模型压缩与优化技术——神经架构搜索（Neural Architecture Search, NAS）

模型压缩与优化技术中的神经架构搜索（Neural Architecture Search, NAS）技术

在深度学习领域，神经网络的架构设计对模型的性能至关重要。传统的手动设计网络架构的过程费时费力，且通常依赖于经验和直觉。为了提升效率与效果，神经架构搜索（Neural Architecture Search, NAS）作为一种自动化的方法，能够通过算法寻找和优化最佳的神经网络架构。NAS可以在图像识别、自然语言处理和语音识别等多个领域取得显著的性能提升。

神经架构搜索是指通过自动化方法来搜索最佳神经网络架构的过程。其目标是找到在特定任务上表现最佳的网络结构。NAS涉及以下几个关键组件：

• 搜索空间：定义所有可供选择的网络架构的集合。
• 搜索策略：决定如何在搜索空间中进行探索的算法或方法。
• 评价标准：用于评估候选架构的性能指标，例如准确率、推理时间和模型大小。

搜索空间的构建是NAS的第一步，通常可以采用以下几种方式：

NAS的应用范围广泛，主要包括：

• 图像分类：自动寻找最佳的卷积神经网络（CNN）架构以提高分类精度。
• 目标检测：通过NAS来优化目标检测网络，提升模型的检测能力。
• 自然语言处理：自动生成适用于语言模型和文本生成任务的网络架构。

在NAS中，搜索空间通常用集合的形式表示：

A = { a 1 , a 2 , … , a N } \mathcal{A} = \{ a_1, a_2, \ldots, a_N \} A={a1​,a2​,…,aN​}

其中 $N$ 是候选架构的数量， a i a_i ai​ 表示第 $i$ 个架构。

NAS的搜索策略可以分为以下几类：

$$E(a)=\alpha \cdot \text{Accuracy}(a)-\beta \cdot \text{Complexity}(a)$$

其中，$\alpha$ 和 $\beta$ 是权重，分别表示准确性和复杂性的影响程度。

在设计NAS时，需要对模型的复杂度进行分析。通常，模型的复杂度可以通过以下几种方式表示：

神经架构搜索的基本流程一般包括以下步骤：

以下是一个简单的NAS示例，使用随机搜索来寻找最佳的神经网络架构。

<span class="token keyword">import</span> random <span class="token keyword">import</span> numpy <span class="token keyword">as</span> np <span class="token keyword">import</span> torch <span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn <span class="token keyword">as</span> nn <span class="token keyword">import</span> torch<span class="token punctuation">.</span>optim <span class="token keyword">as</span> optim  <span class="token keyword">class</span> <span class="token class-name">SimpleNN</span><span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Module<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> num_layers<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token builtin">super</span><span class="token punctuation">(</span>SimpleNN<span class="token punctuation">,</span> self<span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span><span class="token punctuation">)</span> layers <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span> <span class="token keyword">for</span> _ <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>num_layers<span class="token punctuation">)</span><span class="token punctuation">:</span> layers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span>num_units<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span><span class="token punctuation">)</span> layers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>nn<span class="token punctuation">.</span>ReLU<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span> layers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span>num_units<span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">)</span>  self<span class="token punctuation">.</span>model <span class="token operator">=</span> nn<span class="token punctuation">.</span>Sequential<span class="token punctuation">(</span><span class="token operator">*</span>layers<span class="token punctuation">)</span> <span class="token keyword">def</span> <span class="token function">forward</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> x<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token keyword">return</span> self<span class="token punctuation">.</span>model<span class="token punctuation">(</span>x<span class="token punctuation">)</span>  <span class="token keyword">def</span> <span class="token function">evaluate_model</span><span class="token punctuation">(</span>model<span class="token punctuation">,</span> data<span class="token punctuation">,</span> target<span class="token punctuation">)</span><span class="token punctuation">:</span> criterion <span class="token operator">=</span> nn<span class="token punctuation">.</span>MSELoss<span class="token punctuation">(</span><span class="token punctuation">)</span> optimizer <span class="token operator">=</span> optim<span class="token punctuation">.</span>Adam<span class="token punctuation">(</span>model<span class="token punctuation">.</span>parameters<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> lr<span class="token operator">=</span><span class="token number">0.01</span><span class="token punctuation">)</span> model<span class="token punctuation">.</span>train<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">for</span> _ <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">)</span><span class="token punctuation">:</span>  optimizer<span class="token punctuation">.</span>zero_grad<span class="token punctuation">(</span><span class="token punctuation">)</span> output <span class="token operator">=</span> model<span class="token punctuation">(</span>data<span class="token punctuation">)</span> loss <span class="token operator">=</span> criterion<span class="token punctuation">(</span>output<span class="token punctuation">,</span> target<span class="token punctuation">)</span> loss<span class="token punctuation">.</span>backward<span class="token punctuation">(</span><span class="token punctuation">)</span> optimizer<span class="token punctuation">.</span>step<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">return</span> loss<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token keyword">def</span> <span class="token function">search_best_architecture</span><span class="token punctuation">(</span>num_trials<span class="token punctuation">,</span> max_layers<span class="token punctuation">,</span> max_units<span class="token punctuation">)</span><span class="token punctuation">:</span> best_architecture <span class="token operator">=</span> <span class="token boolean">None</span> best_loss <span class="token operator">=</span> <span class="token builtin">float</span><span class="token punctuation">(</span><span class="token string">'inf'</span><span class="token punctuation">)</span> <span class="token keyword">for</span> _ <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>num_trials<span class="token punctuation">)</span><span class="token punctuation">:</span> num_layers <span class="token operator">=</span> random<span class="token punctuation">.</span>randint<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> max_layers<span class="token punctuation">)</span> num_units <span class="token operator">=</span> random<span class="token punctuation">.</span>randint<span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">,</span> max_units<span class="token punctuation">)</span> model <span class="token operator">=</span> SimpleNN<span class="token punctuation">(</span>num_layers<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span>  data <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">100</span><span class="token punctuation">,</span> num_units<span class="token punctuation">)</span>  target <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">100</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span>  loss <span class="token operator">=</span> evaluate_model<span class="token punctuation">(</span>model<span class="token punctuation">,</span> data<span class="token punctuation">,</span> target<span class="token punctuation">)</span> <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string-interpolation"><span class="token string">f"架构: </span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>num_layers<span class="token punctuation">}</span></span><span class="token string"> 层, </span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>num_units<span class="token punctuation">}</span></span><span class="token string"> 单元, 损失: </span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>loss<span class="token punctuation">}</span></span><span class="token string">"</span></span><span class="token punctuation">)</span> <span class="token keyword">if</span> loss <span class="token operator"><</span> best_loss<span class="token punctuation">:</span> best_loss <span class="token operator">=</span> loss best_architecture <span class="token operator">=</span> <span class="token punctuation">(</span>num_layers<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span> <span class="token keyword">return</span> best_architecture  best_arch <span class="token operator">=</span> search_best_architecture<span class="token punctuation">(</span>num_trials<span class="token operator">=</span><span class="token number">20</span><span class="token punctuation">,</span> max_layers<span class="token operator">=</span><span class="token number">5</span><span class="token punctuation">,</span> max_units<span class="token operator">=</span><span class="token number">50</span><span class="token punctuation">)</span> <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"最佳架构:"</span><span class="token punctuation">,</span> best_arch<span class="token punctuation">)</span> 
<span class="token keyword">import</span> random <span class="token keyword">import</span> numpy <span class="token keyword">as</span> np <span class="token keyword">import</span> torch <span class="token keyword">import</span> torch<span class="token punctuation">.</span>nn <span class="token keyword">as</span> nn <span class="token keyword">import</span> torch<span class="token punctuation">.</span>optim <span class="token keyword">as</span> optim  <span class="token keyword">class</span> <span class="token class-name">SimpleNN</span><span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Module<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> num_layers<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token builtin">super</span><span class="token punctuation">(</span>SimpleNN<span class="token punctuation">,</span> self<span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span><span class="token punctuation">)</span> layers <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span> <span class="token keyword">for</span> _ <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>num_layers<span class="token punctuation">)</span><span class="token punctuation">:</span> layers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span>num_units<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span><span class="token punctuation">)</span> layers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>nn<span class="token punctuation">.</span>ReLU<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span> layers<span class="token punctuation">.</span>append<span class="token punctuation">(</span>nn<span class="token punctuation">.</span>Linear<span class="token punctuation">(</span>num_units<span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">)</span>  self<span class="token punctuation">.</span>model <span class="token operator">=</span> nn<span class="token punctuation">.</span>Sequential<span class="token punctuation">(</span><span class="token operator">*</span>layers<span class="token punctuation">)</span> <span class="token keyword">def</span> <span class="token function">forward</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> x<span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token keyword">return</span> self<span class="token punctuation">.</span>model<span class="token punctuation">(</span>x<span class="token punctuation">)</span>  <span class="token keyword">def</span> <span class="token function">evaluate_model</span><span class="token punctuation">(</span>model<span class="token punctuation">,</span> data<span class="token punctuation">,</span> target<span class="token punctuation">)</span><span class="token punctuation">:</span> criterion <span class="token operator">=</span> nn<span class="token punctuation">.</span>MSELoss<span class="token punctuation">(</span><span class="token punctuation">)</span> optimizer <span class="token operator">=</span> optim<span class="token punctuation">.</span>Adam<span class="token punctuation">(</span>model<span class="token punctuation">.</span>parameters<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span> lr<span class="token operator">=</span><span class="token number">0.01</span><span class="token punctuation">)</span> model<span class="token punctuation">.</span>train<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">for</span> _ <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">)</span><span class="token punctuation">:</span>  optimizer<span class="token punctuation">.</span>zero_grad<span class="token punctuation">(</span><span class="token punctuation">)</span> output <span class="token operator">=</span> model<span class="token punctuation">(</span>data<span class="token punctuation">)</span> loss <span class="token operator">=</span> criterion<span class="token punctuation">(</span>output<span class="token punctuation">,</span> target<span class="token punctuation">)</span> loss<span class="token punctuation">.</span>backward<span class="token punctuation">(</span><span class="token punctuation">)</span> optimizer<span class="token punctuation">.</span>step<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">return</span> loss<span class="token punctuation">.</span>item<span class="token punctuation">(</span><span class="token punctuation">)</span>  <span class="token keyword">def</span> <span class="token function">search_best_architecture</span><span class="token punctuation">(</span>num_trials<span class="token punctuation">,</span> max_layers<span class="token punctuation">,</span> max_units<span class="token punctuation">)</span><span class="token punctuation">:</span> best_architecture <span class="token operator">=</span> <span class="token boolean">None</span> best_loss <span class="token operator">=</span> <span class="token builtin">float</span><span class="token punctuation">(</span><span class="token string">'inf'</span><span class="token punctuation">)</span> <span class="token keyword">for</span> _ <span class="token keyword">in</span> <span class="token builtin">range</span><span class="token punctuation">(</span>num_trials<span class="token punctuation">)</span><span class="token punctuation">:</span> num_layers <span class="token operator">=</span> random<span class="token punctuation">.</span>randint<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> max_layers<span class="token punctuation">)</span> num_units <span class="token operator">=</span> random<span class="token punctuation">.</span>randint<span class="token punctuation">(</span><span class="token number">10</span><span class="token punctuation">,</span> max_units<span class="token punctuation">)</span> model <span class="token operator">=</span> SimpleNN<span class="token punctuation">(</span>num_layers<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span>  data <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">100</span><span class="token punctuation">,</span> num_units<span class="token punctuation">)</span>  target <span class="token operator">=</span> torch<span class="token punctuation">.</span>randn<span class="token punctuation">(</span><span class="token number">100</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span>  loss <span class="token operator">=</span> evaluate_model<span class="token punctuation">(</span>model<span class="token punctuation">,</span> data<span class="token punctuation">,</span> target<span class="token punctuation">)</span> <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string-interpolation"><span class="token string">f"架构: </span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>num_layers<span class="token punctuation">}</span></span><span class="token string"> 层, </span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>num_units<span class="token punctuation">}</span></span><span class="token string"> 单元, 损失: </span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>loss<span class="token punctuation">}</span></span><span class="token string">"</span></span><span class="token punctuation">)</span> <span class="token keyword">if</span> loss <span class="token operator"><</span> best_loss<span class="token punctuation">:</span> best_loss <span class="token operator">=</span> loss best_architecture <span class="token operator">=</span> <span class="token punctuation">(</span>num_layers<span class="token punctuation">,</span> num_units<span class="token punctuation">)</span> <span class="token keyword">return</span> best_architecture  best_arch <span class="token operator">=</span> search_best_architecture<span class="token punctuation">(</span>num_trials<span class="token operator">=</span><span class="token number">20</span><span class="token punctuation">,</span> max_layers<span class="token operator">=</span><span class="token number">5</span><span class="token punctuation">,</span> max_units<span class="token operator">=</span><span class="token number">50</span><span class="token punctuation">)</span> <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"最佳架构:"</span><span class="token punctuation">,</span> best_arch<span class="token punctuation">)</span> 
import random import numpy as np import torch import torch.nn as nn import torch.optim as optim  class SimpleNN(nn.Module): def __init__(self, num_layers, num_units): super(SimpleNN, self).__init__() layers = [] for _ in range(num_layers): layers.append(nn.Linear(num_units, num_units)) layers.append(nn.ReLU()) layers.append(nn.Linear(num_units, 1))  self.model = nn.Sequential(*layers) def forward(self, x): return self.model(x)  def evaluate_model(model, data, target): criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.01) model.train() for _ in range(10):  optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() return loss.item()  def search_best_architecture(num_trials, max_layers, max_units): best_architecture = None best_loss = float('inf') for _ in range(num_trials): num_layers = random.randint(1, max_layers) num_units = random.randint(10, max_units) model = SimpleNN(num_layers, num_units)  data = torch.randn(100, num_units)  target = torch.randn(100, 1)  loss = evaluate_model(model, data, target) print(f"架构: {num_layers} 层, {num_units} 单元, 损失: {loss}") if loss < best_loss: best_loss = loss best_architecture = (num_layers, num_units) return best_architecture  best_arch = search_best_architecture(num_trials=20, max_layers=5, max_units=50) print("最佳架构:", best_arch) 

• SimpleNN类定义了一个简单的全连接神经网络，通过指定层数和每层的单元数来创建不同的网络结构。

• evaluate_model函数用于训练模型并评估其性能。使用均方误差（MSE）作为损失函数，优化器为Adam，训练10个epoch。

• search_best_architecture函数通过随机生成不同的网络架构，评估其性能并记录最佳架构。
• 在每次迭代中，随机选择层数和每层的单元数，生成模型，并对其进行评估。

• 调用search_best_architecture函数进行架构搜索，输出最佳架构的信息。

神经架构搜索作为一种重要的自动化模型设计技术，通过算法探索和优化神经网络架构，有助于提升模型在特定任务中的表现。尽管在计算成本和搜索空间的复杂性等方面面临挑战，但随着技术的进步，NAS在深度学习领域的应用前景依然广阔。

原文链接：https://blog.csdn.net/qq_44648285/article/details/143454178?ops_request_misc=%257B%2522request%255Fid%2522%253A%25226bb173574c62335ad988293e9779e0d3%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=6bb173574c62335ad988293e9779e0d3&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-7-143454178-null-null.nonecase&utm_term=%E6%9E%81%E7%A9%BA%E9%97%B4nas