芙蓉区网站建设,广州建设技术职业学院学费,wordpress备份用户,html教程网站目录
1. Loss Function
1.1 L1Loss
1.2 MSELoss
1.3 CrossEntropyLoss
2. 交叉熵与神经网络模型的结合
2.1 反向传播
1. Loss Function
目的#xff1a; a. 计算预测值与真实值之间的差距; b. 可通过此条件#xff0c;进行反向传播。 1.1 L1Loss import torch
from …目录
1. Loss Function
1.1 L1Loss
1.2 MSELoss
1.3 CrossEntropyLoss
2. 交叉熵与神经网络模型的结合
2.1 反向传播
1. Loss Function
目的 a. 计算预测值与真实值之间的差距; b. 可通过此条件进行反向传播。 1.1 L1Loss import torch
from torch.nn import L1Lossinputs torch.tensor([1, 2, 3], dtypetorch.float32)
targets torch.tensor([1, 2, 5], dtypetorch.float32)
inputs torch.reshape(inputs, (1, 1, 1, 3)) # 1-batch_size,1-channel,1×3
targets torch.reshape(targets, (1, 1, 1, 3))
loss L1Loss()
result loss(inputs, targets)
print(result) # tensor(0.6667)
loss1 L1Loss(reductionsum)
result1 loss1(inputs, targets)
print(result1) # tensor(2.)1.2 MSELoss import torch
from torch.nn import L1Loss, MSELossinputs torch.tensor([1, 2, 3], dtypetorch.float32)
targets torch.tensor([1, 2, 5], dtypetorch.float32)
inputs torch.reshape(inputs, (1, 1, 1, 3)) # 1-batch_size,1-channel,1×3
targets torch.reshape(targets, (1, 1, 1, 3))
loss_mse MSELoss()
res loss_mse(inputs, targets)
print(res) # tensor(1.3333)1.3 CrossEntropyLoss
图片来源于b站up主 我是土堆
It is useful when training a classification problem with C classes.
import torch
from torch import nnx torch.tensor([0.1, 0.2, 0.3])
y torch.tensor([1])
x torch.reshape(x, (1, 3)) # 1-batch_size,3 classes
loss_cross nn.CrossEntropyLoss()
res loss_cross(x, y)
print(res) # tensor(1.1019)2. 交叉熵与神经网络模型的结合
nn_loss_network.py import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoaderdataset torchvision.datasets.CIFAR10(./dataset, trainFalse, transformtorchvision.transforms.ToTensor(),downloadTrue)
dataloader DataLoader(dataset, batch_size1)class MyModule(nn.Module):def __init__(self):super(MyModule, self).__init__()self.model1 Sequential(Conv2d(3, 32, 5, padding2),MaxPool2d(2),Conv2d(32, 32, 5, padding2),MaxPool2d(2),Conv2d(32, 64, 5, padding2),MaxPool2d(2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self, x):x self.model1(x)return xmyModule1 MyModule()
for data in dataloader:imgs, targets dataoutputs myModule1(imgs)print(outputs)print(targets)tensor([[-0.1187, 0.1490, -0.1015, 0.0767, -0.0677, -0.0625, 0.0553, -0.0932, -0.0866, 0.0746]], grad_fnAddmmBackward0) tensor([1]) 计算交叉熵损失 loss nn.CrossEntropyLoss()
myModule1 MyModule()
for data in dataloader:imgs, targets dataoutputs myModule1(imgs)res_loss loss(outputs, targets)print(res_loss) tensor(2.4315, grad_fnNllLossBackward0) tensor(2.3594, grad_fnNllLossBackward0) tensor(2.3659, grad_fnNllLossBackward0) ... 2.1 反向传播
for data in dataloader:imgs, targets dataoutputs myModule1(imgs)res_loss loss(outputs, targets)res_loss.backward()
