Struct pyrus_nn::network::Sequential

pub struct Sequential {
    pub lr: f32,
    pub n_epoch: usize,
    pub batch_size: usize,
    pub cost: CostFunc,
    pub verbose: bool,
    // some fields omitted
}

Fields

lr: f32

n_epoch: usize

batch_size: usize

cost: CostFunc

verbose: bool

Methods

impl Sequential

pub fn new(lr: f32, n_epoch: usize, batch_size: usize, cost: CostFunc) -> Self

Create a new Sequential network, with perhaps sensible defaults.

pub fn add(&mut self, layer: impl Layer + 'static) -> Result<(), &'static str>

Add a layer to the network

pub fn len(&self) -> usize

Determine how many layers on held in the network

pub fn predict(&mut self, x: ArrayView2<f32>) -> Array2<f32>

Use the network to predict the outcome of x

pub fn forward(&mut self, x: ArrayView2<f32>) -> Array2<f32>

Apply the network against an input

pub fn backward(&mut self, output: ArrayView2<f32>, expected: ArrayView2<f32>)

Run back propagation on output vs expected

pub fn fit(&mut self, x: ArrayView2<f32>, y: ArrayView2<f32>)

Train the network according to the parameters set given training and target data

pub fn iter_mut(&mut self) -> impl Iterator<Item = &mut Box<dyn Layer>>

Mutably iterate over the layers in the network.

pub fn iter(&self) -> impl Iterator<Item = &Box<dyn Layer>>

Iterate over the layers in the network.

Trait Implementations

impl Default for Sequential

fn default() -> Sequential

Returns the "default value" for a type.

impl Serialize for Sequential

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 Sequential

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

Deserialize this value from the given Serde deserializer.