pytorch 花朵识别代码

以下是使用 PyTorch 实现花朵识别的示例代码：

首先，我们需要导入必要的包：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

然后，我们可以定义数据的预处理方式：

transform = transforms.Compose(
    [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])

接下来，我们可以定义数据集和数据加载器：

train_data = datasets.ImageFolder('path/to/train/dataset', transform=transform)
test_data = datasets.ImageFolder('path/to/test/dataset', transform=transform)

train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
test_loader = DataLoader(test_data, batch_size=32, shuffle=False)

然后，我们可以定义模型：

class FlowerNet(nn.Module):
    def __init__(self):
        super(FlowerNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 102),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

接下来，我们可以定义损失函数和优化器：

model = FlowerNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

然后，我们可以进行训练和测试：

num_epochs = 10

for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0
    model.train()
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    train_loss = train_loss / len(train_data)
    train_acc = train_acc / len(train_data)

    print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.6f}'.format(
        epoch+1, train_loss, train_acc))

    model.eval()
    test_loss = 0.0
    test_acc = 0.0
    for i, (images, labels) in enumerate(test_loader):
        outputs = model(images)
        loss = criterion(outputs, labels)

        test_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        test_acc += torch.sum(preds == labels.data)

    test_loss = test_loss / len(test_data)
    test_acc = test_acc / len(test_data)

    print('Epoch: {} \tTesting Loss: {:.6f} \tTesting Accuracy: {:.6f}'.format(
        epoch+1, test_loss, test_acc))

以上就是使用 PyTorch 实现花朵识别的示例代码。请注意替换代码中的数据集路径和其他参数以适应您自己的应用场景。