From f0d9f6586e1b3447da75a69c386a04d4fecf0c91 Mon Sep 17 00:00:00 2001 From: majianjia Date: Sun, 31 Mar 2019 02:06:51 +0100 Subject: [PATCH] add cnn image --- docs/example_mnist_simple_cn.md | 10 ++++++++++ docs/figures/Typical_cnn.png | Bin 0 -> 71514 bytes 2 files changed, 10 insertions(+) create mode 100644 docs/figures/Typical_cnn.png diff --git a/docs/example_mnist_simple_cn.md b/docs/example_mnist_simple_cn.md index 0e818db..25e646e 100644 --- a/docs/example_mnist_simple_cn.md +++ b/docs/example_mnist_simple_cn.md @@ -7,6 +7,16 @@ MNIST 经常被用来做为分类任务的入门数据库使用。在这个简单的例子里面,我们也用它来试试数据归类。 +这个例子建造了一个卷积网络。这个网络主要包括卷积层(Convolution layer)和全连接层 (Densely-connected,或者也叫 Fully-connected layer). + +![Architecture of a CNN.](figures/Typical_cnn.png) + +[Image source](https://www.mathworks.com/videos/introduction-to-deep-learning-what-are-convolutional-neural-networks--1489512765771.html) + +卷积网络大概是上图所示(这里用的层数不同),每一层通过一定大小的卷积核从上一层提取一定的特征。 +这些特征最后通过全连接层选择后,会选择性激活某个类型的标签。标签对应的数值越大,可能性越大。 + + ## 1. 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