def test_generate_keras_mode_skip_run_eagerly(self):
result = combinations.keras_mode_combinations(run_eagerly=[False])
if tf2.enabled():
self.assertLen(result, 1)
self.assertEqual(result[0], {"mode": "eager", "run_eagerly": False})
else:
self.assertLen(result, 2)
self.assertEqual(result[0], {"mode": "eager", "run_eagerly": False})
self.assertEqual(result[1], {"mode": "graph", "run_eagerly": False})
class ExampleTest(parameterized.TestCase):
def runTest(self):
pass
@combinations.generate(combinations.keras_mode_combinations())
def testBody(self):
mode = "eager" if context.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
test_params.append((mode, should_run_eagerly))
def test_combine_combinations(self):
test_cases = []
@combinations.generate(combinations.times(
combinations.keras_mode_combinations(),
combinations.keras_model_type_combinations()))
class ExampleTest(parameterized.TestCase):
def runTest(self):
pass
@parameterized.named_parameters(dict(testcase_name="_arg",
arg=True))
def testBody(self, arg):
del arg
mode = "eager" if context.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
test_cases.append((mode, should_run_eagerly,
testing_utils.get_model_type()))
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
expected_combinations = [
("eager", False, "functional"),
("eager", False, "sequential"),
("eager", False, "subclass"),
("eager", True, "functional"),
("eager", True, "sequential"),
("eager", True, "subclass"),
]
if not tf2.enabled():
expected_combinations.extend([
("graph", False, "functional"),
("graph", False, "sequential"),
("graph", False, "subclass"),
])
self.assertAllEqual(sorted(test_cases), expected_combinations)
import copy
from absl.testing import parameterized
import numpy as np
from tensorflow.python import keras
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.keras import combinations
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
from tensorflow.python.training import gradient_descent
@combinations.generate(combinations.keras_mode_combinations())
class SimpleRNNLayerTest(test.TestCase, parameterized.TestCase):
def test_return_sequences_SimpleRNN(self):
num_samples = 2
timesteps = 3
embedding_dim = 4
units = 2
testing_utils.layer_test(keras.layers.SimpleRNN,
kwargs={
'units': units,
'return_sequences': True
},
input_shape=(num_samples, timesteps,
embedding_dim))
@testing_utils.run_v2_only
def test_generate_keras_mode_eager_only(self):
result = combinations.keras_mode_combinations(mode=["eager"])
self.assertLen(result, 2)
self.assertEqual(result[0], {"mode": "eager", "run_eagerly": True})
self.assertEqual(result[1], {"mode": "eager", "run_eagerly": False})
