HDC 学习

在学习了如何在超空间中表示和操作信息之后，我们可以实现我们的第一个HDC分类模型！我们将以著名的MNIST数据集为例，该数据集包含手写数字的图像。

我们首先导入Torchhd和其他所需的库，并指定训练参数：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision.datasets import MNIST
# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
import torchmetrics

import torchhd
from torchhd.models import Centroid
from torchhd import embeddings

# Use the GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using {} device".format(device))

DIMENSIONS = 10000
IMG_SIZE = 28
NUM_LEVELS = 1000
BATCH_SIZE = 1  # for GPUs with enough memory we can process multiple images at ones

数据集

接下来，我们加载训练和测试数据集：

transform = torchvision.transforms.ToTensor()

train_ds = MNIST("../data", train=True, transform=transform, download=True)
train_ld = torch.utils.data.DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)

test_ds = MNIST("../data", train=False, transform=transform, download=True)
test_ld = torch.utils.data.DataLoader(test_ds, batch_size=BATCH_SIZE, shuffle=False)

除了Torch生态系统中可用的各种数据集，如MNIST，torchhd.datasets模块还提供了对HDC中几个常用数据集的接口。这些接口继承自PyTorch的数据集类，确保与其他数据集的互操作性。

培训

为了执行训练，我们首先定义一个编码。除了指定基础超向量集之外，学习的核心部分是编码函数。在下面的示例中，我们分别使用随机超向量和级别超向量来编码每个像素的位置和值：

class Encoder(nn.Module):
    def __init__(self, out_features, size, levels):
        super(Encoder, self).__init__()
        self.flatten = torch.nn.Flatten()
        self.position = embeddings.Random(size * size, out_features)
        self.value = embeddings.Level(levels, out_features)

    def forward(self, x):
        x = self.flatten(x)
        sample_hv = torchhd.bind(self.position.weight, self.value(x))
        sample_hv = torchhd.multiset(sample_hv)
        return torchhd.hard_quantize(sample_hv)

encode = Encoder(DIMENSIONS, IMG_SIZE, NUM_LEVELS)
encode = encode.to(device)

num_classes = len(train_ds.classes)
model = Centroid(DIMENSIONS, num_classes)
model = model.to(device)

定义模型后，我们遍历训练样本以创建类向量：

with torch.no_grad():
    for samples, labels in tqdm(train_ld, desc="Training"):
        samples = samples.to(device)
        labels = labels.to(device)

        samples_hv = encode(samples)
        model.add(samples_hv, labels)

测试

模型训练完成后，我们可以通过编码测试样本并将其与类别向量进行比较来进行分类：

accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)

with torch.no_grad():
    model.normalize()

    for samples, labels in tqdm(test_ld, desc="Testing"):
        samples = samples.to(device)

        samples_hv = encode(samples)
        outputs = model(samples_hv, dot=True)
        accuracy.update(outputs.cpu(), labels)

print(f"Testing accuracy of {(accuracy.compute().item() * 100):.3f}%")