Struct DenseLayer 

pub struct DenseLayer<A, B: Backend = DefaultBackend>
where
    A: Activation<B>,
{
    pub weights: B::Tensor<Ix2>,
    pub biases: B::Tensor<Ix1>,
    pub activation: A,
    /* private fields */
}

Fields

weights: B::Tensor<Ix2>

biases: B::Tensor<Ix1>

activation: A

Implementations

impl<A: Activation<B>, B: Backend> DenseLayer<A, B>

pub fn new<IW, IB>( input_size: usize, output_size: usize, activation: A, weight_init: IW, bias_init: IB, ) -> Self

where IW: Initializer<B>, IB: Initializer<B>,

Trait Implementations

impl<'de, A, B: Backend> Deserialize<'de> for DenseLayer<A, B>

where A: Activation<B> + Deserialize<'de>, B::Tensor<Ix2>: Deserialize<'de>, B::Tensor<Ix1>: Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<A: Activation<B>, B: Backend> Layer<B> for DenseLayer<A, B>

type Input = Dim<[usize; 2]>

type Output = Dim<[usize; 2]>

fn forward(&mut self, input: &B::Tensor<Ix2>) -> B::Tensor<Ix2>

fn backward(&mut self, grad_output: &B::Tensor<Ix2>) -> B::Tensor<Ix2>

fn update<O: Optimizer<B>>(&mut self, optimizer: &mut O)

impl<A, B: Backend> Serialize for DenseLayer<A, B>

where A: Activation<B> + Serialize, B::Tensor<Ix2>: Serialize, B::Tensor<Ix1>: Serialize,

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

where __S: Serializer,

Serialize this value into the given Serde serializer.