def train_trigger(self):
train, dev, test = self.t_train, self.t_dev, self.t_test
saver = tf.train.Saver()
maxlen = self.maxlen
print('--Training Trigger--')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
devbest = 0
testbest = (0, 0, 0)
from tqdm import tqdm
for epoch in tqdm(range(constant.t_epoch)):
loss_list = []
for batch in get_batch(train, constant.t_batch_size, True):
loss, _ = sess.run([self.loss, self.train_op],
feed_dict=get_trigger_feeddict(
self, batch, self.stage, maxlen))
loss_list.append(loss)
print('epoch:{}'.format(str(epoch)))
print('loss:', np.mean(loss_list))
pred_labels = []
for batch in get_batch(dev, constant.t_batch_size, False):
pred_label = sess.run(self.pred_label,
feed_dict=get_trigger_feeddict(
self,
batch,
self.stage,
maxlen,
is_train=False))
pred_labels.extend(list(pred_label))
golds = list(dev[0][4])
dev_p, dev_r, dev_f = f_score(pred_labels, golds)
print("dev_Precision: {} dev_Recall:{} dev_F1:{}".format(
str(dev_p), str(dev_r), str(dev_f)))
pred_labels = []
for batch in get_batch(test, constant.t_batch_size, False):
pred_label = sess.run(self.pred_label,
feed_dict=get_trigger_feeddict(
self,
batch,
self.stage,
maxlen,
is_train=False))
pred_labels.extend(list(pred_label))
golds = list(test[0][4])
test_p, test_r, test_f = f_score(pred_labels, golds)
print("test_Precision: {} test_Recall:{} test_F1:{}\n".format(
str(test_p), str(test_r), str(test_f)))
if dev_f > devbest:
devbest = dev_f
testbest = (test_p, test_r, test_f)
saver.save(sess, "saved_models/trigger.ckpt")
test_p, test_r, test_f = testbest
print(
"test best Precision: {} test best Recall:{} test best F1:{}".
format(str(test_p), str(test_r), str(test_f)))
def train_argument(self):
print('--Training Argument--')
train, dev, test = self.a_train, self.a_dev, self.a_test
with tf.Session() as sess:
devbest = 0
testbest = (0, 0, 0)
sess.run(tf.global_variables_initializer())
for epoch in range(constant.a_epoch):
loss_list = []
for batch in get_batch(train,
constant.a_batch_size,
shuffle=True):
loss, _ = sess.run([self.loss, self.train_op],
feed_dict=get_argument_feeddict(
self, batch, True, "argument"))
loss_list.append(loss)
print('epoch:{}'.format(str(epoch)))
print('loss:', np.mean(loss_list))
pred_labels = []
for batch in get_batch(dev, constant.a_batch_size, False):
pred_event_types, feed_dict = get_argument_feeddict(
self, batch, False, "argument")
pred_label = sess.run(self.pred_label, feed_dict=feed_dict)
pred_labels.extend(
list(zip(list(pred_event_types), list(pred_label))))
golds = list(zip(list(dev[1]), list(dev[2])))
dev_p, dev_r, dev_f = f_score(pred_labels, golds,
self.classify)
print("dev_Precision: {} dev_Recall:{} dev_F1:{}".format(
str(dev_p), str(dev_r), str(dev_f)))
pred_labels = []
for batch in get_batch(test, constant.a_batch_size, False):
pred_event_types, feed_dict = get_argument_feeddict(
self, batch, False, "argument")
pred_label = sess.run(self.pred_label, feed_dict=feed_dict)
pred_labels.extend(
list(zip(list(pred_event_types), list(pred_label))))
golds = list(zip(list(test[1]), list(test[2])))
test_p, test_r, test_f = f_score(pred_labels, golds,
self.classify)
print("test_Precision: {} test_Recall:{} test_F1:{}\n".format(
str(test_p), str(test_r), str(test_f)))
if dev_f > devbest:
devbest = dev_f
testbest = (test_p, test_r, test_f)
test_p, test_r, test_f = testbest
print(
"test best Precision: {} test best Recall:{} test best F1:{}".
format(str(test_p), str(test_r), str(test_f)))