How to train two dense layers at different learning rates in Tensorflow?

Question

I am trying to build a multi-task CNN in Tensorflow which has two dense dense layers in parallel ,one for Age prediction and other for Gender prediction. How can I train each Dense layer for different number of epochs since one can converge before the other and training both for same no of epochs would overfit one of them?

Also, if I propagate the gradients of both age and gender to the CNN, would it overfit since it's weights are being updated at twice the rate of Dense layers?

Answer 1

I have ask a similar question and i've finally found the answer : LINK

SOLUTION : You can define 2 different train_step , and each one has his own learning rate . Each train_step can be called a chosen number of times. In addition, you can define some dependencies if you want some variables to be trainable only for a selected train_step. (See the documentation ).