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Fix error in beam gradient calculation

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This commit is contained in:
Matthew Honnibal 2018-05-09 02:44:09 +02:00
parent a6ae1ee6f7
commit a61fd60681
1 changed files with 15 additions and 7 deletions
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22
spacy/syntax/_beam_utils.pyx
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@ -133,7 +133,7 @@ cdef class ParserBeam(object):
if follow_gold:
min_cost = 0
for j in range(beam.nr_class):
if beam.costs[i][j] < min_cost:
if beam.is_valid[i][j] and beam.costs[i][j] < min_cost:
min_cost = beam.costs[i][j]
for j in range(beam.nr_class):
if beam.costs[i][j] > min_cost:
@ -160,7 +160,8 @@ nr_update = 0
def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
states, golds,
state2vec, vec2scores,
int width, losses=None, drop=0.):
int width, losses=None, drop=0.,
early_update=True):
global nr_update
cdef MaxViolation violn
nr_update += 1
@ -201,13 +202,16 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
for indices in p_indices]
g_scores = [numpy.ascontiguousarray(scores[indices], dtype='f')
for indices in g_indices]
# Now advance the states in the beams. The gold beam is contrained to
# Now advance the states in the beams. The gold beam is constrained to
# to follow only gold analyses.
pbeam.advance(p_scores)
gbeam.advance(g_scores, follow_gold=True)
# Track the "maximum violation", to use in the update.
for i, violn in enumerate(violns):
violn.check_crf(pbeam[i], gbeam[i])
if pbeam[i].loss > 0 and pbeam[i].min_score > (gbeam[i].score * 1.5):
pbeam.dones[i] = True
gbeam.dones[i] = True
histories = []
losses = []
for violn in violns:
@ -271,14 +275,15 @@ def get_gradient(nr_class, beam_maps, histories, losses):
Each batch has multiple beams
So history is list of lists of lists of ints
"""
nr_step = len(beam_maps)
grads = []
nr_step = 0
nr_steps = []
for eg_id, hists in enumerate(histories):
nr_step = 0
for loss, hist in zip(losses[eg_id], hists):
if loss != 0.0 and not numpy.isnan(loss):
nr_step = max(nr_step, len(hist))
for i in range(nr_step):
nr_steps.append(nr_step)
for i in range(max(nr_steps)):
grads.append(numpy.zeros((max(beam_maps[i].values())+1, nr_class),
dtype='f'))
if len(histories) != len(losses):
@ -289,8 +294,11 @@ def get_gradient(nr_class, beam_maps, histories, losses):
continue
key = tuple([eg_id])
# Adjust loss for length
# We need to do this because each state in a short path is scored
# multiple times, as we add in the average cost when we run out
# of actions.
avg_loss = loss / len(hist)
loss += avg_loss * (nr_step - len(hist))
loss += avg_loss * (nr_steps[eg_id] - len(hist))
for j, clas in enumerate(hist):
i = beam_maps[j][key]
# In step j, at state i action clas