---

前言

介绍PyTorch中加载数据集的相关操作。Dataset和DataLoader

一、epoch，batch-size和iteration

epoch：所有训练数据完成一次前馈和反馈
batch-size：每次小样本选取的数量
iteration：样本总数除以batch-size

二、示例

1.说明

1.Dataset为抽象类，不能实例化对象，只能继承来用。
2.DataLoader用于帮助加载数据。
train_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=2)
dataset：数据集
batch_size：每一批次的数据大小
shuffle：是否打乱随机取，增加随机性
num_workers：多线程

2.代码示例

代码如下（示例）：

import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt# prepare datasetclass DiabetesDataset(Dataset):def __init__(self, filepath):xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)self.len = xy.shape[0]  # shape(多少行，多少列)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.lendataset = DiabetesDataset('diabetes.csv')
train_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=2)  # num_workers 多线程# design model using classclass 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 xmodel = Model()# construct loss and optimizer
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)# training cycle forward, backward, update
if __name__ == '__main__':epoch_list = []loss_list = []for epoch in range(100):epoch_list.append(epoch)loss_temp = 0for i, data in enumerate(train_loader, 0):  # train_loader 是先shuffle后mini_batchinputs, labels = datay_pred = model(inputs)loss = criterion(y_pred, labels)loss_temp += loss.item()print(epoch, i, loss.item())optimizer.zero_grad()loss.backward()optimizer.step()loss_list.append(loss_temp)plt.plot(epoch_list, loss_list)plt.ylabel('loss')plt.xlabel('epoch')plt.show()

得到如下结果：

总结

PyTorch学习7：加载数据集