Code Generation GANs - 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

# Code Generation GANs

GANs for generating valid Rust AST candidates.

## Configuration

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

    let config = CodeGanConfig {
        generator: GeneratorConfig {
            latent_dim: 128,
            hidden_dims: vec![256, 512, 256],
            vocab_size: 100,
            max_seq_len: 64,
            dropout: 0.1,
            batch_norm: true,
        },
        discriminator: DiscriminatorConfig {
            vocab_size: 100,
            embed_dim: 64,
            hidden_dims: vec![128, 64],
            dropout: 0.1,
        },
        learning_rate_g: 0.0002,
        learning_rate_d: 0.0002,
        beta1: 0.5,
        beta2: 0.999,
    };
    }

## Training Loop

    #![allow(unused)]
    fn main() {
    let mut gan = CodeGan::new(config);

    for epoch in 0..num_epochs {
        for batch in data_loader {
            // Train discriminator
            let d_loss = gan.train_discriminator_step(&batch);

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

            // Monitor for mode collapse
            let collapse_score = gan.detect_mode_collapse(100);
            if collapse_score > 0.8 {
                eprintln!("Warning: Mode collapse detected!");
            }
        }
    }
    }

## Evaluation

    #![allow(unused)]
    fn main() {
    // Generate samples
    let samples = gan.generate_batch(100);

    // Check diversity
    let diversity = gan.compute_diversity(&samples);
    println!("Sample diversity: {:.2}", diversity);

    // Interpolate between codes
    let z1 = gan.sample_latent(1)[0].clone();
    let z2 = gan.sample_latent(1)[0].clone();
    let interpolated = gan.interpolate(&z1, &z2, 10);
    }