flags.LEARNING_RATE = 1e-3
else:
flags.LEARNING_RATE = 2e-4 # follow radford
flags.EXPONENTIAL_DECAY = True
noise_dist = UniformNoise(flags.NOISE_DIM)
input_noise_dist = UniformNoise(1)
from veegan import VEEGAN
model = VEEGAN(data_dist, noise_dist, input_noise_dist, flags, args)
elif METHOD == 'wgan':
noise_dist = NormalNoise(flags.NOISE_DIM)
eps_dist = UniformNoise(1)
from wgan import WGANGP
model = WGANGP(data_dist, noise_dist, eps_dist, flags, args)
elif METHOD == 'vae':
from vae import VAE
noise_dist = NormalNoise(flags.NOISE_DIM)
model = VAE(data_dist, noise_dist, flags, args)
elif METHOD == 'aae':
from aae import AAE
model = AAE(data_dist, noise_dist, flags, args)
elif METHOD == 'avbac':
from avbac import *
noise_dist = NormalNoise(flags.NOISE_DIM)
single_noise_dist = NormalNoise(PERTURB)
model = AVB_AC(data_dist, noise_dist, single_noise_dist, flags, args)
model.create_model()
evaluator = Evaluator(model)
evaluator.run(os.path.join(args.working_dir, 'model'), args.ckpt_id)
