已经获取了每个RNA的特征矩阵 Met2614_P 是正样本数据标签为1 Met2614_N 是负样本数据标签为0 构建一个包含3层全连接层的深度神经网络,用该神经网络预测该数据是正样本还是负样本(用pytorch实现) 其中,70%为训练集,10%为验证集,20%的数据为测试集
时间: 2024-03-02 20:52:47 浏览: 17
好的,我明白了你的问题。以下是用 PyTorch 实现的代码:
```python
import torch
from torch.utils.data import DataLoader, TensorDataset
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
# 加载数据
Met2614_P = torch.load('Met2614_P.pt')
Met2614_N = torch.load('Met2614_N.pt')
# 将数据标签合并
X = torch.cat([Met2614_P, Met2614_N])
y = torch.cat([torch.ones(Met2614_P.shape[0]), torch.zeros(Met2614_N.shape[0])])
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)
# 转换为 DataLoader
train_data = TensorDataset(X_train, y_train)
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
val_data = TensorDataset(X_val, y_val)
val_loader = DataLoader(val_data, batch_size=32, shuffle=False)
test_data = TensorDataset(X_test, y_test)
test_loader = DataLoader(test_data, batch_size=32, shuffle=False)
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2614, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
x = nn.functional.relu(x)
x = self.fc3(x)
x = self.sigmoid(x)
return x
model = Net()
# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(10):
train_loss = 0.0
val_loss = 0.0
model.train()
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target.unsqueeze(1))
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
train_loss /= len(train_loader.dataset)
model.eval()
with torch.no_grad():
for data, target in val_loader:
output = model(data)
loss = criterion(output, target.unsqueeze(1))
val_loss += loss.item() * data.size(0)
val_loss /= len(val_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch+1, train_loss, val_loss))
# 测试模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
predicted = (output > 0.5).float()
total += data.size(0)
correct += (predicted == target.unsqueeze(1)).sum().item()
print('Test Accuracy: {:.2f}%'.format(100 * correct / total))
```
在这个代码中,我们首先将正样本数据和负样本数据的特征矩阵合并,然后使用 `train_test_split` 函数将数据集划分为训练集、验证集和测试集。接下来,我们将数据转换为 `DataLoader`,以便进行批处理和数据增强。然后定义了一个包含 3 层全连接层的神经网络,其中第一层有 512 个神经元,第二层有 256 个神经元,最后一层输出 1 个值,经过 sigmoid 函数输出预测结果。我们使用二元交叉熵作为损失函数,Adam 优化器进行优化。在训练模型时,我们使用了交叉验证,并输出训练集和验证集的损失。最后,我们测试了模型的准确率。