pytorch learning: training

[wsxk]

https://wsxk.github.io/pytorch2/

1. 损失函数与反向传播

   1.1 损失函数（loss function） 1.2 反向传播

1. 损失函数与反向传播 1.1 损失函数（loss function） 损失函数：判断模型训练的好坏标准，你训练了模型，总得有个指标来说明它好不好吧.jpg import torch

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])

平均差，总和差

loss = torch.nn.L1Loss(reduction='sum') result = loss(inputs, targets) print(result)

平方差

loss_mse = torch.nn.MSELoss() result = loss_mse(inputs, targets) print(result)

交叉熵

x = torch.tensor([0.1, 0.2, 0.3]) y = torch.tensor([1]) x = torch.reshape(x, [1, 3]) loss_cross = torch.nn.CrossEntropyLoss() result = loss_cross(x, y) print(result)

1.2 反向传播 反向传播：根据损失函数，反向传播计算梯度，然后更新参数，使得损失函数最小化 import torch import torchvision from torchvision import transforms from torch.utils.data import DataLoader from torch import nn from torch.utils.tensorboard import SummaryWriter

diy_transform = transforms.Compose([transforms.ToTensor()]) dataset = torchvision.datasets.CIFAR10(root=