使用PyCharm实现CIFAR-10卷积神经网络代码
时间: 2024-01-29 10:09:06 浏览: 141
Cifar10-Classify(Resnet)_ResNet_resnetcifar10_ResNetkeras_keras_
以下是使用PyCharm实现的CIFAR-10卷积神经网络的代码:
首先,需要创建一个新的PyCharm项目,并将以下代码复制到一个新的Python文件中:
```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))
```
然后,需要安装PyTorch库。可以在PyCharm的“Terminal”窗口中执行以下命令:
```
pip install torch torchvision
```
接下来,可以通过单击“Run”按钮或使用快捷键“Shift + F10”来运行代码。
在运行过程中,PyCharm将显示训练过程的损失值,并在测试集上计算网络的准确性。
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