def test_save_load_trackable(self, distribution, optimizer):
# TODO(b/123533246): Enable the test for TPU once bug is fixed
if (isinstance(distribution,
(tpu_strategy.TPUStrategy, tpu_strategy.TPUStrategyV1)) and
distribution.extended.steps_per_run > 1):
self.skipTest('MultiStep TPU Strategy deadlocks with optimizer restore.')
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(
optimizer(),
'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp()
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(
optimizer(),
'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_callbacks_in_predict(self, distribution,
experimental_run_tf_function):
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(
optimizer='sgd',
loss='mse',
metrics=['mae'],
experimental_run_tf_function=experimental_run_tf_function)
dataset = keras_test_lib.get_dataset(distribution)
counter = Counter()
model.predict(
keras_test_lib.get_predict_dataset(dataset),
steps=5,
callbacks=[counter])
self.assertDictEqual(
counter.method_counts, {
'on_predict_batch_begin': 5,
'on_predict_batch_end': 5,
'on_predict_begin': 1,
'on_predict_end': 1
})
def test_save_load_h5(self, distribution, optimizer, cloning):
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer(), 'mse', cloning=cloning)
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp('.h5')
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(optimizer(), 'mse', cloning=cloning)
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_save_load_h5(self, distribution, optimizer, cloning):
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer(), 'mse', cloning=cloning)
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp('.h5')
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(optimizer(), 'mse', cloning=cloning)
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_save_load_h5(self, distribution):
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(rms_prop_keras.RMSprop(learning_rate=0.01), 'mse')
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp('.h5')
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(rms_prop_keras.RMSprop(learning_rate=0.01), 'mse')
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
def test_callbacks_in_predict(self, distribution):
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer='sgd', loss='mse', metrics=['mae'])
dataset = keras_test_lib.get_dataset(distribution)
counter = Counter()
model.predict(
keras_test_lib.get_predict_dataset(dataset),
steps=5,
callbacks=[counter])
self.assertDictEqual(
counter.method_counts, {
'on_predict_batch_begin': 5,
'on_predict_batch_end': 5,
'on_predict_begin': 1,
'on_predict_end': 1
})
def test_save_load_trackable(self, distribution, optimizer, cloning):
# TODO(b/123533246): Enable the test for TPU once bug is fixed
if (isinstance(distribution, (tpu_strategy.TPUStrategy,
tpu_strategy.TPUStrategyV1)) and
distribution.extended.steps_per_run > 1):
self.skipTest('MultiStep TPU Strategy deadlocks with optimizer restore.')
with self.cached_session():
dataset = keras_test_lib.get_dataset(distribution)
with distribution.scope():
model = keras_test_lib.get_model()
model.compile(optimizer(), 'mse', cloning=cloning)
model.fit(dataset, epochs=1, steps_per_epoch=1)
weights_file = tempfile.mktemp()
model.save_weights(weights_file)
model_2 = keras_test_lib.get_model()
model_2.compile(optimizer(), 'mse', cloning=cloning)
model_2.load_weights(weights_file)
model_2.predict(
keras_test_lib.get_predict_dataset(distribution), steps=2)
model_2.fit(dataset, epochs=1, steps_per_epoch=1)
