以测试集AUC为评价指标，使用深度学习进行二分类任务预测的代码

以下是一个使用PyTorch进行二分类任务预测的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.metrics import roc_auc_score

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 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

# 加载数据集
data = torch.load('data.pt')
x_train, y_train, x_test, y_test = data

train_dataset = TensorDataset(x_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

test_dataset = TensorDataset(x_test, y_test)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizer = optim.Adam(net.parameters(), lr=0.01)

# 训练模型
net = Net()
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    # 在测试集上计算AUC
    y_pred = []
    y_true = []
    with torch.no_grad():
        for data in test_loader:
            inputs, labels = data
            outputs = net(inputs)
            y_pred += outputs.tolist()
            y_true += labels.tolist()

    auc = roc_auc_score(y_true, y_pred)
    print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))

首先定义了一个包含三个全连接层和一个Sigmoid激活函数的神经网络模型。然后加载数据集并分别定义训练集和测试集的DataLoader。接着定义了交叉熵损失函数和Adam优化器，并使用训练集来训练模型。在每个epoch结束时，使用测试集上的预测结果计算AUC，并输出训练信息。