教程Python代碼如下：

"""

epoch = 1 forward and backward pass of ALL training samples

batch_size = numberlof training samples in one forward & backward pass

number of iterations = number of passes，each pass using [batch_size] number of samples

e.g. 100 samples，batch_size=20 --> 100/20 = 5 iterations for 1 epoch

"""

import torch

import torchvision

from torch.utils.data import Dataset, DataLoader

import numpy as np

import math

#實(shí)現(xiàn)自己的自定義數(shù)據(jù)集

class WineDataset():

??def __init__(self, tranform = None):

????# data loading, 數(shù)據(jù)加載

????xy = np.loadtxt('./Data/wine.csv',delimiter=",", dtype=np.float32, skiprows=1) #delomiter分隔符，skiprows=1跳過(guò)第一行（第一行為標(biāo)題）

????self.n_samples = xy.shape[0]

????# 把數(shù)據(jù)集分成 x 和 y,note that we do not convert to tensor here

????self.x = xy[:,1:] #不要第一行

????self.y = xy[:, [0]] # n_samples, 1：只要第一列，這樣就有了樣品的大小數(shù)

????self.transform = tranform

??def __getitem__(self, index):

????sample = self.x[index], self.y[index]

????if self.transform:

??????sample = self.transform(sample)

????return sample

??def __len__(self):

????# len(dataset), 調(diào)用數(shù)據(jù)集的長(zhǎng)度

????return self.n_samples

# 類方法對(duì)類屬性進(jìn)行的處理是有記憶性的

class ToTensor:

??def __call__(self, sample):

????inputs, targets = sample

????return torch.from_numpy(inputs),torch.from_numpy(targets)

class MulTransform:

??def __init__(self, factor):

????self.factor = factor

??def __call__(self, sample):

????inputs, target = sample

????inputs *= self.factor

????return?inputs, target

dataset = WineDataset(tranform=ToTensor())

first_data = dataset[0]

feautres, labels = first_data

print(feautres)

print(type(feautres), type(labels))

dataset = WineDataset(tranform=None)

first_data = dataset[0]

feautres, labels = first_data

print(feautres)

print(type(feautres), type(labels))

print("\n" + "composed")

composed = torchvision.transforms.Compose([ToTensor(),MulTransform(4)])

dataset = WineDataset(tranform=composed)

first_data = dataset[0]

feautres, labels = first_data

print(feautres)

print(type(feautres), type(labels))