I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.

According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li

As the comments in the function of "ignores_from_label" in layers.py say:

"""Retrieves ignorable pixels from the ground-truth labels. This function returns a binary map in which 1 denotes ignored pixels and 0 means not ignored ones. For those ignored pixels, they are not only the pixels with label value >= num_classes, but also the corresponding neighboring pixels, which are on the the eight co

I'm trying to understand the code but unable to understand your loss calculation function can you please explain what are you doing because it doesn't seem you are doing anything in linear kernel you have commented it out in mmd_loss.py

Hi minihat,
I really love your idea and would like to do a similar Prediction.
While looking through the files in this repository it was pretty hard for me to understand the meaning of each file.
And just by your final report:
did you used Gold earned and Gold spent in the neuronal network?
if so you might have designed a great detector for the team with more unspent gold or more gold in tota