<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Images on 中文AI术语词典</title><link>https://terms-en.ai-term-hub.com/zh/tags/images/</link><description>Recent content in Images on 中文AI术语词典</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/zh/tags/images/index.xml" rel="self" type="application/rss+xml"/><item><title>卷积神经网络</title><link>https://terms-en.ai-term-hub.com/zh/terms/convolutional_neural_network/</link><pubDate>Sat, 18 Jul 2026 07:44:21 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/zh/terms/convolutional_neural_network/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>卷积神经网络（CNN）旨在从视觉输入中自动且自适应地学习特征的空间层次结构。它们利用卷积层应用滤波器来检测局部模式，并通过池化等操作逐步提取高层语义特征。&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>一类专门用于处理网格状数据（如图像）的深度神经网络，通过应用卷积滤波器来实现。&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>卷积层&lt;/li>
&lt;li>池化&lt;/li>
&lt;li>特征图&lt;/li>
&lt;li>空间层次&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>图像分类&lt;/li>
&lt;li>视频流中的目标检测&lt;/li>
&lt;li>医学影像诊断&lt;/li>
&lt;/ul>
&lt;h2 id="code-example">Code Example&lt;/h2>
&lt;div class="highlight">&lt;pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;">&lt;code class="language-python" data-lang="python">&lt;span style="display:flex;">&lt;span>&lt;span style="color:#f92672">import&lt;/span> tensorflow &lt;span style="color:#66d9ef">as&lt;/span> tf
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>model &lt;span style="color:#f92672">=&lt;/span> tf&lt;span style="color:#f92672">.&lt;/span>keras&lt;span style="color:#f92672">.&lt;/span>Sequential([
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span> tf&lt;span style="color:#f92672">.&lt;/span>keras&lt;span style="color:#f92672">.&lt;/span>layers&lt;span style="color:#f92672">.&lt;/span>Conv2D(&lt;span style="color:#ae81ff">32&lt;/span>, (&lt;span style="color:#ae81ff">3&lt;/span>, &lt;span style="color:#ae81ff">3&lt;/span>), activation&lt;span style="color:#f92672">=&lt;/span>&lt;span style="color:#e6db74">&amp;#39;relu&amp;#39;&lt;/span>, input_shape&lt;span style="color:#f92672">=&lt;/span>(&lt;span style="color:#ae81ff">28&lt;/span>, &lt;span style="color:#ae81ff">28&lt;/span>, &lt;span style="color:#ae81ff">1&lt;/span>)),
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span> tf&lt;span style="color:#f92672">.&lt;/span>keras&lt;span style="color:#f92672">.&lt;/span>layers&lt;span style="color:#f92672">.&lt;/span>MaxPooling2D((&lt;span style="color:#ae81ff">2&lt;/span>, &lt;span style="color:#ae81ff">2&lt;/span>)),
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span> tf&lt;span style="color:#f92672">.&lt;/span>keras&lt;span style="color:#f92672">.&lt;/span>layers&lt;span style="color:#f92672">.&lt;/span>Flatten(),
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span> tf&lt;span style="color:#f92672">.&lt;/span>keras&lt;span style="color:#f92672">.&lt;/span>layers&lt;span style="color:#f92672">.&lt;/span>Dense(&lt;span style="color:#ae81ff">10&lt;/span>)
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>])
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/div>&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/deep-learning-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">Deep Learning (深度学习)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/computer-vision-%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/">Computer Vision (计算机视觉)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/backpropagation-%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD/">Backpropagation (反向传播)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/neural-network-%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/">Neural Network (神经网络)&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>