import torch from torch.nn import L1Loss inputs = torch.tensor([1, 2, 3], dtype=torch.float32) targets = torch.tensor([1, 2, 5], dtype=torch.float32) inputs = torch.reshape(inputs, [1, 1, 1, 3]) targets = torch.reshape(targets, [1, 1, 1, 3]) loss = L1Loss() result = loss(inputs, targets) print(result)
D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/nn_los.py tensor(0.6667) Process finished with exit code 0可设置L1Loss相关的计算方式 因为前面看的那个输入输出里面需要有batch_size，但是现在不需要必须输入batch_size这一参数了，所以不reshap也没问题
import torch from torch.nn import L1Loss inputs = torch.tensor([1, 2, 3], dtype=torch.float32) targets = torch.tensor([1, 2, 5], dtype=torch.float32) 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)
D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/nn_los.py tensor(2.) Process finished with exit code 0
MSELoss
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) 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) lose_mse = nn.MSELoss() result_mse = lose_mse(inputs, targets) print(result_mse)
D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/nn_los.py tensor(2.) tensor(1.3333) Process finished with exit code 0CrossEntropyLoss
It is useful when training a classification problem with C classes
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) 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) lose_mse = nn.MSELoss() result_mse = lose_mse(inputs, targets) print(result_mse) 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)
D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/nn_los.py tensor(2.) tensor(1.3333) tensor(1.1019) Process finished with exit code 0示例loss_network
发现CIFAR10的数据结构类似上面所举例
import torch
import torchvision.datasets
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=1)
class Lixinyu(nn.Module):
def __init__(self):
super(Lixinyu, self).__init__()
self.model1 = Sequential(Conv2d(3, 32, 5, stride=1, 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, 10))
def forward(self, x):
x = self.model1(x)
return x
lixinyu = Lixinyu()
for data in dataloader:
imgs, targets = data
outputs = lixinyu(imgs)
print(outputs)
print(targets)
tensor([[-0.0434, 0.0130, -0.1247, -0.0355, -0.0632, -0.0156, 0.0355, 0.0226,
0.0443, 0.1039]], grad_fn=)
tensor([4])
import torch
import torchvision.datasets
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=1)
class Lixinyu(nn.Module):
def __init__(self):
super(Lixinyu, self).__init__()
self.model1 = Sequential(Conv2d(3, 32, 5, stride=1, 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, 10))
def forward(self, x):
x = self.model1(x)
return x
loss = nn.CrossEntropyLoss()
lixinyu = Lixinyu()
for data in dataloader:
imgs, targets = data
outputs = lixinyu(imgs)
result_loss = loss(outputs, targets)
print(result_loss)
tensor(2.3227, grad_fn=backward) tensor(2.3795, grad_fn= ) tensor(2.4423, grad_fn= ) tensor(2.2870, grad_fn= )
import torch
import torchvision.datasets
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Flatten, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=1)
class Lixinyu(nn.Module):
def __init__(self):
super(Lixinyu, self).__init__()
self.model1 = Sequential(Conv2d(3, 32, 5, stride=1, 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, 10))
def forward(self, x):
x = self.model1(x)
return x
loss = nn.CrossEntropyLoss()
lixinyu = Lixinyu()
for data in dataloader:
imgs, targets = data
outputs = lixinyu(imgs)
result_loss = loss(outputs, targets)
result_loss.backward()
print("ok")
运行完backward（）后，grad出现数值，必须有loss.backward（）,否则不会产生grad。
之后可通过相关优化器对其进行优化
