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

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

.. _sphx_glr_auto_examples_train_regr.py:


Example 01: Train a network for regression
==========================================

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


.. code-block:: default


    # Import code
    import msdnet
    import glob

    # Define dilations in [1,10] as in paper.
    dilations = msdnet.dilations.IncrementDilations(10)

    # Create main network object for regression, with 100 layers,
    # [1,10] dilations, 1 input channel, 1 output channel, using
    # the GPU (set gpu=False to use CPU)
    n = msdnet.network.MSDNet(100, dilations, 1, 1, gpu=True)

    # Initialize network parameters
    n.initialize()

    # Define training data
    # First, create lists of input files (noisy) and target files (noiseless)
    flsin = sorted(glob.glob('train/noisy/*.tiff'))
    flstg = sorted(glob.glob('train/noiseless/*.tiff'))
    # Create list of datapoints (i.e. input/target pairs)
    dats = []
    for i in range(len(flsin)):
        # Create datapoint with file names
        d = msdnet.data.ImageFileDataPoint(flsin[i],flstg[i])
        # Augment data by rotating and flipping
        d_augm = msdnet.data.RotateAndFlipDataPoint(d)
        # Add augmented datapoint to list
        dats.append(d_augm)
    # Note: The above can also be achieved using a utility function for such 'simple' cases:
    # dats = msdnet.utils.load_simple_data('train/noisy/*.tiff', 'train/noiseless/*.tiff', augment=True)

    # Normalize input and output of network to zero mean and unit variance using
    # training data images
    n.normalizeinout(dats)

    # Use image batches of a single image
    bprov = msdnet.data.BatchProvider(dats,1)

    # Define validation data (not using augmentation)
    flsin = sorted(glob.glob('val/noisy/*.tiff'))
    flstg = sorted(glob.glob('val/noiseless/*.tiff'))
    datsv = []
    for i in range(len(flsin)):
        d = msdnet.data.ImageFileDataPoint(flsin[i],flstg[i])
        datsv.append(d)
    # Note: The above can also be achieved using a utility function for such 'simple' cases:
    # datsv = msdnet.utils.load_simple_data('val/noisy/*.tiff', 'val/noiseless/*.tiff', augment=False)

    # Validate with Mean-Squared Error
    val = msdnet.validate.MSEValidation(datsv)

    # Use ADAM training algorithms
    t = msdnet.train.AdamAlgorithm(n)

    # Log error metrics to console
    consolelog = msdnet.loggers.ConsoleLogger()
    # Log error metrics to file
    filelog = msdnet.loggers.FileLogger('log_regr.txt')
    # Log typical, worst, and best images to image files
    imagelog = msdnet.loggers.ImageLogger('log_regr', onlyifbetter=True)

    # Train network until program is stopped manually
    # Network parameters are saved in regr_params.h5
    # Validation is run after every len(datsv) (=25)
    # training steps.
    msdnet.train.train(n, t, val, bprov, 'regr_params.h5',loggers=[consolelog,filelog,imagelog], val_every=len(datsv))


.. _sphx_glr_download_auto_examples_train_regr.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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