pytorch loss

参考文献：

loss 测试

import torch

from torch.autograd import Variable

'''

参考文献：　https://blog.csdn.net/zhangxb35/article/details/72464152?utm_source=itdadao&utm_medium=referral

如果 reduce = False，那么 size_average 参数失效，直接返回向量形式的 loss；

如果 reduce = True，那么 loss 返回的是标量

如果 size_average = True，返回 loss.mean();

如果 size_average = True，返回 loss.sum();

'''

# nn.L1Loss: loss(input, target)=|input-target|

if False:

loss_fn = torch.nn.L1Loss(reduce=True, size_average=False)

input = torch.autograd.Variable(torch.randn(3, 4))

target = torch.autograd.Variable(torch.randn(3, 4))

loss = loss_fn(input, target)

print(input)

print(target)

print(loss)

print(input.size(), target.size(), loss.size())

# nn.SmoothL1Loss 　在(-1, 1)上是平方loss, 其他情况是L1 loss

if False:

loss_fn = torch.nn.SmoothL1Loss(reduce=False, size_average=False)

input = torch.autograd.Variable(torch.randn(3, 4))

target = torch.autograd.Variable(torch.randn(3, 4))

loss = loss_fn(input, target)

print(input)

print(target)

print(loss)

print(input.size(), target.size(), loss.size())

# nn.MSELoss 均方损失函数

if False:

loss_fn = torch.nn.MSELoss(reduce=False, size_average=False)

input = torch.autograd.Variable(torch.randn(3, 4))

target = torch.autograd.Variable(torch.randn(3, 4))

loss = loss_fn(input, target)

print(input)

print(target)

print(loss)

print(input.size(), target.size(), loss.size())

# nn.BCELoss

if False:

import torch.nn.functional as F

loss_fn = torch.nn.BCELoss(reduce=False, size_average=False)

input = torch.autograd.Variable(torch.randn(3, 4))

target = torch.autograd.Variable(torch.FloatTensor(3, 4).random_(2))

loss = loss_fn(F.sigmoid(input), target)

print(input, input.shape)

print(F.sigmoid(input))

print(target, target.shape)

print(loss, loss.shape)

# nn.CrossEntropyLoss

if False:

weight = torch.Tensor([1, 2, 1, 1, 10])

loss_fn = torch.nn.CrossEntropyLoss(reduce=False, size_average=False, weight=None)

input = Variable(torch.randn(3, 5)) # (batch_size, C)

target = Variable(torch.LongTensor(3).random_(5))

loss = loss_fn(input, target)

print(input)

print(target)

print(loss)

# nn.NLLLoss 负的　log likehood loss损失.用于训练一个ｎ类分类器.

if False:

m = nn.LogSoftmax()

loss = nn.NLLLoss()

# input is of size nBatch x nClasses = 3 x 5

input = Variable(torch.randn(3, 5), requires_grad=True)

# each element in target has to have 0 <= value < nclasses

target = Variable(torch.LongTensor([1, 0, 4]))

output = loss(m(input), target)

print(output)

# nn.LLLoss2d 对于图片的　negtive log likehood loss.计算每个像素的NLL loss

if False:

m = nn.Conv2d(16, 32, (3, 3)).float()

loss = nn.NLLLoss2d()

# input is of size nBatch x nClasses x height x width

input = Variable(torch.randn(3, 16, 10, 10))

# each element in target has to have 0 <= value < nclasses

target = Variable(torch.LongTensor(3, 8, 8).random_(0, 4))

output = loss(m(input), target)

print(m(input))

print(output)

# nn.MultiLabelMarginLoss input : x --> (N, c), y --> (N, c)其中y是 LongTensor, 且其元素为类别的index

if False:

x=Variable(torch.randn(3, 4))

y=Variable(torch.LongTensor(3, 4).random_(4))

loss=torch.nn.MultiLabelMarginLoss()

output = loss(x, y)

print(output)

# nn.MultiLabelSoftMarginLoss 与MultiLableMarginLoss相同，区别在于y的类型是FloatTensor

if False:

x = Variable(torch.randn(3, 10))

y = Variable(torch.FloatTensor(3, 10).random_(10))

loss = torch.nn.MultiLabelSoftMarginLoss()

output = loss(x, y)

print(output)

# nn.MultiMarginLoss 适用于多分类模型

if True:

x = Variable(torch.randn(3, 10))

y = Variable(torch.LongTensor(3).random_(10))

loss = torch.nn.MultiMarginLoss()

output=loss(x, y)

print(output)