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Fix sample weight generation in MIA tests and add test sharding to single_layer_softmax_test. #648

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Fix sample weight generation in MIA tests and add test sharding to single_layer_softmax_test. #648

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1 change: 1 addition & 0 deletions tensorflow_privacy/privacy/logistic_regression/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,7 @@ py_test(
name = "single_layer_softmax_test",
size = "medium",
srcs = ["single_layer_softmax_test.py"],
shard_count = 4,
deps = [
":datasets",
":single_layer_softmax",
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19 changes: 11 additions & 8 deletions tensorflow_privacy/privacy/logistic_regression/single_layer_softmax_test.py
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Expand Up @@ -12,8 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

from absl.testing import absltest
from absl.testing import parameterized
from tensorflow_privacy.privacy.logistic_regression import datasets
from tensorflow_privacy.privacy.logistic_regression import single_layer_softmax
Expand All @@ -27,13 +26,17 @@ class SingleLayerSoftmaxTest(parameterized.TestCase):
(10000, 1000, 3, 40, 4, 0.1),
(10000, 1000, 4, 40, 4, 0.1),
)
def test_single_layer_softmax(self, num_train, num_test, dimension, epochs,
num_classes, tolerance):
(train_dataset, test_dataset) = datasets.synthetic_linearly_separable_data(
num_train, num_test, dimension, num_classes)
def test_single_layer_softmax(
self, num_train, num_test, dimension, epochs, num_classes, tolerance
):
train_dataset, test_dataset = datasets.synthetic_linearly_separable_data(
num_train, num_test, dimension, num_classes
)
_, accuracy = single_layer_softmax.single_layer_softmax_classifier(
train_dataset, test_dataset, epochs, num_classes, 'sgd')
train_dataset, test_dataset, epochs, num_classes, 'sgd'
)
self.assertAlmostEqual(accuracy[-1], 1, delta=tolerance)


if __name__ == '__main__':
unittest.main()
absltest.main()
10 changes: 6 additions & 4 deletions ...acy/privacy/privacy_tests/membership_inference_attack/membership_inference_attack_test.py
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Expand Up @@ -83,8 +83,9 @@ def get_multilabel_test_input_with_sample_weights(n_train, n_test):
logits_test=rng.randn(n_test, num_classes) + 0.2,
labels_train=get_multihot_labels_for_test(n_train, num_classes),
labels_test=get_multihot_labels_for_test(n_test, num_classes),
sample_weight_train=rng.randn(n_train, 1),
sample_weight_test=rng.randn(n_test, 1))
sample_weight_train=rng.rand(n_train, 1),
sample_weight_test=rng.rand(n_test, 1),
)


def get_test_input_logits_only(n_train, n_test):
Expand All @@ -101,8 +102,9 @@ def get_test_input_logits_only_with_sample_weights(n_train, n_test):
return AttackInputData(
logits_train=rng.randn(n_train, 5) + 0.2,
logits_test=rng.randn(n_test, 5) + 0.2,
sample_weight_train=rng.randn(n_train, 1),
sample_weight_test=rng.randn(n_test, 1))
sample_weight_train=rng.rand(n_train, 1),
sample_weight_test=rng.rand(n_test, 1),
)


class MockTrainedAttacker(object):
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