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

.. only:: html

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

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

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

.. _sphx_glr_beginner_introyt_trainingyt.py:


`Introduction <introyt1_tutorial.html>`_ ||
`Tensors <tensors_deeper_tutorial.html>`_ ||
`Autograd <autogradyt_tutorial.html>`_ ||
`Building Models <modelsyt_tutorial.html>`_ ||
`TensorBoard Support <tensorboardyt_tutorial.html>`_ ||
**Training Models** ||
`Model Understanding <captumyt.html>`_

Training with PyTorch
=====================

Follow along with the video below or on `youtube <https://www.youtube.com/watch?v=jF43_wj_DCQ>`__.

.. raw:: html

   <div style="margin-top:10px; margin-bottom:10px;">
     <iframe width="560" height="315" src="https://www.youtube.com/embed/jF43_wj_DCQ" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
   </div>

Introduction
------------

In past videos, we’ve discussed and demonstrated:

- Building models with the neural network layers and functions of the torch.nn module
- The mechanics of automated gradient computation, which is central to
  gradient-based model training 
- Using TensorBoard to visualize training progress and other activities

In this video, we’ll be adding some new tools to your inventory:

- We’ll get familiar with the dataset and dataloader abstractions, and how
  they ease the process of feeding data to your model during a training loop 
- We’ll discuss specific loss functions and when to use them
- We’ll look at PyTorch optimizers, which implement algorithms to adjust
  model weights based on the outcome of a loss function

Finally, we’ll pull all of these together and see a full PyTorch
training loop in action.


Dataset and DataLoader
----------------------
 
The ``Dataset`` and ``DataLoader`` classes encapsulate the process of
pulling your data from storage and exposing it to your training loop in
batches.

The ``Dataset`` is responsible for accessing and processing single
instances of data.
 
The ``DataLoader`` pulls instances of data from the ``Dataset`` (either
automatically or with a sampler that you define), collects them in
batches, and returns them for consumption by your training loop. The
``DataLoader`` works with all kinds of datasets, regardless of the type
of data they contain.
 
For this tutorial, we’ll be using the Fashion-MNIST dataset provided by
TorchVision. We use ``torchvision.transforms.Normalize()`` to
zero-center and normalize the distribution of the image tile content,
and download both training and validation data splits.

.. GENERATED FROM PYTHON SOURCE LINES 65-96

.. code-block:: Python


    import torch
    import torchvision
    import torchvision.transforms as transforms

    # PyTorch TensorBoard support
    from torch.utils.tensorboard import SummaryWriter
    from datetime import datetime


    transform = transforms.Compose(
        [transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))])

    # Create datasets for training & validation, download if necessary
    training_set = torchvision.datasets.FashionMNIST('./data', train=True, transform=transform, download=True)
    validation_set = torchvision.datasets.FashionMNIST('./data', train=False, transform=transform, download=True)

    # Create data loaders for our datasets; shuffle for training, not for validation
    training_loader = torch.utils.data.DataLoader(training_set, batch_size=4, shuffle=True)
    validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False)

    # Class labels
    classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
            'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')

    # Report split sizes
    print('Training set has {} instances'.format(len(training_set)))
    print('Validation set has {} instances'.format(len(validation_set)))






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

 .. code-block:: none


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    Training set has 60000 instances
    Validation set has 10000 instances




.. GENERATED FROM PYTHON SOURCE LINES 97-99

As always, let’s visualize the data as a sanity check:


.. GENERATED FROM PYTHON SOURCE LINES 99-123

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    # Helper function for inline image display
    def matplotlib_imshow(img, one_channel=False):
        if one_channel:
            img = img.mean(dim=0)
        img = img / 2 + 0.5     # unnormalize
        npimg = img.numpy()
        if one_channel:
            plt.imshow(npimg, cmap="Greys")
        else:
            plt.imshow(np.transpose(npimg, (1, 2, 0)))

    dataiter = iter(training_loader)
    images, labels = next(dataiter)

    # Create a grid from the images and show them
    img_grid = torchvision.utils.make_grid(images)
    matplotlib_imshow(img_grid, one_channel=True)
    print('  '.join(classes[labels[j]] for j in range(4)))





.. image-sg:: /beginner/introyt/images/sphx_glr_trainingyt_001.png
   :alt: trainingyt
   :srcset: /beginner/introyt/images/sphx_glr_trainingyt_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Trouser  Bag  Coat  Shirt




.. GENERATED FROM PYTHON SOURCE LINES 124-130

The Model
---------

The model we’ll use in this example is a variant of LeNet-5 - it should
be familiar if you’ve watched the previous videos in this series.


.. GENERATED FROM PYTHON SOURCE LINES 130-158

.. code-block:: Python


    import torch.nn as nn
    import torch.nn.functional as F

    # PyTorch models inherit from torch.nn.Module
    class GarmentClassifier(nn.Module):
        def __init__(self):
            super(GarmentClassifier, self).__init__()
            self.conv1 = nn.Conv2d(1, 6, 5)
            self.pool = nn.MaxPool2d(2, 2)
            self.conv2 = nn.Conv2d(6, 16, 5)
            self.fc1 = nn.Linear(16 * 4 * 4, 120)
            self.fc2 = nn.Linear(120, 84)
            self.fc3 = nn.Linear(84, 10)

        def forward(self, x):
            x = self.pool(F.relu(self.conv1(x)))
            x = self.pool(F.relu(self.conv2(x)))
            x = x.view(-1, 16 * 4 * 4)
            x = F.relu(self.fc1(x))
            x = F.relu(self.fc2(x))
            x = self.fc3(x)
            return x
    

    model = GarmentClassifier()









.. GENERATED FROM PYTHON SOURCE LINES 159-166

Loss Function
-------------

For this example, we’ll be using a cross-entropy loss. For demonstration
purposes, we’ll create batches of dummy output and label values, run
them through the loss function, and examine the result.


.. GENERATED FROM PYTHON SOURCE LINES 166-182

.. code-block:: Python


    loss_fn = torch.nn.CrossEntropyLoss()

    # NB: Loss functions expect data in batches, so we're creating batches of 4
    # Represents the model's confidence in each of the 10 classes for a given input
    dummy_outputs = torch.rand(4, 10)
    # Represents the correct class among the 10 being tested
    dummy_labels = torch.tensor([1, 5, 3, 7])
    
    print(dummy_outputs)
    print(dummy_labels)

    loss = loss_fn(dummy_outputs, dummy_labels)
    print('Total loss for this batch: {}'.format(loss.item()))






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

 .. code-block:: none

    tensor([[0.5055, 0.0374, 0.7753, 0.2083, 0.7269, 0.9645, 0.4356, 0.7317, 0.7338,
             0.5601],
            [0.2260, 0.8362, 0.0534, 0.0455, 0.6723, 0.0521, 0.8753, 0.4892, 0.5256,
             0.0251],
            [0.0733, 0.4259, 0.4406, 0.0258, 0.0485, 0.4394, 0.3209, 0.3881, 0.8326,
             0.9217],
            [0.6352, 0.9349, 0.4924, 0.5526, 0.1184, 0.2063, 0.8744, 0.2927, 0.3695,
             0.4838]])
    tensor([1, 5, 3, 7])
    Total loss for this batch: 2.700305938720703




.. GENERATED FROM PYTHON SOURCE LINES 183-200

Optimizer
---------

For this example, we’ll be using simple `stochastic gradient
descent <https://pytorch.org/docs/stable/optim.html>`__ with momentum.

It can be instructive to try some variations on this optimization
scheme:

- Learning rate determines the size of the steps the optimizer
  takes. What does a different learning rate do to the your training
  results, in terms of accuracy and convergence time?
- Momentum nudges the optimizer in the direction of strongest gradient over
  multiple steps. What does changing this value do to your results? 
- Try some different optimization algorithms, such as averaged SGD, Adagrad, or
  Adam. How do your results differ?


.. GENERATED FROM PYTHON SOURCE LINES 200-205

.. code-block:: Python


    # Optimizers specified in the torch.optim package
    optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)









.. GENERATED FROM PYTHON SOURCE LINES 206-225

The Training Loop
-----------------

Below, we have a function that performs one training epoch. It
enumerates data from the DataLoader, and on each pass of the loop does
the following:

- Gets a batch of training data from the DataLoader
- Zeros the optimizer’s gradients 
- Performs an inference - that is, gets predictions from the model for an input batch
- Calculates the loss for that set of predictions vs. the labels on the dataset
- Calculates the backward gradients over the learning weights
- Tells the optimizer to perform one learning step - that is, adjust the model’s
  learning weights based on the observed gradients for this batch, according to the
  optimization algorithm we chose
- It reports on the loss for every 1000 batches.
- Finally, it reports the average per-batch loss for the last
  1000 batches, for comparison with a validation run


.. GENERATED FROM PYTHON SOURCE LINES 225-262

.. code-block:: Python


    def train_one_epoch(epoch_index, tb_writer):
        running_loss = 0.
        last_loss = 0.
    
        # Here, we use enumerate(training_loader) instead of
        # iter(training_loader) so that we can track the batch
        # index and do some intra-epoch reporting
        for i, data in enumerate(training_loader):
            # Every data instance is an input + label pair
            inputs, labels = data
        
            # Zero your gradients for every batch!
            optimizer.zero_grad()
        
            # Make predictions for this batch
            outputs = model(inputs)
        
            # Compute the loss and its gradients
            loss = loss_fn(outputs, labels)
            loss.backward()
        
            # Adjust learning weights
            optimizer.step()
        
            # Gather data and report
            running_loss += loss.item()
            if i % 1000 == 999:
                last_loss = running_loss / 1000 # loss per batch
                print('  batch {} loss: {}'.format(i + 1, last_loss))
                tb_x = epoch_index * len(training_loader) + i + 1
                tb_writer.add_scalar('Loss/train', last_loss, tb_x)
                running_loss = 0.
            
        return last_loss









.. GENERATED FROM PYTHON SOURCE LINES 263-276

Per-Epoch Activity
~~~~~~~~~~~~~~~~~~

There are a couple of things we’ll want to do once per epoch: 

- Perform validation by checking our relative loss on a set of data that was not
  used for training, and report this 
- Save a copy of the model

Here, we’ll do our reporting in TensorBoard. This will require going to
the command line to start TensorBoard, and opening it in another browser
tab.


.. GENERATED FROM PYTHON SOURCE LINES 276-326

.. code-block:: Python


    # Initializing in a separate cell so we can easily add more epochs to the same run
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    writer = SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))
    epoch_number = 0

    EPOCHS = 5

    best_vloss = 1_000_000.

    for epoch in range(EPOCHS):
        print('EPOCH {}:'.format(epoch_number + 1))
    
        # Make sure gradient tracking is on, and do a pass over the data
        model.train(True)
        avg_loss = train_one_epoch(epoch_number, writer)
    

        running_vloss = 0.0
        # Set the model to evaluation mode, disabling dropout and using population 
        # statistics for batch normalization.
        model.eval()

        # Disable gradient computation and reduce memory consumption.
        with torch.no_grad():
            for i, vdata in enumerate(validation_loader):
                vinputs, vlabels = vdata
                voutputs = model(vinputs)
                vloss = loss_fn(voutputs, vlabels)
                running_vloss += vloss
    
        avg_vloss = running_vloss / (i + 1)
        print('LOSS train {} valid {}'.format(avg_loss, avg_vloss))
    
        # Log the running loss averaged per batch
        # for both training and validation
        writer.add_scalars('Training vs. Validation Loss',
                        { 'Training' : avg_loss, 'Validation' : avg_vloss },
                        epoch_number + 1)
        writer.flush()
    
        # Track best performance, and save the model's state
        if avg_vloss < best_vloss:
            best_vloss = avg_vloss
            model_path = 'model_{}_{}'.format(timestamp, epoch_number)
            torch.save(model.state_dict(), model_path)
    
        epoch_number += 1






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

 .. code-block:: none

    EPOCH 1:
      batch 1000 loss: 1.9576318949013949
      batch 2000 loss: 0.9126187145933509
      batch 3000 loss: 0.7570243945810944
      batch 4000 loss: 0.6829133056234569
      batch 5000 loss: 0.6194519917643629
      batch 6000 loss: 0.5836719829617069
      batch 7000 loss: 0.5557402149150148
      batch 8000 loss: 0.535657885234803
      batch 9000 loss: 0.5052011218289845
      batch 10000 loss: 0.45554780365002806
      batch 11000 loss: 0.46591379248257725
      batch 12000 loss: 0.43484579073067287
      batch 13000 loss: 0.4310119082017918
      batch 14000 loss: 0.44020615833428745
      batch 15000 loss: 0.402784006381451
    LOSS train 0.402784006381451 valid 0.44769349694252014
    EPOCH 2:
      batch 1000 loss: 0.3899975952416134
      batch 2000 loss: 0.4153860620104242
      batch 3000 loss: 0.3800500175692141
      batch 4000 loss: 0.4061927368853649
      batch 5000 loss: 0.37216635681514165
      batch 6000 loss: 0.3869452087971731
      batch 7000 loss: 0.36553758599137653
      batch 8000 loss: 0.36490986471352516
      batch 9000 loss: 0.3708160625002347
      batch 10000 loss: 0.3504008587963763
      batch 11000 loss: 0.3758990603135899
      batch 12000 loss: 0.3704308765029418
      batch 13000 loss: 0.36135593060984683
      batch 14000 loss: 0.35064337893083575
      batch 15000 loss: 0.3400108248385659
    LOSS train 0.3400108248385659 valid 0.34501272439956665
    EPOCH 3:
      batch 1000 loss: 0.3174629740609089
      batch 2000 loss: 0.32524551963224074
      batch 3000 loss: 0.31630020878865617
      batch 4000 loss: 0.32892000097072743
      batch 5000 loss: 0.32677057264751785
      batch 6000 loss: 0.3121449965004431
      batch 7000 loss: 0.3503868164533469
      batch 8000 loss: 0.322912187736365
      batch 9000 loss: 0.33894188481817766
      batch 10000 loss: 0.3280733678800898
      batch 11000 loss: 0.3263176130118118
      batch 12000 loss: 0.32762141215529117
      batch 13000 loss: 0.31733203681408484
      batch 14000 loss: 0.326167136561824
      batch 15000 loss: 0.3140320948020485
    LOSS train 0.3140320948020485 valid 0.33181536197662354
    EPOCH 4:
      batch 1000 loss: 0.282012564426841
      batch 2000 loss: 0.30304097851185224
      batch 3000 loss: 0.30601019736530727
      batch 4000 loss: 0.3027197667012515
      batch 5000 loss: 0.29386470701214423
      batch 6000 loss: 0.3085742753381419
      batch 7000 loss: 0.30387153060534183
      batch 8000 loss: 0.30424517345192725
      batch 9000 loss: 0.3060073038264309
      batch 10000 loss: 0.3032905082749348
      batch 11000 loss: 0.2930876870978027
      batch 12000 loss: 0.283281282274831
      batch 13000 loss: 0.29611325267382926
      batch 14000 loss: 0.30703694381329116
      batch 15000 loss: 0.27550252123206154
    LOSS train 0.27550252123206154 valid 0.3137122392654419
    EPOCH 5:
      batch 1000 loss: 0.27832344647739227
      batch 2000 loss: 0.28029874832210405
      batch 3000 loss: 0.2761170767475196
      batch 4000 loss: 0.269687148014269
      batch 5000 loss: 0.2757682931441468
      batch 6000 loss: 0.28846762490483707
      batch 7000 loss: 0.269388484995281
      batch 8000 loss: 0.2872645212863208
      batch 9000 loss: 0.29315009839697087
      batch 10000 loss: 0.2734479327071067
      batch 11000 loss: 0.2710334567155878
      batch 12000 loss: 0.28115201150160285
      batch 13000 loss: 0.26809077356300987
      batch 14000 loss: 0.2664093120649536
      batch 15000 loss: 0.27590450757789947
    LOSS train 0.27590450757789947 valid 0.3077177107334137




.. GENERATED FROM PYTHON SOURCE LINES 327-369

To load a saved version of the model:

.. code:: python

    saved_model = GarmentClassifier()
    saved_model.load_state_dict(torch.load(PATH))

Once you’ve loaded the model, it’s ready for whatever you need it for -
more training, inference, or analysis.

Note that if your model has constructor parameters that affect model
structure, you’ll need to provide them and configure the model
identically to the state in which it was saved.

Other Resources
---------------

-  Docs on the `data
   utilities <https://pytorch.org/docs/stable/data.html>`__, including
   Dataset and DataLoader, at pytorch.org
-  A `note on the use of pinned
   memory <https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-pinning>`__
   for GPU training
-  Documentation on the datasets available in
   `TorchVision <https://pytorch.org/vision/stable/datasets.html>`__,
   `TorchText <https://pytorch.org/text/stable/datasets.html>`__, and
   `TorchAudio <https://pytorch.org/audio/stable/datasets.html>`__
-  Documentation on the `loss
   functions <https://pytorch.org/docs/stable/nn.html#loss-functions>`__
   available in PyTorch
-  Documentation on the `torch.optim
   package <https://pytorch.org/docs/stable/optim.html>`__, which
   includes optimizers and related tools, such as learning rate
   scheduling
-  A detailed `tutorial on saving and loading
   models <https://pytorch.org/tutorials/beginner/saving_loading_models.html>`__
-  The `Tutorials section of
   pytorch.org <https://pytorch.org/tutorials/>`__ contains tutorials on
   a broad variety of training tasks, including classification in
   different domains, generative adversarial networks, reinforcement
   learning, and more 



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

   **Total running time of the script:** (2 minutes 59.103 seconds)


.. _sphx_glr_download_beginner_introyt_trainingyt.py:

.. only:: html

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

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

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

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

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

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

      :download:`Download zipped: trainingyt.zip <trainingyt.zip>`


.. only:: html

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

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