写一个用花分类数据集且添加了注意力机制的网络代码

好的，以下是一个使用花分类数据集和注意力机制的网络代码：

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


class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.hidden_size = hidden_size
        self.attention = nn.Linear(hidden_size * 2, hidden_size)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, hidden, encoder_outputs):
        max_len = encoder_outputs.size(0)
        batch_size = encoder_outputs.size(1)
        attention_weights = torch.zeros(batch_size, max_len)

        for i in range(max_len):
            attention_input = torch.cat((hidden, encoder_outputs[i]), 1)
            attention_weights[:, i] = self.attention(attention_input).squeeze(1)

        return self.softmax(attention_weights).unsqueeze(1)


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.decoder = nn.Sequential(
            nn.Linear(32 * 7 * 7, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 5),
            nn.Softmax(dim=1)
        )
        self.attention = Attention(256)

    def forward(self, x):
        batch_size = x.size(0)
        x = self.encoder(x)
        x = x.view(batch_size, -1)
        x = self.decoder(x)
        attention_weights = self.attention(x, encoder_outputs)
        context = attention_weights.bmm(encoder_outputs.transpose(0, 1))
        output = torch.cat((x, context.squeeze(1)), 1)
        return output


if __name__ == '__main__':
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    transform = transforms.Compose(
        [transforms.Resize((28, 28)),
         transforms.ToTensor(),
         transforms.Normalize((0.5,), (0.5,))])

    trainset = datasets.ImageFolder(root='./flowers/train', transform=transform)
    trainloader = DataLoader(trainset, batch_size=64, shuffle=True)

    net = Net().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=0.001)

    for epoch in range(10):  # loop over the dataset multiple times

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data[0].to(device), data[1].to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 100 == 99:    # print every 100 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 100))
                running_loss = 0.0

    print('Finished Training')

在这个代码中，我们定义了一个包含卷积层、全连接层和注意力机制的神经网络，用于对花分类数据集进行分类。注意力机制用于帮助网络在处理图像时更好地捕捉重要的特征。我们使用Adam优化器和交叉熵损失函数来训练网络。