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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 06, 2026 | 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.v2.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
from torchvision.transforms import v2

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


transform = v2.Compose([
    v2.ToImage(),
    v2.ToDtype(torch.float32, scale=True),
    v2.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(f'Training set has {len(training_set)} instances')
print(f'Validation set has {len(validation_set)} instances')

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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)))

Pullover  Dress  Dress  Sandal

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().__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(f'Total loss for this batch: {loss.item()}')

tensor([[0.6146, 0.5996, 0.2258, 0.5280, 0.9899, 0.7108, 0.0979, 0.0627, 0.7203,
         0.3153],
        [0.7424, 0.7364, 0.9516, 0.8597, 0.1745, 0.6204, 0.9570, 0.5694, 0.2583,
         0.3515],
        [0.2817, 0.6510, 0.0586, 0.3483, 0.9941, 0.0245, 0.4253, 0.9692, 0.3378,
         0.4006],
        [0.2602, 0.8881, 0.3159, 0.7279, 0.0308, 0.4146, 0.7582, 0.2557, 0.8852,
         0.2887]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.3989295959472656

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(f'  batch {i + 1} loss: {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(f'runs/fashion_trainer_{timestamp}')
epoch_number = 0

EPOCHS = 5

best_vloss = 1_000_000.

for epoch in range(EPOCHS):
    print(f'EPOCH {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(f'LOSS train {avg_loss} valid {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 = f'model_{timestamp}_{epoch_number}'
        torch.save(model.state_dict(), model_path)

    epoch_number += 1

EPOCH 1:
  batch 1000 loss: 1.8695995131731034
  batch 2000 loss: 0.8097825972158462
  batch 3000 loss: 0.7166740772034973
  batch 4000 loss: 0.6375269831670448
  batch 5000 loss: 0.5870955650696996
  batch 6000 loss: 0.5821583204418421
  batch 7000 loss: 0.5547886421768926
  batch 8000 loss: 0.49789536159858105
  batch 9000 loss: 0.5055540083851665
  batch 10000 loss: 0.46892975261685205
  batch 11000 loss: 0.4703439413950546
  batch 12000 loss: 0.4312088005100377
  batch 13000 loss: 0.43367812463932204
  batch 14000 loss: 0.431104372608941
  batch 15000 loss: 0.38677558897435665
LOSS train 0.38677558897435665 valid 0.4277903735637665
EPOCH 2:
  batch 1000 loss: 0.4108089748605271
  batch 2000 loss: 0.3884809552783554
  batch 3000 loss: 0.40962709004699716
  batch 4000 loss: 0.3865247091604397
  batch 5000 loss: 0.3899723859790538
  batch 6000 loss: 0.37240864015789704
  batch 7000 loss: 0.38396977348154177
  batch 8000 loss: 0.3724400322136935
  batch 9000 loss: 0.351614531144558
  batch 10000 loss: 0.33966147384233775
  batch 11000 loss: 0.3546896719417564
  batch 12000 loss: 0.35413731618088784
  batch 13000 loss: 0.3512592303447891
  batch 14000 loss: 0.34911778155202045
  batch 15000 loss: 0.34582560407966956
LOSS train 0.34582560407966956 valid 0.34605205059051514
EPOCH 3:
  batch 1000 loss: 0.3277154142586514
  batch 2000 loss: 0.3436353682272602
  batch 3000 loss: 0.33548803454889276
  batch 4000 loss: 0.33053843823008355
  batch 5000 loss: 0.3295680404887826
  batch 6000 loss: 0.3144164528474794
  batch 7000 loss: 0.3223921780626406
  batch 8000 loss: 0.29472704230013186
  batch 9000 loss: 0.3060894489788043
  batch 10000 loss: 0.32984772351513675
  batch 11000 loss: 0.3138090755259036
  batch 12000 loss: 0.3007646624220797
  batch 13000 loss: 0.3286551084505918
  batch 14000 loss: 0.3143047702828917
  batch 15000 loss: 0.29775889165730407
LOSS train 0.29775889165730407 valid 0.35488712787628174
EPOCH 4:
  batch 1000 loss: 0.2859347220129785
  batch 2000 loss: 0.2614405706108591
  batch 3000 loss: 0.3026811663096523
  batch 4000 loss: 0.3015303528185905
  batch 5000 loss: 0.31160122281614167
  batch 6000 loss: 0.28420745696956873
  batch 7000 loss: 0.3009077931898937
  batch 8000 loss: 0.28923106571727475
  batch 9000 loss: 0.2857467100047215
  batch 10000 loss: 0.28246191915300006
  batch 11000 loss: 0.3129734639956987
  batch 12000 loss: 0.288689081073906
  batch 13000 loss: 0.2701071563748046
  batch 14000 loss: 0.29800996946467784
  batch 15000 loss: 0.276075417055632
LOSS train 0.276075417055632 valid 0.30846190452575684
EPOCH 5:
  batch 1000 loss: 0.2629861165839065
  batch 2000 loss: 0.27379635428504845
  batch 3000 loss: 0.25427826664778513
  batch 4000 loss: 0.2745738396905508
  batch 5000 loss: 0.25755009193009754
  batch 6000 loss: 0.27176805736006643
  batch 7000 loss: 0.2746723964669727
  batch 8000 loss: 0.29465596913577063
  batch 9000 loss: 0.2777492433016986
  batch 10000 loss: 0.27339272240383117
  batch 11000 loss: 0.2673776623143931
  batch 12000 loss: 0.2726597747898777
  batch 13000 loss: 0.2726985205232013
  batch 14000 loss: 0.2611108816904016
  batch 15000 loss: 0.2635891359810084
LOSS train 0.2635891359810084 valid 0.33936864137649536

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