download_prepare_mnist03
========================

.. py:module:: download_prepare_mnist03


Attributes
----------

.. autoapisummary::

   download_prepare_mnist03.ROOT
   download_prepare_mnist03.DOWNLOAD_DIR
   download_prepare_mnist03.NPZ_DIR
   download_prepare_mnist03.orig


Functions
---------

.. autoapisummary::

   download_prepare_mnist03.load_idx_images
   download_prepare_mnist03.load_idx_labels
   download_prepare_mnist03.save_to_npz
   download_prepare_mnist03.save_to_h5
   download_prepare_mnist03.main


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

.. py:data:: ROOT

.. py:data:: DOWNLOAD_DIR

.. py:data:: NPZ_DIR

.. py:data:: orig

.. py:function:: load_idx_images(path: pathlib.Path)

       Load MNIST image file (idx3 format) from .gz

   Args:
       path: path to raw image data gzip file

   Returns:
       image data as numpy array (n_images, n_rows, n_cols)



.. py:function:: load_idx_labels(path: pathlib.Path)

   Load MNIST label file (idx1 format) from .gz

   Args:
       path: path to raw label data gzip file

   Returns: labels as numpy array (n_labels,)



.. py:function:: save_to_npz(npz_path, data)

   Save the image data and labels with splits "train" and "test" in npz file
   Args:
       npz_path: Path
           output path

       data: dict
           data dict with entries X_train, y_train, X_val, y_val, X_test, y_test


.. py:function:: save_to_h5(h5_path, data)

   Save the image data and labels with splits "train", "val" and "test" in h5 file
   Args:
       h5_path:
           output path

       data: dict
           data dict with entries X_train, y_train, X_val, y_val, X_test, y_test


.. py:function:: main(npz=False, h5=True)

   Open raw MNIST train and test image data and labels, create val split from train data and save as npz and or h5
   Args:
       npz: bool
           If True, save data and labels as npz file
       h5: bool
           If True, save data and labels as h5 file