.. 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

        Click :ref:`here <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:: default


    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

    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz

      0%|          | 0/26421880 [00:00<?, ?it/s]
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     11%|#         | 2883584/26421880 [00:00<00:04, 5545914.99it/s]
     32%|###1      | 8421376/26421880 [00:00<00:01, 14546670.27it/s]
     52%|#####2    | 13860864/26421880 [00:01<00:00, 19782602.44it/s]
     74%|#######3  | 19496960/26421880 [00:01<00:00, 23450572.19it/s]
     95%|#########4| 24969216/26421880 [00:01<00:00, 25572392.17it/s]
    100%|##########| 26421880/26421880 [00:01<00:00, 18133262.41it/s]
    Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw

    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz

      0%|          | 0/29515 [00:00<?, ?it/s]
    100%|##########| 29515/29515 [00:00<00:00, 328536.58it/s]
    Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw

    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz

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    100%|##########| 4422102/4422102 [00:00<00:00, 6054219.45it/s]
    Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw

    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
    Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz

      0%|          | 0/5148 [00:00<?, ?it/s]
    100%|##########| 5148/5148 [00:00<00:00, 41523609.60it/s]
    Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw

    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:: default


    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

    Sandal  Sneaker  Coat  Sneaker




.. 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:: default


    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:: default


    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([[0.7026, 0.1489, 0.0065, 0.6841, 0.4166, 0.3980, 0.9849, 0.6701, 0.4601,
             0.8599],
            [0.7461, 0.3920, 0.9978, 0.0354, 0.9843, 0.0312, 0.5989, 0.2888, 0.8170,
             0.4150],
            [0.8408, 0.5368, 0.0059, 0.8931, 0.3942, 0.7349, 0.5500, 0.0074, 0.0554,
             0.1537],
            [0.7282, 0.8755, 0.3649, 0.4566, 0.8796, 0.2390, 0.9865, 0.7549, 0.9105,
             0.5427]])
    tensor([1, 5, 3, 7])
    Total loss for this batch: 2.428950071334839




.. 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:: default


    # 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:: default


    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:: default


    # 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: 1.6334228584356607
      batch 2000 loss: 0.8325267538074403
      batch 3000 loss: 0.7359380583595484
      batch 4000 loss: 0.6198329215242994
      batch 5000 loss: 0.6000315657821484
      batch 6000 loss: 0.555109024874866
      batch 7000 loss: 0.5260250487388112
      batch 8000 loss: 0.4973462742221891
      batch 9000 loss: 0.4781935699362075
      batch 10000 loss: 0.47880298678041433
      batch 11000 loss: 0.45598648857555235
      batch 12000 loss: 0.4327470133750467
      batch 13000 loss: 0.41800182418141046
      batch 14000 loss: 0.4115047634313814
      batch 15000 loss: 0.4211296908891527
    LOSS train 0.4211296908891527 valid 0.414460688829422
    EPOCH 2:
      batch 1000 loss: 0.3879808729066281
      batch 2000 loss: 0.35912817339546743
      batch 3000 loss: 0.38074520684120944
      batch 4000 loss: 0.3614532373107213
      batch 5000 loss: 0.36850082185724753
      batch 6000 loss: 0.3703581801643886
      batch 7000 loss: 0.38547042514081115
      batch 8000 loss: 0.37846584360170527
      batch 9000 loss: 0.3341486988377292
      batch 10000 loss: 0.3433013284947956
      batch 11000 loss: 0.35607743899174965
      batch 12000 loss: 0.3499939931873523
      batch 13000 loss: 0.33874178926000603
      batch 14000 loss: 0.35130289171106416
      batch 15000 loss: 0.3394507191307202
    LOSS train 0.3394507191307202 valid 0.3581162691116333
    EPOCH 3:
      batch 1000 loss: 0.3319729989422485
      batch 2000 loss: 0.29558994361863006
      batch 3000 loss: 0.3107374766407593
      batch 4000 loss: 0.3298987646112146
      batch 5000 loss: 0.30858693152241906
      batch 6000 loss: 0.33916381367447684
      batch 7000 loss: 0.3105102765217889
      batch 8000 loss: 0.3011080777524912
      batch 9000 loss: 0.3142058177240979
      batch 10000 loss: 0.31458891937109
      batch 11000 loss: 0.31527258940579483
      batch 12000 loss: 0.31501667268342864
      batch 13000 loss: 0.3011875962628328
      batch 14000 loss: 0.30012811454350596
      batch 15000 loss: 0.31833117976446373
    LOSS train 0.31833117976446373 valid 0.3307691514492035
    EPOCH 4:
      batch 1000 loss: 0.2786161053752294
      batch 2000 loss: 0.27965198021690596
      batch 3000 loss: 0.28595415444140965
      batch 4000 loss: 0.292985666413857
      batch 5000 loss: 0.3069892351147719
      batch 6000 loss: 0.29902250939945224
      batch 7000 loss: 0.2863366014406201
      batch 8000 loss: 0.2655441066541243
      batch 9000 loss: 0.3045048695363293
      batch 10000 loss: 0.27626545656517554
      batch 11000 loss: 0.2808379335970967
      batch 12000 loss: 0.29241049340573955
      batch 13000 loss: 0.28030834131941446
      batch 14000 loss: 0.2983542350126445
      batch 15000 loss: 0.3009556676162611
    LOSS train 0.3009556676162611 valid 0.41686952114105225
    EPOCH 5:
      batch 1000 loss: 0.2614263167564495
      batch 2000 loss: 0.2587047562422049
      batch 3000 loss: 0.2642477260621345
      batch 4000 loss: 0.2825975873669813
      batch 5000 loss: 0.26987933717705165
      batch 6000 loss: 0.2759250026817317
      batch 7000 loss: 0.26055969463163275
      batch 8000 loss: 0.29164007206353565
      batch 9000 loss: 0.2893096504513578
      batch 10000 loss: 0.2486029507305684
      batch 11000 loss: 0.2732803234480907
      batch 12000 loss: 0.27927226484491985
      batch 13000 loss: 0.2686819267635074
      batch 14000 loss: 0.24746483912148323
      batch 15000 loss: 0.27903492261294194
    LOSS train 0.27903492261294194 valid 0.31206756830215454




.. 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:** ( 4 minutes  53.168 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-python

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

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

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


.. only:: html

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