Fixing new pylint errors.

privacy/bolton/models.py

@@ -180,7 +180,7 @@ class BoltonModel(Model):  # pylint: disable=abstract-method
                     n_samples=None,
                     steps_per_epoch=None,
                     **kwargs):  # pylint: disable=arguments-differ
-    """Fit with a generator..
+    """Fit with a generator.
 
       This method is the same as fit except for when the passed dataset
       is a generator. See super method and fit for more details.

privacy/bolton/models_test.py

@@ -175,12 +175,12 @@ class InitTests(keras_parameterized.TestCase):
       },
   ])
   def test_compile(self, n_outputs, loss, optimizer):
-    """test compilation of BoltonModel.
+    """Test compilation of BoltonModel.
 
     Args:
       n_outputs: number of output neurons
       loss: instantiated TestLoss instance
-      optimizer: instanced TestOptimizer instance
+      optimizer: instantiated TestOptimizer instance
     """
     # test compilation of valid tf.optimizer and tf.loss
     with self.cached_session():
@@ -206,7 +206,7 @@ class InitTests(keras_parameterized.TestCase):
       Args:
         n_outputs: number of output neurons
         loss: instantiated TestLoss instance
-        optimizer: instanced TestOptimizer instance
+        optimizer: instantiated TestOptimizer instance
       """
     # test compilaton of invalid tf.optimizer and non instantiated loss.
     with self.cached_session():
@@ -505,7 +505,6 @@ class FitTests(keras_parameterized.TestCase):
        'num_classes': 2,
        'err_msg': 'Detected array length:'},
   ])
-
   def test_class_errors(self,
                         class_weights,
                         class_counts,
@@ -516,10 +515,10 @@ class FitTests(keras_parameterized.TestCase):
       This test passes invalid params which should raise the expected errors.
 
       Args:
-        class_weights: the class_weights to use
-        class_counts: count of number of samples for each class
-        num_classes: number of outputs neurons
-        err_msg:
+        class_weights: the class_weights to use.
+        class_counts: count of number of samples for each class.
+        num_classes: number of outputs neurons.
+        err_msg: The expected error message.
       """
     clf = models.BoltonModel(1, 1)
     with self.assertRaisesRegexp(ValueError, err_msg):  # pylint: disable=deprecated-method

privacy/bolton/optimizers.py

@@ -310,8 +310,7 @@ class Bolton(optimizer_v2.OptimizerV2):
 
     Args:
       noise_distribution: the noise distribution to pick.
-                          see _accepted_distributions and get_noise for
-                          possible values.
+        see _accepted_distributions and get_noise for possible values.
       epsilon: privacy parameter. Lower gives more privacy but less utility.
       layers: list of Keras/Tensorflow layers. Can be found as model.layers
       class_weights: class_weights used, which may either be a scalar or 1D