Module delta.ml.config_parser
Functions to support loading custom ML-related objects from dictionaries specified in yaml files. Includes constructing custom neural networks and more.
Functions
def augmentation_from_dict(aug_dict: Union[dict, str])-
Construct an augmenation function from a dictionary.
Parameters
aug_dict:Union[dict, str]- Config dictionary defining an augmentation.
Returns
Callable- The augmentation function.
def callback_from_dict(callback_dict: Union[dict, str]) ‑> keras.src.callbacks.Callback-
Construct a callback from a dictionary.
Parameters
callback_dict:Union[dict, str]- Config dictionary defining a callback.
Returns
tensorflow.keras.callbacks.Callback- The callback object.
def config_augmentation()-
Returns
Callable- Augmentation function that applies all augmentations in configuration.
def config_callbacks() ‑> List[keras.src.callbacks.Callback]-
Returns
List[tensorflow.keras.callbacks.Callback]- List of callbacks specified in the config file.
def config_model(num_bands: int) ‑> Callable[[], keras.src.engine.training.Model]-
Returns
Callable[[], tensorflow.keras.models.Model]- A function to construct the model given in the config file.
def learning_rate_from_dict(spec: Union[dict, float]) ‑> Union[keras.src.optimizers.schedules.learning_rate_schedule.LearningRateSchedule, float]-
Construct a learning rate schedule from a dictionary or float.
Parameters
spec:Union[dict, float]- Config dictionary or float defining a learning rate.
Returns
Union[tensorflow.keras.schedules.LearingRateSchedule, float]- The learning rate schedule or constant learning rate
def loss_from_dict(loss_spec: Union[dict, str]) ‑> keras.src.losses.Loss-
Construct a loss function.
Parameters
loss_spec:Union[dict, str]- Specification of the loss function. Either a string that is compatible with the keras interface (e.g. 'categorical_crossentropy') or an object defined by a dict of the form {'LossFunctionName': {'arg1':arg1_val, …,'argN',argN_val}}
Returns
tensorflow.keras.losses.Loss- The loss object.
def metric_from_dict(metric_spec: Union[dict, str]) ‑> keras.src.metrics.base_metric.Metric-
Construct a metric.
Parameters
metric_spec:Union[dict, str]- Config dictionary or string defining the metric
Returns
tensorflow.keras.metrics.Metric- The metric object.
def model_from_dict(model_dict: dict, exposed_params: dict) ‑> Callable[[], keras.src.engine.training.Model]-
Construct a model.
Parameters
model_dict:dict- Config dictionary describing the model
exposed_params:dict- Dictionary of parameter names and values to substitute.
Returns
Callable[[], tensorflow.keras.models.Model]:- Model constructor function.
def optimizer_from_dict(spec: Union[dict, str]) ‑> keras.src.optimizers.optimizer.Optimizer-
Construct an optimizer from a dictionary or string.
Parameters
spec:Union[dict, str]- Config dictionary or string defining an optimizer
Returns
tensorflow.keras.optimizers.Optimizer- The optimizer object.