Adding loss layer of type INFOGAIN_LOSS

[shaibagon]

I would like to add loss layer of type INFOGAIN_LOSS. This layer requires three inputs: (1) class probabilities, (2) labels and (3) information gain matrix H of size (#labels)-by-(#labels).
I want H to be provided as the third input (bottom) to the loss layer (rather than as a model parameter). How can I do this?
Do I define a new data layer which "top" is H? If so, wouldn't the data of this layer be incremented every training iteration like the training data is incremented? How can I define multiple unrelated input "data" layers, and how does caffe know to read from the training/testing "data" layer batch after batch, while from the H "data" layer it knows to read only once for all the training process?

[shelhamer]

There's no way at present to make data layers load input at different rates. Every forward pass all data layers will advance. However, the constant H input could be done by making an input lmdb / leveldb / hdf5 file that is only H since the data layer will loop and keep loading the same H. This obviously wastes disk IO.

One could orchestrate the training in Python by defining a data layer for the normal inputs and labels but input fields for H, and then assigning H at the beginning of training. The input blobs will persist across forward calls.

[shaibagon]

@shelhamer - thank you for your answer. I'll look into it and give it a try!