Classification and Neural Network using PyTorch

Working with data

PyTorch has two primitives to work with data: 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

Dataset: The datasets retrieves our dataset’s features and labels one sample at a time.

Dataloader: DataLoader is an iterable that abstracts this complexity for us in an easy API.

PyTorch offers domain-specific libraries such as TorchText, TorchVision, and TorchAudio, all of which include datasets. For section, we will be using a TorchVision’s FashionMNIST dataset.

To import the FashionMNIST dataset from TorchVision

root is the path where the train/test data is stored,

train specifies training or test dataset,

download=True downloads the data from the internet if it’s not available at root.

transform and target_transform specify the feature and label transformations

# Download training data from open datasets. 
# this will load the dataset to 'data', 
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, # test data should set train=False
    download=True,
    transform=ToTensor(),
)

DataLoader wraps an iterable over our training and testing dataset and returns it as a batch of 64 features and labels. We can test our dataloader by iterate through test_dataloader.

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

Read more about loading data in PyTorch.

Creating Models

To define a neural network in PyTorch, we create a class that inherits from nn.Module. 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)

Read more about building neural networks in PyTorch.

Optimizing the Model Parameters

To train a model, we need a loss function and an optimizer.

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

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!")

Read more about Training your model.

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")

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}"')

Read more about Saving & Loading your model.

Acknowledgement: The content of this document has been adapted from the original PyTorch quickstart.

Classification and Neural Network using PyTorch

Working with data​

Creating Models​

Optimizing the Model Parameters​

Saving Models​

Loading Models​

Working with data

Creating Models

Optimizing the Model Parameters

Saving Models

Loading Models