加载数据集

1，代码：

# 10月16日
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('deep_learn/diabetes.csv')
train_loader = DataLoader(
    dataset=dataset,
    batch_size=1024,
    shuffle=True,
    num_workers=0 # num_workers 多线程
)

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, 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))
        return x

model = Model()

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr = 0.8)

if __name__ == '__main__':
    for epoch in range(1000):
        for i, data in enumerate(train_loader, 0): # 先shuffle，后mini_batch
            inputs, labels = data
            y_pred = model(inputs)
            loss = criterion(y_pred,labels)
            print(epoch,i,loss.item())

            optimizer.zero_grad()
            loss.backward()

            optimizer.step()