From 6237fcb1a931e8681cfc3a4893175713d2c78f64 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 13 Jun 2019 03:12:48 -0700 Subject: [PATCH] Reactivate SavedModel tests that were broken by b/134660903, b/134662234. PiperOrigin-RevId: 252996041 --- .../integration_tests/saved_model_test.py | 12 +++++------- .../saved_model/integration_tests/use_mnist_cnn.py | 3 +-- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/tensorflow/examples/saved_model/integration_tests/saved_model_test.py b/tensorflow/examples/saved_model/integration_tests/saved_model_test.py index 7d2f9bc554d..29bbac4d5c4 100644 --- a/tensorflow/examples/saved_model/integration_tests/saved_model_test.py +++ b/tensorflow/examples/saved_model/integration_tests/saved_model_test.py @@ -73,14 +73,12 @@ class SavedModelTest(integration_scripts.TestCase, parameterized.TestCase): NAMED_PARAMETERS_FOR_TEST_MNIST_CNN = ( ("", dict()), - # TODO(b/134662234): Re-enable this case when fixed. - # ("_with_retraining", dict( - # retrain=True, - # regularization_loss_multiplier=2, # Test impact of b/134528831. - # )), + ("_with_retraining", dict( + retrain=True, + regularization_loss_multiplier=2, # Test impact of b/134528831. + )), ("_with_mirrored_strategy", dict( - # TODO(b/134662234): Add back retrain=True when fixed. - # retrain=True, # That's the relevant case for distribution. + retrain=True, # That's the relevant case for distribution. use_mirrored_strategy=True, )), ) diff --git a/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py b/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py index 7eeb6800460..cec671b46ac 100644 --- a/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py +++ b/tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py @@ -118,8 +118,7 @@ def main(argv): FLAGS.input_saved_model_dir, FLAGS.retrain, FLAGS.regularization_loss_multiplier) - model = make_classifier(feature_extractor, - dropout_rate=0.0) # TODO(b/134660903): Remove. + model = make_classifier(feature_extractor) model.compile(loss=tf.keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.SGD(),