Training Loop - Entrenar - Training & Optimization Library
Description: A comprehensive guide to building neural network training systems with autograd, optimizers, LoRA/QLoRA, and quantization using EXTREME TDD methodology

# GAN Training Loop

Training GANs requires careful balancing of generator and discriminator.

## Basic Training Loop

    #![allow(unused)]
    fn main() {
    use entrenar::generative::{CodeGan, CodeGanConfig};

    let config = CodeGanConfig::default();
    let mut gan = CodeGan::new(config);

    for epoch in 0..num_epochs {
        for real_batch in data_loader {
            // 1. Train discriminator on real data
            let d_loss_real = gan.train_discriminator_real(&real_batch);

            // 2. Train discriminator on fake data
            let fake_batch = gan.generate_batch(batch_size);
            let d_loss_fake = gan.train_discriminator_fake(&fake_batch);

            // 3. Train generator
            let g_loss = gan.train_generator_step();

            gan.record_step(g_loss, d_loss_real + d_loss_fake);
        }

        // Log progress
        println!(
            "Epoch {}: G_loss={:.4}, D_loss={:.4}",
            epoch,
            gan.stats.avg_generator_loss(),
            gan.stats.avg_discriminator_loss()
        );
    }
    }

## Training Tricks

### n-step Discriminator Updates

Train discriminator more frequently than generator:

    #![allow(unused)]
    fn main() {
    let d_steps_per_g_step = 5;

    for _ in 0..d_steps_per_g_step {
        let d_loss = gan.train_discriminator_step(&real_batch);
    }
    let g_loss = gan.train_generator_step();
    }

### Gradient Penalty (WGAN-GP)

    #![allow(unused)]
    fn main() {
    fn gradient_penalty(
        discriminator: &Discriminator,
        real: &[u32],
        fake: &[u32],
    ) -> f32 {
        let alpha = rand::random::<f32>();
        let interpolated = interpolate_tokens(real, fake, alpha);

        let d_interp = discriminator.forward(&interpolated);
        let gradients = compute_gradients(discriminator, &interpolated);

        let penalty = (gradients.norm() - 1.0).powi(2);
        10.0 * penalty  // Lambda = 10
    }
    }

### Spectral Normalization

    #![allow(unused)]
    fn main() {
    fn spectral_norm(weight: &mut Vec<Vec<f32>>) {
        let (u, sigma, _v) = power_iteration(weight, 10);
        for row in weight.iter_mut() {
            for w in row.iter_mut() {
                *w /= sigma;
            }
        }
    }
    }

## Tracking Training Progress

    #![allow(unused)]
    fn main() {
    // Statistics tracking
    let stats = &gan.stats;

    println!("Steps: {}", stats.steps);
    println!("Avg G loss: {:.4}", stats.avg_generator_loss());
    println!("Avg D loss: {:.4}", stats.avg_discriminator_loss());
    println!("Mode collapse score: {:.4}", stats.mode_collapse_score);
    println!("Unique tokens: {}", stats.unique_tokens);
    }