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Stop skipping KerasTensors for Keras tests that now work with KerasTensors.

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PiperOrigin-RevId: 316543523
Change-Id: Iea54fa7ed735e239cda293304c6a17207b136ab7
This commit is contained in:
Tomer Kaftan 2020-06-15 14:20:38 -07:00 committed by TensorFlower Gardener
parent 0e634188b3
commit fc531df8ce
5 changed files with 18 additions and 27 deletions
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5
tensorflow/python/keras/engine/training_test.py
View File

@ -1563,10 +1563,7 @@ class TrainingTest(keras_parameterized.TestCase):
self.assertEqual(self.evaluate(layer.v), 1.)
@keras_parameterized.run_all_keras_modes(
always_skip_v1=True,
# TODO(kaftan): this is failing with KerasTensors
# in a way that seems orthogonal to what the code is testing
skip_keras_tensors=True)
always_skip_v1=True)
@parameterized.named_parameters(
('numpy_array', 'numpy_array'),
('dataset_array', 'dataset_array'),

26
tensorflow/python/keras/layers/preprocessing/category_encoding_test.py
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@ -31,7 +31,6 @@ from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.keras import backend
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.layers import core
from tensorflow.python.keras.layers.preprocessing import category_encoding
from tensorflow.python.keras.layers.preprocessing import category_encoding_v1
@ -252,24 +251,21 @@ class CategoryEncodingInputTest(keras_parameterized.TestCase,
sparse_ops.sparse_tensor_to_dense(sp_output_dataset, default_value=0),
output_dataset)
# TODO(b/158570051): Support KerasTensor
# Keras functional model doesn't support dense layer stacked with sparse out.
def test_sparse_output_and_dense_layer(self):
with testing_utils.use_keras_tensors_scope(False):
input_array = constant_op.constant([[1, 2, 3], [3, 3, 0]])
input_array = constant_op.constant([[1, 2, 3], [3, 3, 0]])
max_tokens = 4
max_tokens = 4
input_data = keras.Input(shape=(None,), dtype=dtypes.int32)
encoding_layer = get_layer_class()(
max_tokens=max_tokens, output_mode=category_encoding.COUNT,
sparse=True)
int_data = encoding_layer(input_data)
dense_layer = keras.layers.Dense(units=1)
output_data = dense_layer(int_data)
input_data = keras.Input(shape=(None,), dtype=dtypes.int32)
encoding_layer = get_layer_class()(
max_tokens=max_tokens, output_mode=category_encoding.COUNT,
sparse=True)
int_data = encoding_layer(input_data)
dense_layer = keras.layers.Dense(units=1)
output_data = dense_layer(int_data)
model = keras.Model(inputs=input_data, outputs=output_data)
_ = model.predict(input_array, steps=1)
model = keras.Model(inputs=input_data, outputs=output_data)
_ = model.predict(input_array, steps=1)
@keras_parameterized.run_all_keras_modes

3
tensorflow/python/keras/premade/linear_test.py
View File

@ -40,8 +40,7 @@ from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@keras_parameterized.run_all_keras_modes(always_skip_v1=True,
skip_keras_tensors=True)
@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
class LinearModelTest(keras_parameterized.TestCase):
def test_linear_model_with_single_input(self):

3
tensorflow/python/keras/premade/wide_deep_test.py
View File

@ -37,8 +37,7 @@ from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@keras_parameterized.run_all_keras_modes(always_skip_v1=True,
skip_keras_tensors=True)
@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
class WideDeepModelTest(keras_parameterized.TestCase):
def test_wide_deep_model(self):

8
tensorflow/python/keras/regularizers_test.py
View File

@ -83,7 +83,7 @@ class KerasRegularizersTest(keras_parameterized.TestCase,
self.assertEqual(len(model.losses), 1)
model.fit(x_train, y_train, batch_size=10, epochs=1, verbose=0)
@keras_parameterized.run_all_keras_modes(skip_keras_tensors=True)
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters([
('l1', regularizers.l1()),
('l2', regularizers.l2()),
@ -126,7 +126,7 @@ class KerasRegularizersTest(keras_parameterized.TestCase,
model.get_config(), custom_objects={'my_regularizer': my_regularizer})
self.assertEqual(model2.layers[1].kernel_regularizer, my_regularizer)
@keras_parameterized.run_all_keras_modes(skip_keras_tensors=True)
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters([
('l1', regularizers.l1()),
('l2', regularizers.l2()),
@ -144,7 +144,7 @@ class KerasRegularizersTest(keras_parameterized.TestCase,
run_eagerly=testing_utils.should_run_eagerly())
self.assertLen(model.losses, 5)
@keras_parameterized.run_all_keras_modes(skip_keras_tensors=True)
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters([
('l1', regularizers.l1()),
('l2', regularizers.l2()),
@ -166,7 +166,7 @@ class KerasRegularizersTest(keras_parameterized.TestCase,
run_eagerly=testing_utils.should_run_eagerly())
self.assertLen(model.losses, 6)
@keras_parameterized.run_all_keras_modes(skip_keras_tensors=True)
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters([
('l1', regularizers.l1()),
('l2', regularizers.l2()),