dataeval.utils.torch.trainer.AETrainer
======================================

.. py:class:: dataeval.utils.torch.trainer.AETrainer(model, device = 'auto', batch_size = 8)

   A class to train and evaluate an autoencoder<Autoencoder>` model.

   :param model: The model to be trained.
   :type model: nn.Module
   :param device: The hardware device to use for training.
                  If "auto", the device will be set to "cuda" if available, otherwise "cpu".
   :type device: str or torch.device, default "auto"
   :param batch_size: The number of images to process in a batch.
   :type batch_size: int, default 8


   .. py:method:: encode(dataset)

      Create image :term:`embeddings<Embeddings>` for the dataset using the model's encoder.

      If the model has an `encode` method, it will be used; otherwise,
      `model.forward` will be used.

      :param dataset: The dataset to encode.
                      Torch Dataset containing images in the first return position.
      :type dataset: Dataset

      :returns: Data encoded by the model
      :rtype: torch.Tensor

      .. note:: This function should be run after the model has been trained and evaluated.



   .. py:method:: eval(dataset)

      Basic image reconstruction evaluation function for :term:`autoencoder<Autoencoder>` models

      Uses `torch.nn.MSELoss` as default loss function.

      :param dataset: The dataset to evaluate on.
                      Torch Dataset containing images in the first return position.
      :type dataset: Dataset

      :returns: Total reconstruction loss over the entire dataset
      :rtype: float

      .. note::

         To replace this function with a custom function, do:
             AETrainer.eval = custom_function



   .. py:method:: train(dataset, epochs = 25)

      Basic image reconstruction training function for :term:`Autoencoder` models

      Uses `torch.optim.Adam` and `torch.nn.MSELoss` as default hyperparameters

      :param dataset: The dataset to train on.
                      Torch Dataset containing images in the first return position.
      :type dataset: Dataset
      :param epochs: Number of full training loops
      :type epochs: int, default 25

      :returns: A list of average loss values for each :term:`epoch<Epoch>`.
      :rtype: List[float]

      .. note::

         To replace this function with a custom function, do:
             AETrainer.train = custom_function