A total variation loss

最新推荐文章于 2023-08-02 14:00:18 发布

Sag_ittarius

最新推荐文章于 2023-08-02 14:00:18 发布

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分类专栏： Python Pytorch

本文链接：https://blog.youkuaiyun.com/u012871045/article/details/84960853

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本文探讨了Total Variation Loss在图像处理中的应用，揭示了损失值与视觉效果的关系。通过实验调整λ和lr参数，寻找最优的去噪效果，并提供了相关资源链接深入理解variance与variation的区别。

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import matplotlib
import torch

x = torch.FloatTensor([1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3,3,1,1,1,1,1,0,0,1,1,1,1])
#x = torch.FloatTensor([1,1,1,1,1,20,20,20,20,20,20,3,3,3,3,3,3,3,3,1,1,1,1,1,0,0,1,1,1,1])
m = torch.distributions.normal.Normal(torch.tensor([0.0]),torch.tensor([0.3]))

noise = torch.squeeze(m.sample((x.size()[0],)))

x_ = x + noise
x_ = torch.autograd.Variable(x_)
#matplotlib.pyplot.plot(x_.numpy())
#matplotlib.pyplot.plot(x.numpy())

y = torch.zeros([x_.size()[0]])
y = torch.autograd.Variable(y,requires_grad=True)

#optimizer = torch.optim.Adam([{'params':[y]}])
#torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, #amsgrad=False)
optimizer = torch.optim.SGD([{'params':[y], 'lr':1.0e-2}])
#torch.optim.SGD(params, lr=<required parameter>, momentum=0, dampening=0, #weight_decay=0,