def discriminator_cate(self, zs, reuse=False, name='discriminator_cate'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(zs)
layer.linear_block(256, relu)
layer.linear_block(256, relu)
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def discriminator(self, X, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
layer = Stacker(X)
layer.conv_block(128, CONV_FILTER_5522, lrelu)
layer.conv_block(256, CONV_FILTER_5522, lrelu)
layer.reshape([self.batch_size, -1])
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def generator(self, z, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
layer = Stacker(z)
layer.add_layer(linear, 7 * 7 * 128)
layer.reshape([self.batch_size, 7, 7, 128])
layer.upscale_2x_block(256, CONV_FILTER_5522, relu)
layer.conv2d_transpose(self.Xs_shape, CONV_FILTER_5522)
layer.conv2d(self.input_c, CONV_FILTER_3311)
layer.sigmoid()
return layer.last_layer
def discriminator(self, X, net_shapes, reuse=False, name='discriminator'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(flatten(X))
for shape in net_shapes:
layer.linear(shape)
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def generator(self, z, net_shapes, reuse=False, name='generator'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(z)
for shape in net_shapes:
layer.linear(shape)
layer.linear(self.X_flatten_size)
layer.sigmoid()
layer.reshape(self.Xs_shape)
return layer.last_layer
def discriminator(self, X, Y, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
Y = linear(Y, self.input_h * self.input_w)
Y = reshape(Y, [self.batch_size, self.input_h, self.input_w, 1])
layer = Stacker(tf.concat((X, Y), axis=3))
layer.conv_block(128, CONV_FILTER_5522, lrelu)
layer.conv_block(256, CONV_FILTER_5522, lrelu)
layer.reshape([self.batch_size, -1])
layer.linear(1)
layer.sigmoid()
return layer.last_layer
def discriminator_gauss(self,
zs,
net_shapes,
reuse=False,
name='discriminator_gauss'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(zs)
for shape in net_shapes:
layer.linear_block(shape, relu)
layer.linear(1)
layer.sigmoid()
return layer.last_layer
