Stable version for tf2.0a0, b0.

privacy/bolt_on/README.md

@@ -44,11 +44,12 @@ https://arxiv.org/pdf/1811.04911.pdf
 
 ## Stability
 
-As we are pegged on tensorflow2.0.0, this package may encounter stability
-issues in the ongoing development of this package.
+As we are pegged on tensorflow2.0, this package may encounter stability
+issues in the ongoing development of tensorflow2.0.
 
-We are aware of issues in model fitting using the BoltOnModel and are actively
-working towards solving these issues.
+This sub-package is currently stable for 2.0.0a0 and 2.0.0b0. We are aware of 
+issues in model fitting using the BoltOnModel in beta1, the latest release, 
+and are actively working towards solving these issues.
 
 ## Contacts
 

privacy/bolt_on/models.py

@@ -217,7 +217,10 @@ class BoltOnModel(Model):  # pylint: disable=abstract-method
     elif hasattr(generator, '__len__'):
       data_size = len(generator)
     else:
-      data_size = None
+      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)

privacy/bolt_on/models_test.py

@@ -227,8 +227,8 @@ def _cat_dataset(n_samples, input_dim, n_classes, batch_size, generator=False):
     n_samples: number of rows
     input_dim: input dimensionality
     n_classes: output dimensionality
+    batch_size: The desired batch_size
     generator: False for array, True for generator
-    batch_size: The desired batch_size.
 
   Returns:
     X as (n_samples, input_dim), Y as (n_samples, n_outputs)
@@ -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,
@@ -303,7 +309,6 @@ def _do_fit(n_samples,
         generator=generator)
     if reset_n_samples:
       n_samples = None
-
     clf.fit(x,
             y,
             batch_size=batch_size,

privacy/bolt_on/optimizers.py

@@ -139,7 +139,7 @@ class BoltOn(optimizer_v2.OptimizerV2):
                                 'n_samples',
                                 'layers',
                                 'batch_size',
-                                '_is_init'
+                                '_is_init',
                                 ]
     self._internal_optimizer = optimizer
     self.learning_rate = GammaBetaDecreasingStep()  # use the BoltOn Learning
@@ -250,8 +250,7 @@ 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 +318,7 @@ 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 +345,7 @@ 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

@@ -199,7 +199,6 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
        'result': None,
        'test_attr': ''},
   ])
-
   def test_fn(self, fn, args, result, test_attr):
     """test that a fn of BoltOn optimizer is working as expected.
 
@@ -270,7 +269,6 @@ 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 +356,8 @@ 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 +367,6 @@ 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 +460,6 @@ 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 +574,5 @@ class SchedulerTest(keras_parameterized.TestCase):
 
 if __name__ == '__main__':
   test.main()
+  import unittest
+  unittest.main()

tutorials/bolton_tutorial.py

@@ -124,10 +124,9 @@ except ValueError as e:
 # And now, re running with the parameter set.
 # -------
 n_samples = 20
-bolt.fit(generator,
+bolt.fit_generator(generator,
                    epsilon=epsilon,
                    class_weight=class_weight,
-         batch_size=batch_size,
                    n_samples=n_samples,
                    noise_distribution=noise_distribution,
                    verbose=0)