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Use mean loss within each microbatch.

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PiperOrigin-RevId: 233832864
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Steve Chien 2019-02-13 14:41:52 -08:00 committed by A. Unique TensorFlower
parent 72305bcb10
commit f37c9d1ea1
2 changed files with 8 additions and 8 deletions
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4
privacy/optimizers/dp_optimizer.py
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@ -71,7 +71,8 @@ def make_optimizer_class(cls):
def process_microbatch(i, sample_state): def process_microbatch(i, sample_state):
"""Process one microbatch (record) with privacy helper.""" """Process one microbatch (record) with privacy helper."""
grads, _ = zip(*super(cls, self).compute_gradients( grads, _ = zip(*super(cls, self).compute_gradients(
tf.gather(microbatches_losses, [i]), var_list, gate_gradients, tf.reduce_mean(tf.gather(microbatches_losses,
[i])), var_list, gate_gradients,
aggregation_method, colocate_gradients_with_ops, grad_loss)) aggregation_method, colocate_gradients_with_ops, grad_loss))
grads_list = list(grads) grads_list = list(grads)
sample_state = self._dp_average_query.accumulate_record( sample_state = self._dp_average_query.accumulate_record(
@ -155,4 +156,3 @@ DPAdagradGaussianOptimizer = make_gaussian_optimizer_class(
DPAdamGaussianOptimizer = make_gaussian_optimizer_class(tf.train.AdamOptimizer) DPAdamGaussianOptimizer = make_gaussian_optimizer_class(tf.train.AdamOptimizer)
DPGradientDescentGaussianOptimizer = make_gaussian_optimizer_class( DPGradientDescentGaussianOptimizer = make_gaussian_optimizer_class(
tf.train.GradientDescentOptimizer) tf.train.GradientDescentOptimizer)

12
privacy/optimizers/dp_optimizer_test.py
View file

@ -37,16 +37,16 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
# Parameters for testing: optimizer, num_microbatches, expected answer. # Parameters for testing: optimizer, num_microbatches, expected answer.
@parameterized.named_parameters( @parameterized.named_parameters(
('DPGradientDescent 1', dp_optimizer.DPGradientDescentOptimizer, 1, ('DPGradientDescent 1', dp_optimizer.DPGradientDescentOptimizer, 1,
[-10.0, -10.0]), [-2.5, -2.5]),
('DPGradientDescent 2', dp_optimizer.DPGradientDescentOptimizer, 2, ('DPGradientDescent 2', dp_optimizer.DPGradientDescentOptimizer, 2,
[-5.0, -5.0]), [-2.5, -2.5]),
('DPGradientDescent 4', dp_optimizer.DPGradientDescentOptimizer, 4, ('DPGradientDescent 4', dp_optimizer.DPGradientDescentOptimizer, 4,
[-2.5, -2.5]), [-2.5, -2.5]),
('DPAdagrad 1', dp_optimizer.DPAdagradOptimizer, 1, [-10.0, -10.0]), ('DPAdagrad 1', dp_optimizer.DPAdagradOptimizer, 1, [-2.5, -2.5]),
('DPAdagrad 2', dp_optimizer.DPAdagradOptimizer, 2, [-5.0, -5.0]), ('DPAdagrad 2', dp_optimizer.DPAdagradOptimizer, 2, [-2.5, -2.5]),
('DPAdagrad 4', dp_optimizer.DPAdagradOptimizer, 4, [-2.5, -2.5]), ('DPAdagrad 4', dp_optimizer.DPAdagradOptimizer, 4, [-2.5, -2.5]),
('DPAdam 1', dp_optimizer.DPAdamOptimizer, 1, [-10.0, -10.0]), ('DPAdam 1', dp_optimizer.DPAdamOptimizer, 1, [-2.5, -2.5]),
('DPAdam 2', dp_optimizer.DPAdamOptimizer, 2, [-5.0, -5.0]), ('DPAdam 2', dp_optimizer.DPAdamOptimizer, 2, [-2.5, -2.5]),
('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5])) ('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5]))
def testBaseline(self, cls, num_microbatches, expected_answer): def testBaseline(self, cls, num_microbatches, expected_answer):
with self.cached_session() as sess: with self.cached_session() as sess: