def l2sgd(model,
instances,
nr_epoth,
init_learning_rate,
adjust_learning_rate = False):
_sigma = 1.
_gamma = init_learning_rate
_t = 1.
_eta = 0.
samples = random.sample(instances, min(int(len(instances) * 0.1), 1000))
for epoth in xrange(nr_epoth):
LOG(INFO, "Training epoth [%d]" % epoth)
# randomly shuffle the training instances
random.shuffle(instances)
# loop over the training instances
for index, instance in enumerate(instances):
# first need to clear the cache
build_instance(model.w, model.attrs, model.tags, instance)
for k, v in expectation(model, instance).iteritems():
model.w[k] -= v * _gamma
for k, v in instance.correct_features.iteritems():
model.w[k] += v * _gamma
# re-calculate the scale
_gamma = init_learning_rate / (1 + sqrt(float(epoth)))
_t += 1.
if (index + 1) % 1000 == 0:
LOG(INFO, "%d instances is trained" % (index + 1))
destroy_instance(instance)
LOG(INFO, "%d instances is trained" % (index + 1))
LOG(INFO, "Parameters norm %f" % norm(model.w))
def l2sgd(model,
instances,
nr_epoth,
init_learning_rate,
adjust_learning_rate=False):
_sigma = 1.
_gamma = init_learning_rate
_t = 1.
_eta = 0.
samples = random.sample(instances, min(int(len(instances) * 0.1), 1000))
for epoth in xrange(nr_epoth):
LOG(INFO, "Training epoth [%d]" % epoth)
# randomly shuffle the training instances
random.shuffle(instances)
# loop over the training instances
for index, instance in enumerate(instances):
# first need to clear the cache
build_instance(model.w, model.attrs, model.tags, instance)
for k, v in expectation(model, instance).iteritems():
model.w[k] -= v * _gamma
for k, v in instance.correct_features.iteritems():
model.w[k] += v * _gamma
# re-calculate the scale
_gamma = init_learning_rate / (1 + sqrt(float(epoth)))
_t += 1.
if (index + 1) % 1000 == 0:
LOG(INFO, "%d instances is trained" % (index + 1))
destroy_instance(instance)
LOG(INFO, "%d instances is trained" % (index + 1))
LOG(INFO, "Parameters norm %f" % norm(model.w))
def viterbi(model, instance):
'''
'''
L = len(instance)
T = model.nr_tags
A = model.nr_attrs
build_instance(model.attrs, model.tags, instance, False)
g0, g = build_score_cache(model.w, L, T, A, instance)
destroy_instance(instance)
s, p = argmax(g0, g, L, T)
v, i = s[L - 1].argmax(), L - 1
ret = []
while i >= 0:
ret.append(v)
v = p[i][v]
i -= 1
ret.reverse()
return ret
def viterbi(model, instance):
'''
'''
L = len(instance)
T = model.nr_tags
A = model.nr_attrs
build_instance(model.attrs, model.tags, instance, False)
g0, g = build_score_cache(model.w, L, T, A, instance)
destroy_instance(instance)
s, p = argmax(g0, g, L, T)
v, i = s[L -1].argmax(), L -1
ret = []
while i >= 0:
ret.append(v)
v = p[i][v]
i -= 1
ret.reverse()
return ret
