如何将多个渐变与TensorFlow渐变带一起使用？

Question

有3个神经网络像下面的代码一样连接，我们如何从初始网络中获取两个梯度？？第一个梯度工作，但第二个梯度返回None张量。看起来它们之间并没有关系，两个可以得到梯度。这里有什么问题

with tf.GradientTape() as tape1:
    with tf.GradientTape() as tape2:
        output1 = NN_model1(input1, training=True)
        output2 = NN_model2(output1, training=True)
        output3 = NN_model3([input1, output1, output2], training=True)
        loss1 = -tf.math.reduce_mean(output3)
        loss2 = -tf.math.reduce_mean(output2)
    grad1 = tape2.gradient(loss1, NN_model1.trainable_variables)
grad2 = tape1.gradient(loss2, grad1)
optimizer.apply_gradients(zip(grad2, NN_model1.trainable_variables))

Answer 1

我认为正确的方法应该是：

with tf.GradientTape() as tape:
    output1 = NN_model1(input1, training=True)
    output2 = NN_model2(output1, training=True)
    output3 = NN_model3([input1, output1, output2], training=True)
    loss1 = -tf.math.reduce_mean(output3)
    loss2 = -tf.math.reduce_mean(output2)
grad = tape.gradient([loss1, loss2], NN_model1.trainable_variables)
optimizer.apply_gradients(zip(grad, NN_model1.trainable_variables))