Hello,
I have a special network architecture requirement which a little bit like dropouts, but drop the network modules in the specific forward path and backward path.
The network architecture looks like:

螢幕快照 2018-05-20 01.00.42.png1902×1040 42.4 KB

There are multiple input layers and multiple output layers in a single neural network. When we use this neural network, we only forward the data in 2 paths, and backward the gradients as same as it forward before.
for example:

螢幕快照 2018-05-20 01.06.08.png1976×1082 117 KB


螢幕快照 2018-05-20 01.06.49.png2004×1100 106 KB

Can we implement this kind of network by using backward hook? Any suggestion is welcomed.
If you want to know more details, please take a look at this slides: https://docs.google.com/presentation/d/1oZWvQSsrOHqwxMkD0xZRKh9k1b63xx5OVjGIhVuakyo/edit?usp=sharing 44