帮我根据上面的代码补充下面的代码 net = ... epochs = 10 optimizer = GD(net.parameters(), 0.002) loss_fn = ... losses, states = ... fig = plot_losses(losses) iplot(fig)

Sure, here's the code snippet that satisfies your requirements: ``` import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm import matplotlib.pyplot as plt from IPython.display import display, clear_output # Define your OneFCNet model class OneFCNet(nn.Module): def __init__(self): super().__init__() self.fc = nn.Linear(100, 10) def forward(self, x): x = self.fc(x) return x # Define your Gradient Descent optimizer class GD(): def __init__(self, params, lr): self.params = list(params) self.lr = lr def step(self): with torch.no_grad(): for p in self.params: p -= self.lr * p.grad p.grad.zero_() # Define your training function def train(net, loader, optimizer, loss_fn): net.train() losses = [] states = [] for epoch in range(epochs): running_loss = 0.0 for i, data in tqdm(enumerate(loader)): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = loss_fn(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() epoch_loss = running_loss / len(loader) losses.append(epoch_loss) states.append([p.detach().clone() for p in net.parameters()]) clear_output(wait=True) fig = plot_losses(losses) display(fig) return losses, states # Define your data loader class MyDataset(torch.utils.data.Dataset): def __init__(self, data, labels): self.data = data self.labels = labels def __getitem__(self, index): x = self.data[index] y = self.labels[index] return x, y def __len__(self): return len(self.data) # Create your OneFCNet object net = OneFCNet() # Define number of epochs, learning rate, Gradient Descent optimizer, and Cross Entropy loss epochs = 10 lr = 0.002 optimizer = GD(net.parameters(), lr) loss_fn = nn.CrossEntropyLoss() # Create your data and labels tensors data = torch.randn(1000, 100) labels = torch.randint(0, 10, (1000,)) # Create your data loader dataset = MyDataset(data, labels) loader = DataLoader(dataset, batch_size=32, shuffle=True) # Call your train function to train the model losses, states = train(net, loader, optimizer, loss_fn) ``` Note that I assumed that you already have a `plot_losses` function that plots the training loss over time. If you don't have one already, you can define it as follows: ``` def plot_losses(losses): fig, ax = plt.subplots(figsize=(8, 6)) ax.plot(losses) ax.set_xlabel('Epochs') ax.set_ylabel('Loss') ax.set_title('Training Loss') plt.show() return fig ```

下面是根据上述要求补充完整的代码： ```python 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。