281 lines
9.1 KiB
Python
281 lines
9.1 KiB
Python
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# Copyright 2018, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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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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"""Loss functions for bolton method"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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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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class StrongConvexLoss(losses.Loss):
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"""
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Strong Convex Loss base class for any loss function that will be used with
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Bolton model. Subclasses must be strongly convex and implement the
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associated constants. They must also conform to the requirements of tf losses
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(see super class)
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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 = 1,
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reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
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name: str = None,
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dtype=tf.float32,
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**kwargs):
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"""
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Args:
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reg_lambda: Weight regularization constant
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c: Additional constant for strongly convex convergence. Acts
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as a global weight.
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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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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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super(StrongConvexLoss, self).__init__(reduction=reduction,
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name=name,
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**kwargs)
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self._sample_weight = tf.Variable(initial_value=c,
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trainable=False,
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dtype=tf.float32)
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self._reg_lambda = reg_lambda
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self.radius_constant = tf.Variable(initial_value=radius_constant,
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trainable=False,
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dtype=tf.float32)
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self.dtype = dtype
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def radius(self):
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"""Radius of R-Ball (value to normalize weights to after each batch)
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Returns: radius
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"""
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raise NotImplementedError("Radius not implemented for StrongConvex Loss"
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"function: %s" % str(self.__class__.__name__))
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def gamma(self):
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""" Gamma strongly convex
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Returns: gamma
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"""
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raise NotImplementedError("Gamma not implemented for StrongConvex Loss"
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"function: %s" % str(self.__class__.__name__))
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def beta(self, class_weight):
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"""Beta smoothess
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Args:
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class_weight: the class weights used.
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Returns: Beta
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"""
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raise NotImplementedError("Beta not implemented for StrongConvex Loss"
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"function: %s" % str(self.__class__.__name__))
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def lipchitz_constant(self, class_weight):
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""" L lipchitz continuous
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Args:
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class_weight: class weights used
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Returns: L
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"""
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raise NotImplementedError("lipchitz constant not implemented for "
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"StrongConvex Loss"
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"function: %s" % str(self.__class__.__name__))
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def reg_lambda(self, convert_to_tensor: bool = False):
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""" returns the lambda weight regularization constant, as a tensor if
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desired
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Args:
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convert_to_tensor: True to convert to tensor, False to leave as
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python numeric.
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Returns: reg_lambda
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"""
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if convert_to_tensor:
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return _ops.convert_to_tensor_v2(self._reg_lambda, dtype=self.dtype)
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return self._reg_lambda
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def max_class_weight(self, class_weight):
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class_weight = _ops.convert_to_tensor_v2(class_weight, dtype=self.dtype)
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return tf.math.reduce_max(class_weight)
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class Huber(StrongConvexLoss, losses.Huber):
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"""Strong Convex version of huber loss using l2 weight regularization.
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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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delta: float,
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reduction: str = losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
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name: str = 'huber',
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dtype=tf.float32):
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"""Constructor. Passes arguments to StrongConvexLoss and Huber Loss.
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Args:
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reg_lambda: Weight regularization constant
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c: Additional constant for strongly convex convergence. Acts
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as a global weight.
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radius_constant: constant defining the length of the radius
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delta: delta value in huber loss. When to switch from quadratic to
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absolute deviation.
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reduction: reduction type to use. See super class
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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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Returns:
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Loss values per sample.
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"""
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# self.delta = tf.Variable(initial_value=delta, trainable=False)
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super(Huber, self).__init__(
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reg_lambda,
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c,
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radius_constant,
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delta=delta,
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name=name,
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reduction=reduction,
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dtype=dtype
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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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Args:
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y_true: Ground truth values.
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y_pred: The predicted values.
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Returns:
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Loss values per sample.
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"""
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return super(Huber, self).call(y_true, y_pred, **self._fn_kwargs) * \
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self._sample_weight
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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(True)
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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(True)
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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)
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return self._sample_weight * max_class_weight / \
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(self.delta * tf.Variable(initial_value=2, trainable=False)) + \
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self.reg_lambda(True)
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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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# if class_weight is provided,
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# it should be a vector of the same size of number of classes
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max_class_weight = self.max_class_weight(class_weight)
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lc = self._sample_weight * max_class_weight + \
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self.reg_lambda(True) * self.radius()
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return lc
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class BinaryCrossentropy(StrongConvexLoss, losses.BinaryCrossentropy):
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"""
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Strong Convex version of BinaryCrossentropy loss using l2 weight
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regularization.
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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: Additional constant for strongly convex convergence. Acts
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as a global weight.
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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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super(BinaryCrossentropy, self).__init__(reg_lambda,
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c,
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radius_constant,
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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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dtype=dtype
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)
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self.radius_constant = radius_constant
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def call(self, y_true, y_pred):
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"""Compute loss
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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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Returns:
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Loss values per sample.
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"""
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loss = tf.nn.sigmoid_cross_entropy_with_logits(
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labels=y_true,
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logits=y_pred
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)
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loss = loss * self._sample_weight
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return loss
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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(True)
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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(True)
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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)
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return self._sample_weight * max_class_weight + self.reg_lambda(True)
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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)
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return self._sample_weight * max_class_weight + \
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self.reg_lambda(True) * self.radius()
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