用Python代码实现基于RNA序列数据集实验，数据集在model文件夹中： ALKBH5_Baltz2012.train.negatives.fa 是训练集负样本； ALKBH5_Baltz2012.train.positives.fa 是训练集正样本； ALKBH5_Baltz2012.val.negatives.fa 是验证集负样本； ALKBH5_Baltz2012.val.positives.fa 是验证集正样本。 用Pytorch框架搭建循环神经网络，输出最终分类正确率。

好的，这是一个很有趣的问题，需要一些时间和代码实现。首先，我们需要导入 PyTorch 模块和其他必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
from sklearn.model_selection import train_test_split

然后，我们需要定义一个数据集类，用于加载并处理 RNA 序列数据集。这个类需要实现 __len__() 方法和 __getitem__() 方法。

class RNADataSet(Dataset):
    def __init__(self, file_path):
        self.data = []
        self.labels = []
        self.vocab = {'A': 0, 'C': 1, 'G': 2, 'U': 3} # 将四个核苷酸映射到数字
        
        with open(file_path, 'r') as f:
            lines = f.readlines()
            for i in range(0, len(lines), 2):
                seq = lines[i+1].strip() # RNA序列
                label = int(lines[i].strip()[1]) # 类别标签（正样本为1，负样本为0）
                seq_list = [self.vocab[c] for c in seq] # 将RNA序列转换为数字列表
                self.data.append(seq_list)
                self.labels.append(label)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return torch.tensor(self.data[idx]), torch.tensor(self.labels[idx])

然后，我们需要定义一个循环神经网络模型：

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        
        out, _ = self.rnn(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        
        return out

接下来，我们需要加载数据集，划分训练集和验证集：

train_dataset = RNADataSet('./model/ALKBH5_Baltz2012.train.negatives.fa')
train_dataset += RNADataSet('./model/ALKBH5_Baltz2012.train.positives.fa')
val_dataset = RNADataSet('./model/ALKBH5_Baltz2012.val.negatives.fa')
val_dataset += RNADataSet('./model/ALKBH5_Baltz2012.val.positives.fa')

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)

train_size = len(train_dataset)
val_size = len(val_dataset)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

然后，我们需要定义损失函数和优化器：

model = RNN(input_size=4, hidden_size=128, num_layers=2, output_size=2).to(device) # 输入维度为4，即四种核苷酸
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

最后，我们需要训练模型，并输出最终分类正确率：

num_epochs = 10

for epoch in range(num_epochs):
    train_loss = 0.0
    train_correct = 0
    
    for data, label in train_loader:
        data = data.to(device)
        label = label.to(device)
        
        optimizer.zero_grad()
        
        output = model(data.float())
        
        loss = criterion(output, label)
        train_loss += loss.item()
        
        _, predicted = torch.max(output.data, 1)
        train_correct += (predicted == label).sum().item()
        
        loss.backward()
        optimizer.step()
        
    train_loss /= train_size
    train_acc = train_correct / train_size
    
    val_loss = 0.0
    val_correct = 0
    
    with torch.no_grad():
        for data, label in val_loader:
            data = data.to(device)
            label = label.to(device)
            
            output = model(data.float())
            
            loss = criterion(output, label)
            val_loss += loss.item()
            
            _, predicted = torch.max(output.data, 1)
            val_correct += (predicted == label).sum().item()
        
    val_loss /= val_size
    val_acc = val_correct / val_size
    
    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))

最终，我们可以得到模型在验证集上的分类正确率。