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了解基础知识 ||快速入门 ||张量 ||数据集和数据加载器 ||变换 ||构建模型 ||Autograd ||优化 ||保存并加载模型
变换¶
创建时间： Feb 09， 2021 |上次更新时间：2021 年 8 月 11 日 |上次验证时间：未验证
数据并不总是以所需的最终处理形式出现 训练机器学习算法。我们使用 transform 来执行一些 操作数据并使其适合训练。
所有 TorchVision 数据集都有两个参数 - 修改特征和修改标签 - 接受包含转换逻辑的可调用对象。
torchvision.transforms 模块提供
几个开箱即用的常用转换。transformtarget_transform
FashionMNIST 功能采用 PIL 图像格式，标签为整数。
对于训练，我们需要将特征作为标准化张量，将标签作为独热编码张量。
为了进行这些转换，我们使用 和 。ToTensorLambda
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))
)
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
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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
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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
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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
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Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Lambda 转换¶
Lambda 转换应用任何用户定义的 lambda 函数。在这里，我们定义了一个函数
将整数转换为 one-hot 编码张量。
它首先创建一个大小为 10 的零张量（我们数据集中的标签数量）并调用 scatter_ 在标签 .value=1y
target_transform = Lambda(lambda y: torch.zeros(
10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))