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<h1><a href="https://nn.labml.ai/conv_mixer/index.html">补丁是你所需要的吗？</a></h1>
<p>这是 <a href="https://pytorch.org">PayTorch</a> 实现的纸质<a href="https://papers.labml.ai/paper/2201.09792">补丁是你所需要的吗？</a>。</p>
<p>ConvMixer 与 <a href="https://nn.labml.ai/transformers/mlp_mixer/index.html">MLP 混音器</a>类似。MLP-Mixer 将空间维度和信道维度的混合分开，方法是跨空间维度应用 MLP，然后在通道维度上应用 MLP（空间 MLP 取代 <a href="https://nn.labml.ai/transformers/vit/index.html">ViT</a> 注意力，通道 MLP 是ViT 的 <a href="https://nn.labml.ai/transformers/feed_forward.html">FFN</a>）。</p>
C@@ <p>onvMixer 使用 1x1 卷积进行通道混合，使用深度卷积进行空间混合。由于它是卷积而不是整个空间的完整MLP，因此与 ViT 或 MLP 混音器相比，它只混合附近的批次。此外，MLP-Mixer 在每次混音时使用两层的 MLP，而 ConvMixer 为每次混音使用单个层。</p>
<p>本文建议移除通道混音中的残余连接（逐点卷积），并且在空间混合（深度卷积）上只有一个剩余连接。他们还使用<a href="https://nn.labml.ai/normalization/batch_norm/index.html">批量归一化</a>而不是<a href="../normalization/layer_norm/index.html">图层规范化</a>。</p>
<p><a href="https://nn.labml.ai/conv_mixer/experiment.html">这是一个在 CIFAR-10 上训练 ConvMixer 的实验</a>。</p>
<p><a href="https://app.labml.ai/run/0fc344da2cd011ecb0bc3fdb2e774a3d"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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