def testUnrollMicrobatches(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([1.0, 2.0])
data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
num_microbatches = 4
dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(
dp_sum_query, 1e6, num_microbatches / 1e6)
opt = cls(
dp_sum_query,
num_microbatches=num_microbatches,
learning_rate=2.0,
unroll_microbatches=True)
self.evaluate(tf.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
# Expected gradient is sum of differences divided by number of
# microbatches.
gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
grads_and_vars = sess.run(gradient_op)
self.assertAllCloseAccordingToType([-2.5, -2.5], grads_and_vars[0][0])
def linear_model_fn(features, labels, mode):
preds = tf.keras.layers.Dense(
1, activation='linear', name='dense').apply(features['x'])
vector_loss = tf.squared_difference(labels, preds)
scalar_loss = tf.reduce_mean(vector_loss)
dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
optimizer = dp_optimizer.DPGradientDescentOptimizer(
dp_sum_query,
num_microbatches=1,
learning_rate=1.0)
global_step = tf.train.get_global_step()
train_op = optimizer.minimize(loss=vector_loss, global_step=global_step)
return tf.estimator.EstimatorSpec(
mode=mode, loss=scalar_loss, train_op=train_op)
def __init__(self,
l2_norm_clip,
noise_multiplier,
num_microbatches=None,
ledger=None,
unroll_microbatches=False,
*args,
**kwargs):
dp_sum_query = gaussian_query.GaussianSumQuery(
l2_norm_clip, l2_norm_clip * noise_multiplier)
if ledger:
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query,
ledger=ledger)
super(DPGaussianOptimizerClass,
self).__init__(dp_sum_query, num_microbatches,
unroll_microbatches, *args, **kwargs)
def testClippingNorm(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0, 0.0])
data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
self.evaluate(tf.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([0.0, 0.0], self.evaluate(var0))
# Expected gradient is sum of differences.
gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
grads_and_vars = sess.run(gradient_op)
self.assertAllCloseAccordingToType([-0.6, -0.8], grads_and_vars[0][0])
def testNoiseMultiplier(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0])
data0 = tf.Variable([[0.0]])
dp_sum_query = gaussian_query.GaussianSumQuery(4.0, 8.0)
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
self.evaluate(tf.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([0.0], self.evaluate(var0))
gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
grads = []
for _ in range(1000):
grads_and_vars = sess.run(gradient_op)
grads.append(grads_and_vars[0][0])
# Test standard deviation is close to l2_norm_clip * noise_multiplier.
self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
def test_sum_query(self):
record1 = tf.constant([2.0, 0.0])
record2 = tf.constant([-1.0, 1.0])
population_size = tf.Variable(0)
selection_probability = tf.Variable(1.0)
query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0)
query = privacy_ledger.QueryWithLedger(query, population_size,
selection_probability)
# First sample.
tf.compat.v1.assign(population_size, 10)
tf.compat.v1.assign(selection_probability, 0.1)
test_utils.run_query(query, [record1, record2])
expected_queries = [[10.0, 0.0]]
formatted = query.ledger.get_formatted_ledger_eager()
sample_1 = formatted[0]
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
# Second sample.
tf.compat.v1.assign(population_size, 20)
tf.compat.v1.assign(selection_probability, 0.2)
test_utils.run_query(query, [record1, record2])
formatted = query.ledger.get_formatted_ledger_eager()
sample_1, sample_2 = formatted
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
self.assertAllClose(sample_2.population_size, 20.0)
self.assertAllClose(sample_2.selection_probability, 0.2)
self.assertAllClose(sample_2.queries, expected_queries)