def test_nllloss_WITH_OUT_SIZE_AVERAGE(self):
loss = NLLLoss(size_average=False)
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss(size_average=False)
for output, target in zip(self.outputs, self.targets):
loss.eval_step(output, target)
pytorch_loss += pytorch_criterion(output, target)
loss_val = loss.get_loss()
self.assertAlmostEqual(loss_val, pytorch_loss.item())
def test_perplexity(self):
nll = NLLLoss()
ppl = Perplexity()
for output, target in zip(self.outputs, self.targets):
nll.eval_step(output, target)
ppl.eval_step(output, target)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
def test_nllloss(self):
loss = NLLLoss()
pytorch_loss = 0
pytorch_criterion = torch.nn.NLLLoss()
for output, target in zip(self.outputs, self.targets):
loss.eval_step(output, target)
pytorch_loss += pytorch_criterion(output, target)
loss_val = loss.get_loss()
pytorch_loss /= self.num_batch
self.assertAlmostEqual(loss_val, pytorch_loss.item())
