liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-15 08:46:56 +08:00
Code Activity

Add torch.no_grad for evaluation

Browse Source
This commit is contained in:
D-X-Y
2021-03-12 02:46:04 +00:00
parent 67fbdafe76
commit db59713d36
5 changed files with 35 additions and 37 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
qlib/contrib/model/pytorch_tabnet.py
View File

@@ -219,7 +219,7 @@ class TabnetModel(Model):
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
self.tabnet_model.load_state_dict(best_param)
torch.save(best_param, save_path)
if self.use_gpu:
torch.cuda.empty_cache()
@@ -272,12 +272,12 @@ class TabnetModel(Model):
label = y_values[indices[i : i + self.batch_size]].float().to(self.device)
priors = torch.ones(self.batch_size, self.d_feat).to(self.device)
with torch.no_grad():
pred = self.tabnet_model(feature, priors)
loss = self.loss_fn(pred, label)
losses.append(loss.item())
pred = self.tabnet_model(feature, priors)
loss = self.loss_fn(pred, label)
losses.append(loss.item())
score = self.metric_fn(pred, label)
scores.append(score.item())
score = self.metric_fn(pred, label)
scores.append(score.item())
return np.mean(losses), np.mean(scores)
@@ -361,10 +361,10 @@ class TabnetModel(Model):
S_mask = S_mask.to(self.device)
priors = 1 - S_mask
with torch.no_grad():
(vec, sparse_loss) = self.tabnet_model(feature, priors)
f = self.tabnet_decoder(vec)
(vec, sparse_loss) = self.tabnet_model(feature, priors)
f = self.tabnet_decoder(vec)
loss = self.pretrain_loss_fn(label, f, S_mask)
loss = self.pretrain_loss_fn(label, f, S_mask)
losses.append(loss.item())
return np.mean(losses)