text_gen_text.py 文件源码

def fetch(self):
        # cut the text in semi-redundant sequences of maxlen characters
        #text=self.text
        text=self.next_text()
        chars=self.chars
        maxlen=self.maxlen
        step=self.step

        maxlen = 20
        step = 3
        sentences = []
        next_chars = []
        for i in range(0, len(text) - maxlen, step):
            sentences.append(text[i: i + maxlen])
            next_chars.append(text[i + maxlen])
        print('nb sequences:', len(sentences))

        print('Vectorization...')
        X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
        y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
        for i, sentence in enumerate(sentences):
            for t, char in enumerate(sentence):
                X[i, t, self.char_indices[char]] = 1
            y[i, self.char_indices[next_chars[i]]] = 1
        return text,X,y