# Path: tensorflow_constrained_optimization/python/rates/loss.py
# def _convert_to_binary_classification_predictions(predictions):
#   def is_differentiable(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def __ne__(self, other):
#   def is_normalized(self):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_normalized(self):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def __init__(self, margin=1.0):
#   def margin(self):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
# class Loss(helpers.RateObject):
# class BinaryClassificationLoss(Loss):
# class MulticlassLoss(BinaryClassificationLoss):
# class ZeroOneLoss(MulticlassLoss):
# class HingeLoss(MulticlassLoss):
# class SoftmaxLoss(MulticlassLoss):
# class SoftmaxCrossEntropyLoss(MulticlassLoss):


from tensorflow_constrained_optimization.python.rates import loss
# Copyright 2018 The TensorFlow Constrained Optimization Authors. All Rights
# Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, 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.
# ==============================================================================
"""Contains internal global defaults."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


DENOMINATOR_LOWER_BOUND_KEY = "denominator_lower_bound"
GLOBAL_STEP_KEY = "global_step"
VARIABLE_FN_KEY = "variable_fn"

DEFAULT_PENALTY_LOSS = loss.HingeLoss()

# Path: tensorflow_constrained_optimization/python/rates/loss.py
# def _convert_to_binary_classification_predictions(predictions):
#   def is_differentiable(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def __ne__(self, other):
#   def is_normalized(self):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_normalized(self):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def __init__(self, margin=1.0):
#   def margin(self):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
#   def is_differentiable(self):
#   def is_normalized(self):
#   def __hash__(self):
#   def __eq__(self, other):
#   def evaluate_multiclass(self, predictions, weights):
#   def evaluate_binary_classification(self, predictions, weights):
# class Loss(helpers.RateObject):
# class BinaryClassificationLoss(Loss):
# class MulticlassLoss(BinaryClassificationLoss):
# class ZeroOneLoss(MulticlassLoss):
# class HingeLoss(MulticlassLoss):
# class SoftmaxLoss(MulticlassLoss):
# class SoftmaxCrossEntropyLoss(MulticlassLoss):


# Path: tensorflow_constrained_optimization/python/rates/defaults.py
from tensorflow_constrained_optimization.python.rates import loss

# Copyright 2018 The TensorFlow Constrained Optimization Authors. All Rights
# Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, 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.
# ==============================================================================
"""Contains internal global defaults."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


DENOMINATOR_LOWER_BOUND_KEY = "denominator_lower_bound"
GLOBAL_STEP_KEY = "global_step"
VARIABLE_FN_KEY = "variable_fn"

DEFAULT_PENALTY_LOSS = loss.HingeLoss()

DEFAULT_CONSTRAINT_LOSS = loss.ZeroOneLoss()