def test_dp_monitor_gpu():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
batch_size = 16
batches = 128
epochs = 1
rdp = PrivacyMonitorFactory.create(policy='rdp',
num_samples=60000,
batch_size=batch_size,
initial_noise_multiplier=0.4,
noise_decay_rate=6e-3)
suggest_epoch = rdp.max_epoch_suggest()
LOGGER.info(TAG, 'The recommended maximum training epochs is: %s',
suggest_epoch)
network = Net()
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
model = Model(network, net_loss, net_opt)
LOGGER.info(TAG, "============== Starting Training ==============")
ds1 = ds.GeneratorDataset(dataset_generator(batch_size, batches),
["data", "label"])
model.train(epochs, ds1, callbacks=[rdp], dataset_sink_mode=False)
# or 'AdaGaussian', in which noise would be decayed with 'AdaGaussian'
# mechanism while be constant with 'Gaussian' mechanism.
noise_mech = NoiseMechanismsFactory().create(
cfg.noise_mechanisms,
norm_bound=cfg.norm_bound,
initial_noise_multiplier=cfg.initial_noise_multiplier,
decay_policy='Exp')
net_opt = nn.Momentum(params=network.trainable_params(),
learning_rate=cfg.lr,
momentum=cfg.momentum)
# Create a monitor for DP training. The function of the monitor is to
# compute and print the privacy budget(eps and delta) while training.
rdp_monitor = PrivacyMonitorFactory.create(
'rdp',
num_samples=60000,
batch_size=cfg.batch_size,
initial_noise_multiplier=cfg.initial_noise_multiplier,
per_print_times=234)
# Create the DP model for training.
model = DPModel(micro_batches=cfg.micro_batches,
norm_bound=cfg.norm_bound,
noise_mech=noise_mech,
network=network,
loss_fn=net_loss,
optimizer=net_opt,
metrics={"Accuracy": Accuracy()})
LOGGER.info(TAG, "============== Starting Training ==============")
model.train(cfg['epoch_size'],
ds_train,
callbacks=[ckpoint_cb, LossMonitor(), rdp_monitor],