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() 帮我根据上面的要求补充完整下面的代码 net = ... epochs = 10 optimizer = GD(net.parameters(), 0.02) losses, states = ... fig = plot_losses(losses) conf_pltly() init_notebook_mode(connected=False) iplot(fig)

下面是根据上述要求补充完整的代码：

import torch.nn as nn
from torch.optim import SGD as GD
from plotly.offline import iplot, init_notebook_mode
from utils import plot_losses

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()

net = get_GarmentClassifier()
epochs = 10
optimizer = GD(net.parameters(), 0.02)
losses, states = train(net, optimizer, epochs)
fig = plot_losses(losses)
init_notebook_mode(connected=False)
iplot(fig)

其中 `train` 函数是用来训练模型的，需要根据具体的数据集和训练方式来实现。`plot_losses` 函数是用来绘制训练过程中损失函数的变化情况。请注意，上面的代码中省略了这两个函数的具体实现。