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

好的，我可以给你提供一个基于PyTorch的代码框架，用Transformer模型对时序数据进行二分类检测。你可以根据自己的数据集进行一定的修改。

首先，需要安装必要的Python库，包括PyTorch、Numpy、Pandas等。可以使用以下命令安装：

pip install torch numpy pandas

然后，我们需要准备数据集。假设我们的数据集包含两个类别，每个样本包含10个时序数据，我们可以使用Pandas库读取数据并将其转换为PyTorch Tensor：

import pandas as pd
import numpy as np
import torch

# 读取数据
data = pd.read_csv('data.csv')

# 将数据转换为PyTorch Tensor
x = torch.tensor(data.iloc[:, :-1].values, dtype=torch.float32)
y = torch.tensor(data.iloc[:, -1].values, dtype=torch.long)

接下来，我们需要对数据进行划分，以便进行训练、验证和测试。可以使用PyTorch中的SubsetRandomSampler实现：

from torch.utils.data import DataLoader, SubsetRandomSampler

# 定义训练、验证、测试集的比例
train_ratio = 0.6
val_ratio = 0.2
test_ratio = 0.2

# 计算划分后的数据集大小
train_size = int(train_ratio * len(data))
val_size = int(val_ratio * len(data))
test_size = len(data) - train_size - val_size

# 随机划分数据集
indices = np.arange(len(data))
np.random.shuffle(indices)
train_indices = indices[:train_size]
val_indices = indices[train_size:train_size+val_size]
test_indices = indices[train_size+val_size:]

# 定义数据加载器
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = SubsetRandomSampler(val_indices)
test_sampler = SubsetRandomSampler(test_indices)

train_loader = DataLoader(dataset, batch_size=32, sampler=train_sampler)
val_loader = DataLoader(dataset, batch_size=32, sampler=val_sampler)
test_loader = DataLoader(dataset, batch_size=32, sampler=test_sampler)

现在，我们可以定义Transformer模型。这里我们使用PyTorch官方的Transformer模型实现：

import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer

class TransformerModel(nn.Module):
    def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.src_mask = None
        self.pos_encoder = PositionalEncoding(ninp, dropout)
        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.ninp = ninp
        self.decoder = nn.Linear(ninp, 2)

        self.init_weights()

    def _generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src):
        if self.src_mask is None or self.src_mask.size(0) != len(src):
            device = src.device
            mask = self._generate_square_subsequent_mask(len(src)).to(device)
            self.src_mask = mask

        src = self.encoder(src) * math.sqrt(self.ninp)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, self.src_mask)
        output = output.mean(dim=0)
        output = self.decoder(output)
        return output

接下来，我们可以定义训练和评估函数：

import torch.optim as optim

def train(model, optimizer, criterion, train_loader):
    model.train()
    total_loss = 0
    total_correct = 0
    for batch in train_loader:
        optimizer.zero_grad()
        x, y = batch
        y_pred = model(x)
        loss = criterion(y_pred, y)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        total_correct += (y_pred.argmax(dim=1) == y).sum().item()
    return total_loss / len(train_loader), total_correct / len(train_loader.dataset)

def evaluate(model, criterion, data_loader):
    model.eval()
    total_loss = 0
    total_correct = 0
    with torch.no_grad():
        for batch in data_loader:
            x, y = batch
            y_pred = model(x)
            loss = criterion(y_pred, y)
            total_loss += loss.item()
            total_correct += (y_pred.argmax(dim=1) == y).sum().item()
    return total_loss / len(data_loader), total_correct / len(data_loader.dataset)

最后，我们可以开始训练模型：

# 定义模型超参数
ntokens = 10
emsize = 128
nhead = 8
nhid = 256
nlayers = 6
dropout = 0.2

# 创建模型、优化器和损失函数
model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(10):
    train_loss, train_acc = train(model, optimizer, criterion, train_loader)
    val_loss, val_acc = evaluate(model, criterion, val_loader)
    print(f'Epoch {epoch+1}: Train Loss={train_loss:.4f}, Train Acc={train_acc:.4f}, Val Loss={val_loss:.4f}, Val Acc={val_acc:.4f}')

# 在测试集上评估模型
test_loss, test_acc = evaluate(model, criterion, test_loader)
print(f'Test Loss={test_loss:.4f}, Test Acc={test_acc:.4f}')

这里我们训练了10个epoch，并在测试集上评估了模型。你可以根据自己的数据集和需求调整模型超参数和训练轮数。