神经网络cifar代码是什么意思
时间: 2023-12-20 20:04:45 浏览: 22
"CIFAR"是指“Canadian Institute for Advanced Research”,是一种经典的图像分类数据集,其中包含10个类别的60000张32x32像素的彩色图像。"神经网络CIFAR代码"指的是使用神经网络模型对CIFAR数据集进行训练和分类的代码。这样的代码通常包括数据预处理、模型定义、训练和评估等部分。通过这样的代码,我们可以训练出一个能够自动分类CIFAR图像的神经网络模型。
相关问题
神经网络cifar代码和cnn代码
以下是使用PyTorch实现CIFAR-10数据集分类的神经网络代码和卷积神经网络(CNN)代码的示例。
神经网络代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(32 * 32 * 3, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
x = x.view(-1, 32 * 32 * 3)
x = torch.relu(self.fc1(x))
x = torch.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)
```
CNN代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
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, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.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)
```
这些代码使用PyTorch框架实现,包括网络的定义、损失函数和优化器。神经网络和CNN的区别在于网络结构的不同,CNN包括卷积层和池化层来提取图像特征。
CIFAR-10卷积神经网络代码
以下是使用PyTorch实现的CIFAR-10卷积神经网络的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
# 定义数据预处理方式
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# 加载CIFAR-10数据集
trainset = datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128,
shuffle=False, num_workers=2)
# 定义卷积神经网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(256 * 4 * 4, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(torch.relu(self.bn1(self.conv1(x))))
x = self.pool(torch.relu(self.bn2(self.conv2(x))))
x = self.pool(torch.relu(self.bn3(self.conv3(x))))
x = x.view(-1, 256 * 4 * 4)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 初始化网络和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
# 训练网络
for epoch in range(100):
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 % 100 == 99:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 在测试集上测试网络
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
inputs, labels = data
outputs = net(inputs)
_, 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))
```
这个网络使用3个卷积层和2个全连接层,使用ReLU激活函数和批归一化来提高性能。训练过程中使用了随机裁剪和水平翻转等数据扩增技术来提高模型的泛化能力。
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