PyTorch 深度学习实践第八讲（Dataset and DataLoader）

上课代码

import torch
import numpy as np
from torch.utils.data import Dataset   #Data是一个抽先类
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('diabetes.csv.gz')    #实例化
train_loader = DataLoader(dataset = dataset,batch_size = 32,shuffle = True,num_workers = 2)

class Model(torch.nn.Module):
    def __init__(self):  #构造函数
        super(Model,self).__init__()
        #进行降维
        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()     #改变激活函数
        
    def forward(self,x):
        x = self.sigmoid(self.linear1(x))     #加入逻辑变换
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x   #全程只用一个x
    
model = Model()     #模型实例化

criterion = torch.nn.BCELoss(size_average = False)   #将原来MSE变为BCE
optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)   #优化器

if __name__ == '__main__':
    for epoch in range(100):
        for i,data in enumerate(train_loader,0):
            #prepare data
            inputs,labels = data
            #forward
            y_pred  =model(inputs)
            loss = criterion(y_pred,labels)
            print(epoch,i,loss.item())
            #backward
            optimizer.zero_grad()
            loss.backward()
            #update
            optimizer.step()