Pytorch personal learning record summary 10

Table of contents

optimizer

---

optimizer

Official document address: torch.optim https://pytorch.org/docs/stable/optim.html 

The location of the grad viewed during the Debug process:

model --> Protected Atributes --> _modules --> 'model' --> Protected Atributes --> _modules --> '0' (choose a conv layer) --> weight (check the data and grad under weight The change)

 Simple training code, added Loss, Optim.

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torchvision.transforms import transforms

dataset = torchvision.datasets.CIFAR10('./dataset', train=False, transform=transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=2, shuffle=True)


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.model = Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2, stride=2),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2, stride=2),
            Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),
            MaxPool2d(kernel_size=2, stride=2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):  # 模型前向传播
        return self.model(x)


model = Model()  # 定义模型
loss_cross = nn.CrossEntropyLoss()  # 定义损失函数
optim = torch.optim.SGD(model.parameters(), lr=0.01)  # lr不能过大或者过小。刚开始的lr可设置得较大一点，后面再对lr进行调节
len = len(dataloader)

for epoch in range(20):
    total_loss = 0.0
    for imgs, targets in dataloader:
        outputs = model(imgs)
        res_loss = loss_cross(outputs, targets)

        optim.zero_grad()  # 优化器对model中的每一个参数进行梯度清零
        res_loss.backward()  # 损失反向传播
        optim.step()  # 对model参数开始调优
        total_loss += res_loss
    print('epoch:{}\ttotal_loss:{}\tmean_loss:{}.'.format(epoch, total_loss, total_loss / len))
# epoch:0	total_loss:9374.806640625	mean_loss:1.8749613761901855.
# epoch:1	total_loss:7721.240234375	mean_loss:1.544248104095459.
# epoch:2	total_loss:6830.775390625	mean_loss:1.3661550283432007.