Struct BayesianNeuralNetwork 

pub struct BayesianNeuralNetwork<F> {
    pub architecture: Vec<usize>,
    pub activations: Vec<ActivationType>,
    pub weight_priors: Vec<DistributionType<F>>,
    pub bias_priors: Vec<DistributionType<F>>,
    pub weight_samples: Option<Vec<Array2<F>>>,
    pub bias_samples: Option<Vec<Array1<F>>>,
}

Bayesian neural network implementation

Fields

architecture: Vec<usize>

Network architecture

activations: Vec<ActivationType>

Activation functions per layer

weight_priors: Vec<DistributionType<F>>

Weight priors

bias_priors: Vec<DistributionType<F>>

Bias priors

weight_samples: Option<Vec<Array2<F>>>

Posterior samples of weights

bias_samples: Option<Vec<Array1<F>>>

Posterior samples of biases

Implementations

impl<F> BayesianNeuralNetwork<F>

where F: Float + NumCast + SimdUnifiedOps + Zero + One + PartialOrd + Copy + Send + Sync + Display,

pub fn new( architecture: Vec<usize>, activations: Vec<ActivationType>, ) -> StatsResult<Self>

Create new Bayesian neural network

pub fn forward( &self, x: &ArrayView2<'_, F>, weights: &[Array2<F>], biases: &[Array1<F>], ) -> StatsResult<Array2<F>>

Forward pass through the network

pub fn predict_with_uncertainty( &self, x: &ArrayView2<'_, F>, _n_samples_: usize, ) -> StatsResult<(Array2<F>, Array2<F>)>

Make predictions with uncertainty quantification

Trait Implementations

impl<F: Clone> Clone for BayesianNeuralNetwork<F>

fn clone(&self) -> BayesianNeuralNetwork<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for BayesianNeuralNetwork<F>

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

Formats the value using the given formatter.