Paper:
DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
WGAN: Wasserstein GAN
WGAN-GP: Improved Training of Wasserstein GANs
LSGAN: Least Squares Generative Adversarial Networks
SNGAN: Spectral normalization for generative adversarial networks
RSGAN: The relativistic discriminator: a key element missing from standard GAN
损失函数如下表所示：
| DCGAN |   |
| WGAN |   |
| WGAN-GP |   |
| LSGAN |   |
| SNGAN |   |
| RSGAN |
|
五种GAN不同迭代次数生成样本，对比结果如下图所示：
代码具体请参看我的Github:
https://github.com/MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_RaSGAN_TensorFlow
class GAN:
#Architecture of generator and discriminator just like DCGAN.
def __init__(self):
self.Z = tf.placeholder("float", [batchsize, 100])
self.img = tf.placeholder("float", [batchsize, img_H, img_W, img_C])
D = Discriminator("discriminator")
G = Generator("generator")
self.fake_img = G(self.Z)
if GAN_type == "DCGAN":
#DCGAN, paper: UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
self.fake_logit = tf.nn.sigmoid(D(self.fake_img))
self.real_logit = tf.nn.sigmoid(D(self.img, reuse=True))
self.d_loss = - (tf.reduce_mean(tf.log(self.real_logit + epsilon)) + tf.reduce_mean(tf.log(1 - self.fake_logit + epsilon)))
self.g_loss = - tf.reduce_mean(tf.log(self.fake_logit + epsilon))
self.opt_D = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "WGAN":
#WGAN, paper: Wasserstein GAN
self.fake_logit = D(self.fake_img)
self.real_logit = D(self.img, reuse=True)
self.d_loss = -tf.reduce_mean(self.real_logit) + tf.reduce_mean(self.fake_logit)
self.g_loss = -tf.reduce_mean(self.fake_logit)
self.clip = []
for _, var in enumerate(D.var):
self.clip.append(tf.clip_by_value(var, -0.01, 0.01))
self.opt_D = tf.train.RMSPropOptimizer(5e-5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.RMSPropOptimizer(5e-5).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "WGAN-GP":
#WGAN-GP, paper: Improved Training of Wasserstein GANs
self.fake_logit = D(self.fake_img)
self.real_logit = D(self.img, reuse=True)
e = tf.random_uniform([batchsize, 1, 1, 1], 0, 1)
x_hat = e * self.img + (1 - e) * self.fake_img
grad = tf.gradients(D(x_hat, reuse=True), x_hat)[0]
self.d_loss = tf.reduce_mean(self.fake_logit - self.real_logit) + 10 * tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_sum(tf.square(grad), axis=[1, 2, 3])) - 1))
self.g_loss = tf.reduce_mean(-self.fake_logit)
self.opt_D = tf.train.AdamOptimizer(1e-4, beta1=0., beta2=0.9).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(1e-4, beta1=0., beta2=0.9).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "LSGAN":
#LSGAN, paper: Least Squares Generative Adversarial Networks
self.fake_logit = D(self.fake_img)
self.real_logit = D(self.img, reuse=True)
self.d_loss = tf.reduce_mean(0.5 * tf.square(self.real_logit - 1) + 0.5 * tf.square(self.fake_logit))
self.g_loss = tf.reduce_mean(0.5 * tf.square(self.fake_logit - 1))
self.opt_D = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "SNGAN":
#SNGAN, paper: SPECTRAL NORMALIZATION FOR GENERATIVE ADVERSARIAL NETWORKS
self.fake_logit = tf.nn.sigmoid(D(self.fake_img, is_sn=True))
self.real_logit = tf.nn.sigmoid(D(self.img, reuse=True, is_sn=True))
self.d_loss = - (tf.reduce_mean(tf.log(self.real_logit + epsilon) + tf.log(1 - self.fake_logit + epsilon)))
self.g_loss = - tf.reduce_mean(tf.log(self.fake_logit + epsilon))
self.opt_D = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "RSGAN":
# RSGAN, paper: The relativistic discriminator: a key element missing from standard GAN
self.fake_logit = D(self.fake_img)
self.real_logit = D(self.img, reuse=True)
self.d_loss = - tf.reduce_mean(tf.log(tf.nn.sigmoid(self.real_logit - self.fake_logit) + epsilon))
self.g_loss = - tf.reduce_mean(tf.log(tf.nn.sigmoid(self.fake_logit - self.real_logit) + epsilon))
self.opt_D = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "RaHingeGAN":
self.fake_logit = D(self.fake_img)
self.real_logit = D(self.img, reuse=True)
d_tiled_r = self.real_logit - tf.reduce_mean(self.fake_logit, 0)
d_tiled_f = self.fake_logit - tf.reduce_mean(self.real_logit, 0)
self.d_loss = tf.reduce_mean(tf.maximum(0., 1. - d_tiled_r)) + tf.reduce_mean(tf.maximum(0., 1. + d_tiled_f))
self.g_loss = tf.reduce_mean(tf.maximum(0., 1. - d_tiled_f)) + tf.reduce_mean(tf.maximum(0., 1. + d_tiled_r))
self.opt_D = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.g_loss, var_list=G.var)
elif GAN_type == "RSGAN-GP":
self.fake_logit = D(self.fake_img)
self.real_logit = D(self.img, reuse=True)
e = tf.random_uniform([batchsize, 1, 1, 1], 0, 1)
x_hat = e * self.img + (1 - e) * self.fake_img
grad = tf.gradients(D(x_hat, reuse=True), x_hat)[0]
self.d_loss = - tf.reduce_mean(tf.log(tf.nn.sigmoid(self.real_logit - self.fake_logit) + epsilon)) + 10 * tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_sum(tf.square(grad), axis=[1, 2, 3])) - 1))
self.g_loss = - tf.reduce_mean(tf.log(tf.nn.sigmoid(self.fake_logit - self.real_logit) + epsilon))
self.opt_D = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.d_loss, var_list=D.var)
self.opt_G = tf.train.AdamOptimizer(2e-4, beta1=0.5).minimize(self.g_loss, var_list=G.var)
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
来源：CSDN
作者：MarTin Guo
链接：https://blog.csdn.net/Geoffrey_MT/article/details/81198504