TensorFlow выдает ValueError: последовательный слой ожидает 1 входной сигнал, но получил 2 входных тензора ⇐ Python

[Anonymous]

Я пытаюсь создать свою первую нейронную сеть на основе кода, который дал мне учитель, но когда я пытаюсь подогнать сеть, я получаю следующую ошибку:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1224 test_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1215 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1208 run_step **
outputs = model.test_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1174 test_step
y_pred = self(x, training=False)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__
self.name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:158 assert_input_compatibility
' input tensors. Inputs received: ' + str(inputs))

ValueError: Layer sequential expects 1 inputs, but it received 2 input tensors. Inputs received: [, ]

строка, которая выдает ошибку:
model.fit(x=x_train,
y=y_train,
batch_size=10,
epochs=10,
verbose=1,
validation_data = [x_test,y_test])

Я пытаюсь изменить скобки на круглые, но не получается
Данные
from keras.datasets import mnist
import matplotlib.pyplot as plt
from keras.utils import np_utils
import seaborn as sns

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0],x_train.shape[1]*x_train.shape[2])
x_test = x_test.reshape(x_test.shape[0],x_test.shape[1]*x_test.shape[2])

x_train = x_train/255
x_test = x_test/255

y_train = np_utils.to_categorical(y_train,10)
y_test = np_utils.to_categorical(y_test,10)

модель:
model = Sequential()
model.add(Dense(32, input_dim = 784))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))


Подробнее здесь: https://stackoverflow.com/questions/634 ... t-received