GAN-Base-on-Matlab-master.zip_GAN matlab_GaN_GaN matlab_goo7w_m

# GAN-Base-on-Matlab ## data ### mnist_uint8 [download link](https://github.com/rasmusbergpalm/DeepLearnToolbox/blob/master/data/mnist_uint8.mat) ## Example ### example_1 * network structure: ``` generator.layers = { struct('type', 'input', 'output_shape', [100, batch_size]) struct('type', 'fully_connect', 'output_shape', [3136, batch_size], 'activation', 'leaky_relu') struct('type', 'reshape', 'output_shape', [7,7,64, batch_size]) struct('type', 'conv2d_transpose', 'output_shape', [14, 14, 32, batch_size], 'kernel_size', 5, 'stride', 2, 'padding', 'same', 'activation', 'leaky_relu') struct('type', 'conv2d_transpose', 'output_shape', [28, 28, 1, batch_size], 'kernel_size', 5, 'stride', 2, 'padding', 'same', 'activation', 'sigmoid') }; discriminator.layers = { struct('type', 'input', 'output_shape', [28, 28, 1, batch_size]) struct('type', 'conv2d', 'output_maps', 32, 'kernel_size', 5, 'padding', 'same', 'activation', 'leaky_relu') struct('type', 'sub_sampling', 'scale', 2) struct('type', 'conv2d', 'output_maps', 64, 'kernel_size', 5, 'padding', 'same', 'activation', 'leaky_relu') struct('type', 'sub_sampling', 'scale', 2) struct('type', 'reshape', 'output_shape', [3136, batch_size]) struct('type', 'fully_connect', 'output_shape', [1, batch_size], 'activation', 'sigmoid') }; ``` * result: <p align="center"> <img src="https://github.com/JZhaoCH/GAN-Base-on-Matlab/blob/master/readme_images/1.png"> </p> <p align="center"> <img src="https://github.com/JZhaoCH/GAN-Base-on-Matlab/blob/master/readme_images/2.png"> </p> ### example_2 * network structure: ``` generator.layers = { struct('type', 'input', 'output_shape', [100, batch_size]) struct('type', 'fully_connect', 'output_shape', [1024, batch_size], 'activation', 'relu') struct('type', 'fully_connect', 'output_shape', [28*28, batch_size], 'activation', 'sigmoid') struct('type', 'reshape', 'output_shape', [28, 28, 1, batch_size]) }; discriminator.layers = { struct('type', 'input', 'output_shape', [28,28,1, batch_size]) struct('type', 'reshape', 'output_shape', [28*28, batch_size]) struct('type', 'fully_connect', 'output_shape', [1024, batch_size], 'activation', 'relu') struct('type', 'fully_connect', 'output_shape', [1, batch_size], 'activation', 'sigmoid') }; ``` * result: <p align="center"> <img src="https://github.com/JZhaoCH/GAN-Base-on-Matlab/blob/master/readme_images/3.png"> </p> ## Reference 1. `https://grzegorzgwardys.wordpress.com/2016/04/22/8/` 2. `Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning[J]. 2016.` 3. `https://github.com/rasmusbergpalm/DeepLearnToolbox/tree/master/CNN` 4. `http://neuralnetworksanddeeplearning.com/index.html`