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Fix a few more failed test in OSS keras.

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PiperOrigin-RevId: 336967487
Change-Id: I496078cee0aa946c636b35df3f3037f9f7aef556
This commit is contained in:
Scott Zhu 2020-10-13 15:23:35 -07:00 committed by TensorFlower Gardener
parent 8572e0189a
commit c290421e1d
3 changed files with 50 additions and 47 deletions
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47
tensorflow/python/keras/saving/hdf5_format_test.py
View File

@ -831,29 +831,30 @@ class TestWholeModelSaving(keras_parameterized.TestCase):
saved_model_dir = self._save_model_dir()
save_format = testing_utils.get_save_format()
model = _make_model()
model.compile(
loss=keras.losses.SparseCategoricalCrossentropy(),
optimizer=optimizers.gradient_descent_v2.SGD(),
metrics=[keras.metrics.SparseCategoricalCrossentropy()])
x = np.random.normal(size=(32, 4))
y = np.random.randint(0, 3, size=32)
model.train_on_batch(x, y)
evaluation_results = model.evaluate(x, y)
# Save and reload model.
model.save(saved_model_dir, save_format=save_format)
del model # Prevent misuse.
loaded_model = keras.models.load_model(saved_model_dir)
loaded_model_eval_results = loaded_model.evaluate(x, y)
# Assert all evaluation results are the same.
self.assertAllClose(evaluation_results, loaded_model_eval_results, 1e-9)
# Check correctness of the loss calculation.
self.assertAllGreater(evaluation_results, 0.)
evaluation_results = dict(
zip(loaded_model.metrics_names, evaluation_results))
self.assertNear(
evaluation_results['sparse_categorical_crossentropy'] +
evaluation_results['custom_loss'], evaluation_results['loss'], 1e-6)
with self.cached_session():
model = _make_model()
model.compile(
loss=keras.losses.SparseCategoricalCrossentropy(),
optimizer=optimizers.gradient_descent_v2.SGD(),
metrics=[keras.metrics.SparseCategoricalCrossentropy()])
x = np.random.normal(size=(32, 4))
y = np.random.randint(0, 3, size=32)
model.train_on_batch(x, y)
evaluation_results = model.evaluate(x, y)
# Save and reload model.
model.save(saved_model_dir, save_format=save_format)
del model # Prevent misuse.
loaded_model = keras.models.load_model(saved_model_dir)
loaded_model_eval_results = loaded_model.evaluate(x, y)
# Assert all evaluation results are the same.
self.assertAllClose(evaluation_results, loaded_model_eval_results, 1e-9)
# Check correctness of the loss calculation.
self.assertAllGreater(evaluation_results, 0.)
evaluation_results = dict(
zip(loaded_model.metrics_names, evaluation_results))
self.assertNear(
evaluation_results['sparse_categorical_crossentropy'] +
evaluation_results['custom_loss'], evaluation_results['loss'], 1e-6)
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
def test_save_uncompiled_model_with_optimizer(self):

3
tensorflow/python/keras/saving/save_test.py
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@ -33,6 +33,7 @@ from tensorflow.python.keras import losses
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.engine import sequential
from tensorflow.python.keras.feature_column import dense_features
from tensorflow.python.keras.feature_column import sequence_feature_column as ksfc
from tensorflow.python.keras.layers import core
from tensorflow.python.keras.saving import model_config
from tensorflow.python.keras.saving import save
@ -190,7 +191,7 @@ class TestSaveModel(test.TestCase, parameterized.TestCase):
shape=(None, 1), sparse=True, name='b', dtype='string')
}
fc_layer, _ = feature_column_lib.SequenceFeatures(cols)(input_layers)
fc_layer, _ = ksfc.SequenceFeatures(cols)(input_layers)
# TODO(tibell): Figure out the right dtype and apply masking.
# sequence_length_mask = array_ops.sequence_mask(sequence_length)
# x = keras.layers.GRU(32)(fc_layer, mask=sequence_length_mask)

47
tensorflow/python/keras/saving/saved_model/saved_model_test.py
View File

@ -965,33 +965,34 @@ class MetricTest(test.TestCase, parameterized.TestCase):
num_tensor_args,
shape=(1, 5),
test_sample_weight=True):
tf_save.save(metric, save_dir)
loaded = keras_load.load(save_dir)
self.evaluate([v.initializer for v in loaded.variables])
self.assertEqual(metric.name, loaded.name)
self.assertEqual(metric.dtype, loaded.dtype)
with self.cached_session():
tf_save.save(metric, save_dir)
loaded = keras_load.load(save_dir)
self.evaluate([v.initializer for v in loaded.variables])
self.assertEqual(metric.name, loaded.name)
self.assertEqual(metric.dtype, loaded.dtype)
inputs = self.generate_inputs(num_tensor_args, shape)
actual = self.evaluate(metric(*inputs))
self.assertAllClose(actual, loaded(*inputs))
self.assertAllClose(metric.variables, loaded.variables)
# Test with separate calls to update state and result.
inputs = self.generate_inputs(num_tensor_args, shape)
self.evaluate(metric.update_state(*inputs))
self.evaluate(loaded.update_state(*inputs))
actual = self.evaluate(metric.result())
self.assertAllClose(actual, loaded.result())
if test_sample_weight:
# Test with sample weights input.
inputs = self.generate_inputs(num_tensor_args, shape)
sample_weight = self.generate_inputs(1, [])[0]
inputs.append(sample_weight)
actual = self.evaluate(metric(*inputs))
self.assertAllClose(actual, loaded(*inputs))
return loaded
self.assertAllClose(metric.variables, loaded.variables)
# Test with separate calls to update state and result.
inputs = self.generate_inputs(num_tensor_args, shape)
self.evaluate(metric.update_state(*inputs))
self.evaluate(loaded.update_state(*inputs))
actual = self.evaluate(metric.result())
self.assertAllClose(actual, loaded.result())
if test_sample_weight:
# Test with sample weights input.
inputs = self.generate_inputs(num_tensor_args, shape)
sample_weight = self.generate_inputs(1, [])[0]
inputs.append(sample_weight)
actual = self.evaluate(metric(*inputs))
self.assertAllClose(actual, loaded(*inputs))
return loaded
@parameterized.named_parameters([
('mean', keras.metrics.Mean, 1, (1, 5)),