def __init__(self, n_word_types, n_trig_types, n_role_types,
n_entity_types, trigger_type2id, entity_type2id, DIM_EMBED,
DIM_EVENT, DIM_BILSTM, DIM_TRIG_TYPE, DIM_ROLE_TYPE,
DIM_ARG_TYPE, DIM_IO, DROPOUT, REPLACE_TYPE, GENERALISATION,
THRESHOLD):
super(Loader, self).__init__()
with self.init_scope():
self.DIM_EMBED = DIM_EMBED
self.DIM_EVENT = DIM_EVENT
self.DIM_BILSTM = DIM_BILSTM
self.DIM_TRIG_TYPE = DIM_TRIG_TYPE
self.DIM_ROLE_TYPE = DIM_ROLE_TYPE
self.DIM_ARG_TYPE = DIM_ARG_TYPE
self.DIM_IO = DIM_IO
self.DROPOUT = DROPOUT
self.THRESHOLD = THRESHOLD
self.GENERALISATION = GENERALISATION
self.REPLACE_TYPE = REPLACE_TYPE
self.DROPOUT = DROPOUT
self.DIM_TREE_LSTM_INPUT = self.DIM_TRIG_TYPE + (
self.DIM_BILSTM *
2) + self.DIM_ROLE_TYPE + self.DIM_TRIG_TYPE + (
self.DIM_BILSTM * 2) + self.DIM_IO
self.DIM_ARG = self.DIM_TRIG_TYPE + (self.DIM_BILSTM * 2)
self.id2triggertype = {v: k for k, v in trigger_type2id.items()}
self.id2entitytype = {v: k for k, v in entity_type2id.items()}
self.embed = L.EmbedID(n_word_types,
self.DIM_EMBED,
ignore_label=-1)
self.bilstm = L.NStepBiLSTM(1, self.DIM_EMBED, self.DIM_BILSTM, 0)
self.embed_trigtype = L.EmbedID(n_trig_types,
self.DIM_TRIG_TYPE,
ignore_label=-1)
self.embed_roletype = L.EmbedID(n_role_types,
self.DIM_ROLE_TYPE,
ignore_label=-1)
self.embed_argtype = L.EmbedID(n_entity_types,
self.DIM_ARG_TYPE,
ignore_label=-1)
self.embed_io = L.EmbedID(2, self.DIM_IO, ignore_label=-1)
self.treelstm = L.ChildSumTreeLSTM(self.DIM_TREE_LSTM_INPUT,
self.DIM_EVENT)
self.l1 = L.Linear(None, self.DIM_EVENT)
self.y = L.Linear(None, self.DIM_EVENT)
self.final = L.Linear(None, 1) # event or non-event
self.reducedEvent = L.Linear(None, self.DIM_ARG)
self.len_type_and_arg = self.DIM_TRIG_TYPE + (self.DIM_BILSTM * 2)
self.len_relation = self.DIM_TRIG_TYPE + (self.DIM_BILSTM * 2) + self.DIM_ROLE_TYPE + self.DIM_ARG_TYPE + \
(self.DIM_BILSTM * 2) + self.DIM_IO
self.trigger_type2id = trigger_type2id
self.entity_type2id = entity_type2id
def setUp(self):
if self.model_type == 'ChildSumTreeLSTM':
self.link = links.ChildSumTreeLSTM(self.in_size, self.out_size)
elif self.model_type == 'NaryTreeLSTM':
if self.n_ary == 0:
# n_ary=0 test should be skipped for NaryTreeLSTM
self.n_ary = 1
self.link = links.NaryTreeLSTM(self.in_size,
self.out_size,
n_ary=self.n_ary)
else:
NotImplementedError()
for p in self.link.params():
p.data[:] = numpy.random.uniform(-1, 1, p.shape).astype(self.dtype)
self.c_prevs = [
numpy.random.uniform(-1, 1, (5, self.out_size)).astype(self.dtype)
for _ in range(self.n_ary)
]
self.h_prevs = [
numpy.random.uniform(-1, 1, (5, self.out_size)).astype(self.dtype)
for _ in range(self.n_ary)
]
self.x = numpy.random.uniform(-1, 1,
(5, self.in_size)).astype(self.dtype)
self.inputs = self.c_prevs + self.h_prevs + [self.x]
self.gc = numpy.random.uniform(-1, 1,
(5, self.out_size)).astype(self.dtype)
self.gh = numpy.random.uniform(-1, 1,
(5, self.out_size)).astype(self.dtype)
self.check_forward_options = {}
self.check_backward_options = {'dtype': numpy.float64}
if self.dtype == numpy.float16:
self.check_forward_options = {'atol': 5e-4, 'rtol': 5e-3}
self.check_backward_options = {
'dtype': numpy.float64,
'atol': 5e-4,
'rtol': 5e-3
}