def _compile_model(
self,
loss_function: Callable,
ground_distance_path: Path,
**loss_function_kwargs
):
if loss_function == self_guided_earth_mover_distance:
self.emd_weight_head_start = EmdWeightHeadStart()
self.ground_distance_manager = GroundDistanceManager(ground_distance_path)
lr_schedule = ExponentialDecay(
self.learning_rate,
decay_steps=429,
decay_rate=0.995
)
self.compile(
loss=loss_function(
model=self,
**loss_function_kwargs
),
optimizer=self._OPTIMIZER(
learning_rate=lr_schedule,
nesterov=True,
momentum=self._OPTIMIZER_MOMENTUM
),
metrics=self._METRICS,
run_eagerly=True
)
def alxs(lr_index, fold_index):
ground_distance_manager = GroundDistanceManager(
Path('ground_distances'))
ground_distance_matrix = ground_distance_manager.load_ground_distance_matrix(
'159')
evaluate_adience_model(evaluation_model=Alxs,
learning_rate_index=lr_index,
fold_index=fold_index,
loss_function=approximate_earth_mover_distance,
final_activation=softmax,
entropic_regularizer=10,
distance_matrix=ground_distance_matrix,
matrix_scaling_operations=100)
class EvaluationModel(ABC, Model):
"""Base class for the models to be evaluated."""
_OPTIMIZER: ClassVar[Optimizer] = SGD
_OPTIMIZER_MOMENTUM: ClassVar[float] = 0.98
_METRICS: ClassVar[List[Metric]] = [
categorical_accuracy,
one_off_accuracy
]
_MODEL_NAME: ClassVar[str] = 'base_model'
model: Model = None
def __init__(
self,
number_of_classes: int,
dataset_name: DatasetName,
final_activation: Union[softmax, linear],
loss_function: Callable,
learning_rate: float,
fold_index: int,
ground_distance_path: Path,
**loss_function_kwargs,
):
super(EvaluationModel, self).__init__()
self.number_of_classes = number_of_classes
self.learning_rate = learning_rate
self.fold_index = fold_index
self.dataset_name = dataset_name
self.loss_function = loss_function
self.second_to_last_layer = None
self._build_model(
number_of_classes=number_of_classes,
final_activation=final_activation
)
self._compile_model(
ground_distance_path=ground_distance_path,
loss_function=loss_function,
**loss_function_kwargs
)
def compute_output_shape(self, input_shape):
return TensorShape((
input_shape[0],
self.number_of_classes
))
@abstractmethod
def _build_model(
self,
number_of_classes: int,
final_activation: Union[softmax, linear]
):
pass
def call(self, inputs, **kwargs):
y = inputs
for layer in self.layers[:-2]:
y = layer(y, **kwargs)
self.second_to_last_layer = self.layers[-2](y, **kwargs)
output = self.layers[-1](self.second_to_last_layer, **kwargs)
if not kwargs['training']:
y = inputs[:, :, ::-1, :]
for layer in self.layers[:-2]:
y = layer(y, **kwargs)
self.second_to_last_layer = self.layers[-2](y, **kwargs)
mirrored_output = self.layers[-1](self.second_to_last_layer, **kwargs)
return (mirrored_output + output) / 2
else:
return output
def _compile_model(
self,
loss_function: Callable,
ground_distance_path: Path,
**loss_function_kwargs
):
if loss_function == self_guided_earth_mover_distance:
self.emd_weight_head_start = EmdWeightHeadStart()
self.ground_distance_manager = GroundDistanceManager(ground_distance_path)
lr_schedule = ExponentialDecay(
self.learning_rate,
decay_steps=429,
decay_rate=0.995
)
self.compile(
loss=loss_function(
model=self,
**loss_function_kwargs
),
optimizer=self._OPTIMIZER(
learning_rate=lr_schedule,
nesterov=True,
momentum=self._OPTIMIZER_MOMENTUM
),
metrics=self._METRICS,
run_eagerly=True
)
def test(self, **kwargs):
return self.predict(**kwargs)
def train(self, **kwargs):
callbacks = [
get_checkpoint_file(
loss_name=self.loss_function.__name__,
data_set_name=self.dataset_name,
learning_rate=self.learning_rate,
model_name=self._MODEL_NAME,
fold_index=self.fold_index
),
get_tensorboard_callback(
loss_name=self.loss_function.__name__,
data_set_name=self.dataset_name,
learning_rate=self.learning_rate,
model_name=self._MODEL_NAME,
fold_index=self.fold_index
)
]
if hasattr(self, 'ground_distance_manager'):
labels = [batch[1] for batch in kwargs['x']]
self.ground_distance_manager.set_labels(labels)
callbacks.extend([self.emd_weight_head_start, self.ground_distance_manager])
return self.fit(
callbacks=callbacks,
**kwargs
)