Struct maver::NeuralNetwork

pub struct NeuralNetwork {
    pub layers: Vec<Vec<Perceptron>>,
    // some fields omitted
}

Fields

layers: Vec<Vec<Perceptron>>

Methods

impl NeuralNetwork

pub fn new(
    layer_sizes: Vec<usize>,
    activation: Activation,
    output_act: Activation
) -> NeuralNetwork

Create new neural network layer_sizes: neural network layout. activation: activation function for hidden perceptrons output_act: activation function for output perceptrons

pub fn forward_prop(&mut self, row: &[f64]) -> Vec<f64>

Get neural network output

pub fn forward_set(&mut self, row: &Vec<f64>) -> Vec<f64>

Set & get neural network output (used for backpropagation)

pub fn train(
    &mut self,
    epochs: usize,
    shuffle: bool,
    data: Vec<(Vec<f64>, Vec<f64>)>,
    lrate: Option<f64>
)

Train neural network epochs: how many times we will iterate shuffle: shuffle train data data: vector of training data: Vec<(input,expected output)> lrate: learning rate if none = 0.1

pub fn backpropagate(
    &mut self,
    input: &Vec<f64>,
    expected: &Vec<f64>,
    lrate: Option<f64>
)

Backpropagation algorithm implementation input: input data expected: expected output lrate: learning rate if none = 0.1

pub fn save_to_file(&self, file_path: &str) -> Result<()>

Trait Implementations

impl Clone for NeuralNetwork

fn clone(&self) -> NeuralNetwork

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for NeuralNetwork

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

Formats the value using the given formatter.

impl Serialize for NeuralNetwork

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<'de> Deserialize<'de> for NeuralNetwork

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.