Pytorch - Cross Entropy Loss

class torch.nn.CrossEntropyLoss(weight=None, size_average=True, ignore_index=-100, reduce=True)[source]

# 1D

import torch 
import torch.nn as nn

loss = nn.CrossEntropyLoss()

# input, NxC=2x3

input = torch.randn(2, 3, requires_grad=True)

# target, N

target = torch.empty(2, dtype=torch.long).random_(3)
output = loss(input, target)
output.backward()

class torch.nn.KLDivLoss(size_average=True, reduce=True

class torch.nn.BCELoss(weight=None, size_average=True, reduce=True)

import torch 
import torch.nn as nn

sig = nn.Sigmoid()
loss = nn.BCELoss()
input = torch.randn(3, requires_grad=True)
target = torch.empty(3).random_(2)
output = loss(sig(input), target)
output.backward()

class torch.nn.BCEWithLogitsLoss(weight=None, size_average=True, reduce=True)

class torch.nn.MultiLabelSoftMarginLoss(weight=None, size_average=True, reduce=True)

class torch.nn.MultiLabelMarginLoss(size_average=True, reduce=True)