pytorch_simple_fullynet.py

"""
A simple walkthrough of how to code a fully connected neural network
using the PyTorch library. This code is designed to solve a classic problem in machine learning: recognizing handwritten digits. 
We use the MNIST dataset, which contains images of handwritten digits from 0 to 9. 
The goal is to train a neural network to recognize these digits with high accuracy.
In this code we go through how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.

Programmed by Aladdin Persson
* 2020-04-08: Initial coding
* 2021-03-24: Added more detailed comments also removed part of
              check_accuracy which would only work specifically on MNIST.
* 2022-09-23: Updated with more detailed comments, docstrings to functions, and checked code still functions as intended.
"""

# Imports
import torch
import torch.nn.functional as F  # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets  # Standard datasets
import torchvision.transforms as transforms  # Transformations we can perform on our dataset for augmentation
from torch import optim  # For optimizers like SGD, Adam, etc.
from torch import nn  # All neural network modules
from torch.utils.data import (
    DataLoader,
)  # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm  # For nice progress bar! (Type "pip install tqdm" in your Terminal if u don't have the module to install it)

# Here we create our simple neural network. For more details here we are subclassing and
# inheriting from nn.Module, this is the most general way to create your networks and
# allows for more flexibility. I encourage you to also check out nn.Sequential which
# would be easier to use in this scenario but I wanted to show you something that
# "always" works and is a general approach.
class NN(nn.Module):
    def __init__(self, input_size, num_classes):
        """
        Here we define the layers of the network. We create two fully connected layers

        Parameters:
            input_size: the size of the input, in this case 784 (28x28)
            num_classes: the number of classes we want to predict, in this case 10 (0-9)

        """
        super(NN, self).__init__()
        # Our first linear layer take input_size, in this case 784 nodes to 50
        # and our second linear layer takes 50 to the num_classes we have, in
        # this case 10.
        self.fc1 = nn.Linear(input_size, 50)
        self.fc2 = nn.Linear(50, num_classes)

    def forward(self, x):
        """
        x here is the mnist images and we run it through fc1, fc2 that we created above.
        we also add a ReLU activation function in between and for that (since it has no parameters)
        I recommend using nn.functional (F)

        Parameters:
            x: mnist images

        Returns:
            out: the output of the network
        """

        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x


# Set device cuda for GPU if it's available otherwise run on the CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Hyperparameters
input_size = 784
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 3

# Load Data
train_dataset = datasets.MNIST(
    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
test_dataset = datasets.MNIST(
    root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

# Initialize network
model = NN(input_size=input_size, num_classes=num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Train Network
for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
        # Get data to cuda if possible
        data = data.to(device=device)
        targets = targets.to(device=device)

        # Get to correct shape
        data = data.reshape(data.shape[0], -1)

        # Forward
        scores = model(data)
        loss = criterion(scores, targets)

        # Backward
        optimizer.zero_grad()
        loss.backward()

        # Gradient descent or adam step
        optimizer.step()


# Check accuracy on training & test to see how good our model
def check_accuracy(loader, model):
    """
    Check accuracy of our trained model given a loader and a model

    Parameters:
        loader: torch.utils.data.DataLoader
            A loader for the dataset you want to check accuracy on
        model: nn.Module
            The model you want to check accuracy on

    Returns:
        acc: float
            The accuracy of the model on the dataset given by the loader
    """

    num_correct = 0
    num_samples = 0
    model.eval()

    # We don't need to keep track of gradients here so we wrap it in torch.no_grad()
    with torch.no_grad():
        # Loop through the data
        for x, y in loader:

            # Move data to device
            x = x.to(device=device)
            y = y.to(device=device)

            # Get to correct shape
            x = x.reshape(x.shape[0], -1)

            # Forward pass
            scores = model(x)
            _, predictions = scores.max(1)

            # Check how many we got correct
            num_correct += (predictions == y).sum()

            # Keep track of number of samples
            num_samples += predictions.size(0)

    model.train()
    return num_correct / num_samples


# Check accuracy on training & test to see how good our model
print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")