Kubernetes Initializers工作原理介绍.pptx下载

weixin_39820535 2021-10-11 09:35:56

Kubernetes Initializers工作原理介绍.pptx , 相关下载链接：https://download.csdn.net/download/njbaige/31035333?utm_source=bbsseo

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Kubernetes Initializers工作原理介绍.pptx

java反编译工具jad 1.5.8g支持 jdk1.5,jdk1.6。说明很多记住一个万能的命令基本就够用了。jad -sjava -r -8 -o **\*.class ---------------This is README file for Jad - the fast Java Decompiler.Jad home page: http://www.kpdus.com/jad.htmlCopyright 2001 Pavel Kouznetsov (jad@kpdus.com).0. Please read the disclaimer on the Jad home page.1. Installation.Unzip jad.zip file into any appropriate directory on your hard drive.This will create two files: - an executable file named 'jad.exe' (Windows *) or 'jad' (*n*x) - this README fileNo other setup is required.2. How to use JadTo decompile a single JAVA class file 'example1.class' type the following: jad example1.classThis command creates file 'example1.jad' in the current directory.If such file already exists Jad asks whether you want to overwrite it or not.Option -o permits overwriting without a confirmation.You can omit .class extension and/or use wildcards in the names ofinput files.Option -s allows to change output file extension: jad -sjava example1.classThis command creates file 'example1.java'. Be careful when usingoptions -o and -sjava together, because Jad can accidentally overwriteyour own source files.Jad uses JAVA class name as an output file name. For example, if classfile 'example1.class' contains JAVA class 'test1' then Jad will createfile 'test1.jad' rather than 'example1.jad'. If you want to specifyyour own output file name use the output redirection: jad -p example1.class > myexm1.javaOption -d allows you to specify another directory for output files,which are created, by default, in the current directory. For example: jad -o -dtest -sjava *.class (or jad -o -d test -s java *.class, which has the same effect)This command decompiles all .class files in the current directory <

Class Initializer Initializer base class: all initializers inherit from this class. Aliases: Class tf.compat.v2.initializers.Initializer Class tf.compat.v2.keras.initializers.Initializer Class tf...

Xavier Glorot 和 Yoshua Bengio 在 2010年提出了一种很有效的神经网络的初始化方法:Xavier 1.为什么需要参数初始化？深度学习模型的权重初始化得太小，容易产生梯度消失。权重初始化得太大，容易产生梯度爆炸 Xavier初始化器得作用，就是在初始化深度学习网络得时候让权重不大不小。 2.w分布: 3.下面给出公式推导：考虑线性...

其实很简单，GlorotUniform初始化等同于Pytorch中的Xavier均匀初始化器！

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