Validation Module

nnodely.operators.validator.Validator.analyzeModel(self, dataset: str | list | dict | None = None, *, tag: str | None = None, splits: list | None = None, minimize_gain: dict = {}, closed_loop: dict = {}, connect: dict = {}, prediction_samples: int | str = 0, step: int = 0, batch_size: int | None = None) → None

The function is used to analyze the performance of the model on the provided dataset.

Parameters:

  • dataset (str | list | dict) – Dataset to analyze the performance of the model on.

  • tag (str or None) – Label to be used in the plots

  • minimize_gain (dict) – A dictionary specifying the gain for each minimization loss function.

  • closed_loop (dict or None, optional) – A dictionary specifying closed loop connections. The keys are input names and the values are output names. Default is None.

  • connect (dict or None, optional) – A dictionary specifying connections. The keys are input names and the values are output names. Default is None.

  • step (int or None, optional) – The step size to analyze the model on the provided dataset. A big value will result in less data used for each epochs and a faster train. Default is None.

  • prediction_samples (int or None, optional) – The size of the prediction horizon. Number of samples at each recurrent window Default is None.

  • batch_size – The batch size use for analyse the performance of the model on the provided dataset.