Struct Variable 

pub struct Variable { /* private fields */ }

Variable that participates in automatic differentiation

Implementations

impl Variable

pub fn new(tensor: Tensor, requires_grad: bool) -> Self

Create a new variable from a tensor

pub fn new_with_name(tensor: Tensor, requires_grad: bool, name: String) -> Self

Create a new variable with a name

pub fn from_graph(graph: GraphRef, node_id: NodeId, requires_grad: bool) -> Self

Create a new variable from an existing graph

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

Get the tensor data

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

Get the gradient

pub fn node_id(&self) -> NodeId

Get the node ID

pub fn requires_grad(&self) -> bool

Check if this variable requires gradients

pub fn graph(&self) -> GraphRef

Get the graph reference

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

Get the shape of the tensor

pub fn item(&self) -> Result<f32>

Convert to a scalar value

pub fn backward(&self) -> Result<()>

Compute backward pass for this variable

pub fn backward_with_grad(&self, grad: Tensor) -> Result<()>

Compute backward pass with custom gradient

pub fn zero_grad(&self)

Zero the gradients

pub fn detach(&self) -> Result<Variable>

Detach this variable from the computation graph

pub fn requires_grad_(&self) -> Result<Variable>

Create a copy of this variable that requires gradients

pub fn set_data(&self, tensor: Tensor) -> Result<()>

Update the value of this variable

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

Add another variable

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

Subtract another variable

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

Multiply by another variable

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

Divide by another variable

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

Matrix multiplication

pub fn neg(&self) -> Result<Variable>

Negation

pub fn square(&self) -> Result<Variable>

Square

pub fn sqrt(&self) -> Result<Variable>

Square root

pub fn log(&self) -> Result<Variable>

Natural logarithm

pub fn exp(&self) -> Result<Variable>

Exponential

pub fn sigmoid(&self) -> Result<Variable>

Sigmoid activation

pub fn tanh(&self) -> Result<Variable>

Tanh activation

pub fn relu(&self) -> Result<Variable>

ReLU activation

pub fn leaky_relu(&self, alpha: f32) -> Result<Variable>

Leaky ReLU activation

pub fn softmax(&self) -> Result<Variable>

Softmax activation

pub fn reshape(&self, shape: Vec<usize>) -> Result<Variable>

Reshape the tensor

pub fn transpose(&self, permutation: Vec<usize>) -> Result<Variable>

Transpose the tensor

pub fn sum(&self, axes: Option<Vec<usize>>) -> Result<Variable>

Sum along specified axes

pub fn mean(&self, axes: Option<Vec<usize>>) -> Result<Variable>

Mean along specified axes

pub fn max(&self, axes: Option<Vec<usize>>) -> Result<Variable>

Max along specified axes

pub fn min(&self, axes: Option<Vec<usize>>) -> Result<Variable>

Min along specified axes

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

Add a scalar

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

Subtract a scalar

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

Multiply by a scalar

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

Divide by a scalar

pub fn set_requires_grad(&mut self, requires_grad: bool)

Set whether this variable requires gradients

pub fn from_tensor(tensor: Tensor) -> Self

Create a variable from a tensor (with requires_grad = false by default)

impl Variable

Convenience functions for creating variables

pub fn scalar(value: f32, requires_grad: bool) -> Result<Self>

Create a variable from a scalar

pub fn zeros(shape: &[usize], requires_grad: bool) -> Result<Self>

Create a variable with zeros

pub fn ones(shape: &[usize], requires_grad: bool) -> Result<Self>

Create a variable with ones

pub fn randn(shape: &[usize], requires_grad: bool) -> Result<Self>

Create a variable with random normal distribution

pub fn rand(shape: &[usize], requires_grad: bool) -> Result<Self>

Create a variable with random uniform distribution

Trait Implementations

impl Add for &Variable

type Output = Result<Variable, TrustformersError>

The resulting type after applying the + operator.

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

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

Formats the value using the given formatter.

impl Div for &Variable

type Output = Result<Variable, TrustformersError>

The resulting type after applying the / operator.

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

Performs the / operation.

impl Mul for &Variable

type Output = Result<Variable, TrustformersError>

The resulting type after applying the * operator.

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

Performs the * operation.

impl Neg for &Variable

type Output = Result<Variable, TrustformersError>

The resulting type after applying the - operator.

fn neg(self) -> Self::Output

Performs the unary - operation.

impl Sub for &Variable

type Output = Result<Variable, TrustformersError>

The resulting type after applying the - operator.

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

Performs the - operation.