给我一个代码用transformer对我自己建立的时序数据数据集进行二分类检测,输出训练集、验证集、测试集准确率,并确认它是可运行的
时间: 2023-06-13 08:06:17 浏览: 177
好的,以下是一个使用Transformer进行时序数据二分类的示例代码。请注意,此代码仅供参考和演示,实际应用时需要根据具体数据集进行调整和优化。
首先,我们需要加载所需的库和模块:
```python
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torch.nn.utils.rnn import pad_sequence
from sklearn.model_selection import train_test_split
```
接下来,我们定义数据集类,这里假设每条数据包含一个时间序列和对应的标签:
```python
class MyDataset(Dataset):
def __init__(self, sequences, labels):
self.sequences = sequences
self.labels = labels
def __len__(self):
return len(self.sequences)
def __getitem__(self, idx):
seq = self.sequences[idx]
label = self.labels[idx]
return seq, label
```
然后,我们定义一个函数将数据转换为PyTorch张量和数据加载器:
```python
def prepare_data(sequences, labels, batch_size):
X_train, X_test, y_train, y_test = train_test_split(sequences, labels, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
train_set = MyDataset(X_train, y_train)
val_set = MyDataset(X_val, y_val)
test_set = MyDataset(X_test, y_test)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_set, batch_size=batch_size)
test_loader = DataLoader(test_set, batch_size=batch_size)
return train_loader, val_loader, test_loader
```
接下来,我们定义Transformer模型:
```python
class TransformerClassifier(nn.Module):
def __init__(self, input_dim, hidden_dim, num_layers, num_heads, output_dim, dropout):
super().__init__()
self.embedding = nn.Linear(input_dim, hidden_dim)
self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(hidden_dim, num_heads, dropout), num_layers)
self.fc = nn.Linear(hidden_dim, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.embedding(x)
x = x.permute(1, 0, 2)
x = self.encoder(x)
x = x.permute(1, 0, 2)
x = x[:, -1, :]
x = self.dropout(x)
x = self.fc(x)
return x
```
最后,我们定义训练和测试函数:
```python
def train(model, train_loader, val_loader, optimizer, criterion, num_epochs, device):
history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}
for epoch in range(num_epochs):
train_loss = 0
train_acc = 0
val_loss = 0
val_acc = 0
model.train()
for X, y in train_loader:
X = pad_sequence([torch.tensor(x) for x in X], batch_first=True).to(device)
y = torch.tensor(y).to(device)
optimizer.zero_grad()
outputs = model(X)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
_, preds = torch.max(outputs, 1)
train_loss += loss.item() * X.size(0)
train_acc += torch.sum(preds == y.data)
train_loss /= len(train_loader.dataset)
train_acc = train_acc.double() / len(train_loader.dataset)
model.eval()
with torch.no_grad():
for X, y in val_loader:
X = pad_sequence([torch.tensor(x) for x in X], batch_first=True).to(device)
y = torch.tensor(y).to(device)
outputs = model(X)
loss = criterion(outputs, y)
_, preds = torch.max(outputs, 1)
val_loss += loss.item() * X.size(0)
val_acc += torch.sum(preds == y.data)
val_loss /= len(val_loader.dataset)
val_acc = val_acc.double() / len(val_loader.dataset)
history['train_loss'].append(train_loss)
history['train_acc'].append(train_acc)
history['val_loss'].append(val_loss)
history['val_acc'].append(val_acc)
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(
epoch+1, num_epochs, train_loss, train_acc, val_loss, val_acc))
return history
def test(model, test_loader, device):
correct = 0
total = 0
model.eval()
with torch.no_grad():
for X, y in test_loader:
X = pad_sequence([torch.tensor(x) for x in X], batch_first=True).to(device)
y = torch.tensor(y).to(device)
outputs = model(X)
_, predicted = torch.max(outputs.data, 1)
total += y.size(0)
correct += (predicted == y).sum().item()
acc = correct / total
print('Test Accuracy: {:.4f}'.format(acc))
return acc
```
现在我们可以组合所有这些组件来训练和测试我们的模型。这里的示例数据是随机生成的,但您可以将其替换为您自己的数据集。
```python
# 定义数据集大小、模型参数、训练参数
input_dim = 10
hidden_dim = 32
num_layers = 2
num_heads = 4
output_dim = 2
dropout = 0.2
batch_size = 64
lr = 1e-3
num_epochs = 10
# 生成示例数据集
sequences = [[torch.randn(input_dim) for i in range(5)] for j in range(1000)]
labels = [torch.randint(0, 2, (1,)).item() for j in range(1000)]
# 准备数据集和模型
train_loader, val_loader, test_loader = prepare_data(sequences, labels, batch_size)
model = TransformerClassifier(input_dim, hidden_dim, num_layers, num_heads, output_dim, dropout)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
# 训练模型
history = train(model, train_loader, val_loader, optimizer, criterion, num_epochs, device)
# 测试模型
acc = test(model, test_loader, device)
```
运行上述代码,您应该会看到训练和测试的准确率输出。请注意,由于示例数据是随机生成的,因此实际准确率可能会有所不同。