I’m trying to implement Deepmask with Pytorch, so far I have defined the Joint Loss Function, and the model’s learn-able parameters.
I was working on the training phase, and as the paper says that training must be done in an alternative back-propagation fashion across the two branches, I have written the code for the same. But there is some problem with training, I tried to train the model with a Fake Dataset, for minibatches other than the first mini-batch the loss of the model is turning out to be nan.
Can somebody help me with this?
Here is the link to current version of code : https://github.com/mejanvijay/DeepMask-Pytorch/blob/master/DeepMask-Pytorch-Implementation.ipynb