sublime Licence keys

2步破解官方sublime4 2步破解官方sublime4。 阅读详情

[2017.09.15]Sublime Text 2.X, 3.X 注册码 License Key 集合(更新至3144)

虽然Sublime Text不用购买注册码也能使用,但是使用过程中偶尔会弹出让购买的小窗口也不是很爽。记录一些搜集的 Sublime Text 3 注册码,适用于 Windows、Linux 及 Mac系统,方便以后使用。(以下注册码只适用于我等屌丝,土豪门请务必支持正版,到官网购买)

推荐阅读:《JetBrains PhpStorm 所有版本通用注册码》

使用方法

打开 Sublime Text 3 的 “Help”–“Enter Licence”,然后根据版本选择输入下面的注册码。

注册码

所有这些注册码都经过测试(2016-05-12),适用于所有 Mac、Linux、Windows 系统下 2.X 及 3.X 版本(Build 2221; 3062~3126)。

1. Sublime Text 2.x 版本


   
  1. —– BEGIN LICENSE —–
  2. Andrew Weber
  3. Single User License
  4. EA7E-855605
  5. 813A03DD 5E4AD9E6 6C0EEB94 BC99798F
  6. 942194A6 02396E98 E62C9979 4BB979FE
  7. 91424C9D A45400BF F6747D88 2FB88078
  8. 90F5CC94 1CDC92DC 8457107A F151657B
  9. 1D22E383 A997F016 42397640 33F41CFC
  10. E1D0AE85 A0BBD039 0E9C8D55 E1B89D5D
  11. 5CDB7036 E56DE1C0 EFCC0840 650CD3A6
  12. B98FC99C 8FAC73EE D2B95564 DF450523
  13. —— END LICENSE ——

2. Sublime Text 3.x(Bulid 309X 之前版本)

适用于3065 / 3083 等低于 309x 的版本


   
  1. —– BEGIN LICENSE —–
  2. Andrew Weber
  3. Single User License
  4. EA7E-855605
  5. 813A03DD 5E4AD9E6 6C0EEB94 BC99798F
  6. 942194A6 02396E98 E62C9979 4BB979FE
  7. 91424C9D A45400BF F6747D88 2FB88078
  8. 90F5CC94 1CDC92DC 8457107A F151657B
  9. 1D22E383 A997F016 42397640 33F41CFC
  10. E1D0AE85 A0BBD039 0E9C8D55 E1B89D5D
  11. 5CDB7036 E56DE1C0 EFCC0840 650CD3A6
  12. B98FC99C 8FAC73EE D2B95564 DF450523
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. K-20
  3. Single User License
  4. EA7E-940129
  5. 3A099EC1 C0B5C7C5 33EBF0CF BE82FE3B
  6. EAC2164A 4F8EC954 4E87F1E5 7E4E85D6
  7. C5605DE6 DAB003B4 D60CA4D0 77CB1533
  8. 3C47F579 FB3E8476 EB3AA9A7 68C43CD9
  9. 8C60B563 80FE367D 8CAD14B3 54FB7A9F
  10. 4123FFC4 D63312BA 141AF702 F6BBA254
  11. B094B9C0 FAA4B04C 06CC9AFC FD412671
  12. 82E3AEE0 0F0FAAA7 8FA773C9 383A9E18
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. J2TeaM
  3. 2 User License
  4. EA7E-940282
  5. 45CB0D8F 09100037 7D1056EB A1DDC1A2
  6. 39C102C5 DF8D0BF0 FC3B1A94 4F2892B4
  7. 0AEE61BA 65758D3B 2EED551F A3E3478C
  8. C1C0E04E CA4E4541 1FC1A2C1 3F5FB6DB
  9. CFDA1551 51B05B5D 2D3C8CFE FA8B4285
  10. 051750E3 22D1422A 7AE3A8A1 3B4188AC
  11. 346372DA 37AA8ABA 6EB30E41 781BC81F
  12. B5CA66E3 A09DBD3A 3FE85BBD 69893DBD
  13. —— END LICENSE ——

3. Sublime Text 3.X(Bulid 309x之后版本)

适用于 3103 等高于 309x 的版本


   
  1. —– BEGIN LICENSE —–
  2. MinBan
  3. Single User License
  4. EA7E-806395
  5. 318133A3 8F202A61 B0DBB8EB 21E17D2E
  6. 97D540E6 34079344 54620650 71E47589
  7. 9EF87857 345F5042 0D728DD1 8D8C979D
  8. 6A4F4DD2 67BB0345 746CA297 515BDA91
  9. 6CEAB381 4DB56700 D77DCD14 977BD326
  10. 1AC309ED 0EB414B8 4730DA10 99DBD291
  11. FC88E0EF DCC7E3A9 56E4FFED 7629746B
  12. E529AECA 92A96B60 72AE8928 8A240AAC
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Michael Barnes
  3. Single User License
  4. EA7E-821385
  5. 8A353C41 872A0D5C DF9B2950 AFF6F667
  6. C458EA6D 8EA3C286 98D1D650 131A97AB
  7. AA919AEC EF20E143 B361B1E7 4C8B7F04
  8. B085E65E 2F5F5360 8489D422 FB8FC1AA
  9. 93F6323C FD7F7544 3F39C318 D95E6480
  10. FCCC7561 8A4A1741 68FA4223 ADCEDE07
  11. 200C25BE DBBC4855 C4CFB774 C5EC138C
  12. 0FEC1CEF D9DCECEC D3A5DAD1 01316C36
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Free Communities Consultoria em Informática Ltda
  3. Single User License
  4. EA7E-801302
  5. C154C122 4EFA4415 F1AAEBCC 315F3A7D
  6. 2580735A 7955AA57 850ABD88 72A1DDD8
  7. 8D2CE060 CF980C29 890D74F2 53131895
  8. 281E324E 98EA1FEF 7FF69A12 17CA7784
  9. 490862AF 833E133D FD22141D D8C89B94
  10. 4C10A4D2 24693D70 AE37C18F 72EF0BE5
  11. 1ED60704 651BC71F 16CA1B77 496A0B19
  12. 463EDFF9 6BEB1861 CA5BAD96 89D0118E
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Nicolas Hennion
  3. Single User License
  4. EA7E-866075
  5. 8A01AA83 1D668D24 4484AEBC 3B04512C
  6. 827B0DE5 69E9B07A A39ACCC0 F95F5410
  7. 729D5639 4C37CECB B2522FB3 8D37FDC1
  8. 72899363 BBA441AC A5F47F08 6CD3B3FE
  9. CEFB3783 B2E1BA96 71AAF7B4 AFB61B1D
  10. 0CC513E7 52FF2333 9F726D2C CDE53B4A
  11. 810C0D4F E1F419A3 CDA0832B 8440565A
  12. 35BF00F6 4CA9F869 ED10E245 469C233E
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Anthony Sansone
  3. Single User License
  4. EA7E-878563
  5. 28B9A648 42B99D8A F2E3E9E0 16DE076E
  6. E218B3DC F3606379 C33C1526 E8B58964
  7. B2CB3F63 BDF901BE D31424D2 082891B5
  8. F7058694 55FA46D8 EFC11878 0868F093
  9. B17CAFE7 63A78881 86B78E38 0F146238
  10. BAE22DBB D4EC71A1 0EC2E701 C7F9C648
  11. 5CF29CA3 1CB14285 19A46991 E9A98676
  12. 14FD4777 2D8A0AB6 A444EE0D CA009B54
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Alexey Plutalov
  3. Single User License
  4. EA7E-860776
  5. 3DC19CC1 134CDF23 504DC871 2DE5CE55
  6. 585DC8A6 253BB0D9 637C87A2 D8D0BA85
  7. AAE574AD BA7D6DA9 2B9773F2 324C5DEF
  8. 17830A4E FBCF9D1D 182406E9 F883EA87
  9. E585BBA1 2538C270 E2E857C2 194283CA
  10. 7234FF9E D0392F93 1D16E021 F1914917
  11. 63909E12 203C0169 3F08FFC8 86D06EA8
  12. 73DDAEF0 AC559F30 A6A67947 B60104C6
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Peter Halliday
  3. Single User License
  4. EA7E-855988
  5. 3997BFF0 2856413A 7A555954 67069B78
  6. 06D8CE12 63EAF079 AD039757 79E16D13
  7. C555AD90 465CBE53 10F6DFC4 D3A3C611
  8. 411106F8 0CFEB15F 0A7BB891 111F5ED2
  9. C6AA8429 77913528 FA6291A9 B88D4550
  10. F1D6AB13 BF9153BC 91B4DFFE D296CFE0
  11. C1D8EB22 13D5F14E 75A699EC 49EDDC23
  12. D89D0F9B D240B10A A3712467 09DE7870
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Fred Zirdung
  3. Single User License
  4. EA7E-844672
  5. 6089C0EC 22936E1A 1EADEBE2 B8654BBA
  6. 5C98FFA6 C0FD1599 0364779B 071C74FB
  7. EEFE9EAB 92B3D867 CD1B32FE D190269F
  8. 6FC08F8F 8D24191D 32828465 942CE58E
  9. AECE5307 08B62229 D788560A 6E0AAC4B
  10. 48A2D9EE 24FD8CAA 07BEBDF2 28EA86D4
  11. CCB96084 6C34CAD2 E8A04F39 3B5A3CBC
  12. 3B668BB7 C94D0B4B 847D6D7F 4BC07375
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Wixel
  3. Single User License
  4. EA7E-848235
  5. 103D2969 8700C7ED 8173CF61 537000C0
  6. EB3C7ECB 5E750F17 6B42B67C A190090B
  7. 7669164F C6F371A8 5A1D88D5 BDD0DA70
  8. C065892B 7CC1BB2B 1C8B8C7C F08E7789
  9. 7C2A5241 35F86328 4C8F70D9 C023D7C2
  10. 11245C36 59A730DB 72BDB9A7 D5B20304
  11. 90E90E72 9F08CA25 73F49C20 179D938E
  12. 5BC8BEDA 13457A69 39E6265F 233767F9
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Daniel Russel
  3. Single User License
  4. EA7E-917420
  5. 9327EC62 44020C2A 45172A68 12FE13F1
  6. 1D22245B 680892EE F551F8EB C183D032
  7. 8B4EDB4B 479CB7E4 07E42EDD A780021D
  8. 56BADF42 AC05238B 023B47B1 EBA1B7DE
  9. 6DF9A383 159F32AE 04EBE100 1278B1D2
  10. 52E81B60 C68AA2E8 F84A20BE FE7990EB
  11. 5D44E4B6 16369263 1DDAACBC 280FF19E
  12. 86CF4319 0B8615A8 4FF0512E B123B8EC
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Peter Eriksson
  3. Single User License
  4. EA7E-890068
  5. 8E107C71 3100D6FC 2AC805BF 9E627C77
  6. 72E710D7 43392469 D06A2F5B F9304FBD
  7. F5AB4DB2 7A95F172 FE68E300 42745819
  8. E94AB2DF C1893094 ECABADC8 71FEE764
  9. 20224821 3EABF931 745AF882 87AD0A4B
  10. 33C6E377 0210D712 CD2B1178 82601542
  11. C7FD8098 F45D2824 BC7DFB38 F1EBD38A
  12. D7A3AFE0 96F938EA 2D90BD72 9E34CDF0
  13. —— END LICENSE ——

或者


   
  1. —– BEGIN LICENSE —–
  2. Ryan Clark
  3. Single User License
  4. EA7E-812479
  5. 2158A7DE B690A7A3 8EC04710 006A5EEB
  6. 34E77CA3 9C82C81F 0DB6371B 79704E6F
  7. 93F36655 B031503A 03257CCC 01B20F60
  8. D304FA8D B1B4F0AF 8A76C7BA 0FA94D55
  9. 56D46BCE 5237A341 CD837F30 4D60772D
  10. 349B1179 A996F826 90CDB73C 24D41245
  11. FD032C30 AD5E7241 4EAA66ED 167D91FB
  12. 55896B16 EA125C81 F550AF6B A6820916
  13. —— END LICENSE ——

4. Sublime Text 3.x (after Build 312X)

   
  1. —– BEGIN LICENSE —–
  2. riku
  3. Single User License
  4. EA7E-806996
  5. 60C55C64D0195F15A118D93ECE0849B3
  6. 30C432F529F7BFAAF6568C6BFDDA1868
  7. D6DF14D0464281D64A7E2EBB32558D84
  8. 148EF8041694AC00B9FA17D6119A6286
  9. 611D11E26BB48DCF19F76CB1CC7B995E
  10. F41F7BFAB3348963FF69F163A70ABBEA
  11. 2526B73B523AA28BF66AFEF3ED3D1D21
  12. BC6CB3B5B6D183FF5C755DE7007C6C41
  13. —— END LICENSE ——

5. Sublime Text 3.x (after Build 3133)

   
  1. —– BEGIN LICENSE —–
  2. TwitterInc
  3. 200 User License
  4. EA7E-890007
  5. 1D77F72E 390CDD93 4DCBA022 FAF60790
  6. 61AA12C0 A37081C5 D0316412 4584D136
  7. 94D7F7D4 95BC8C1C 527DA828 560BB037
  8. D1EDDD8C AE7B379F 50C9D69D B35179EF
  9. 2FE898C4 8E4277A8 555CE714 E1FB0E43
  10. D5D52613 C3D12E98 BC49967F 7652EED2
  11. 9D2D2E61 67610860 6D338B72 5CF95C69
  12. E36B85CC 84991F19 7575D828 470A92AB
  13. —— END LICENSE ——

或者

   
  1. —– BEGIN LICENSE —–
  2. Morin
  3. 2 User License
  4. EA7E-924018
  5. 184B9FDB 02612F57 33B15E69 BBC567F1
  6. E20FA231 C077EA95 CC14B48B 71DD2536
  7. E209843A 94D13692 03AC2FAA 895B688D
  8. B8F4A0E6 FDC15964 A5573FD7 6405ED1E
  9. 6F205469 7F34C69D 3D36E475 52AF6A5B
  10. DFD15C31 85BA64EF F95DD592 4B42C314
  11. AC655762 C0F0F5A1 018824E4 17C56E16
  12. AC5AA84C 034F7A53 2C9A801B 8AED239F
  13. —— END LICENSE ——

6. Sublime Text 2.x(对于所有 3.x beta 版本依然有效)

   
  1. — BEGIN LICENSE —–
  2. ZYNGA INC.
  3. 50 User License
  4. EA7E-811825
  5. 927BA117 84C9300F 4A0CCBC4 34A56B44
  6. 985E4562 59F2B63B CCCFF92F 0E646B83
  7. 0FD6487D 1507AE29 9CC4F9F5 0A6F32E3
  8. 0343D868 C18E2CD5 27641A71 25475648
  9. 309705B3 E468DDC4 1B766A18 7952D28C
  10. E627DDBA 960A2153 69A2D98A C87C0607
  11. 45DC6049 8C04EC29 D18DFA40 442C680B
  12. 1342224D 44D90641 33A3B9F2 46AADB8F
  13. —— END LICENSE ——

或者

   
  1. —– BEGIN LICENSE —–
  2. Molex, Inc.
  3. Single User License
  4. EA7E-818977
  5. AE248E13 C08A1CDF 2D6751EF 32243003
  6. 53F5EF8A 1522F537 6A09E721 030DC36C
  7. C477DA16 2BD1681C 0164D104 BB58D77F
  8. 1C221EE9 3296957E 5784C10F 51AA0020
  9. 6F2E553C D8147ADF 7A5B7A4C F843F2A6
  10. 2489CCA4 05759326 1D16227F 4813084D
  11. 61821A38 F4CD6E01 CFC5A7A5 C7D8B685
  12. 1D56D4DB 6B3C3094 57A6F2F3 7CD4177B
  13. —— END LICENSE ——

 

注意:在 “—– BEGIN LICENSE —–” 之前和 “—— END LICENSE ——”之后不要有任何额外的字符串。

via:appnee.com,本文遵循 Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License。

PS:如何查看版本号呢?只需打开“Help”–“About Sublime Text”即可看到。

sublime-text-version

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sublime3 bulid3176 license key(注册码) Sublime Text 3 build 3176 license key Windows Mac Unbuntu16.04 实测有限 立即下载

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2007 年 ~ 2025 年,深耕 SAP 技术 18 年 787

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运行Sublime Text,菜单Help->Enter License->输入下面的激活码->Use License->确定,软件就激活了。操作完成后,按下Ctrl+S,将原文件备份后(例如改名为sublime_text.exe.bak),保存。关闭许可证检查:按Ctrl+F搜索。破解:按Ctrl+F搜索。

学亮编程手记 4194

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AI问数技术正在改变企业与数据交互的方式,它以自然语言为桥梁,让数据不再是少数专家的专利,而是成为所有业务人员的决策伙伴。这不仅是技术的创新,更是管理理念的革新。在数字化转型的关键阶段,企业应积极拥抱AI问数技术,通过降低数据使用门槛、提升数据利用效率、释放数据潜在价值,实现真正的数据驱动决策,为企业的高质量发展注入源源不断的智慧动能。AI问数,正在开启一个数据普惠的新时代。

getusushu的博客 64

Camera2Demo

在Camera2 的基础上进行封装,使得Camera2一堆初始化逻辑与Activity分离,降低耦合度,完成预览拍照并保存的操作

Sublime Text 2.02注册码

----- BEGIN LICENSE ----- Andrew Weber Single User License EA7E-855605 813A03DD 5E4AD9E6 6C0EEB94 BC99798F 942194A6 02396E98 E62C9979 4BB979FE 91424C9D A45400BF F6747D88 2FB88078 90F5CC94 1CDC92DC 845

milife2012的专栏 5014

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

sublime text4 license

sublime text4 license sublime text 4 license sublime text 4 license 本着独乐乐不如众乐乐的原则: <pre class="md-fences md-end-block ty-contain-cm modeLoaded" spellcheck="false" lang="bash" cid="n198" mdtype="fences" style="box-sizing: border-box; overflow: visible; f

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汉书HE8,好用的汇编代码编写工具

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廖雪峰的python教程整理的,已经排好版了,而且还是完整的,适合小白

Sublime Text2 License Key真实可用

直接上干货: —– BEGIN LICENSE —– J2TeaM 2 User License EA7E-940282 45CB0D8F 09100037 7D1056EB A1DDC1A2 39C102C5 DF8D0BF0 FC3B1A94 4F2892B4 0AEE61BA 65758D3B 2EED551F A3E3478C C1C0E04E CA4E4541 1FC1A2

Yerasel的专栏 2440

Sublime Text3可用注册码

最近觉得Sublime Text3比Notepad++好使,可惜需要购买,于是网上搜了一下,屌丝的福音啊: Sublime Text Build 3065 License key 复制如下三个任意一个正版注册码即可 —– BEGIN LICENSE —– Andrew Weber Single User License EA7E-855605 813A03DD 5

小白兔的窝 1741

Sublime3注册码失效问题解决方案: license key no longer valid and has been removed

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window下(或Ubuntu下)Sublime Text 2.01注册码license key

 点击help输入license key [plain] view plaincopyprint? ----- ----- BEGIN LICENSE  Alexander  Single User License  EA7E-814345  51F47F09 4EAB1285 7827EFF0 8B1207DC  A76A6EA3 E1A1CA7A DC

hhczy1003的专栏 619

Sublime Text3 Build 3207破解

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Monitor的博客 1773

Sublime text 3 License key 持续更新 直到我挣够钱不当程序员那天

Sublime text 3 (Build 3143) —– BEGIN LICENSE —– TwitterInc 200 User License EA7E-890007 1D77F72E 390CDD93 4DCBA022 FAF60790 61AA12C0 A37081C5 D0316412 4584D136 94D7F7D4 95BC8C1C 527DA828 56

DJQTDJのグラム 4495
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