Kaggle project - Spaceship Titanic

I did Kaggle's Spaceship Titanic project, which can be regarded as a summary of CNN learning and a response to returning to machine learning (the exercises in Chapter 3 suggest that I can do Kaggle's Titanic case), the specific code is as follows:

# 定义网络模型
from torch import nn
import torch.nn.functional as F


class mymodule(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(20, 64)
        self.fcc = nn.Linear(64, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 2)
        self.relu = nn.ReLU()
        self.drop = nn.Dropout(p=0.2)
        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(32)

    def forward(self, x):
        x = x.to(torch.float32)  # 解决数据类型的问题
        x = self.relu(self.fc1(x))
        x = self.bn1(x)
        x = self.drop(x)
        x = self.relu(self.fcc(x))
        x = self.bn1(x)
        x = self.drop(x)
        x = self.relu(self.fc2(x))
        x = self.bn2(x)
        x = self.fc3(x)
        x = torch.sigmoid(x)
        return x


'''
----------------------------------------------------------------------
'''

# 设置随机数种子，保证结果可复现
seed = 548814
torch.manual_seed(seed)  # 设置CPU
# torch.cuda.manual_seed(seed)  # 设置GPU


# 训练模型
from torch import optim

model = mymodule()  # 实例化模型
# 适应设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)


# 选用Adam优化器，传入模型参数，设置学习率为0.002，正则化参数weight_decay=0.1
optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=0.01)
criterion = nn.CrossEntropyLoss()  # 选用交叉熵作为损失函数Loss
epoch = 70  # 设定迭代次数

for epoch in range(epoch+1):  # 开始迭代循环
    for x, y in train_dr:  # 从dataloader中取x,y
        pred = model(x)  # 正向传播
        loss = criterion(pred, y)  # 计算损失函数
        optimizer.zero_grad()  # 优化器的梯度清零
        loss.backward()  # 反向传播
        optimizer.step()  # 参数更新
    # 计算准确率
    with torch.no_grad():  # 在该模块下，所有计算得出的tensor的requires_grad都自动设置为False。
        y_pred = model(x_train_tensor)  # 得到训练集的预测标签
        acc_train = (y_pred.argmax(dim=1) == y_train_tensor).float().mean().item()  # 计算训练集的准确率
        y_pred = model(x_test_tensor)  # 得到测试集的预测标签
        acc_test = (y_pred.argmax(dim=1) == y_test_tensor).float().mean().item()  # 计算训练集的准确率
        print('epoch:', epoch, '  Accuracy for train:', acc_train, '  Accuracy for test:', acc_test)

The above is an attempt to use only the fully connected layer. The code for using the CNN method has been given before (the data preprocessing process is also), and finally Score: 0.80079 on Kaggle, which is a good attempt and start. Keep working hard!