From 33c3f058ac4943980c027292f8d8c5b24ff6c0f9 Mon Sep 17 00:00:00 2001
From: npapernot <papernot@google.com>
Date: Mon, 29 Jul 2019 21:34:02 +0000
Subject: [PATCH] conflicts in models

---
 privacy/bolton/models.py | 58 +++++-----------------------------------
 1 file changed, 7 insertions(+), 51 deletions(-)

diff --git a/privacy/bolton/models.py b/privacy/bolton/models.py
index 80b9d14..8883d34 100644
--- a/privacy/bolton/models.py
+++ b/privacy/bolton/models.py
@@ -81,24 +81,12 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
     """See super class. Default optimizer used in Bolton method is SGD.
 
       Args:
-<<<<<<< HEAD
-        optimizer:
-        loss:
-        metrics:
-        loss_weights:
-        sample_weight_mode:
-        weighted_metrics:
-        target_tensors:
-        distribute:
-        kernel_initializer:
-=======
         optimizer: The optimizer to use. This will be automatically wrapped
           with the Bolton Optimizer.
         loss: The loss function to use. Must be a StrongConvex loss (extend the
           StrongConvexMixin).
         kernel_initializer: The kernel initializer to use for the single layer.
         kwargs: kwargs to keras Model.compile. See super.
->>>>>>> 71c4a11eb9ad66a78fb13428987366887ea20beb
     """
     if not isinstance(loss, StrongConvexMixin):
       raise ValueError('loss function must be a Strongly Convex and therefore '
@@ -137,25 +125,17 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
         4. Use a strongly convex loss function (see compile)
       See super implementation for more details.
 
-<<<<<<< HEAD
-      Args:
-        n_samples: the number of individual samples in x.
-        epsilon: privacy parameter, which trades off between utility an privacy.
-                  See the bolton paper for more description.
-        noise_distribution: the distribution to pull noise from.
-        class_weight: the class weights to be used. Can be a scalar or 1D tensor
-                      whose dim == n_classes.
-=======
     Args:
+      x:
+      y:
+      batch_size:
+      class_weight: the class weights to be used. Can be a scalar or 1D tensor
+                    whose dim == n_classes.
       n_samples: the number of individual samples in x.
       epsilon: privacy parameter, which trades off between utility an privacy.
                 See the bolton paper for more description.
       noise_distribution: the distribution to pull noise from.
-      class_weight: the class weights to be used. Can be a scalar or 1D tensor
-                    whose dim == n_classes.
->>>>>>> 71c4a11eb9ad66a78fb13428987366887ea20beb
-
-      See the super method for descriptions on the rest of the arguments.
+      steps_per_epoch:
     """
     if class_weight is None:
       class_weight_ = self.calculate_class_weights(class_weight)
@@ -206,8 +186,7 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
 
       This method is the same as fit except for when the passed dataset
       is a generator. See super method and fit for more details.
-<<<<<<< HEAD
- 
+
       Args:
         generator:
         class_weight: the class weights to be used. Can be a scalar or 1D tensor
@@ -217,18 +196,6 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
                   Bolton paper for more description.
         n_samples: number of individual samples in x
         steps_per_epoch:
-=======
-
-    Args:
-      n_samples: number of individual samples in x
-      noise_distribution: the distribution to get noise from.
-      epsilon: privacy parameter, which trades off utility and privacy. See
-                Bolton paper for more description.
-      class_weight: the class weights to be used. Can be a scalar or 1D tensor
-                    whose dim == n_classes.
-
-      See the super method for descriptions on the rest of the arguments.
->>>>>>> 71c4a11eb9ad66a78fb13428987366887ea20beb
     """
     if class_weight is None:
       class_weight = self.calculate_class_weights(class_weight)
@@ -262,7 +229,6 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
                               num_classes=None):
     """Calculates class weighting to be used in training.
 
-<<<<<<< HEAD
       Args:
         class_weights: str specifying type, array giving weights, or None.
         class_counts: If class_weights is not None, then an array of
@@ -271,16 +237,6 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
                         classes.
       Returns:
         class_weights as 1D tensor, to be passed to model's fit method.
-=======
-    Args:
-      class_weights: str specifying type, array giving weights, or None.
-      class_counts: If class_weights is not None, then an array of
-                    the number of samples for each class
-      num_classes: If class_weights is not None, then the number of
-                      classes.
-    Returns:
-      class_weights as 1D tensor, to be passed to model's fit method.
->>>>>>> 71c4a11eb9ad66a78fb13428987366887ea20beb
     """
     # Value checking
     class_keys = ['balanced']