Struct Tensor 

pub struct Tensor {
    pub storage: Storage,
    /* private fields */
}

Fields

storage: Storage

Implementations

impl Tensor

pub fn new(storage: Storage, device: Device, requires_grad: bool) -> Self

pub fn view(&self, tensor: Storage) -> Self

pub fn tensor(&self) -> &Storage

pub fn tensor_mut(&mut self) -> &mut Storage

pub fn device(&self) -> Device

pub fn requires_grad(&self) -> &bool

pub fn grad_fn(&self) -> Option<Rc<dyn GradientFunction>>

pub fn set_grad_fn(&mut self, grad_fn: Option<Rc<dyn GradientFunction>>)

pub fn grad(&self) -> Option<GradientStorage>

pub fn grad_mut(&mut self) -> GradientStorage

pub fn shape(&self) -> &Vec<usize>

pub fn backward(&mut self)

impl Tensor

pub fn zeros( shape: Vec<usize>, device: Device, requires_grad: Option<bool>, ) -> Self

pub fn ones( shape: Vec<usize>, device: Device, requires_grad: Option<bool>, ) -> Self

pub fn from_ndarray<S, D, T>( data: &ArrayBase<S, D>, device: Device, requires_grad: Option<bool>, ) -> Self

where S: Data<Elem = T>, T: AsPrimitive<f32>, D: Dimension,

pub fn uniform( l_bound: f32, r_bound: f32, shape: Vec<usize>, device: Device, requires_grad: Option<bool>, ) -> Self

Trait Implementations

impl ActivationOps for Tensor

fn binary_step(&self) -> Self

fn sigmoid(&self) -> Self

fn tanh(&self) -> Self

fn relu(&self) -> Self

fn leaky_relu(&self) -> Self

fn parametric_relu(&self, a: f32) -> Self

fn elu(&self, alpha: f32) -> Self

fn softmax(&self, dim: usize) -> Self

fn swish(&self) -> Self

impl Add<&Tensor> for &Tensor

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: &Tensor) -> Self::Output

Performs the + operation.

impl Add<f32> for &Tensor

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: f32) -> Self::Output

Performs the + operation.

impl AddAssign<&Tensor> for Tensor

fn add_assign(&mut self, rhs: &Tensor)

Performs the += operation.

impl AddAssign<f32> for Tensor

fn add_assign(&mut self, rhs: f32)

Performs the += operation.

impl ArithmeticOps for Tensor

fn add_tensor(&self, other: &Self) -> Self

fn sub_tensor(&self, other: &Self) -> Self

fn mul_tensor(&self, other: &Self) -> Self

fn div_tensor(&self, other: &Self) -> Self

fn pow_f32(&self, other: f32) -> Self

fn add_f32(&self, other: f32) -> Self

fn sub_f32(&self, other: f32) -> Self

fn mul_f32(&self, other: f32) -> Self

fn div_f32(&self, other: f32) -> Self

fn abs(&self) -> Self

fn add_tensor_assign(&mut self, other: &Self)

fn sub_tensor_assign(&mut self, other: &Self)

fn mul_tensor_assign(&mut self, other: &Self)

fn div_tensor_assign(&mut self, other: &Self)

fn add_f32_assign(&mut self, other: f32)

fn sub_f32_assign(&mut self, other: f32)

fn mul_f32_assign(&mut self, other: f32)

fn div_f32_assign(&mut self, other: f32)

fn pow_f32_assign(&mut self, other: f32)

fn abs_assign(&mut self)

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

fn greater_than_f32(&self, other: f32, make_binary: bool) -> Self

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

fn less_than_f32(&self, other: f32, make_binary: bool) -> Self

fn sign(&self) -> Self

impl BlasOps for Tensor

fn matmul(&self, other: &Self, trans_a: bool, trans_b: bool) -> Self

impl Clone for Tensor

fn clone(&self) -> Tensor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Tensor

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

Formats the value using the given formatter.

impl Display for Tensor

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

Formats the value using the given formatter.

impl Display for Tensor

fn print(&self)

fn print_data_recursive<'a>( data: &'a [f32], shape: &'a [usize], stride: &'a [usize], ) -> String

fn print_data(&self)

impl Div<&Tensor> for &Tensor

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: &Tensor) -> Self::Output

Performs the / operation.

impl Div<f32> for &Tensor

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: f32) -> Self::Output

Performs the / operation.

impl DivAssign<&Tensor> for Tensor

fn div_assign(&mut self, rhs: &Tensor)

Performs the /= operation.

impl DivAssign<f32> for Tensor

fn div_assign(&mut self, rhs: f32)

Performs the /= operation.

impl Mul<&Tensor> for &Tensor

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: &Tensor) -> Self::Output

Performs the * operation.

impl Mul<f32> for &Tensor

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: f32) -> Self::Output

Performs the * operation.

impl MulAssign<&Tensor> for Tensor

fn mul_assign(&mut self, rhs: &Tensor)

Performs the *= operation.

impl MulAssign<f32> for Tensor

fn mul_assign(&mut self, rhs: f32)

Performs the *= operation.

impl ReductionOps for Tensor

fn sum(&self) -> Self

fn sum_axis(&self, axis: usize) -> Self

fn mean(&self) -> Self

fn product(&self) -> Self

impl Sub<&Tensor> for &Tensor

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: &Tensor) -> Self::Output

Performs the - operation.

impl Sub<f32> for &Tensor

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: f32) -> Self::Output

Performs the - operation.

impl SubAssign<&Tensor> for Tensor

fn sub_assign(&mut self, rhs: &Tensor)

Performs the -= operation.

impl SubAssign<f32> for Tensor

fn sub_assign(&mut self, rhs: f32)

Performs the -= operation.

impl TransformOps for Tensor

fn apply<F>(&self, op: F) -> Self

where F: Fn(f32) -> f32,

fn apply_assign<F>(&mut self, op: F)

where F: Fn(f32) -> f32,

fn elementwise_op<F>(&self, other: &Self, op: F) -> Self

where F: Fn(f32, f32) -> f32,

fn scalar_op<F>(&self, scalar: f32, op: F) -> Self

where F: Fn(f32, f32) -> f32,

fn elementwise_op_assign<F>(&mut self, other: &Self, op: F)

where F: Fn(f32, f32) -> f32,

fn scalar_op_assign<F>(&mut self, scalar: f32, op: F)

where F: Fn(f32, f32) -> f32,

fn sum_dim(&self, dims: &[bool]) -> Self

fn reshape(&mut self, new_shape: Vec<usize>)

fn transpose(&self) -> Self

fn permute(&mut self, dims: &[usize])

fn flatten(&mut self)

fn squeeze(&mut self)

fn unsqueeze(&mut self, dim: usize)

fn broadcast(&self, new_shape: &[usize]) -> Self

fn compute_broadcast_shape(&self, target_shape: &[usize]) -> Vec<usize>

fn compute_broadcast_strides(&self, broadcast_shape: &[usize]) -> Vec<usize>

fn pad_shape(&self, target_rank: usize) -> Vec<usize>

fn broadcast_tensors(a: &Self, b: &Self) -> (Self, Self)

where Self: Sized,