Struct dfdx::tensor::Tensor3D

pub struct Tensor3D<const M: usize, const N: usize, const O: usize, TapeHolder = NoTape> { /* private fields */ }

A 3d [Tensor] with shape (M, N, O). Backed by data [[[f32; O]; N]; M].

Implementations

impl<const M: usize, const N: usize, const O: usize> Tensor3D<M, N, O, WithTape>

pub fn backward(self) -> Gradients

Calls backward on self

impl<const M: usize, const N: usize, const O: usize> Tensor3D<M, N, O, NoTape>

pub fn trace(&self) -> Tensor3D<M, N, O, WithTape>

Clones self and returns a copy with WithTape as the TapeHolder.

See traced for a version that takes ownership of the tensor.

pub fn traced(self) -> Tensor3D<M, N, O, WithTape>

Takes ownership of self and inserts WithTape as the TapeHolder.

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn relu(self) -> Self

Calls [$func_name] on self

pub fn sin(self) -> Self

Calls [$func_name] on self

pub fn cos(self) -> Self

Calls [$func_name] on self

pub fn ln(self) -> Self

Calls [$func_name] on self

pub fn exp(self) -> Self

Calls [$func_name] on self

pub fn sigmoid(self) -> Self

Calls [$func_name] on self

pub fn tanh(self) -> Self

Calls [$func_name] on self

pub fn square(self) -> Self

Calls [$func_name] on self

pub fn sqrt(self) -> Self

Calls [$func_name] on self

pub fn abs(self) -> Self

Calls [$func_name] on self

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn clamp(self, min: f32, max: f32) -> Self

Calls clamp on self

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn value_mask(self, mask: &Tensor3D<M, N, O, NoTape>, value: f32) -> Self

Calls value_mask on self

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn mean(self) -> Tensor0D<<Self as Tensor>::TapeHolder>

Calls mean() on self.

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn nans_to(self, value: f32) -> Self

Calls nans_to on self.

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn logsumexp(self) -> <Self as Tensor>::LastDimReduced

Calls logsumexp on self.

pub fn log_softmax(self) -> Self

Calls log_softmax on self

pub fn softmax(self) -> Self

Calls softmax on self

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor3D<M, N, O, H>

pub fn sum_last_dim(self) -> <Self as Tensor>::LastDimReduced

Calls sum_last_dim() on self.

Trait Implementations

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Add<Tensor3D<M, N, O, H>> for &Tensor3D<M, N, O, NoTape>

fn add(self, rhs: Tensor3D<M, N, O, H>) -> Self::Output

Calls add() - implements &T<NoTape> + T<H>

type Output = Tensor3D<M, N, O, H>

The resulting type after applying the + operator.

impl<const M: usize, const N: usize, const O: usize, HIn, HOut> CanPutTapeHolder<HOut> for Tensor3D<M, N, O, HIn> where
    HIn: TapeHolder,
    HOut: TapeHolder, 

type Output = Tensor3D<M, N, O, HOut>

fn with_tape_holder(self, tape: HOut) -> Self::Output

impl<const M: usize, const N: usize, const O: usize, TapeHolder: Clone> Clone for Tensor3D<M, N, O, TapeHolder>

fn clone(&self) -> Tensor3D<M, N, O, TapeHolder>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<const M: usize, const N: usize, const O: usize, TapeHolder: Debug> Debug for Tensor3D<M, N, O, TapeHolder>

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

Formats the value using the given formatter.

impl<const M: usize, const N: usize, const O: usize> Default for Tensor3D<M, N, O, NoTape>

fn default() -> Self

Returns the “default value” for a type.

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Div<Tensor3D<M, N, O, H>> for &Tensor3D<M, N, O, NoTape>

fn div(self, rhs: Tensor3D<M, N, O, H>) -> Self::Output

Calls div() - implements &T<NoTape> / T<H>

type Output = Tensor3D<M, N, O, H>

The resulting type after applying the / operator.

impl<const M: usize, const N: usize, const O: usize, H> HasArrayData for Tensor3D<M, N, O, H>

fn data(&self) -> &Self::Array

fn mut_data(&mut self) -> &mut Self::Array

impl<const M: usize, const N: usize, const O: usize, H> HasArrayType for Tensor3D<M, N, O, H>

type Dtype = f32

type Array = [[[f32; O]; N]; M]

impl<const M: usize, const N: usize, const O: usize, H> HasDevice for Tensor3D<M, N, O, H>

type Device = Cpu

impl<const M: usize, const N: usize, const O: usize, H> HasUniqueId for Tensor3D<M, N, O, H>

fn id(&self) -> &UniqueId

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Mul<Tensor3D<M, N, O, H>> for &Tensor3D<M, N, O, NoTape>

fn mul(self, rhs: Tensor3D<M, N, O, H>) -> Self::Output

Calls mul() - implements &T<NoTape> * T<H>

type Output = Tensor3D<M, N, O, H>

The resulting type after applying the * operator.

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Neg for Tensor3D<M, N, O, H>

fn neg(self) -> Self::Output

Calls negate on self.

type Output = Tensor3D<M, N, O, H>

The resulting type after applying the - operator.

impl<const M: usize, const N: usize, const O: usize, H> Randomize<f32> for Tensor3D<M, N, O, H>

fn randomize<R: Rng, D: Distribution<f32>>(&mut self, rng: &mut R, dist: &D)

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Sub<Tensor3D<M, N, O, H>> for &Tensor3D<M, N, O, NoTape>

fn sub(self, rhs: Tensor3D<M, N, O, H>) -> Self::Output

Calls sub() - implements &T<NoTape> - T<H>

type Output = Tensor3D<M, N, O, H>

The resulting type after applying the - operator.

impl<const M: usize, const N: usize, const O: usize, H: TapeHolder> Tensor for Tensor3D<M, N, O, H>

type TapeHolder = H

type NoTape = Tensor3D<M, N, O, NoTape>

type WithTape = Tensor3D<M, N, O, WithTape>

type LastDimReduced = Tensor2D<M, N, H>

fn split_tape_holder(self) -> (Self::NoTape, Self::TapeHolder)

Removes whatever TapeHolder this tensor has and returns itself without a tape.

fn duplicate(&self) -> Self::NoTape

Clones the data & UniqueId of this tensor and returns something with NoTape.

impl<const M: usize, const N: usize, const O: usize> TensorCreator for Tensor3D<M, N, O, NoTape>

fn new_boxed(data: Box<Self::Array>) -> Self

Create a new tensor with a Box<Self::Array>.

fn new(data: Self::Array) -> Self

Create a new tensor with Self::Array on the stack. This just boxes Self::Array and calls TensorCreator::new_boxed.

fn zeros() -> Self

Creates a tensor filled with all 0s.

fn ones() -> Self where
    Self::Dtype: One, 

Creates a tensor filled with all 1s.

fn rand<R: Rng>(rng: &mut R) -> Self where
    Standard: Distribution<Self::Dtype>, 

Creates a tensor filled with values sampled from Standard distribution.

fn randn<R: Rng>(rng: &mut R) -> Self where
    StandardNormal: Distribution<Self::Dtype>, 

Creates a tensor filled with values sampled from StandardNormal distribution.