From 510dd207d57c2fe18d6bd06c1289f21930b6eaa4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 17 Jul 2020 02:43:29 -0700
Subject: [PATCH]   Internal change.

PiperOrigin-RevId: 321742857
---
 .../data_structures_test.py                   | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 tensorflow_privacy/privacy/membership_inference_attack/data_structures_test.py

diff --git a/tensorflow_privacy/privacy/membership_inference_attack/data_structures_test.py b/tensorflow_privacy/privacy/membership_inference_attack/data_structures_test.py
new file mode 100644
index 0000000..721f9e1
--- /dev/null
+++ b/tensorflow_privacy/privacy/membership_inference_attack/data_structures_test.py
@@ -0,0 +1,39 @@
+# Copyright 2020, The TensorFlow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Tests for tensorflow_privacy.privacy.membership_inference_attack.data_structures."""
+from absl.testing import absltest
+import numpy as np
+from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData
+
+
+class AttackInputDataTest(absltest.TestCase):
+
+  def test_get_loss(self):
+    attack_input = AttackInputData(
+        logits_train=np.array([[0.3, 0.5, 0.2], [0.2, 0.3, 0.5]]),
+        logits_test=np.array([[0.2, 0.3, 0.5], [0.3, 0.5, 0.2]]),
+        labels_train=np.array([1, 0]),
+        labels_test=np.array([0, 1])
+    )
+
+    np.testing.assert_almost_equal(
+        attack_input.get_loss_train().tolist(), [0.5, 0.2])
+    np.testing.assert_almost_equal(
+        attack_input.get_loss_test().tolist(), [0.2, 0.5])
+
+
+if __name__ == '__main__':
+  absltest.main()