transformer代码 机器学习
时间: 2023-09-04 12:06:56 浏览: 61
您好!对于Transformer模型的机器学习代码,您可以参考以下示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
# 定义Transformer模型
class Transformer(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(Transformer, self).__init__()
self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(input_dim, nhead=8), num_layers=6)
self.decoder = nn.Linear(input_dim, hidden_dim)
self.output = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
x = self.output(x)
return x
# 定义训练函数
def train(model, train_data, optimizer, criterion, epochs):
for epoch in range(epochs):
total_loss = 0.0
for input, target in train_data:
optimizer.zero_grad()
output = model(input)
loss = criterion(output, target)
loss.backward()
optimizer.step()
total_loss += loss.item()
print("Epoch {}/{} - Loss: {:.4f}".format(epoch+1, epochs, total_loss))
# 设置超参数
input_dim = 128
hidden_dim = 256
output_dim = 10
learning_rate = 0.001
epochs = 10
# 准备训练数据
train_data = [(torch.randn(input_dim), torch.randint(0, output_dim, (hidden_dim,))) for _ in range(100)]
# 初始化模型、优化器和损失函数
model = Transformer(input_dim, hidden_dim, output_dim)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()
# 开始训练
train(model, train_data, optimizer, criterion, epochs)
```
以上是一个简单的Transformer模型的机器学习代码示例,包括了模型的定义、训练函数的实现以及超参数的设置。您可以根据自己的需求进行修改和扩展。希望能对您有所帮助!如果您还有其他问题,请随时提问。
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