PyTorch搭建LSTM实现服装分类（FashionMNIST）

FashionMNIST 数据集官网：https://github.com/zalandoresearch/fashion-mnist.

这里不再介绍该数据集，如需了解请前往官网。

思路： 数据集中的每张图片都是尺寸为 $(28,28)$ 的灰度图。我们可以将其看作 $28\times 28$ 的数字矩阵，将该矩阵按行进行逐行分块可得一个长度为 $28$ 的序列，且序列中的每个 “词元” 对应的特征维数也是 $28$。

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运行环境：

• 系统：Ubuntu 20.04；
• GPU：RTX 3090；
• Pytorch：1.11；
• Python：3.8

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import numpy as np
import matplotlib.pyplot as plt

import torch
import torchvision
import torch.nn as nn
from torch.utils.data import DataLoader

# Data Preprocessing
train_data = torchvision.datasets.FashionMNIST(root='data',
                                               train=True,
                                               transform=torchvision.transforms.ToTensor(),
                                               download=True)
test_data = torchvision.datasets.FashionMNIST(root='data',
                                              train=False,
                                              transform=torchvision.transforms.ToTensor(),
                                              download=True)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True, num_workers=4)
test_loader = DataLoader(test_data, batch_size=64, num_workers=4)


# Model building
class LSTM(nn.Module):

    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(28, 64, num_layers=2)
        self.linear = nn.Linear(64, 10)

    def forward(self, x):
        output, (h_n, c_n) = self.lstm(x, None)
        return self.linear(h_n[0])


def setup_seed(seed):
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


# Setup
setup_seed(42)

NUM_EPOCHS = 20
LR = 4e-3
train_loss, test_loss, test_acc = [], [], []

device = 'cuda' if torch.cuda.is_available() else 'cpu'
lstm = LSTM()
lstm.to(device)

critertion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(lstm.parameters(), lr=LR)

# Training and testing
for epoch in range(NUM_EPOCHS):
    print(f'[Epoch {epoch + 1}]', end=' ')
    avg_train_loss, avg_test_loss, correct = 0, 0, 0

    # train
    lstm.train()
    for batch_idx, (X, y) in enumerate(train_loader):
        # (64, 1, 28, 28) -> (28, 64, 28)
        X = X.squeeze().movedim(0, 1)
        X, y = X.to(device), y.to(device)

        # forward
        output = lstm(X)
        loss = critertion(output, y)
        avg_train_loss += loss

        # backward
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    avg_train_loss /= (batch_idx + 1)
    train_loss.append(avg_train_loss.item())

    # test
    lstm.eval()
    with torch.no_grad():
        for batch_idx, (X, y) in enumerate(test_loader):
            X = X.squeeze().movedim(0, 1)
            X, y = X.to(device), y.to(device)

            pred = lstm(X)
            loss = critertion(pred, y)
            avg_test_loss += loss
            correct += (pred.argmax(1) == y).sum().item()

    avg_test_loss /= (batch_idx + 1)
    test_loss.append(avg_test_loss.item())
    correct /= len(test_loader.dataset)
    test_acc.append(correct)

    print(
        f"train loss: {train_loss[-1]:.4f} | test loss: {test_loss[-1]:.4f} | test acc: {correct:.4f}"
    )

# Plot
x = np.arange(1, 21)
plt.plot(x, train_loss, label="train loss")
plt.plot(x, test_loss, label="test loss")
plt.plot(x, test_acc, label="test acc")
plt.xlabel("epoch")
plt.legend(loc="best", fontsize=12)
plt.show()
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输出结果：

[Epoch 1] train loss: 0.6602 | test loss: 0.5017 | test acc: 0.8147
[Epoch 2] train loss: 0.4089 | test loss: 0.3979 | test acc: 0.8566
[Epoch 3] train loss: 0.3577 | test loss: 0.3675 | test acc: 0.8669
[Epoch 4] train loss: 0.3268 | test loss: 0.3509 | test acc: 0.8751
[Epoch 5] train loss: 0.3098 | test loss: 0.3395 | test acc: 0.8752
[Epoch 6] train loss: 0.2962 | test loss: 0.3135 | test acc: 0.8854
[Epoch 7] train loss: 0.2823 | test loss: 0.3377 | test acc: 0.8776
[Epoch 8] train loss: 0.2720 | test loss: 0.3196 | test acc: 0.8835
[Epoch 9] train loss: 0.2623 | test loss: 0.3120 | test acc: 0.8849
[Epoch 10] train loss: 0.2547 | test loss: 0.2981 | test acc: 0.8931
[Epoch 11] train loss: 0.2438 | test loss: 0.3140 | test acc: 0.8882
[Epoch 12] train loss: 0.2372 | test loss: 0.3043 | test acc: 0.8909
[Epoch 13] train loss: 0.2307 | test loss: 0.2977 | test acc: 0.8918
[Epoch 14] train loss: 0.2219 | test loss: 0.2888 | test acc: 0.8970
[Epoch 15] train loss: 0.2187 | test loss: 0.2946 | test acc: 0.8959
[Epoch 16] train loss: 0.2132 | test loss: 0.2894 | test acc: 0.8985
[Epoch 17] train loss: 0.2061 | test loss: 0.2835 | test acc: 0.9014
[Epoch 18] train loss: 0.2028 | test loss: 0.2954 | test acc: 0.8971
[Epoch 19] train loss: 0.1966 | test loss: 0.2952 | test acc: 0.8986
[Epoch 20] train loss: 0.1922 | test loss: 0.2910 | test acc: 0.9011
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相应的曲线：

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一些心得 ：

• 切勿直接使用 X = X.reshape(28, -1, 28)，否则 X 对应的将不是原来的图片（读者可自行尝试使用 torchvision.transforms.ToPILImage 去输出 X 对应的图片观察效果）。
• 学习率相同的情况下，SGD 的效果没有 Adam 好。