PyTorch加载数据集

一、实现过程

1、准备数据

与PyTorch实现多维度特征输入的逻辑回归的方法不同的是：本文使用DataLoader方法，并继承DataSet抽象类，可实现对数据集进行mini_batch梯度下降优化。代码如下：

import torch
import numpy as np
from torch.utils.data import Dataset,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('G:/datasets/diabetes/diabetes.csv')
train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=0)

2、设计模型

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.activate = torch.nn.Sigmoid()
    
    def forward(self, x):
        x = self.activate(self.linear1(x))
        x = self.activate(self.linear2(x))
        x = self.activate(self.linear3(x))
        return x
model = Model()

3、构造损失函数和优化器

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

4、训练过程

每次拿出mini_batch个样本进行训练，代码如下：

epoch_list = []
loss_list = []
for epoch in range(100):
    count = 0
    loss1 = 0
    for i, data in enumerate(train_loader,0):
        # 1.Prepare data
        inputs, labels = data
        # 2.Forward
        y_pred = model(inputs)
        loss = criterion(y_pred,labels)
        print(epoch,i,loss.item())
        count += 1
        loss1 += loss.item()
        # 3.Backward
        optimizer.zero_grad()
        loss.backward()
        # 4.Update
        optimizer.step()
        
    epoch_list.append(epoch)
    loss_list.append(loss1/count)

5、结果展示

plt.plot(epoch_list,loss_list,'b')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.show()

二、参考文献

[1] https://www.bilibili.com/video/BV1Y7411d7Ys?p=8