def _handle_batch(self, batch):
# model train/valid step
x, y = batch
y_hat = self.model(x.view(x.size(0), -1))
loss = F.cross_entropy(y_hat, y)
accuracy01, accuracy03 = metrics.accuracy(y_hat, y, topk=(1, 3))
self.state.batch_metrics.update({
"loss": loss,
"accuracy01": accuracy01,
"accuracy03": accuracy03
})
if self.state.is_train_loader:
loss.backward()
self.state.optimizer.step()
self.state.optimizer.zero_grad()
def _handle_batch(self, batch):
x, y = batch
# y_hat, attention = self.model(x)
outputs = self.model(x)
loss = F.cross_entropy(outputs['logits'], y)
accuracy01, accuracy02 = metrics.accuracy(
outputs['logits'], y, topk=(1, 2))
self.batch_metrics = {
"loss": loss,
"accuracy01": accuracy01,
"accuracy02": accuracy02,
}
if self.is_train_loader:
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
def _handle_batch(self, batch):
x, y = batch
x = x.view(x.size(0), -1)
y_hat, x_ = self.model(x)
loss_clf = F.cross_entropy(y_hat, y)
loss_ae = F.mse_loss(x_, x)
loss = loss_clf + loss_ae
accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat,
y,
topk=(1, 3, 5))
self.batch_metrics = {
"loss_clf": loss_clf,
"loss_ae": loss_ae,
"loss": loss,
"accuracy01": accuracy01,
"accuracy03": accuracy03,
"accuracy05": accuracy05,
}
if self.is_train_loader:
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
def _handle_batch(self, batch):
"""
Docs.
"""
x, y = batch
x = x.view(x.size(0), -1)
y_hat, x_, z_logprob, loc, log_scale = self.model(x)
loss_clf = F.cross_entropy(y_hat, y)
loss_ae = F.mse_loss(x_, x)
loss_kld = (
-0.5
* torch.mean(1 + log_scale - loc.pow(2) - log_scale.exp())
* 0.1
)
loss_logprob = torch.mean(z_logprob) * 0.01
loss = loss_clf + loss_ae + loss_kld + loss_logprob
accuracy01, accuracy03, accuracy05 = metrics.accuracy(
y_hat, y, topk=(1, 3, 5)
)
self.state.batch_metrics = {
"loss_clf": loss_clf,
"loss_ae": loss_ae,
"loss_kld": loss_kld,
"loss_logprob": loss_logprob,
"loss": loss,
"accuracy01": accuracy01,
"accuracy03": accuracy03,
"accuracy05": accuracy05,
}
if self.state.is_train_loader:
loss.backward()
self.state.optimizer.step()
self.state.optimizer.zero_grad()
