自动节点分类网络
自动节点分类网络 在深度学习网络中一般都是以（卷积层，池化层，等...）作为特征提取层再接一个全链接再加一个输出(sofmax,sigmod,等),这种形式的网络是固定不变，这会带来一些问题，比如训练太慢，精度不够，不可变，内存占用太大，等问题，其中最大的问题在于（训练与精度），如果权重太多训练变慢，权重太小精度不够，作者为了解决这个问题提出了这个网络。
接下来会以一个经典的数字分类来说明网络做了啥，为什么要这样做。 我们现在用一个普通的cnn分类模型来做这个试验，任务是：分类0到9这10个数字,我们可以看到在训练次数不足够的情况下分类模型往往是把一些数字给搞混我们可以看到搞混的数字是(0,8)和(1,7)这里只是举例没有针对性。为什么会这样？
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