Pytorch学习日记——完整的模型训练套路（一）

学习视频——B站【小土堆】

代码

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

train_data = torchvision.datasets.CIFAR10("../data", train=True, transform=torchvision.transforms.ToTensor(), download=True)

test_data = torchvision.datasets.CIFAR10("../data", train=False, transform=torchvision.transforms.ToTensor(), download=True)

train_data_size = len(train_data)
test_data_size = len(test_data)
# 如果train_data_size = 10, 训练数据集的长度为：10
print(f"训练数据集的长度为：{format(train_data_size)}")
print(f"测试数据集的长度为：{format(test_data_size)}")

# 利用Dataloader加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

# 搭建神经网络
class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.model = nn.Sequential(
            Conv2d(3, 32, 5, 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.model(x)
        return x

# 创建网络模型
tudui = Tudui()

# 损失函数
loss_fn = nn.CrossEntropyLoss()

# 优化器
optimizer = torch.optim.SGD(tudui.parameters(), lr=0.01)

# 设置训练网络的一些参数
# 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 = 10

for i in range(epoch):
    print(f"-----第{format(i+1)}轮训练开始-----")
    # 训练步骤开始
    for data in train_dataloader:
        imgs, targets = data
        outputs = tudui(imgs)
        loss = loss_fn(outputs, targets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_train_step = total_train_step + 1
        print(f"训练次数：{format(total_train_step)}, Loss: {format(loss.item())}")

结果