相关疑难解决方法(0)

我正在关注 tensorflow 神经机器翻译教程：https ://www.tensorflow.org/tutorials/text/nmt_with_attention

我正在尝试保存作为 tf.keras.Model 子类的 Encoder 和 Decoder 模型，并在训练和推理期间正常工作，但是我想保存模型。当我尝试这样做时，我收到以下错误：

TypeError: call() missing 1 required positional argument: 'initial_state'

这是代码：

class Encoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_matrix, n_units, batch_size):
        super(Encoder, self).__init__()
        self.n_units = n_units
        self.batch_size = batch_size

        self.embedding = Embedding(vocab_size, embedding_matrix.shape[1], weights=[embedding_matrix], trainable=True, mask_zero=True)
        self.lstm = LSTM(n_units, return_sequences=True, return_state=True, recurrent_initializer="glorot_uniform")

    def call(self, input_utterence, initial_state):
        input_embed = self.embedding(input_utterence)
        encoder_states, h1, c1 = self.lstm(input_embed, initial_state=initial_state)
        return encoder_states, h1, c1

    def create_initial_state(self):
        return tf.zeros((self.batch_size, self.n_units))

encoder = Encoder(vocab_size, embedding_matrix, LSTM_DIM, BATCH_SIZE)
# do …