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Creation of Simple Neural Network for Classifying the MNIST Dataset

import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms # Define a simple neural network class SimpleNN(nn.Module): def __init__(self): super(SimpleNN, self).__init__() self.fc1 = nn.Linear(28*28, 10) # Input layer to output layer def forward(self, x): x = x.view(-1, 28*28) # Flatten the input x = self.fc1(x) # Forward pass return x # Load the MNIST dataset transform = transforms.Compose([transforms.ToTensor()]) train_loader = torch.utils.data.DataLoader(datasets.MNIST('.', train=True, download=True, transform=transform), batch_size=64, shuffle=True) # Initialize network, loss, and optimizer model = SimpleNN() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Training loop (1 epoch for simplicity) for images, labels in train_loader: optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() print("Training complete!")

Explanation:

• Network Architecture:
  • SimpleNN Class: Contains a single fully connected layer (fc1) that maps 784 features (flattened 28x28 images) to 10 output classes.
• Data Loading:
  • MNIST Dataset: Uses transforms.ToTensor() to convert images to tensors and loads them in batches.
• Training Process:
  • Training Loop: Iterates over the dataset once (1 epoch), performs a forward pass, calculates the loss, and updates the model weights.

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