def _train(self):
for _ in range(self.n_steps_per_epoch):
self.optimizer.zero_grad()
y = self.model.matrix()[:, self.perm]
loss = nn.functional.mse_loss(y, self.target_matrix)
if (not self.model.fixed_order) and hasattr(self, 'semantic_loss_weight'):
semantic_loss = semantic_loss_exactly_one(nn.functional.log_softmax(self.model.logit, dim=-1))
loss += self.semantic_loss_weight * semantic_loss.mean()
loss.backward()
self.optimizer.step()
return {'negative_loss': -loss.item()}
def _train(self):
for _ in range(self.n_steps_per_epoch):
self.optimizer.zero_grad()
y = self.model.matrix()[:, self.br_perm]
loss = nn.functional.mse_loss(y, self.target_matrix)
semantic_loss = semantic_loss_exactly_one(
nn.functional.log_softmax(self.model.logit, dim=-1))
total_loss = loss + self.semantic_loss_weight * semantic_loss.mean(
)
total_loss.backward()
self.optimizer.step()
return {'negative_loss': -loss.item()}
def _train(self):
temperature = 1.0 / (0.3 * self._iteration + 1)
for _ in range(self.n_steps_per_epoch):
self.optimizer.zero_grad()
y = self.model.matrix(temperature)
loss = nn.functional.mse_loss(y, self.target_matrix)
if (not self.model.fixed_order) and hasattr(
self, 'semantic_loss_weight'):
semantic_loss = semantic_loss_exactly_one(
nn.functional.log_softmax(self.model.logit, dim=-1))
loss += self.semantic_loss_weight * semantic_loss.mean()
loss.backward()
self.optimizer.step()
return {'negative_loss': -polished_loss_fft_learn_perm(self)}