Python 简单LSTM错误：检查输入时出错：预期LSTM_20_输入具有形状（无，10，3），但获得具有形状（1，64，3）的数组

我有一个批处理生成器功能，它没有将正确的批处理形状提供给LSTM。当我测试函数时，它似乎返回了正确的形状[n_samples，n_timesteps，n_features]，但这会在拟合模型时引发错误

我通过在生成器上循环检查批处理形状来检查函数，它们返回正确的样本数、时间步长等

from keras.models import Sequential
from keras.layers import Dense, LSTM, TimeDistributed, RepeatVector


def batch_generator(x_train_scaled, y_train_scaled, batch_size, sequence_length):
    """
    Generator function to develop sequential batches of data.
    Args:
      batch_size:
      sequence_length: 
    """
    # Infinite loop.
    while True: 
        # Allocate a new array for the batch of input-signals.
        x_shape = np.array((batch_size, sequence_length,     num_x_signals))
        x_batch = np.zeros(shape=x_shape, dtype=np.float16)

        # Allocate a new array for the batch of output-signals.
        y_shape = np.array((batch_size, sequence_length, 1))
        y_batch = np.zeros(shape=y_shape, dtype=np.float16)

        for i in range(batch_size):
            # Copy the sequences of data starting at this index.
            x_batch[i] = x_train_scaled[i:i+sequence_length]
            y_batch[i] = y_train_scaled[i:i+sequence_length]

        yield (x_batch, y_batch)



# test function
batch_size = 10
sequence_length = 10

batch_gen = batch_generator(x_train_scaled, y_train_scaled,batch_size=batch_size,
                            sequence_length=sequence_length)

x_batch, y_batch = next(batch_gen)

# test that returns correct shape (10, 10, 3) and (10, 10, 1)
print(x_batch.shape)
print(y_batch.shape)


def build_model(generator, n_outputs):
  # define encoder/decoder architecture, use Time Distributed layer
  model = Sequential()
  model.add(LSTM(10, activation='relu', input_shape=(x_batch.shape[1], 
                                                     x_batch.shape[2])))
  model.add(RepeatVector(n_outputs))
  model.add(LSTM(10, activation='relu', return_sequences=True))
  model.add(TimeDistributed(Dense(5, activation='relu')))
  model.add(TimeDistributed(Dense(1)))
  model.compile(loss='mse', optimizer='adam')

  # fit network
  model.fit_generator(generator=batch_gen,
                      epochs=20,
                      steps_per_epoch=10,
                      validation_data=validation_data,
                      verbose = 1)
  return model