老师针对

keras.layers.SimpleRNN(units = rnn_units,
                               stateful = True,
                               recurrent_initializer = 'glorot_uniform',
                               return_sequences = True),

的stateful = True有几个问题：
1、stateful=true时,batch的顺序不能被打乱，那么

seq_dataset = seq_dataset.shuffle(buffer_size).batch(
    batch_size, drop_remainder=True)

的.shuffle(buffer_size)是否应该去掉
2、

history = model.fit(seq_dataset, epochs = epochs,
                    callbacks = [checkpoint_callback])

fit函数是不是应该加上shuffle=False参数
3、在预测阶段

model.reset_states()
    temperature = 2
    for _ in range(num_generate):
        predictions = model(input_eval)
        predictions = predictions / temperature
        predictions = tf.squeeze(predictions, 0)
        predicted_id = tf.random.categorical(
            predictions, num_samples = 1)[-1, 0].numpy()
        text_generated.append(idx2char[predicted_id])
        input_eval = tf.expand_dims([predicted_id], 0)

由于batch_size=1,timestep=1,对于循环for _ in range(num_generate)中每次进行predictions = model(input_eval)前向传播得到的状态之所以能传到下一次循环作为初始状态，是否是依赖于stateful = True这个机制呢，若stateful = False，则循环中每次调用predictions = model(input_eval)是都将自动调用model.reset_states()将状态初始化，而和上一次循环完全无关，不知道这么理解的是否正确