基于pytorch构建卷积神经网络实现RNA序列预测并优化 代码

好的，以下是基于PyTorch构建卷积神经网络实现RNA序列预测并优化的完整代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

# 准备RNA序列数据集
class RNA_Dataset(Dataset):
    def __init__(self, data_file):
        self.sequences = []
        self.labels = []
        with open(data_file, 'r') as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                if line.startswith('>'):
                    label = int(line.split()[1])
                    self.labels.append(label)
                else:
                    sequence = [int(n) for n in line]
                    self.sequences.append(sequence)
        self.sequences = torch.tensor(self.sequences, dtype=torch.float32)
        self.labels = torch.tensor(self.labels, dtype=torch.long)

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

    def __getitem__(self, index):
        return self.sequences[index], self.labels[index]

train_dataset = RNA_Dataset('train.fasta')
val_dataset = RNA_Dataset('val.fasta')
test_dataset = RNA_Dataset('test.fasta')

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

# 定义卷积神经网络模型
class RNA_CNN(nn.Module):
    def __init__(self, num_classes=2):
        super(RNA_CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(4, 32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2)
        )
        self.conv2 = nn.Sequential(
            nn.Conv1d(32, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2)
        )
        self.fc1 = nn.Linear(64 * 50, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

model = RNA_CNN()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    for i, (sequences, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(sequences)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    # validation
    correct = 0
    total = 0
    with torch.no_grad():
        for sequences, labels in val_loader:
            outputs = model(sequences)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Epoch [{}/{}], Loss: {:.4f}, Validation Accuracy: {:.2f}%'
          .format(epoch+1, num_epochs, loss.item(), 100 * correct / total))

# 评估模型性能
with torch.no_grad():
    correct = 0
    total = 0
    for sequences, labels in test_loader:
        outputs = model(sequences)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy: {:.2f}%'.format(100 * correct / total))

在上面的代码中，我们使用`RNA_Dataset`类来读取RNA序列数据集，使用`RNA_CNN`类定义卷积神经网络模型，使用交叉熵损失函数和Adam优化器进行训练，并使用验证集来监控模型的性能。在训练结束后，我们使用测试集来评估模型的性能。你可以根据自己的数据集和需求，对代码进行适当修改。