Struct nncombinator::cuda::mem::CachedTensor

pub struct CachedTensor<U, T>where
    U: Debug + Default,
    T: AsRawSlice<U>,{ /* private fields */ }

Object that collectively manages cuda memory paired with a value of a specified type

Implementations

impl<U, T> CachedTensor<U, T>where
    U: Debug + Default,
    T: AsRawSlice<U>,

pub fn new(
    value: T,
    memory_pool: &Arc<Mutex<MemoryPool>>
) -> Result<CachedTensor<U, T>, CudaError>

Creation of CachedTensor instance

Arguments

• value - Wrapping value
• memory_pool - Memory pool for allocating cuda memory paired with value

Errors

This function may return the following errors

• CudaError

pub fn scoped_mut<'a>(&'a mut self) -> ScopedMut<'a, U, T>

Returns the ScopedMut object associated with the value it has

Trait Implementations

impl<U, T> AsPtr<U> for CachedTensor<U, T>where
    U: Debug + Default,
    T: AsRawSlice<U>,

fn as_ptr(&self) -> *const U

impl<U, T> AsVoidPtr for CachedTensor<U, T>where
    U: Debug + Default,
    T: AsRawSlice<U>,

fn as_void_ptr(&self) -> *const c_void

impl<U, T> Deref for CachedTensor<U, T>where
    U: Debug + Default,
    T: AsRawSlice<U>,

type Target = T

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl<const NI: usize, const NO: usize> DeviceLinear<f32, CachedTensor<f32, Arr2<f32, NI, NO>>, NI, NO> for DeviceGpu<f32>

fn forward_linear(
    &self,
    bias: &Arr<f32, NO>,
    units: &CachedTensor<f32, Arr2<f32, NI, NO>>,
    input: &Arr<f32, NI>
) -> Result<Arr<f32, NO>, EvaluateError>

Forward propagation calculation

fn backward_linear(
    &self,
    units: &CachedTensor<f32, Arr2<f32, NI, NO>>,
    input: &Arr<f32, NO>
) -> Result<Arr<f32, NI>, TrainingError>

Error back propagation calculation

fn backward_weight_gradient(
    &self,
    o: &Arr<f32, NI>,
    loss: &Arr<f32, NO>
) -> Result<Arr2<f32, NI, NO>, TrainingError>

Calculate the gradient of the weights

fn batch_forward_linear(
    &self,
    bias: &Arr<f32, NO>,
    units: &CachedTensor<f32, Arr2<f32, NI, NO>>,
    input: &VecArr<f32, Arr<f32, NI>>
) -> Result<VecArr<f32, Arr<f32, NO>>, TrainingError>

Forward propagation calculation in batch

fn batch_backward_linear(
    &self,
    units: &CachedTensor<f32, Arr2<f32, NI, NO>>,
    input: &VecArr<f32, Arr<f32, NO>>
) -> Result<VecArr<f32, Arr<f32, NI>>, TrainingError>

Error back propagation in batch

fn batch_backward_weight_gradient(
    &self,
    o: &VecArr<f32, Arr<f32, NI>>,
    loss: &VecArr<f32, Arr<f32, NO>>
) -> Result<Arr2<f32, NI, NO>, TrainingError>

Calculate the gradient of the weights in batch

impl<const NI: usize, const NO: usize> DeviceLinear<f64, CachedTensor<f64, Arr2<f64, NI, NO>>, NI, NO> for DeviceGpu<f64>

fn forward_linear(
    &self,
    bias: &Arr<f64, NO>,
    units: &CachedTensor<f64, Arr2<f64, NI, NO>>,
    input: &Arr<f64, NI>
) -> Result<Arr<f64, NO>, EvaluateError>

Forward propagation calculation

fn backward_linear(
    &self,
    units: &CachedTensor<f64, Arr2<f64, NI, NO>>,
    input: &Arr<f64, NO>
) -> Result<Arr<f64, NI>, TrainingError>

Error back propagation calculation

fn backward_weight_gradient(
    &self,
    o: &Arr<f64, NI>,
    loss: &Arr<f64, NO>
) -> Result<Arr2<f64, NI, NO>, TrainingError>

Calculate the gradient of the weights

fn batch_forward_linear(
    &self,
    bias: &Arr<f64, NO>,
    units: &CachedTensor<f64, Arr2<f64, NI, NO>>,
    input: &VecArr<f64, Arr<f64, NI>>
) -> Result<VecArr<f64, Arr<f64, NO>>, TrainingError>

Forward propagation calculation in batch

fn batch_backward_linear(
    &self,
    units: &CachedTensor<f64, Arr2<f64, NI, NO>>,
    input: &VecArr<f64, Arr<f64, NO>>
) -> Result<VecArr<f64, Arr<f64, NI>>, TrainingError>

Error back propagation in batch

fn batch_backward_weight_gradient(
    &self,
    o: &VecArr<f64, Arr<f64, NI>>,
    loss: &VecArr<f64, Arr<f64, NO>>
) -> Result<Arr2<f64, NI, NO>, TrainingError>

Calculate the gradient of the weights in batch

impl<U, T> Index<(usize, usize)> for CachedTensor<U, T>where
    U: Debug + Default,
    T: Index<(usize, usize), Output = U> + AsRawSlice<U>,

type Output = U

The returned type after indexing.

fn index(&self, index: (usize, usize)) -> &Self::Output

Performs the indexing (container[index]) operation.