第一种方法
python
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 x
tudui = 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 = 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
if 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 += 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
if 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 + 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")第二种方法
python
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 x
tudui = 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 = 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
imgs = 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 += 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
imgs = 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 + 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")