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Update Huber loss regularization term and some small changes across loss parameters.

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Christopher Choquette Choo 2019-06-17 14:46:04 -04:00
parent 935d6e8480
commit ed7fa73a99
1 changed files with 8 additions and 6 deletions
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14
privacy/bolton/loss.py
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@ -58,7 +58,8 @@ class StrongConvexMixin:
"""Smoothness, beta
Args:
class_weight: the class weights used.
class_weight: the class weights as scalar or 1d tensor, where its
dimensionality is equal to the number of outputs.
Returns: Beta
@ -154,7 +155,7 @@ class StrongConvexHuber(losses.Loss, StrongConvexMixin):
"""Compute loss
Args:
y_true: Ground truth values. One
y_true: Ground truth values. One hot encoded using -1 and 1.
y_pred: The predicted values.
Returns:
@ -211,7 +212,7 @@ class StrongConvexHuber(losses.Loss, StrongConvexMixin):
this loss function to be strongly convex.
:return:
"""
return L1L2(l2=self.reg_lambda)
return L1L2(l2=self.reg_lambda/2)
class StrongConvexBinaryCrossentropy(
@ -230,7 +231,6 @@ class StrongConvexBinaryCrossentropy(
from_logits: bool = True,
label_smoothing: float = 0,
reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
name: str = 'binarycrossentropy',
dtype=tf.float32):
"""
Args:
@ -239,7 +239,9 @@ class StrongConvexBinaryCrossentropy(
radius_constant: constant defining the length of the radius
reduction: reduction type to use. See super class
label_smoothing: amount of smoothing to perform on labels
relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x)
relaxation of trust in labels, e.g. (1 -> 1-x, 0 -> 0+x).
Note, the impact of this parameter's effect on privacy
is not known and thus the default should be used.
name: Name of the loss instance
dtype: tf datatype to use for tensor conversions.
"""
@ -256,7 +258,7 @@ class StrongConvexBinaryCrossentropy(
self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
super(StrongConvexBinaryCrossentropy, self).__init__(
reduction=reduction,
name=name,
name='binarycrossentropy',
from_logits=from_logits,
label_smoothing=label_smoothing,
)