deep

How do you make a deep copy of an object in Java?

匿名 (未验证) 提交于 2019-12-03 02:04:01
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: In java it's a bit difficult to implement a deep object copy function. What steps you take to ensure the original object and the cloned one share no reference? 回答1: A safe way is to serialize the object, then deserialize. This ensures everything is a brand new reference. Here's an article about how to do this efficiently. Caveats: It's possible for classes to override serialization such that new instances are not created, e.g. for singletons. Also this of course doesn't work if your classes aren't Serializable. 回答2: A few people have

Dynamic Scoping - Deep Binding vs Shallow Binding

匿名 (未验证) 提交于 2019-12-03 01:59:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I've been trying to get my head around shallow binding and deep binding, wikipedia doesn't do a good job of explaining it properly. Say I have the following code, what would the output be if the language uses dynamic scoping with a) deep binding b) shallow binding? x: integer := 1 y: integer := 2 procedure add x := x + y procedure second(P:procedure) x:integer := 2 P() procedure first y:integer := 3 second(add) ----main starts here--- first() write_integer(x) 回答1: Deep binding binds the environment at the time the procedure is passed as an

How do I deep copy a DateTime object?

匿名 (未验证) 提交于 2019-12-03 01:55:01
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: $date1 = $date2 = new DateTime(); $date2->add(new DateInterval('P3Y')); Now $date1 and $date2 contain the same date -- three years from now. I'd like to create two separate datetimes, one which is parsed from a string and one with three years added to it. Currently I've hacked it up like this: $date2 = new DateTime($date1->format(DateTime::ISO8601)); but that seems like a horrendous hack. Is there a "correct" way to deep copy a DateTime object? 回答1: $date1 = new DateTime(); $date2 = new DateTime(); $date2->add(new DateInterval('P3Y'));

JS: Does Object.assign() create deep copy or shallow copy

匿名 (未验证) 提交于 2019-12-03 01:54:01
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I just came across this concept of var copy = Object.assign({}, originalObject); which creates a copy of original object into the " copy " object. However, my question is, does this way of cloning object create a deep copy or a shallow copy? PS: The confusion is, if it creates a deep copy, then it would be the easiest way to clone an object. 回答1: Forget about deep copy, even shallow copy isn't safe, if the object you're copying has a property with enumerable attribute set to false. MDN : The Object.assign() method only copies enumerable and

Memory Usage During running a Deep learning CNN Model in Colab

匿名 (未验证) 提交于 2019-12-03 01:47:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I am conducting a research which requires me to know the memory used during run time by the model when i run a deep learning model(CNN) in google colab. Is there any code i can use to know the same .Basically I want to know how much memory has been used in total model run .(after all epoch has been complete). I am coding in python Regards Avik 回答1: As explained in this post and my own observations, Tensorflow always tries to allocate the entire memory , no matter how small or big is your model. Unlike for example MXNet that only allocates

OpenCL / AMD: Deep Learning

匿名 (未验证) 提交于 2019-12-03 01:23:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: While "googl'ing" and doing some research I were not able to find any serious/popular framework/sdk for scientific GPGPU-Computing and OpenCL on AMD hardware. Is there any literature and/or software I missed? Especially I am interested in deep learning . For all I know deeplearning.net recommends NVIDIA hardware and CUDA frameworks. Additionally all big deep learning frameworks I know, such as Caffe , Theano , Torch , DL4J , ... are focussed on CUDA and do not plan to support OpenCL/AMD . Furthermore one can find plenty of scientific papers

The use of /deep/ and >>> in Angular 2

匿名 (未验证) 提交于 2019-12-03 01:10:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I've been reading up on this selector, and getting conflicting answers. In: What do /deep/ and ::shadow mean in a CSS selector? We see: As Joel H. points out in the comments, Chrome has since deprecated the /deep/ combinator, and it gives a syntax error in IE. In: https://github.com/Microsoft/vscode/issues/7002 We see: /deep/ no longer exists, so I don't think we should support it. >>> is the new version, which should probably be supported However, in the Angular 2 docs: https://angular.io/docs/ts/latest/guide/component-styles.html We see:

React: Nested defaultProps deep merge

匿名 (未验证) 提交于 2019-12-03 00:46:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I have a track prop with the following definition: propTypes: { track: React.PropTypes.shape({ cover: React.PropTypes.string, title: React.PropTypes.string, artist: React.PropTypes.string }) } I would like the track.cover to get a default value if undefined: getDefaultProps: function() { return { track: { cover: 'placeholder.png' } } } Any chance I can do that at the view level? Does getDefaultProps does a Deep Merge? Or do I need to handle this at the model level? Thanks 回答1: getDefaultProps does not merge passed property values with the

Deep Learning 9: Performance

匿名 (未验证) 提交于 2019-12-03 00:38:01
http://rduin.nl/presentations/ROC%20Tutorial%20Peter%20Flach/ROCtutorialPartI.pdf https://stats.stackexchange.com/questions/7207/roc-vs-precision-and-recall-curves https://machinelearningmastery.com/assessing-comparing-classifier-performance-roc-curves-2/ Deep Learning 9: Performance 原文:https://www.cnblogs.com/wordchao/p/9240087.html

Deep Learning Ups and Downs

匿名 (未验证) 提交于 2019-12-03 00:30:01
1958: Perceptron(Linear model) 1969: Perceptron has limitation 1980s: Multi-layer perceptron 1986: Backpropagation 1989: 1 hidden layer is "good enough",why deep? 2006: RBM initialization(breakthrough) // Restricted Boltzmann Machine 2009: GPU 2011: Start to be popular in speech recognition 2012: win ILSVRC image competition 转载请标明出处: Deep Learning Ups and Downs 文章来源: Deep Learning Ups and Downs