Struct Variable 

pub struct Variable { /* private fields */ }

A tensor with automatic differentiation support.

Variable wraps a Tensor and tracks operations performed on it to enable automatic gradient computation. When requires_grad is true, all operations are recorded in a computational graph.

Implementations

impl Variable

pub fn new(data: Tensor<f32>, requires_grad: bool) -> Variable

Creates a new variable from a tensor.

Arguments

• data - The tensor data
• requires_grad - Whether to track gradients for this variable

pub fn from_tensor(data: Tensor<f32>) -> Variable

Creates a variable that doesn’t require gradients.

pub fn data(&self) -> Tensor<f32>

Returns a reference to the underlying tensor data.

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

Returns the shape of the tensor.

pub fn ndim(&self) -> usize

Returns the number of dimensions.

pub fn numel(&self) -> usize

Returns the total number of elements.

pub fn requires_grad(&self) -> bool

Returns whether this variable requires gradients.

pub fn is_leaf(&self) -> bool

Returns whether this is a leaf variable.

pub fn grad(&self) -> Option<Tensor<f32>>

Returns the gradient of this variable.

Only available for leaf variables after backward() has been called.

pub fn grad_fn(&self) -> Option<&GradFn>

Returns the gradient function.

pub fn set_grad(&self, grad: Tensor<f32>)

Sets the gradient (used during backward pass).

pub fn accumulate_grad(&self, grad: &Tensor<f32>)

Accumulates gradient (adds to existing gradient).

pub fn zero_grad(&self)

Clears the gradient.

pub fn detach(&self) -> Variable

Detaches this variable from the computation graph.

Returns a new variable with the same data but no gradient history.

pub fn requires_grad_(self, requires_grad: bool) -> Variable

Returns a new variable with requires_grad set.

pub fn backward(&self)

Computes gradients via backpropagation.

This should only be called on scalar (single-element) tensors, typically the loss value.

pub fn add_var(&self, other: &Variable) -> Variable

Element-wise addition.

pub fn sub_var(&self, other: &Variable) -> Variable

Element-wise subtraction.

pub fn mul_var(&self, other: &Variable) -> Variable

Element-wise multiplication.

pub fn div_var(&self, other: &Variable) -> Variable

Element-wise division.

pub fn neg_var(&self) -> Variable

Negation.

pub fn matmul(&self, other: &Variable) -> Variable

Matrix multiplication.

pub fn pow(&self, exponent: f32) -> Variable

Power operation.

pub fn relu(&self) -> Variable

ReLU activation.

pub fn sigmoid(&self) -> Variable

Sigmoid activation.

pub fn tanh(&self) -> Variable

Tanh activation.

pub fn sum(&self) -> Variable

Sum all elements.

pub fn mean(&self) -> Variable

Mean of all elements.

pub fn mse_loss(&self, target: &Variable) -> Variable

Mean Squared Error loss.

pub fn binary_cross_entropy(&self, target: &Variable) -> Variable

Binary Cross Entropy loss (expects sigmoid output).

pub fn reshape(&self, shape: &[usize]) -> Variable

Reshapes the variable to a new shape.

pub fn transpose(&self, dim0: usize, dim1: usize) -> Variable

Transposes two dimensions.

pub fn slice(&self, ranges: &[Range<usize>]) -> Variable

Slices the variable along specified ranges.

pub fn expand(&self, shape: &[usize]) -> Variable

Expands the variable to a new shape (broadcast).

pub fn mul_scalar(&self, scalar: f32) -> Variable

Multiplies by a scalar.

pub fn add_scalar(&self, scalar: f32) -> Variable

Adds a scalar.

pub fn sub_scalar(&self, scalar: f32) -> Variable

Subtracts a scalar.

pub fn div_scalar(&self, scalar: f32) -> Variable

Divides by a scalar.

pub fn gelu(&self) -> Variable

GELU activation function (Gaussian Error Linear Unit).

pub fn silu(&self) -> Variable

SiLU/Swish activation function (x * sigmoid(x)).

pub fn sqrt(&self) -> Variable

Square root.

pub fn softmax(&self, dim: i32) -> Variable

Softmax along specified dimension.

pub fn log_softmax(&self, dim: i32) -> Variable

Log softmax along specified dimension.

pub fn mean_dim(&self, dim: i32, keepdim: bool) -> Variable

Mean along a dimension, optionally keeping the dimension.

pub fn var_dim(&self, dim: i32, keepdim: bool) -> Variable

Variance along a dimension, optionally keeping the dimension.

pub fn from_tensor_with_grad(data: Tensor<f32>, requires_grad: bool) -> Variable

Creates a Variable from a tensor and requires_grad flag (for weight access). This is typically used internally by Parameter types.

pub fn clone_var(&self) -> Variable

Clones the variable (alias for Clone trait).

pub fn add(&self, other: &Variable) -> Variable

Adds another variable (alias for add_var for method chaining).

pub fn sub(&self, other: &Variable) -> Variable

Subtracts another variable (alias for sub_var for method chaining).

pub fn mul(&self, other: &Variable) -> Variable

Multiplies by another variable (alias for mul_var for method chaining).

pub fn div(&self, other: &Variable) -> Variable

Divides by another variable (alias for div_var for method chaining).

Trait Implementations

impl Add for &Variable

type Output = Variable

The resulting type after applying the + operator.

fn add(self, other: &Variable) -> Variable

Performs the + operation.

impl Clone for Variable

fn clone(&self) -> Variable

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Variable

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

Formats the value using the given formatter.

impl Div for &Variable

type Output = Variable

The resulting type after applying the / operator.

fn div(self, other: &Variable) -> Variable

Performs the / operation.

impl Mul for &Variable

type Output = Variable

The resulting type after applying the * operator.

fn mul(self, other: &Variable) -> Variable

Performs the * operation.

impl Neg for &Variable

type Output = Variable

The resulting type after applying the - operator.

fn neg(self) -> Variable

Performs the unary - operation.

impl Sub for &Variable

type Output = Variable

The resulting type after applying the - operator.

fn sub(self, other: &Variable) -> Variable

Performs the - operation.