keras でCNNをしようと思ったんですが　inputでエラーが出ます

inputのところでエラーが出ます　なんで出るのかさっぱりわかりません
CNNは縦＊横＊RGBのはず

python
1コード
2learning_rate = 0.00001
3action_size=2 #モデルのoutput
4statesize=[400,400,3]   #モデルのinput
5
6
7class QNetwork :
8    def __init__(self,learning_rate, state_size, action_size):
9        self.inputs = Input(shape=(statesize[0],statesize[1],statesize[2],))
10        
11        self.a=Conv2D(32,kernel_size=(3,3),padding='same',activation=LeakyReLU(alpha=0.01),
12                      use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
13                      bias_constraint=max_norm(2.))(self.inputs)
14        self.a=Conv2D(32,(3,3),strides=1,padding='same',activation=LeakyReLU(alpha=0.01),
15                      use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
16                      bias_constraint=max_norm(2.))(self.a)
17        self.a=MaxPooling2D(pool_size=(2, 2))
18        
19        self.a=Conv2D(64,kernel_size=(3, 3), padding='same', data_format=None, dilation_rate=(1, 1),activation=LeakyReLU(alpha=0.01),
20                      use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
21                      bias_constraint=max_norm(2.))(self.a)
22        self.a=MaxPooling2D(pool_size=(2, 2))(self.a)
23        
24        self.a=Conv2D(128,kernel_size=(3, 3), padding='same', data_format=None, dilation_rate=(1, 1),activation=LeakyReLU(alpha=0.01),
25                      use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
26                      bias_constraint=max_norm(2.))(self.a)
27        self.a=MaxPooling2D(pool_size=(2, 2))(self.a)
28        self.a=Flatten()(self.a)
29        
30        
31        self.a=Dense(250,activation='relu',
32                     use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
33                     bias_constraint=max_norm(2.))(self.a)
34        self.a=Dense(250,activation='relu',
35                     use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
36                     bias_constraint=max_norm(2.))(self.a)
37
38        
39        self.v=Dense(1,      
40                     use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
41                     bias_constraint=max_norm(2.))(self.v)
42        
43        self.dv=Dense(500,activation='relu'
44                     ,use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
45                     bias_constraint=max_norm(2.))(self.a)   
46        self.dv=Dense(action_size
47                     ,use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
48                     bias_constraint=max_norm(2.))(self.dv)
49        
50        self.a = Concatenate()([self.v,self.dv])
51        self.a = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], axis=1, keepdims=True), 
52                        output_shape=(action_size,))(self.a)
53        
54        self.adm = Adam(lr=learning_rate,beta_1=0.9, beta_2=0.999, amsgrad=False)
55        self.model = Model(inputs=self.inputs, outputs=self.a)
56        self.model.compile(loss=huberloss, optimizer=self.adm,metrics=['accuracy'])
57
58
59
60mainQN = QNetwork(learning_rate=learning_rate,state_size=statesize,action_size=action_size)```ここに言語を入力
61

ValueError Traceback (most recent call last)
c:\users\pc_user\anaconda3\envs\ml-agents\lib\site-packages\keras\engine\base_layer.py in assert_input_compatibility(self, inputs)
309 try:
--> 310 K.is_keras_tensor(x)
311 except ValueError:

c:\users\pc_user\anaconda3\envs\ml-agents\lib\site-packages\keras\backend\tensorflow_backend.py in is_keras_tensor(x)
696 raise ValueError('Unexpectedly found an instance of type ' + --> 697 str(type(x)) + '. '
698 'Expected a symbolic tensor instance.')

ValueError: Unexpectedly found an instance of type <class 'keras.layers.pooling.MaxPooling2D'>. Expected a symbolic tensor instance.

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
<ipython-input-4-b9642f280ab9> in <module>
71 statesize=[400,400,3] #モデルのinput
72
---> 73 mainQN = QNetwork(learning_rate=learning_rate,state_size=statesize,action_size=action_size) # メインQネットワーク
74 targetQN = QNetwork(learning_rate=learning_rate,state_size=statesize,action_size=action_size) # 価値計算Qネットワーク
75

<ipython-input-4-b9642f280ab9> in init(self, learning_rate, state_size, action_size)
29 self.a=Conv2D(64,kernel_size=(3, 3), padding='same', data_format=None, dilation_rate=(1, 1),activation=LeakyReLU(alpha=0.01),
30 use_bias=True,kernel_initializer='he_normal',bias_initializer='zeros',kernel_constraint=max_norm(2.),
---> 31 bias_constraint=max_norm(2.))(self.a)
32 self.a=MaxPooling2D(pool_size=(2, 2))(self.a)
33

c:\users\pc_user\anaconda3\envs\ml-agents\lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)

c:\users\pc_user\anaconda3\envs\ml-agents\lib\site-packages\keras\engine\base_layer.py in call(self, inputs, **kwargs)
444 # Raise exceptions in case the input is not compatible
445 # with the input_spec specified in the layer constructor.
--> 446 self.assert_input_compatibility(inputs)
447
448 # Collect input shapes to build layer.

c:\users\pc_user\anaconda3\envs\ml-agents\lib\site-packages\keras\engine\base_layer.py in assert_input_compatibility(self, inputs)
314 'Received type: ' +
315 str(type(x)) + '. Full input: ' +
--> 316 str(inputs) + '. All inputs to the layer '
317 'should be tensors.')
318

ValueError: Layer conv2d_9 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.pooling.MaxPooling2D'>. Full input: [<keras.layers.pooling.MaxPooling2D object at 0x00000212F146F2E8>]. All inputs to the layer should be tensors.