Struct LayerBase

pub struct LayerBase<F, S, D = Ix2>
where
    D: Dimension,
    S: RawData,
{ /* private fields */ }

Implementations

impl<S, D> LayerBase<Linear, S, D>

where D: Dimension, S: RawData<Elem = f32>,

pub fn linear(params: ParamsBase<S, D>) -> Self

impl<S, D> LayerBase<Sigmoid, S, D>

where D: Dimension, S: RawData<Elem = f32>,

pub fn sigmoid(params: ParamsBase<S, D>) -> Self

impl<S, D> LayerBase<Tanh, S, D>

where D: Dimension, S: RawData<Elem = f32>,

pub fn tanh(params: ParamsBase<S, D>) -> Self

impl<S, D> LayerBase<ReLU, S, D>

where D: Dimension, S: RawData<Elem = f32>,

pub fn relu(params: ParamsBase<S, D>) -> Self

impl<F, S, A, D> LayerBase<F, S, D>

where D: Dimension, S: RawData<Elem = A>,

pub fn new(rho: F, params: ParamsBase<S, D>) -> Self

pub fn params(&self) -> &ParamsBase<S, D>

returns an immutable reference to the layer’s parameters

pub fn params_mut(&mut self) -> &mut ParamsBase<S, D>

returns a mutable reference to the layer’s parameters

pub fn rho(&self) -> &F

returns an immutable reference to the activation function of the layer

pub fn rho_mut(&mut self) -> &mut F

returns a mutable reference to the activation function of the layer

pub fn with_rho<G>(self, rho: G) -> LayerBase<G, S, D>

where G: Activator<S::Elem>, F: Activator<S::Elem>, S: RawData<Elem = A>,

consumes the current instance and returns another with the given activation function. This is useful during the creation of the model, when the activation function is not known yet.

pub fn forward<X, Y>(&self, input: &X) -> Result<Y>

where F: Activator<<ParamsBase<S, D> as Forward<X>>::Output, Output = Y>, ParamsBase<S, D>: Forward<X, Output = Y>, X: Clone, Y: Clone,

Methods from Deref<Target = ParamsBase<S, D>>

pub fn bias(&self) -> &ArrayBase<S, <D as Dimension>::Smaller>

returns an immutable reference to the bias

pub fn bias_mut(&mut self) -> &mut ArrayBase<S, <D as Dimension>::Smaller>

returns a mutable reference to the bias

pub fn weights(&self) -> &ArrayBase<S, D>

returns an immutable reference to the weights

pub fn weights_mut(&mut self) -> &mut ArrayBase<S, D>

returns a mutable reference to the weights

pub fn assign_bias( &mut self, bias: &ArrayBase<S, <D as Dimension>::Smaller>, ) -> &mut ParamsBase<S, D>

where A: Clone, S: DataMut,

assign the bias

pub fn assign_weights( &mut self, weights: &ArrayBase<S, D>, ) -> &mut ParamsBase<S, D>

where A: Clone, S: DataMut,

assign the weights

pub fn replace_bias( &mut self, bias: ArrayBase<S, <D as Dimension>::Smaller>, ) -> ArrayBase<S, <D as Dimension>::Smaller>

replace the bias and return the previous state; uses replace

pub fn replace_weights(&mut self, weights: ArrayBase<S, D>) -> ArrayBase<S, D>

replace the weights and return the previous state; uses replace

pub fn set_bias( &mut self, bias: ArrayBase<S, <D as Dimension>::Smaller>, ) -> &mut ParamsBase<S, D>

set the bias

pub fn set_weights(&mut self, weights: ArrayBase<S, D>) -> &mut ParamsBase<S, D>

set the weights

pub fn backward<X, Y, Z>( &mut self, input: &X, grad: &Y, lr: A, ) -> Result<Z, Error>

where A: Clone, S: Data, ParamsBase<S, D>: Backward<X, Y, Elem = A, Output = Z>,

perform a single backpropagation step

pub fn forward<X, Y>(&self, input: &X) -> Result<Y, Error>

where A: Clone, S: Data, ParamsBase<S, D>: Forward<X, Output = Y>,

forward propagation

pub fn dim(&self) -> <D as Dimension>::Pattern

returns the dimensions of the weights

pub fn is_empty(&self) -> bool

returns true if both the weights and bias are empty; uses is_empty

pub fn is_weights_empty(&self) -> bool

returns true if the weights are empty

pub fn is_bias_empty(&self) -> bool

returns true if the bias is empty

pub fn count_weight(&self) -> usize

the total number of elements within the weight tensor

pub fn count_bias(&self) -> usize

the total number of elements within the bias tensor

pub fn raw_dim(&self) -> D

returns the raw dimensions of the weights;

pub fn shape(&self) -> &[usize]

returns the shape of the parameters; uses the shape of the weight tensor

pub fn shape_bias(&self) -> &[usize]

returns the shape of the bias tensor; the shape should be equivalent to that of the weight tensor minus the “zero-th” axis

pub fn size(&self) -> usize

returns the total number of parameters within the layer

pub fn to_owned(&self) -> ParamsBase<OwnedRepr<A>, D>

where A: Clone, S: DataOwned,

returns an owned instance of the parameters

pub fn to_shape<Sh>( &self, shape: Sh, ) -> Result<ParamsBase<CowRepr<'_, A>, <Sh as ShapeBuilder>::Dim>, Error>

where A: Clone, S: DataOwned, Sh: ShapeBuilder, <Sh as ShapeBuilder>::Dim: Dimension + RemoveAxis,

change the shape of the parameters; the shape of the bias parameters is determined by removing the “zero-th” axis of the given shape

pub fn to_shared(&self) -> ParamsBase<OwnedArcRepr<A>, D>

where A: Clone, S: Data,

returns a new ParamsBase instance with the same paramaters, but using a shared representation of the data;

pub fn view(&self) -> ParamsBase<ViewRepr<&A>, D>

where S: Data,

returns a “view” of the parameters; see view for more information

pub fn view_mut(&mut self) -> ParamsBase<ViewRepr<&mut A>, D>

where S: DataMut,

returns mutable view of the parameters; see view_mut for more information

pub fn ncols(&self) -> usize

returns the number of columns in the weights matrix

pub fn nrows(&self) -> usize

returns the number of rows in the weights matrix

pub fn iter(&self) -> Iter<'_, A, D>

where D: RemoveAxis, S: Data,

an iterator of the parameters; the created iterator zips together an axis iterator over the columns of the weights and an iterator over the bias

pub fn iter_mut(&mut self) -> IterMut<'_, A, D>

where D: RemoveAxis, S: DataMut,

returns a mutable iterator of the parameters, IterMut, which essentially zips together a mutable axis iterator over the columns of the weights against a mutable iterator over the elements of the bias

pub fn iter_bias(&self) -> Iter<'_, A, <D as Dimension>::Smaller>

where S: Data,

returns an iterator over the bias

pub fn iter_bias_mut(&mut self) -> IterMut<'_, A, <D as Dimension>::Smaller>

where S: DataMut,

returns a mutable iterator over the bias

pub fn iter_weights(&self) -> Iter<'_, A, D>

where S: Data,

returns an iterator over the weights

pub fn iter_weights_mut(&mut self) -> IterMut<'_, A, D>

where S: DataMut,

returns a mutable iterator over the weights; see iter_mut for more

pub fn l1_norm(&self) -> A

Returns the L1 norm of the parameters (bias and weights).

pub fn l2_norm(&self) -> A

Returns the L2 norm of the parameters (bias and weights).

pub fn apply_gradient<Delta, Z>( &mut self, grad: &Delta, lr: A, ) -> Result<Z, Error>

where S: DataMut, ParamsBase<S, D>: ApplyGradient<Delta, A, Output = Z>,

a convenience method used to apply a gradient to the parameters using the given learning rate.

pub fn apply_gradient_with_decay<Grad, Z>( &mut self, grad: &Grad, lr: A, decay: A, ) -> Result<Z, Error>

where S: DataMut, ParamsBase<S, D>: ApplyGradient<Grad, A, Output = Z>,

pub fn apply_gradient_with_momentum<Grad, V, Z>( &mut self, grad: &Grad, lr: A, momentum: A, velocity: &mut V, ) -> Result<Z, Error>

where S: DataMut, ParamsBase<S, D>: ApplyGradientExt<Grad, A, Output = Z, Velocity = V>,

pub fn apply_gradient_with_decay_and_momentum<Grad, V, Z>( &mut self, grad: &Grad, lr: A, decay: A, momentum: A, velocity: &mut V, ) -> Result<Z, Error>

where S: DataMut, ParamsBase<S, D>: ApplyGradientExt<Grad, A, Output = Z, Velocity = V>,

Trait Implementations

impl<U, V, F, S, D> Activator<U> for LayerBase<F, S, D>

where F: Activator<U, Output = V>, D: Dimension, S: RawData,

type Output = V

fn activate(&self, x: U) -> Self::Output

Applies the activation function to the input tensor.

impl<U, F, S, D> ActivatorGradient<U> for LayerBase<F, S, D>

where F: ActivatorGradient<U>, D: Dimension, S: RawData,

type Input = <F as ActivatorGradient<U>>::Input

type Delta = <F as ActivatorGradient<U>>::Delta

fn activate_gradient(&self, inputs: F::Input) -> F::Delta

compute the gradient of some input

impl<F, S, D> Deref for LayerBase<F, S, D>

where D: Dimension, S: RawData,

type Target = ParamsBase<S, D>

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl<F, S, D> DerefMut for LayerBase<F, S, D>

where D: Dimension, S: RawData,

fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.

impl<A, F, S, D> Layer<S, D> for LayerBase<F, S, D>

where F: ActivatorGradient<A>, D: Dimension, S: RawData<Elem = A>,

type Scalar = A

fn params(&self) -> &ParamsBase<S, D>

returns an immutable reference to the parameters of the layer

fn params_mut(&mut self) -> &mut ParamsBase<S, D>

returns a mutable reference to the parameters of the layer

fn set_params(&mut self, params: ParamsBase<S, D>)

update the layer parameters

fn backward<X, Y, Z, Delta>( &mut self, input: X, error: Y, gamma: Self::Scalar, ) -> Result<Z>

where S: Data, Self: ActivatorGradient<X, Input = Y, Delta = Delta>, Self::Scalar: Clone, ParamsBase<S, D>: Backward<X, Delta, Elem = Self::Scalar, Output = Z>,

backward propagate error through the layer

fn forward<X, Y>(&self, input: &X) -> Result<Y>

where Y: NdTensor<S::Elem, D, Repr = S>, ParamsBase<S, D>: Forward<X, Output = Y>, Self: Activator<Y, Output = Y>,

complete a forward pass through the layer