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Click :ref:`here <sphx_glr_download_beginner_introyt_trainingyt.py>` to download the full example code
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.. _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.
.. 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, num_workers=2)
validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False, num_workers=2)
# 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)))
As always, let’s visualize the data as a sanity check:
.. 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 = dataiter.next()
# 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)))
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.
.. 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()
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.
.. 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()))
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?
.. code-block:: default
# Optimizers specified in the torch.optim package
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
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
.. 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
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.
.. 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)
# We don't need gradients on to do reporting
model.train(False)
running_vloss = 0.0
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
To load a saved version of the model:
::
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
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