def test(self):
trace('loading model ...')
src_vocab = Vocabulary.load(self.model + '.srcvocab')
trg_vocab = Vocabulary.load(self.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(self.model + '.spec')
serializers.load_hdf5(self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source),
self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward(src_batch, None, src_vocab, trg_vocab,
encdec, False, self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " +
"".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' + ''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def test(self):
trace('loading model ...')
src_vocab = Vocabulary.load(self.model + '.srcvocab')
trg_vocab = Vocabulary.load(self.model + '.trgvocab')
encdec = EncoderDecoder.load_spec(self.model + '.spec')
serializers.load_hdf5(self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
for src_batch in gens.batch(gens.word_list(self.source), self.minibatch):
src_batch = fill_batch(src_batch)
K = len(src_batch)
trace('sample %8d - %8d ...' % (generated + 1, generated + K))
hyp_batch = self.forward(src_batch, None, src_vocab, trg_vocab, encdec, False, self.generation_limit)
source_cuont = 0
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print("src : " + "".join(src_batch[source_cuont]).replace("</s>", ""))
print('hyp : ' +''.join(hyp))
print(' '.join(hyp), file=fp)
source_cuont = source_cuont + 1
generated += K
trace('finished.')
def __predict_sentence(self, src_batch):
dialogue = EncoderDecoderModelForwardSlack(self.parameter)
src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
model = EncoderDecoder.load_spec(self.model_name + '.spec')
serializers.load_hdf5(dialogue.model + '.weights', model)
hyp_batch = dialogue.forward(src_batch, None, src_vocab, trg_vocab, model, False, self.generation_limit)
return hyp_batch
def __predict_sentence(self, src_batch):
dialogue = EncoderDecoderModelForwardSlack(self.parameter)
src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
model = EncoderDecoder.load_spec(self.model_name + '.spec')
serializers.load_hdf5(dialogue.model + '.weights', model)
hyp_batch = dialogue.forward(src_batch, None, src_vocab, trg_vocab,
model, False, self.generation_limit)
return hyp_batch
def __judge_print(self):
if len(self.data) >= 1 and "text" in self.data[0]:
print(self.data[0]["text"])
if "chainer:" in self.data[0]["text"]:
# input sentence
src_batch = self.__input_sentence()
#predict
hyp_batch = self.__predict_sentence(src_batch)
#show predict word
word = ''.join(hyp_batch[0]).replace("</s>", "")
print(self.sc.api_call("chat.postMessage", user=self.usr, channel = self.chan, text = word))
if "chainer_train" in self.data[0]["text"]:
self.__setting_parameter()
model = EncoderDecoder.load_spec(self.model_name + '.spec')
dialogue = EncoderDecoderModelForwardSlack(self.parameter)
serializers.load_hdf5(dialogue.model + '.weights', model)
dialogue.encdec = model
dialogue.word2vecFlag = False
dialogue.train()
def test(self):
trace('loading model ...')
self.trg_vocab = Vocabulary.load("model/" + self.model + '.trgvocab')
self.batch_size = len(trg_batch)
encdec = EncoderDecoder.load_spec("model/" + self.model + '.spec')
serializers.load_hdf5("model/" + self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
self.__forward_img()
trace('sample %8d ...' % (generated + 1))
hyp_batch = self.__forward_word(self.trg_batch, encdec, False, self.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print('hyp : ' +''.join(hyp))
print(' '.join(hyp), file=fp)
trace('finished.')
def __judge_print(self):
if len(self.data) >= 1 and "text" in self.data[0]:
print(self.data[0]["text"])
if "chainer:" in self.data[0]["text"]:
# input sentence
src_batch = self.__input_sentence()
#predict
hyp_batch = self.__predict_sentence(src_batch)
#show predict word
word = ''.join(hyp_batch[0]).replace("</s>", "")
print(
self.sc.api_call("chat.postMessage",
user=self.usr,
channel=self.chan,
text=word))
if "chainer_train" in self.data[0]["text"]:
self.__setting_parameter()
model = EncoderDecoder.load_spec(self.model_name + '.spec')
dialogue = EncoderDecoderModelForwardSlack(self.parameter)
serializers.load_hdf5(dialogue.model + '.weights', model)
dialogue.encdec = model
dialogue.word2vecFlag = False
dialogue.train()
def test(self):
trace('loading model ...')
self.trg_vocab = Vocabulary.load("model/" + self.model + '.trgvocab')
self.batch_size = len(trg_batch)
encdec = EncoderDecoder.load_spec("model/" + self.model + '.spec')
serializers.load_hdf5("model/" + self.model + '.weights', encdec)
trace('generating translation ...')
generated = 0
with open(self.target, 'w') as fp:
self.__forward_img()
trace('sample %8d ...' % (generated + 1))
hyp_batch = self.__forward_word(self.trg_batch, encdec, False,
self.generation_limit)
for hyp in hyp_batch:
hyp.append('</s>')
hyp = hyp[:hyp.index('</s>')]
print('hyp : ' + ''.join(hyp))
print(' '.join(hyp), file=fp)
trace('finished.')
