神經架構搜索（Neural Architecture Search，NAS）介紹

1. 定義 神經架構搜索（NAS）是一種自動化設計神經網絡架構的方法，通過搜索算法在預定義的搜索空間中尋找最優的網絡結構。其目標是在給定的任務上找到性能最優的網絡架構，減少人工設計網絡結構的工作量。
2. 搜索空間 搜索空間定義了所有可能的網絡結構，包括層的類型、數量、連接方式等。一個好的搜索空間設計能夠提高搜索效率，減少搜索時間。
3. 搜索策略 搜索策略決定了如何在搜索空間中尋找最優結構，常見的策略包括：
4. 性能評估 性能評估是指如何評價一個網絡結構的好壞，通常是通過在驗證集上的準確率或其他指標來衡量。

神經架構搜索（NAS）代碼示例 以下是一個使用 PyTorch 實現的簡單 NAS 示例，基於隨機搜索策略：

Python

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import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data import DataLoader import random

定義一個簡單的卷積神經網絡

class SimpleCNN(nn.Module): def init(self, num_conv_layers, num_dense_layers, num_filters, kernel_size, dense_units): super(SimpleCNN, self).init() layers = [] layers.append(nn.Conv2d(1, num_filters, kernel_size=kernel_size, stride=1, padding=1)) layers.append(nn.ReLU()) layers.append(nn.MaxPool2d(kernel_size=2, stride=2))

for _ in range(num_conv_layers - 1):
        layers.append(nn.Conv2d(num_filters, num_filters, kernel_size=kernel_size, stride=1, padding=1))
        layers.append(nn.ReLU())
        layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
    
    layers.append(nn.Flatten())
    
    for _ in range(num_dense_layers):
        layers.append(nn.Linear(dense_units, dense_units))
        layers.append(nn.ReLU())
    
    layers.append(nn.Linear(dense_units, 10))
    layers.append(nn.LogSoftmax(dim=1))
    
    self.model = nn.Sequential(*layers)

def forward(self, x):
    return self.model(x)

定義訓練函數

def train(model, device, train_loader, optimizer, epoch): 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 = nn.NLLLoss()(output, target) loss.backward() optimizer.step()

定義測試函數

def test(model, device, test_loader): model.eval（) test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += nn.NLLLoss()(output, target).item() pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) accuracy = 100. * correct / len(test_loader.dataset) return accuracy

隨機搜索

def random_search(num_trials): best_accuracy = 0.0 best_model = None device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

for i in range(num_trials):
    # 隨機生成超參數
    num_conv_layers = random.choice([1, 2, 3])
    num_dense_layers = random.choice([1, 2])
    num_filters = random.choice([32, 64, 128])
    kernel_size = random.choice([3, 5])
    dense_units = random.choice([64, 128, 256])
    
    # 創建模型
    model = SimpleCNN(num_conv_layers, num_dense_layers, num_filters, kernel_size, dense_units).to(device)
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    # 訓練模型
    train_loader = DataLoader(datasets.MNIST('', train=True, download=True, transform=transforms.ToTensor()), batch_size=64, shuffle=True)
    test_loader = DataLoader(datasets.MNIST('', train=False, download=True, transform=transforms.ToTensor()), batch_size=1000, shuffle=False)
    for epoch in range(5):
        train(model, device, train_loader, optimizer, epoch)
    
    # 測試模型
    accuracy = test(model, device, test_loader)
    print(f"Trial {i+1}: Conv layers={num_conv_layers}, Dense layers={num_dense_layers}, Filters={num_filters}, Kernel size={kernel_size}, Dense units={dense_units}, Accuracy={accuracy:.2f}%")
    
    # 更新最佳模型
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_model = model

print(f"Best accuracy: {best_accuracy:.2f}%")
return best_model

執行隨機搜索

best_model = random_search(num_trials=5) 代碼説明

通過上述代碼，你可以實現一個簡單的神經架構搜索過程，基於隨機搜索策略在 MNIST 數據集上尋找最優的卷積神經網絡架構。