使用pytorch写猫种类识别代码
时间: 2024-05-02 13:22:45 浏览: 12
好的,针对你的问题,以下是大致的步骤:
1. 导入所需的库和数据集
```
import torch
import torchvision
import torchvision.transforms as transforms
```
```
transform = transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(224), # 裁剪为224大小的正方形
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # 对图像进行标准化
])
trainset = torchvision.datasets.ImageFolder(root='path/to/training_set', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=0)
testset = torchvision.datasets.ImageFolder(root='path/to/test_set', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=0)
classes = trainset.classes # 获取所有种类
```
2. 定义模型
```
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 * 54 * 54, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, len(classes))
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 * 54 * 54)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
```
3. 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)
```
4. 训练模型
```
for epoch in range(10): # 训练10个epoch
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: # 每2000个mini-batch打印一次loss
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
```
5. 测试模型
```
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 test images: %d %%' % (
100 * correct / total))
```
至此,完整的猫种类识别代码就完成了。其中,主要的步骤包括数据集预处理、模型定义、损失函数和优化器定义、模型训练和测试。
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