def discriminator2_32X32_dc(x,
training=True,
reuse=True,
name="discriminator"):
# Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
# Architecture : (64)4c2s-(128)4c2s_BL-FC1024_BL-FC1_S
with tf.variable_scope(name, reuse=reuse):
net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name='d_conv1'))
net = lrelu(
bn(conv2d(net, 64 * 2, 4, 4, 2, 2, name='d_conv2'),
is_training=training,
scope='d_bn2'))
net = lrelu(
bn(conv2d(net, 64 * 4, 4, 4, 2, 2, name='d_conv3'),
is_training=training,
scope='d_bn3'))
net = lrelu(
bn(conv2d(net, 64 * 8, 4, 4, 2, 2, name='d_conv4'),
is_training=training,
scope='d_bn4'))
# net = tf.reshape(net, [batch_size, -1])
# net = lrelu(bn(linear(net, 1024, scope='d_fc3'), is_training=training, scope='d_bn3'))
# net = fc(net, 1024)
# net = lrelu(bn(net, is_training=training, scope='d_bn5'))
# net = fc(net, 1024)
# net = lrelu(bn(net, is_training=training, scope='d_bn3'))
# out_logit = linear(net, 1, scope='d_fc4')
# out_logit = linear(tf.reshape(net, [64, -1]), 1, 'd_h4_lin')
# out_logit = linear(net, 1, scope='d_fc4')
# out = tf.nn.sigmoid(out_logit)
out_logit = fc(net, 1)
return out_logit
def mgan_dis(x, training=True, reuse=True, name = "discriminator"):
with tf.variable_scope(name, reuse=reuse):
net = lrelu(conv2d(x, 128, 5, 5, 2, 2, name='d_conv1'))
net = lrelu(bn(conv2d(net, 256, 5, 5, 2, 2, name='d_conv2'), is_training=training, scope='d_bn2'))
net = lrelu(bn(conv2d(net, 512, 5, 5, 2, 2, name='d_conv3'), is_training=training, scope='d_bn3'))
# net = tf.reshape(net, [batch_size, -1])
# net = lrelu(bn(linear(net, 1024, scope='d_fc3'), is_training=training, scope='d_bn3'))
out_logit = fc(net,1)
# net = lrelu(bn(net, is_training=training, scope='d_bn3'))
# out_logit = linear(net, 1, scope='d_fc4')
return out_logit