conf.generator_shared_hidden_units = [1000] conf.generator_hidden_units = [1000] conf.generator_activation_function = "elu" conf.generator_apply_dropout = False conf.generator_apply_batchnorm = False conf.generator_apply_batchnorm_to_input = False conf.decoder_hidden_units = [1000, 1000] conf.decoder_activation_function = "elu" conf.decoder_apply_dropout = False conf.decoder_apply_batchnorm = False conf.decoder_apply_batchnorm_to_input = False conf.discriminator_z_hidden_units = [1000, 1000] conf.discriminator_z_activation_function = "elu" conf.discriminator_z_apply_dropout = False conf.discriminator_z_apply_batchnorm = False conf.discriminator_z_apply_batchnorm_to_input = False conf.discriminator_y_hidden_units = [1000, 1000] conf.discriminator_y_activation_function = "elu" conf.discriminator_y_apply_dropout = False conf.discriminator_y_apply_batchnorm = False conf.discriminator_y_apply_batchnorm_to_input = False conf.q_z_x_type = aae.Q_Z_X_TYPE_GAUSSIAN aae = AAE(conf, name="aae") aae.load(args.model_dir)
if config.distribution_z == "deterministic":
generator_z.add(Linear(None, config.ndim_z))
elif config.distribution_z == "gaussian":
generator_z.add(Gaussian(None,
config.ndim_z)) # outputs mean and ln(var)
else:
raise Exception()
generator_y = Sequential()
generator_y.add(Linear(None, config.ndim_y))
params = {
"config": config.to_dict(),
"decoder": decoder.to_dict(),
"generator_shared": generator_shared.to_dict(),
"generator_z": generator_z.to_dict(),
"generator_y": generator_y.to_dict(),
"discriminator_y": discriminator_y.to_dict(),
"discriminator_z": discriminator_z.to_dict(),
}
with open(model_filename, "w") as f:
json.dump(params, f, indent=4, sort_keys=True, separators=(',', ': '))
aae = AAE(params)
aae.load(args.model_dir)
if args.gpu_device != -1:
cuda.get_device(args.gpu_device).use()
aae.to_gpu()