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()