Merge pull request #70 from georgianpartners:master

PiperOrigin-RevId: 265056745

privacy/bolt_on/README.md

@@ -42,6 +42,16 @@ delta-epsilon privacy in machine learning, some of which can be explored here:
 https://medium.com/apache-mxnet/epsilon-differential-privacy-for-machine-learning-using-mxnet-a4270fe3865e
 https://arxiv.org/pdf/1811.04911.pdf
 
+## Stability
+
+As we are pegged on tensorflow2.0, this package may encounter stability
+issues in the ongoing development of tensorflow2.0.
+
+This sub-package is currently stable for 2.0.0a0, 2.0.0b0, and 2.0.0.b1 If you
+would like to use this subpackage, please do use one of these versions as we
+cannot guarantee it will work for all latest releases. If you do find issues,
+feel free to raise an issue to the contributors listed below.
+
 ## Contacts
 
 In addition to the maintainers of tensorflow/privacy listed in the root

privacy/bolt_on/models.py

@@ -217,9 +217,11 @@ class BoltOnModel(Model):  # pylint: disable=abstract-method
     elif hasattr(generator, '__len__'):
       data_size = len(generator)
     else:
-      data_size = None
-    batch_size = self._validate_or_infer_batch_size(None,
-                                                    steps_per_epoch,
+      raise ValueError('The number of samples could not be determined. '
+                       'Please make sure that if you are using a generator'
+                       'to call this method directly with n_samples kwarg '
+                       'passed.')
+    batch_size = self._validate_or_infer_batch_size(None, steps_per_epoch,
                                                     generator)
     if batch_size is None:
       batch_size = 32

privacy/bolt_on/models_test.py

@@ -294,6 +294,12 @@ def _do_fit(n_samples,
     # x = x.batch(batch_size)
     x = x.shuffle(n_samples//2)
     batch_size = None
+    if reset_n_samples:
+      n_samples = None
+    clf.fit_generator(x,
+                      n_samples=n_samples,
+                      noise_distribution=distribution,
+                      epsilon=epsilon)
   else:
     x, y = _cat_dataset(
         n_samples,
@@ -301,15 +307,14 @@ def _do_fit(n_samples,
         n_outputs,
         batch_size,
         generator=generator)
-  if reset_n_samples:
-    n_samples = None
-
-  clf.fit(x,
-          y,
-          batch_size=batch_size,
-          n_samples=n_samples,
-          noise_distribution=distribution,
-          epsilon=epsilon)
+    if reset_n_samples:
+      n_samples = None
+    clf.fit(x,
+            y,
+            batch_size=batch_size,
+            n_samples=n_samples,
+            noise_distribution=distribution,
+            epsilon=epsilon)
   return clf
 
 

privacy/bolt_on/optimizers.py

@@ -129,18 +129,19 @@ class BoltOn(optimizer_v2.OptimizerV2):
     if not isinstance(loss, StrongConvexMixin):
       raise ValueError('loss function must be a Strongly Convex and therefore '
                        'extend the StrongConvexMixin.')
-    self._private_attributes = ['_internal_optimizer',
-                                'dtype',
-                                'noise_distribution',
-                                'epsilon',
-                                'loss',
-                                'class_weights',
-                                'input_dim',
-                                'n_samples',
-                                'layers',
-                                'batch_size',
-                                '_is_init'
-                               ]
+    self._private_attributes = [
+        '_internal_optimizer',
+        'dtype',
+        'noise_distribution',
+        'epsilon',
+        'loss',
+        'class_weights',
+        'input_dim',
+        'n_samples',
+        'layers',
+        'batch_size',
+        '_is_init',
+    ]
     self._internal_optimizer = optimizer
     self.learning_rate = GammaBetaDecreasingStep()  # use the BoltOn Learning
     # rate scheduler, as required for privacy guarantees. This will still need
@@ -250,8 +251,7 @@ class BoltOn(optimizer_v2.OptimizerV2):
           "Neither '{0}' nor '{1}' object has attribute '{2}'"
           "".format(self.__class__.__name__,
                     self._internal_optimizer.__class__.__name__,
-                    name
-                   )
+                    name)
           )
 
   def __setattr__(self, key, value):
@@ -319,8 +319,7 @@ class BoltOn(optimizer_v2.OptimizerV2):
                layers,
                class_weights,
                n_samples,
-               batch_size
-              ):
+               batch_size):
     """Accepts required values for bolton method from context entry point.
 
     Stores them on the optimizer for use throughout fitting.
@@ -347,8 +346,7 @@ class BoltOn(optimizer_v2.OptimizerV2):
                                               _accepted_distributions))
     self.noise_distribution = noise_distribution
     self.learning_rate.initialize(self.loss.beta(class_weights),
-                                  self.loss.gamma()
-                                 )
+                                  self.loss.gamma())
     self.epsilon = tf.constant(epsilon, dtype=self.dtype)
     self.class_weights = tf.constant(class_weights, dtype=self.dtype)
     self.n_samples = tf.constant(n_samples, dtype=self.dtype)

privacy/bolt_on/optimizers_test.py

@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import unittest
 from absl.testing import parameterized
 import tensorflow as tf
 from tensorflow.python import ops as _ops
@@ -270,7 +271,6 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
       result: the expected output after projection.
     """
     tf.random.set_seed(1)
-    @tf.function
     def project_fn(r):
       loss = TestLoss(1, 1, r)
       bolton = opt.BoltOn(TestOptimizer(), loss)
@@ -358,7 +358,8 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
       {'testcase_name': 'fn: get_noise',
        'fn': 'get_noise',
        'args': [1, 1],
-       'err_msg': 'ust be called from within the optimizer\'s context'},
+       'err_msg': 'This method must be called from within the '
+                  'optimizer\'s context'},
   ])
   def test_not_in_context(self, fn, args, err_msg):
     """Tests that the expected functions raise errors when not in context.
@@ -368,7 +369,6 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
         args: the arguments for said function
         err_msg: expected error message
     """
-    @tf.function
     def test_run(fn, args):
       loss = TestLoss(1, 1, 1)
       bolton = opt.BoltOn(TestOptimizer(), loss)
@@ -462,7 +462,6 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
       fn: fn to test
       args: arguments to that fn
     """
-    @tf.function
     def test_run(fn, args):
       loss = TestLoss(1, 1, 1)
       bolton = opt.BoltOn(TestOptimizer(), loss)
@@ -577,3 +576,4 @@ class SchedulerTest(keras_parameterized.TestCase):
 
 if __name__ == '__main__':
   test.main()
+  unittest.main()

tutorials/bolton_tutorial.py

@@ -124,13 +124,12 @@ except ValueError as e:
 # And now, re running with the parameter set.
 # -------
 n_samples = 20
-bolt.fit(generator,
-         epsilon=epsilon,
-         class_weight=class_weight,
-         batch_size=batch_size,
-         n_samples=n_samples,
-         noise_distribution=noise_distribution,
-         verbose=0)
+bolt.fit_generator(generator,
+                   epsilon=epsilon,
+                   class_weight=class_weight,
+                   n_samples=n_samples,
+                   noise_distribution=noise_distribution,
+                   verbose=0)
 # -------
 # You don't have to use the BoltOn model to use the BoltOn method.
 # There are only a few requirements: