synthval.configs
================

.. py:module:: synthval.configs

.. autoapi-nested-parse::

   Module for defining default parameters for network training and testing using PyTorch and pyNeVer.

   This module provides default configurations for setting up neural networks and training them
   using the PyTorch framework, with an emphasis on integrating with pyNeVer strategies.

   .. attribute:: DEFAULT_NETWORK_PARAMS

      Default parameters for the neural network architecture. The dictionary contains:

      - network_id : str
          Identifier for the network architecture.
      - num_hidden_neurons : list of int
          Number of neurons for each hidden layer in the network.

      :type: dict

   .. attribute:: DEFAULT_TRAINING_PARAMS

      Default parameters for training the network using PyTorch. The dictionary contains:

      - optimizer_con : torch.optim.Optimizer
          Constructor for the optimizer to be used during training.
      - opt_params : dict
          Parameters for the optimizer, such as learning rate (`lr`).
      - n_epochs : int
          Number of epochs for training.
      - validation_percentage : float
          Proportion of the data used for validation.
      - train_batch_size : int
          Batch size used for training.
      - validation_batch_size : int
          Batch size used for validation.
      - r_split : bool
          Whether to perform a random data split for training/validation.
      - scheduler_con : torch.optim.lr_scheduler, optional
          Constructor for a learning rate scheduler (default is None).
      - sch_params : dict or None
          Parameters for the learning rate scheduler (default is None).
      - precision_metric : pynever.strategies.training.PytorchMetrics
          Metric to evaluate training precision, e.g., accuracy or inaccuracy.
      - network_transform : callable, optional
          Transformation function to apply to the network (default is None).
      - device : str
          The device on which the training will be performed (e.g., 'cpu' or 'cuda').
      - train_patience : int
          Number of epochs to wait before early stopping if no improvement in validation loss.
      - checkpoints_root : str
          Path to the directory where model checkpoints will be saved.
      - verbose_rate : int
          Frequency (in epochs) for printing training progress.

      :type: dict

   .. attribute:: DEFAULT_TESTING_PARAMS

      Default parameters for testing the trained network. The dictionary contains:

      - metric : pynever.strategies.training.PytorchMetrics
          Metric used to evaluate the model performance on the test set.
      - metric_params : dict
          Additional parameters for the testing metric.
      - test_batch_size : int
          Batch size for testing.
      - device : str
          The device on which testing will be performed (e.g., 'cpu' or 'cuda').

      :type: dict