Struct SparseAutoencoder 

pub struct SparseAutoencoder {
    pub encoder_w: Vec<Vec<f32>>,
    pub encoder_b: Vec<f32>,
    pub decoder_w: Vec<Vec<f32>>,
    pub decoder_b: Vec<f32>,
}

Sparse autoencoder with k-winner-takes-all activation.

Fields

encoder_w: Vec<Vec<f32>>

Encoder weight matrix (hidden_dim × input_dim).

encoder_b: Vec<f32>

Encoder bias vector.

decoder_w: Vec<Vec<f32>>

Decoder weight matrix (input_dim × hidden_dim).

decoder_b: Vec<f32>

Decoder bias vector.

Implementations

impl SparseAutoencoder

pub fn new(config: &SaeConfig, rng: &mut impl Rng) -> Self

Create a new sparse autoencoder with Xavier initialization.

pub fn encode(&self, x: &[f32]) -> Vec<f32>

Encode with ReLU followed by k-winner-takes-all.

pub fn decode(&self, z: &[f32]) -> Vec<f32>

Decode the latent representation back to the input space.

pub fn reconstruction_loss(&self, x: &[f32]) -> f32

Mean squared reconstruction error.

pub fn sparsity_loss(&self, z: &[f32]) -> f32

KL-divergence sparsity penalty on hidden activations.

pub fn total_loss(&self, x: &[f32], config: &SaeConfig) -> f32

Total loss = reconstruction + sparsity_weight * sparsity.

pub fn kl_divergence_bernoulli(rho: f32, rho_hat: f32) -> f32

KL divergence between Bernoulli(rho) and Bernoulli(rho_hat).

Trait Implementations

impl Clone for SparseAutoencoder

fn clone(&self) -> SparseAutoencoder

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SparseAutoencoder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.