我有个问题。我建立了一个ConvNet。在最终输出之前隐藏的那个隐藏层的输出形状是（None，64,32,32）。我想要的是取这64个通道的元素明智的平均值。我已经试过了：
main_inputs=[]
outputs=[]
def convnet(channels,rows,columns):
input=Input(shape=(channels,rows,columns))
main_inputs.append(input)
conv1=Convolution2D(kernel_size=(3,3) ,filters=64, padding="same")(input)
activation1= Activation('relu')(conv1)
conv2=Convolution2D(kernel_size=(3,3), filters=64, padding="same")(activation1)
activation2 = Activation('relu')(conv2)
conv3=Convolution2D(kernel_size=(3,3), filters=64, padding="same")(activation2)
activation3 = Activation('relu')(conv3)
conv4=Convolution2D(kernel_size=(3,3), filters=channels, padding="same")(activation3)
out=keras.layers.Average()(conv4)
activation4 = Activation('linear')(out)
outputs.append(activation4)
print(np.shape(outputs))
model = Model(inputs=main_inputs, outputs=outputs)
return model
但是当我遇到错误时：
ValueError: A merge layer should be called on a list of inputs
之后，我尝试使用后端文档，而不是keras.layer.average：
out=K.mean(conv4,axis=1)
但我收到此错误：
'Tensor' object has no attribute '_keras_history'
有任何想法吗？