Python tensorFlow向我抛出错误值error:Layer sequential需要1个输入，但它收到2个输入张量

我正试图根据老师给我的代码建立我的第一个神经网络，但当我尝试拟合网络时，我得到以下错误：

/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: [<tf.Tensor 'IteratorGetNext:0' shape=(10, 784) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(10, 10) dtype=float32>]

我试着用括号换括号，但没用

数据是

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)

模型：

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

---

您只需将验证数据放入元组而不是列表中

因此，改变这一点：

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

为此：

model.fit( x=x_train , y=y_train , batch_size=10 , epochs=10 , verbose=1 , validation_data = (x_test,y_test))

---

尝试省去batch_size参数。否则你需要提供更多的细节。我试过了，但没用。还要添加我需要的数据use@lautarogonzalez你能展示一下模型摘要吗？

model.fit( x=x_train , y=y_train , batch_size=10 , epochs=10 , verbose=1 , validation_data = (x_test,y_test))