https://terms-en.ai-term-hub.com/zh/tags/hyperparameters/Recent content in Hyperparameters on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/bayesian_optimization/Sat, 18 Jul 2026 11:08:51 +0000https://terms-en.ai-term-hub.com/zh/terms/bayesian_optimization/<h2 id="definition">Definition</h2> <p>贝叶斯优化使用概率代理模型（通常为高斯过程）来建模目标函数。它采用采集函数来平衡探索与利用，从而高效地找到最优参数配置。</p> <h3 id="summary">Summary</h3> <p>一种用于全局优化昂贵评估黑盒函数的顺序设计策略。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>代理模型</li> <li>采集函数</li> <li>探索与利用权衡</li> <li>黑盒优化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>深度学习模型的超参数调优</li> <li>科学实验设计的优化</li> <li>机器人控制参数调整</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter_tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">hyperparameter_tuning (超参数调优)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/gaussian_processes-%E9%AB%98%E6%96%AF%E8%BF%87%E7%A8%8B/">gaussian_processes (高斯过程)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/acquisition_function-%E9%87%87%E9%9B%86%E5%87%BD%E6%95%B0/">acquisition_function (采集函数)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/grid_search-%E7%BD%91%E6%A0%BC%E6%90%9C%E7%B4%A2/">grid_search (网格搜索)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/batch_size/Sat, 18 Jul 2026 11:08:51 +0000https://terms-en.ai-term-hub.com/zh/terms/batch_size/<h2 id="definition">Definition</h2> <p>批大小是一个关键的超参数，决定了在更新模型内部参数之前处理多少个样本。较大的批大小能提供更准确的梯度估计，但可能增加内存需求并影响收敛特性。</p> <h3 id="summary">Summary</h3> <p>在随机梯度下降算法的一次迭代中使用的训练样本数量。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>梯度估计</li> <li>内存限制</li> <li>收敛稳定性</li> <li>超参数调整</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>调整模型收敛速度</li> <li>训练期间管理GPU内存限制</li> <li>通过噪声梯度提高泛化能力</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/learning_rate-%E5%AD%A6%E4%B9%A0%E7%8E%87/">learning_rate (学习率)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/stochastic_gradient_descent-%E9%9A%8F%E6%9C%BA%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/">stochastic_gradient_descent (随机梯度下降)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/mini_batch-%E5%B0%8F%E6%89%B9%E9%87%8F/">mini_batch (小批量)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/memory_usage-%E5%86%85%E5%AD%98%E4%BD%BF%E7%94%A8/">memory_usage (内存使用)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/learning_rate/Sat, 18 Jul 2026 11:00:35 +0000https://terms-en.ai-term-hub.com/zh/terms/learning_rate/<h2 id="definition">Definition</h2> <p>学习率决定了在每次训练迭代中，模型权重相对于计算出的梯度更新了多少。如果学习率过高，可能导致模型在优化过程中越过最优解；如果过低，则可能导致训练收敛缓慢。</p> <h3 id="summary">Summary</h3> <p>控制模型优化过程中步长的超参数，旨在最小化损失函数。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>梯度下降</li> <li>超参数调优</li> <li>收敛</li> <li>优化器</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>神经网络训练</li> <li>深度学习模型优化</li> <li>强化学习策略更新</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">import</span> torch.optim <span style="color:#66d9ef">as</span> optim </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> MyModel() </span></span><span style="display:flex;"><span>optimizer <span style="color:#f92672">=</span> optim<span style="color:#f92672">.</span>SGD(model<span style="color:#f92672">.</span>parameters(), lr<span style="color:#f92672">=</span><span style="color:#ae81ff">0.01</span>) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/gradient_descent-%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/">gradient_descent (梯度下降)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/optimizer-%E4%BC%98%E5%8C%96%E5%99%A8/">optimizer (优化器)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter-%E8%B6%85%E5%8F%82%E6%95%B0/">hyperparameter (超参数)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/convergence-%E6%94%B6%E6%95%9B/">convergence (收敛)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/rate/Sat, 18 Jul 2026 10:54:02 +0000https://terms-en.ai-term-hub.com/zh/terms/rate/<h2 id="definition">Definition</h2> <p>在AI中，“率”最常指学习率，这是一个超参数，控制每次更新模型权重时，根据估计误差对模型进行调整的幅度。一个适……</p> <h3 id="summary">Summary</h3> <p>频率或速度的度量，通常指优化中的学习率或令牌生成速度。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>学习率</li> <li>优化</li> <li>吞吐量</li> <li>超参数</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>调整梯度下降优化</li> <li>监控API使用限制</li> <li>测量推理延迟</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span>optimizer <span style="color:#f92672">=</span> torch<span style="color:#f92672">.</span>optim<span style="color:#f92672">.</span>SGD(model<span style="color:#f92672">.</span>parameters(), lr<span style="color:#f92672">=</span><span style="color:#ae81ff">0.01</span>) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/optimizer-%E4%BC%98%E5%8C%96%E5%99%A8/">Optimizer (优化器)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/convergence-%E6%94%B6%E6%95%9B/">Convergence (收敛)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/speed-%E9%80%9F%E5%BA%A6/">Speed (速度)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/latency-%E5%BB%B6%E8%BF%9F/">Latency (延迟)</a></li> </ul>