def discriminator(self, image, is_training, reuse=False):
with tf.variable_scope("discriminator"):
if reuse:
tf.get_variable_scope().reuse_variables()
h0 = lrelu(conv2d(image, self.discriminator_dim,
scope="d_h0_conv"))
h1 = lrelu(
batch_norm(conv2d(h0,
self.discriminator_dim * 2,
scope="d_h1_conv"),
is_training,
scope="d_bn_1"))
h2 = lrelu(
batch_norm(conv2d(h1,
self.discriminator_dim * 4,
scope="d_h2_conv"),
is_training,
scope="d_bn_2"))
h3 = lrelu(
batch_norm(conv2d(h2,
self.discriminator_dim * 8,
scope="d_h3_conv"),
is_training,
scope="d_bn_3"))
# h4 = lrelu(batch_norm(conv2d(h3, self.discriminator_dim * 8, scope="d_h4_conv"),
# is_training, scope="d_bn_4"))
# h5 = lrelu(batch_norm(conv2d(h4, self.discriminator_dim * 8, sh=1, sw=1, scope="d_h5_conv"),
# is_training, scope="d_bn_5"))
# real or fake binary loss
fc1 = fc(tf.reshape(h3, [self.batch_size, -1]), 8, scope="d_fc1")
fc2 = fc(fc1, 1, scope="d_fc2")
return tf.nn.sigmoid(fc2), fc2
def discriminator(self, image, is_training, reuse=False):
with tf.variable_scope("discriminator"):
if reuse:
tf.get_variable_scope().reuse_variables()
# [batch,256,256,1] -> [batch,128,128,64]
h0 = lrelu(conv2d(image, self.discriminator_dim,
scope="d_h0_conv"))
# [batch,128,128,64] -> [batch,64,64,64*2]
h1 = lrelu(
batch_norm(conv2d(h0,
self.discriminator_dim * 2,
scope="d_h1_conv"),
is_training,
scope="d_bn_1"))
# [batch,64,64,64*2] -> [batch,32,32,64*4]
h2 = lrelu(
batch_norm(conv2d(h1,
self.discriminator_dim * 4,
scope="d_h2_conv"),
is_training,
scope="d_bn_2"))
# [batch,32,32,64*4] -> [batch,31,31,64*8]
h3 = lrelu(
batch_norm(conv2d(h2,
self.discriminator_dim * 8,
sh=1,
sw=1,
scope="d_h3_conv"),
is_training,
scope="d_bn_3"))
# real or fake binary loss
fc1 = fc(tf.reshape(h3, [self.batch_size, -1]), 1, scope="d_fc1")
return tf.sigmoid(fc1), fc1