NutShell

torch_criterion.py

import torch
import torch.nn as nn
import torch.nn.functional as F

# * [1] Inputs and labels
inputs = torch.randn(4,3)
targets = torch.LongTensor([0,2,1,2])
print('[---Inputs and Targets:---]\n',inputs)
print(targets,'\n')

# * [2] Softmax   
sfm1 = nn.Softmax(dim=1) #object and instance
sfm2 = F.softmax(input, dim=1)  #function requires arguments
print('[---Softmax:---]\n',sfm1(inputs))
print(sfm2,'\n')

# * [3] log_softmax
log1 = torch.log(sfm1(inputs))
log2 = nn.LogSoftmax(dim=1)
print('[---Log-Softmax:---]\n',log1)
print(log2(inputs),'\n')

# * [4] NLLLoss vs CrossEntropyLoss
loss1 = nn.NLLLoss()
print('[---NLLLoss:---]\n', loss1(log2(inputs),targets))
loss2 = nn.CrossEntropyLoss()
print(loss2(inputs, targets))

# * [5] Check
import torch 
import torch.nn as nn
logits = torch.randn(4,3)
targets = torch.LongTensor([0,2,1,2])
print(nn.NLLLoss()(torch.log(nn.Softmax(dim=1)(logits)),targets))
print(nn.CrossEntropyLoss()(logits,targets))