def testAggregateH5FormatSaveLoad(self):
model_config = configs.AggregateFunctionConfig(
feature_configs=feature_configs,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-4),
configs.RegularizerConfig('torsion', l2=1e-3),
],
middle_calibration=True,
middle_monotonicity='increasing',
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=8,
output_initialization=[0.0, 1.0])
model = premade.AggregateFunction(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
# Note: because of naming clashes in the optimizer, we cannot include it
# when saving in HDF5. The keras team has informed us that we should not
# push to support this since SavedModel format is the new default and no
# new HDF5 functionality is desired.
tf.keras.models.save_model(model, f.name, include_optimizer=False)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
def testLatticeEnsembleH5FormatSaveLoad(self):
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(feature_configs),
lattices=[['numerical_1', 'categorical'],
['numerical_2', 'categorical']],
num_lattices=2,
lattice_rank=2,
separate_calibrators=True,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-4),
],
output_min=-1.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=5,
output_initialization=[-1.0, 1.0])
model = premade.CalibratedLatticeEnsemble(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
tf.keras.models.save_model(model, f.name)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
def testLatticeEnsembleFromConfig(self):
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=copy.deepcopy(feature_configs),
lattices=[['numerical_1', 'categorical'],
['numerical_2', 'categorical']],
num_lattices=2,
lattice_rank=2,
separate_calibrators=True,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-4),
],
output_min=-1.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=5,
output_initialization=[-1.0, 1.0])
model = premade.CalibratedLatticeEnsemble(model_config)
loaded_model = premade.CalibratedLatticeEnsemble.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(),
sort_keys=True,
cls=self.Encoder))
def testLatticeH5FormatSaveLoad(self, parameterization, num_terms):
model_config = configs.CalibratedLatticeConfig(
feature_configs=copy.deepcopy(feature_configs),
parameterization=parameterization,
num_terms=num_terms,
regularizer_configs=[
configs.RegularizerConfig('calib_wrinkle', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-3),
],
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
if parameterization == 'kronecker_factored':
model_config.regularizer_configs = None
for feature_config in model_config.feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
model = premade.CalibratedLattice(model_config)
# Compile and fit model.
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))
model.fit(fake_data['train_xs'], fake_data['train_ys'])
# Save model using H5 format.
with tempfile.NamedTemporaryFile(suffix='.h5') as f:
tf.keras.models.save_model(model, f.name)
loaded_model = tf.keras.models.load_model(
f.name, custom_objects=premade.get_custom_objects())
self.assertAllClose(
model.predict(fake_data['eval_xs']),
loaded_model.predict(fake_data['eval_xs']))
def testLatticeFromConfig(self):
model_config = configs.CalibratedLatticeConfig(
feature_configs=copy.deepcopy(feature_configs),
regularizer_configs=[
configs.RegularizerConfig('calib_wrinkle', l2=1e-3),
configs.RegularizerConfig('torsion', l2=1e-3),
],
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=6,
output_initialization=[0.0, 1.0])
model = premade.CalibratedLattice(model_config)
loaded_model = premade.CalibratedLattice.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
def testAggregateFromConfig(self):
model_config = configs.AggregateFunctionConfig(
feature_configs=feature_configs,
regularizer_configs=[
configs.RegularizerConfig('calib_hessian', l2=1e-4),
configs.RegularizerConfig('torsion', l2=1e-3),
],
middle_calibration=True,
middle_monotonicity='increasing',
output_min=0.0,
output_max=1.0,
output_calibration=True,
output_calibration_num_keypoints=8,
output_initialization=[0.0, 1.0])
model = premade.AggregateFunction(model_config)
loaded_model = premade.AggregateFunction.from_config(
model.get_config(), custom_objects=premade.get_custom_objects())
self.assertEqual(
json.dumps(model.get_config(), sort_keys=True, cls=self.Encoder),
json.dumps(loaded_model.get_config(), sort_keys=True, cls=self.Encoder))
