p27完整的模型训练套路1

# 搭建神经网络
import torch
from torch import nn
class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024, 64),
            nn.Linear(64, 10)
        )
    def forward(self, x):
        x = self.model(x)
        return x

# 主方法测试正确性
if __name__ == '__main__':
    lixinyu = Lixinyu()
    input = torch.ones(64, 3, 32, 32)
    output = lixinyu(input)
    print(output.shape)

# 准备数据集
import torch.optim
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from model import *

train_data = torchvision.datasets.CIFAR10("./dataset", train=True, transform=torchvision.transforms.ToTensor(),
                                          download=True)
test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                         download=True)
# length
train_data_size = len(train_data)
test_data_siz = len(test_data)
print(f"训练数据集的长度为：{train_data_size}")
print(f"测试数据集的长度为：{test_data_siz}")

# 利用DataLoader加载
train_data_loader = DataLoader(train_data, batch_size=64)
test_data_loader = DataLoader(test_data, batch_size=64)

# 创建网络模型
lixinyu = Lixinyu()

# 损失函数
loss_fn = nn.CrossEntropyLoss()

# 优化器
# learning_rate = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(lixinyu.parameters(), learning_rate)

# 设置训练网络参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 5

for i in range(epoch):
    print(f"--------------------第{i+1}轮训练开始了-----------------")

    # 训练步骤开始
    for data in train_data_loader:
        imgs, targets = data
        output = lixinyu(imgs)
        loss = loss_fn(output, targets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_train_step += 1
        print(f"训练次数{total_train_step} 损失值{loss.item()}")  # loss.item()标准写法，这里直接写loss也行

import torch
a = torch.tensor(5)
print(a)
print(a.item())

D:anacondapython.exe C:/Users/ASUS/Desktop/tudui/loss_item.py
tensor(5)
5

Process finished with exit code 0