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GAN-master

于 2018-04-04 发布 文件大小:13228KB
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代码说明：

  GAN标准生成对抗网络基于tensorflow的实现(Implementation of GAN standard generation confrontation network based on tensorflow.)

文件列表：

GAN-master, 0 , 2017-05-07
GAN-master\Datas, 0 , 2017-05-07
GAN-master\Datas\mnist, 0 , 2017-05-07
GAN-master\Datas\mnist\t10k-images-idx3-ubyte.gz, 1648877 , 2017-05-07
GAN-master\Datas\mnist\t10k-labels-idx1-ubyte.gz, 4542 , 2017-05-07
GAN-master\Datas\mnist\train-images-idx3-ubyte.gz, 9912422 , 2017-05-07
GAN-master\Datas\mnist\train-labels-idx1-ubyte.gz, 28881 , 2017-05-07
GAN-master\README.md, 12072 , 2017-05-07
GAN-master\README, 0 , 2017-05-07
GAN-master\README\images, 0 , 2017-05-07
GAN-master\README\images\cgan.png, 7178 , 2017-05-07
GAN-master\README\images\gan.png, 6753 , 2017-05-07
GAN-master\README\images\infogan1.png, 4228 , 2017-05-07
GAN-master\README\images\infogan2.png, 4777 , 2017-05-07
GAN-master\README\results, 0 , 2017-05-07
GAN-master\README\results\cgan_mlp.png, 47347 , 2017-05-07
GAN-master\README\results\face3D_dcgan.png, 151303 , 2017-05-07
GAN-master\Samples, 0 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier, 0 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\000_0.png, 32085 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\001_1.png, 14637 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\002_2.png, 13206 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\003_3.png, 12778 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\004_4.png, 12493 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\005_5.png, 12609 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\006_6.png, 12861 , 2017-05-07
GAN-master\Samples\mnist_cgan_classifier\348_8.png, 11793 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv, 0 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\000_0.png, 31917 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\001_1.png, 13567 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\002_2.png, 11557 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\005_5.png, 10345 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\008_8.png, 12074 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\039_9.png, 12607 , 2017-05-07
GAN-master\Samples\mnist_cgan_conv\043_3.png, 13035 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp, 0 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\000_0.png, 31964 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\001_1.png, 21156 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\002_2.png, 21020 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\003_3.png, 19724 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\036_6.png, 14516 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\060_0.png, 14986 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\061_1.png, 9493 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\062_2.png, 16208 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\063_3.png, 14668 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\064_4.png, 15572 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\065_5.png, 14644 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\066_6.png, 13623 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\067_7.png, 13277 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\068_8.png, 14995 , 2017-05-07
GAN-master\Samples\mnist_cgan_mlp\069_9.png, 13014 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier, 0 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\000_0.png, 31984 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\001_1.png, 14636 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\002_2.png, 13766 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\003_3.png, 13297 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\027_7.png, 13322 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\028_8.png, 13479 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\029_9.png, 13732 , 2017-05-07
GAN-master\Samples\mnist_cgan_wgan_classifier\030_0.png, 13388 , 2017-05-07
GAN-master\Samples\mnist_dcgan, 0 , 2017-05-07
GAN-master\Samples\mnist_dcgan\000.png, 31961 , 2017-05-07
GAN-master\Samples\mnist_dcgan\001.png, 17299 , 2017-05-07
GAN-master\Samples\mnist_dcgan\002.png, 13444 , 2017-05-07
GAN-master\Samples\mnist_dcgan\025.png, 12733 , 2017-05-07
GAN-master\Samples\mnist_dcgan\028.png, 12899 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv, 0 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\000_0.png, 32046 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\001_1.png, 12917 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\002_2.png, 12055 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\003_3.png, 12830 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\084_4.png, 12281 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\085_5.png, 12314 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\086_6.png, 12085 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\087_7.png, 12116 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\088_8.png, 12608 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\089_9.png, 12133 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\090_0.png, 12237 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\091_1.png, 11868 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\092_2.png, 11524 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\093_3.png, 12472 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\094_4.png, 11529 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv\095_5.png, 12117 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share, 0 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\000_0.png, 31894 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\001_1.png, 13656 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\002_2.png, 12970 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\003_3.png, 13400 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\048_8.png, 12517 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\049_9.png, 13065 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\050_0.png, 12801 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\051_1.png, 12187 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\052_2.png, 12247 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\054_4.png, 11925 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\055_5.png, 12350 , 2017-05-07
GAN-master\Samples\mnist_infogan_conv_without_share\058_8.png, 12323 , 2017-05-07
GAN-master\Samples\mnist_infogan_mlp, 0 , 2017-05-07
GAN-master\Samples\mnist_infogan_mlp\000_0.png, 32026 , 2017-05-07
GAN-master\Samples\mnist_infogan_mlp\001_1.png, 20314 , 2017-05-07
GAN-master\Samples\mnist_infogan_mlp\002_2.png, 17154 , 2017-05-07

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    谈到在Windows创建线程的例子，在网上的很多的参考都是基于MFC的。其实，就操作系统实验这个前提而言，大可不必去碰那个大型的MFC的框架。[1]中提到了在Windows命令控制台下创建进程及线程的方法，本文对[1]中的方法加以了实现，并做些简单的进程及线程的测试程序。 -On Windows create a thread in the examples of the many online reference are based on the MFC. In fact, experiments on this premise in terms of the operating system, no need to touch the large MFC framework. [1] mentioned in the Windows command console to create processes and threads under way, this paper [1] in the methods to achieve, and do a simple process and thread test procedures.
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