def _pairwise_gradients(self, pxs, nxs):
# indices of positive examples
sp, pp, op = unzip_triples(pxs)
# indices of negative examples
sn, pn, on = unzip_triples(nxs)
pscores = self.af.f(self._scores(sp, pp, op))
nscores = self.af.f(self._scores(sn, pn, on))
#print("avg = %f/%f, min = %f/%f, max = %f/%f" % (pscores.mean(), nscores.mean(), pscores.min(), nscores.min(), pscores.max(), nscores.max()))
# find examples that violate margin
ind = np.where(nscores + self.margin > pscores)[0]
self.nviolations = len(ind)
if len(ind) == 0:
return
# aux vars
sp, sn = list(sp[ind]), list(sn[ind])
op, on = list(op[ind]), list(on[ind])
pp, pn = list(pp[ind]), list(pn[ind])
gpscores = -self.af.g_given_f(pscores[ind])[:, np.newaxis]
gnscores = self.af.g_given_f(nscores[ind])[:, np.newaxis]
# object role gradients
ridx, Sm, n = grad_sum_matrix(pp + pn)
grp = gpscores * ccorr(self.E[sp], self.E[op])
grn = gnscores * ccorr(self.E[sn], self.E[on])
#gr = (Sm.dot(np.vstack((grp, grn))) + self.rparam * self.R[ridx]) / n
gr = Sm.dot(np.vstack((grp, grn))) / n
gr += self.rparam * self.R[ridx]
# filler gradients
eidx, Sm, n = grad_sum_matrix(sp + sn + op + on)
geip = gpscores * ccorr(self.R[pp], self.E[op])
gein = gnscores * ccorr(self.R[pn], self.E[on])
gejp = gpscores * cconv(self.E[sp], self.R[pp])
gejn = gnscores * cconv(self.E[sn], self.R[pn])
ge = Sm.dot(np.vstack((geip, gein, gejp, gejn))) / n
#ge += self.rparam * self.E[eidx]
return {'E': (ge, eidx), 'R':(gr, ridx)}
def _gradients(self, xys):
ss, ps, os, ys = unzip_triples(xys, with_ys=True)
yscores = ys * self._scores(ss, ps, os)
self.loss = np.sum(np.logaddexp(0, -yscores))
#preds = af.Sigmoid.f(yscores)
fs = -(ys * af.Sigmoid.f(-yscores))[:, np.newaxis]
#self.loss -= np.sum(np.log(preds))
ridx, Sm, n = grad_sum_matrix(ps)
gr = Sm.dot(fs * ccorr(self.E[ss], self.E[os])) / n
gr += self.rparam * self.R[ridx]
eidx, Sm, n = grad_sum_matrix(list(ss) + list(os))
ge = Sm.dot(np.vstack((
fs * ccorr(self.R[ps], self.E[os]),
fs * cconv(self.E[ss], self.R[ps])
))) / n
ge += self.rparam * self.E[eidx]
return {'E': (ge, eidx), 'R':(gr, ridx)}
def _scores(self, ss, ps, os):
return np.sum(self.R[ps] * ccorr(self.E[ss], self.E[os]), axis=1)