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