PyTorch入门四 || 定义DataLoader 和 Dataset

shuffle用来将数据随机打乱，batch_size为2，则两个为一组

import torch
from torch.utils.data import Dataset        #抽象类，需要继承
from torch.utils.data import DataLoader
import numpy as np

#此处的Dataset，表示继承这个类
class DiabetesDataset(Dataset):
    def __init__(self,filepath):
        xy=np.loadtxt(filepath,delimiter=',',dtype=np.float32)
        self.len = xy.shape[0]      #xy.shape 是一个元祖，该元祖包含xy的维数：N行9列
        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

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()

dataset = DiabetesDataset('diabetes.csv.gz')
#第四个参数，需要多少个并行进程来读取数据，2个进程，每一个读取32个数据
train_loader = DataLoader(dataset=dataset,
                          batch_size=32,
                          shuffle=True,
                          num_workers=2)

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

if __name__ == '__main__':
    for epoch in range(100):
        #enumerate 可以反馈当前的迭代次数，即第几次迭代
        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()