Transformer实现一维单特征时间序列信号端到端分类代码,有100个样本,每个样本数据是一个2000长的数据,每个数据只有一个数,希望输出2000个0-6七分类的标签,每类之间的变化位置不明确需要模型去学习
时间: 2024-04-05 12:31:20 浏览: 59
以下是利用PyTorch实现一维单特征时间序列信号端到端分类的代码:
```python
import torch
import torch.nn as nn
class TransformerModel(nn.Module):
def __init__(self, input_dim, output_dim, num_layers=6, hidden_dim=512, dropout=0.1):
super(TransformerModel, self).__init__()
self.num_layers = num_layers
self.hidden_dim = hidden_dim
self.embedding = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU()
)
self.pos_encoder = PositionalEncoding(hidden_dim, dropout)
encoder_layers = nn.TransformerEncoderLayer(hidden_dim, nhead=4)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
self.decoder = nn.Linear(hidden_dim, output_dim)
def forward(self, src):
src = self.embedding(src)
src = self.pos_encoder(src)
output = self.transformer_encoder(src)
output = self.decoder(output)
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=2000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
# 数据准备
input_dim = 1
output_dim = 7
num_samples = 100
sequence_length = 2000
data = torch.randn(num_samples, sequence_length, input_dim)
labels = torch.randint(output_dim, size=(num_samples, sequence_length))
# 模型训练
model = TransformerModel(input_dim, output_dim)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
for i in range(num_samples):
optimizer.zero_grad()
output = model(data[i])
loss = criterion(output.view(-1, output_dim), labels[i].view(-1))
loss.backward()
optimizer.step()
running_loss += loss.item()
epoch_loss = running_loss / num_samples
print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}")
```
在这个例子中,我们使用了一个Transformer模型,其中包括一个嵌入层、位置编码层、经过多个TransformerEncoder层的编码器和一个全连接层的解码器。为了方便,我们使用了PyTorch中提供的`nn.TransformerEncoder`和`nn.TransformerEncoderLayer`。
我们利用随机数据和标签进行训练,随机数据是一个形状为`(100, 2000, 1)`的张量,其中100是样本数量,2000是时间序列长度,1是特征数。标签是一个形状为`(100, 2000)`的张量,其中每个元素是0-6之间的整数,表示这个时间点的分类标签。
训练过程中,我们使用交叉熵损失函数和Adam优化器。每个样本都是独立训练的,所以我们需要在每个时间序列上进行循环,并在每个时间点上计算损失和梯度,并执行反向传播和优化步骤。在训练期间,我们打印出每个epoch的平均损失。
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