Trait dfdx::tensor_ops::MeanTo

pub trait MeanTo: HasErr + HasShape {
    // Required method
    fn try_mean<Dst: Shape, Ax: Axes>(
        self
    ) -> Result<Self::WithShape<Dst>, Self::Err>
       where Self::Shape: HasAxes<Ax> + ReduceShapeTo<Dst, Ax>;

    // Provided method
    fn mean<Dst: Shape, Ax: Axes>(self) -> Self::WithShape<Dst>
       where Self::Shape: HasAxes<Ax> + ReduceShapeTo<Dst, Ax> { ... }
}

Reduction along multiple axes using mean.

Required Methods

fn try_mean<Dst: Shape, Ax: Axes>( self ) -> Result<Self::WithShape<Dst>, Self::Err>where Self::Shape: HasAxes<Ax> + ReduceShapeTo<Dst, Ax>,

Fallible version of MeanTo::mean

Provided Methods

fn mean<Dst: Shape, Ax: Axes>(self) -> Self::WithShape<Dst>where Self::Shape: HasAxes<Ax> + ReduceShapeTo<Dst, Ax>,

Mean reduction. Pytorch equivalent: t.mean(Axes)

Example:

let t = dev.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]);
let r = t.mean::<Rank0, _>(); // or `mean::<_, Axes2<0, 1>>()`
assert_eq!(r.array(), 3.5);

Reducing 1 axis:

let t = dev.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]);
let r = t.mean::<Rank1<2>, _>(); // or `mean::<_, Axis<1>>()`
assert_eq!(r.array(), [2.0, 5.0]);

Implementors

impl<S: Shape, E: Dtype, D: Device<E>, T: Tape<E, D>> MeanTo for Tensor<S, E, D, T>