3 变换
数据并不总是以训练机器学习算法所需的最终处理形式出现。我们使用转换来对数据进行一些操作并使其适合训练。
所有 TorchVision 数据集都有两个参数 -transform
修改特征和 target_transform
修改标签 -
接受包含转换逻辑的可调用对象。该torchvision.transforms模块提供几种常用的变换开箱。
FashionMNIST 特征采用 PIL Image 格式,标签为整数。对于训练,我们需要将特征作为归一化张量,将标签作为单热编码张量。为了进行这些转换,我们使用ToTensor
和Lambda
。
import torch
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda
ds = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=ToTensor(),
target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))
)
结果:
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
3.1 ToTensor()
ToTensor
将 PIL 图像或 NumPyndarray
转换为FloatTensor
. 并在 [0., 1.] 范围内缩放图像的像素强度值
3.2 Lambda 变换
Lambda 转换适用于任何用户定义的 lambda 函数。在这里,我们定义了一个函数来将整数转换为单热编码的张量。它首先创建一个大小为 10(我们数据集中的标签数量)的零张量,并调用 scatter_,它value=1
在标签给定的索引上分配 a y
。
target_transform = Lambda(lambda y: torch.zeros(
10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))