请写一个Transformer训练代码
时间: 2023-11-14 19:23:16 浏览: 55
好的,请参考以下代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from transformer import Transformer
from dataset import MyDataset
# 定义超参数
batch_size = 64
embedding_size = 256
hidden_size = 512
num_layers = 6
num_heads = 8
dropout = 0.1
learning_rate = 0.001
num_epochs = 100
# 加载数据集
dataset = MyDataset()
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# 初始化模型和优化器
model = Transformer(dataset.vocab_size, embedding_size, hidden_size, num_layers, num_heads, dropout)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 定义损失函数
criterion = nn.CrossEntropyLoss()
# 训练
for epoch in range(num_epochs):
total_loss = 0
for i, (inputs, targets) in enumerate(dataloader):
# 清空梯度
optimizer.zero_grad()
# 前向传播
outputs = model(inputs, targets[:, :-1])
# 计算损失
loss = criterion(outputs.reshape(-1, dataset.vocab_size), targets[:, 1:].reshape(-1))
# 反向传播
loss.backward()
# 更新参数
optimizer.step()
# 累加损失
total_loss += loss.item()
# 打印训练信息
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(dataset)//batch_size, total_loss/(i+1)))
# 保存模型
torch.save(model.state_dict(), 'transformer.pth')
```
这里使用了一个自定义的数据集 `MyDataset`,并且假设已经实现了 Transformer 模型。在训练过程中,我们使用了交叉熵损失函数,并且每个 epoch 结束后保存模型参数。