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您好,我有2个问题想请教。
1)请问为何在val模式下, Loss的值没有求平均呢,即:最后在tensorboard上显示的loss值。 `
val_loss += loss.item() val_loss_seg += loss_seg.item() val_loss_exist += loss_exist.item() progressbar.set_description("batch loss: {:.3f}".format(loss.item())) progressbar.update(1) progressbar.close() iter_idx = (epoch + 1) * len(train_loader) # keep align with training process iter_idx tensorboard.scalar_summary("val_loss", val_loss, iter_idx)
`
2)关于分布式训练 我发现用一张显卡时, train_loss =a 。 用2张显卡是train_loss≈2a 。 是因为求和了吗。 若求和了,不需要求平均吗, 因为感觉有点问题。
期待您的解答!
The text was updated successfully, but these errors were encountered:
loss不平均也行吧,反正你也要调整learning rate,或者不同loss之间的balance weight
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大佬,在训练CULane数据集时,我发现在前10个eopch,train_loss下降, val_loss经常维持在某个数值左右(例如0.5),请问您有遇到过吗?不知道这是否是过拟合
调参挺麻烦的。我之前不知道怎么就调出了一个。。。
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您好,我有2个问题想请教。
1)请问为何在val模式下, Loss的值没有求平均呢,即:最后在tensorboard上显示的loss值。
`
`
2)关于分布式训练
我发现用一张显卡时, train_loss =a 。 用2张显卡是train_loss≈2a 。
是因为求和了吗。 若求和了,不需要求平均吗, 因为感觉有点问题。
期待您的解答!
The text was updated successfully, but these errors were encountered: