18 lines
484 B
Python
18 lines
484 B
Python
import torch
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import torch.nn.functional as F
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__all__ = ['kl_loss', 'huber_loss']
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def kl_loss(x, y):
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x = F.softmax(x.detach(), dim=1)
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y = F.log_softmax(y, dim=1)
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return torch.mean(torch.sum(x * (torch.log(x) - y), dim=1))
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def huber_loss(error, delta):
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abs_error = torch.abs(error)
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quadratic = torch.min(abs_error, torch.full_like(abs_error, fill_value=delta))
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losses = 0.5 * (quadratic ** 2) + delta * (abs_error - quadratic)
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return torch.mean(losses)
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