def _build_graph(self, input_vars):
input, output = input_vars
input, output = input / 128.0 - 1, output / 128.0 - 1
with argscope([Conv2D, Deconv2D],
W_init=tf.truncated_normal_initializer(stddev=0.02)), \
argscope(LeakyReLU, alpha=0.2):
with tf.variable_scope('gen'):
fake_output = self.generator(input)
with tf.variable_scope('discrim'):
real_pred = self.discriminator(input, output)
with tf.variable_scope('discrim', reuse=True):
fake_pred = self.discriminator(input, fake_output)
self.g_loss, self.d_loss = build_GAN_losses(real_pred, fake_pred)
errL1 = tf.reduce_mean(tf.abs(fake_output - output), name='L1_loss')
self.g_loss = tf.add(self.g_loss, LAMBDA * errL1, name='total_g_loss')
add_moving_summary(errL1, self.g_loss)
# tensorboard visualization
if IN_CH == 1:
input = tf.image.grayscale_to_rgb(input)
if OUT_CH == 1:
output = tf.image.grayscale_to_rgb(output)
fake_output = tf.image.grayscale_to_rgb(fake_output)
viz = (tf.concat(2, [input, output, fake_output]) + 1.0) * 128.0
viz = tf.cast(viz, tf.uint8, name='viz')
tf.image_summary('gen', viz, max_images=max(30, BATCH))
all_vars = tf.trainable_variables()
self.g_vars = [v for v in all_vars if v.name.startswith('gen/')]
self.d_vars = [v for v in all_vars if v.name.startswith('discrim/')]
def _build_graph(self, input_vars):
image_pos = input_vars[0]
image_pos = tf.expand_dims(image_pos * 2.0 - 1, -1)
prior_prob = tf.constant([0.1] * 10, name='prior_prob')
# assume first 10 is categorical
ids = tf.multinomial(tf.zeros([BATCH, 10]), num_samples=1)[:, 0]
zc = tf.one_hot(ids, 10, name='zc_train')
zc = tf.placeholder_with_default(zc, [None, 10], name='zc')
z = tf.random_uniform(tf.pack([tf.shape(zc)[0], 90]),
-1,
1,
name='z_train')
z = tf.placeholder_with_default(z, [None, 90], name='z')
z = tf.concat(1, [zc, z], name='fullz')
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.generator(z)
tf.image_summary('gen', image_gen, max_images=30)
with tf.variable_scope('discrim'):
vecpos, _ = self.discriminator(image_pos)
with tf.variable_scope('discrim', reuse=True):
vecneg, dist_param = self.discriminator(image_gen)
logprob = tf.nn.log_softmax(
dist_param) # log prob of each category
# Q(c|x) = Q(zc | image_gen)
log_qc_given_x = tf.reduce_sum(logprob * zc, 1, name='logQc_x') # bx1
log_qc = tf.reduce_sum(prior_prob * zc, 1, name='logQc')
Elog_qc_given_x = tf.reduce_mean(log_qc_given_x, name='ElogQc_x')
Hc = tf.reduce_mean(-log_qc, name='Hc')
MIloss = tf.mul(Hc + Elog_qc_given_x, -1.0, name='neg_MI')
self.g_loss, self.d_loss = build_GAN_losses(vecpos, vecneg)
self.g_loss = tf.add(self.g_loss, MIloss, name='total_g_loss')
self.d_loss = tf.add(self.d_loss, MIloss, name='total_g_loss')
summary.add_moving_summary(MIloss, self.g_loss, self.d_loss, Hc,
Elog_qc_given_x)
all_vars = tf.trainable_variables()
self.g_vars = [v for v in all_vars if v.name.startswith('gen/')]
self.d_vars = [v for v in all_vars if v.name.startswith('discrim/')]
def _build_graph(self, input_vars):
image_pos = input_vars[0]
image_pos = image_pos / 128.0 - 1
z = tf.random_uniform([CFG.BATCH, CFG.Z_DIM], -1, 1, name='z_train')
z = tf.placeholder_with_default(z, [None, CFG.Z_DIM], name='z')
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.generator(z)
tf.image_summary('gen', image_gen, max_images=30)
with tf.variable_scope('discrim'):
vecpos = self.discriminator(image_pos)
with tf.variable_scope('discrim', reuse=True):
vecneg = self.discriminator(image_gen)
self.g_loss, self.d_loss = build_GAN_losses(vecpos, vecneg)
all_vars = tf.trainable_variables()
self.g_vars = [v for v in all_vars if v.name.startswith('gen/')]
self.d_vars = [v for v in all_vars if v.name.startswith('discrim/')]
