LinearRegressionModel.py

#This code is a ton of GPT, still learning/sifting through this.
#Import the PyTorch Neural Network Library
from torch import torch as torch, nn as nn, optim as optim
#Import the PyTorch DataLoader Library
from torch.utils.data import DataLoader
#Import the PyTorch Datasets Library
from torchvision import datasets, transforms

#Code below here is un-edited

# Define transformations for the training data
transform = transforms.Compose(
    [
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
    ]
)

# Load the training and test datasets
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# Define the neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)

    def forward(self, x):
        x = x.view(-1, 28 * 28)  # Flatten the image
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = SimpleNN()

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Train the model
num_epochs = 5
for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Test the model
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy: {100 * correct / total}%')