def test_perplexity(self):
nll = NLLLoss()
ppl = Perplexity()
nll.eval_batch(self.outputs, self.batch)
ppl.eval_batch(self.outputs, self.batch)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
def test_perplexity(self):
nll = NLLLoss()
ppl = Perplexity()
for output, target in zip(self.outputs, self.targets):
nll.eval_batch(output, target)
ppl.eval_batch(output, target)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
def test_perplexity(self):
num_class = 5
num_batch = 10
batch_size = 5
outputs = [F.softmax(Variable(torch.randn(batch_size, num_class)))
for _ in range(num_batch)]
targets = [Variable(torch.LongTensor([random.randint(0, num_class - 1)
for _ in range(batch_size)]))
for _ in range(num_batch)]
nll = NLLLoss()
ppl = Perplexity()
for output, target in zip(outputs, targets):
nll.eval_batch(output, target)
ppl.eval_batch(output, target)
nll_loss = nll.get_loss()
ppl_loss = ppl.get_loss()
self.assertAlmostEqual(ppl_loss, math.exp(nll_loss))
