PyTorch快速入门教程【小土堆】之完整模型训练套路

视频地址完整的模型训练套路（一）_哔哩哔哩_bilibili

import torch
import torchvision
from model import *
from torch import nn
from torch.utils.data import DataLoader

# 准备数据集
train_data = torchvision.datasets.CIFAR10(root="CIFAR10", train=True, transform=torchvision.transforms.ToTensor(),
                                          download=True)
test_data = torchvision.datasets.CIFAR10(root="CIFAR10", train=False, transform=torchvision.transforms.ToTensor(),
                                         download=True)
# Length 长度
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("训练数据集的长度为: {}".format(train_data_size))
# print("测试数据集的长度为: {}".format(test_data_size))

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

# 创建网络模型
tudui = Tudui()

# 损失函数
loss_fn = nn.CrossEntropyLoss()

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

# 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("--------第{}轮训练开始---------".format(i + 1))

    # 训练步骤开始
    for data in train_dataloader:
        imgs, targets = data
        outputs = tudui(imgs)
        loss = loss_fn(outputs, targets)

        # 优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step += 1
        if total_train_step % 100 == 0:
            print("训练次数：{},Loss：{}".format(total_train_step, loss.item()))

    # 测试步骤开始
    total_test_loss =0
    total_accuracy = 0
    with torch.no_grad():# 保证不会调优
        for data in test_dataloader:
            imgs, targets = data
            outputs = tudui(imgs)
            loss = loss_fn(outputs, targets)
            total_test_loss = total_test_loss + loss.item()
            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy = total_accuracy + accuracy

    print("整体测试集上的Loss: {}".format(total_test_loss))
    print("整体测试集上的正确率：{}".format(total_accuracy / test_data_size))
    torch.save(tudui, "tudui_{}.pth".format(i))
    print("模型已保存")