.. 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]
      0%|          | 65536/26421880 [00:00<01:12, 364465.30it/s]
      1%|          | 229376/26421880 [00:00<00:38, 682909.25it/s]
      4%|3         | 950272/26421880 [00:00<00:11, 2191234.44it/s]
     15%|#4        | 3833856/26421880 [00:00<00:02, 7611836.35it/s]
     38%|###7      | 10027008/26421880 [00:00<00:00, 17191604.34it/s]
     60%|######    | 15925248/26421880 [00:01<00:00, 22395298.10it/s]
     82%|########1 | 21594112/26421880 [00:01<00:00, 29635784.15it/s]
     95%|#########4| 25001984/26421880 [00:01<00:00, 26269199.26it/s]
    100%|##########| 26421880/26421880 [00:01<00:00, 19317528.61it/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, 327674.80it/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

      0%|          | 0/4422102 [00:00<?, ?it/s]
      1%|1         | 65536/4422102 [00:00<00:12, 359332.09it/s]
      5%|5         | 229376/4422102 [00:00<00:06, 678893.02it/s]
     21%|##1       | 950272/4422102 [00:00<00:01, 2178733.25it/s]
     85%|########5 | 3768320/4422102 [00:00<00:00, 8195917.17it/s]
    100%|##########| 4422102/4422102 [00:00<00:00, 6061102.41it/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, 51288068.86it/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: 1.6334228541590274
      batch 2000 loss: 0.8324381597135216
      batch 3000 loss: 0.7350949151031673
      batch 4000 loss: 0.6221513676682953
      batch 5000 loss: 0.6008665340302978
      batch 6000 loss: 0.5533551393696107
      batch 7000 loss: 0.5268192595622968
      batch 8000 loss: 0.4953766325986944
      batch 9000 loss: 0.4763272075761342
      batch 10000 loss: 0.48026260716759134
      batch 11000 loss: 0.4555706014999887
      batch 12000 loss: 0.43150419856602096
      batch 13000 loss: 0.41889463035896185
      batch 14000 loss: 0.4101380754457787
      batch 15000 loss: 0.4188491042831447
    LOSS train 0.4188491042831447 valid 0.42083388566970825
    EPOCH 2:
      batch 1000 loss: 0.39033183104451746
      batch 2000 loss: 0.35730057470843896
      batch 3000 loss: 0.3797398313785088
      batch 4000 loss: 0.3595128281345387
      batch 5000 loss: 0.3674602470536483
      batch 6000 loss: 0.3695404906652402
      batch 7000 loss: 0.38634192156628705
      batch 8000 loss: 0.37888678515458013
      batch 9000 loss: 0.32936658181797246
      batch 10000 loss: 0.3460305611458316
      batch 11000 loss: 0.355949883276422
      batch 12000 loss: 0.34613123371596155
      batch 13000 loss: 0.3435088261961791
      batch 14000 loss: 0.35190882972519466
      batch 15000 loss: 0.34078337761512373
    LOSS train 0.34078337761512373 valid 0.3449384272098541
    EPOCH 3:
      batch 1000 loss: 0.3336456001721235
      batch 2000 loss: 0.2948776570415939
      batch 3000 loss: 0.30873254264354183
      batch 4000 loss: 0.3269525112561532
      batch 5000 loss: 0.3081500146031831
      batch 6000 loss: 0.33906219027831686
      batch 7000 loss: 0.3114977335120493
      batch 8000 loss: 0.3028961390093173
      batch 9000 loss: 0.31883212575598735
      batch 10000 loss: 0.3121348040100274
      batch 11000 loss: 0.3204089922408457
      batch 12000 loss: 0.3172754702415841
      batch 13000 loss: 0.3022056705406212
      batch 14000 loss: 0.29925711060611504
      batch 15000 loss: 0.3158802612772852
    LOSS train 0.3158802612772852 valid 0.32655972242355347
    EPOCH 4:
      batch 1000 loss: 0.2793223039015138
      batch 2000 loss: 0.2759745200898469
      batch 3000 loss: 0.2885438525550344
      batch 4000 loss: 0.29715126178535867
      batch 5000 loss: 0.3092308461628054
      batch 6000 loss: 0.29819886386692085
      batch 7000 loss: 0.28212033420058286
      batch 8000 loss: 0.2652145917697999
      batch 9000 loss: 0.30505836525483027
      batch 10000 loss: 0.28172129570529797
      batch 11000 loss: 0.2760911153540328
      batch 12000 loss: 0.29349113235381813
      batch 13000 loss: 0.28226990548134745
      batch 14000 loss: 0.2974613601177407
      batch 15000 loss: 0.3016561955644138
    LOSS train 0.3016561955644138 valid 0.3930961787700653
    EPOCH 5:
      batch 1000 loss: 0.2611404411364929
      batch 2000 loss: 0.25894880425418887
      batch 3000 loss: 0.2585991551137176
      batch 4000 loss: 0.2808971864393097
      batch 5000 loss: 0.26857244527151486
      batch 6000 loss: 0.2778763904040534
      batch 7000 loss: 0.2556428771363862
      batch 8000 loss: 0.2892738865161955
      batch 9000 loss: 0.2898595165217885
      batch 10000 loss: 0.24955335284502145
      batch 11000 loss: 0.27326060194405
      batch 12000 loss: 0.2833696024138153
      batch 13000 loss: 0.2705353221144751
      batch 14000 loss: 0.24937306600230658
      batch 15000 loss: 0.27901125454565046
    LOSS train 0.27901125454565046 valid 0.3100835084915161




.. 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:** ( 3 minutes  4.024 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

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

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