为您的 base_model 赋予了构建下面代码块的正确方法...

given your base_model this the correct way to build the code block below...

l1 = model_base.layers[2].output
l1 = GlobalAveragePooling2D()(l1) 
c2 = model_base.layers[4].output
c2 = GlobalAveragePooling2D()(c2) 
c3 = model_base.layers[6].output

c = c3.shape[-1] ### this is important for the dimesionality
l1 = Dense(c)(l1)
c2 = Dense(c)(c2) 

c13 = Lambda(lambda lam: K.squeeze(K.map_fn(lambda xy: K.dot(xy[0], xy[1]), 
                                            elems=(lam[0], K.expand_dims(lam[1], -1)), dtype='float32'), 3), name='cdp1')([c3, l1])  # batch*x*y

c23 = Lambda(lambda lam: K.squeeze(K.map_fn(lambda xy: K.dot(xy[0], xy[1]), 
                                            elems=(lam[0], K.expand_dims(lam[1], -1)), dtype='float32'), 3), name='cdp2')([c3, c2])  # batch*x*y

flatc13 = Flatten(name='flatc1')(c13)  # batch*xy
flatc23 = Flatten(name='flatc2')(c23)  # batch*xy

a1 = Activation('softmax', name='softmax1')(flatc13) # batch*xy
a2 = Activation('softmax', name='softmax2')(flatc23) # batch*xy

reshaped = Reshape((-1,c), name='reshape1')(c3)  # batch*xy*c

g1 = Lambda(lambda lam: K.squeeze(K.batch_dot(K.expand_dims(lam[0], 1), lam[1]), 1), 
            name='g1')([a1,reshaped])  # batch*c
g2 = Lambda(lambda lam: K.squeeze(K.batch_dot(K.expand_dims(lam[0], 1), lam[1]), 1), 
            name='g2')([a2,reshaped])  # batch*c

注意维数（在您的情况下，您不能使用512而是使用256进行操作，这由 c 变量）。还应注意Lambda操作中使用的层的顺序（例如，在c13中为（[[c3，l1]）而不是（[ l1，c3]））

pay attention to the dimensionality (in your case you can't operate with 512 but with 256, this is handled automatically by the c variable). pay attention also to the order of the layer used in the Lambda operations (for example in c13 it's ([c3, l1]) and not ([l1, c3]))

正在运行的笔记本在这里： https://colab.research.google.com/drive/1m0pB5GlYRtIsOnHUTz6LxRQblcvtVU3Y?usp=sharing

here the running notebook: https://colab.research.google.com/drive/1m0pB5GlYRtIsOnHUTz6LxRQblcvtVU3Y?usp=sharing