def test_loss_init(self):
name = "name"
shortname = "shortname"
inputs = "decoder_output"
targets = "decoder_output"
loss = Loss(name, shortname, inputs, targets, torch.nn.NLLLoss())
self.assertEqual(loss.name, name)
self.assertEqual(loss.log_name, shortname)
def test_loss_backward_WITH_NO_LOSS(self):
loss = Loss("name", "shortname", "decoder_output", "decoder_output", torch.nn.NLLLoss())
cls.targets = [torch.LongTensor([random.randint(0, num_class - 1)
for _ in range(batch_size)])
for _ in range(num_batch)]
>>>>>>> upstream/master
def test_loss_init(self):
name = "name"
shortname = "shortname"
<<<<<<< HEAD
inputs="decoder_output"
targets="decoder_output"
=======
inputs = "decoder_output"
targets = "decoder_output"
>>>>>>> upstream/master
loss = Loss(name, shortname, inputs, targets, torch.nn.NLLLoss())
self.assertEqual(loss.name, name)
self.assertEqual(loss.log_name, shortname)
def test_loss_init_WITH_NON_LOSS(self):
<<<<<<< HEAD
self.assertRaises(ValueError, lambda: Loss("name", "shortname", "decoder_outputs", "decoder_targets", "loss"))
def test_loss_backward_WITH_NO_LOSS(self):
loss = Loss("name", "shortname", "decoder_output", "decoder_output", torch.nn.NLLLoss())
=======
self.assertRaises(ValueError, lambda: Loss("name", "shortname",
"decoder_outputs", "decoder_targets", "loss"))
def test_loss_backward_WITH_NO_LOSS(self):
loss = Loss("name", "shortname", "decoder_output",
def test_loss_backward_WITH_NO_LOSS(self):
loss = Loss("name", "shortname", "decoder_output", "decoder_output",
torch.nn.NLLLoss())
self.assertRaises(ValueError, lambda: loss.backward())
def test_loss_init_WITH_NON_LOSS(self):
self.assertRaises(
ValueError, lambda: Loss("name", "shortname", "decoder_outputs",
"decoder_targets", "loss"))