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use grad_clip from config.json

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Eren Golge
2019-03-12 16:54:42 +01:00
parent abc7b8e366
commit bbd90c3b4d
1 changed files with 1 additions and 1 deletions
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2
train.py
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@@ -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