深度神经收集可表明性要领汇总，附Tensorflow代码实现

领略神经收集：人们一向认为深度进修可表明性较弱。然而，领略神经收集的研究一向也没有遏制过，本文就来先容几种神经收集的可表明性要领，并配有可以或许在Jupyter下运行的代码毗连。

通过激活最化来表明深度神经收集的要领一共有两种，详细如下：

1.1 Activation Maximization (AM)

相干代码如下：

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/1.1%20Activation%20Maximization.ipynb 

1.2 Performing AM in Code Space

相干代码如下：

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/1.3%20Performing%20AM%20in%20Code%20Space.ipynb

层偏向的关联撒播，一共有5种可表明要领。Sensitivity Analysis、Simple Taylor Decomposition、Layer-wise Relevance Propagation、Deep Taylor Decomposition、DeepLIFT。它们的处理赏罚要领是：先通过敏感性说明引入关联分数的观念，操作简朴的Taylor Decomposition试探根基的关联解析，进而成立各类分层的关联撒播要领。详细如下：

2.1 Sensitivity Analysis

相干代码如下：

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/2.1%20Sensitivity%20Analysis.ipynb

2.2 Simple Taylor Decomposition

相干代码如下：

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/2.2%20Simple%20Taylor%20Decomposition.ipynb

2.3 Layer-wise Relevance Propagation

相干代码如下：

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/2.3%20Layer-wise%20Relevance%20Propagation%20%281%29.ipynb

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/2.3%20Layer-wise%20Relevance%20Propagation%20%282%29.ipynb

2.4 Deep Taylor Decomposition

相干代码如下：

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/2.4%20Deep%20Taylor%20Decomposition%20%281%29.ipynb

http://nbviewer.jupyter.org/github/1202kbs/Understanding-NN/blob/master/2.4%20Deep%20Taylor%20Decomposition%20%282%29.ipynb

2.5 DeepLIFT

相干代码如下：

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