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Stable version for tf2.0a0, b0.

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This commit is contained in:
Christopher Choquette Choo 2019-08-21 22:57:35 -04:00
parent 7d885640ec
commit 18ce9c2335
6 changed files with 39 additions and 35 deletions
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9
privacy/bolt_on/README.md
View file

@ -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

5
privacy/bolt_on/models.py
View file

@ -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)

25
privacy/bolt_on/models_test.py
View file

@ -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,
@ -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

13
privacy/bolt_on/optimizers.py
View file

@ -139,8 +139,8 @@ 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
# rate scheduler, as required for privacy guarantees. This will still need
@ -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)

9
privacy/bolt_on/optimizers_test.py
View file

@ -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()

13
tutorials/bolton_tutorial.py
View file

@ -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: