dot

　　Dropout是深度学习中的一种防止过拟合手段，在面试中也经常会被问到，因此有必要搞懂其原理。 1 Dropout的运作方式 　　在神经网络的训练过程中，对于一次迭代中的某一层神经网络，先随机选择中的一些神经元并将其临时隐藏(丢弃)，然后再进行本次训练和优化。在下一次迭代中，继续随机隐藏一些神经元，如此直至训练结束。由于是随机丢弃，故而每一个mini-batch都在训练不同的网络。 　　在训练时，每个神经单元以概率$p$被保留(Dropout丢弃率为$1-p$)；在预测阶段（测试阶段），每个神经单元都是存在的，权重参数$w$要乘以$p$，输出是：$pw$。示意图如下： 预测阶段需要乘上$p$的原因： 　　前一层隐藏层的一个神经元在$dropout$之前的输出是$x$，训练时$dropout$之后的期望值是$E=px+(1−p) \dot 0$; 在预测阶段该层神经元总是激活，为了保持同样的输出期望值并使下一层也得到同样的结果，需要调整$x->px$. 其中$p$是Bernoulli分布（0-1分布）中值为1的概率。 2 Dropout 实现 　　 如前文所述，在训练时随机隐藏部分神经元，在预测时必须要乘上p。代码如下： 1 import numpy as np 2 3 p = 0.5 # 神经元激活概率 4 5 def train_step(X): 6 """ X

I'm feeding this simple input script defining record based nodes to dot in order to create a SVG from it (the SVG part actually doesn't matter): graph mygraph{ node [shape=record, fontsize=10, fontname=Arial]; rankdir=TB; ranksep=0.5; rank=max; splines=true; overlap=false; mindist=0.2; "d1" [style=solid, label="{\N|{<0> 0|<1> 1}}"]; "d2" [style=solid, label="{\N|{<0> 0|<1> 1|<2> 2|<3> 3}}"]; "d1":0 -- "d2":0[color=blue, penwidth=3, tooltip="d1:0 -- d2:0", URL="#"]; } This yields a graph where ports 0 of d1 and port 0 of d2 are connected by a blue spline: Fine. Now I have the need to colorize

I am building a dot file to represent computer hardware and the physical connections to a network switch and displays. I have it looking ok when processed by the dot program but I think I really want it processed by neato to create a more "free form" picture as it starts to grom. Right now when I run my large file with neato, everything is overlapping. I am trying to figure out the syntax on where to define the overlap attribute. Below is a subset of my dot file. graph g { node [shape=record,height=.1]; PC8[label="{{<GigE1>GigE1|<GigE2>GigE2}|{<name>PC8}|{<dvi1>dvi1|<dvi2>dvi2|<dvi3>dvi3|<dvi4