pytorch 泰坦尼克

好的，我明白了。关于 PyTorch 和泰坦尼克数据集的问题，我猜测你是想问如何使用 PyTorch 对泰坦尼克数据集进行分类预测。如果是这样，可以按照以下步骤进行：

1. 导入必要的库和数据集

import torch
import torch.nn as nn
import pandas as pd
from sklearn.model_selection import train_test_split

# 读取数据集
data = pd.read_csv('titanic.csv')

2. 数据预处理

# 去除无用特征
data.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1, inplace=True)

# 处理缺失值
data['Age'].fillna(data['Age'].mean(), inplace=True)
data['Embarked'].fillna('S', inplace=True)

# 将分类变量转换为数值变量
data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})
data['Embarked'] = data['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop('Survived', axis=1), data['Survived'], test_size=0.2, random_state=42)

# 转换为 PyTorch 张量
X_train = torch.tensor(X_train.values, dtype=torch.float32)
X_test = torch.tensor(X_test.values, dtype=torch.float32)
y_train = torch.tensor(y_train.values, dtype=torch.float32)
y_test = torch.tensor(y_test.values, dtype=torch.float32)

3. 定义模型

class TitanicModel(nn.Module):
    def __init__(self):
        super(TitanicModel, self).__init__()
        self.fc1 = nn.Linear(7, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.relu(out)
        out = self.fc3(out)
        out = self.sigmoid(out)
        return out

model = TitanicModel()

4. 训练模型

criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(1000):
    optimizer.zero_grad()
    y_pred = model(X_train)
    loss = criterion(y_pred.squeeze(), y_train)
    loss.backward()
    optimizer.step()

    if epoch % 100 == 0:
        print('Epoch {}: train loss: {}'.format(epoch, loss.item()))

5. 评估模型

with torch.no_grad():
    y_pred = model(X_test)
    y_pred_class = y_pred.round()
    accuracy = (y_pred_class == y_test).sum() / float(len(y_test))
    print('Test accuracy:', accuracy.item())