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
