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:mod:`torchfilter.train._train_filter`
======================================

.. py:module:: torchfilter.train._train_filter

.. autoapi-nested-parse::

   Private module; avoid importing from directly.



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


Functions
~~~~~~~~~

.. autoapisummary::

   torchfilter.train._train_filter.train_filter


.. function:: train_filter(buddy: fannypack.utils.Buddy, filter_model: torchfilter.base.Filter, dataloader: DataLoader, initial_covariance: torch.Tensor, *, loss_function: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = F.mse_loss, log_interval: int = 10, measurement_initialize=False, optimizer_name='train_filter_recurrent') -> None

   Trains a filter end-to-end via backpropagation through time for 1 epoch over a
   subsequence dataset.

   :param buddy: Training helper.
   :type buddy: fannypack.utils.Buddy
   :param filter_model: Model to train.
   :type filter_model: torchfilter.base.DynamicsModel
   :param dataloader: Loader for a SubsequenceDataset.
   :type dataloader: DataLoader
   :param initial_covariance: Covariance matrix of error in initial
                              posterior, whose mean is sampled from a Gaussian centered at the
                              ground-truth start state. Shape should be ``(state_dim, state_dim)``.
   :type initial_covariance: torch.Tensor

   :keyword loss_function: Loss function, from ``torch.nn.functional``.
                           Defaults to MSE.
   :kwtype loss_function: callable, optional
   :keyword log_interval: Minibatches between each Tensorboard log.
   :kwtype log_interval: int, optional