def step(self, samples):
# descriminator inference using true images
self.discriminator = model.Descriminator(FLAGS.batch_size, FLAGS.dc_dim)
self.D1, D1_logits, D1_inter = self.discriminator.inference(samples)
return self.D1
def step(self, images, z):
self.generator = model.Generator(FLAGS.batch_size, FLAGS.gc_dim)
self.G = self.generator.inference(z)
# descriminator inference using true images
self.discriminator_list = []
self.D1_list = []
self.D2_list = []
D1_logits_list = []
D2_logits_list = []
D1_inter_list = []
D2_inter_list = []
self.samples = self.generator.sampler(z, reuse=True)
for i in range(self.d_num):
discriminator = model.Descriminator(FLAGS.batch_size, FLAGS.dc_dim)
D1, D1_logits, D1_inter = discriminator.inference(images, num=i)
# descriminator inference using sampling with G
D2, D2_logits, D2_inter = discriminator.inference(self.G, reuse=True, num=i)
self.D1_list.append(D1)
self.D2_list.append(D2)
D1_logits_list.append(D1_logits)
D2_logits_list.append(D2_logits)
D1_inter_list.append(D1_inter)
D2_inter_list.append(D2_inter)
return images, D1_logits_list, D2_logits_list, D1_inter_list, D2_inter_list
def step(self, z):
z_sum = tf.summary.histogram("z", z)
self.generator = model.Generator(FLAGS.batch_size_v, FLAGS.gc_dim_v)
self.G = self.generator.inference(z)
# descriminator inference using true images
self.discriminator = model.Descriminator(FLAGS.batch_size_v,
FLAGS.dc_dim_v)
#self.D1, D1_logits = self.discriminator.inference(images)
# descriminator inference using sampling with G
self.samples = self.generator.sampler(z, reuse=True, trainable=False)
def step(self, images, z):
z_sum = tf.summary.histogram("z", z)
self.generator = model.Generator(FLAGS.batch_size, FLAGS.gc_dim)
G = self.generator.inference(z)
# descriminator inference using true images
discriminator = model.Descriminator(FLAGS.batch_size, FLAGS.dc_dim)
self.D1, D1_logits = discriminator.inference(images)
# descriminator inference using sampling with G
samples = self.generator.inference(z, reuse=True)
self.D2, D2_logits = discriminator.inference(samples, reuse=True)
d1_sum = tf.summary.histogram("d1", self.D1)
d2_sum = tf.summary.histogram("d2", self.D1)
G_sum = tf.summary.histogram("G", G)
return images, D1_logits, D2_logits, G_sum, z_sum, d1_sum, d2_sum
def step(self, z):
z_sum = tf.summary.histogram("z", z)
self.generator = model.Generator(FLAGS.batch_size, FLAGS.gc_dim)
self.G = self.generator.inference(z)
# descriminator inference using true images
self.discriminator = model.Descriminator(FLAGS.batch_size, FLAGS.dc_dim)
#self.D1, D1_logits = self.discriminator.inference(images)
# descriminator inference using sampling with G
self.samples = self.generator.sampler(z, reuse=True)
self.D2, D2_logits, D2_inter = self.discriminator.inference(self.G, reuse=False)
# d1_sum = tf.summary.histogram("d1", self.D1)
d2_sum = tf.summary.histogram("d2", self.D2)
G_sum = tf.summary.histogram("G", self.G)
# return images, D1_logits, D2_logits, G_sum, z_sum, d1_sum, d2_sum
# return D2_logits, G_sum, z_sum, d1_sum, d2_sum
return D2_logits, G_sum, z_sum, d2_sum