> For the complete documentation index, see [llms.txt](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/47-shi-yao-shi-shen-du-juan-ji-wang-luo-ff1f-ff08-what-are-deep-convnets-learning.md).

# 4.6 什么是深度卷积网络？（What are deep ConvNets learning?）

假如训练了一个**Alexnet**轻量级网络，不同层之间隐藏单元的计算结果如下：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/6d489f040214efb27bf0f109874b3918.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/6d489f040214efb27bf0f109874b3918.png)

从第一层的隐藏单元开始，将训练集经过神经网络，然后弄明白哪一张图片最大限度地激活特定的单元。在第一层的隐藏单元，只能看到小部分卷积神经，只有一小块图片块是有意义的，因为这就是特定单元所能看到的全部

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/1472cbf93948173ac314ceb4eb5e4c97.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/1472cbf93948173ac314ceb4eb5e4c97.png)

然后选一个另一个第一层的隐藏单元，重复刚才的步骤：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/5b097161aa8a3e22c081185a69a367a3.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/5b097161aa8a3e22c081185a69a367a3.png)

对其他隐藏单元也进行处理，会发现其他隐藏单元趋向于激活类似于这样的图片：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/eb711a9de1a7c8681c25c9c6e3bf71cd.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/eb711a9de1a7c8681c25c9c6e3bf71cd.png)

以此类推，这是9个不同的代表性神经元，每一个不同的图片块都最大化地激活了。可以理解为第一层的隐藏单元通常会找一些简单的特征，比如说边缘或者颜色阴影

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/4000d4a71a5820691197d506654216bd.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/4000d4a71a5820691197d506654216bd.png)

在深层部分，一个隐藏单元会看到一张图片更大的部分，在极端的情况下，可以假设每一个像素都会影响到神经网络更深层的输出，靠后的隐藏单元可以看到更大的图片块

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/2ccff4b8e125893f330414574cd03af8.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/2ccff4b8e125893f330414574cd03af8.png)

第一层，第一个被高度激活的单元：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/d7fd293116929c0e6e807e10156d7e5a.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/d7fd293116929c0e6e807e10156d7e5a.png)

第二层检测的特征变得更加复杂：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/83f73c165fe6ec9c98ab2993d3efaf7f.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/83f73c165fe6ec9c98ab2993d3efaf7f.png)

第三层明显检测到更复杂的模式

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/62ac4181d43c9f937b33a70428d1fca1.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/62ac4181d43c9f937b33a70428d1fca1.png)

第四层，检测到的模式和特征更加复杂：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/96585e7bfa539245870080d4db16f255.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/96585e7bfa539245870080d4db16f255.png)

第五层检测到更加复杂的事物：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/ac77f5f5dd63264cf8af597c3aa20d59.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/ac77f5f5dd63264cf8af597c3aa20d59.png)