PyTorch深度学习实践——加载数据集

参考资料

参考资料1：https://blog.csdn.net/bit452/article/details/109686474
参考资料2：http://biranda.top/Pytorch%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0009%E2%80%94%E2%80%94%E5%85%B3%E4%BA%8E%E6%95%B0%E6%8D%AE%E9%9B%86/#%E6%95%B0%E6%8D%AE%E9%9B%86

加载数据集

#Definition: Epoch   One forward pass and one backward pass of all the training examples.
#Definition: Batch-Size   The number of training examples in one forward backward pass.
#Definition: Iteration   Number of passes, each pass(传递) using [batch size] number of examples.
#例如有10000 个样本， Batch—Size为1000，Iteration为10

import torch
import numpy as np
#DataSet是抽象类，无法实例化
from torch.utils.data import Dataset
#DataLoader可实例化
from torch.utils.data import DataLoader

class DiabetesDataset(Dataset):
    def __init__(self,filepath):
        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
        #获得数据集长度
        self.len=xy.shape[0] #shape(多少行，多少列)
        self.x_data = torch.from_numpy(xy[:, :-1]) #取所有行，除最后一列的所有列
        self.y_data = torch.from_numpy(xy[:, [-1]])#取所有行，和最后一列

    #↓要实现__getitem__和__len__，不然DataLoader时会报错。

    #获得索引方法
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index] #return x,y 相当于返回元组（x，y）
    #获得数据集长度
    def __len__(self):
        return self.len

dataset = DiabetesDataset('../diabetes.csv')
#num_workers表示多线程的读取
train_loader = DataLoader(dataset=dataset,
                          batch_size=32,
                          shuffle=True,
                          num_workers=2)# batch_size是batch数量，shuffle是是否打乱顺序，num_workers是设计几个线程

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()

criterion = torch.nn.BCELoss(reduction='mean')

optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

#在windows中利用多线程读取，需要将主程序（对数据操作的程序）封装到函数中.这里不加main的话，会报错
#如果设置了num_workers之后，会出现运行时错误，需要将其封装到函数里面或者if语句里面，比如这里加上了if __name__ =='__main__':就不报错了
if __name__ =='__main__':
    for epoch in range(100):
        #enumerate:可获得当前迭代的次数
        for i,data in enumerate(train_loader,0):#enumerate中的o是指起始位置（下标）为0,即i从0开始，i=0,1,2......
            #准备数据dataloader会将按batch_size返回的数据整合成矩阵加载
            inputs, labels = data#input.shape=([32, 8]),最后一次是([23, 8])
            print(inputs.shape)
            #前馈
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())
            #反向传播
            optimizer.zero_grad()
            loss.backward()
            #更新
            optimizer.step()