# loss_function

We can initialize a loss function with a single line of code, e.g.:

# For a basic classification problem we would use: criterion = nn.CrossEntropyLoss() # This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class. # In a basic regression problem instead we could use something like: criterion = nn.MSELoss()

From time to time, it may happen that we have to write our own custom loss function,
in order to do this, I would suggest to write it in a different file,
in our project package to keep things clean, e.g., *project_name/main_package/myloss.py*.
and we just have to define the forward step, the backward will be automatically inferred and
computed.
We generally average the outpus before returning the loss array.

import torch import numpy as np class DistanceLoss(torch.nn.Module): def __init__(self): super(DistanceLoss, self).__init__() def forward(self, dist_a, dist_b, tanh_output): x = tanh_output y = dist_b - dist_a # Normalize the difference between distances to be in the range [-1,1] y = (2*(y - dist_min)/(dist_max - dist_min)) -1 y = y.view(-1,1) # To not have all the operations on a single line # I preferred to keep track of InterMediate steps # through variables called 'im' x_squared = torch.pow(x, 2) y_squared = torch.pow(y, 2) xy = x * y x2y = torch.mul(xy, 2) im1 = torch.add(x_squared, -y_squared) im2 = torch.add(im1, -x2y) im3 = torch.mul(im2, 50) loss = torch.add(im3, 100) loss = loss.mean()