神经网络值错误：形状 (2115,13) 和 (1,) 未对齐：13 (dim 1) != 1 (dim 0)

如何解决神经网络值错误：形状 (2115,13) 和 (1,) 未对齐：13 (dim 1) != 1 (dim 0)

我现在很无助，虽然我找了几天的答案，但找不到合适的。我也看到了，有人问了类似的问题，但我还是找不到解决我的维度问题的方法。

我的目标是预测电影院的票价。我将数据拆分为训练数据和测试数据。 直到“创建神经网络”部分一切顺利。 我是初学者。请帮忙！

ValueError: 形状 (8457,13) 和 (1,) 未对齐：13 (dim 1) != 1 (dim 0)

    data_perc= data_filtered.head(int(len(data_filtered)*(10/100)))

y=data_perc.ticket_price
x=data_perc.drop('ticket_price',axis=1) # 1=colums und 0=rows

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)
x_train.head()
x_train.shape


inputs = x_train
outputs = y_train

class NeuralNetwork:

    # intialize variables in class
    def __init__(self,inputs,outputs):
        self.inputs  = inputs
        self.outputs = outputs
        # initialize weights as .50 for simplicity
        self.weights = np.array([.50])
        self.error_history = []
        self.epoch_list = []

    #activation function ==> S(x) = 1/1+e^(-x)
    def sigmoid(self,x,deriv=False):
        if deriv == True:
            return x * (1 - x)
        return 1 / (1 + np.exp(-x))

    # data will flow through the neural network.
    def feed_forward(self):
        self.hidden = self.sigmoid(np.squeeze(np.dot(self.inputs,self.weights)))

    # going backwards through the network to update weights
    def backpropagation(self):
        self.error  = self.outputs - self.hidden
        delta = self.error * self.sigmoid(self.hidden,deriv=True)
        self.weights += np.dot(self.inputs.T,delta)

    # train the neural net for 25,000 iterations
    def train(self,epochs=25000):
        for epoch in range(epochs):
            # flow forward and produce an output
            self.feed_forward()
            # go back though the network to make corrections based on the output
            self.backpropagation()    
            # keep track of the error history over each epoch
            self.error_history.append(np.average(np.abs(self.error)))
            self.epoch_list.append(epoch)

    # function to predict output on new and unseen input data                               
    def predict(self,new_input):
        prediction = self.sigmoid(np.dot(new_input,self.weights))
        return prediction

# create neural network   
NN = NeuralNetwork(inputs,outputs)
# train neural network
NN.train()