OptunaEstimator

class mergernet.estimators.automl.OptunaEstimator

Bases: Estimator

Attributes

registry
tf_model

_objective(trial: Trial) → float

Objective function that will be optimized by oputna

Parameters:

trial (optuna.trial.Trial) – The current optuna trial

Returns:

The metric that will be optimized by optuna

Return type:

float

_save_optuna_db(study: Study)

build()

compile_model(tf_model: Model, optimizer: Optimizer, metrics: list = [], label_smoothing: float = 0.0)

download(config: EstimatorConfig, replace: bool = False)

get_conv_arch(pretrained_arch: str) → Tuple[Callable, Callable]

get_dataaug_block(flip_horizontal: bool = True, flip_vertical: bool = True, rotation: Tuple[float, float] | bool = (-0.08, 0.08), zoom: Tuple[float, float] | bool = (-0.15, 0.0))

get_metric(metric: str)

get_optimizer(optimizer: str, lr: float | LearningRateSchedule) → Optimizer

get_scheduler(scheduler: str, lr: float) → LearningRateSchedule

For cosine_restarts scheduler, the learning rate multiplier first decays from 1 to alpha for first_decay_steps steps. Then, a warm restart is performed. Each new warm restart runs for t_mul times more steps and with m_mul times initial learning rate as the new learning rate.

Parameters:

• scheduler (str) – Scheduler name

• lr (float) – Initial learning rate

Returns:

A LearningRateSchedule instance

Return type:

tf.keras.optimizers.schedules.LearningRateSchedule

plot(filename: str = 'model.png')

predict()

set_trainable(tf_model: Model, layer: str, trainable: bool)

train(*args, **kwargs) → Tuple[Model, History]

Starts the optuna optimization and returns the best model trained

Returns:

The best model trained

Return type:

tf.keras.Model

_abc_impl = <_abc_data object>

registry = <mergernet.estimators.base.EstimatorRegistry object>

property tf_model