mltk.core.EvaluateClassifierMixin¶

class EvaluateClassifierMixin[source]¶

Provides evaluation properties and methods to the base MltkModel

Note

This mixin is specific to “classification” models

Refer to the Model Evaluation guide for more details.

Properties

`eval_augment`	Enable random augmentations during evaluation Default: False NOTE: This is only used if the DataGeneratorDatasetMixin or sub-class is used by the MltkModel
`eval_custom_function`	Custom evaluation callback
`eval_max_samples_per_class`	The maximum number of samples for a given class to use during evaluation.
`eval_shuffle`	Shuffle data during evaluation Default: False
`eval_steps_per_epoch`	Total number of steps (batches of samples) before declaring the prediction round finished.

Methods

__init__

property eval_shuffle¶: Shuffle data during evaluation Default: False

property eval_augment¶: Enable random augmentations during evaluation Default: False NOTE: This is only used if the DataGeneratorDatasetMixin or sub-class is used by the MltkModel

property eval_custom_function¶

Custom evaluation callback

This is invoked during the mltk.core.evaluate_model() API.

The given function should have the following signature:

my_custom_eval_function(my_model:MyModel, built_model: Union[KerasModel, TfliteModel]) -> EvaluationResults:
    results = EvaluationResults(name=my_model.name)

    if isinstance(built_model, KerasModel):
        results['overall_accuracy] = calculate_accuracy(built_model)
    return results

property eval_steps_per_epoch¶: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, predict will run until the input dataset is exhausted.

property eval_max_samples_per_class¶: The maximum number of samples for a given class to use during evaluation. If -1 then use all available samples Default: -1