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這篇文章主要講解了“PyTorch reduction的作用是什么”,文中的講解內(nèi)容簡單清晰,易于學(xué)習(xí)與理解,下面請大家跟著小編的思路慢慢深入,一起來研究和學(xué)習(xí)“PyTorch reduction的作用是什么”吧!
損失函數(shù)的reduction有三種模式,它們的作用分別是什么?
當(dāng)inputs和target及weight分別如以下參數(shù)時,reduction=’mean’模式時,loss是如何計算得到的?
inputs = torch.tensor([[1, 2], [1, 3], [1, 3]], dtype=torch.float)
target = torch.tensor([0, 1, 1], dtype=torch.long)
weights = torch.tensor([1, 2]
import torch import torch.nn as nn inputs = torch.tensor([[1, 2], [1, 3], [1, 3]], dtype=torch.float) target = torch.tensor([0, 1, 1], dtype=torch.long) # def loss function weights = torch.tensor([1, 200], dtype=torch.float) loss_f_none_w = nn.CrossEntropyLoss(weight=weights, reduction='none') loss_f_sum = nn.CrossEntropyLoss(weight=weights, reduction='sum') loss_f_mean = nn.CrossEntropyLoss(weight=weights, reduction='mean') # forward loss_none_w = loss_f_none_w(inputs, target) loss_sum = loss_f_sum(inputs, target) loss_mean = loss_f_mean(inputs, target) # view print("\nweights: ", weights) print(loss_none_w, loss_sum, loss_mean)
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