tensorFlow向我抛出错误ValueError：图层顺序需要1个输入，但收到2个输入张量

如何解决tensorFlow向我抛出错误ValueError：图层顺序需要1个输入，但收到2个输入张量

我正在尝试根据老师给我的代码建立我的第一个神经网络，但是当我尝试拟合该网络时，出现以下错误：

var indexOf=Array.prototype.indexOf

抛出一个错误的行是这个

/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>]

我尝试通过括号将方括号更改，但不起作用

数据是

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

模型：

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)

解决方法

您需要做的就是将验证数据放入一个元组而不是一个列表中。

所以改变这个：

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

对此：

model.fit( x=x_train,validation_data = (x_test,y_test))