Implement PyTorch training loop

Q: Implement PyTorch training loop

This question evaluates practical implementation skills in PyTorch, focusing on model and device management, batch-wise training mechanics, gradient handling, and optimizer interaction.

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Question

Implement a basic PyTorch training loop

You are given a PyTorch neural network model, a DataLoader that yields (inputs, targets) batches, an optimizer, and a loss function.

Write a function train(model, train_loader, optimizer, loss_fn, device, num_epochs) that:

Moves the model and input batches to the specified device (CPU or GPU).
Runs for num_epochs epochs.
For each batch, performs a forward pass, computes the loss, runs backpropagation, and updates the model parameters.
Properly zeros gradients at the right time.
Optionally prints or returns the average training loss per epoch.

Clearly show the order of operations inside the training loop (zeroing gradients, forward pass, loss computation, backward pass, optimizer step).

Implement PyTorch training loop

Overview

Implement a basic PyTorch training loop

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