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

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

.. _sphx_glr_auto_examples_trainer.py:


Trainer Example
================================

This example should illustrate how to use the trainer class.




.. code-block:: python


    import torch.nn as nn
    from inferno.io.box.cifar import get_cifar10_loaders
    from inferno.trainers.basic import Trainer
    from inferno.trainers.callbacks.logging.tensorboard import TensorboardLogger
    from inferno.extensions.layers import ConvELU2D
    from inferno.extensions.layers import Flatten
    from inferno.utils.python_utils import ensure_dir

    from inferno.extensions.layers import SELU


change directories to your needs



.. code-block:: python

    LOG_DIRECTORY = ensure_dir('log')
    SAVE_DIRECTORY = ensure_dir('save')
    DATASET_DIRECTORY = ensure_dir('dataset')


shall models be downloaded



.. code-block:: python

    DOWNLOAD_CIFAR = True
    USE_CUDA = True


Build torch model



.. code-block:: python

    model = nn.Sequential(
        ConvELU2D(in_channels=3, out_channels=256, kernel_size=3),
        nn.MaxPool2d(kernel_size=2, stride=2),
        ConvELU2D(in_channels=256, out_channels=256, kernel_size=3),
        nn.MaxPool2d(kernel_size=2, stride=2),
        ConvELU2D(in_channels=256, out_channels=256, kernel_size=3),
        nn.MaxPool2d(kernel_size=2, stride=2),
        Flatten(),
        nn.Linear(in_features=(256 * 4 * 4), out_features=10),
        nn.Softmax()
    )


data loaders



.. code-block:: python

    train_loader, validate_loader = get_cifar10_loaders(DATASET_DIRECTORY,
                                            download=DOWNLOAD_CIFAR)


Build trainer



.. code-block:: python

    trainer = Trainer(model)
    trainer.build_criterion('CrossEntropyLoss')
    trainer.build_metric('CategoricalError')
    trainer.build_optimizer('Adam')
    trainer.validate_every((2, 'epochs'))
    trainer.save_every((5, 'epochs'))
    trainer.save_to_directory(SAVE_DIRECTORY)
    trainer.set_max_num_epochs(10)
    trainer.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
                                    log_images_every='never'), 
                  log_directory=LOG_DIRECTORY)


Bind loaders



.. code-block:: python

    trainer.bind_loader('train', train_loader)
    trainer.bind_loader('validate', validate_loader)


activate cuda



.. code-block:: python

    if USE_CUDA:
        trainer.cuda()


fit



.. code-block:: python

    trainer.fit()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_trainer.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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