def _get_gan(with_dp=False):
gan_loss_fns = gan_losses.get_gan_loss_fns('wasserstein')
server_gen_optimizer = tf.keras.optimizers.Adam()
client_disc_optimizer = tf.keras.optimizers.Adam()
train_generator_fn = gan_training_tf_fns.create_train_generator_fn(
gan_loss_fns, server_gen_optimizer)
train_discriminator_fn = gan_training_tf_fns.create_train_discriminator_fn(
gan_loss_fns, client_disc_optimizer)
if with_dp:
dp_average_query = tensorflow_privacy.QuantileAdaptiveClipAverageQuery(
initial_l2_norm_clip=BEFORE_DP_L2_NORM_CLIP,
noise_multiplier=0.3,
target_unclipped_quantile=3,
learning_rate=0.1,
clipped_count_stddev=0.0,
expected_num_records=10,
denominator=10.0)
else:
dp_average_query = None
return tff_gans.GanFnsAndTypes(
generator_model_fn=one_dim_gan.create_generator,
discriminator_model_fn=one_dim_gan.create_discriminator,
dummy_gen_input=next(iter(one_dim_gan.create_generator_inputs())),
dummy_real_data=next(iter(one_dim_gan.create_real_data())),
train_generator_fn=train_generator_fn,
train_discriminator_fn=train_discriminator_fn,
server_disc_update_optimizer_fn=lambda: tf.keras.optimizers.SGD(lr=1.0
),
train_discriminator_dp_average_query=dp_average_query)
def _get_gan(gen_model_fn, disc_model_fn, gan_loss_fns, gen_optimizer,
disc_optimizer, server_gen_inputs_dataset,
client_real_images_tff_data, use_dp, dp_l2_norm_clip,
dp_noise_multiplier, clients_per_round):
"""Construct instance of tff_gans.GanFnsAndTypes class."""
dummy_gen_input = next(iter(server_gen_inputs_dataset))
dummy_real_data = next(
iter(
client_real_images_tff_data.create_tf_dataset_for_client(
client_real_images_tff_data.client_ids[0])))
train_generator_fn = gan_training_tf_fns.create_train_generator_fn(
gan_loss_fns, gen_optimizer)
train_discriminator_fn = gan_training_tf_fns.create_train_discriminator_fn(
gan_loss_fns, disc_optimizer)
dp_average_query = None
if use_dp:
dp_average_query = tensorflow_privacy.GaussianAverageQuery(
l2_norm_clip=dp_l2_norm_clip,
sum_stddev=dp_l2_norm_clip * dp_noise_multiplier,
denominator=clients_per_round)
return tff_gans.GanFnsAndTypes(
generator_model_fn=gen_model_fn,
discriminator_model_fn=disc_model_fn,
dummy_gen_input=dummy_gen_input,
dummy_real_data=dummy_real_data,
train_generator_fn=train_generator_fn,
train_discriminator_fn=train_discriminator_fn,
server_disc_update_optimizer_fn=lambda: tf.keras.optimizers.SGD(lr=1.0
),
train_discriminator_dp_average_query=dp_average_query)
def _get_train_generator_and_discriminator_fns():
gan_loss_fns = gan_losses.get_gan_loss_fns('wasserstein')
train_generator_fn = gan_training_tf_fns.create_train_generator_fn(
gan_loss_fns, tf.keras.optimizers.Adam())
train_discriminator_fn = gan_training_tf_fns.create_train_discriminator_fn(
gan_loss_fns, tf.keras.optimizers.Adam())
return train_generator_fn, train_discriminator_fn
def test_create_train_generator_fn(self):
train_generator_fn = gan_training_tf_fns.create_train_generator_fn(
GAN_LOSS_FNS, tf.keras.optimizers.Adam())
self.assertListEqual(
['generator', 'discriminator', 'generator_inputs'],
train_generator_fn.function_spec.fullargspec.args)
def _get_train_generator_and_discriminator_fns():
train_generator_fn = gan_training_tf_fns.create_train_generator_fn(
GAN_LOSS_FNS, tf.keras.optimizers.Adam())
train_discriminator_fn = gan_training_tf_fns.create_train_discriminator_fn(
GAN_LOSS_FNS, tf.keras.optimizers.Adam())
return train_generator_fn, train_discriminator_fn
