diff --git a/.gitignore b/.gitignore index 505a3b1..919728d 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ build/ dist/ wheels/ *.egg-info +data # Virtual environments .venv diff --git a/.python-version b/.python-version deleted file mode 100644 index e4fba21..0000000 --- a/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.12 diff --git a/README.md b/README.md index 0cd17b4..5c03bc5 100644 --- a/README.md +++ b/README.md @@ -24,10 +24,10 @@ DMAdam优化器代码实现见 `dmadam.py`。算法流程如下： > 5.   $x^{k+1} = x^k - \eta_k \cdot \frac{m^k}{\sqrt{v^k + \varepsilon}}$ > 6. **end while** - - ## 实验设置 +### 参数说明 + - 迭代次数: 2000 - 其余优化器参数见各小节表格 - DMAdam优化器中: @@ -39,6 +39,15 @@ $$ \end{align*} $$ +### 实验环境 + +``` +python >= 3.12 +torch==2.10.0 +numpy==2.4.2 +matplotlib==3.10.8 +``` + --- ## 1. Sphere 函数 diff --git a/main.py b/benchmark_main.py similarity index 100% rename from main.py rename to benchmark_main.py diff --git a/cudatest.py b/cudatest.py new file mode 100644 index 0000000..73c2464 --- /dev/null +++ b/cudatest.py @@ -0,0 +1,3 @@ +import torch + +print(torch.cuda.is_available()) \ No newline at end of file diff --git a/dl-main.py b/dl-main.py new file mode 100644 index 0000000..3cefedf --- /dev/null +++ b/dl-main.py @@ -0,0 +1,149 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +from torchvision import datasets, transforms, models +import matplotlib.pyplot as plt +from dmadam import DMAdam +import os + +# ResNet-34 for CIFAR-10 (adapted from torchvision) +def get_resnet34(): + model = models.resnet34(weights=None) + model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + model.maxpool = nn.Identity() + model.fc = nn.Linear(512, 10) + return model + +# VGG-13 for CIFAR-10 (adapted from torchvision) +def get_vgg13(): + model = models.vgg13(weights=None) + model.classifier[6] = nn.Linear(4096, 10) + return model + +def train_epoch(model, loader, optimizer, criterion, device): + model.train() + correct = 0 + total = 0 + for data, target in loader: + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + output = model(data) + loss = criterion(output, target) + loss.backward() + optimizer.step() + pred = output.argmax(dim=1) + correct += pred.eq(target).sum().item() + total += target.size(0) + return 100. * correct / total + +def test(model, loader, device): + model.eval() + correct = 0 + total = 0 + with torch.no_grad(): + for data, target in loader: + data, target = data.to(device), target.to(device) + output = model(data) + pred = output.argmax(dim=1) + correct += pred.eq(target).sum().item() + total += target.size(0) + return 100. * correct / total + +def create_optimizer(name, params, config): + if name == 'SGD': + return torch.optim.SGD(params, lr=config['lr'], weight_decay=config.get('weight_decay', 0)) + elif name == 'SGDM': + return torch.optim.SGD(params, lr=config['lr'], momentum=config['momentum'], weight_decay=config.get('weight_decay', 0)) + elif name == 'Adagrad': + return torch.optim.Adagrad(params, lr=config['lr'], weight_decay=config.get('weight_decay', 0)) + elif name == 'Adam': + return torch.optim.Adam(params, lr=config['lr'], betas=config['betas'], weight_decay=config.get('weight_decay', 0)) + elif name == 'AdamW': + return torch.optim.AdamW(params, lr=config['lr'], betas=config['betas'], weight_decay=config['weight_decay']) + elif name == 'DMAdam': + return DMAdam(params, lr=config['lr'], eta0=config['eta0'], eps=config.get('eps', 1e-8)) + +def run_cifar10(model_name='ResNet-34'): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + print(f"Using device: {device}") + + transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) + ]) + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) + ]) + + train_dataset = datasets.CIFAR10('./data', train=True, download=True, transform=transform_train) + test_dataset = datasets.CIFAR10('./data', train=False, transform=transform_test) + train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2) + test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2) + + # DMAdam parameters from Table 2 + # VGG-13: η_k=0.9, α_k=0.001, β_k=0.999, ε=10^-8, weight_decay=0.0005 + # ResNet-34: η_k=1.4, α_k=0.001, β_k=0.999, ε=10^-8, weight_decay=0.0005 + dmadam_eta0 = 0.9 if model_name == 'VGG-13' else 1.4 + + optimizers_config = { + 'SGD': {'lr': 0.1, 'weight_decay': 0.0005}, + 'SGDM': {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0005}, + 'Adagrad': {'lr': 0.01, 'weight_decay': 0.0005}, + 'Adam': {'lr': 0.001, 'betas': (0.9, 0.999), 'weight_decay': 0.0005}, + 'AdamW': {'lr': 0.001, 'betas': (0.9, 0.999), 'weight_decay': 0.0005}, + 'DMAdam': {'lr': 0.001, 'eta0': dmadam_eta0, 'eps': 1e-8} + } + + results = {} + criterion = nn.CrossEntropyLoss() + + for opt_name, config in optimizers_config.items(): + print(f"\nTraining CIFAR-10 ({model_name}) with {opt_name}") + + if model_name == 'ResNet-34': + model = get_resnet34().to(device) + elif model_name == 'VGG-13': + model = get_vgg13().to(device) + + optimizer = create_optimizer(opt_name, model.parameters(), config) + scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1) + + test_accs = [] + + for epoch in range(200): + train_acc = train_epoch(model, train_loader, optimizer, criterion, device) + test_acc = test(model, test_loader, device) + test_accs.append(test_acc) + scheduler.step() + + if (epoch + 1) % 10 == 0: + print(f"Epoch {epoch+1}/200: Train Acc={train_acc:.2f}%, Test Acc={test_acc:.2f}%") + + results[opt_name] = test_accs + print(f"{opt_name} Final Test Acc: {test_accs[-1]:.2f}%") + + plt.figure(figsize=(10, 5)) + for name, accs in results.items(): + plt.plot(range(1, 201), accs, label=name) + plt.xlabel('Epoch') + plt.ylabel('Test Accuracy (%)') + plt.title(f'CIFAR-10 Test Accuracy ({model_name})') + plt.legend() + plt.grid(True) + os.makedirs('results', exist_ok=True) + plt.savefig(f'results/cifar10_{model_name.lower().replace("-", "")}_accuracy.png', dpi=140, bbox_inches='tight') + plt.close() + + return results + +if __name__ == '__main__': + print("Running CIFAR-10 experiment with ResNet-34...") + run_cifar10('ResNet-34') + + print("\n" + "="*50) + print("Running CIFAR-10 experiment with VGG-13...") + run_cifar10('VGG-13') diff --git a/dl-reproduce.pdf b/dl-reproduce.pdf new file mode 100644 index 0000000..51ac213 Binary files /dev/null and b/dl-reproduce.pdf differ diff --git a/parameter.png b/parameter.png new file mode 100644 index 0000000..0fca8fe Binary files /dev/null and b/parameter.png differ diff --git a/pyproject.toml b/pyproject.toml index 9ee27c9..260b5f8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,27 +3,19 @@ name = "reproduce" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.12" +requires-python = ">=3.13" dependencies = [ - "contourpy==1.3.3", - "cycler==0.12.1", - "filelock==3.20.3", - "fonttools==4.61.1", - "fsspec==2026.1.0", - "jinja2==3.1.6", - "kiwisolver==1.4.9", - "markupsafe==3.0.3", - "matplotlib==3.10.8", - "mpmath==1.3.0", - "networkx==3.6.1", - "numpy==2.4.2", - "packaging==26.0", - "pillow==12.1.0", - "pyparsing==3.3.2", - "python-dateutil==2.9.0.post0", - "setuptools==80.10.2", - "six==1.17.0", - "sympy==1.14.0", - "torch==2.10.0", - "typing-extensions==4.15.0", + "matplotlib>=3.10.8", + "numpy>=2.4.2", + "torch>=2.10.0", + "torchvision>=0.25.0", ] + 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