char_predictor.py 文件源码

def build_model(self):
        assert self.seq_len>1
        assert len(self.alphabet.alphabet)>0
        bits_per_char = self.alphabet.nb_chars
        rnn_size = bits_per_char
        model = Sequential()
        model.add( Masking( mask_value=0, input_shape=(self.seq_len, bits_per_char), name='input_layer' ) )
        model.add( recurrent.LSTM( rnn_size, input_shape=(self.seq_len, bits_per_char), return_sequences=False ) )
        model.add( Dense( units=rnn_size, activation='sigmoid') )
        model.add( Dense( units=bits_per_char, activation='softmax', name='output_layer') )
        model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
        return model