如何在keras中的层之间共享卷积内核？

Question

假设我想用深度卷积NN比较两个图像。如何在keras中使用相同的内核实现两个不同的路径？

像这样：

我需要卷积层1,2和3使用并训练相同的内核。

有可能吗？

我还在考虑连接下面的图像

但问题是关于如何在第一张照片上实施托盘学。

Answer 1

您可以在模型中使用相同的图层，创建nodes：

from keras.models import Model    
from keras.layers import *

#create the shared layers
layer1 = Conv2D(filters, kernel_size.....)
layer2 = Conv2D(...)    
layer3 = ....

#create one input tensor for each side
input1 = Input((imageX, imageY, channels))
input2 = Input((imageX, imageY, channels))   

#use the layers in side 1
out1 = layer1(input1)   
out1 = layer2(out1)   
out1 = layer3(out1)

#use the layers in side 2
out2 = layer1(input2)   
out2 = layer2(out2)   
out2 = layer3(out2)

#concatenate and add the fully connected layers
out = Concatenate()([out1,out2])
out = Flatten()(out)
out = Dense(...)(out)   
out = Dense(...)(out)   

#create the model taking 2 inputs with one output
model = Model([input1,input2],out)

您也可以使用相同的模型，使其成为更大的子模型：

#have a previously prepared model 
convModel = some model previously prepared

#define two different inputs
input1 = Input((imageX, imageY, channels))
input2 = Input((imageX, imageY, channels))   

#use the model to get two different outputs:
out1 = convModel(input1)
out2 = convModel(input2)

#concatenate the outputs and add the final part of your model: 
out = Concatenate()([out1,out2])
out = Flatten()(out)
out = Dense(...)(out)   
out = Dense(...)(out)   

#create the model taking 2 inputs with one output
model = Model([input1,input2],out)

Answer 2

确实两次使用相同（实例）图层可确保共享权重。

看看siamese example，我只是在这里放一个模型的摘录来展示一个例子：

# because we re-use the same instance `base_network`,
# the weights of the network
# will be shared across the two branches
processed_a = base_network(input_a)
processed_b = base_network(input_b)