use grad_clip from config.json

2026-09-06 16:30:13 +08:00 · 2019-03-12 16:54:42 +01:00
parent abc7b8e366
commit bbd90c3b4d
1 changed files with 1 additions and 1 deletions
@@ -134,7 +134,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
        # backpass and check the grad norm for spec losses
        loss.backward(retain_graph=True)
        optimizer, current_lr = weight_decay(optimizer, c.wd)
-        grad_norm, _ = check_update(model, 1.0)
+        grad_norm, _ = check_update(model, c.grad_clip)
        optimizer.step()

        # backpass and check the grad norm for stop loss