''' GAN implementation, just a demo. ''' ```python # pd for short, should be more concise. from paddle.v2 as pd import numpy as np import logging ```


The original GAN paper.

# Conditional-GAN should be a class. ### Class member function: the initializer. ```python class DCGAN(object): def __init__(self, y_dim=None): # hyper parameters self.y_dim = y_dim # conditional gan or not self.batch_size = 100 self.z_dim = z_dim # input noise dimension # define parameters of discriminators self.D_W0 = pd.Variable(shape=[3,3, 1, 128], data=pd.gaussian_normal_randomizer()) self.D_b0 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data self.D_W1 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer()) self.D_b1 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data self.D_W2 = pd.Varialble(np.random.rand(128, 1)) self.D_b2 = pd.Variable(np.zeros(128)) self.theta_D = [self.D_W0, self.D_b0, self.D_W1, self.D_b1, self.D_W2, self.D_b2] # define parameters of generators self.G_W0 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer()) self.G_b0 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data self.G_W1 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer()) self.G_b1 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data self.G_W2 = pd.Varialble(np.random.rand(128, 1)) self.G_b2 = pd.Variable(np.zeros(128)) self.theta_G = [self.G_W0, self.G_b0, self.G_W1, self.G_b1, self.G_W2, self.G_b2] ``` ### Class member function: Generator Net ```python def generator(self, z, y = None): # Generator Net if not self.y_dim: z = pd.concat(1, [z, y]) G_h0 = pd.fc(z, self.G_w0, self.G_b0) G_h0_bn = pd.batch_norm(G_h0) G_h0_relu = pd.relu(G_h0_bn) G_h1 = pd.deconv(G_h0_relu, self.G_w1, self.G_b1) G_h1_bn = pd.batch_norm(G_h1) G_h1_relu = pd.relu(G_h1_bn) G_h2 = pd.deconv(G_h1_relu, self.G_W2, self.G_b2)) G_im = pd.tanh(G_im) return G_im ``` ### Class member function: Discriminator Net ```python def discriminator(self, image): # Discriminator Net D_h0 = pd.conv2d(image, self.D_w0, self.D_b0) D_h0_bn = pd.batchnorm(h0) D_h0_relu = pd.lrelu(h0_bn) D_h1 = pd.conv2d(D_h0_relu, self.D_w1, self.D_b1) D_h1_bn = pd.batchnorm(D_h1) D_h1_relu = pd.lrelu(D_h1_bn) D_h2 = pd.fc(D_h1_relu, self.D_w2, self.D_b2) return D_h2 ``` ### Class member function: Build the model ```python def build_model(self): # input data if self.y_dim: self.y = pd.data(pd.float32, [self.batch_size, self.y_dim]) self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) self.z = pd.data(tf.float32, [None, self.z_size]) # if conditional GAN if self.y_dim: self.G = self.generator(self.z, self.y) self.D_t = self.discriminator(self.images) # generated fake images self.sampled = self.sampler(self.z, self.y) self.D_f = self.discriminator(self.images) else: # original version of GAN self.G = self.generator(self.z) self.D_t = self.discriminator(self.images) # generate fake images self.sampled = self.sampler(self.z) self.D_f = self.discriminator(self.images) self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size)) self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size)) self.d_loss = self.d_loss_real + self.d_loss_fake self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_f, np.ones(self.batch_szie)) ``` # Main function for the demo: ```python if __name__ == "__main__": # dcgan dcgan = DCGAN() dcgan.build_model() # load mnist data data_X, data_y = self.load_mnist() # Two subgraphs required!!! d_optim = pd.train.Adam(lr = .001, beta= .1).minimize(self.d_loss, ) g_optim = pd.train.Adam(lr = .001, beta= .1).minimize(self.g_loss) # executor sess = pd.executor() # training for epoch in xrange(10000): for batch_id in range(N / batch_size): idx = ... # sample a batch batch_im, batch_label = data_X[idx:idx+batch_size], data_y[idx:idx+batch_size] # sample z batch_z = np.random.uniform(-1., 1., [batch_size, z_dim]) if batch_id % 2 == 0: sess.eval(d_optim, feed_dict = {dcgan.images: batch_im, dcgan.y: batch_label, dcgan.z: batch_z}) else: sess.eval(g_optim, feed_dict = {dcgan.z: batch_z}) ```