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 npclass 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.lendef __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 xmodel = 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 = datay_pred = model(inputs)loss = criterion(y_pred, labels)optimizer.zero_grad()loss.backward()optimizer.step()print(epoch, i, loss.data)