视频地址利用GPU训练（一）_哔哩哔哩_bilibili
第一种方法
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
class Tudui(nn.Module):def __init__(self):super(Tudui, 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(64 * 4 * 4, 64),nn.Linear(64, 10))def forward(self, x):x = self.model(x)return xtudui = Tudui()
if torch.cuda.is_available():tudui = tudui.cuda()# 损失函数
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():loss_fn = loss_fn.cuda()# 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 = 10for 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 = dataif torch.cuda.is_available():imgs = imgs.cuda()targets = targets.cuda()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 += 1if 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 = 0total_accuracy = 0with torch.no_grad(): # 保证不会调优for data in test_dataloader:imgs, targets = dataif torch.cuda.is_available():imgs = imgs.cuda()targets = targets.cuda()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 + accuracyprint("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")
第二种方法
import torch
import torchvision
# from model import *
from torch import nn
from torch.utils.data import DataLoader# 定义训练的设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")# 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
class Tudui(nn.Module):def __init__(self):super(Tudui, 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(64 * 4 * 4, 64),nn.Linear(64, 10))def forward(self, x):x = self.model(x)return xtudui = Tudui()
tudui = tudui.to(device)# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)# 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 = 10for 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 = dataimgs = imgs.to(device)targets = targets.to(device)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 += 1if 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 = 0total_accuracy = 0with torch.no_grad(): # 保证不会调优for data in test_dataloader:imgs, targets = dataimgs = imgs.to(device)targets = targets.to(device)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 + accuracyprint("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")