diff options
Diffstat (limited to 'tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py')
| -rw-r--r-- | tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py | 47 |
1 files changed, 44 insertions, 3 deletions
diff --git a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py index 13968457f7..27da0b4f55 100644 --- a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py +++ b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py @@ -92,6 +92,7 @@ def make_variable_dict(max_age, max_gender): return dict(sparse_features_weights=[age_weights, gender_weights], dense_features_weights=[]) + def make_dense_variable_dict(num_dense_features, num_examples): feature_weights = ([ tf.Variable(tf.zeros([1], @@ -130,6 +131,7 @@ def tearDown(): pass +# TODO(katsiapis): Add tests that exercise L1 and Shrinking. class SdcaOptimizerTest(TensorFlowTestCase): def _single_threaded_test_session(self): @@ -180,6 +182,44 @@ class SdcaOptimizerTest(TensorFlowTestCase): rtol=1e-2, atol=1e-2) + def testSimpleLogisticNoL2(self): + # Same as test above (so comments from above apply) but without an L2. + # The algorithm should behave as if we have an L2 of 1 in optimization but + # 0 in regularized_loss. + example_protos = [ + make_example_proto( + {'age': [0], + 'gender': [0]}, 0), + make_example_proto( + {'age': [1], + 'gender': [1]}, 1), + ] + example_weights = [1.0, 1.0] + with self._single_threaded_test_session(): + examples = make_example_dict(example_protos, example_weights) + variables = make_variable_dict(1, 1) + options = dict(symmetric_l2_regularization=0, + symmetric_l1_regularization=0, + loss_type='logistic_loss') + + lr = SdcaModel(CONTAINER, examples, variables, options) + tf.initialize_all_variables().run() + unregularized_loss = lr.unregularized_loss(examples) + loss = lr.regularized_loss(examples) + predictions = lr.predictions(examples) + self.assertAllClose(0.693147, unregularized_loss.eval()) + self.assertAllClose(0.693147, loss.eval()) + for _ in xrange(5): + lr.minimize().run() + self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11) + self.assertAllClose(0.371705, loss.eval(), atol=0.01) + predicted_labels = get_binary_predictions_for_logistic(predictions) + self.assertAllEqual([0, 1], predicted_labels.eval()) + self.assertAllClose(0.01, + lr.approximate_duality_gap().eval(), + rtol=1e-2, + atol=1e-2) + def testSomeUnweightedExamples(self): # Setup test data with 4 examples, but should produce the same # results as testSimple. @@ -272,10 +312,11 @@ class SdcaOptimizerTest(TensorFlowTestCase): lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval()) - with self.assertRaisesOpError( - 'No weighted examples in 2 training examples'): - lr.minimize().run() + lr.minimize().run() self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval()) + with self.assertRaisesOpError( + 'No examples found or all examples have zero weight.'): + lr.approximate_duality_gap().eval() def testDuplicateExampleIds(self): # Setup test data with 1 positive, and 1 negative example. |