Building a model in keras

Question

I have tried to build my model in Keras. My model has 2 inputs and 2 outputs. The model includes 5 convolution layers and the weights of the layers must be shared. But, the new layers after the convolutional layers should not share the weights. I used concatenate() but it affected the other layers. The figure of my model is below the page. How can I do that?

The Network-Model:

Answer 1

I think you should upload the image of model architecture again. And the part of shared weights confused me. I think you mean just one layer with one input.

input = Input(shape=(64,))

layer_1 = Dense(32, activation="relu")(input)

layer_2 = Dense(16, activation="relu")(layer_1)
layer_3 = Dense(16, activation="relu")(layer_1)

combined = concatenate([layer_2 , layer_3])

output = Dense(8, activation="relu")(combined)
output = Dense(1, activation="linear")(z)

model = Model(inputs=[input], outputs=output)