pub struct InputData<Data, Algebra>where
Data: HasDims,{ /* private fields */ }
Expand description
A network that takes a single user data as input and returns it (after a dimension check).
Implementations§
Trait Implementations§
Source§impl<Data, Value, Dims, Algebra> Net<Algebra> for InputData<Data, Algebra>where
Algebra: HasGradientReader + CoreAlgebra<Data, Value = Value>,
Data: HasDims<Dims = Dims>,
Dims: Clone + PartialEq + Debug,
impl<Data, Value, Dims, Algebra> Net<Algebra> for InputData<Data, Algebra>where
Algebra: HasGradientReader + CoreAlgebra<Data, Value = Value>,
Data: HasDims<Dims = Dims>,
Dims: Clone + PartialEq + Debug,
Source§type GradientInfo = ()
type GradientInfo = ()
How to read the gradients of the weights after a backward pass.
fn eval_with_gradient_info( &self, graph: &mut Algebra, input: Self::Input, ) -> Result<(Self::Output, Self::GradientInfo)>
fn get_weights(&self) -> Self::Weights
fn set_weights(&mut self, _weights: Self::Weights) -> Result<()>
fn update_weights(&mut self, _delta: Self::Weights) -> Result<()>
fn read_weight_gradients( &self, _info: Self::GradientInfo, _reader: &Algebra::GradientReader, ) -> Result<Self::Weights>
fn eval(&self, graph: &mut Algebra, input: Self::Input) -> Result<Self::Output>
fn map<F, O>(self, f: F) -> Map<Self, F>
fn using<N>(self, net: N) -> Using<Self, N>
fn then<N>(self, net: N) -> Then<Self, N>
fn and<N>(self, net: N) -> (Self, N)
Auto Trait Implementations§
impl<Data, Algebra> Freeze for InputData<Data, Algebra>
impl<Data, Algebra> RefUnwindSafe for InputData<Data, Algebra>
impl<Data, Algebra> Send for InputData<Data, Algebra>
impl<Data, Algebra> Sync for InputData<Data, Algebra>
impl<Data, Algebra> Unpin for InputData<Data, Algebra>
impl<Data, Algebra> UnwindSafe for InputData<Data, Algebra>
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
Source§impl<T> CloneToUninit for Twhere
T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
Source§impl<Data, Algebra, N> SingleOutputNet<Data, Algebra> for Nwhere
N: Net<Algebra>,
Algebra: HasGradientReader + CoreAlgebra<Data, Value = <N as Net<Algebra>>::Output>,
impl<Data, Algebra, N> SingleOutputNet<Data, Algebra> for Nwhere
N: Net<Algebra>,
Algebra: HasGradientReader + CoreAlgebra<Data, Value = <N as Net<Algebra>>::Output>,
Source§fn add_square_loss(self) -> SquareLoss<Self, Data>where
Self: Sized,
fn add_square_loss(self) -> SquareLoss<Self, Data>where
Self: Sized,
A network that takes an additional input and returns the L2-distance with
the output of the initial network.