Modely Class

class nnodely.nnodely.Modely(*, visualizer: str | EmptyVisualizer | None = 'Standard', exporter: str | EmptyExporter | None = 'Standard', seed: int | None = None, workspace: str | None = None, log_internal: bool = False, save_history: bool = False)[source]

Bases: Composer, Trainer, Loader, Validator, Exporter

Create the main object, the nnodely object, that will be used to create the network, train and export it.

Parameters:

  • visualizer (str, Visualizer, optional) – The visualizer to be used. Default is the ‘Standard’ visualizer.

  • exporter (str, Exporter, optional) – The exporter to be used. Default is the ‘Standard’ exporter.

  • seed (int, optional) – Set the seed for all the random modules inside the nnodely framework. Default is None.

  • workspace (str) – The path of the workspace where all the exported files will be saved.

  • log_internal (bool) – Whether or not save the logs. Default is False.

  • save_history (bool) – Whether or not save the history. Default is False.

Example

>>> model = Modely()
property constants
property internals
property json
property neuralized
property parameters
resetSeed(seed: int) → None[source]

Resets the random seed for reproducibility.

This method sets the seed for various random number generators used in the project to ensure reproducibility of results.

Parameters:

seed (int) – The seed value to be used for the random number generators.

Example

>>> model = nnodely()
>>> model.resetSeed(42)
property states
property traced
trainAndAnalyze(*, test_dataset: str | list | dict | None = None, test_batch_size: int = 128, **kwargs)[source]

Trains the model using the provided datasets and parameters. After training, it analyzes the results on the training, validation, and test datasets.

Parameters:

  • test_dataset (str or None, optional) – The name of the datasets used for test. Default is None.

  • test_batch_size (int, optional) – The batch size for testing. Default is 1.

  • models (list or None, optional) – A list of models to train. Default is None.

  • train_dataset (str or None, optional) – The name of datasets to use for training. Default is None.

  • validation_dataset (str or None, optional) – The name of datasets to use for validation. Default is None.

  • dataset (str or None, optional) – The name of the datasets to use for training, validation and test.

  • splits (list or None, optional) – A list of 3 elements specifying the percentage of splits for training, validation, and testing. The three elements must sum up to 100! The parameter splits is only used when dataset is not None

  • 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 for training. 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.

  • shuffle_data (bool or None, optional) – Whether to shuffle the data during training. Default is None.

  • early_stopping (Callable or None, optional) – A callable for early stopping. Default is None.

  • early_stopping_params (dict or None, optional) – A dictionary of parameters for early stopping. Default is None.

  • select_model (Callable or None, optional) – A callable for selecting the best model. Default is None.

  • select_model_params (dict or None, optional) – A dictionary of parameters for selecting the best model. Default is None.

  • minimize_gain (dict or None, optional) – A dictionary specifying the gain for each minimization loss function. Default is None.

  • num_of_epochs (int or None, optional) – The number of epochs to train the model. Default is None.

  • train_batch_size (int or None, optional) – The batch size for training. Default is None.

  • val_batch_size (int or None, optional) – The batch size for validation. Default is None.

  • optimizer (Optimizer or None, optional) – The optimizer to use for training. Default is None.

  • lr (float or None, optional) – The learning rate. Default is None.

  • lr_param (dict or None, optional) – A dictionary of learning rate parameters. Default is None.

  • optimizer_params (list or dict or None, optional) – A dictionary of optimizer parameters. Default is None.

  • optimizer_defaults (dict or None, optional) – A dictionary of default optimizer settings. Default is None.

  • training_params (dict or None, optional) – A dictionary of training parameters. Default is None.

  • add_optimizer_params (list or None, optional) – Additional optimizer parameters. Default is None.

  • add_optimizer_defaults (dict or None, optional) – Additional default optimizer settings. Default is None.

Raises:

  • RuntimeError – If no data is loaded or if there are no modules with learnable parameters.

  • KeyError – If the sample horizon is not positive.

  • ValueError – If an input or output variable is not in the model definition.