
er = dom.beta
mx = F.relu(dom.head + er)
mn = F.relu(dom.head - er)
return dom.new((mn + mx) / 2, (mx - mn) / 2 + 2e-6, None)
aber = torch.abs(dom.errors)
sm = torch.sum(aber, 0)
if not dom.beta is None:
sm += dom.beta
mx = dom.head + sm
mn = dom.head - sm
should_box = mn.lt(0) * mx.gt(0)
gtz = dom.head.gt(0).to_dtype()
mx /= 2
newhead = h.ifThenElse(should_box, mx, gtz * dom.head)
newbeta = h.ifThenElse(should_box, mx + 2e-6,
gtz * (dom.beta if not dom.beta is None else 0))
newerr = (1 - should_box.to_dtype()) * gtz * dom.errors
return dom.new(newhead, newbeta, newerr)
def softplus(self):
if self.errors is None:
if self.beta is None:
return self.new(F.softplus(self.head), None, None)
tp = F.softplus(self.head + self.beta)
bt = F.softplus(self.head - self.beta)
return self.new((tp + bt) / 2, (tp - bt) / 2, None)
errors = self.concreteErrors()
o = h.ones(self.head.size())
def sp(hd):
return F.softplus(
hd) # torch.log(o + torch.exp(hd)) # not very stable
def spp(hd):
ehd = torch.exp(hd)
return ehd.div(ehd + o)
def sppp(hd):
ehd = torch.exp(hd)
md = ehd + o
return ehd.div(md.mul(md))
fa = sp(self.head)
fpa = spp(self.head)
a = self.head
k = torch.sum(errors.abs(), 0)
def evalG(r):
return r.mul(r).mul(sppp(a + r))
m = torch.max(evalG(h.zeros(k.size())), torch.max(evalG(k), evalG(-k)))
m = h.ifThenElse(a.abs().lt(k),
torch.max(m, torch.max(evalG(a), evalG(-a))), m)
m /= 2
return self.new(fa, m if self.beta is None else m + self.beta.mul(fpa),
None if self.errors is None else self.errors.mul(fpa))
