登录
首页 » matlab » GAN-Base-on-Matlab-master

GAN-Base-on-Matlab-master

于 2019-01-09 发布
0 148
下载积分: 1 下载次数: 21
我要下载

代码说明：

说明：  生成手写数字图片，一共提供了三种算法，example_1和example_2训练GAN来生成手写数字图片，搭建了一个简单的GAN的网络结构，example_3难度较大(There are three algorithms to generate handwritten digital pictures. Example_1 and example_2 train GAN to generate handwritten digital pictures, and build a simple network structure of GAN. Example_3 is more difficult.)

文件列表：

GAN-Base-on-Matlab-master, 0 , 2018-12-12
GAN-Base-on-Matlab-master\.gitignore, 5 , 2018-12-12
GAN-Base-on-Matlab-master\LICENSE, 1350 , 2018-12-12
GAN-Base-on-Matlab-master\README.md, 2762 , 2018-12-12
GAN-Base-on-Matlab-master\activation, 0 , 2018-12-12
GAN-Base-on-Matlab-master\activation\activate_z.m, 472 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_activation_function.m, 559 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_leaky_relu.m, 157 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_relu.m, 144 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_sigmoid.m, 213 , 2018-12-12
GAN-Base-on-Matlab-master\activation\delta_tanh.m, 67 , 2018-12-12
GAN-Base-on-Matlab-master\activation\leaky_relu.m, 169 , 2018-12-12
GAN-Base-on-Matlab-master\activation\relu.m, 83 , 2018-12-12
GAN-Base-on-Matlab-master\activation\sigmoid.m, 60 , 2018-12-12
GAN-Base-on-Matlab-master\error_term, 0 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\delta_sigmoid_cross_entropy.m, 694 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_atrous_conv2d_layer.m, 554 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_batch_norm_layer.m, 1441 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_conv2d_layer.m, 554 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_conv2d_transpose_layer.m, 1936 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_fully_connect_layer.m, 121 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_reshape_layer.m, 137 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\get_error_term_from_sub_sampling_layer.m, 374 , 2018-12-12
GAN-Base-on-Matlab-master\error_term\sigmoid_cross_entropy.m, 284 , 2018-12-12
GAN-Base-on-Matlab-master\example_1.m, 1582 , 2018-12-12
GAN-Base-on-Matlab-master\example_2.m, 1144 , 2018-12-12
GAN-Base-on-Matlab-master\example_3.m, 1277 , 2018-12-12
GAN-Base-on-Matlab-master\gan_train.m, 2922 , 2018-12-12
GAN-Base-on-Matlab-master\gradient, 0 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_atrous_conv2d_layer.m, 773 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_batch_norm_layer.m, 731 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_conv2d_layer.m, 739 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_conv2d_transpose_layer.m, 1260 , 2018-12-12
GAN-Base-on-Matlab-master\gradient\calculate_gradient_for_fully_connect_layer.m, 179 , 2018-12-12
GAN-Base-on-Matlab-master\layer, 0 , 2018-12-12
GAN-Base-on-Matlab-master\layer\atrous_conv2d.m, 893 , 2018-12-12
GAN-Base-on-Matlab-master\layer\batch_norm.m, 1074 , 2018-12-12
GAN-Base-on-Matlab-master\layer\check_layer_field_names.m, 579 , 2018-12-12
GAN-Base-on-Matlab-master\layer\conv2d.m, 1062 , 2018-12-12
GAN-Base-on-Matlab-master\layer\conv2d_transpose.m, 1933 , 2018-12-12
GAN-Base-on-Matlab-master\layer\reshape_operation.m, 300 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_atrous_conv2d_layer.m, 1631 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_batch_norm_layer.m, 751 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_conv2d_layer.m, 1594 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_conv2d_transpose_layer.m, 5976 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_fully_connect_layer.m, 704 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_reshape_layer.m, 454 , 2018-12-12
GAN-Base-on-Matlab-master\layer\setup_sub_sampling_layer.m, 389 , 2018-12-12
GAN-Base-on-Matlab-master\layer\sub_sample.m, 346 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow, 0 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_applygrads_adam.m, 1408 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_applygrads_sgd.m, 602 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_bp_d.m, 2904 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_bp_g.m, 2981 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_ff.m, 1633 , 2018-12-12
GAN-Base-on-Matlab-master\nerual_network_flow\nn_setup.m, 1157 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images, 0 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images\1.png, 3660 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images\2.png, 3589 , 2018-12-12
GAN-Base-on-Matlab-master\readme_images\3.png, 4283 , 2018-12-12
GAN-Base-on-Matlab-master\setup_environment.m, 296 , 2018-12-12
GAN-Base-on-Matlab-master\test, 0 , 2018-12-12
GAN-Base-on-Matlab-master\test\convolution_process.m, 1821 , 2018-12-12
GAN-Base-on-Matlab-master\util, 0 , 2018-12-12
GAN-Base-on-Matlab-master\util\argparse.m, 259 , 2018-12-12
GAN-Base-on-Matlab-master\util\expand.m, 1958 , 2018-12-12
GAN-Base-on-Matlab-master\util\flipall.m, 77 , 2018-12-12
GAN-Base-on-Matlab-master\util\insert_zeros_into_array.m, 329 , 2018-12-12
GAN-Base-on-Matlab-master\util\padding_height_width_in_array.m, 476 , 2018-12-12
GAN-Base-on-Matlab-master\util\save_images.m, 623 , 2018-12-12

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
粗体
下划线
斜体
删除线
文字颜色
链接
取消链接
表情
图片
插入代码
全屏


0 个回复

  • Image-segmentation-based-on-texture
    利用灰度图 建共生矩阵 利用特征值 对纹理图像分割(The use of grayscale co-occurrence matrix to build the use of characteristic values of the texture map would like to partition)
    2016-04-05 15:40:46下载
    积分：1
  • fourier
    基于radon的图像运动模糊参数估计程序，估计了模糊角度与模糊尺度，但过大的图片需先处理后才能使用(Radon-based image motion blur parameter estimation procedure, estimates of the fuzzy point and fuzzy scale, but the picture is too large to be treated before use)
    2011-06-20 09:46:15下载
    积分：1
  • 火灾预警
    说明：  火灾预警系统，实现火焰的特征提取，结合SVM分类算法(Fire early warning system, to achieve the feature extraction of flame, combined with SVM classification algorithm)
    2020-04-28 17:41:17下载
    积分：1
  • 粒子群算法的Pareto多目标函数优化
    粒子群算法的Pareto多目标函数优化，适用于多目标问题的寻优(Particle swarm optimization Pareto multi-objective function optimization, suitable for multi-objective optimization.)
    2020-12-23 17:29:06下载
    积分：1
  • DOA
    波束空间DOA的估计,从图中可以很好的看出角度的估计还是可以的。(Beam-space DOA estimation, from the figure we can see the point of a good estimate is still possible.)
    2020-07-22 14:48:44下载
    积分：1
  • unet-master 2
    说明：  使用unet对图像进行分割的源码，里面有训练集，可以根据自己的需要更换训练数据。(Use the source code of the image segmentation using UNET, which has a training set, you can change the training data according to your own needs.)
    2020-06-29 21:22:43下载
    积分：1
  • IsoMap
    说明：  isomap matlab的实现源程序，使用时，需要添加主程序。(isomap Matlab implementation source program, when used, need to add the main program.)
    2019-05-23 20:54:39下载
    积分：1
  • GMM高斯混合模型进行背景建模（Matlab）
    转：https://blog.csdn.net/jinshengtao/article/details/26278725 混合高斯背景建模是基于像素样本统计信息的背景表示方法，利用像素在较长时间内大量样本值的概率密度等统计信息(如模式数量、每个模式的均值和标准差)表示背景，然后使用统计差分(如3σ原则)进行目标像素判断，可以对复杂动态背景进行建模，计算量较大。 在混合高斯背景模型中，认为像素之间的颜色信息互不相关，对各像素点的处理都是相互独立的。对于视频图像中的每一个像素点，其值在序列图像中的变化可看作是不断产生像素值的随机过程，即用高斯分布来描述每个像素点的颜色呈现规律单模态(单峰)，多模态(多峰)(Gaussian mixture background modeling is a background representation method based on the statistical information of pixel samples. Statistical information such as the number of patterns, the mean and standard deviation of each pattern are used to represent the background. Statistical difference (such as 3_principle) is used to judge the target pixel. Complex dynamic background modeling has a large amount of computation. In the Gaussian mixture background model, it is considered that the color information between pixels is uncorrelated and the processing of each pixel is independent of each other. For each pixel in a video image, the change of its value in a sequential image can be seen as a random process that produces pixel values continuously, i.e. Gaussian distribution is used to describe the regularity of color rendering of each pixel in single mode (single peak) and multi-mode (multi-peak).)
    2020-11-01 09:49:54下载
    积分：1
  • fastNLmeans2
    快速非局部均值，用来消除噪声，效果良好，适合于细节较多的图像(Fast nonlocal means is used to eliminate noise and achieve good results, which is suitable for more detailed images.)
    2021-04-01 17:29:08下载
    积分：1
  • C_V
    CV水平集模型实现对图像的分割，收敛快，收敛效果优于GAC 模型。(CV levelset model to realize image segementation)
    2012-07-11 08:51:55下载
    积分：1
  • 696518资源总数
  • 104544会员总数
  • 20今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2025 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！