QUICKSTART

This section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.

Working with data

PyTorch has two primitives to work with data (opens new window): torch.utils.data.DataLoader and torch.utils.data.Dataset. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset.

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

PyTorch offers domain-specific libraries such as TorchText (opens new window), TorchVision (opens new window), and TorchAudio (opens new window), all of which include datasets. For this tutorial, we will be using a TorchVision dataset.

The torchvision.datasets module contains Dataset objects for many real-world vision data like CIFAR, COCO (full list here (opens new window)). In this tutorial, we use the FashionMNIST dataset. Every TorchVision Dataset includes two arguments: transform and target_transform to modify the samples and labels respectively.

# Download training data from open datasets.
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# Download test data from open datasets.
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

Out:

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
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
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
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
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw

We pass the Dataset as an argument to DataLoader. This wraps an iterable over our dataset, and supports automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element in the dataloader iterable will return a batch of 64 features and labels.

batch_size = 64

# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
    print(f"Shape of X [N, C, H, W]: {X.shape}")
    print(f"Shape of y: {y.shape} {y.dtype}")
    break

Out:

Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64

Read more about loading data in PyTorch (opens new window).

---

Creating Models

To define a neural network in PyTorch, we create a class that inherits from nn.Module (opens new window). We define the layers of the network in the __init__ function and specify how data will pass through the network in the forward function. To accelerate operations in the neural network, we move it to the GPU if available.

# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")

# Define model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10)
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

model = NeuralNetwork().to(device)
print(model)

Out:

Using cuda device
NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear_relu_stack): Sequential(
    (0): Linear(in_features=784, out_features=512, bias=True)
    (1): ReLU()
    (2): Linear(in_features=512, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=10, bias=True)
  )
)

Read more about building neural networks in PyTorch (opens new window).

---

Optimizing the Model Parameters

To train a model, we need a loss function (opens new window) and an optimizer (opens new window).

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and backpropagates the prediction error to adjust the model’s parameters.

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

We also check the model’s performance against the test dataset to ensure it is learning.

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

The training process is conducted over several iterations (epochs). During each epoch, the model learns parameters to make better predictions. We print the model’s accuracy and loss at each epoch; we’d like to see the accuracy increase and the loss decrease with every epoch.

epochs = 5
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(train_dataloader, model, loss_fn, optimizer)
    test(test_dataloader, model, loss_fn)
print("Done!")

Out:

Epoch 1
-------------------------------
loss: 2.302418  [    0/60000]
loss: 2.292112  [ 6400/60000]
loss: 2.263742  [12800/60000]
loss: 2.261939  [19200/60000]
loss: 2.246309  [25600/60000]
loss: 2.211567  [32000/60000]
loss: 2.222588  [38400/60000]
loss: 2.184552  [44800/60000]
loss: 2.181921  [51200/60000]
loss: 2.149043  [57600/60000]
Test Error:
 Accuracy: 39.3%, Avg loss: 2.141805

Epoch 2
-------------------------------
loss: 2.158010  [    0/60000]
loss: 2.149895  [ 6400/60000]
loss: 2.079702  [12800/60000]
loss: 2.100497  [19200/60000]
loss: 2.038944  [25600/60000]
loss: 1.982337  [32000/60000]
loss: 2.017102  [38400/60000]
loss: 1.928107  [44800/60000]
loss: 1.938422  [51200/60000]
loss: 1.860414  [57600/60000]
Test Error:
 Accuracy: 52.8%, Avg loss: 1.856996

Epoch 3
-------------------------------
loss: 1.900860  [    0/60000]
loss: 1.867335  [ 6400/60000]
loss: 1.738796  [12800/60000]
loss: 1.787248  [19200/60000]
loss: 1.663797  [25600/60000]
loss: 1.628784  [32000/60000]
loss: 1.656449  [38400/60000]
loss: 1.553097  [44800/60000]
loss: 1.582812  [51200/60000]
loss: 1.476982  [57600/60000]
Test Error:
 Accuracy: 60.3%, Avg loss: 1.495178

Epoch 4
-------------------------------
loss: 1.570129  [    0/60000]
loss: 1.536125  [ 6400/60000]
loss: 1.379916  [12800/60000]
loss: 1.455038  [19200/60000]
loss: 1.332352  [25600/60000]
loss: 1.337494  [32000/60000]
loss: 1.350402  [38400/60000]
loss: 1.275247  [44800/60000]
loss: 1.310407  [51200/60000]
loss: 1.212858  [57600/60000]
Test Error:
 Accuracy: 63.3%, Avg loss: 1.241025

Epoch 5
-------------------------------
loss: 1.320390  [    0/60000]
loss: 1.305637  [ 6400/60000]
loss: 1.133990  [12800/60000]
loss: 1.242127  [19200/60000]
loss: 1.116368  [25600/60000]
loss: 1.145456  [32000/60000]
loss: 1.163464  [38400/60000]
loss: 1.101201  [44800/60000]
loss: 1.141440  [51200/60000]
loss: 1.058561  [57600/60000]
Test Error:
 Accuracy: 64.7%, Avg loss: 1.082234

Done!

Read more about Training your model (opens new window).

---

Saving Models

A common way to save a model is to serialize the internal state dictionary (containing the model parameters).

torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")

Out:

Saved PyTorch Model State to model.pth

Loading Models

The process for loading a model includes re-creating the model structure and loading the state dictionary into it.

model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))

This model can now be used to make predictions.

classes = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

model.eval()
x, y = test_data[0][0], test_data[0][1]
with torch.no_grad():
    pred = model(x)
    predicted, actual = classes[pred[0].argmax(0)], classes[y]
    print(f'Predicted: "{predicted}", Actual: "{actual}"')

Out:

Predicted: "Ankle boot", Actual: "Ankle boot"

Read more about Saving & Loading your model (opens new window).