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Fix bug in keras optimizers where tape was not being used with tensor (as opposed to callable) losses.

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PiperOrigin-RevId: 327276721
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Steve Chien 2020-08-18 11:59:56 -07:00 committed by A. Unique TensorFlower
parent 193ac3b1c8
commit 6dccd9b537
2 changed files with 37 additions and 3 deletions
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1
tensorflow_privacy/privacy/optimizers/dp_optimizer_keras.py
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@ -83,6 +83,7 @@ def make_keras_optimizer_class(cls):
if callable(var_list): if callable(var_list):
var_list = var_list() var_list = var_list()
else: else:
with tape:
microbatch_losses = tf.reduce_mean( microbatch_losses = tf.reduce_mean(
tf.reshape(loss, [self._num_microbatches, -1]), axis=1) tf.reshape(loss, [self._num_microbatches, -1]), axis=1)

35
tensorflow_privacy/privacy/optimizers/dp_optimizer_keras_test.py
View file

@ -42,7 +42,8 @@ class DPOptimizerComputeGradientsTest(tf.test.TestCase, parameterized.TestCase):
('DPAdagrad 4', dp_optimizer_keras.DPKerasAdagradOptimizer, 4, ('DPAdagrad 4', dp_optimizer_keras.DPKerasAdagradOptimizer, 4,
[-2.5, -2.5], [-0.5]), [-2.5, -2.5], [-0.5]),
) )
def testBaseline(self, cls, num_microbatches, expected_grad0, expected_grad1): def testBaselineWithCallableLoss(self, cls, num_microbatches, expected_grad0,
expected_grad1):
var0 = tf.Variable([1.0, 2.0]) var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0]) var1 = tf.Variable([3.0])
data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]]) data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
@ -60,6 +61,38 @@ class DPOptimizerComputeGradientsTest(tf.test.TestCase, parameterized.TestCase):
self.assertAllCloseAccordingToType(expected_grad0, grads_and_vars[0][0]) self.assertAllCloseAccordingToType(expected_grad0, grads_and_vars[0][0])
self.assertAllCloseAccordingToType(expected_grad1, grads_and_vars[1][0]) self.assertAllCloseAccordingToType(expected_grad1, grads_and_vars[1][0])
# Parameters for testing: optimizer, num_microbatches, expected gradient for
# var0, expected gradient for var1.
@parameterized.named_parameters(
('DPGradientDescent 1', dp_optimizer_keras.DPKerasSGDOptimizer, 1,
[-2.5, -2.5], [-0.5]),
('DPAdam 2', dp_optimizer_keras.DPKerasAdamOptimizer, 2, [-2.5, -2.5
], [-0.5]),
('DPAdagrad 4', dp_optimizer_keras.DPKerasAdagradOptimizer, 4,
[-2.5, -2.5], [-0.5]),
)
def testBaselineWithTensorLoss(self, cls, num_microbatches, expected_grad0,
expected_grad1):
var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0])
data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
data1 = tf.Variable([[8.0], [2.0], [3.0], [1.0]])
opt = cls(
l2_norm_clip=100.0,
noise_multiplier=0.0,
num_microbatches=num_microbatches,
learning_rate=2.0)
tape = tf.GradientTape()
with tape:
loss = self._loss(data0, var0) + self._loss(data1, var1)
grads_and_vars = opt._compute_gradients(
loss, [var0, var1], tape=tape)
self.assertAllCloseAccordingToType(expected_grad0, grads_and_vars[0][0])
self.assertAllCloseAccordingToType(expected_grad1, grads_and_vars[1][0])
@parameterized.named_parameters( @parameterized.named_parameters(
('DPGradientDescent', dp_optimizer_keras.DPKerasSGDOptimizer),) ('DPGradientDescent', dp_optimizer_keras.DPKerasSGDOptimizer),)
def testClippingNorm(self, cls): def testClippingNorm(self, cls):