登录
首页 » Python » GAN-master

GAN-master

于 2018-04-04 发布 文件大小:13228KB
0 154
下载积分: 1 下载次数: 21

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

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • GTS4轴点位2轴插补多线程能处理
    固高GTS控制卡,4轴运动,2轴插补多线程程序处理(GTS ,4-axis Motion, 2-axis Interpolation Multithread Processing)
    2020-09-10 06:08:03下载
    积分:1
  • life__akplication
    频繁树增长算法的特点以及在现实生活中的应用(The characteristics of frequent tree growth algorithm and its application in real life)
    2017-04-03 15:38:29下载
    积分:1
  • 减少使用盲提取循环平稳信号
    Blind extraction of a cyclostationary signal using reduced-rank cyclic regression―A unifying approach-Blind extraction of a cyclostationary signal using reduced-rank cyclic regression-A unifying approach
    2022-01-26 01:32:40下载
    积分:1
  • Fig2
    说明:  仿真了叶绿素浓度和散射系数和吸收系数之间的关系(The relationship between chlorophyll concentration, scattering coefficient and absorption coefficient is simulated)
    2020-01-07 10:56:05下载
    积分:1
  • C#用子查询作表达式
    C#用子查询作表达式,查询学生表中指定科目的成绩超过其科目平均成绩的学生信息,一个结合数据库实现的程序,学习子查询的实现方法,如何使用子查询表达式。
    2023-08-27 00:20:03下载
    积分:1
  • images
    miffy image file. do you like this one.
    2017-07-24 03:28:29下载
    积分:1
  • GoogleEarthManual
    一份有关google maps的使用方法(Introduce a description of how to use Google maps.)
    2020-06-22 08:00:01下载
    积分:1
  • 熵权法
    说明:  按照信息论基本原理的解释,信息是系统有序程度的一个度量,熵是系统无序程度的一个度量;如果指标的信息熵越大,该指标提供的信息量越大,在综合评价中所起作用理当越大,权重就应该越高。因此,可利用信息熵这个工具,计算出各个指标的权重,为多指标综合评价提供依据(According to the interpretation of the basic principles of information theory, information is a measure of the order degree of the system, and entropy is a measure of the disorder degree of the system. If the larger the information entropy of an index is, the greater the amount of information provided by the index is, and the greater the role it plays in the comprehensive evaluation, the higher the weight should be. Therefore, we can use the tool of information entropy to calculate the weight of each index and provide the basis for multi index comprehensive evaluation)
    2020-03-26 19:35:54下载
    积分:1
  • Zhuang201SCI
    Zhuang2011_Article_Modified-VSIMMAlgorithmWithAnA
    2020-06-22 19:00:02下载
    积分:1
  • 矩阵按键
    STM32F103RCT6矩阵键盘,4×4键盘,按键串口打印(STM32F103RCT6 Matrix Keyboard, 4*4 Keyboard, Key Serial Port Printing)
    2020-06-19 02:00:01下载
    积分:1
  • 696518资源总数
  • 106222会员总数
  • 14今日下载