neural network - Share weights between two dense layers in keras

Question

I have a code as follows. What I want to do is to share the same weights in two dense layers.

The equation for op1 and op2 layer will be like that

op1 = w1y1 + w2y2 + w3y3 + w4y4 + w5y5 + b1

op2 = w1z1 + w2z2 + w3z3 + w4z4 + w5z5 + b1

here w1 to w5 weights are shared between op1 and op2 layer inputs which are (y1 to y5) and (z1 to z5) respectively.

ip_shape1 = Input(shape=(5,))
ip_shape2 = Input(shape=(5,))

op1 = Dense(1, activation = "sigmoid", kernel_initializer = "ones")(ip_shape1)
op2 = Dense(1, activation = "sigmoid", kernel_initializer = "ones")(ip_shape2)

merge_layer = concatenate([op1, op2])
predictions = Dense(1, activation='sigmoid')(merge_layer)

model = Model(inputs=[ip_shape1, ip_shape2], outputs=predictions)

Thanks in advance.

Answer 1

This uses the same layer for both sides. (Weighs and bias are shared)

ip_shape1 = Input(shape=(5,))
ip_shape2 = Input(shape=(5,))

dense = Dense(1, activation = "sigmoid", kernel_initializer = "ones")

op1 = dense(ip_shape1)
op2 = dense(ip_shape2)

merge_layer = Concatenate()([op1, op2])
predictions = Dense(1, activation='sigmoid')(merge_layer)

model = Model(inputs=[ip_shape1, ip_shape2], outputs=predictions)