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Hotfix: Compile Keras WN models without tf_function #429

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Merged
WindQAQ merged 1 commit into from
Aug 19, 2019
Merged

Hotfix: Compile Keras WN models without tf_function #429

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2 changes: 1 addition & 1 deletion tensorflow_addons/layers/wrappers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,6 @@ def build(self, input_shape):

super(WeightNormalization, self).build()

@tf.function
def call(self, inputs):
"""Call `Layer`"""
if not self._initialized:
Expand Down Expand Up @@ -131,6 +130,7 @@ def _init_norm(self):
self.g.assign(
tf.reshape(tf.linalg.norm(flat, axis=0), (self.layer_depth,)))

# TODO: Get data init to work with tf_function compile #428
def _data_dep_init(self, inputs):
"""Data dependent initialization."""

Expand Down
8 changes: 6 additions & 2 deletions tensorflow_addons/layers/wrappers_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,14 +26,16 @@

@test_utils.run_all_in_graph_and_eager_modes
class WeightNormalizationTest(tf.test.TestCase):
# TODO: Get data init to work with tf_function compile #428
def test_weightnorm_dense_train(self):
model = tf.keras.models.Sequential()
model.add(
wrappers.WeightNormalization(
tf.keras.layers.Dense(2), input_shape=(3, 4)))
model.compile(
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.001),
loss='mse')
loss='mse',
experimental_run_tf_function=False)
model.fit(
np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
Expand All @@ -58,6 +60,7 @@ def test_weightnorm_dense_train_notinit(self):
self.assertTrue(hasattr(model.layers[0], 'g'))

def test_weightnorm_conv2d(self):
# TODO: Get data init to work with tf_function compile #428
model = tf.keras.models.Sequential()
model.add(
wrappers.WeightNormalization(
Expand All @@ -67,7 +70,8 @@ def test_weightnorm_conv2d(self):
model.add(tf.keras.layers.Activation('relu'))
model.compile(
optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.001),
loss='mse')
loss='mse',
experimental_run_tf_function=False)
model.fit(
np.random.random((2, 4, 4, 3)),
np.random.random((2, 4, 4, 5)),
Expand Down