My question is related with model-weights optimization during back propagation. In this image I'm trying to represent an auto-encoder having 7 layers where 4th one is center layer.
If my understanding is correct, during back propagation outcome of jth+1 layer, will help in optimizing the weights of jth layer. If it is correct, I want to restrict the weights training of Encoder layer and want to perform my customized training.
Customized training should be like this:
- Till layer 4 normal back propagation.
- Layer 3 weights will get trained(optimized) using the output of 4th layer as mentioned in PINK arrow.
- Layer 2 weights will get trained with the outcome of layer 5 as mentioned in YELLOW color arrow.
- Layer 1 weights will get trained with the outcome of layer 6 as mentioned in BLUE color.
I want to know how I can implement such customized trainable architecture?I'm familiar with Tensorflow, but I'm not able to figure out any solution and kind of stuck.
NOTE: Bear with my drawing.