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 __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()
