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conflicts in opt test

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npapernot 2019-07-29 21:27:54 +00:00
commit d10d7b0148
6 changed files with 105 additions and 60 deletions
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59
privacy/bolton/models.py
View file

@ -76,17 +76,12 @@ class BoltonModel(Model): # pylint: disable=abstract-method
def compile(self,
optimizer,
loss,
metrics=None,
loss_weights=None,
sample_weight_mode=None,
weighted_metrics=None,
target_tensors=None,
distribute=None,
kernel_initializer=tf.initializers.GlorotUniform,
**kwargs): # pylint: disable=arguments-differ
"""See super class. Default optimizer used in Bolton method is SGD.
Args:
<<<<<<< HEAD
optimizer:
loss:
metrics:
@ -96,6 +91,14 @@ class BoltonModel(Model): # pylint: disable=abstract-method
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 '
@ -112,15 +115,7 @@ class BoltonModel(Model): # pylint: disable=abstract-method
optimizer = optimizers.get(optimizer)
optimizer = Bolton(optimizer, loss)
super(BoltonModel, self).compile(optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode,
weighted_metrics=weighted_metrics,
target_tensors=target_tensors,
distribute=distribute,
**kwargs)
super(BoltonModel, self).compile(optimizer, loss=loss, **kwargs)
def fit(self,
x=None,
@ -142,6 +137,7 @@ 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.
@ -149,6 +145,15 @@ class BoltonModel(Model): # pylint: disable=abstract-method
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:
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.
"""
@ -201,6 +206,7 @@ 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:
@ -211,6 +217,18 @@ 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)
@ -244,6 +262,7 @@ 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
@ -252,6 +271,16 @@ 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']

22
privacy/bolton/models_test.py
View file

@ -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():
@ -206,8 +206,13 @@ class InitTests(keras_parameterized.TestCase):
Args:
n_outputs: number of output neurons
loss: instantiated TestLoss instance
<<<<<<< HEAD
optimizer: instanced TestOptimizer instance
"""
=======
optimizer: instantiated TestOptimizer instance
"""
>>>>>>> 71c4a11eb9ad66a78fb13428987366887ea20beb
# test compilaton of invalid tf.optimizer and non instantiated loss.
with self.cached_session():
with self.assertRaises((ValueError, AttributeError)):
@ -506,13 +511,13 @@ class FitTests(keras_parameterized.TestCase):
'num_classes': 2,
'err_msg': 'Detected array length:'},
])
def test_class_errors(self,
class_weights,
class_counts,
num_classes,
err_msg):
"""Tests the BOltonModel calculate_class_weights method.
<<<<<<< HEAD
This test passes invalid params which should raise the expected errors.
@ -522,6 +527,17 @@ class FitTests(keras_parameterized.TestCase):
num_classes: number of outputs neurons
err_msg:
"""
=======
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: The expected error message.
"""
>>>>>>> 71c4a11eb9ad66a78fb13428987366887ea20beb
clf = models.BoltonModel(1, 1)
with self.assertRaisesRegexp(ValueError, err_msg): # pylint: disable=deprecated-method
clf.calculate_class_weights(class_weights,

3
privacy/bolton/optimizers.py
View file

@ -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

17
privacy/bolton/optimizers_test.py
View file

@ -263,11 +263,11 @@ class BoltonOptimizerTest(keras_parameterized.TestCase):
"""test that a fn of Bolton optimizer is working as expected.
Args:
r:
shape:
n_out:
init_value:
result:
r: Radius value for StrongConvex loss function.
shape: input_dimensionality
n_out: output dimensionality
init_value: the initial value for 'constant' kernel initializer
result: the expected output after projection.
"""
tf.random.set_seed(1)
@tf.function
@ -539,7 +539,7 @@ class SchedulerTest(keras_parameterized.TestCase):
"""Test attribute of internal opt correctly rerouted to the internal opt.
Args:
err_msg:
err_msg: The expected error message from the scheduler bad call.
"""
scheduler = opt.GammaBetaDecreasingStep()
with self.assertRaisesRegexp(Exception, err_msg): # pylint: disable=deprecated-method
@ -558,13 +558,12 @@ class SchedulerTest(keras_parameterized.TestCase):
])
def test_call(self, step, res):
"""Test call.
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer
Args:
step:
res:
step: step number to 'GammaBetaDecreasingStep' 'Scheduler'.
res: expected result from call to 'GammaBetaDecreasingStep' 'Scheduler'.
"""
beta = _ops.convert_to_tensor_v2(2, dtype=tf.float32)
gamma = _ops.convert_to_tensor_v2(1, dtype=tf.float32)

2
tutorials/bolton_tutorial.py
View file

@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tutorial for bolton module, the model and the optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=wrong-import-position
from privacy.bolton import losses # pylint: disable=wrong-import-position