class ARMA4NC(nn.Module): def __init__(self, in_dim, hid_dim, out_dim, num_stacks, num_layers, activation=None, dropout=0.0): super(ARMA4NC, self).__init__() self.conv1 = ARMAConv(in_dim=in_dim, out_dim=hid_dim, num_stacks=num_stacks, num_layers=num_layers, activation=activation, dropout=dropout) self.conv2 = ARMAConv(in_dim=hid_dim, out_dim=out_dim, num_stacks=num_stacks, num_layers=num_layers, activation=activation, dropout=dropout) self.dropout = nn.Dropout(p=dropout) def forward(self, g, feats): feats = F.relu(self.conv1(g, feats)) feats = self.dropout(feats) feats = self.conv2(g, feats) return feats。写出对这个进行训练的代码
时间: 2023-12-05 17:04:00 浏览: 182
以下是一个示例训练代码,包括定义损失函数、优化器,以及数据加载器和训练循环:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
# Define the loss function
criterion = nn.CrossEntropyLoss()
# Define the optimizer
optimizer = optim.Adam(model.parameters(), lr=0.01)
# Create a DataLoader for the training data
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
# Train the model
for epoch in range(num_epochs):
for batch_idx, (g, feats, labels) in enumerate(train_loader):
# Zero the gradients
optimizer.zero_grad()
# Forward pass
output = model(g, feats)
loss = criterion(output, labels)
# Backward pass and optimization step
loss.backward()
optimizer.step()
# Print progress
if batch_idx % log_interval == 0:
print('Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(g), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
```
在此示例中,`train_dataset` 是一个包含图形、特征和标签的数据集,`log_interval` 是打印日志的间隔(例如每100个批次)。在训练过程中,每个批次的图形、特征和标签被加载到 `g`、`feats` 和 `labels` 变量中,分别表示图形、特征和标签的张量。在每个批次中,模型的输出和损失都被计算,并且通过反向传播和优化器进行了更新。最后,训练循环打印进度并在每个日志间隔时输出当前损失。
阅读全文
相关推荐













