WSGAN matlab代码

以下是Wasserstein GAN（WGANGP）的MATLAB代码示例：

生成器代码：

function [generated_image] = generator(z, output_size)
% This function defines the generator network architecture
% Inputs:
% z: Input noise vector
% output_size: Desired output image size
% Outputs:
% generated_image: Generated image

% Define the generator network architecture
num_filters = 128;
num_layers = 4;
kernel_size = 5;
stride = 2;

% Define the initial layer
layer_input = z;

% Define the hidden layers
for i=1:num_layers
    num_filters = num_filters * 2;
    layer_output = transposed_conv2d(layer_input, num_filters, kernel_size, stride);
    layer_output = batch_norm(layer_output);
    layer_output = leaky_relu(layer_output);
    layer_input = layer_output;
end

% Define the final output layer
generated_image = tanh(transposed_conv2d(layer_input, output_size(3), kernel_size, stride));

end

判别器代码：

function [discriminator_output] = discriminator(x, input_size)
% This function defines the discriminator network architecture
% Inputs:
% x: Input image
% input_size: Input image size
% Outputs:
% discriminator_output: Discriminator output

% Define the discriminator network architecture
num_filters = 64;
num_layers = 4;
kernel_size = 5;
stride = 2;

% Define the initial layer
layer_input = x;

% Define the hidden layers
for i=1:num_layers
    layer_output = conv2d(layer_input, num_filters, kernel_size, stride);
    layer_output = leaky_relu(layer_output);
    layer_input = layer_output;
    num_filters = num_filters * 2;
end

% Define the final output layer
discriminator_output = fully_connected(flatten(layer_input), 1);

end

训练代码：

% Define the hyperparameters
batch_size = 64;
noise_size = 100;
num_epochs = 200;
learning_rate = 0.0002;
beta1 = 0.5;
beta2 = 0.999;

% Load the dataset
dataset = load_dataset('path/to/dataset');

% Define the generator and discriminator networks
generator_network = @(z) generator(z, [64, 64, 3]);
discriminator_network = @(x) discriminator(x, [64, 64, 3]);

% Define the loss function
loss_function = @(real_images, fake_images, discriminator_network) wgan_loss(real_images, fake_images, discriminator_network);

% Train the WGAN
[wgan_model, wgan_loss_history] = wgan_train(dataset, generator_network, discriminator_network, loss_function, ...
    'BatchSize', batch_size, 'NoiseSize', noise_size, 'NumEpochs', num_epochs, ...
    'LearningRate', learning_rate, 'Beta1', beta1, 'Beta2', beta2);

注意：上述代码仅为示例，可能需要根据实际情况进行修改和调整。