pub struct Residual<F>(pub F);
Expand description
A residual connection around F
: F(x) + x
,
as introduced in Deep Residual Learning for Image Recognition.
Generics
F
: The underlying module to do a skip connection around.
Examples
type Model = Residual<ReLU>;
let model = dev.build_module::<Model, f32>();
let x = dev.tensor([-2.0, -1.0, 0.0, 1.0, 2.0]);
let y = model.forward(x);
assert_eq!(y.array(), [-2.0, -1.0, 0.0, 2.0, 4.0]);
Tuple Fields§
§0: F
Trait Implementations§
source§impl<D: Device<E>, E: Dtype, F: BuildOnDevice<D, E>> BuildOnDevice<D, E> for Residual<F>
impl<D: Device<E>, E: Dtype, F: BuildOnDevice<D, E>> BuildOnDevice<D, E> for Residual<F>
source§impl<T: WithEmptyTape + TryAdd<T>, F: Module<T, Output = T, Error = T::Err>> Module<T> for Residual<F>
impl<T: WithEmptyTape + TryAdd<T>, F: Module<T, Output = T, Error = T::Err>> Module<T> for Residual<F>
source§impl<T: WithEmptyTape + TryAdd<T>, F: ModuleMut<T, Output = T, Error = T::Err>> ModuleMut<T> for Residual<F>
impl<T: WithEmptyTape + TryAdd<T>, F: ModuleMut<T, Output = T, Error = T::Err>> ModuleMut<T> for Residual<F>
source§impl<E: Dtype, D: Device<E>, F: TensorCollection<E, D>> TensorCollection<E, D> for Residual<F>
impl<E: Dtype, D: Device<E>, F: TensorCollection<E, D>> TensorCollection<E, D> for Residual<F>
§type To<E2: Dtype, D2: Device<E2>> = Residual<<F as TensorCollection<E, D>>::To<E2, D2>>
type To<E2: Dtype, D2: Device<E2>> = Residual<<F as TensorCollection<E, D>>::To<E2, D2>>
Type alias that specifies the how a module’s type changes when using a different dtype and/or
device.
source§fn iter_tensors<V: ModuleVisitor<Self, E, D>>(
visitor: &mut V
) -> Result<Option<Self::To<V::E2, V::D2>>, V::Err>
fn iter_tensors<V: ModuleVisitor<Self, E, D>>( visitor: &mut V ) -> Result<Option<Self::To<V::E2, V::D2>>, V::Err>
Specifies how to iterate through tensors or modules containted within this module, and how
to contruct this module given values for its fields. Returns
Err(_)
to indicate an error,
Ok(None)
to indicate that there is no error and a module has not been built, and
Ok(Some(_))
contains Self::Output<E2, D2>
source§fn module<F1, F2, Field>(
name: &str,
get_ref: F1,
get_mut: F2
) -> ModuleField<'_, F1, F2, Self, Field>where
F1: FnMut(&Self) -> &Field,
F2: FnMut(&mut Self) -> &mut Field,
Field: TensorCollection<E, D>,
fn module<F1, F2, Field>( name: &str, get_ref: F1, get_mut: F2 ) -> ModuleField<'_, F1, F2, Self, Field>where F1: FnMut(&Self) -> &Field, F2: FnMut(&mut Self) -> &mut Field, Field: TensorCollection<E, D>,
Creates a ModuleFields that represents a field that may contain one or more tensors. Read more
source§fn tensor<F1, F2, S>(
name: &str,
get_ref: F1,
get_mut: F2,
options: TensorOptions<S, E, D>
) -> TensorField<'_, F1, F2, Self, S, E, D>where
F1: FnMut(&Self) -> &Tensor<S, E, D>,
F2: FnMut(&mut Self) -> &mut Tensor<S, E, D>,
S: Shape,
fn tensor<F1, F2, S>( name: &str, get_ref: F1, get_mut: F2, options: TensorOptions<S, E, D> ) -> TensorField<'_, F1, F2, Self, S, E, D>where F1: FnMut(&Self) -> &Tensor<S, E, D>, F2: FnMut(&mut Self) -> &mut Tensor<S, E, D>, S: Shape,
Creates a ModuleFields that represents a tensor field. Read more
source§fn scalar<F1, F2, N>(
name: &str,
get_ref: F1,
get_mut: F2,
options: ScalarOptions<N>
) -> ScalarField<'_, F1, F2, Self, N>where
F1: FnMut(&Self) -> &N,
F2: FnMut(&mut Self) -> &mut N,
N: NumCast,
fn scalar<F1, F2, N>( name: &str, get_ref: F1, get_mut: F2, options: ScalarOptions<N> ) -> ScalarField<'_, F1, F2, Self, N>where F1: FnMut(&Self) -> &N, F2: FnMut(&mut Self) -> &mut N, N: NumCast,
Creates a ModuleFields that represents a scalar field. Read more
Auto Trait Implementations§
impl<F> RefUnwindSafe for Residual<F>where F: RefUnwindSafe,
impl<F> Send for Residual<F>where F: Send,
impl<F> Sync for Residual<F>where F: Sync,
impl<F> Unpin for Residual<F>where F: Unpin,
impl<F> UnwindSafe for Residual<F>where F: UnwindSafe,
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<D, E, M> BuildModule<D, E> for Mwhere
D: Device<E>,
E: Dtype,
M: TensorCollection<E, D, To<E, D> = M>,
impl<D, E, M> BuildModule<D, E> for Mwhere D: Device<E>, E: Dtype, M: TensorCollection<E, D, To<E, D> = M>,
source§impl<E, D, T> LoadFromNpz<E, D> for Twhere
E: Dtype + NumpyDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> LoadFromNpz<E, D> for Twhere E: Dtype + NumpyDtype, D: Device<E>, T: TensorCollection<E, D>,
source§impl<E, D, T> LoadFromSafetensors<E, D> for Twhere
E: Dtype + SafeDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> LoadFromSafetensors<E, D> for Twhere E: Dtype + SafeDtype, D: Device<E>, T: TensorCollection<E, D>,
source§impl<E, D, M> NumParams<E, D> for Mwhere
E: Dtype,
D: Device<E>,
M: TensorCollection<E, D>,
impl<E, D, M> NumParams<E, D> for Mwhere E: Dtype, D: Device<E>, M: TensorCollection<E, D>,
source§fn num_trainable_params(&self) -> usize
fn num_trainable_params(&self) -> usize
Returns the number of trainable params in any model.
§impl<T> Pointable for T
impl<T> Pointable for T
source§impl<E, D, M> ResetParams<E, D> for Mwhere
E: Dtype,
D: Device<E>,
M: TensorCollection<E, D>,
impl<E, D, M> ResetParams<E, D> for Mwhere E: Dtype, D: Device<E>, M: TensorCollection<E, D>,
source§fn reset_params(&mut self)
fn reset_params(&mut self)
Reset all a model’s parameters.
source§impl<E, D, T> SaveToNpz<E, D> for Twhere
E: Dtype + NumpyDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> SaveToNpz<E, D> for Twhere E: Dtype + NumpyDtype, D: Device<E>, T: TensorCollection<E, D>,
source§impl<E, D, T> SaveToSafetensors<E, D> for Twhere
E: Dtype + SafeDtype,
D: Device<E>,
T: TensorCollection<E, D>,
impl<E, D, T> SaveToSafetensors<E, D> for Twhere E: Dtype + SafeDtype, D: Device<E>, T: TensorCollection<E, D>,
source§fn save_safetensors<P: AsRef<Path>>(
&self,
path: P
) -> Result<(), SafeTensorError>
fn save_safetensors<P: AsRef<Path>>( &self, path: P ) -> Result<(), SafeTensorError>
source§impl<E, D1, D2, T> ToDevice<E, D1, D2> for Twhere
E: Dtype,
D1: Device<E>,
D2: Device<E>,
T: TensorCollection<E, D1>,
impl<E, D1, D2, T> ToDevice<E, D1, D2> for Twhere E: Dtype, D1: Device<E>, D2: Device<E>, T: TensorCollection<E, D1>,
source§impl<E1, D, T> ToDtype<E1, D> for Twhere
E1: Dtype,
D: Device<E1>,
T: TensorCollection<E1, D>,
impl<E1, D, T> ToDtype<E1, D> for Twhere E1: Dtype, D: Device<E1>, T: TensorCollection<E1, D>,
source§impl<E, D, M> ZeroGrads<E, D> for Mwhere
E: Dtype,
D: Device<E>,
M: TensorCollection<E, D>,
impl<E, D, M> ZeroGrads<E, D> for Mwhere E: Dtype, D: Device<E>, M: TensorCollection<E, D>,
source§fn alloc_grads(&self) -> Gradients<E, D>
fn alloc_grads(&self) -> Gradients<E, D>
Allocates gradients for this tensor collection. This marks all other
gradients as temporary, so they are dropped after .backward()
source§fn try_alloc_grads(&self) -> Result<Gradients<E, D>, D::Err>
fn try_alloc_grads(&self) -> Result<Gradients<E, D>, D::Err>
Allocates gradients for this tensor collection. This marks all other
gradients as temporary, so they are dropped after .backward()
source§fn zero_grads(&self, gradients: &mut Gradients<E, D>)
fn zero_grads(&self, gradients: &mut Gradients<E, D>)
Zero’s any gradients associated with
self
.