import torch import torch.nn as nn import torch.optim as optim import pandas as pd

读取Excel表格

data = pd.read_excel('C:\Users\lenovo\Desktop\HIV\DNN神经网络测试\data1.xlsx') x = data.iloc[:, 1:].values # 取除第一列以外的所有列，即基因的表达量 y = data.iloc[:, 0].values # 取第一列，即患者状态标志state

定义第一个模型，输出为8分类

class Model1(nn.Module): def init(self): super(Model1, self).init() self.fc1 = nn.Linear(46, 128) self.fc2 = nn.Linear(128, 64) self.fc3 = nn.Linear(64, 32) self.fc4 = nn.Linear(32, 16) self.fc5 = nn.Linear(16, 8) self.dropout = nn.Dropout(0.2)

def forward(self, x):
    x = nn.functional.relu(self.fc1(x))
    x = self.dropout(x)
    x = nn.functional.relu(self.fc2(x))
    x = self.dropout(x)
    x = nn.functional.relu(self.fc3(x))
    x = self.dropout(x)
    x = nn.functional.relu(self.fc4(x))
    x = self.dropout(x)
    x = self.fc5(x)
    return x

定义第二个模型，输入为第一个模型的8分类输出，输出为4分类

class Model2(nn.Module): def init(self): super(Model2, self).init() self.fc1 = nn.Linear(8, 16) self.fc2 = nn.Linear(16, 8) self.fc3 = nn.Linear(8, 4) self.dropout = nn.Dropout(0.2)

def forward(self, x):
    x = nn.functional.relu(self.fc1(x))
    x = self.dropout(x)
    x = nn.functional.relu(self.fc2(x))
    x = self.dropout(x)
    x = self.fc3(x)
    return x

定义第三个模型，第三个模型为二分类模型，输入为第二个模型的4分类输出

class Model3(nn.Module): def init(self): super(Model3, self).init() self.fc1 = nn.Linear(4, 8) self.fc2 = nn.Linear(8, 1) self.dropout = nn.Dropout(0.2)

def forward(self, x):
    x = nn.functional.relu(self.fc1(x))
    x = self.dropout(x)
    x = torch.sigmoid(self.fc2(x))
    return x

将数据转换为张量

x = torch.tensor(x).float() y = torch.tensor(y).float().unsqueeze(1)

定义损失函数和优化器

criterion1 = nn.CrossEntropyLoss() criterion2 = nn.CrossEntropyLoss() criterion3 = nn.BCELoss() model1 = Model1() optimizer1 = optim.Adam(model1.parameters(), lr=0.001)

训练第一个模型

for epoch in range(100): optimizer1.zero_grad() output1 = model1(x) loss1 = criterion1(output1, y.long()) loss1.backward() optimizer1.step() acc1 = (output1.argmax(dim=1) == y).float().mean() print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, 100, loss1.item(), acc1.item()))

训练第二个模型

model2 = Model2() output1 = model1(x) output2 = model2(output1) optimizer2 = optim.Adam(model2.parameters(), lr=0.001) for epoch in range(100): optimizer2.zero_grad() loss2 = criterion2(output2, output1.argmax(dim=1).long()) loss2.backward() optimizer2.step() acc2 = (output2.argmax(dim=1) == output1.argmax(dim=1)).float().mean() print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, 100, loss2.item(), acc2.item()))

训练第三个模型

model3 = Model3() output3 = model3(output2) optimizer3 = optim.Adam(model3.parameters(), lr=0.001) for epoch in range(100): optimizer3.zero_grad() loss3 = criterion3(output3, y) loss3.backward() optimizer3.step() acc3 = ((output3 > 0.5) == y).float().mean() print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, 100, loss3.item(), acc3.item()))