请问各路大佬，我的TensorFlow2.0 搭建的BP神经网络为什么预测值是一条直线？

dataSet=pd.read_csv('data2.csv',engine='python')
train=dataSet.iloc[ :16000,:]#前800行
#print(train)
test= dataSet.iloc[16000:,]#800行后
#print(test)
#训练输入数据
train_x=train.iloc[ :,:-1]
print('train_x',type(train_x))
#print(train_data)
train_y=train.iloc[:,-1]
#print(train_label)
#测试数据
test_x=test.iloc[ :,:-1]
test_y=test.iloc[:,-1]


#加载的训练集和测试集转化为tensor格式
x_train = tf.convert_to_tensor(train_x)
y_train = tf.convert_to_tensor(train_y)

x_test =tf.convert_to_tensor(test_x)
y_test =tf.convert_to_tensor(test_y)


#构建一个结构为[18,15,1]的BP神经网络
model = tf.keras.Sequential( [tf.keras.layers.Dense(15, activation='tanh',input_shape=(18,)),
                              tf.keras.layers.Dense(1)])

model.compile(optimizer='adam',loss='mse',metrics=[tf.keras.metrics.sparse_categorical_accuracy])#tf.keras.optimizers.Adam(lr=0.00035)

model.fit(x_train, y_train, epochs=10)

model.summary()
print('x_test',x_test)
y_predict = model.predict(x_test)
print('y_predict',y_predict)
test_loss,test_accuracy=model.evaluate(x_test,y_test)
print('test_accuracy是',test_loss,test_accuracy)


plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号


plt.figure(1

)
plt.plot( y_test, color = 'red', linewidth=0.2,label = '实际值')
plt.plot(y_predict,color = 'green',label = '预测值')
plt.xlabel('空预器运行时间')
plt.ylabel('压差（KPa）')
plt.title('空气预热器压差预测结果图')
plt.show()

请问一下解决了吗，我也有相同的问题

我也是。。。不管是增加深度，修改batch，初始参数，激活函数，都是直线。