Struct TensorOpsContext 

pub struct TensorOpsContext { /* private fields */ }

Tensor operations context

Implementations

impl TensorOpsContext

pub fn new(config: TensorConfig) -> Self

Create new tensor operations context

pub fn from_array<D: Dimension>( &mut self, array: &Array<Float, D>, ) -> Result<Tensor>

Create tensor from ndarray

pub fn full(&mut self, shape: &[usize], value: Float) -> Result<Tensor>

Create tensor with specific shape and fill value

pub fn zeros(&mut self, shape: &[usize]) -> Result<Tensor>

Create zero tensor

pub fn ones(&mut self, shape: &[usize]) -> Result<Tensor>

Create ones tensor

pub fn randn(&mut self, shape: &[usize]) -> Result<Tensor>

Create random tensor

pub fn add(&mut self, a: &Tensor, b: &Tensor) -> Result<Tensor>

Element-wise addition

pub fn sub(&mut self, a: &Tensor, b: &Tensor) -> Result<Tensor>

Element-wise subtraction

pub fn mul(&mut self, a: &Tensor, b: &Tensor) -> Result<Tensor>

Element-wise multiplication

pub fn matmul(&mut self, a: &Tensor, b: &Tensor) -> Result<Tensor>

Matrix multiplication

pub fn activation( &mut self, tensor: &Tensor, activation: ActivationType, ) -> Result<Tensor>

Apply activation function

pub fn reduce( &mut self, tensor: &Tensor, reduction: ReductionType, axis: Option<usize>, ) -> Result<Tensor>

Reduction operations

pub fn reshape( &mut self, tensor: &Tensor, new_shape: &[usize], ) -> Result<Tensor>

Reshape tensor

pub fn transpose(&mut self, tensor: &Tensor, axes: &[usize]) -> Result<Tensor>

Transpose tensor

pub fn concat(&mut self, tensors: &[&Tensor], axis: usize) -> Result<Tensor>

Concatenate tensors along axis

pub fn ensemble_aggregate( &mut self, predictions: &[&Tensor], weights: Option<&Tensor>, aggregation: AggregationType, ) -> Result<Tensor>

Ensemble-specific operations

pub fn batch_ensemble_forward( &mut self, inputs: &[&Tensor], models: &[&Tensor], ) -> Result<Vec<Tensor>>

Batch operations for ensemble training

pub fn backward(&mut self, loss: &Tensor) -> Result<HashMap<String, Tensor>>

Backward pass for gradient computation

pub fn get_computation_graph(&self) -> &ComputationGraph

Get computation graph

pub fn clear_graph(&mut self)

Clear computation graph