model.claim: claim,
model.warrant0_len: warrant0_len,
model.warrant1_len: warrant1_len,
model.reason_len: reason_len,
model.claim_len: claim_len,
model.input_keep_prob: 1,
model.output_keep_prob: 1,
model.keep_prob: 1
}
p_labels = sess.run(model.prob_labels, feed_dict)
return p_labels
print 'start train..........'
batches = data_helper.iter_batch(batch_size, num_epochs,
train_warrant0, train_warrant1,
train_reason, train_claim,
train_labels, train_warrant0_len,
train_warrant1_len, train_reason_len,
train_claim_len)
train_acc_list = []
train_loss_list = []
max_dev_acc = float('-inf')
min_dev_loss = float('inf')
num_undesc = 0
for current_epoch, batch in batches:
if num_undesc > max_num_undsc:
break
warrant0, warrant1, reason, claim, labels, warrant0_len, warrant1_len, reason_len, claim_len = batch
step, train_batch_loss, train_batch_acc = train_step(
warrant0, warrant1, reason, claim, labels, warrant0_len,
warrant1_len, reason_len, claim_len)
current_step = tf.train.global_step(sess, global_step)
feed_dict = {
model.sen1_ids: sen1_ids,
model.sen2_ids: sen2_ids,
model.sen1_len: sen1_len,
model.sen2_len: sen2_len,
model.s_labels: s_labels,
model.input_keep_prob: 1.0,
model.output_keep_prob: 1.0,
}
step, loss, acc = sess.run(
[global_step, model.s_loss, model.s_acc], feed_dict)
return loss, acc
print 'Start Source training........'
batches = data_helper.iter_batch(s_batch_size, s_num_epochs,
sen1_train, sen2_train,
sen1_train_len, sen2_train_len,
s_train_labels)
s_train_loss_list = []
s_train_acc_list = []
s_max_dev_acc = float('-inf')
s_min_dev_loss = 0
s_num_undesc = 0
for current_epoch, batch in batches:
if s_num_undesc > s_max_num_undesc:
break
sen1_ids, sen2_ids, sen1_len, sen2_len, s_labels = batch
step, s_train_batch_loss, s_train_batch_acc = s_train_step(
sen1_ids, sen2_ids, sen1_len, sen2_len, s_labels)
current_step = tf.train.global_step(sess, global_step)
s_train_loss_list.append(s_train_batch_loss)
s_train_acc_list.append(s_train_batch_acc)