forked from 626_privacy/tensorflow_privacy
Bolton implementation and unit tests. Has two pre-implemented loss functions.
This commit is contained in:
Christopher Choquette Choo
parent
ed7fa73a99
commit
f41be2c598
8 changed files with 49 additions and 308 deletions
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@ -41,3 +41,9 @@ else:
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from privacy.optimizers.dp_optimizer import DPAdamOptimizer
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from privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
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from privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
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from privacy.bolton.models import BoltonModel
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from privacy.bolton.optimizers import Bolton
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from privacy.bolton.losses import StrongConvexMixin
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from privacy.bolton.losses import StrongConvexBinaryCrossentropy
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from privacy.bolton.losses import StrongConvexHuber
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@ -9,6 +9,7 @@ if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
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if hasattr(sys, 'skip_tf_privacy_import'): # Useful for standalone scripts.
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pass
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else:
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from privacy.bolton.model import Bolton
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from privacy.bolton.loss import StrongConvexHuber
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from privacy.bolton.loss import StrongConvexBinaryCrossentropy
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from privacy.bolton.models import BoltonModel
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from privacy.bolton.optimizers import Bolton
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from privacy.bolton.losses import StrongConvexHuber
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from privacy.bolton.losses import StrongConvexBinaryCrossentropy
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@ -21,6 +21,7 @@ from tensorflow.python.keras import losses
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from tensorflow.python.keras.utils import losses_utils
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.keras.regularizers import L1L2
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from tensorflow.python.platform import tf_logging as logging
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class StrongConvexMixin:
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@ -147,7 +148,7 @@ class StrongConvexHuber(losses.Loss, StrongConvexMixin):
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self.dtype = dtype
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self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
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super(StrongConvexHuber, self).__init__(
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name='huber',
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name='strongconvexhuber',
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reduction=reduction,
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)
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@ -245,6 +246,11 @@ class StrongConvexBinaryCrossentropy(
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name: Name of the loss instance
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dtype: tf datatype to use for tensor conversions.
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"""
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if label_smoothing != 0:
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logging.warning('The impact of label smoothing on privacy is unknown. '
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'Use label smoothing at your own risk as it may not '
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'guarantee privacy.')
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if reg_lambda <= 0:
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raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
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if C <= 0:
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@ -258,7 +264,7 @@ class StrongConvexBinaryCrossentropy(
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self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
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super(StrongConvexBinaryCrossentropy, self).__init__(
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reduction=reduction,
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name='binarycrossentropy',
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name='strongconvexbinarycrossentropy',
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from_logits=from_logits,
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label_smoothing=label_smoothing,
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)
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@ -313,280 +319,3 @@ class StrongConvexBinaryCrossentropy(
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return L1L2(l2=self.reg_lambda/2)
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# class StrongConvexSparseCategoricalCrossentropy(
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# losses.CategoricalCrossentropy,
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# StrongConvexMixin
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# ):
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# """
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# Strong Convex version of CategoricalCrossentropy loss using l2 weight
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# regularization.
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# """
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#
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# def __init__(self,
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# reg_lambda: float,
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# C: float,
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# radius_constant: float,
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# from_logits: bool = True,
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# label_smoothing: float = 0,
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# reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
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# name: str = 'binarycrossentropy',
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# dtype=tf.float32):
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# """
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# Args:
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# reg_lambda: Weight regularization constant
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# C: Penalty parameter C of the loss term
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# radius_constant: constant defining the length of the radius
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# reduction: reduction type to use. See super class
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# label_smoothing: amount of smoothing to perform on labels
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# relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
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# name: Name of the loss instance
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# dtype: tf datatype to use for tensor conversions.
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# """
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# if reg_lambda <= 0:
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# raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
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# if C <= 0:
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# raise ValueError('c: {0}, should be >= 0'.format(C))
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# if radius_constant <= 0:
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# raise ValueError('radius_constant: {0}, should be >= 0'.format(
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# radius_constant
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# ))
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#
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# self.C = C
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# self.dtype = dtype
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# self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
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# super(StrongConvexSparseCategoricalCrossentropy, self).__init__(
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# reduction=reduction,
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# name=name,
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# from_logits=from_logits,
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# label_smoothing=label_smoothing,
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# )
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# self.radius_constant = radius_constant
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#
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# def call(self, y_true, y_pred):
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# """Compute loss
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#
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# Args:
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# y_true: Ground truth values.
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# y_pred: The predicted values.
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#
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# Returns:
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# Loss values per sample.
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# """
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# loss = super()
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# loss = loss * self.C
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# return loss
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#
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# def radius(self):
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# """See super class.
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# """
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# return self.radius_constant / self.reg_lambda
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#
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# def gamma(self):
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# """See super class.
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# """
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# return self.reg_lambda
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#
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# def beta(self, class_weight):
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# """See super class.
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# """
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# max_class_weight = self.max_class_weight(class_weight, self.dtype)
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# return self.C * max_class_weight + self.reg_lambda
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#
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# def lipchitz_constant(self, class_weight):
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# """See super class.
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# """
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# max_class_weight = self.max_class_weight(class_weight, self.dtype)
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# return self.C * max_class_weight + self.reg_lambda * self.radius()
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#
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# def kernel_regularizer(self):
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# """
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# l2 loss using reg_lambda as the l2 term (as desired). Required for
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# this loss function to be strongly convex.
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# :return:
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# """
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# return L1L2(l2=self.reg_lambda)
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#
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# class StrongConvexSparseCategoricalCrossentropy(
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# losses.SparseCategoricalCrossentropy,
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# StrongConvexMixin
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# ):
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# """
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# Strong Convex version of SparseCategoricalCrossentropy loss using l2 weight
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# regularization.
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# """
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#
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# def __init__(self,
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# reg_lambda: float,
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# C: float,
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# radius_constant: float,
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# from_logits: bool = True,
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# label_smoothing: float = 0,
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# reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
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# name: str = 'binarycrossentropy',
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# dtype=tf.float32):
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# """
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# Args:
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# reg_lambda: Weight regularization constant
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# C: Penalty parameter C of the loss term
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# radius_constant: constant defining the length of the radius
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# reduction: reduction type to use. See super class
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# label_smoothing: amount of smoothing to perform on labels
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# relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
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# name: Name of the loss instance
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# dtype: tf datatype to use for tensor conversions.
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# """
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# if reg_lambda <= 0:
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# raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
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# if C <= 0:
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# raise ValueError('c: {0}, should be >= 0'.format(C))
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# if radius_constant <= 0:
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# raise ValueError('radius_constant: {0}, should be >= 0'.format(
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# radius_constant
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# ))
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#
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# self.C = C
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# self.dtype = dtype
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# self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
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# super(StrongConvexHuber, self).__init__(reduction=reduction,
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# name=name,
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# from_logits=from_logits,
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# label_smoothing=label_smoothing,
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# )
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# self.radius_constant = radius_constant
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#
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# def call(self, y_true, y_pred):
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# """Compute loss
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#
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# Args:
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# y_true: Ground truth values.
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# y_pred: The predicted values.
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#
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# Returns:
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# Loss values per sample.
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# """
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# loss = super()
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# loss = loss * self.C
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# return loss
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#
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# def radius(self):
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# """See super class.
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# """
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# return self.radius_constant / self.reg_lambda
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#
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# def gamma(self):
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# """See super class.
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# """
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# return self.reg_lambda
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#
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# def beta(self, class_weight):
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# """See super class.
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# """
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# max_class_weight = self.max_class_weight(class_weight, self.dtype)
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# return self.C * max_class_weight + self.reg_lambda
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#
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# def lipchitz_constant(self, class_weight):
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# """See super class.
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# """
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# max_class_weight = self.max_class_weight(class_weight, self.dtype)
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# return self.C * max_class_weight + self.reg_lambda * self.radius()
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#
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# def kernel_regularizer(self):
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# """
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# l2 loss using reg_lambda as the l2 term (as desired). Required for
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# this loss function to be strongly convex.
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# :return:
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# """
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# return L1L2(l2=self.reg_lambda)
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#
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#
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# class StrongConvexCategoricalCrossentropy(
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# losses.CategoricalCrossentropy,
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# StrongConvexMixin
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# ):
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# """
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# Strong Convex version of CategoricalCrossentropy loss using l2 weight
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# regularization.
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# """
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#
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# def __init__(self,
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# reg_lambda: float,
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# C: float,
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# radius_constant: float,
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# from_logits: bool = True,
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# label_smoothing: float = 0,
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# reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
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# name: str = 'binarycrossentropy',
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# dtype=tf.float32):
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# """
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# Args:
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# reg_lambda: Weight regularization constant
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# C: Penalty parameter C of the loss term
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# radius_constant: constant defining the length of the radius
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# reduction: reduction type to use. See super class
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# label_smoothing: amount of smoothing to perform on labels
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# relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
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# name: Name of the loss instance
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# dtype: tf datatype to use for tensor conversions.
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# """
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# if reg_lambda <= 0:
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# raise ValueError("reg lambda: {0} must be positive".format(reg_lambda))
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# if C <= 0:
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# raise ValueError('c: {0}, should be >= 0'.format(C))
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# if radius_constant <= 0:
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# raise ValueError('radius_constant: {0}, should be >= 0'.format(
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# radius_constant
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# ))
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#
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# self.C = C
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# self.dtype = dtype
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# self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
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# super(StrongConvexHuber, self).__init__(reduction=reduction,
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# name=name,
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# from_logits=from_logits,
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# label_smoothing=label_smoothing,
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# )
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# self.radius_constant = radius_constant
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#
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# def call(self, y_true, y_pred):
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# """Compute loss
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#
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# Args:
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# y_true: Ground truth values.
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# y_pred: The predicted values.
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#
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# Returns:
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# Loss values per sample.
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# """
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# loss = super()
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# loss = loss * self.C
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# return loss
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#
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# def radius(self):
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# """See super class.
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# """
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# return self.radius_constant / self.reg_lambda
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#
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# def gamma(self):
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# """See super class.
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# """
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# return self.reg_lambda
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#
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# def beta(self, class_weight):
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# """See super class.
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# """
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# max_class_weight = self.max_class_weight(class_weight, self.dtype)
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# return self.C * max_class_weight + self.reg_lambda
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#
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# def lipchitz_constant(self, class_weight):
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# """See super class.
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# """
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# max_class_weight = self.max_class_weight(class_weight, self.dtype)
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# return self.C * max_class_weight + self.reg_lambda * self.radius()
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#
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# def kernel_regularizer(self):
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# """
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# l2 loss using reg_lambda as the l2 term (as desired). Required for
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# this loss function to be strongly convex.
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# :return:
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# """
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# return L1L2(l2=self.reg_lambda)
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@ -11,7 +11,7 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Unit testing for loss.py"""
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"""Unit testing for losses.py"""
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from __future__ import absolute_import
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from __future__ import division
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@ -22,9 +22,9 @@ from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.framework import test_util
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from tensorflow.python.keras.regularizers import L1L2
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from absl.testing import parameterized
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from privacy.bolton.loss import StrongConvexBinaryCrossentropy
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from privacy.bolton.loss import StrongConvexHuber
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from privacy.bolton.loss import StrongConvexMixin
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from privacy.bolton.losses import StrongConvexBinaryCrossentropy
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from privacy.bolton.losses import StrongConvexHuber
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from privacy.bolton.losses import StrongConvexMixin
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class StrongConvexMixinTests(keras_parameterized.TestCase):
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@ -355,7 +355,7 @@ class HuberTests(keras_parameterized.TestCase):
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'fn': 'kernel_regularizer',
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'init_args': [1, 1, 1, 1],
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'args': [],
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'result': L1L2(l2=1),
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'result': L1L2(l2=0.5),
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},
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])
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def test_fns(self, init_args, fn, args, result):
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@ -20,8 +20,8 @@ import tensorflow as tf
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from tensorflow.python.keras.models import Model
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from tensorflow.python.keras import optimizers
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from tensorflow.python.framework import ops as _ops
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from privacy.bolton.loss import StrongConvexMixin
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from privacy.bolton.optimizer import Bolton
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from privacy.bolton.losses import StrongConvexMixin
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from privacy.bolton.optimizers import Bolton
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class BoltonModel(Model):
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@ -142,7 +142,9 @@ class BoltonModel(Model):
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"""
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if class_weight is None:
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class_weight = self.calculate_class_weights(class_weight)
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class_weight_ = self.calculate_class_weights(class_weight)
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else:
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class_weight_ = class_weight
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if n_samples is not None:
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data_size = n_samples
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elif hasattr(x, 'shape'):
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@ -160,10 +162,13 @@ class BoltonModel(Model):
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if batch_size_ is None:
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raise ValueError('batch_size: {0} is an '
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'invalid value'.format(batch_size_))
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if data_size is None:
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raise ValueError('Could not infer the number of samples. Please pass '
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'this in using n_samples.')
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with self.optimizer(noise_distribution,
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epsilon,
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self.layers,
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class_weight,
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class_weight_,
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data_size,
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self.n_outputs,
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batch_size_,
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@ -11,7 +11,7 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Unit testing for model.py"""
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"""Unit testing for models.py"""
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from __future__ import absolute_import
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from __future__ import division
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@ -25,9 +25,9 @@ from tensorflow.python.keras import losses
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.keras.regularizers import L1L2
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from absl.testing import parameterized
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from privacy.bolton import model
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from privacy.bolton.optimizer import Bolton
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from privacy.bolton.loss import StrongConvexMixin
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from privacy.bolton import models
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from privacy.bolton.optimizers import Bolton
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from privacy.bolton.losses import StrongConvexMixin
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class TestLoss(losses.Loss, StrongConvexMixin):
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"""Test loss function for testing Bolton model"""
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@ -130,8 +130,8 @@ class InitTests(keras_parameterized.TestCase):
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n_outputs: number of output neurons
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"""
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# test valid domains for each variable
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clf = model.BoltonModel(n_outputs)
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self.assertIsInstance(clf, model.BoltonModel)
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clf = models.BoltonModel(n_outputs)
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self.assertIsInstance(clf, models.BoltonModel)
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@parameterized.named_parameters([
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{'testcase_name': 'invalid n_outputs',
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@ -146,7 +146,7 @@ class InitTests(keras_parameterized.TestCase):
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"""
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# test invalid domains for each variable, especially noise
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with self.assertRaises(ValueError):
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model.BoltonModel(n_outputs)
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models.BoltonModel(n_outputs)
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@parameterized.named_parameters([
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{'testcase_name': 'string compile',
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|
@ -170,7 +170,7 @@ class InitTests(keras_parameterized.TestCase):
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"""
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# test compilation of valid tf.optimizer and tf.loss
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with self.cached_session():
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clf = model.BoltonModel(n_outputs)
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clf = models.BoltonModel(n_outputs)
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clf.compile(optimizer, loss)
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self.assertEqual(clf.loss, loss)
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@ -197,7 +197,7 @@ class InitTests(keras_parameterized.TestCase):
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# test compilaton of invalid tf.optimizer and non instantiated loss.
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with self.cached_session():
|
||||
with self.assertRaises((ValueError, AttributeError)):
|
||||
clf = model.BoltonModel(n_outputs)
|
||||
clf = models.BoltonModel(n_outputs)
|
||||
clf.compile(optimizer, loss)
|
||||
|
||||
|
||||
|
|
@ -261,7 +261,7 @@ def _do_fit(n_samples,
|
|||
|
||||
Returns: BoltonModel instsance
|
||||
"""
|
||||
clf = model.BoltonModel(n_outputs)
|
||||
clf = models.BoltonModel(n_outputs)
|
||||
clf.compile(optimizer, loss)
|
||||
if generator:
|
||||
x = _cat_dataset(
|
||||
|
|
@ -355,7 +355,7 @@ class FitTests(keras_parameterized.TestCase):
|
|||
input_dim = 5
|
||||
batch_size = 1
|
||||
n_samples = 10
|
||||
clf = model.BoltonModel(n_classes)
|
||||
clf = models.BoltonModel(n_classes)
|
||||
clf.compile(optimizer, loss)
|
||||
x = _cat_dataset(
|
||||
n_samples,
|
||||
|
|
@ -441,7 +441,7 @@ class FitTests(keras_parameterized.TestCase):
|
|||
num_classes: number of outputs neurons
|
||||
result: expected result
|
||||
"""
|
||||
clf = model.BoltonModel(1, 1)
|
||||
clf = models.BoltonModel(1, 1)
|
||||
expected = clf.calculate_class_weights(class_weights,
|
||||
class_counts,
|
||||
num_classes
|
||||
|
|
@ -508,7 +508,7 @@ class FitTests(keras_parameterized.TestCase):
|
|||
num_classes: number of outputs neurons
|
||||
result: expected result
|
||||
"""
|
||||
clf = model.BoltonModel(1, 1)
|
||||
clf = models.BoltonModel(1, 1)
|
||||
with self.assertRaisesRegexp(ValueError, err_msg): # pylint: disable=deprecated-method
|
||||
clf.calculate_class_weights(class_weights,
|
||||
class_counts,
|
||||
|
|
@ -21,7 +21,7 @@ import tensorflow as tf
|
|||
from tensorflow.python.keras.optimizer_v2 import optimizer_v2
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python import ops as _ops
|
||||
from privacy.bolton.loss import StrongConvexMixin
|
||||
from privacy.bolton.losses import StrongConvexMixin
|
||||
|
||||
_accepted_distributions = ['laplace'] # implemented distributions for noising
|
||||
|
||||
|
|
@ -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.
|
||||
"""Unit testing for optimizer.py"""
|
||||
"""Unit testing for optimizers.py"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
|
|
@ -29,8 +29,8 @@ from tensorflow.python.framework import ops as _ops
|
|||
from tensorflow.python.framework import test_util
|
||||
from tensorflow.python import ops as _ops
|
||||
from absl.testing import parameterized
|
||||
from privacy.bolton.loss import StrongConvexMixin
|
||||
from privacy.bolton import optimizer as opt
|
||||
from privacy.bolton.losses import StrongConvexMixin
|
||||
from privacy.bolton import optimizers as opt
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in a new issue