def discriminator(x, f_dim, output_size, c_dim, is_training=True):
# Network
net = x
with slim.arg_scope([conv2d], activation_fn=lrelu, is_spectral_norm=True,
kernel_size=[5, 5], stride=[2, 2], update_collection=None):
net = conv2d(net, f_dim, name='conv0')
net = conv2d(net, f_dim, name='conv1')
net = conv2d(net, f_dim, name='conv2')
net = conv2d(net, f_dim, name='conv3')
net = tf.reshape(net, [-1, output_size // 16 * output_size // 16 * f_dim])
logits = fully_connected(net, 1, activation_fn=None, is_spectral_norm=True,
update_collection=None, name='fc4')
logits = tf.squeeze(logits, -1)
return logits
def discriminator(x, f_dim, output_size, c_dim, is_training=True):
# Network
net = x
with slim.arg_scope([conv2d],
activation_fn=lrelu,
is_spectral_norm=False,
update_collection=None):
net = conv2d(net,
f_dim,
kernel_size=[3, 3],
stride=[1, 1],
name='conv0_0')
net = conv2d(net,
f_dim,
kernel_size=[4, 4],
stride=[2, 2],
name='conv0_1')
net = conv2d(net,
2 * f_dim,
kernel_size=[3, 3],
stride=[1, 1],
name='conv1_0')
net = conv2d(net,
2 * f_dim,
kernel_size=[4, 4],
stride=[2, 2],
name='conv1_1')
net = conv2d(net,
4 * f_dim,
kernel_size=[3, 3],
stride=[1, 1],
name='conv2_0')
net = conv2d(net,
4 * f_dim,
kernel_size=[4, 4],
stride=[2, 2],
name='conv2_1')
net = conv2d(net,
8 * f_dim,
kernel_size=[3, 3],
stride=[1, 1],
name='conv3_0')
net = tf.reshape(net,
[-1, output_size // 8 * output_size // 8 * 8 * f_dim])
# net = slim.fully_connected(net, 512, activation_fn=lrelu)
logits = fully_connected(net,
1,
activation_fn=None,
is_spectral_norm=False,
update_collection=None)
logits = tf.squeeze(logits, -1)
return logits
def discriminator(x, f_dim, output_size, c_dim, is_training=True):
# Network
net = x
argscope_conv2d = slim.arg_scope([conv2d], kernel_size=[4, 4], stride=[2, 2],
activation_fn=tf.nn.relu, is_spectral_norm=True, update_collection=None)
with argscope_conv2d:
net = conv2d(net, f_dim, name='conv0_0')
dnet = conv2d(net, f_dim, kernel_size=[3, 3], stride=[1, 1], name='conv0_1')
net += 1e-1 * conv2d(dnet, f_dim, kernel_size=[3, 3], stride=[1, 1], name='conv0_2')
net = conv2d(net, f_dim, name='conv1_0')
dnet = conv2d(net, f_dim // 2, kernel_size=[3, 3], stride=[1, 1], name='conv1_1')
net += 1e-1 * conv2d(dnet, f_dim, kernel_size=[3, 3], stride=[1, 1], name='conv1_2')
net = conv2d(net, f_dim, name='conv2_0')
dnet = conv2d(net, f_dim // 2, kernel_size=[3, 3], stride=[1, 1], name='conv2_1')
net += 1e-1 * conv2d(dnet, f_dim, kernel_size=[3, 3], stride=[1, 1], name='conv2_2')
net = conv2d(net, f_dim, name='conv3_0')
dnet = conv2d(net, f_dim // 2, kernel_size=[3, 3], stride=[1, 1], name='conv3_1')
net += 1e-1 * conv2d(dnet, f_dim, kernel_size=[3, 3], stride=[1, 1], name='conv3_2')
net = tf.reshape(net, [-1, output_size // 16 * output_size // 16 * f_dim])
logits = fully_connected(net, 1, activation_fn=None, is_spectral_norm=True, update_collection=None, name='fc4')
logits = tf.squeeze(logits, -1)
return logits