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npapernot 2019-07-29 21:43:19 +00:00
parent 33c3f058ac
commit f06443d50e
1 changed files with 41 additions and 29 deletions
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70
privacy/bolton/optimizers.py
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@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Bolton Optimizer for bolton method"""
"""Bolton Optimizer for bolton method."""
from __future__ import absolute_import
from __future__ import division
@ -28,8 +28,10 @@ _accepted_distributions = ['laplace'] # implemented distributions for noising
class GammaBetaDecreasingStep(
optimizer_v2.learning_rate_schedule.LearningRateSchedule):
"""Computes LR as minimum of 1/beta and 1/(gamma * step) at each step.
A required step for privacy guarantees.
This is a required step for privacy guarantees.
"""
def __init__(self):
self.is_init = False
self.beta = None
@ -37,11 +39,13 @@ class GammaBetaDecreasingStep(
def __call__(self, step):
"""Computes and returns the learning rate.
Args:
step: the current iteration number
Returns:
decayed learning rate to minimum of 1/beta and 1/(gamma * step) as per
the Bolton privacy requirements.
Args:
step: the current iteration number
Returns:
decayed learning rate to minimum of 1/beta and 1/(gamma * step) as per
the Bolton privacy requirements.
"""
if not self.is_init:
raise AttributeError('Please initialize the {0} Learning Rate Scheduler.'
@ -49,13 +53,13 @@ class GammaBetaDecreasingStep(
'{1} as a context manager, '
'as desired'.format(self.__class__.__name__,
Bolton.__class__.__name__
)
)
)
)
dtype = self.beta.dtype
one = tf.constant(1, dtype)
return tf.math.minimum(tf.math.reduce_min(one/self.beta),
one/(self.gamma*math_ops.cast(step, dtype))
)
)
def get_config(self):
"""Return config to setup the learning rate scheduler."""
@ -107,22 +111,24 @@ class Bolton(optimizer_v2.OptimizerV2):
Bolt-on Differential Privacy for Scalable Stochastic Gradient
Descent-based Analytics by Xi Wu et. al.
"""
def __init__(self, # pylint: disable=super-init-not-called
optimizer,
loss,
dtype=tf.float32,
):
):
"""Constructor.
Args:
optimizer: Optimizer_v2 or subclass to be used as the optimizer
(wrapped).
loss: StrongConvexLoss function that the model is being compiled with.
Args:
optimizer: Optimizer_v2 or subclass to be used as the optimizer
(wrapped).
loss: StrongConvexLoss function that the model is being compiled with.
dtype: dtype
"""
if not isinstance(loss, StrongConvexMixin):
raise ValueError("loss function must be a Strongly Convex and therefore "
"extend the StrongConvexMixin.")
raise ValueError('loss function must be a Strongly Convex and therefore '
'extend the StrongConvexMixin.')
self._private_attributes = ['_internal_optimizer',
'dtype',
'noise_distribution',
@ -134,7 +140,7 @@ class Bolton(optimizer_v2.OptimizerV2):
'layers',
'batch_size',
'_is_init'
]
]
self._internal_optimizer = optimizer
self.learning_rate = GammaBetaDecreasingStep() # use the Bolton Learning
# rate scheduler, as required for privacy guarantees. This will still need
@ -154,6 +160,9 @@ class Bolton(optimizer_v2.OptimizerV2):
Args:
force: True to normalize regardless of previous weight values.
False to check if weights > R-ball and only normalize then.
Raises:
Exception:
"""
if not self._is_init:
raise Exception('This method must be called from within the optimizer\'s '
@ -171,14 +180,17 @@ class Bolton(optimizer_v2.OptimizerV2):
)
def get_noise(self, input_dim, output_dim):
"""Sample noise to be added to weights for privacy guarantee
"""Sample noise to be added to weights for privacy guarantee.
Args:
input_dim: the input dimensionality for the weights
output_dim the output dimensionality for the weights
Args:
input_dim: the input dimensionality for the weights
output_dim the output dimensionality for the weights
Returns:
Noise in shape of layer's weights to be added to the weights.
Returns:
Noise in shape of layer's weights to be added to the weights.
Raises:
Exception:
"""
if not self._is_init:
raise Exception('This method must be called from within the optimizer\'s '
@ -206,7 +218,7 @@ class Bolton(optimizer_v2.OptimizerV2):
beta=1 / beta,
seed=1,
dtype=self.dtype
)
)
return unit_vector * gamma
raise NotImplementedError('Noise distribution: {0} is not '
'a valid distribution'.format(distribution))
@ -236,7 +248,7 @@ class Bolton(optimizer_v2.OptimizerV2):
"".format(self.__class__.__name__,
self._internal_optimizer.__class__.__name__,
name
)
)
)
def __setattr__(self, key, value):
@ -304,7 +316,7 @@ class Bolton(optimizer_v2.OptimizerV2):
class_weights,
n_samples,
batch_size
):
):
"""Accepts required values for bolton method from context entry point.
Stores them on the optimizer for use throughout fitting.
@ -328,7 +340,7 @@ class Bolton(optimizer_v2.OptimizerV2):
self.noise_distribution = noise_distribution
self.learning_rate.initialize(self.loss.beta(class_weights),
self.loss.gamma()
)
)
self.epsilon = tf.constant(epsilon, dtype=self.dtype)
self.class_weights = tf.constant(class_weights, dtype=self.dtype)
self.n_samples = tf.constant(n_samples, dtype=self.dtype)
@ -354,7 +366,7 @@ class Bolton(optimizer_v2.OptimizerV2):
output_dim = layer.units
noise = self.get_noise(input_dim,
output_dim,
)
)
layer.kernel = tf.math.add(layer.kernel, noise)
self.noise_distribution = None
self.learning_rate.de_initialize()