def train(self):
self.opt_op()
init = tf.global_variables_initializer()
self.sess.run(init)
# 划分数据batch
iterations, next_iterator = dataset_iterator(
self.train_data[tr_sents_up],
self.train_data[tr_positions_up],
self.train_data[tr_pos_up],
self.train_data[tr_sts_up],
self.train_data[tr_sents_down],
self.train_data[tr_positions_down],
self.train_data[tr_pos_down],
self.train_data[tr_sts_down],
self.train_data[tr_labels],
len(self.train_data[tr_sents_up])
)
for epoch in range(self.n_epochs):
count = 0
train_loss = 0
train_acc = 0
print("-----------Now we begin the %dth epoch-----------" % (epoch))
for iter in range(iterations):
word_up_batch, position_up_batch, poses_up_batch, sts_up_batch, \
word_down_batch, position_down_batch, poses_down_batch, sts_down_batch, \
labels_batch = self.sess.run(next_iterator)
if len(word_up_batch) < self.batch_size:
continue
f_dict = {
self.word_up: word_up_batch,
self.positons_up: position_up_batch,
self.poses_up: poses_up_batch,
self.sts_up: sts_up_batch,
self.word_down: word_down_batch,
self.positons_down: position_down_batch,
self.poses_down: poses_down_batch,
self.sts_down: sts_down_batch,
self.labels: labels_batch,
self.dropout_keep_prob: 0.5
}
_, loss, acc = self.sess.run([self.train_op, self.loss_op, self.accuracy_op], feed_dict=f_dict)
train_loss = train_loss+loss
train_acc = train_acc+acc
count = count + 1
train_loss = train_loss / count
train_acc = train_acc / count
print("-----------After the %dth epoch, the train loss is: %f, the train acc is: %f-----------" % (epoch, train_loss, train_acc))
# test
iterations_test, next_iterator_test = dataset_iterator(
self.test_data[te_sents_up],
self.test_data[te_positions_up],
self.test_data[te_pos_up],
self.test_data[te_sts_up],
self.test_data[te_sents_down],
self.test_data[te_positions_down],
self.test_data[te_pos_down],
self.test_data[te_sts_down],
self.test_data[te_labels],
len(self.test_data[te_sents_up])
)
self.test(iterations_test, next_iterator_test, epoch, self.loss_op, self.accuracy_op)
def train(self):
self.opt_op()
init = tf.global_variables_initializer()
self.sess.run(init)
saver = tf.train.Saver()
# 划分数据batch
iterations, next_iterator = dataset_iterator(
self.train_data[tr_sents_up], self.train_data[tr_positions_up],
self.train_data[tr_pos_up], self.train_data[tr_sts_up],
self.train_data[tr_sents_down], self.train_data[tr_positions_down],
self.train_data[tr_pos_down], self.train_data[tr_sts_down],
self.train_data[tr_labels], len(self.train_data[tr_sents_up]))
# max_test_acc = 0
for epoch in range(self.n_epochs):
count = 0
train_loss = 0
train_acc = 0
print("-----------Now we begin the %dth epoch-----------" %
(epoch))
for iter in range(iterations):
word_up_batch, position_up_batch, poses_up_batch, sts_up_batch, \
word_down_batch, position_down_batch, poses_down_batch, sts_down_batch, \
labels_batch = self.sess.run(next_iterator)
if len(word_up_batch) < self.batch_size:
continue
f_dict = {
self.word_up: word_up_batch,
self.positons_up: position_up_batch,
self.poses_up: poses_up_batch,
self.sts_up: sts_up_batch,
self.word_down: word_down_batch,
self.positons_down: position_down_batch,
self.poses_down: poses_down_batch,
self.sts_down: sts_down_batch,
self.labels: labels_batch,
self.dropout_keep_prob: 0.5
}
_, loss, acc, att_up, att_down = self.sess.run(
[
self.train_op, self.loss_op, self.accuracy_op,
self.alpha_up, self.alpha_down
],
feed_dict=f_dict)
train_loss = train_loss + loss
train_acc = train_acc + acc
count = count + 1
if iter == 2:
print(str(att_up.shape) + str(att_down.shape))
print("up att: ",
[float("%.3f" % e[0]) for e in att_up[3][:]])
print("%d up id: ", [e for e in word_up_batch[3][:]])
print("%d down att: ",
[float("%.3f" % e[0]) for e in att_down[3][:]])
print("%d down id: ", [e for e in word_down_batch[3][:]])
train_loss = train_loss / count
train_acc = train_acc / count
print("---%dth epoch train loss: %f, train acc: %f---" %
(epoch, train_loss, train_acc))
# test
iterations_test, next_iterator_test = dataset_iterator(
self.test_data[te_sents_up], self.test_data[te_positions_up],
self.test_data[te_pos_up], self.test_data[te_sts_up],
self.test_data[te_sents_down],
self.test_data[te_positions_down], self.test_data[te_pos_down],
self.test_data[te_sts_down], self.test_data[te_labels],
len(self.test_data[te_sents_up]))
self.test(iterations_test, next_iterator_test, epoch, self.loss_op,
self.accuracy_op)