Garment Classifier Below there is a net given, that start to use convolutions to consider the advantages of the spatial structure of images. Test the gradient descent method on this net and compare it.def get_GarmentClassifier(): return nn.Sequential( nn.Conv2d(1, 6, kernel_size=5), nn.ReLU(), nn.MaxPool2d(2,2), nn.Conv2d(6, 16, kernel_size=5), nn.ReLU(), nn.MaxPool2d(2,2), nn.Flatten(), nn.Linear(16 * 4 * 4, 120), nn.ReLU(), nn.Linear(120, 84), nn.ReLU(), nn.Linear(84, 10) ).cuda()帮我根据上面的补全下面的代码net = ... epochs = 10 optimizer = GD(net.parameters(), 0.02) losses, states = ... fig = plot_losses(losses) conf_pltly() init_notebook_mode(connected=False) iplot(fig)

以下是根据提供的代码补全的代码：

import torch.nn as nn
import torch.optim as optim

# 定义网络结构
def get_GarmentClassifier():
    return nn.Sequential(
        nn.Conv2d(1, 6, kernel_size=5),
        nn.ReLU(),
        nn.MaxPool2d(2,2),
        nn.Conv2d(6, 16, kernel_size=5),
        nn.ReLU(),
        nn.MaxPool2d(2,2),
        nn.Flatten(),
        nn.Linear(16 * 4 * 4, 120),
        nn.ReLU(),
        nn.Linear(120, 84),
        nn.ReLU(),
        nn.Linear(84, 10)
    ).cuda()

# 初始化网络、优化器和损失函数
net = get_GarmentClassifier()
epochs = 10
optimizer = optim.SGD(net.parameters(), lr=0.02)
loss_fn = nn.CrossEntropyLoss()

# 训练网络
losses = []
states = []
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data[0].cuda(), data[1].cuda()
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = loss_fn(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    losses.append(running_loss/len(trainloader))
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss/len(trainloader)))

# 绘制损失函数曲线
fig = plot_losses(losses)
conf_pltly()
init_notebook_mode(connected=False)
iplot(fig)

其中`optim.SGD`是使用随机梯度下降（SGD）方法进行训练，损失函数为交叉熵损失（`nn.CrossEntropyLoss()`）。在训练过程中通过`enumerate`函数遍历`trainloader`中的数据，对网络进行前向传播和反向传播，并使用优化器更新网络参数。最后将每个epoch的平均损失记录到`losses`列表中，并绘制损失函数曲线。