:py:mod:`orcanet.lib.dataset_modifiers`
=======================================

.. py:module:: orcanet.lib.dataset_modifiers


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


Functions
~~~~~~~~~

.. autoapisummary::

   orcanet.lib.dataset_modifiers.as_array
   orcanet.lib.dataset_modifiers.as_recarray
   orcanet.lib.dataset_modifiers.as_recarray_dist
   orcanet.lib.dataset_modifiers.as_recarray_dist_split



.. py:function:: as_array(info_blob)

   
   Save network output as ndarrays to h5. This is the default dataset modifier.

   Every output layer will get one dataset each for both the label and
   the prediction. E.g. if the model has an output layer called "energy",
   the datasets "label_energy" and "pred_energy" will be made.















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.. py:function:: as_recarray(info_blob)

   
   Save network output as recarray to h5. Intended for when network
   outputs are 2D, i.e. (batchsize, X).

   Output from network:
   Dict with arrays, shapes (batchsize, x_i).
   E.g. {"foo": ndarray, "bar": ndarray}

   dtypes that will get saved to h5:
   (foo_1, foo_2, ..., bar_1, bar_2, ... )















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.. py:function:: as_recarray_dist(info_blob)

   
   Save network output as recarray to h5. Intended for when network
   outputs are distributions and thus 3D (for example when using
   OutputRegNormal as output layer block).
   I.e. (batchsize, 2, X), with [:, 0] being mu and [:, 1] being std.

   Example output from network:
   shape {"A": (bs, 2), "B": (bs, 2, 3)}
       [:, 0] is reco, [:, 1] is err

   dtypes that will get saved to h5:
   A_1, A_err_1, B_1, B_2, B_3, B_err_1, B_err_2, B_err_3















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.. py:function:: as_recarray_dist_split(info_blob)

   
   Save network output as recarray to h5. Intended for networks that
   output recos and errs in seperate towers (for example when using
   OutputRegNormalSplit as output layer block).

   Example output from network:
   shape {"A": (bs, 1), "A_err": (bs, 2, 1),
          "B": (bs, 3), "B_err": (bs, 2, 3)}
   In "A_err": [:, 0] is mu, [:, 1] is sigma

   dtypes that will get saved to h5:
   A_1, A_err_1, B_1, B_1_err, B_2, B_err_2, ...















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