ncalab.models.applications.classification.classificationNCA
===========================================================

.. py:module:: ncalab.models.applications.classification.classificationNCA


Classes
-------

.. autoapisummary::

   ncalab.models.applications.classification.classificationNCA.ClassificationNCAModel


Module Contents
---------------

.. py:class:: ClassificationNCAModel(device: torch.device, num_image_channels: int, num_hidden_channels: int, num_classes: int, fire_rate: float = 0.8, hidden_size: int = 128, use_alive_mask: bool = False, pixel_wise_loss: bool = False, num_learned_filters: int = 2, filter_padding: str = 'reflect', use_laplace: bool = False, kernel_size: int = 3, pad_noise: bool = False, use_temporal_encoding: bool = False, use_classifier: bool = True, class_names: Optional[List[str]] = None, avg_pool_size: int = 5, **kwargs)

   Bases: :py:obj:`ncalab.models.basicNCA.BasicNCAModel`


   Abstract base class for NCA models.

   BasicNCAModel is a composition of an NCA backbone model (called "rule"), and
   an (optional) head module for downstream tasks.

   :param device: Pytorch device descriptor.
   :param num_image_channels: _description_
   :param num_hidden_channels: _description_
   :param num_classes: _description_
   :param fire_rate: Fire rate for stochastic weight update. Defaults to 0.8.
   :param hidden_size: Number of neurons in hidden layer. Defaults to 128.
   :param use_alive_mask: Whether to use alive masking (channel 3) during training. Defaults to False.
   :param pixel_wise_loss: Whether a prediction per pixel is desired, like in self-classifying MNIST. Defaults to False.
   :param num_learned_filters: Number of learned filters. If zero, use two sobel filters instead. Defaults to 2.
   :param filter_padding: Padding type to use. Might affect reliance on spatial cues. Defaults to "circular".
   :param pad_noise: Whether to pad input image tensor with noise in hidden / output channels


   .. py:attribute:: _num_classes


   .. py:attribute:: pixel_wise_loss
      :value: False



   .. py:attribute:: use_classifier
      :value: True



   .. py:attribute:: avg_pool_size
      :value: 5



   .. py:property:: num_classes
      :type: int



   .. py:method:: classify(image: torch.Tensor, steps: int = 100, reduce: bool = False) -> torch.Tensor

      Predict classification for an input image.

      :param image: Input image.
      :param steps: Inference steps. Defaults to 100.
      :param reduce: Return a single softmax probability. Defaults to False.

      :returns: Single class index or vector of logits.



   .. py:method:: loss(pred: ncalab.prediction.Prediction, label: torch.Tensor) -> Dict[str, torch.Tensor]

      Return the classification loss.

      :param pred: Prediction.
      :param label: Ground truth.

      :returns: Dictionary of identifiers mapped to computed losses.



   .. py:method:: metrics(pred: ncalab.prediction.Prediction, label: torch.Tensor) -> Dict[str, float]

      Return dict of standard evaluation metrics.

      :param pred: Prediction
      :type pred: Prediction
      :param label: Ground truth label.
      :type label: Tensor