def PG_GAN(G,D,latent_size,label_size,resolution,num_channels):
print("Latent size:")
print(latent_size)
print("Label size:")
print(label_size)
#inputs = [Input(shape=[latent_size], name='GANlatents')]
#if label_size:
# inputs += [Input(shape=[label_size], name='GANlabels')]
#fake = G(inputs)
#GAN_out = D(fake)
#G_train = Model(inputs = inputs,outputs = [GAN_out],name = "PG_GAN")
G_train = Sequential([G, D])
G_train.cur_lod = G.cur_lod
shape = D.get_input_shape_at(0)[1:]
gen_input, real_input, interpolation = Input(shape), Input(shape), Input(shape)
sub = Subtract()([D(gen_input), D(real_input)])
norm = GradNorm()([D(interpolation), interpolation])
D_train = Model([gen_input, real_input, interpolation], [sub, norm])
D_train.cur_lod = D.cur_lod
return G_train,D_train
def PG_GAN(G, D, label_size, resolution, num_channels):
print("Label size:")
print(label_size)
G_train = Sequential([G, D])
G_train.cur_lod = G.cur_lod
shape = D.get_input_shape_at(0)[1:]
gen_input, real_input, interpolation = Input(shape), Input(shape), Input(shape)
sub = Subtract()([D(gen_input), D(real_input)])
norm = GradNorm()([D(interpolation), interpolation])
D_train = Model([gen_input, real_input, interpolation], [sub, norm])
D_train.cur_lod = D.cur_lod
return G_train, D_train