transformer分类代码
时间: 2023-09-30 08:02:32 浏览: 190
下面是一个使用Transformer模型进行分类的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.datasets import IMDB
from torchtext.data import Field, LabelField, BucketIterator
class TransformerClassifier(nn.Module):
def __init__(self, input_dim, embedding_dim, n_heads, hidden_dim, n_layers, output_dim, dropout):
super(TransformerClassifier, self).__init__()
self.embedding = nn.Embedding(input_dim, embedding_dim)
self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(embedding_dim, n_heads, hidden_dim, dropout), n_layers)
self.fc = nn.Linear(embedding_dim, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
embedded = self.embedding(x) # [seq_len, batch_size, embedding_dim]
embedded = self.dropout(embedded)
encoded = self.encoder(embedded) # [seq_len, batch_size, embedding_dim]
pooled = F.avg_pool1d(encoded.permute(1, 2, 0), encoded.shape[0]).squeeze(2) # [batch_size, embedding_dim]
output = self.fc(pooled)
return output
# 数据预处理
TEXT = Field(tokenize='spacy', lower=True)
LABEL = LabelField(dtype=torch.float)
train_data, test_data = IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train_data, max_size=25000, vectors="glove.6B.100d", unk_init=torch.Tensor.normal_)
LABEL.build_vocab(train_data)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, test_iterator = BucketIterator.splits((train_data, test_data), batch_size=64, device=device)
# 模型参数
input_dim = len(TEXT.vocab)
embedding_dim = 100
n_heads = 4
hidden_dim = 256
n_layers = 2
output_dim = 1
dropout = 0.5
model = TransformerClassifier(input_dim, embedding_dim, n_heads, hidden_dim, n_layers, output_dim, dropout).to(device)
# 损失函数和优化器
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())
# 训练和评估函数
def train(model, iterator, optimizer, criterion):
model.train()
epoch_loss = 0
for batch in iterator:
optimizer.zero_grad()
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
return epoch_loss / len(iterator)
def evaluate(model, iterator, criterion):
model.eval()
epoch_loss = 0
with torch.no_grad():
for batch in iterator:
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
epoch_loss += loss.item()
return epoch_loss / len(iterator)
# 训练模型
N_EPOCHS = 5
for epoch in range(N_EPOCHS):
train_loss = train(model, train_iterator, optimizer, criterion)
test_loss = evaluate(model, test_iterator, criterion)
print(f'Epoch: {epoch+1:02}, Train Loss: {train_loss:.3f}, Test Loss: {test_loss:.3f}')
```
注意:这只是一个简单的示例,可能需要根据你的具体需求进行修改和调整。