pytorch学习笔记（9）--损失函数

1、损失函数的作用：

（1）计算实际输出和目标输出之间的差距；

（2）为我们更新输出提供一定的依据（也就是反向传播）

官网链接：https://pytorch.org/docs/1.8.1/nn.html

2、损失函数的使用

2.1、L1Loss

注：reduction = “sum” 表示求和 / reduction = "mean" 表示求平均值默认求平均值

代码：

# file     : nn_lose.py

# time     : 2022/8/2 上午10:31

# function : L1Loss

import torch

from torch.nn import L1Loss

from torch import nn

inputs = torch.tensor([1, 2, 3], dtype=torch.float32)

targets = torch.tensor([1, 2, 5], dtype=torch.float32)

# reshape()添加维度，原来tensor是二维

inputs = torch.reshape(inputs, (1, 1, 1, 3))

targets = torch.reshape(targets, (1, 1, 1, 3))

loss = L1Loss()

result = loss(inputs, targets)

print(result)

上述代码计算了实际输出[1, 2, 3]和目标输出[1, 2, 5]之间的L1Loss，代码输出结果为：

tensor(0.6667)

2.2MSELoss 均方损失函数：可以设置reduction参数来决定具体的计算方法

代码:

# file     : nn_lose.py

# time     : 2022/8/2 上午10:31

# function :

import torch

from torch.nn import L1Loss

from torch import nn

inputs = torch.tensor([1, 2, 3], dtype=torch.float32)

targets = torch.tensor([1, 2, 5], dtype=torch.float32)

# reshape

inputs = torch.reshape(inputs, (1, 1, 1, 3))

targets = torch.reshape(targets, (1, 1, 1, 3))

loss = L1Loss(reduction="sum")

result = loss(inputs, targets)

print(result)

# MSELoss 均方损失函数

loss_mse = nn.MSELoss(reduction="sum")

result_mse = loss_mse(inputs, targets)

print(result_mse)

结果:

tensor(2.)

tensor(4.)  #均方误差损失函数计算结果

2.3 CrossEntropyLoss交叉熵损失函数----没懂

交叉熵损失函数计算方法的细节可以参照这个博文：交叉熵损失函数。（看上去很牛）

代码:

# file     : nn_lose.py

# time     : 2022/8/2 上午10:31

# function :

import torch

from torch.nn import L1Loss

from torch import nn

inputs = torch.tensor([1, 2, 3], dtype=torch.float32)

targets = torch.tensor([1, 2, 5], dtype=torch.float32)

# reshape

inputs = torch.reshape(inputs, (1, 1, 1, 3))

targets = torch.reshape(targets, (1, 1, 1, 3))

loss = L1Loss(reduction="sum")

result = loss(inputs, targets)

print(result)

# MSELoss 均方损失函数

loss_mse = nn.MSELoss(reduction="sum")

result_mse = loss_mse(inputs, targets)

print(result_mse)

# CrossEntropyLoss

x = torch.tensor([0.1, 0.2, 0.3])

y = torch.tensor([1])

x = torch.reshape(x, (1, 3))

loss_cross = nn.CrossEntropyLoss()

result_cross = loss_cross(x, y)

print(result_cross)

结果:

tensor(1.1019)

用了之前的一个简单神经网络，测试了损失函数及反向传播

# file     : nn_loss_network.py

# time     : 2022/8/2 下午2:39

# function :

import torch

import torchvision.datasets

from torch import nn

from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential

from torch.utils.data import DataLoader

dataset = torchvision.datasets.CIFAR10("../dataset", train=False, transform=torchvision.transforms.ToTensor(), download=False)

dataloader = DataLoader(dataset, batch_size=1)

class Tudui(nn.Module):

    def __init__(self):

        super(Tudui, self).__init__()

        self.model1 = Sequential(

            Conv2d(3, 32, 5, padding=2),

            MaxPool2d(2),

            Conv2d(32, 32, 5, padding=2),

            MaxPool2d(2),

            Conv2d(32, 64, 5, padding=2),

            MaxPool2d(2),

            Flatten(),

            Linear(1024, 64),

            Linear(64, 64)

        )

    def forward(self, x):

        x = self.model1(x)

        return x

loss = nn.CrossEntropyLoss()

tudui = Tudui()

for data in dataloader:

    imgs, targets = data

    outputs = tudui(imgs)

    result_loss = loss(outputs, targets)

    result_loss.backward()

    print("ok")

巴特西

pytorch学习笔记（9）--损失函数

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