类型错误：添加的图层必须是 Layer 类的实例。找到：

导入库和模型，

from __future__ import print_function
import keras
from keras.datasets import mnist
from tensorflow.keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
#from tensorflow.keras.layers import backend as k

batch_size = 128
num_classes = 10
epochs = 12

在编写的代码下面，

model = Sequential()
    model.add(Conv2D(32, kernel_size=(3,3), strides=(1,1), activation="relu", input_shape=(28, 28, 1) ))
    model.add(Conv2D(32, kernel_size=(3,3), strides=(1,1), activation="relu"))
    
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2) ))
    
    model.add(Dropout(0.5))
    model.add(Flatten())
    
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))

在类型错误下方，我遇到了严重的问题，但无法找到解决方案，

TypeError                                 Traceback (most recent call last)
<ipython-input-6-6c99a01e13d4> in <module>
      7 model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2) ))
      8 
----> 9 model.add(Dropout(0.5))
     10 model.add(Flatten())

TypeError: The added layer must be an instance of class Layer. Found: <keras.layers.core.Dropout object at 0x000001622999A5F8>

现在，我应该如何解决此类错误？需要帮忙，

Use Keras or tensorflow.keras，不要同时使用它们。

from __future__ import print_function
from tensorflow import keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras import backend as k

batch_size = 128
num_classes = 10
epochs = 12

model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), strides=(1,1), activation="relu", input_shape=(28, 28, 1) ))
model.add(Conv2D(32, kernel_size=(3,3), strides=(1,1), activation="relu"))

model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2) ))

model.add(Dropout(0.5))
model.add(Flatten())

model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))