pytorch训练2个类别的cifar10数据集
时间: 2023-12-09 11:04:19 浏览: 50
可以使用PyTorch的torchvision.datasets模块中提供的CIFAR10数据集,然后通过划分数据来创建2个类别的数据集。具体实现可以参考PyTorch官方文档中的教程:https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html 。
相关问题
pytorch训练2个类别的cifar10数据集的代码
可以参考以下代码:
```
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainset.targets = [1 if label == 0 else 0 for label in trainset.targets] # 将第一类样本的标签设为1,第二类样本的标签设为0
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testset.targets = [1 if label == 0 else 0 for label in testset.targets] # 将第一类样本的标签设为1,第二类样本的标签设为0
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('class 2', 'class 1')
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
这份代码将CIFAR10数据集中的第一类样本设为正样本,第二类样本设为负样本,训练一个二分类模型。
pytorch训练2个类别的cifar10数据集的python代码
以下是一个使用 PyTorch 训练 CIFAR10 数据集的代码示例,其中仅包含两个类别。
```
import torch
import torchvision
import torchvision.transforms as transforms
# 定义数据集转换
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# 加载 CIFAR10 数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
# 将数据集分成两个类别(飞机和汽车)
classes = ('plane', 'car')
trainset.targets = [1 if target == 1 else 0 for target in trainset.targets]
# 定义数据加载器
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
# 定义神经网络模型
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
# 定义损失函数和优化器
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练神经网络
for epoch in range(2): # 多次迭代数据集
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# 获取输入数据
inputs, labels = data
# 清零梯度缓存、前向传播、计算损失、反向传播、更新权重
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 打印统计信息
running_loss += loss.item()
if i % 2000 == 1999: # 每 2000 个小批量数据打印一次
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
```
希望这个代码示例能够帮到你!
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