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model.evaluate

logger_config​

logger​

use_gpu​

eval_temporal​

def eval_temporal(cfg: dict,
                  use_gpu: bool,
                  model_name: str,
                  fixed: bool,
                  snapshot: Optional[str] = None,
                  suffix: Optional[str] = None) -> None

Evaluate the temporal aspects of the trained model.

Parameters

  • cfg (dict): Configuration dictionary.
  • use_gpu (bool): Flag indicating whether to use GPU for evaluation.
  • model_name (str): Name of the model.
  • fixed (bool): Flag indicating whether the data is fixed or not.
  • snapshot (str, optional): Path to the model snapshot. Defaults to None.
  • suffix (str, optional): Suffix for the saved plot filename. Defaults to None.

Returns

  • None

evaluate_model​

@save_state(model=EvaluateModelFunctionSchema)
def evaluate_model(config: dict,
                   use_snapshots: bool = False,
                   save_logs: bool = False) -> None

Evaluate the trained model. Fills in the values in the "evaluate_model" key of the states.json file. Saves the evaluation results to:

  • project_name/
    • model/
      • evaluate/

Parameters

  • config (dict): Configuration dictionary.
  • use_snapshots (bool, optional): Whether to plot for all snapshots or only the best model. Defaults to False.
  • save_logs (bool, optional): Flag indicating whether to save logs. Defaults to False.

Returns

  • None