在Keras中的两个密集层之间共享权重

Question

我有如下代码。我想要做的是在两个密集层中共享相同的权重。

op1和op2层的等式如下所示

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

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

这里，w1到w5的权重在op1和op2层输入之间共享，它们分别是（y1到y5）和（z1到z5）。

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)

提前致谢。

Answer 1

双方使用相同的图层。（权衡和偏见是共享的）

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)