PyTorch Training Loop from Scratch

What you'll learn

• Defining Custom Dataset and DataLoader classes
• Subclassing nn.Module to build a feedforward neural network
• Writing the fundamental forward/backward pass loop manually
• Tracking training metrics with TensorBoard or Weights & Biases

Architecture

Custom Dataset (PyTorch)
  → DataLoader (Batching, Shuffling)
  → Neural Network (nn.Module)
  → Loss Function & Optimizer (SGD/Adam)
  → Training Loop (Epochs)

Steps

1. Pick a standard dataset (e.g., FashionMNIST or a tabular regression dataset) and write a custom PyTorch Dataset class for it.
2. Initialize PyTorch DataLoaders for batching.
3. Define your network architecture by subclassing nn.Module. Include __init__ for layers and forward for the computational graph.
4. Set up an optimizer (like Adam) and a loss function (like CrossEntropyLoss).
5. Write the core training loop:
   • Zero gradients
   • Forward pass
   • Calculate loss
   • Backward pass
   • Optimizer step
6. Integrate TensorBoard to log loss and accuracy at every epoch.
7. Save the model weights using torch.save.