Struct ParamsBase

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

The ParamsBase struct is a generic container for a set of weights and biases for a model. The implementation is designed around the ArrayBase type from the ndarray crate, which allows for flexible and efficient storage of multi-dimensional arrays.

Implementations

impl<A, S, D> ParamsBase<S, D>

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

pub const fn new( bias: ArrayBase<S, D::Smaller>, weights: ArrayBase<S, D>, ) -> Self

create a new instance of the ParamsBase with the given bias and weights

pub fn from_elems<Sh>(shape: Sh, elem: A) -> Self

where A: Clone, D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

create a new instance of the [ModelParams] from the given shape and element;

pub fn default<Sh>(shape: Sh) -> Self

where A: Clone + Default, D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

create an instance of the parameters with all values set to the default value

pub fn ones<Sh>(shape: Sh) -> Self

where A: Clone + One, D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

initialize the parameters with all values set to zero

pub fn zeros<Sh>(shape: Sh) -> Self

where A: Clone + Zero, D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

create an instance of the parameters with all values set to zero

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

returns an immutable reference to the bias

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

returns a mutable reference to the bias

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

returns an immutable reference to the weights

pub const 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::Smaller>) -> &mut Self

where A: Clone, S: DataMut,

assign the bias

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

where A: Clone, S: DataMut,

assign the weights

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

set the bias

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

set the weights

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

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

perform a single backpropagation step

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

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

forward propagation

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

returns the dimensions of the weights

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, ) -> Zip<AxisIterMut<'_, A, D::Smaller>, IterMut<'_, A, D::Smaller>>

where D: RemoveAxis, S: DataMut,

a mutable iterator of the parameters

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

where S: Data,

returns an iterator over the bias

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

where A: Clone, S: DataOwned, Sh: ShapeBuilder, Sh::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 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

impl<A, S> ParamsBase<S, Ix1>

where S: RawData<Elem = A>,

pub fn from_scalar_bias(bias: A, weights: ArrayBase<S, Ix1>) -> Self

where A: Clone, S: DataOwned,

pub fn nrows(&self) -> usize

impl<A, S> ParamsBase<S, Ix2>

where S: RawData<Elem = A>,

pub fn ncols(&self) -> usize

pub fn nrows(&self) -> usize

impl<A, S, D> ParamsBase<S, D>

where A: Float + FromPrimitive + ScalarOperand, D: Dimension, S: RawData<Elem = A>,

pub fn init_rand<G, Dst, Sh>(shape: Sh, distr: G) -> Self

where D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>, Dst: Clone + Distribution<A>, G: Fn(&Sh) -> Dst,

impl<A, S, D> ParamsBase<S, D>

where A: ScalarOperand + Float + FromPrimitive, D: Dimension, S: Data<Elem = A>,

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>

where S: DataMut, Self: 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>

where S: DataMut, Self: 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>

where S: DataMut, Self: 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>

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

Trait Implementations

impl<A, S, T, D> ApplyGradient<ParamsBase<T, D>, A> for ParamsBase<S, D>

where A: Float + FromPrimitive + ScalarOperand, S: DataMut<Elem = A>, T: Data<Elem = A>, D: Dimension,

type Output = ()

fn apply_gradient( &mut self, grad: &ParamsBase<T, D>, lr: A, ) -> Result<Self::Output>

fn apply_gradient_with_decay( &mut self, grad: &ParamsBase<T, D>, lr: A, decay: A, ) -> Result<Self::Output>

impl<A, S, T, D> ApplyGradientExt<ParamsBase<T, D>, A> for ParamsBase<S, D>

where A: Float + FromPrimitive + ScalarOperand, S: DataMut<Elem = A>, T: Data<Elem = A>, D: Dimension,

type Velocity = ParamsBase<OwnedRepr<A>, D>

fn apply_gradient_with_momentum( &mut self, grad: &ParamsBase<T, D>, lr: A, momentum: A, velocity: &mut Self::Velocity, ) -> Result<()>

fn apply_gradient_with_decay_and_momentum( &mut self, grad: &ParamsBase<T, D>, lr: A, decay: A, momentum: A, velocity: &mut Self::Velocity, ) -> Result<()>

impl<S, D> Biased<S, D> for ParamsBase<S, D>

where S: RawData, D: Dimension,

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

returns the bias of the model

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

returns a mutable reference to the bias of the model

fn assign_bias(&mut self, bias: &ArrayBase<S, D::Smaller>) -> &mut Self

where S: DataMut, S::Elem: Clone,

assigns the given bias to the current bias

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

replaces the current bias with the given bias

fn set_bias(&mut self, bias: ArrayBase<S, D::Smaller>) -> &mut Self

sets the bias of the model

fn iter_bias<'a>(&'a self) -> Iter<'a, S::Elem, D::Smaller>

where S: Data + 'a, D: 'a,

returns an iterator over the bias

fn iter_bias_mut<'a>(&'a mut self) -> IterMut<'a, S::Elem, D::Smaller>

where S: DataMut + 'a, D: 'a,

returns a mutable iterator over the bias

impl<A, S, D> Clone for ParamsBase<S, D>

where D: Dimension, S: RawDataClone<Elem = A>, A: Clone,

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<A, S, D> Debug for ParamsBase<S, D>

where D: Dimension, S: Data<Elem = A>, A: Debug,

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

Formats the value using the given formatter.

impl<A, X, Y, Z, S, D> Forward<X> for ParamsBase<S, D>

where A: Clone, D: Dimension, S: Data<Elem = A>, for<'a> X: Dot<ArrayBase<S, D>, Output = Y>, Y: for<'a> Add<&'a ArrayBase<S, D::Smaller>, Output = Z>,

type Output = Z

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

a single forward step

fn forward_then<F>(&self, input: &Rhs, then: F) -> Result<Self::Output>

where F: FnOnce(Self::Output) -> Self::Output,

this method enables the forward pass to be generically activated using some closure. This is useful for isolating the logic of the forward pass from that of the activation function and is often used by layers and models.

impl<A, S, D> Initialize<S, D> for ParamsBase<S, D>

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

fn rand<Sh, Ds>(shape: Sh, distr: Ds) -> Self

where Ds: Distribution<A>, Sh: ShapeBuilder<Dim = D>, S: DataOwned,

fn rand_with<Sh, Ds, R>(shape: Sh, distr: Ds, rng: &mut R) -> Self

where R: Rng + ?Sized, Ds: Distribution<A>, Sh: ShapeBuilder<Dim = D>, S: DataOwned,

fn bernoulli<Sh>(shape: Sh, p: f64) -> Result<Self, BernoulliError>

where Bernoulli: Distribution<S::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

fn glorot_normal<Sh: ShapeBuilder<Dim = D>>(shape: Sh) -> Self

where StandardNormal: Distribution<S::Elem>, S: DataOwned, S::Elem: Float + FromPrimitive,

Initialize the object according to the Glorot Initialization scheme.

fn glorot_uniform<Sh>(shape: Sh) -> UniformResult<Self>

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, S::Elem: Float + FromPrimitive + SampleUniform, <S::Elem as SampleUniform>::Sampler: Clone,

Initialize the object according to the Glorot Initialization scheme.

fn lecun_normal<Sh>(shape: Sh) -> Self

where StandardNormal: Distribution<S::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>, S::Elem: Float,

Initialize the object according to the Lecun Initialization scheme. LecunNormal distributions are truncated Normal distributions centered at 0 with a standard deviation equal to the square root of the reciprocal of the number of inputs.

fn normal<Sh>( shape: Sh, mean: S::Elem, std: S::Elem, ) -> Result<Self, NormalError>

where StandardNormal: Distribution<S::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>, S::Elem: Float,

Given a shape, mean, and standard deviation generate a new object using the Normal distribution

fn randc<Sh>(shape: Sh, re: S::Elem, im: S::Elem) -> Self

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, ComplexDistribution<S::Elem, S::Elem>: Distribution<S::Elem>,

fn stdnorm<Sh>(shape: Sh) -> Self

where StandardNormal: Distribution<S::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

Generate a random array using the StandardNormal distribution

fn stdnorm_from_seed<Sh>(shape: Sh, seed: u64) -> Self

where StandardNormal: Distribution<S::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

Generate a random array using the StandardNormal distribution with a given seed

fn truncnorm<Sh>( shape: Sh, mean: S::Elem, std: S::Elem, ) -> Result<Self, NormalError>

where StandardNormal: Distribution<S::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>, S::Elem: Float,

Initialize the object using the TruncatedNormal distribution

fn uniform<Sh>(shape: Sh, dk: S::Elem) -> UniformResult<Self>

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, S::Elem: Clone + Neg<Output = S::Elem> + SampleUniform, <S::Elem as SampleUniform>::Sampler: Clone,

initialize the object using the Uniform distribution with values bounded by +/- dk

fn uniform_from_seed<Sh>( shape: Sh, start: S::Elem, stop: S::Elem, key: u64, ) -> UniformResult<Self>

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, S::Elem: Clone + SampleUniform, <S::Elem as SampleUniform>::Sampler: Clone,

randomly initialize the object using the Uniform distribution with values between the start and stop params using some random seed.

fn uniform_along<Sh>(shape: Sh, axis: usize) -> UniformResult<Self>

where Sh: ShapeBuilder<Dim = D>, S: DataOwned, S::Elem: Float + FromPrimitive + SampleUniform, <S::Elem as SampleUniform>::Sampler: Clone,

initialize the object using the Uniform distribution with values bounded by the size of the specified axis. The values are bounded by +/- dk where dk = 1 / size(axis).

fn uniform_between<Sh>(shape: Sh, a: S::Elem, b: S::Elem) -> UniformResult<Self>

where Sh: ShapeBuilder<Dim = D>, S: DataOwned, S::Elem: Clone + SampleUniform, <S::Elem as SampleUniform>::Sampler: Clone,

initialize the object using the Uniform distribution with values between then given bounds, a and b.

impl<A, S, D> PartialEq for ParamsBase<S, D>

where D: Dimension, S: Data<Elem = A>, A: PartialEq,

fn eq(&self, other: &Self) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<S, D> Weighted<S, D> for ParamsBase<S, D>

where S: RawData, D: Dimension,

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

returns the weights of the model

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

returns a mutable reference to the weights of the model

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

where S: DataMut, S::Elem: Clone,

assigns the given bias to the current weight

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

replaces the current weights with the given weights

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

sets the weights of the model

fn iter_weights<'a>(&'a self) -> Iter<'a, S::Elem, D>

where S: Data + 'a, D: 'a,

returns an iterator over the weights

fn iter_weights_mut<'a>(&'a mut self) -> IterMut<'a, S::Elem, D>

where S: DataMut + 'a, D: 'a,

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

impl<A, S, D> Copy for ParamsBase<S, D>

where D: Dimension + Copy, <D as Dimension>::Smaller: Copy, S: RawDataClone<Elem = A> + Copy, A: Copy,

impl<A, S, D> Eq for ParamsBase<S, D>

where D: Dimension, S: Data<Elem = A>, A: Eq,