lecture08数据集导入和构建
课程网址
Pytorch深度学习实践

部分课件内容：

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

class DiabetesDataset(Dataset):
    def __init__(self):
        xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:,:-1])  #第一列开始最后一列不要
        self.y_data = torch.from_numpy(xy[:,[-1]])  # 取最后一列

    def __len__(self):
        return self.len

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

dataset = DiabetesDataset()
train_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True,num_workers=2)

class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel, 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 = LogisticRegressionModel()

criterion = torch.nn.BCELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

if __name__ == '__main__':
    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)

            optimizer.zero_grad()
            loss.backward()

            optimizer.step()
            print(epoch, i, loss.data)