def get_factor_expressions(fws,
bws,
tfemb,
tfdict,
valid_fes,
sentence,
spaths_x=None,
cpaths_x=None):
pw_z = parameter(w_z)
pb_z = parameter(b_z)
pw_f = parameter(w_f)
pb_f = parameter(b_f)
factexprs = {}
sentlen = len(fws)
sortedtfd = sorted(tfdict.keys())
targetspan = (sortedtfd[0], sortedtfd[-1])
for j in xrange(sentlen):
istart = 0
if USE_SPAN_CLIP and j > ALLOWED_SPANLEN:
istart = max(0, j - ALLOWED_SPANLEN)
for i in xrange(istart, j + 1):
spanlen = scalarInput(j - i + 1)
logspanlen = scalarInput(math.log(j - i + 1))
spanwidth = sp_x[SpanWidth.howlongisspan(i, j)]
spanpos = ap_x[ArgPosition.whereisarg((i, j), targetspan)]
fbemb_ij_basic = concatenate([
fws[i][j], bws[i][j], tfemb, spanlen, logspanlen, spanwidth,
spanpos
])
if USE_DEPS:
outs = oh_s[OutHeads.getnumouts(i, j, sentence.outheads)]
shp = spaths_x[sentence.shortest_paths[(i, j, targetspan[0])]]
fbemb_ij = concatenate([fbemb_ij_basic, outs, shp])
elif USE_CONSTITS:
isconstit = scalarInput((i, j) in sentence.constitspans)
lca = ct_x[sentence.lca[(i, j)][1]]
phrp = cpaths_x[sentence.cpaths[(i, j, targetspan[0])]]
fbemb_ij = concatenate([fbemb_ij_basic, isconstit, lca, phrp])
else:
fbemb_ij = fbemb_ij_basic
for y in valid_fes:
fctr = Factor(i, j, y)
if USE_HIER and y in feparents:
fefixed = esum([fe_x[y]] +
[fe_x[par] for par in feparents[y]])
else:
fefixed = fe_x[y]
fbemb_ijy = concatenate([fefixed, fbemb_ij])
factexprs[fctr] = pw_f * rectify(pw_z * fbemb_ijy +
pb_z) + pb_f
return factexprs
ALL_FEATS_DIM = 2 * LSTMDIM \
+ LUDIM \
+ LUPOSDIM \
+ FRMDIM \
+ LSTMINPDIM \
+ LSTMDIM \
+ FEDIM \
+ ARGPOSDIM \
+ SPANDIM \
+ 2 # spanlen and log spanlen features and is a constitspan
if USE_DEPS:
DEPHEADDIM = LSTMINPDIM + POSDIM
DEPRELDIM = configuration["dependency_relation_dim"]
OUTHEADDIM = OutHeads.size()
PATHLSTMINPDIM = DEPHEADDIM + DEPRELDIM
ALL_FEATS_DIM += OUTHEADDIM + PATHDIM
if USE_CONSTITS:
ALL_FEATS_DIM += 1 + PHRASEDIM # is a constit and what is it
ALL_FEATS_DIM += PATHDIM
NUMEPOCHS = configuration["num_epochs"]
PATIENCE = configuration["patience"]
LOSS_EVAL_EPOCH = configuration["eval_after_every_epochs"]
DEV_EVAL_EPOCHS = configuration["dev_eval_epoch_frequency"] * LOSS_EVAL_EPOCH
trainexamples = filter_long_ex(trainexamples, USE_SPAN_CLIP, ALLOWED_SPANLEN,
NOTANFEID)