前言:
在训练的时候,都需要对样本进行随机采样。
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 14 09:49:07 2022
@author: chengxf2
"""
from torch.utils.data import Dataset, DataLoader
import torch
#需要继承data.Dataset
class MyDataset(Dataset):
def __init__(self, data, target):
self.x = data
self.y = target
self.len = self.x.shape[0] #样本个数
def __getitem__(self, index):
x = self.x[index]
y = self.y[index]
return x,y
def __len__(self):
return self.len
def sample(data, label):
mydata = MyDataset(data,label)
train_loader = DataLoader(dataset = mydata, batch_size =6, shuffle = True,drop_last =True)
max_iter =6
for epoch in range(max_iter):
for step, (batch_x, batch_y) in enumerate(train_loader):
print('Epoch: ', epoch, '| Step:', step, '\n batch x:\n ', batch_x.numpy(), '\n| batch y:', batch_y.numpy())
if __name__ == "__main__":
xList =[[1,0],[1,1],[1,2],[1,3],[1,4],[1,5],[1,6],[1,7],[1,8],[1,9]]
yList = [0,1,2,3,4,5,6,7,8,9]
x = torch.tensor(xList,dtype= torch.float)
y = torch.tensor(yList,dtype= torch.float)
sample(x,y)