lstm_trainer_generator.py 文件源码

def generate_sentences(self, len_sentences=60, load=True):
        """
        Generate sentences given a trained LSTM model
        :param len_sentences: the length of the sentences to be generated
        :param load: whether to load in a model, or to use the trained one, only works if you ran train_model before
        :return:
        """

        model = lstm_model.create_model(word_coding)

        if load:
            model.load_weights('lstm-weights')

        else:
            if not ModelTrainer.model_trained:
                raise Exception("The model hasn't been trained. Either train it or load in the weights from a file")
            model = self.model

        seedSrc = i_D
        outSentences = []
        while len(outSentences) < len_sentences:
            start_index = random.randint(0, len(seedSrc) - 1)
            sentence = seedSrc[start_index: start_index + 1]

            sentOutput = ''

            for iteration in range(500):
                vecsentence = []
                for vcode in sentence[0]:
                    vecsentence.append(self.vmodel[coded_word[vcode]])
                vecsentence = np.reshape(vecsentence, (1, len(vecsentence), 300))
                preds = model.predict({'input': vecsentence}, verbose=0)['output1'][0]
                next_index = sample(preds, 0.2)
                if next_index in coded_word:
                    next_char = coded_word[next_index]
                    sentence = np.append(sentence[0][1:], [next_index]).reshape(np.asarray(sentence).shape)
                    sentOutput += next_char + ' '
            print(sentOutput)