import QA_similiarity
import CharacterBA
from nltk.translate import bleu
from Evaluation_Package.rouge.rouge import Rouge
from Evaluation_Package.meteor.meteor import Meteor
from Evaluation_Package.bleu.bleu import Bleu
from Evaluation_Package.cider.cider import Cider
SOS = 0
EOS = 1
PAD = 2
batches_in_epoch = 200
epoch_print = 2
Bleu_obj = Bleu()
Rouge_obj = Rouge()
Meteor_obj = Meteor()
cIDER_obj = Cider()
# import os
# print os.getcwd()
def RL_tuning_model(config):
emd_file = config.nemd_dir, config.cemd_dir
train_data, test_data, eval_data, vocab, embd = load_data(config.data_dir, emd_file, "BA")
##train_Id = [SOS] + clean_Id, ground_truth = clean_Id + [EOS], clean_len = get_length(ground_truth)
train_noisy_Id, train_noisy_len, train_char_noisy_Id, train_char_noisy_len, train_nemd, train_target_Id, train_input_Id, train_clean_Id, train_clean_len, train_answer_Id, train_answer_len, max_char, max_word = train_data
# noisy_Id, noisy_len, char_noisy_Id, char_noisy_len, ground_truth, train_Id, clean_Id, clean_len, answer_Id, answer_len, max_char, max_word = train_data
test_noisy_Id, test_noisy_len, test_char_noisy_Id, test_char_noisy_len,test_nemd, test_ground_truth, test_train_Id, test_clean_Id, test_clean_len, test_answer_Id, test_answer_len = test_data
# eval_noisy_Id, eval_noisy_len, eval_char_noisy_Id, eval_char_noisy_len,eval_target_Id, eval_input_Id, eval_clean_Id, eval_clean_len, eval_answer_Id, eval_answer_len =eval_data
def main():
SOS = 0
EOS = 1
PAD = 2
UNK = 3
epoch = 20
batches_in_epoch = 100
batch_size = 64
num_units = 200
char_dim = 50 ## the size of char dimention
char_hidden_units = 100 ##the length of the char_lstm (word embedding:2 * char_hidden)
epoch_print = 2
Bidirection = False
Attention = False
Embd_train = False
model_type = 'testing'
Dataset_name = "YAHOO"
# Dataset_name1 = "wrongorder1"
# Dataset_name1 = "back"
Dataset_name1 = "back"
Dataset_name1 = "threeopt"
print "this is the " + Dataset_name1 + " char"
logs_path = "/mnt/WDRed4T/ye/Qrefine/ckpt/" + Dataset_name + "/seq2seq_BIA_board"
# FileName = "/mnt/WDRed4T/ye/DataR/" + Dataset_name + "/wrongword_Id_1"
FileName = '/mnt/WDRed4T/ye/DataR/' + Dataset_name + '/' + Dataset_name1 + "_final"
input = open(FileName, 'rb')
data_com = pickle.load(input)
train_noisy_Id = data_com["train_noisy_Id"]
test_noisy_Id = data_com["test_noisy_Id"]
eval_noisy_Id = data_com["eval_noisy_Id"]
train_noisy_len = data_com["train_noisy_len"]
test_noisy_len = data_com["test_noisy_len"]
eval_noisy_len = data_com["eval_noisy_len"]
train_noisy_char_Id = data_com["train_noisy_char_Id"]
test_noisy_char_Id = data_com["test_noisy_char_Id"]
eval_noisy_char_Id = data_com["eval_noisy_char_Id"]
train_noisy_char_len = data_com["train_noisy_char_len"]
test_noisy_char_len = data_com["test_noisy_char_len"]
eval_noisy_char_len = data_com["eval_noisy_char_len"]
train_target_Id = data_com["train_ground_truth"]
test_target_Id = data_com["test_ground_truth"]
eval_target_Id = data_com["eval_ground_truth"]
train_input_Id = data_com["train_input_Id"]
test_input_Id = data_com["test_input_Id"]
eval_input_Id = data_com["eval_input_Id"]
train_clean_len = data_com["train_clean_len"]
test_clean_len = data_com["test_clean_len"]
eval_clean_len = data_com["eval_clean_len"]
max_char = data_com['max_char']
char_num = 44
max_word = data_com['max_word']
FileName = '/mnt/WDRed4T/ye/DataR/' + Dataset_name + '/' + Dataset_name1 + '_vocab_embd'
input = open(FileName, 'rb')
vocab_embd = pickle.load(input)
vocab = vocab_embd['vocab']
embd = vocab_embd['embd']
embd = np.asarray(embd)
vocdic = zip(vocab, range(len(vocab)))
index_voc = dict((index, vocab) for vocab, index in vocdic)
voc_index = dict(
(vocab, index) for vocab, index in vocdic) ##dic[char]= index
SOS = voc_index[u"<SOS>"]
EOS = voc_index[u"<EOS>"]
PAD = voc_index[u"<PAD>"]
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
print "the number of training question is:", len(train_noisy_Id), len(
train_noisy_char_Id), len(train_noisy_char_len)
print "the number of eval question is:", len(eval_noisy_Id), len(
eval_noisy_char_Id), len(eval_noisy_char_len)
vocab_size = len(vocab)
Bleu_obj = Bleu()
Rouge_obj = Rouge()
Meteor_obj = Meteor()
cIDER_obj = Cider()
if model_type == 'training':
Seq2Seq_model = Char_Seq2Seq(batch_size, vocab_size, num_units, embd,
model_type, Bidirection, Embd_train,
char_hidden_units, Attention, char_num,
char_dim)
# print "the number of evaluate question is:", len(eval_noisy_Id)
patience_cnt = 0
summary_writer = tf.summary.FileWriter(logs_path,
graph=tf.get_default_graph())
merge = tf.summary.merge_all()
saver = tf.train.Saver(sharded=False)
with tf.Session(config=config) as sess:
# sess.run(tf.global_variables_initializer())
saver.restore(
sess, "/mnt/WDRed4T/ye/Qrefine/ckpt/CharS2S/" + Dataset_name +
"/" + Dataset_name1 + "_" + str(Bidirection) + "_Att_" +
str(Attention) + "_Emb_" + str(Embd_train) + "_hiddenunits_" +
str(num_units) + "_bleu_0.491625950768")
val_loss_epo = []
# print [v for v in tf.trainable_variables()]
for epo in range(epoch):
idx = np.arange(0, len(train_noisy_Id))
idx = list(np.random.permutation(idx))
print " Epoch {}".format(epo)
Bleu_score1 = []
Bleu_score2 = []
Bleu_score3 = []
Bleu_score4 = []
Rouge_score = []
Meteor_score = []
Cider_score = []
for batch in range(len(train_noisy_Id) / batch_size):
source_shuffle, source_len, char_Id, char_len, train_shuffle, target_shuffle, target_len = next_batch(
train_noisy_Id, train_noisy_len, train_noisy_char_Id,
train_noisy_char_len, train_input_Id, train_target_Id,
train_clean_len, batch_size, batch, idx)
source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
source_shuffle,
maxlen=max_word,
padding='post',
value=EOS)
train_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
train_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
target_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
target_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
whole_noisy_char_Id = []
for sen_char in char_Id:
sen_len = len(sen_char)
for i in range(len(source_shuffle[0]) - sen_len):
sen_char.append(
[0] * max_char
) ## fix the char with the length of max_word
whole_noisy_char_Id.append(sen_char)
whole_noisy_char_Id = np.asarray(whole_noisy_char_Id)
# print whole_noisy_char_Id.shape
#
# print len(char_len[0])
# target_len = np.tile(max(target_len) + 1, batch_size)
fd = {
Seq2Seq_model.encoder_inputs: source_shuffle,
Seq2Seq_model.encoder_inputs_length: source_len,
Seq2Seq_model.encoder_char_ids: whole_noisy_char_Id,
Seq2Seq_model.encoder_char_len: char_len,
Seq2Seq_model.decoder_inputs: train_shuffle,
Seq2Seq_model.decoder_length: target_len,
Seq2Seq_model.ground_truth: target_shuffle,
Seq2Seq_model.dropout_rate: 1
}
mask, logit_id, logit_id_pad, l_fun, pro_op = sess.run([
Seq2Seq_model.mask, Seq2Seq_model.ids,
Seq2Seq_model.logits_padded,
Seq2Seq_model.loss_seq2seq, Seq2Seq_model.train_op
], fd)
# mask, logit_id, logit_id_pad= sess.run(
# [Seq2Seq_model.mask, Seq2Seq_model.ids, Seq2Seq_model.logits_padded], fd)
# l_fun =0
for t in range(batch_size):
ref = []
hyp = []
for tar_id in target_shuffle[t]:
if tar_id != EOS and tar_id != PAD:
ref.append(vocab[tar_id])
# print vocab[tar_id]
for pre_id in logit_id[t]:
if pre_id != EOS and pre_id != PAD:
hyp.append(vocab[pre_id])
hyp_sen = u" ".join(hyp).encode('utf-8')
ref_sen = u" ".join(ref).encode('utf-8')
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(
dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(
dic_ref, dic_hyp)
Bleu_score1.append(sen_bleu[0])
Bleu_score2.append(sen_bleu[1])
Bleu_score3.append(sen_bleu[2])
Bleu_score4.append(sen_bleu[3])
Rouge_score.append(sen_rouge[0])
Meteor_score.append(sen_meteor)
Cider_score.append(sen_cider)
if batch == 0 or batch % batches_in_epoch == 0:
##print the training
print('batch {}'.format(batch))
print(' minibatch loss: {}'.format(l_fun))
for t in xrange(3):
print 'Training Question {}'.format(t)
print "NQ: " + " ".join(
map(lambda i: vocab[i], list(
source_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "CQ: " + " ".join(
map(lambda i: vocab[i], list(
target_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "GQ: " + " ".join(
map(lambda i: vocab[i], list(
logit_id[t]))).strip().replace(
"<EOS>", " ")
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
sum(Bleu_score1) / float(len(Bleu_score1)),
sum(Bleu_score2) / float(len(Bleu_score2)),
sum(Bleu_score3) / float(len(Bleu_score3)),
sum(Bleu_score4) / float(len(Bleu_score4)),
sum(Rouge_score) / float(len(Rouge_score)),
sum(Meteor_score) / float(len(Meteor_score)),
sum(Cider_score) / float(len(Cider_score))))
val_loss = []
val_Bleu_score1 = []
val_Bleu_score2 = []
val_Bleu_score3 = []
val_Bleu_score4 = []
val_Rouge_score = []
val_Meteor_score = []
val_Cider_score = []
for batch_val in range(
len(eval_noisy_Id) / batch_size):
idx_v = np.arange(0, len(eval_noisy_Id))
idx_v = list(np.random.permutation(idx_v))
val_source_shuffle, val_source_len, val_char_Id, val_char_len, val_train_shuffle, val_target_shuffle, val_target_len = next_batch(
eval_noisy_Id, eval_noisy_len,
eval_noisy_char_Id, eval_noisy_char_len,
eval_input_Id, eval_target_Id, eval_clean_len,
batch_size, batch_val, idx_v)
val_source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
val_source_shuffle,
maxlen=max_word,
padding='post',
value=EOS)
val_target_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
val_target_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
val_train_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
val_train_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
val_batch_noisy_char_Id = []
for sen_char in val_char_Id:
sen_len = len(sen_char)
for i in range(
len(val_source_shuffle[0]) - sen_len):
sen_char.append(
[0] * max_char
) ## fix the char with the length of max_word
val_batch_noisy_char_Id.append(sen_char)
val_batch_noisy_char_Id = np.asarray(
val_batch_noisy_char_Id)
# val_target_len = np.tile(max(val_target_len) + 1, batch_size)
fd_val = {
Seq2Seq_model.encoder_inputs:
val_source_shuffle,
Seq2Seq_model.encoder_inputs_length:
val_source_len,
Seq2Seq_model.encoder_char_ids:
val_batch_noisy_char_Id,
Seq2Seq_model.encoder_char_len: val_char_len,
Seq2Seq_model.decoder_length: val_target_len,
Seq2Seq_model.decoder_inputs:
val_train_shuffle,
Seq2Seq_model.ground_truth: val_target_shuffle,
Seq2Seq_model.dropout_rate: 1
}
val_ids, val_loss_seq = sess.run([
Seq2Seq_model.ids, Seq2Seq_model.loss_seq2seq
], fd_val)
val_loss.append(val_loss_seq)
for t in range(batch_size):
ref = []
hyp = []
for tar_id in val_target_shuffle[t]:
if tar_id != EOS and tar_id != PAD:
ref.append(vocab[tar_id])
for pre_id in val_ids[t]:
if pre_id != EOS and pre_id != PAD:
hyp.append(vocab[pre_id])
# print vocab[pre_id]
# sen_bleu1 = bleu([ref], hyp, weights=(1, 0, 0, 0))
# sen_bleu2 = bleu([ref], hyp, weights=(0.5, 0.5, 0, 0))
# val_Bleu_score1.append(sen_bleu1)
# val_Bleu_score2.append(sen_bleu2)
hyp_sen = u" ".join(hyp).encode('utf-8')
ref_sen = u" ".join(ref).encode('utf-8')
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(
dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(
dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(
dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(
dic_ref, dic_hyp)
val_Bleu_score1.append(sen_bleu[0])
val_Bleu_score2.append(sen_bleu[1])
val_Bleu_score3.append(sen_bleu[2])
val_Bleu_score4.append(sen_bleu[3])
val_Rouge_score.append(sen_rouge[0])
val_Meteor_score.append(sen_meteor)
val_Cider_score.append(sen_cider)
for t in xrange(3):
print 'Validation Question {}'.format(t)
print "NQ: " + " ".join(
map(lambda i: vocab[i],
list(val_source_shuffle[t]))).strip(
).replace("<EOS>", " ")
print "CQ: " + " ".join(
map(lambda i: vocab[i],
list(val_target_shuffle[t]))).strip(
).replace("<EOS>", " ")
print "GQ: " + " ".join(
map(lambda i: vocab[i], list(
val_ids[t]))).strip().replace(
"<EOS>", " ")
avg_val_loss = sum(val_loss) / float(len(val_loss))
print(' minibatch loss of validation: {}'.format(
avg_val_loss))
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
sum(val_Bleu_score1) /
float(len(val_Bleu_score1)),
sum(val_Bleu_score2) /
float(len(val_Bleu_score2)),
sum(val_Bleu_score3) /
float(len(val_Bleu_score3)),
sum(val_Bleu_score4) /
float(len(val_Bleu_score4)),
sum(val_Rouge_score) /
float(len(val_Rouge_score)),
sum(val_Meteor_score) /
float(len(val_Meteor_score)),
sum(val_Cider_score) /
float(len(val_Cider_score))))
val_loss_epo.append(avg_val_loss)
if epo > 0:
for loss in val_loss_epo:
print "the val_loss_epo:", loss
print "the loss difference:", val_loss_epo[
-2] - val_loss_epo[-1]
if val_loss_epo[-2] - val_loss_epo[-1]:
patience_cnt = 0
else:
patience_cnt += 1
print patience_cnt
if patience_cnt > 5:
print("early stopping...")
saver.save(
sess, "/mnt/WDRed4T/ye/Qrefine/ckpt/CharS2S/" +
Dataset_name + "/" + Dataset_name1 + "_" +
str(Bidirection) + "_Att_" + str(Attention) +
"_Emb_" + str(Embd_train) + "_hiddenunits_" +
str(num_units))
break
# if epo % epoch_print == 0:
# for t in range(10):
# print 'Question {}'.format(t)
# print " ".join(map(lambda i: vocab[i], list(target_shuffle[t]))).strip()
# print " ".join(map(lambda i: vocab[i], list(logit_id[t, :]))).strip()
avg_bleu_val = sum(val_Bleu_score1) / float(
len(val_Bleu_score1))
saver.save(
sess, "/mnt/WDRed4T/ye/Qrefine/ckpt/CharS2S/" +
Dataset_name + "/" + Dataset_name1 + "_" +
str(Bidirection) + "_Att_" + str(Attention) + "_Emb_" +
str(Embd_train) + "_hiddenunits_" + str(num_units) +
"_bleu_" + str(avg_bleu_val))
elif model_type == 'testing':
Seq2Seq_model = Char_Seq2Seq(batch_size, vocab_size, num_units, embd,
model_type, Bidirection, Embd_train,
char_hidden_units, Attention, char_num,
char_dim)
with tf.Session(config=config) as sess:
saver_word_rw = tf.train.Saver()
saver_word_rw.restore(
sess, "/mnt/WDRed4T/ye/Qrefine/ckpt/CharS2S/" + Dataset_name +
"/" + Dataset_name1 + str(Bidirection) + "_Att_" +
str(Attention) + "_Emb_" + str(Embd_train) + "_hiddenunits_" +
str(num_units) + "_bleu_0.286359797692")
max_batches = len(test_noisy_Id) / batch_size
idx = np.arange(0, len(test_noisy_Id))
# idx = list(np.random.permutation(idx))
test_Bleu_score1 = []
test_Bleu_score2 = []
test_Bleu_score3 = []
test_Bleu_score4 = []
test_Rouge_score = []
test_Meteor_score = []
test_Cider_score = []
generated_test_sen = []
test_answer_sen = []
test_noisy_sen = []
test_clean_sen = []
for batch in range(max_batches):
source_shuffle, source_len, test_char_Id, char_len, train_shuffle, target_shuffle, target_len = next_batch(
test_noisy_Id, test_noisy_len, test_noisy_char_Id,
test_noisy_char_len, test_input_Id, test_target_Id,
test_clean_len, batch_size, batch, idx)
for no in source_shuffle:
test_noisy_sen.append(vocab[si] for si in no)
for cl in target_shuffle:
test_clean_sen.append(vocab[ci] for ci in cl)
source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
source_shuffle, maxlen=max_word, padding='post', value=EOS)
test_batch_noisy_char_Id = []
for sen_char in test_char_Id:
sen_len = len(sen_char)
for i in range(len(source_shuffle[0]) - sen_len):
sen_char.append(
[0] * max_char
) ## fix the char with the length of max_word
test_batch_noisy_char_Id.append(sen_char)
test_batch_noisy_char_Id = np.asarray(test_batch_noisy_char_Id)
fd = {
Seq2Seq_model.encoder_inputs: source_shuffle,
Seq2Seq_model.encoder_inputs_length: source_len,
Seq2Seq_model.encoder_char_ids: test_batch_noisy_char_Id,
Seq2Seq_model.encoder_char_len: char_len,
Seq2Seq_model.dropout_rate: 1
}
ids = sess.run([Seq2Seq_model.ids], fd)
for t in range(batch_size):
ref = []
hyp = []
for tar_id in target_shuffle[t]:
if tar_id != 2:
ref.append(vocab[tar_id])
for pre_id in ids[0][t]:
if pre_id != 2 and pre_id != 0:
hyp.append(vocab[pre_id])
generated_test_sen.append(hyp)
# sen_bleu1 = bleu([ref], hyp, weights=(1, 0, 0, 0))
# sen_bleu2 = bleu([ref], hyp, weights=(0.5, 0.5, 0, 0))
# sen_bleu3 = bleu([ref], hyp, weights=(0.333, 0.333, 0.333, 0))
# sen_bleu4 = bleu([ref], hyp, weights=(0.25, 0.25, 0.25, 0.25))
# Bleu_score1.append(sen_bleu1)
# Bleu_score2.append(sen_bleu2)
# Bleu_score3.append(sen_bleu3)
# Bleu_score4.append(sen_bleu4)
hyp_sen = u" ".join(hyp).encode('utf-8')
ref_sen = u" ".join(ref).encode('utf-8')
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(dic_ref, dic_hyp)
test_Bleu_score1.append(sen_bleu[0])
test_Bleu_score2.append(sen_bleu[1])
test_Bleu_score3.append(sen_bleu[2])
test_Bleu_score4.append(sen_bleu[3])
test_Rouge_score.append(sen_rouge[0])
test_Meteor_score.append(sen_meteor)
test_Cider_score.append(sen_cider)
for t in xrange(5):
print 'Training Question {}'.format(t)
print "NQ: " + " ".join(
map(lambda i: vocab[i], list(
source_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "CQ: " + " ".join(
map(lambda i: vocab[i], list(
target_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "GQ: " + " ".join(
map(lambda i: vocab[i], list(
ids[0][t]))).strip().replace("<EOS>", " ")
bleu_score1 = sum(test_Bleu_score1) / float(
len(test_Bleu_score1))
bleu_score2 = sum(test_Bleu_score2) / float(
len(test_Bleu_score2))
bleu_score3 = sum(test_Bleu_score3) / float(
len(test_Bleu_score3))
bleu_score4 = sum(test_Bleu_score4) / float(
len(test_Bleu_score4))
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
bleu_score1, bleu_score2, bleu_score3, bleu_score4,
sum(test_Rouge_score) / float(len(test_Rouge_score)),
sum(test_Meteor_score) / float(len(test_Meteor_score)),
sum(test_Cider_score) / float(len(test_Cider_score))))
# len(test_answer_sen)
fname = "/mnt/WDRed4T/ye/Qrefine/ckpt/CharS2S/" + Dataset_name + "/" + Dataset_name1 + "_bleu1_" + str(
bleu_score1)
f = open(fname, "wb")
print "the length test set is:", len(generated_test_sen), len(
test_noisy_sen), len(test_clean_sen)
f.write(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
f.write(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f} \n"
.format(bleu_score1, bleu_score2, bleu_score3, bleu_score4,
sum(test_Rouge_score) / float(len(test_Rouge_score)),
sum(test_Meteor_score) / float(len(test_Meteor_score)),
sum(test_Cider_score) / float(len(test_Cider_score))))
for i in range(len(generated_test_sen)):
f.write("question" + str(i) + "\n")
# f.write("answer: " + " ".join(test_answer_sen[i]) + "\n")
f.write("noisy question: " + " ".join(test_noisy_sen[i]) +
"\n")
f.write("clean question: " + " ".join(test_clean_sen[i]) +
"\n")
f.write("generated question: " +
" ".join(generated_test_sen[i]) + "\n")
f.close()
def seq_run_BA(model_type, config):
SOS = 0
EOS = 1
PAD = 2
UNK = 3
data_file = config.data_dir
emd_file = [config.nemd_dir, config.cemd_dir]
train_data, test_data, eval_data, vocab, embd = load_data(
data_file, emd_file, "BA")
batch_size = config.batch_size
num_units = config.num_units
Bidirection = config.Bidirection
Attention = config.Attention
Embd_train = config.Embd_train
char_hidden_units = config.char_hidden_units
char_num = config.char_num
char_dim = config.char_dim
batches_in_epoch = config.batches_in_epoch
epoch = config.epoch
S2S_ckp_dir = config.S2S_ckp_dir
train_noisy_Id, train_noisy_len, train_noisy_char_Id, train_noisy_char_len, train_nemd, train_target_Id, train_input_Id, train_clean_Id, train_clean_len, train_answer_Id, train_answer_len, max_char, max_word = train_data
test_noisy_Id, test_noisy_len, test_noisy_char_Id, test_noisy_char_len, test_nemd, test_target_Id, test_input_Id, test_clean_Id, test_clean_len, test_answer_Id, test_answer_len = test_data
eval_noisy_Id, eval_noisy_len, eval_noisy_char_Id, eval_noisy_char_len, eval_nemd, eval_target_Id, eval_input_Id, eval_clean_Id, eval_clean_len, eval_answer_Id, eval_answer_len = eval_data
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
print "the number of training question is:", len(train_noisy_Id), len(
train_noisy_char_Id), len(train_noisy_char_len)
print "the number of eval question is:", len(eval_noisy_Id), len(
eval_noisy_char_Id), len(eval_noisy_char_len)
vocab_size = len(vocab)
Bleu_obj = Bleu()
Rouge_obj = Rouge()
Meteor_obj = Meteor()
cIDER_obj = Cider()
if model_type == 'training':
Seq2Seq_model = Seq2Seq(batch_size, vocab_size, num_units, embd,
model_type, Bidirection, Embd_train,
char_hidden_units, Attention, char_num,
char_dim)
# print "the number of evaluate question is:", len(eval_noisy_Id)
patience_cnt = 0
# summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
# merge = tf.summary.merge_all()
saver = tf.train.Saver(sharded=False)
f = open(config.perf_path, "w")
# config_gpu = tf.ConfigProto()
# config_gpu.gpu_options.allow_growth = True
# config=config_gpu
with tf.Session() as sess:
if os.path.isfile(S2S_ckp_dir):
saver.restore(sess, S2S_ckp_dir)
else:
sess.run(tf.global_variables_initializer())
# sess.run(tf.global_variables_initializer())
# saver.restore(sess, config.S2S_ckp_dir)
val_loss_epo = []
# print [v for v in tf.trainable_variables()]
for epo in range(epoch):
idx = np.arange(0, len(train_noisy_Id))
idx = list(np.random.permutation(idx))
print " Epoch {}".format(epo)
Bleu_score1 = []
Bleu_score2 = []
Bleu_score3 = []
Bleu_score4 = []
Rouge_score = []
Meteor_score = []
Cider_score = []
for batch in range(len(train_noisy_Id) / batch_size):
source_shuffle, source_len, source_nemd, train_shuffle, target_shuffle, target_len = next_batch(
train_noisy_Id, train_noisy_len, train_nemd,
train_input_Id, train_target_Id, train_clean_len,
batch_size, batch, idx)
source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
source_shuffle,
maxlen=max_word,
padding='post',
value=EOS)
source_emd = []
for n in source_nemd:
source_emd.append(n[0:source_shuffle.shape[1]])
source_emd = np.asarray(source_emd)
train_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
train_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
# target_emd = []
# for c in train_cemd:
# target_emd.append(c[0:train_shuffle.shape[1]])
# target_emd = np.asarray(target_emd)
target_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
target_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
# whole_noisy_char_Id =[]
# for sen_char in char_Id:
# sen_len = len(sen_char)
# for i in range(len(source_shuffle[0]) - sen_len):
# sen_char.append([0] * max_char) ## fix the char with the length of max_word
# whole_noisy_char_Id.append(sen_char)
#
# whole_noisy_char_Id = np.asarray(whole_noisy_char_Id)
# print whole_noisy_char_Id.shape
#
# print len(char_len[0])
# target_len = np.tile(max(target_len) + 1, batch_size)
fd = {
Seq2Seq_model.encoder_inputs:
source_shuffle,
Seq2Seq_model.encoder_inputs_length:
source_len,
Seq2Seq_model.encoder_emb:
source_emd,
Seq2Seq_model.decoder_inputs:
train_shuffle,
Seq2Seq_model.decoder_length:
target_len,
# Seq2Seq_model.target_emb: target_emd,
Seq2Seq_model.ground_truth:
target_shuffle,
Seq2Seq_model.dropout_rate:
1
}
mask, logit_id, logit_id_pad, l_fun, pro_op = sess.run([
Seq2Seq_model.mask, Seq2Seq_model.ids,
Seq2Seq_model.logits_padded,
Seq2Seq_model.loss_seq2seq, Seq2Seq_model.train_op
], fd)
# mask, logit_id, logit_id_pad= sess.run(
# [Seq2Seq_model.mask, Seq2Seq_model.ids, Seq2Seq_model.logits_padded], fd)
# l_fun =0
for t in range(batch_size):
ref = []
hyp = []
for tar_id in target_shuffle[t]:
if tar_id != EOS and tar_id != PAD:
ref.append(vocab[tar_id])
# print vocab[tar_id]
for pre_id in logit_id[t]:
if pre_id != EOS and pre_id != PAD:
hyp.append(vocab[pre_id])
hyp_sen = u" ".join(hyp).encode('utf-8')
ref_sen = u" ".join(ref).encode('utf-8')
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(
dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(
dic_ref, dic_hyp)
Bleu_score1.append(sen_bleu[0])
Bleu_score2.append(sen_bleu[1])
Bleu_score3.append(sen_bleu[2])
Bleu_score4.append(sen_bleu[3])
Rouge_score.append(sen_rouge[0])
Meteor_score.append(sen_meteor)
Cider_score.append(sen_cider)
if batch == 0 or batch % batches_in_epoch == 0:
##print the training
print('batch {}'.format(batch))
print(' minibatch loss: {}'.format(l_fun))
print(" the loss_epo:", epo)
for t in xrange(3):
print 'Training Question {}'.format(t)
print "NQ: " + " ".join(
map(lambda i: vocab[i], list(
source_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "CQ: " + " ".join(
map(lambda i: vocab[i], list(
target_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "GQ: " + " ".join(
map(lambda i: vocab[i], list(
logit_id[t]))).strip().replace(
"<EOS>", " ")
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
sum(Bleu_score1) / float(len(Bleu_score1)),
sum(Bleu_score2) / float(len(Bleu_score2)),
sum(Bleu_score3) / float(len(Bleu_score3)),
sum(Bleu_score4) / float(len(Bleu_score4)),
sum(Rouge_score) / float(len(Rouge_score)),
sum(Meteor_score) / float(len(Meteor_score)),
sum(Cider_score) / float(len(Cider_score))))
val_loss = []
val_Bleu_score1 = []
val_Bleu_score2 = []
val_Bleu_score3 = []
val_Bleu_score4 = []
val_Rouge_score = []
val_Meteor_score = []
val_Cider_score = []
for batch_val in range(
len(eval_noisy_Id) / batch_size):
idx_v = np.arange(0, len(eval_noisy_Id))
idx_v = list(np.random.permutation(idx_v))
val_source_shuffle, val_source_len, val_nemd, val_train_shuffle, val_target_shuffle, val_target_len = next_batch(
eval_noisy_Id, eval_noisy_len, eval_nemd,
eval_input_Id, eval_target_Id, eval_clean_len,
batch_size, batch_val, idx_v)
val_source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
val_source_shuffle,
maxlen=max_word,
padding='post',
value=EOS)
val_target_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
val_target_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
val_train_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
val_train_shuffle,
maxlen=max_word + 1,
padding='post',
value=EOS)
val_source_emd = []
for n in val_nemd:
val_source_emd.append(
n[0:val_source_shuffle.shape[1]])
val_source_emd = np.asarray(val_source_emd)
# val_target_emd = []
# for c in val_train_cemd:
# val_target_emd.append(c[0:val_train_shuffle.shape[1]])
# val_target_emd = np.asarray(val_target_emd)
# val_batch_noisy_char_Id = []
# for sen_char in val_char_Id:
# sen_len = len(sen_char)
# for i in range(len(val_source_shuffle[0]) - sen_len):
# sen_char.append([0] * max_char) ## fix the char with the length of max_word
# val_batch_noisy_char_Id.append(sen_char)
#
# val_batch_noisy_char_Id = np.asarray(val_batch_noisy_char_Id)
# val_target_len = np.tile(max(val_target_len) + 1, batch_size)
fd_val = {
Seq2Seq_model.encoder_inputs:
val_source_shuffle,
Seq2Seq_model.encoder_inputs_length:
val_source_len,
Seq2Seq_model.encoder_emb:
val_source_emd,
# Seq2Seq_model.target_emb: val_target_emd,
Seq2Seq_model.decoder_length:
val_target_len,
Seq2Seq_model.decoder_inputs:
val_train_shuffle,
Seq2Seq_model.ground_truth:
val_target_shuffle,
Seq2Seq_model.dropout_rate:
1
}
val_ids, val_loss_seq = sess.run([
Seq2Seq_model.ids, Seq2Seq_model.loss_seq2seq
], fd_val)
val_loss.append(val_loss_seq)
for t in range(batch_size):
ref = []
hyp = []
for tar_id in val_target_shuffle[t]:
if tar_id != EOS and tar_id != PAD:
ref.append(vocab[tar_id])
for pre_id in val_ids[t]:
if pre_id != EOS and pre_id != PAD:
hyp.append(vocab[pre_id])
# print vocab[pre_id]
# sen_bleu1 = bleu([ref], hyp, weights=(1, 0, 0, 0))
# sen_bleu2 = bleu([ref], hyp, weights=(0.5, 0.5, 0, 0))
# val_Bleu_score1.append(sen_bleu1)
# val_Bleu_score2.append(sen_bleu2)
hyp_sen = u" ".join(hyp).encode('utf-8')
ref_sen = u" ".join(ref).encode('utf-8')
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(
dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(
dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(
dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(
dic_ref, dic_hyp)
val_Bleu_score1.append(sen_bleu[0])
val_Bleu_score2.append(sen_bleu[1])
val_Bleu_score3.append(sen_bleu[2])
val_Bleu_score4.append(sen_bleu[3])
val_Rouge_score.append(sen_rouge[0])
val_Meteor_score.append(sen_meteor)
val_Cider_score.append(sen_cider)
for t in xrange(3):
print 'Validation Question {}'.format(t)
print "NQ: " + " ".join(
map(lambda i: vocab[i],
list(val_source_shuffle[t]))).strip(
).replace("<EOS>", " ")
print "CQ: " + " ".join(
map(lambda i: vocab[i],
list(val_target_shuffle[t]))).strip(
).replace("<EOS>", " ")
print "GQ: " + " ".join(
map(lambda i: vocab[i], list(
val_ids[t]))).strip().replace(
"<EOS>", " ")
avg_val_loss = sum(val_loss) / float(len(val_loss))
print(' minibatch loss of validation: {}'.format(
avg_val_loss))
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
sum(val_Bleu_score1) /
float(len(val_Bleu_score1)),
sum(val_Bleu_score2) /
float(len(val_Bleu_score2)),
sum(val_Bleu_score3) /
float(len(val_Bleu_score3)),
sum(val_Bleu_score4) /
float(len(val_Bleu_score4)),
sum(val_Rouge_score) /
float(len(val_Rouge_score)),
sum(val_Meteor_score) /
float(len(val_Meteor_score)),
sum(val_Cider_score) /
float(len(val_Cider_score))))
val_loss_epo.append(avg_val_loss)
if epo > 0:
for loss in val_loss_epo:
print "the val_loss_epo:", loss
print "the loss difference:", val_loss_epo[
-2] - val_loss_epo[-1]
if val_loss_epo[-2] - val_loss_epo[-1]:
patience_cnt = 0
else:
patience_cnt += 1
print patience_cnt
if patience_cnt > 5:
print("early stopping...")
saver.save(sess, S2S_ckp_dir)
break
# if epo % epoch_print == 0:
# for t in range(10):
# print 'Question {}'.format(t)
# print " ".join(map(lambda i: vocab[i], list(target_shuffle[t]))).strip()
# print " ".join(map(lambda i: vocab[i], list(logit_id[t, :]))).strip()
avg_bleu_val = sum(val_Bleu_score1) / float(
len(val_Bleu_score1))
if epo % 5 == 0:
saver.save(sess, S2S_ckp_dir)
f.write("the performance: " + S2S_ckp_dir + "_epoch_" +
str(epo) + "avg_eval_bleu_" + str(avg_bleu_val))
f.close()
print "save file"
elif model_type == 'testing':
Seq2Seq_model = Seq2Seq(batch_size, vocab_size, num_units, embd,
model_type, Bidirection, Embd_train,
char_hidden_units, Attention, char_num,
char_dim)
with tf.Session() as sess:
saver_word_rw = tf.train.Saver()
saver_word_rw.restore(sess, S2S_ckp_dir)
max_batches = len(test_noisy_Id) / batch_size
idx = np.arange(0, len(test_noisy_Id))
# idx = list(np.random.permutation(idx))
test_Bleu_score1 = []
test_Bleu_score2 = []
test_Bleu_score3 = []
test_Bleu_score4 = []
test_Rouge_score = []
test_Meteor_score = []
test_Cider_score = []
generated_test_sen = []
test_answer_sen = []
test_noisy_sen = []
test_clean_sen = []
for batch in range(max_batches):
test_source_shuffle, test_source_len, test_source_nemd, test_train_shuffle, test_target_shuffle, test_target_len = next_batch(
test_noisy_Id, test_noisy_len, test_nemd, test_input_Id,
test_target_Id, test_clean_len, batch_size, batch, idx)
for no in test_source_shuffle:
test_noisy_sen.append(vocab[si] for si in no)
for cl in test_target_shuffle:
test_clean_sen.append(vocab[ci] for ci in cl)
test_source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
test_source_shuffle,
maxlen=max_word,
padding='post',
value=EOS)
test_source_emd = []
for n in test_source_nemd:
test_source_emd.append(n[0:test_source_shuffle.shape[1]])
test_source_emd = np.asarray(test_source_emd)
# test_batch_noisy_char_Id = []
# for sen_char in test_char_Id:
# sen_len = len(sen_char)
# for i in range(len(test_source_shuffle[0]) - sen_len):
# sen_char.append([0] * max_char) ## fix the char with the length of max_word
# test_batch_noisy_char_Id.append(sen_char)
# test_batch_noisy_char_Id = np.asarray(test_batch_noisy_char_Id)
fd = {
Seq2Seq_model.encoder_inputs: test_source_shuffle,
Seq2Seq_model.encoder_inputs_length: test_source_len,
Seq2Seq_model.encoder_emb: test_source_emd,
Seq2Seq_model.dropout_rate: 1
}
ids = sess.run([Seq2Seq_model.ids], fd)
for t in range(batch_size):
ref = []
hyp = []
for tar_id in test_target_shuffle[t]:
if tar_id != 2:
ref.append(vocab[tar_id])
for pre_id in ids[0][t]:
if pre_id != 2 and pre_id != 0:
hyp.append(vocab[pre_id])
generated_test_sen.append(hyp)
hyp_sen = u" ".join(hyp).encode('utf-8')
ref_sen = u" ".join(ref).encode('utf-8')
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(dic_ref, dic_hyp)
test_Bleu_score1.append(sen_bleu[0])
test_Bleu_score2.append(sen_bleu[1])
test_Bleu_score3.append(sen_bleu[2])
test_Bleu_score4.append(sen_bleu[3])
test_Rouge_score.append(sen_rouge[0])
test_Meteor_score.append(sen_meteor)
test_Cider_score.append(sen_cider)
for t in xrange(5):
print 'Training Question {}'.format(t)
print "NQ: " + " ".join(
map(lambda i: vocab[i], list(
test_source_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "CQ: " + " ".join(
map(lambda i: vocab[i], list(
test_target_shuffle[t]))).strip().replace(
"<EOS>", " ")
print "GQ: " + " ".join(
map(lambda i: vocab[i], list(
ids[0][t]))).strip().replace("<EOS>", " ")
bleu_score1 = sum(test_Bleu_score1) / float(
len(test_Bleu_score1))
bleu_score2 = sum(test_Bleu_score2) / float(
len(test_Bleu_score2))
bleu_score3 = sum(test_Bleu_score3) / float(
len(test_Bleu_score3))
bleu_score4 = sum(test_Bleu_score4) / float(
len(test_Bleu_score4))
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
bleu_score1, bleu_score2, bleu_score3, bleu_score4,
sum(test_Rouge_score) / float(len(test_Rouge_score)),
sum(test_Meteor_score) / float(len(test_Meteor_score)),
sum(test_Cider_score) / float(len(test_Cider_score))))
# len(test_answer_sen)
fname = S2S_ckp_dir + "_bleu1_" + str(bleu_score1)
f = open(fname, "wb")
print "the length test set is:", len(generated_test_sen), len(
test_noisy_sen), len(test_clean_sen)
f.write(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
f.write(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f} \n"
.format(bleu_score1, bleu_score2, bleu_score3, bleu_score4,
sum(test_Rouge_score) / float(len(test_Rouge_score)),
sum(test_Meteor_score) / float(len(test_Meteor_score)),
sum(test_Cider_score) / float(len(test_Cider_score))))
for i in range(len(generated_test_sen)):
f.write("question" + str(i) + "\n")
# f.write("answer: " + " ".join(test_answer_sen[i]) + "\n")
f.write("noisy question: " + " ".join(test_noisy_sen[i]) +
"\n")
f.write("clean question: " + " ".join(test_clean_sen[i]) +
"\n")
f.write("generated question: " +
" ".join(generated_test_sen[i]) + "\n")
f.close()
def RL_tuning_model(
data_comb,
epoch,
batch_size=20,
num_units=300,
beam_width=5,
discount_factor=0.1,
sen_reward_rate=2,
dropout=1,
dec_L=1,
Bidirection=False,
Embd_train=False,
Attention=True,
):
max_noisy_len = data_comb[3]["max_noisy_len"]
max_clean_len = data_comb[3]["max_clean_len"]
max_answer_len = data_comb[3]["max_answer_len"]
L = max_clean_len
noisy_Id = data_comb[0]['noisy_Id']
noisy_len = data_comb[0]['noisy_len']
ground_truth = data_comb[0]['ground_truth']
clean_len = data_comb[0]['clean_len']
answer_Id = data_comb[0]['answer_Id']
answer_len = data_comb[0]['answer_len']
train_Id = data_comb[0]['train_Id']
vocab = data_comb[0]['vocab']
embd = data_comb[0]['embd']
embd = np.array(embd)
val_noisy_Id = data_comb[1]['noisy_Id']
val_noisy_len = data_comb[1]['noisy_len']
val_ground_truth = data_comb[1]['ground_truth']
val_clean_len = data_comb[1]['clean_len']
val_answer_Id = data_comb[1]['answer_Id']
val_answer_len = data_comb[1]['answer_len']
val_train_Id = data_comb[1]['train_Id']
test_noisy_Id = data_comb[2]['noisy_Id']
test_noisy_len = data_comb[2]['noisy_len']
test_ground_truth = data_comb[2]['ground_truth']
test_clean_len = data_comb[2]['clean_len']
test_answer_Id = data_comb[2]['answer_Id']
test_answer_len = data_comb[2]['answer_len']
test_train_Id = data_comb[2]['train_Id']
vocab_size = len(vocab)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
model_name = "BS_"
values = {}
checkpoint_path = "/home/ye/PycharmProjects/Qrefine/Seq_ckpt/Wiki/ALL_seq2seq_Bi_" + str(
Bidirection) + "_Att_" + str(Attention) + "_Emb_" + str(
Embd_train) + "_hiddenunits_" + str(num_units)
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in var_to_shape_map:
if 'loss_fun' not in key:
values[model_name + key + ':0'] = reader.get_tensor(key)
model_cond = "training"
G_Seq2Seq = tf.Graph()
sess_word_rw = tf.Session(config=config, graph=G_Seq2Seq)
with G_Seq2Seq.as_default():
Seq2Seq_model = BA_Seq2Seq.Bi_Att_Seq2Seq(batch_size, vocab_size,
num_units, embd, model_cond,
Bidirection, Embd_train,
Attention)
saver_word_rw = tf.train.Saver()
# saver_word_rw.restore(sess_word_rw,
# "Seq_ckpt/pretrainALL-seq2seq_Bi_" + str(Bidirection) + "_Att_" + str(
# Attention) + "_Emb_" + str(
# Embd_train))
saver_word_rw.restore(
sess_word_rw,
"/home/ye/PycharmProjects/Qrefine/Seq_ckpt/Wiki/ALL_seq2seq_Bi_" +
str(Bidirection) + "_Att_" + str(Attention) + "_Emb_" +
str(Embd_train) + "_hiddenunits_" + str(num_units))
model_type = "testing"
G_QA_similiarity = tf.Graph()
sess_QA_rw = tf.Session(config=config, graph=G_QA_similiarity)
with G_QA_similiarity.as_default():
QA_simi_model = QA_similiarity.QA_similiarity(batch_size, num_units,
embd, model_type)
saver_sen_rw = tf.train.Saver()
# saver_sen_rw.restore(sess_QA_rw, "Seq_ckpt/Wiki_qa_similiarity")
saver_sen_rw.restore(
sess_QA_rw,
"/home/ye/PycharmProjects/Qrefine/Seq_ckpt/Wiki/qa_similiarity")
G_BeamSearch = tf.Graph()
with G_BeamSearch.as_default():
BeamSearch_seq2seq = seq_last.BeamSearch_Seq2seq(
vocab_size=vocab_size,
num_units=num_units,
beam_width=beam_width,
model_name=model_name,
embd=embd,
Bidirection=Bidirection,
Embd_train=Embd_train,
Attention=Attention,
max_target_length=L)
seq2seq_len = L
RL_len = 0
Bleu_obj = Bleu()
Rouge_obj = Rouge()
Meteor_obj = Meteor()
cIDER_obj = Cider()
with tf.Session(config=config, graph=G_BeamSearch) as sess_beamsearch:
sess_beamsearch.run(tf.global_variables_initializer())
for v in tf.trainable_variables():
if v.name in values.keys():
v.load(values[v.name], sess_beamsearch)
val_loss_epo = []
patience_cnt = 0
for epo in range(epoch):
print(" epoch: {}".format(epo))
# RL_len = epo
RL_len = L - seq2seq_len
idx = np.arange(0, len(noisy_Id))
idx = list(np.random.permutation(idx))
Bleu_score1 = []
for batch in range(len(noisy_Id) / batch_size):
if seq2seq_len < 0:
seq2seq_len = 0
source_shuffle, source_len, train_shuffle, target_shuffle, target_len, answer_shuffle, ans_len = next_batch(
noisy_Id, noisy_len, train_Id, ground_truth, clean_len,
answer_Id, answer_len, batch_size, batch, idx)
source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
source_shuffle,
maxlen=None,
padding='post',
truncating="post",
value=EOS)
target_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
target_shuffle,
maxlen=seq2seq_len,
padding='post',
truncating="post",
value=EOS)
train_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
train_shuffle,
maxlen=seq2seq_len,
padding='post',
truncating="post",
value=EOS)
initial_input_in = [[EOS] for i in range(batch_size)]
target_len = np.tile(0, batch_size)
fd = {
BeamSearch_seq2seq.encoder_inputs: source_shuffle,
BeamSearch_seq2seq.encoder_inputs_length: source_len,
BeamSearch_seq2seq.decoder_length: target_len,
BeamSearch_seq2seq.decoder_inputs: train_shuffle,
BeamSearch_seq2seq.decoder_targets: target_shuffle,
BeamSearch_seq2seq.initial_input: initial_input_in
}
generated_que, policy = sess_beamsearch.run([
BeamSearch_seq2seq.RL_ids,
BeamSearch_seq2seq.max_policy
], fd)
generated_que_input = np.insert(generated_que,
0,
SOS,
axis=1)[:, 0:-1]
generated_target_len = np.tile(
generated_que_input.shape[1], batch_size)
fd_seq = {
Seq2Seq_model.encoder_inputs: source_shuffle,
Seq2Seq_model.encoder_inputs_length: source_len,
Seq2Seq_model.decoder_inputs: generated_que_input,
Seq2Seq_model.decoder_length: generated_target_len,
Seq2Seq_model.ground_truth: generated_que,
Seq2Seq_model.dropout_rate: dropout
}
logits_pro = sess_word_rw.run(Seq2Seq_model.softmax_logits,
fd_seq)
answer_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
answer_shuffle,
maxlen=None,
padding='post',
truncating="post",
value=EOS)
generated_len = np.tile(generated_que.shape[1], batch_size)
fd_qa = {
QA_simi_model.answer_inputs: answer_shuffle,
QA_simi_model.answer_inputs_length: ans_len,
QA_simi_model.question1_inputs: source_shuffle,
QA_simi_model.question1_inputs_length: source_len,
QA_simi_model.question2_inputs: generated_que,
QA_simi_model.question2_inputs_length: generated_len
}
QA_similiarity_rd = sess_QA_rw.run(
[QA_simi_model.two_distance], fd_qa)
beam_QA_similar.append(QA_similiarity_rd[0])
# print QA_similiarity_rd
reward = np.zeros((batch_size, RL_len))
for i in range(generated_que.shape[0]):
for j in range(seq2seq_len, generated_que.shape[1]):
max_index = generated_que[i][j]
reward[i][L - 1 - j] = logits_pro[i, j, max_index]
if j == generated_que.shape[1] - 1:
reward[i][L - 1 - j] = reward[i][
L - 1 - j] + sen_reward_rate * (
QA_similiarity_rd[0][i])
discounted_rewards = discounted_rewards_cal(
reward, discount_factor)
RL_rewards = discounted_rewards
fd = {
BeamSearch_seq2seq.encoder_inputs: source_shuffle,
BeamSearch_seq2seq.encoder_inputs_length: source_len,
BeamSearch_seq2seq.decoder_length: target_len,
BeamSearch_seq2seq.decoder_inputs: train_shuffle,
BeamSearch_seq2seq.decoder_targets: target_shuffle,
BeamSearch_seq2seq.initial_input: initial_input_in,
BeamSearch_seq2seq.dis_rewards: RL_rewards
}
_, rl_reward = sess_beamsearch.run([
BeamSearch_seq2seq.RL_train_op,
BeamSearch_seq2seq.rl_reward
], fd)
for t in range(batch_size):
ref = []
hyp = []
bleu_s = []
for tar_id in target_shuffle[t]:
if tar_id != 2:
ref.append(vocab[tar_id])
for pre_id in generated_que[t, :]:
if pre_id != 0 and pre_id != -1 and pre_id != 2:
hyp.append(vocab[pre_id])
sen_bleu1 = bleu([ref], hyp, weights=(1, 0, 0, 0))
bleu_s.append(sen_bleu1)
Bleu_score1.append(bleu_s)
top1_bleu = []
for i in range(len(Bleu_score1)):
top1_bleu.append(Bleu_score1[i][0])
if batch == 0 or batch % 100 == 0:
print(' batch {}'.format(batch))
print(' minibatch reward of training: {}'.format(
-rl_reward))
print("the current whole bleu_score1 is:",
sum(top1_bleu) / float(len(top1_bleu)))
beam_QA_similar = []
# beam_cnQA_similar = []
source_shuffle, source_len, train_shuffle, target_shuffle, target_len, answer_shuffle, ans_len = next_batch(
noisy_Id, noisy_len, train_Id, ground_truth, clean_len,
answer_Id, answer_len, batch_size, batch, idx)
source_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
source_shuffle,
maxlen=None,
padding='post',
truncating="post",
value=EOS)
target_shuffle_in = tf.keras.preprocessing.sequence.pad_sequences(
target_shuffle,
maxlen=seq2seq_len,
padding='post',
truncating="post",
value=EOS)
train_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
train_shuffle,
maxlen=seq2seq_len,
padding='post',
truncating="post",
value=EOS)
initial_input_in = [
target_shuffle_in[i][-1] for i in range(batch_size)
]
target_len = np.tile(seq2seq_len, batch_size)
fd = {
BeamSearch_seq2seq.encoder_inputs: source_shuffle,
BeamSearch_seq2seq.encoder_inputs_length: source_len,
BeamSearch_seq2seq.decoder_length: target_len,
BeamSearch_seq2seq.decoder_inputs: train_shuffle,
BeamSearch_seq2seq.decoder_targets: target_shuffle_in,
BeamSearch_seq2seq.initial_input: initial_input_in
}
cl_loss, _, S2S_ids = sess_beamsearch.run([
BeamSearch_seq2seq.loss_seq2seq,
BeamSearch_seq2seq.train_op, BeamSearch_seq2seq.ids
], fd)
# tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')
if batch == 0 or batch % batches_in_epoch == 0:
print(' batch {}'.format(batch))
print(' minibatch loss of training: {}'.format(cl_loss))
val_loss = []
for batch_val in range(len(val_noisy_Id) / batch_size):
idx_v = np.arange(0, len(val_noisy_Id))
idx_v = list(np.random.permutation(idx_v))
val_source_shuffle, val_source_len, val_train_shuffle, val_target_shuffle, val_target_len, val_answer_shuffle, val_ans_len = next_batch(
val_noisy_Id, val_noisy_len, val_train_Id,
val_ground_truth, val_clean_len, val_answer_Id,
val_answer_len, batch_size, batch_val, idx_v)
val_source_shuffle_in = tf.keras.preprocessing.sequence.pad_sequences(
val_source_shuffle,
maxlen=None,
padding='post',
value=EOS)
val_target_shuffle_in = tf.keras.preprocessing.sequence.pad_sequences(
val_target_shuffle,
maxlen=seq2seq_len,
padding='post',
value=EOS)
val_train_shuffle_in = tf.keras.preprocessing.sequence.pad_sequences(
val_train_shuffle,
maxlen=seq2seq_len,
padding='post',
value=EOS)
val_initial_input = [
val_target_shuffle_in[i][-1]
for i in range(batch_size)
]
val_target_len = np.tile(seq2seq_len, batch_size)
fd_val = {
BeamSearch_seq2seq.encoder_inputs:
val_source_shuffle_in,
BeamSearch_seq2seq.encoder_inputs_length:
val_source_len,
BeamSearch_seq2seq.decoder_length: val_target_len,
BeamSearch_seq2seq.decoder_inputs:
val_train_shuffle_in,
BeamSearch_seq2seq.decoder_targets:
val_target_shuffle_in,
BeamSearch_seq2seq.initial_input: val_initial_input
}
val_loss.append(
sess_beamsearch.run(
BeamSearch_seq2seq.loss_seq2seq, fd_val))
avg_val_loss = sum(val_loss) / float(len(val_loss))
print(' minibatch loss of validation: {}'.format(
avg_val_loss))
# val_loss_epo.append(avg_val_loss)
gc.collect()
if L - seq2seq_len == 0: continue
RL_logits, policy = sess_beamsearch.run(
[BeamSearch_seq2seq.RL_ids, BeamSearch_seq2seq.max_policy],
fd)
# print "The size of Rl:", RL_logits.shape[0], RL_logits.shape[1]
# print "The size of Policy:", policy.shape[0], policy.shape[1]
max_target_length = RL_logits.shape[1]
# for batch in range(batch_size):
# label = 0
# for i in range(RL_logits.shape[1]):
# if RL_logits[batch][i]==2 and label ==0:
# label = 1
# for id in range(i+1, max_target_length):
# RL_logits[batch][id]=2
# continue
Sequnce_len = seq2seq_len + max_target_length
# print "the max_target_length is:", max_target_length
RL_rewards = np.zeros((batch_size, max_target_length))
generated_que = []
for i in range(batch_size):
generated_que.append(
list(np.append(S2S_ids[i], RL_logits[i])))
generated_que = np.asarray(generated_que)
logits_batch = []
# for i in range(generated_que.shape[0]):
# for j in range(generated_que.shape[1]):
# if generated_que[i][j] == 2 or j == generated_que.shape[1] - 1:
# logits_batch.append(j)
# continue
#
# reward = np.zeros((batch_size, max_target_length))
# # generated_que = SOS + generated_que - PAD
generated_que_input = np.insert(generated_que, 0, SOS,
axis=1)[:, 0:-1]
# target_shuffle = tf.keras.preprocessing.sequence.pad_sequences(target_shuffle, maxlen=None,
# padding='post',truncating="post", value=EOS)
generated_target_len = np.tile(generated_que_input.shape[1],
batch_size)
fd_seq = {
Seq2Seq_model.encoder_inputs: source_shuffle,
Seq2Seq_model.encoder_inputs_length: source_len,
Seq2Seq_model.decoder_inputs: generated_que_input,
Seq2Seq_model.decoder_length: generated_target_len,
Seq2Seq_model.ground_truth: generated_que,
Seq2Seq_model.dropout_rate: dropout
}
logits_pro = sess_word_rw.run(Seq2Seq_model.softmax_logits,
fd_seq)
# logits_pro = logits_pro[:, :, :]
# print "the logits_pro is:", logits_pro.shape[1]
answer_shuffle = tf.keras.preprocessing.sequence.pad_sequences(
answer_shuffle,
maxlen=None,
padding='post',
truncating="post",
value=EOS)
generated_len = np.tile(generated_que.shape[1], batch_size)
fd_qa = {
QA_simi_model.answer_inputs: answer_shuffle,
QA_simi_model.answer_inputs_length: ans_len,
QA_simi_model.question1_inputs: source_shuffle,
QA_simi_model.question1_inputs_length: source_len,
QA_simi_model.question2_inputs: generated_que,
QA_simi_model.question2_inputs_length: generated_len
}
QA_similiarity_rd = sess_QA_rw.run(
[QA_simi_model.two_distance], fd_qa)
beam_QA_similar.append(QA_similiarity_rd[0])
# print QA_similiarity_rd
# reward = np.zeros((batch_size, RL_len))
# only use word reward
for i in range(generated_que.shape[0]):
label = 0
for j in range(seq2seq_len, generated_que.shape[1]):
max_index = generated_que[i][j]
RL_rewards[i][j - seq2seq_len] = logits_pro[i, j,
max_index]
# only use QA similiarity
# for i in range(generated_que.shape[0]):
# label = 0
# for j in range(seq2seq_len, generated_que.shape[1]):
# max_index = generated_que[i][j]
# RL_rewards[i][j - seq2seq_len] = 0
# if max_index == 2 and label == 0:
# label = 1
# RL_rewards[i][j - seq2seq_len] = 0 + sen_reward_rate * (QA_similiarity_rd[0][i])
# break
# # use both QA similiarity and Word reward
for i in range(generated_que.shape[0]):
label = 0
for j in range(seq2seq_len, generated_que.shape[1]):
max_index = generated_que[i][j]
RL_rewards[i][j - seq2seq_len] = logits_pro[i, j,
max_index]
if max_index == 2 and label == 0:
label = 1
RL_rewards[i][j - seq2seq_len] = logits_pro[
i, j, max_index] + sen_reward_rate * (
QA_similiarity_rd[0][i])
break
# fd = {an_Lstm.answer_inputs: answer_shuffle, an_Lstm.answer_inputs_length: ans_len}
# reward_similiarity = sess_sen_rw.run([an_Lstm.answer_state], fd)
discounted_rewards = discounted_rewards_cal(
RL_rewards, discount_factor)
RL_rewards = discounted_rewards
fd = {
BeamSearch_seq2seq.encoder_inputs: source_shuffle,
BeamSearch_seq2seq.encoder_inputs_length: source_len,
BeamSearch_seq2seq.decoder_length: target_len,
BeamSearch_seq2seq.decoder_inputs: train_shuffle,
BeamSearch_seq2seq.decoder_targets: target_shuffle_in,
BeamSearch_seq2seq.initial_input: initial_input_in,
BeamSearch_seq2seq.dis_rewards: RL_rewards
}
_, rl_reward, word_policy, policy = sess_beamsearch.run([
BeamSearch_seq2seq.RL_train_op,
BeamSearch_seq2seq.rl_reward,
BeamSearch_seq2seq.word_log_prob,
BeamSearch_seq2seq.softmax_policy
], fd)
for t in range(batch_size):
ref = []
hyp = []
bleu_s = []
for tar_id in target_shuffle[t]:
if tar_id != 2:
ref.append(vocab[tar_id])
for pre_id in generated_que[t, :]:
if pre_id != 0 and pre_id != -1 and pre_id != 2:
hyp.append(vocab[pre_id])
sen_bleu1 = bleu([ref], hyp, weights=(1, 0, 0, 0))
bleu_s.append(sen_bleu1)
Bleu_score1.append(bleu_s)
top1_bleu = []
for i in range(len(Bleu_score1)):
top1_bleu.append(Bleu_score1[i][0])
if batch == 0 or batch % 100 == 0:
print(' batch {}'.format(batch))
print(' minibatch reward of training: {}'.format(
-rl_reward))
# print the result
for t in range(5): ## five sentences
print('Question {}'.format(t))
print("noisy question:")
print(" ".join(
map(lambda i: vocab[i],
list(source_shuffle[t]))).strip().replace(
"<EOS>", " "))
print("clean question:")
print(" ".join(
map(lambda i: vocab[i],
list(target_shuffle[t]))).strip().replace(
"<EOS>", " "))
print("the generated question:")
pre_sen = []
for log_id in generated_que[t, :]:
if log_id != -1 and log_id != 2 and log_id != 0:
pre_sen.append(log_id)
print(" ".join(map(lambda i: vocab[i],
pre_sen)).strip())
print("\n")
print("the current whole bleu_score1 is:",
sum(top1_bleu) / float(len(top1_bleu)))
gc.collect()
seq2seq_len = seq2seq_len - dec_L
##testing set result:
test_Bleu_score1 = []
test_Bleu_score2 = []
test_Bleu_score3 = []
test_Bleu_score4 = []
test_Rouge_score = []
test_Meteor_score = []
test_Cider_score = []
generated_test_sen = []
test_answer_sen = []
test_noisy_sen = []
test_clean_sen = []
for batch_test in range(len(test_noisy_Id) / batch_size):
idx_t = np.arange(0, len(test_noisy_Id))
# idx_t = list(np.random.permutation(idx_t))
val_source_shuffle, val_source_len, val_train_shuffle, val_target_shuffle, val_target_len, val_answer_shuffle, val_ans_len = next_batch(
test_noisy_Id, test_noisy_len, test_train_Id,
test_ground_truth, test_clean_len, test_answer_Id,
test_answer_len, batch_size, batch_test, idx_t)
for an in val_answer_shuffle:
test_answer_sen.append(vocab[anum] for anum in an)
for no in val_source_shuffle:
test_noisy_sen.append(vocab[si] for si in no)
for cl in val_target_shuffle:
test_clean_sen.append(vocab[ci] for ci in cl)
val_source_shuffle_in = tf.keras.preprocessing.sequence.pad_sequences(
val_source_shuffle,
maxlen=None,
padding='post',
truncating="post",
value=EOS)
val_target_shuffle_in = []
for val in val_target_shuffle:
val_target_shuffle_in.append([val[0]])
val_train_shuffle_in = []
for tra in val_train_shuffle:
val_train_shuffle_in.append([tra[0]])
initial_input_in = [SOS for i in range(batch_size)]
# [SOS for i in range(batch_size)]
val_target_len_in = np.tile(1, batch_size)
fd = {
BeamSearch_seq2seq.encoder_inputs: val_source_shuffle_in,
BeamSearch_seq2seq.encoder_inputs_length: val_source_len,
BeamSearch_seq2seq.decoder_length: val_target_len_in,
BeamSearch_seq2seq.decoder_inputs: val_train_shuffle_in,
BeamSearch_seq2seq.decoder_targets: val_target_shuffle_in,
BeamSearch_seq2seq.initial_input: initial_input_in
}
val_id = sess_beamsearch.run([BeamSearch_seq2seq.RL_ids], fd)
final_id = val_id[0]
for t in range(batch_size):
ref = []
hyp = []
for tar_id in val_target_shuffle[t]:
if tar_id != 2:
ref.append(vocab[tar_id])
for pre_id in final_id[t, :]:
if pre_id != 2 and pre_id != 0 and pre_id != -1:
hyp.append(vocab[pre_id])
generated_test_sen.append(hyp)
hyp_sen = " ".join(hyp)
ref_sen = " ".join(ref)
dic_hyp = {}
dic_hyp[0] = [hyp_sen]
dic_ref = {}
dic_ref[0] = [ref_sen]
sen_bleu, _ = Bleu_obj.compute_score(dic_ref, dic_hyp)
sen_rouge = Rouge_obj.compute_score(dic_ref, dic_hyp)
sen_meteor, _ = Meteor_obj.compute_score(dic_ref, dic_hyp)
sen_cider, _ = cIDER_obj.compute_score(dic_ref, dic_hyp)
test_Bleu_score1.append(sen_bleu[0])
test_Bleu_score2.append(sen_bleu[1])
test_Bleu_score3.append(sen_bleu[2])
test_Bleu_score4.append(sen_bleu[3])
test_Rouge_score.append(sen_rouge[0])
test_Meteor_score.append(sen_meteor)
test_Cider_score.append(sen_cider)
bleu_score1 = sum(test_Bleu_score1) / float(
len(test_Bleu_score1))
bleu_score2 = sum(test_Bleu_score2) / float(
len(test_Bleu_score2))
bleu_score3 = sum(test_Bleu_score3) / float(
len(test_Bleu_score3))
bleu_score4 = sum(test_Bleu_score4) / float(
len(test_Bleu_score4))
print(
"\n Bleu_score1, Bleu_score2, Bleu_score3, Bleu_score4, Rouge, Meteor Cider\n"
)
print(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}"
.format(
bleu_score1, bleu_score2, bleu_score3, bleu_score4,
sum(test_Rouge_score) / float(len(test_Rouge_score)),
sum(test_Meteor_score) / float(len(test_Meteor_score)),
sum(test_Cider_score) / float(len(test_Cider_score))))
print "the bleu score on test is:", bleu_score1
if bleu_score1 > 0.55:
fname = "Wiki_result_whole/wiki_bleu1_" + str(bleu_score1)
f = open(fname, "wb")
f.write(
" {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f} \n"
.format(
bleu_score1, bleu_score2, bleu_score3, bleu_score4,
sum(test_Rouge_score) /
float(len(test_Rouge_score)),
sum(test_Meteor_score) /
float(len(test_Meteor_score)),
sum(test_Cider_score) /
float(len(test_Cider_score))))
f.write("\n" + "reward_rate " + str(sen_reward_rate) +
" discount_factor " + str(discount_factor) +
" dec_L " + str(dec_L) + " batch_size " +
str(batch_size) + " epoch " + str(epoch) + "\n")
for i in range(len(generated_test_sen)):
f.write("question " + str(i) + "\n")
f.write("answer: " + " ".join(test_answer_sen[i]) +
"\n")
f.write("noisy question: " +
" ".join(test_noisy_sen[i]) + "\n")
f.write("clean question: " +
" ".join(test_clean_sen[i]) + "\n")
f.write("generated question: " +
" ".join(generated_test_sen[i]) + "\n")