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 where the bias tensor is always n-1 dimensions smaller than the weights tensor. Consequently, this constrains the ParamsBase implementation to only support dimensions that can be reduced by one axis (i.e. $\mbox{rank}(D)>0$), which is typically the “zero-th” axis.

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 init_from_fn<Sh, F>(shape: Sh, init: F) -> Self

where A: Clone, D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>, F: Fn() -> A,

returns a new instance of the ParamsBase using the initialization routine

pub fn from_shape_fn<Sh, F>(shape: Sh, f: F) -> Self

where A: Clone, D: RemoveAxis, S: DataOwned, Sh: ShapeBuilder<Dim = D>, D::Smaller: Dimension + ShapeArg, F: Fn(<D::Smaller as Dimension>::Pattern) -> A + Fn(<D as Dimension>::Pattern) -> A,

returns a new instance of the ParamsBase initialized use the given shape_function

pub fn from_bias<Sh>(shape: Sh, bias: ArrayBase<S, D::Smaller>) -> Self

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

create a new instance of the ParamsBase with the given bias used the default weights

pub fn from_weights<Sh>(shape: Sh, weights: ArrayBase<S, D>) -> Self

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

create a new instance of the ParamsBase with the given weights used the default bias

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

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

create a new instance of the ParamsBase 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 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 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

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,

returns a new instance of the ParamsBase initialized using a scalar bias along with the given, one-dimensional weight tensor.

pub fn nrows(&self) -> usize

returns the number of rows in the weights matrix

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

where S: RawData<Elem = A>,

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

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,

generates a randomly initialized set of parameters with the given shape using the output of the given distribution function G

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

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

Here, we implement various iterators for the parameters and its constituents. The core iterators are:

• immutable and mutable iterators over each parameter (weights and bias) respectively;
• an iterator over the parameters, which zips together an axis iterator over the columns of the weights and an iterator over the bias;

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::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

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>,

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

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

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

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

returns a new instance of the ParamsBase with the given shape and values initialized according to the provided random distribution distr.

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.

const 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, A, S, D> Deserialize<'a> for ParamsBase<S, D>

where D: Dimension + Deserialize<'a>, S: DataOwned<Elem = A>, A: Deserialize<'a>, <D as Dimension>::Smaller: Deserialize<'a>,

fn deserialize<De>(deserializer: De) -> Result<Self, De::Error>

where De: Deserializer<'a>,

Deserialize this value from the given Serde deserializer.

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

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

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

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

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

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

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

Initialize the object according to the Glorot Initialization scheme.

fn glorot_uniform<Sh>(shape: Sh) -> Result<Self, InitError>

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, <S as RawData>::Elem: Float + FromPrimitive + SampleUniform, <<S as RawData>::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 as RawData>::Elem>, S: DataOwned, Sh: ShapeBuilder<Dim = D>, <S as RawData>::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 as RawData>::Elem, std: <S as RawData>::Elem, ) -> Result<Self, Error>

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

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

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

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

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

where StandardNormal: Distribution<<S as RawData>::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 as RawData>::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 as RawData>::Elem, std: <S as RawData>::Elem, ) -> Result<Self, InitError>

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

Initialize the object using the TruncatedNormal distribution

fn uniform<Sh>(shape: Sh, dk: <S as RawData>::Elem) -> Result<Self, InitError>

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, <S as RawData>::Elem: Clone + Neg<Output = <S as RawData>::Elem> + SampleUniform, <<S as RawData>::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 as RawData>::Elem, stop: <S as RawData>::Elem, key: u64, ) -> Result<Self, InitError>

where S: DataOwned, Sh: ShapeBuilder<Dim = D>, <S as RawData>::Elem: Clone + SampleUniform, <<S as RawData>::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) -> Result<Self, InitError>

where Sh: ShapeBuilder<Dim = D>, S: DataOwned, <S as RawData>::Elem: Float + FromPrimitive + SampleUniform, <<S as RawData>::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 as RawData>::Elem, b: <S as RawData>::Elem, ) -> Result<Self, InitError>

where Sh: ShapeBuilder<Dim = D>, S: DataOwned, <S as RawData>::Elem: Clone + SampleUniform, <<S as RawData>::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> IntoIterator for ParamsBase<S, D>

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

type Item = ParamsBase<S, D>

The type of the elements being iterated over.

type IntoIter = Once<ParamsBase<S, D>>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

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

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

fn eq(&self, other: &&ParamsBase<S, D>) -> bool

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

const 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<A, S, D> PartialEq<&mut ParamsBase<S, D>> for ParamsBase<S, D>

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

fn eq(&self, other: &&mut ParamsBase<S, D>) -> bool

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

const 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<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 ==.

const 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<A, S, D> Serialize for ParamsBase<S, D>

where A: Serialize, D: Dimension + Serialize, S: Data<Elem = A>, <D as Dimension>::Smaller: Serialize,

fn serialize<Ser>(&self, serializer: Ser) -> Result<Ser::Ok, Ser::Error>

where Ser: Serializer,

Serialize this value into the given Serde serializer.

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,