ZoobotEstimator

class mergernet.estimators.decals.ZoobotEstimator

Bases: Estimator

Attributes

registry
tf_model

_prepare_dataset()

build(include_top: bool = True) → Model

cnn_representations(filename: str | Path | None = None, rebuild: bool = False, include_cols: Dict[str, List[Any]] | None = None)

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

pca(features: ndarray, n_components: int, filename: str | None = None, include_iauname: bool = True)

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

predict(n_samples: int = 5, filename: str | Path | None = None, rebuild: bool = False)

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

train()

_abc_impl = <_abc_data object>

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

property tf_model