pub enum LayerType {
Dense,
Convolutional,
Recurrent,
Attention,
}Expand description
Neural layer types
Variants§
Dense
Dense/fully connected layer
Convolutional
Convolutional layer
Recurrent
Recurrent layer
Attention
Attention layer
Trait Implementations§
Auto Trait Implementations§
impl Freeze for LayerType
impl RefUnwindSafe for LayerType
impl Send for LayerType
impl Sync for LayerType
impl Unpin for LayerType
impl UnwindSafe for LayerType
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
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fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
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T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
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impl<T> IntoEither for T
Source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
Converts
self into a Left variant of Either<Self, Self>
if into_left is true.
Converts self into a Right variant of Either<Self, Self>
otherwise. Read moreSource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
Converts
self into a Left variant of Either<Self, Self>
if into_left(&self) returns true.
Converts self into a Right variant of Either<Self, Self>
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impl<T> Pointable for T
Source§impl<SS, SP> SupersetOf<SS> for SPwhere
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impl<SS, SP> SupersetOf<SS> for SPwhere
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fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self from the equivalent element of its
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fn is_in_subset(&self) -> bool
Checks if
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fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset but without any property checks. Always succeeds.Source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self to the equivalent element of its superset.