建立序列

simple_seq= [x for x in list(range(1000)) if x % 3 == 0]

重塑并拆分后

x_train,x_test shape = (159,5,1)
y_train,y_test shape = (159,2)

型号

model = Sequential(name='acc_test')
model.add(Conv1D(
  kernel_size = 2,filters= 128,strides= 1,use_bias= True,activation= 'relu',padding='same',input_shape=(x_train.shape[1],x_train.shape[2])))

model.add(AveragePooling1D(pool_size =(2),strides= [1]))
model.add(flatten())
model.add(Dense(2))

optimizer = Adam(lr=0.001)
model.compile( optimizer= optimizer,loss= 'mse',metrics=['accuracy'])

火车

hist = model.fit(
          x=x_train,y = y_train,epochs=100,validation_split=0.2)

结果：

Epoch 100/100
127/127 [==============================] - 0s 133us/sample - loss: 0.0096 - acc: 1.0000 - val_loss: 0.6305 - val_acc: 1.0000

但是如果使用此模型进行预测：

x_test[-1:] = array([[[9981],[9984],[9987],[9990],[9993]]])

model.predict(x_test[-1:])
result is: array([[10141.571,10277.236]],dtype=float32)

如果结果与事实相去甚远，结果是

step    1          2
true [9996,9999     ]
pred [10141.571,10277.236]