pytorch-08.加载数据集

import torch
from torch.utils.data import Dataset # Dataset抽象类，不可实例化，只能继承
from torch.utils.data import DataLoader # DataLoader 可实例化
import numpy as np

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):  #返回数据集长度
        return self.len

dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=2)  #num_workers 并行读数据

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()
        self.activate = torch.nn.ReLU()

    def forward(self,x):
        x = self.activate(self.linear1(x))
        x = self.activate(self.linear2(x))
        x = self.sigmoid(self.linear3(x))  #RELU，x小于0时的的y值都是0，算损失时有可能出现ln0
        return x

model = Model()

criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

'''
enumerate多用于在for循环中得到计数，利用它可以同时获得索引和值，即需要index和value值的时候可以使用enumerate

'''
if __name__ == '__main__':
    for epoch in range(100):
        for i,data in enumerate(train_loader,0):  #0代表从指定索引0开始
            print('train_loader:',train_loader)
            # 1 Prepare data
            inputs,labels = data

            # 2 Forward
            y_pred = model(inputs)
            loss = criterion(y_pred,labels)
            print(epoch,i,loss.item())

            # 3 Backward
            optimizer.zero_grad()
            loss.backward()

            #4 Update
            optimizer.step()