.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_apply_regr.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_apply_regr.py:


Example 02: Apply trained network for regression
================================================

This script applies a trained MS-D network for regression (i.e. denoising/artifact removal)
Run generatedata.py first to generate required training data and train_regr.py to train
a network.


.. code-block:: default


    # Import code
    import msdnet
    import glob
    import tifffile
    import os

    # Make folder for output
    os.makedirs('results', exist_ok=True)

    # Load network from file
    n = msdnet.network.MSDNet.from_file('regr_params.h5', gpu=True)

    # Process all test images
    flsin = sorted(glob.glob('test/noisy/*.tiff'))
    for i in range(len(flsin)):
        # Create datapoint with only input image
        d = msdnet.data.ImageFileDataPoint(flsin[i])
        # Compute network output
        output = n.forward(d.input)
        # Save network output to file
        tifffile.imsave('results/regr_{:05d}.tiff'.format(i), output[0])


.. _sphx_glr_download_auto_examples_apply_regr.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: apply_regr.py <apply_regr.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: apply_regr.ipynb <apply_regr.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_