【疑难杂症】if __name__ == '__main__'的理解

今天进行小批量梯度下降时，代码给我报错，具体代码如下

import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader


class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:, :-1])
        self.y_data = torch.from_numpy(xy[:, [-1]])

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len


dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=2)


class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(8, 6)
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 2)
        self.linear4 = torch.nn.Linear(2, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        x = self.sigmoid(self.linear4(x))
        return x


model = Model()
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        y_pred = model(inputs)
        loss = criterion(y_pred, labels)
        print(epoch, i, loss.item())
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

报错内容如下

室友告诉我，需要在主运行的代码，也就是for前面加上

if __name__ == '__main__':

通过查阅大致知道了我这句代码的意思，原因就是我上面有一句

from torch.utils.data import Dataset
from torch.utils.data import DataLoader

这句话的意思就是，当模块被直接运行时，以下代码块将被运行，当模块是被导入时，代码块不被运行。
这样就可以很好的决定模块中那些代码运行，那些代码不运行

还有一个警告就是

UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead.
  warnings.warn(warning.format(ret))

这里是版本更新导致的问题
把

criterion = torch.nn.BCELoss(size_average=True)

改为：

criterion = torch.nn.BCELoss(reduction='mean')

即可