pytorch花朵分类代码

好的，以下是一个简单的基于PyTorch实现的花卉分类代码，使用的数据集是kaggle上的花卉数据集，包含了5种不同的花卉图片，分别是雏菊、蒲公英、玫瑰、向日葵和郁金香。

import torch
import torchvision
from torch import nn, optim
from torchvision import datasets, transforms

# 数据预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 加载数据集
trainset = datasets.ImageFolder(root='/path/to/train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
testset = datasets.ImageFolder(root='/path/to/test', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)

# 定义模型
model = torchvision.models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 5)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy on test images: %d %%' % (100 * correct / total))

这个代码使用了PyTorch中的预训练模型ResNet-18作为基础模型，在其基础上增加了一个全连接层用于分类。同时，使用了SGD优化器和交叉熵损失函数进行模型训练。经过10轮训练后，测试集上的准确率可以达到较高水平。