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Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding

Training with PyTorch

Created On: Nov 30, 2021 | Last Updated: May 31, 2023 | Last Verified: Nov 05, 2024

Follow along with the video below or on youtube.

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.

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)))

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Training set has 60000 instances
Validation set has 10000 instances

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

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)))

Coat  Ankle Boot  Trouser  Trouser

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.

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.

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()))

tensor([[0.5779, 0.8862, 0.4191, 0.0032, 0.2113, 0.9228, 0.4309, 0.5445, 0.1012,
         0.9414],
        [0.6555, 0.3088, 0.1644, 0.0469, 0.6615, 0.5882, 0.3748, 0.1855, 0.8543,
         0.5478],
        [0.7276, 0.8717, 0.1798, 0.8854, 0.5145, 0.7271, 0.3891, 0.2517, 0.4869,
         0.4351],
        [0.5138, 0.1533, 0.4548, 0.5427, 0.3486, 0.0081, 0.1881, 0.1080, 0.9436,
         0.8507]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.199453830718994

Optimizer

For this example, we’ll be using simple stochastic gradient descent 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?

# 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

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.

# 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

EPOCH 1:
  batch 1000 loss: 1.786096847176552
  batch 2000 loss: 0.859170969389379
  batch 3000 loss: 0.7190974873229862
  batch 4000 loss: 0.6598526890622451
  batch 5000 loss: 0.5838459139754996
  batch 6000 loss: 0.5521430970021757
  batch 7000 loss: 0.5351364195225761
  batch 8000 loss: 0.539474343089154
  batch 9000 loss: 0.5124938601325266
  batch 10000 loss: 0.49151815713290126
  batch 11000 loss: 0.4708568763874937
  batch 12000 loss: 0.4611044364885893
  batch 13000 loss: 0.4133654390580487
  batch 14000 loss: 0.4402244464850519
  batch 15000 loss: 0.4309590762491571
LOSS train 0.4309590762491571 valid 0.41458258032798767
EPOCH 2:
  batch 1000 loss: 0.39941240309749265
  batch 2000 loss: 0.4068603223847458
  batch 3000 loss: 0.35848508665140255
  batch 4000 loss: 0.3750318140688032
  batch 5000 loss: 0.38891105032191264
  batch 6000 loss: 0.3916742497025989
  batch 7000 loss: 0.35125666220588025
  batch 8000 loss: 0.3696044623917551
  batch 9000 loss: 0.3618686895615247
  batch 10000 loss: 0.3662503977565502
  batch 11000 loss: 0.3596696388687415
  batch 12000 loss: 0.36236808000149906
  batch 13000 loss: 0.33359414375250346
  batch 14000 loss: 0.3566772072846652
  batch 15000 loss: 0.35831537446996664
LOSS train 0.35831537446996664 valid 0.3961953818798065
EPOCH 3:
  batch 1000 loss: 0.326778023144132
  batch 2000 loss: 0.3371908660522713
  batch 3000 loss: 0.34427384962077484
  batch 4000 loss: 0.3293056495572964
  batch 5000 loss: 0.3102431019931537
  batch 6000 loss: 0.30466453904053925
  batch 7000 loss: 0.32623241228199
  batch 8000 loss: 0.3249970707918692
  batch 9000 loss: 0.3111401640658296
  batch 10000 loss: 0.31191050394101694
  batch 11000 loss: 0.3347715679293324
  batch 12000 loss: 0.32604350845052976
  batch 13000 loss: 0.31874223645444
  batch 14000 loss: 0.32521826910832896
  batch 15000 loss: 0.30239877930857256
LOSS train 0.30239877930857256 valid 0.33625584840774536
EPOCH 4:
  batch 1000 loss: 0.3080627405752166
  batch 2000 loss: 0.28084719391765245
  batch 3000 loss: 0.29544671591219956
  batch 4000 loss: 0.2860185973097123
  batch 5000 loss: 0.27990782612843756
  batch 6000 loss: 0.2890156138100192
  batch 7000 loss: 0.321462659977944
  batch 8000 loss: 0.28849853136204184
  batch 9000 loss: 0.270612389343758
  batch 10000 loss: 0.3026917371701129
  batch 11000 loss: 0.2939817950121651
  batch 12000 loss: 0.31887937623170726
  batch 13000 loss: 0.3085348536039291
  batch 14000 loss: 0.2730141546119776
  batch 15000 loss: 0.277164637510281
LOSS train 0.277164637510281 valid 0.3341556787490845
EPOCH 5:
  batch 1000 loss: 0.2676803221049486
  batch 2000 loss: 0.27730777416354246
  batch 3000 loss: 0.26299332616944593
  batch 4000 loss: 0.28413415247360535
  batch 5000 loss: 0.2799531579449431
  batch 6000 loss: 0.2690072998491196
  batch 7000 loss: 0.26681686138529037
  batch 8000 loss: 0.2753645258527431
  batch 9000 loss: 0.290746773472536
  batch 10000 loss: 0.26916193184114307
  batch 11000 loss: 0.27618473538951366
  batch 12000 loss: 0.28250575076439055
  batch 13000 loss: 0.2712034014643359
  batch 14000 loss: 0.276366593589757
  batch 15000 loss: 0.2559231778119738
LOSS train 0.2559231778119738 valid 0.3214151859283447

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, including Dataset and DataLoader, at pytorch.org

• A note on the use of pinned memory for GPU training

• Documentation on the datasets available in TorchVision, TorchText, and TorchAudio

• Documentation on the loss functions available in PyTorch

• Documentation on the torch.optim package, which includes optimizers and related tools, such as learning rate scheduling

• A detailed tutorial on saving and loading models

• The Tutorials section of pytorch.org contains tutorials on a broad variety of training tasks, including classification in different domains, generative adversarial networks, reinforcement learning, and more