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MNIST 手写数字分类

news 来源：原创 2025/5/13 20:11:35

转自我的个人博客: https://shar-pen.github.io/2025/05/04/torch-distributed-series/1.MNIST/

基础的单卡训练

本笔记本演示了训练一个卷积神经网络（CNN）来对 MNIST 数据集中的手写数字进行分类的过程。工作流程包括：

  1. 数据准备：加载和预处理 MNIST 数据集。
  2. 模型定义：使用 PyTorch 构建 CNN 模型。
  3. 模型训练：在 MNIST 训练数据集上训练模型。
  4. 模型评估：在 MNIST 测试数据集上测试模型并评估其性能。
  5. 可视化：展示样本图像及其对应的标签。

参考 pytorch 官方示例 https://github.com/pytorch/examples/blob/main/mnist/main.py 。

至于为什么选择 MNIST 分类任务, 因为它就是深度学习里的 Hello World.

import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torchvision import datasets, transforms
from time import time

在深度学习里，真正必要的超参数，大致是下面这些：

  1. 学习率（learning rate）

    • 最最核心的超参数。
    • 决定每次参数更新的步幅大小。
    • 学习率不合适，训练几乎一定失败。

  2. 优化器（optimizer）

    • 比如 SGD、Adam、AdamW 等。
    • 不同优化器，收敛速度、最终效果差异很大。
    • 有时也需要设置优化器内部超参（比如 Adam 的 β 1 , β 2 \beta_1, \beta_2 β1​,β2​）。

  3. 批大小（batch size）

    • 多少样本合成一批送进模型训练。
    • 影响训练稳定性、收敛速度、硬件占用。

  4. 训练轮次（epoch） 或 最大步数（max steps）

    • 总共训练多久。
    • 如果训练不够长，模型欠拟合；太久则过拟合或资源浪费。

  5. 损失函数（loss function）

    • 明确训练目标，比如分类用 CrossEntropyLoss，回归用 MSELoss。
    • 不同任务必须选对损失。

超参设置

我们设置些最基础的超参: epoch, batch size, device, lr

EPOCHS = 5
BATCH_SIZE = 512
LR = 0.001
LR_DECAY_STEP_NUM = 1
LR_DECAY_FACTOR = 0.5
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

数据构建

直接用库函数生成 dataset 和 dataloader, 前者其实只是拿来生成 dataloader

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))
])train_data = datasets.MNIST(root = './mnist',train=True,       # 设置True为训练数据，False为测试数据transform = transform,# download=True  # 设置True后就自动下载，下载完成后改为False即可
)train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)test_data = datasets.MNIST(root = './mnist',train=False,       # 设置True为训练数据，False为测试数据transform = transform,
)test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=BATCH_SIZE, shuffle=True)# plot one exampleprint(f'dataset: input shape: {train_data.data.size()}, label shape: {train_data.targets.size()}')
print(f'dataloader iter: input shape: {next(iter(train_loader))[0].size()}, label shape: {next(iter(train_loader))[1].size()}')
plt.imshow(train_data.data[0].numpy(), cmap='gray')
plt.title(f'Label: {train_data.targets[0]}')
plt.show()

​ dataset: input shape: torch.Size([60000, 28, 28]), label shape: torch.Size([60000])
​ dataloader iter: input shape: torch.Size([512, 1, 28, 28]), label shape: torch.Size([512])

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

网络

设计简单的 ConvNet, 几层 CNN + MLP。初始化新模型后，先将其放到 DEVICE 上

class ConvNet(nn.Module):"""A neural network model for MNIST digit classification.This model is designed to classify images from the MNIST dataset, which consists of grayscale images of handwritten digits (0-9). The network architecture includes convolutional layers for feature extraction, followed by fully connected layers for classification.Attributes:features (nn.Sequential): A sequential container of convolutional layers, activation functions, pooling, and dropout for feature extraction.classifier (nn.Sequential): A sequential container of fully connected layers, activation functions, and dropout for classification.Methods:forward(x):Defines the forward pass of the network. Takes an input tensor `x`, processes it through the feature extractor and classifier, and returns the log-softmax probabilities for each class."""def __init__(self):super(ConvNet, self).__init__()self.features = nn.Sequential(nn.Conv2d(1, 32, 3, 1),nn.ReLU(),nn.Conv2d(32, 64, 3, 1),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25))self.classifier = nn.Sequential(nn.Linear(9216, 128),nn.ReLU(),nn.Dropout(0.5),nn.Linear(128, 10))def forward(self, x):x = self.features(x)x = torch.flatten(x, 1)x = self.classifier(x)output = F.log_softmax(x, dim=1)return output

训练和评估函数

将训练和评估函数分别封装为函数,使主循环更简洁

def train(model, device, train_loader, optimizer):model.train()for batch_idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)optimizer.zero_grad()output = model(data)loss = F.nll_loss(output, target)loss.backward()optimizer.step()if (batch_idx + 1) % 30 == 0: print('Train: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(batch_idx * len(data), len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.item()))def test(model, device, test_loader):model.eval()test_loss = 0correct = 0with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += F.nll_loss(output, target, reduction='sum').item() # 将一批的损失相加pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标correct += pred.eq(target.view_as(pred)).sum().item()test_loss /= len(test_loader.dataset)print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(test_loss, correct, len(test_loader.dataset),100. * correct / len(test_loader.dataset)))

主训练循环

model = ConvNet().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=LR)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=LR_DECAY_STEP_NUM, gamma=LR_DECAY_FACTOR)start_time = time()  # Record the start time
for epoch in range(EPOCHS):epoch_start_time = time()  # Record the start time of the current epochprint(f'Epoch {epoch}/{EPOCHS}')print(f'Learning Rate: {scheduler.get_last_lr()[0]}')train(model, DEVICE, train_loader, optimizer)test(model, DEVICE, test_loader)scheduler.step()epoch_end_time = time()  # Record the end time of the current epochprint(f"Time for epoch {epoch}: {epoch_end_time - epoch_start_time:.2f} seconds")end_time = time()  # Record the end time
print(f"Total training time: {end_time - start_time:.2f} seconds")
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A   1795609      C   ...st/anaconda3/envs/xprepo/bin/python        448MiB |
|    0   N/A  N/A   1814253      C   ...st/anaconda3/envs/xprepo/bin/python       1036MiB |
|    7   N/A  N/A   4167010      C   ...guest/anaconda3/envs/QDM/bin/python      19416MiB |
+-----------------------------------------------------------------------------------------+

0 卡的占用 1484 MB

完整代码

import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torchvision import datasets, transforms
from time import time
import argparseclass ConvNet(nn.Module):def __init__(self):super(ConvNet, self).__init__()self.features = nn.Sequential(nn.Conv2d(1, 32, 3, 1),nn.ReLU(),nn.Conv2d(32, 64, 3, 1),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25))self.classifier = nn.Sequential(nn.Linear(9216, 128),nn.ReLU(),nn.Dropout(0.5),nn.Linear(128, 10))def forward(self, x):x = self.features(x)x = torch.flatten(x, 1)x = self.classifier(x)output = F.log_softmax(x, dim=1)return outputdef arg_parser():parser = argparse.ArgumentParser(description="MNIST Training Script")parser.add_argument("--epochs", type=int, default=5, help="Number of training epochs")parser.add_argument("--batch_size", type=int, default=512, help="Batch size for training")parser.add_argument("--lr", type=float, default=0.0005, help="Learning rate")parser.add_argument("--lr_decay_step_num", type=int, default=1, help="Step size for learning rate decay")parser.add_argument("--lr_decay_factor", type=float, default=0.5, help="Factor by which learning rate is decayed")parser.add_argument("--cuda_id", type=int, default=0, help="CUDA device ID to use")return parser.parse_args()def prepare_data(batch_size):transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])train_data = datasets.MNIST(root = './mnist',train=True,       # 设置True为训练数据，False为测试数据transform = transform,# download=True  # 设置True后就自动下载，下载完成后改为False即可)train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)test_data = datasets.MNIST(root = './mnist',train=False,       # 设置True为训练数据，False为测试数据transform = transform,)test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=batch_size, shuffle=True)return train_loader, test_loaderdef train(model, device, train_loader, optimizer):model.train()for batch_idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)optimizer.zero_grad()output = model(data)loss = F.nll_loss(output, target)loss.backward()optimizer.step()if (batch_idx + 1) % 30 == 0: print('Train: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(batch_idx * len(data), len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.item()))def test(model, device, test_loader):model.eval()test_loss = 0correct = 0with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += F.nll_loss(output, target, reduction='sum').item() # 将一批的损失相加pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标correct += pred.eq(target.view_as(pred)).sum().item()test_loss /= len(test_loader.dataset)print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(test_loss, correct, len(test_loader.dataset),100. * correct / len(test_loader.dataset)))def train_mnist_classification():args = arg_parser()print(args)EPOCHS = args.epochsBATCH_SIZE = args.batch_sizeLR = args.lrLR_DECAY_STEP_NUM = args.lr_decay_step_numLR_DECAY_FACTOR = args.lr_decay_factorCUDA_ID = args.cuda_idDEVICE = torch.device(f"cuda:{CUDA_ID}")train_loader, test_loader = prepare_data(BATCH_SIZE)model = ConvNet().to(DEVICE)optimizer = optim.Adam(model.parameters(), lr=LR)scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=LR_DECAY_STEP_NUM, gamma=LR_DECAY_FACTOR)start_time = time()  # Record the start timefor epoch in range(EPOCHS):epoch_start_time = time()  # Record the start time of the current epochprint(f'Epoch {epoch}/{EPOCHS}')print(f'Learning Rate: {scheduler.get_last_lr()[0]}')train(model, DEVICE, train_loader, optimizer)test(model, DEVICE, test_loader)scheduler.step()epoch_end_time = time()  # Record the end time of the current epochprint(f"Time for epoch {epoch}: {epoch_end_time - epoch_start_time:.2f} seconds")end_time = time()  # Record the end timeprint(f"Total training time: {end_time - start_time:.2f} seconds")if __name__ == "__main__":train_mnist_classification()

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