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Update optimizers to v2 in saved model test.

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PiperOrigin-RevId: 258907912
This commit is contained in:
Yanhui Liang 2019-07-18 22:27:02 -07:00 committed by TensorFlower Gardener
parent 238dcdfdee
commit 69359f86c9
1 changed files with 16 additions and 6 deletions
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22
tensorflow/python/keras/saving/saved_model_experimental_test.py
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@ -32,8 +32,11 @@ from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.engine import training as model_lib
from tensorflow.python.keras.optimizer_v2 import adadelta
from tensorflow.python.keras.optimizer_v2 import rmsprop
from tensorflow.python.keras.saving import saved_model_experimental as keras_saved_model
from tensorflow.python.keras.utils import mode_keys
from tensorflow.python.keras.utils import tf_utils
@ -44,7 +47,8 @@ from tensorflow.python.saved_model import model_utils
from tensorflow.python.training import training as training_module
class TestModelSavingandLoading(test.TestCase):
@keras_parameterized.run_all_keras_modes()
class TestModelSavingandLoading(parameterized.TestCase, test.TestCase):
def _save_model_dir(self, dirname='saved_model'):
temp_dir = self.get_temp_dir()
@ -59,9 +63,11 @@ class TestModelSavingandLoading(test.TestCase):
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(
loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
optimizer=rmsprop.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
sample_weight_mode='temporal',
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
@ -102,8 +108,10 @@ class TestModelSavingandLoading(test.TestCase):
model = keras.models.Model(inputs, output)
model.compile(
loss=keras.losses.MSE,
optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
optimizer=rmsprop.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
@ -159,7 +167,9 @@ class TestModelSavingandLoading(test.TestCase):
loaded_model.compile(
loss='mse',
optimizer=training_module.RMSPropOptimizer(0.1),
metrics=['acc'])
metrics=['acc'],
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
y = loaded_model.predict(x)
self.assertAllClose(ref_y, y, atol=1e-05)