def main():
###########################
#### create dictionary ####
###########################
if os.path.exists('./data/corpus/dictionary.dict'):
if args.lang == 'ja':
corpus = JaConvCorpus(file_path=None, batch_size=batchsize)
else:
corpus = ConvCorpus(file_path=None, batch_size=batchsize)
corpus.load(load_dir='./data/corpus/')
else:
if args.lang == 'ja':
corpus = JaConvCorpus(file_path=data_file, batch_size=batchsize)
else:
corpus = ConvCorpus(file_path=data_file, batch_size=batchsize)
corpus.save(save_dir='./data/corpus/')
print('Vocabulary Size (number of words) :', len(corpus.dic.token2id))
######################
#### create model ####
######################
model = Seq2Seq(len(corpus.dic.token2id),
feature_num=feature_num,
hidden_num=hidden_num,
batch_size=batchsize,
gpu_flg=args.gpu)
if args.gpu >= 0:
model.to_gpu()
optimizer = optimizers.Adam(alpha=0.001)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(5))
# optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
##########################
#### create ID corpus ####
##########################
input_mat = []
output_mat = []
max_input_ren = max_output_ren = 0
for input_text, output_text in zip(corpus.posts, corpus.cmnts):
# convert to list
input_text.reverse() # encode words in a reverse order
input_text.insert(0, corpus.dic.token2id["<eos>"])
output_text.append(corpus.dic.token2id["<eos>"])
# update max sentence length
max_input_ren = max(max_input_ren, len(input_text))
max_output_ren = max(max_output_ren, len(output_text))
input_mat.append(input_text)
output_mat.append(output_text)
# padding
for li in input_mat:
insert_num = max_input_ren - len(li)
for _ in range(insert_num):
li.insert(0, corpus.dic.token2id['<pad>'])
for li in output_mat:
insert_num = max_output_ren - len(li)
for _ in range(insert_num):
li.append(corpus.dic.token2id['<pad>'])
# create batch matrix
input_mat = np.array(input_mat, dtype=np.int32).T
output_mat = np.array(output_mat, dtype=np.int32).T
# separate corpus into Train and Test
perm = np.random.permutation(len(corpus.posts))
test_input_mat = input_mat[:, perm[0:0 + testsize]]
test_output_mat = output_mat[:, perm[0:0 + testsize]]
train_input_mat = input_mat[:, perm[testsize:]]
train_output_mat = output_mat[:, perm[testsize:]]
list_of_references = []
for text_ndarray in test_output_mat.T:
reference = text_ndarray.tolist()
references = [[w_id for w_id in reference if w_id is not -1]]
list_of_references.append(references)
#############################
#### train seq2seq model ####
#############################
accum_loss = 0
train_loss_data = []
test_loss_data = []
bleu_score_data = []
wer_score_data = []
for num, epoch in enumerate(range(n_epoch)):
total_loss = test_loss = 0
batch_num = 0
perm = np.random.permutation(len(corpus.posts) - testsize)
# for training
for i in range(0, len(corpus.posts) - testsize, batchsize):
# select batch data
input_batch = train_input_mat[:, perm[i:i + batchsize]]
output_batch = train_output_mat[:, perm[i:i + batchsize]]
# Encode a sentence
model.initialize() # initialize cell
model.encode(input_batch,
train=True) # encode (output: hidden Variable)
# Decode from encoded context
end_batch = xp.array(
[corpus.dic.token2id["<start>"] for _ in range(batchsize)])
first_words = output_batch[0]
loss, predict_mat = model.decode(end_batch,
first_words,
train=True)
next_ids = first_words
accum_loss += loss
for w_ids in output_batch[1:]:
loss, predict_mat = model.decode(next_ids, w_ids, train=True)
next_ids = w_ids
accum_loss += loss
# learn model
model.cleargrads() # initialize all grad to zero
accum_loss.backward() # back propagation
optimizer.update()
total_loss += float(accum_loss.data)
batch_num += 1
print('Epoch: ', num, 'Batch_num', batch_num,
'batch loss: {:.2f}'.format(float(accum_loss.data)))
accum_loss = 0
# for testing
list_of_hypotheses = []
for i in range(0, testsize, batchsize):
# select test batch data
input_batch = test_input_mat[:, i:i + batchsize]
output_batch = test_output_mat[:, i:i + batchsize]
# Encode a sentence
model.initialize() # initialize cell
model.encode(input_batch,
train=True) # encode (output: hidden Variable)
# Decode from encoded context
end_batch = xp.array(
[corpus.dic.token2id["<start>"] for _ in range(batchsize)])
first_words = output_batch[0]
loss, predict_mat = model.decode(end_batch,
first_words,
train=True)
next_ids = xp.argmax(predict_mat.data, axis=1)
test_loss += loss
if args.gpu >= 0:
hypotheses = [cuda.to_cpu(next_ids)]
else:
hypotheses = [next_ids]
for w_ids in output_batch[1:]:
loss, predict_mat = model.decode(next_ids, w_ids, train=True)
next_ids = xp.argmax(predict_mat.data, axis=1)
test_loss += loss
if args.gpu >= 0:
hypotheses.append(cuda.to_cpu(next_ids))
else:
hypotheses.append(next_ids)
# collect hypotheses for calculating BLEU score
hypotheses = np.array(hypotheses).T
for hypothesis in hypotheses:
text_list = hypothesis.tolist()
list_of_hypotheses.append(
[w_id for w_id in text_list if w_id is not -1])
# calculate BLEU score from test (develop) data
bleu_score = nltk.translate.bleu_score.corpus_bleu(list_of_references,
list_of_hypotheses,
weights=(0.25, 0.25,
0.25,
0.25))
bleu_score_data.append(bleu_score)
print('Epoch: ', num, 'BLEU SCORE: ', bleu_score)
# calculate WER score from test (develop) data
wer_score = 0
for index, references in enumerate(list_of_references):
wer_score += wer(references[0], list_of_hypotheses[index])
wer_score /= len(list_of_references)
wer_score_data.append(wer_score)
print('Epoch: ', num, 'WER SCORE: ', wer_score)
# save model and optimizer
if (epoch + 1) % 10 == 0:
print('-----', epoch + 1, ' times -----')
print('save the model and optimizer')
serializers.save_hdf5('data/' + str(epoch) + '.model', model)
serializers.save_hdf5('data/' + str(epoch) + '.state', optimizer)
# display the on-going status
print('Epoch: ', num, 'Train loss: {:.2f}'.format(total_loss),
'Test loss: {:.2f}'.format(float(test_loss.data)))
train_loss_data.append(float(total_loss / batch_num))
test_loss_data.append(float(test_loss.data))
# evaluate a test loss
check_loss = test_loss_data[-10:] # check out the last 10 loss data
end_flg = [
j for j in range(len(check_loss) - 1)
if check_loss[j] < check_loss[j + 1]
]
if len(end_flg) > 8:
print('Probably it is over-fitting. So stop to learn...')
break
# save loss data
with open('./data/loss_train_data.pkl', 'wb') as f:
pickle.dump(train_loss_data, f)
with open('./data/loss_test_data.pkl', 'wb') as f:
pickle.dump(test_loss_data, f)
with open('./data/bleu_score_data.pkl', 'wb') as f:
pickle.dump(bleu_score_data, f)
with open('./data/wer_score_data.pkl', 'wb') as f:
pickle.dump(wer_score_data, f)
