.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/4_train_model.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_4_train_model.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_4_train_model.py:


Training a deep learning model
===========================

This example shows how to train a deep learning model using the dataset created in the previous example.

.. GENERATED FROM PYTHON SOURCE LINES 9-18

Loading the dataset
-------------------
To load the dataset we need to use the EMGDatasetLoader class.

Two parameters are required:

- data_path: Path to the dataset file.

- dataloader_parameters: Parameters for the DataLoader.

.. GENERATED FROM PYTHON SOURCE LINES 18-23

.. code-block:: Python

    from pathlib import Path
    from doc_octopy.datasets.loader import EMGDatasetLoader

    loader = EMGDatasetLoader(Path("data/dataset.zarr").resolve(), dataloader_parameters={"batch_size": 16, "drop_last": True})








.. GENERATED FROM PYTHON SOURCE LINES 24-26

Training the model
------------------

.. GENERATED FROM PYTHON SOURCE LINES 26-58

.. code-block:: Python

    from doc_octopy.models.definitions.raul_net.online.v16 import RaulNetV16
    import lightning as L

    # Create the model
    model = RaulNetV16(
        learning_rate=1e-4,
        nr_of_input_channels=2,
        input_length__samples=192,
        nr_of_outputs=60,
        nr_of_electrode_grids=5,
        nr_of_electrodes_per_grid=64,

        # Multiply following by 4, 8, 16 to have a useful network
        cnn_encoder_channels=(4, 1, 1),
        mlp_encoder_channels=(8, 8),

        event_search_kernel_length=31,
        event_search_kernel_stride=8,
    )

    trainer = L.Trainer(
        accelerator="auto",
        devices=1,
        precision="16-mixed",
        max_epochs=1,
        log_every_n_steps=50,
        logger=None,
        enable_checkpointing=False,
        deterministic=False,
    )

    trainer.fit(model, datamodule=loader)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:512: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.
    /home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.

    Sanity Checking: |          | 0/? [00:00<?, ?it/s]/home/runner/work/DocOctopy/DocOctopy/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.

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    Epoch 0:  75%|███████▍  | 68/91 [01:41<00:34,  0.67it/s, v_num=0, loss_step=40.60]
    Epoch 0:  76%|███████▌  | 69/91 [01:42<00:32,  0.67it/s, v_num=0, loss_step=40.60]
    Epoch 0:  76%|███████▌  | 69/91 [01:42<00:32,  0.67it/s, v_num=0, loss_step=41.20]
    Epoch 0:  77%|███████▋  | 70/91 [01:44<00:31,  0.67it/s, v_num=0, loss_step=41.20]
    Epoch 0:  77%|███████▋  | 70/91 [01:44<00:31,  0.67it/s, v_num=0, loss_step=41.40]
    Epoch 0:  78%|███████▊  | 71/91 [01:45<00:29,  0.67it/s, v_num=0, loss_step=41.40]
    Epoch 0:  78%|███████▊  | 71/91 [01:45<00:29,  0.67it/s, v_num=0, loss_step=40.90]
    Epoch 0:  79%|███████▉  | 72/91 [01:47<00:28,  0.67it/s, v_num=0, loss_step=40.90]
    Epoch 0:  79%|███████▉  | 72/91 [01:47<00:28,  0.67it/s, v_num=0, loss_step=39.80]
    Epoch 0:  80%|████████  | 73/91 [01:48<00:26,  0.67it/s, v_num=0, loss_step=39.80]
    Epoch 0:  80%|████████  | 73/91 [01:48<00:26,  0.67it/s, v_num=0, loss_step=39.10]
    Epoch 0:  81%|████████▏ | 74/91 [01:50<00:25,  0.67it/s, v_num=0, loss_step=39.10]
    Epoch 0:  81%|████████▏ | 74/91 [01:50<00:25,  0.67it/s, v_num=0, loss_step=40.60]
    Epoch 0:  82%|████████▏ | 75/91 [01:51<00:23,  0.67it/s, v_num=0, loss_step=40.60]
    Epoch 0:  82%|████████▏ | 75/91 [01:51<00:23,  0.67it/s, v_num=0, loss_step=39.30]
    Epoch 0:  84%|████████▎ | 76/91 [01:53<00:22,  0.67it/s, v_num=0, loss_step=39.30]
    Epoch 0:  84%|████████▎ | 76/91 [01:53<00:22,  0.67it/s, v_num=0, loss_step=39.50]
    Epoch 0:  85%|████████▍ | 77/91 [01:54<00:20,  0.67it/s, v_num=0, loss_step=39.50]
    Epoch 0:  85%|████████▍ | 77/91 [01:54<00:20,  0.67it/s, v_num=0, loss_step=40.30]
    Epoch 0:  86%|████████▌ | 78/91 [01:56<00:19,  0.67it/s, v_num=0, loss_step=40.30]
    Epoch 0:  86%|████████▌ | 78/91 [01:56<00:19,  0.67it/s, v_num=0, loss_step=40.40]
    Epoch 0:  87%|████████▋ | 79/91 [01:57<00:17,  0.67it/s, v_num=0, loss_step=40.40]
    Epoch 0:  87%|████████▋ | 79/91 [01:57<00:17,  0.67it/s, v_num=0, loss_step=38.90]
    Epoch 0:  88%|████████▊ | 80/91 [01:59<00:16,  0.67it/s, v_num=0, loss_step=38.90]
    Epoch 0:  88%|████████▊ | 80/91 [01:59<00:16,  0.67it/s, v_num=0, loss_step=39.40]
    Epoch 0:  89%|████████▉ | 81/91 [02:00<00:14,  0.67it/s, v_num=0, loss_step=39.40]
    Epoch 0:  89%|████████▉ | 81/91 [02:00<00:14,  0.67it/s, v_num=0, loss_step=39.40]
    Epoch 0:  90%|█████████ | 82/91 [02:02<00:13,  0.67it/s, v_num=0, loss_step=39.40]
    Epoch 0:  90%|█████████ | 82/91 [02:02<00:13,  0.67it/s, v_num=0, loss_step=39.20]
    Epoch 0:  91%|█████████ | 83/91 [02:03<00:11,  0.67it/s, v_num=0, loss_step=39.20]
    Epoch 0:  91%|█████████ | 83/91 [02:03<00:11,  0.67it/s, v_num=0, loss_step=39.20]
    Epoch 0:  92%|█████████▏| 84/91 [02:05<00:10,  0.67it/s, v_num=0, loss_step=39.20]
    Epoch 0:  92%|█████████▏| 84/91 [02:05<00:10,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  93%|█████████▎| 85/91 [02:06<00:08,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  93%|█████████▎| 85/91 [02:06<00:08,  0.67it/s, v_num=0, loss_step=39.30]
    Epoch 0:  95%|█████████▍| 86/91 [02:08<00:07,  0.67it/s, v_num=0, loss_step=39.30]
    Epoch 0:  95%|█████████▍| 86/91 [02:08<00:07,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  96%|█████████▌| 87/91 [02:09<00:05,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  96%|█████████▌| 87/91 [02:09<00:05,  0.67it/s, v_num=0, loss_step=39.90]
    Epoch 0:  97%|█████████▋| 88/91 [02:11<00:04,  0.67it/s, v_num=0, loss_step=39.90]
    Epoch 0:  97%|█████████▋| 88/91 [02:11<00:04,  0.67it/s, v_num=0, loss_step=40.30]
    Epoch 0:  98%|█████████▊| 89/91 [02:12<00:02,  0.67it/s, v_num=0, loss_step=40.30]
    Epoch 0:  98%|█████████▊| 89/91 [02:12<00:02,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  99%|█████████▉| 90/91 [02:14<00:01,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0:  99%|█████████▉| 90/91 [02:14<00:01,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0: 100%|██████████| 91/91 [02:15<00:00,  0.67it/s, v_num=0, loss_step=38.70]
    Epoch 0: 100%|██████████| 91/91 [02:15<00:00,  0.67it/s, v_num=0, loss_step=40.50]

    Validation: |          | 0/? [00:00<?, ?it/s]

    Validation:   0%|          | 0/2 [00:00<?, ?it/s]

    Validation DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]

    Validation DataLoader 0:  50%|█████     | 1/2 [00:00<00:00,  2.31it/s]

    Validation DataLoader 0: 100%|██████████| 2/2 [00:00<00:00,  3.06it/s]

                                                                          
    Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=40.50, val_loss=40.30]
    Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=40.50, val_loss=40.30, loss_epoch=47.00]
    Epoch 0: 100%|██████████| 91/91 [02:16<00:00,  0.67it/s, v_num=0, loss_step=40.50, val_loss=40.30, loss_epoch=47.00]





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (2 minutes 17.994 seconds)

**Estimated memory usage:**  112 MB


.. _sphx_glr_download_auto_examples_4_train_model.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 4_train_model.ipynb <4_train_model.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 4_train_model.py <4_train_model.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 4_train_model.zip <4_train_model.zip>`


.. only:: html

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

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