Struct nncombinator::activation::ReLu

source · [−]

pub struct ReLu<U, D>where
    U: UnitValue<U>,
    D: Device<U>,{ /* private fields */ }

Expand description

ReLu Implementation

Implementations

source

impl<U, D> ReLu<U, D>where
U: UnitValue,
D: Device,

source

pub fn new(_: &D) -> ReLu<U, D>

Create an instance of ReLu

Trait Implementations

source

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu> for ReLu<U, DeviceCpu>where
U: UnitValue,

source

fn apply(
 &self,
 device: &DeviceCpu,
 input: &Arr<U, N>
) -> Result<Arr<U, N>, EvaluateError>

Apply the activation function Read more

source

fn derive(
 &self,
 device: &DeviceCpu,
 o: &Arr<U, N>,
 loss: &Arr<U, N>,
 u: &Arr<U, N>
) -> Result<Arr<U, N>, TrainingError>

Apply derivatives of the activation function Read more

source

fn is_canonical_link<L: LossFunction>(&self, _: &L) -> bool

Returns whether or not the canonical linkage function can be used. Read more

source

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for ReLu<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 ReLuForward: Kernel<Args = ActivationForwardArgs>,
 ReLuBackward: Kernel<Args = ActivationBackwardArgs>,

source

fn apply(
 &self,
 _: &DeviceGpu,
 input: &Arr<U, N>
) -> Result<Arr<U, N>, EvaluateError>

Apply the activation function Read more

source

fn derive(
 &self,
 _: &DeviceGpu,
 _: &Arr<U, N>,
 loss: &Arr<U, N>,
 u: &Arr<U, N>
) -> Result<Arr<U, N>, TrainingError>

Apply derivatives of the activation function Read more

source

fn is_canonical_link<L: LossFunction>(&self, _: &L) -> bool

Returns whether or not the canonical linkage function can be used. Read more

source

impl<U, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu> for ReLu<U, DeviceCpu>where
U: UnitValue,
I: Iterator<Item = U> + Clone,

source

fn apply(&self, _: &DeviceCpu, input: &I) -> Result<Arr<U, N>, EvaluateError>

Apply the activation function Read more

source

fn derive(
 &self,
 _: &DeviceCpu,
 _: &I,
 loss: &I,
 u: &I
) -> Result<Arr<U, N>, TrainingError>

Apply derivatives of the activation function Read more

source

fn is_canonical_link<L: LossFunction>(&self, _: &L) -> bool

Returns whether or not the canonical linkage function can be used. Read more

source

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceCpu> for ReLu<U, DeviceCpu>where
U: UnitValue,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

source

fn batch_apply(
 &self,
 device: &DeviceCpu,
 input: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

Apply the activation function Read more

source

fn batch_derive(
 &self,
 device: &DeviceCpu,
 o: &VecArr<U, Arr<U, N>>,
 loss: &VecArr<U, Arr<U, N>>,
 u: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

Apply derivatives of the activation function Read more

source

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for ReLu<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 ReLuForward: Kernel<Args = ActivationForwardArgs>,
 ReLuBackward: Kernel<Args = ActivationBackwardArgs>,

source

fn batch_apply(
 &self,
 _: &DeviceGpu,
 input: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

Apply the activation function Read more

source

fn batch_derive(
 &self,
 _: &DeviceGpu,
 _: &VecArr<U, Arr<U, N>>,
 loss: &VecArr<U, Arr<U, N>>,
 u: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

Apply derivatives of the activation function Read more

Auto Trait Implementations

impl<U, D> RefUnwindSafe for ReLu<U, D>where
D: RefUnwindSafe,
U: RefUnwindSafe,

impl<U, D> Send for ReLu<U, D>where
D: Send,

impl<U, D> Sync for ReLu<U, D>where
D: Sync,

impl<U, D> Unpin for ReLu<U, D>where
D: Unpin,
U: Unpin,

impl<U, D> UnwindSafe for ReLu<U, D>where
D: UnwindSafe,
U: UnwindSafe,

Blanket Implementations

source

impl<T> Any for Twhere
T: 'static + ?Sized,

source

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more

source

impl<T> Borrow<T> for Twhere
T: ?Sized,

const: unstable · source

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more

source

impl<T> BorrowMut<T> for Twhere
T: ?Sized,

const: unstable · source

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more

source

impl<T> From<T> for T

const: unstable · source

fn from(t: T) -> T

Returns the argument unchanged.

source

impl<T, U> Into for Twhere
U: From<T>,

const: unstable · source

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

impl<T> Pointable for T

const ALIGN: usize = mem::align_of::<T>()

The alignment of pointer.

type Init = T

The type for initializers.

unsafe fn init(init: <T as Pointable>::Init) -> usize

Initializes a with the given initializer. Read more

unsafe fn deref<'a>(ptr: usize) -> &'a T

Dereferences the given pointer. Read more

unsafe fn deref_mut<'a>(ptr: usize) -> &'a mut T

Mutably dereferences the given pointer. Read more

unsafe fn drop(ptr: usize)

Drops the object pointed to by the given pointer. Read more

source

impl<T, U> TryFrom for Twhere
U: Into<T>,

type Error = Infallible

The type returned in the event of a conversion error.

const: unstable · source

fn try_from(value: U) -> Result<T, <T as TryFrom>::Error>

Performs the conversion.

source

impl<T, U> TryInto for Twhere
U: TryFrom<T>,

type Error = >::Error

The type returned in the event of a conversion error.

const: unstable · source

fn try_into(self) -> Result<U, >::Error>

Performs the conversion.

Struct nncombinator::activation::ReLu

Implementations

impl<U, D> ReLu<U, D>where U: UnitValue<U>, D: Device<U>,

pub fn new(_: &D) -> ReLu<U, D>

Trait Implementations

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for ReLu<U, DeviceCpu<U>>where U: UnitValue<U>,

fn apply( &self, device: &DeviceCpu<U>, input: &Arr<U, N>) -> Result<Arr<U, N>, EvaluateError>

fn derive( &self, device: &DeviceCpu<U>, o: &Arr<U, N>, loss: &Arr<U, N>, u: &Arr<U, N>) -> Result<Arr<U, N>, TrainingError>

fn is_canonical_link<L: LossFunction<U>>(&self, _: &L) -> bool

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for ReLu<U, DeviceGpu<U>>where U: UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, ReLuForward<U>: Kernel<Args = ActivationForwardArgs<U>>, ReLuBackward<U>: Kernel<Args = ActivationBackwardArgs<U>>,

fn apply( &self, _: &DeviceGpu<U>, input: &Arr<U, N>) -> Result<Arr<U, N>, EvaluateError>

fn derive( &self, _: &DeviceGpu<U>, _: &Arr<U, N>, loss: &Arr<U, N>, u: &Arr<U, N>) -> Result<Arr<U, N>, TrainingError>

fn is_canonical_link<L: LossFunction<U>>(&self, _: &L) -> bool

impl<U, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for ReLu<U, DeviceCpu<U>>where U: UnitValue<U>, I: Iterator<Item = U> + Clone,

fn apply(&self, _: &DeviceCpu<U>, input: &I) -> Result<Arr<U, N>, EvaluateError>

fn derive( &self, _: &DeviceCpu<U>, _: &I, loss: &I, u: &I) -> Result<Arr<U, N>, TrainingError>

fn is_canonical_link<L: LossFunction<U>>(&self, _: &L) -> bool

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for ReLu<U, DeviceCpu<U>>where U: UnitValue<U>, Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

fn batch_apply( &self, device: &DeviceCpu<U>, input: &VecArr<U, Arr<U, N>>) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

fn batch_derive( &self, device: &DeviceCpu<U>, o: &VecArr<U, Arr<U, N>>, loss: &VecArr<U, Arr<U, N>>, u: &VecArr<U, Arr<U, N>>) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for ReLu<U, DeviceGpu<U>>where U: UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, ReLuForward<U>: Kernel<Args = ActivationForwardArgs<U>>, ReLuBackward<U>: Kernel<Args = ActivationBackwardArgs<U>>,

fn batch_apply( &self, _: &DeviceGpu<U>, input: &VecArr<U, Arr<U, N>>) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

fn batch_derive( &self, _: &DeviceGpu<U>, _: &VecArr<U, Arr<U, N>>, loss: &VecArr<U, Arr<U, N>>, u: &VecArr<U, Arr<U, N>>) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

Auto Trait Implementations

impl<U, D> RefUnwindSafe for ReLu<U, D>where D: RefUnwindSafe, U: RefUnwindSafe,

impl<U, D> Send for ReLu<U, D>where D: Send,

impl<U, D> Sync for ReLu<U, D>where D: Sync,

impl<U, D> Unpin for ReLu<U, D>where D: Unpin, U: Unpin,

impl<U, D> UnwindSafe for ReLu<U, D>where D: UnwindSafe, U: UnwindSafe,

Blanket Implementations

impl<T> Any for Twhere T: 'static + ?Sized,

fn type_id(&self) -> TypeId

impl<T> Borrow<T> for Twhere T: ?Sized,

fn borrow(&self) -> &T

impl<T> BorrowMut<T> for Twhere T: ?Sized,

fn borrow_mut(&mut self) -> &mut T

impl<T> From<T> for T

fn from(t: T) -> T

impl<T, U> Into<U> for Twhere U: From<T>,

fn into(self) -> U

impl<T> Pointable for T

const ALIGN: usize = mem::align_of::<T>()

type Init = T

unsafe fn init(init: <T as Pointable>::Init) -> usize

unsafe fn deref<'a>(ptr: usize) -> &'a T

unsafe fn deref_mut<'a>(ptr: usize) -> &'a mut T

unsafe fn drop(ptr: usize)

impl<T, U> TryFrom<U> for Twhere U: Into<T>,

type Error = Infallible

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

impl<T, U> TryInto<U> for Twhere U: TryFrom<T>,

type Error = <U as TryFrom<T>>::Error

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

impl<U, D> ReLu<U, D>where
U: UnitValue<U>,
D: Device<U>,

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for ReLu<U, DeviceCpu<U>>where
U: UnitValue<U>,

fn apply(
&self,
device: &DeviceCpu<U>,
input: &Arr<U, N>
) -> Result<Arr<U, N>, EvaluateError>

fn derive(
&self,
device: &DeviceCpu<U>,
o: &Arr<U, N>,
loss: &Arr<U, N>,
u: &Arr<U, N>
) -> Result<Arr<U, N>, TrainingError>

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for ReLu<U, DeviceGpu<U>>where
U: UnitValue<U> + DataTypeInfo,
DeviceGpu<U>: Device<U>,
ReLuForward<U>: Kernel<Args = ActivationForwardArgs<U>>,
ReLuBackward<U>: Kernel<Args = ActivationBackwardArgs<U>>,

fn apply(
&self,
_: &DeviceGpu<U>,
input: &Arr<U, N>
) -> Result<Arr<U, N>, EvaluateError>

fn derive(
&self,
_: &DeviceGpu<U>,
_: &Arr<U, N>,
loss: &Arr<U, N>,
u: &Arr<U, N>
) -> Result<Arr<U, N>, TrainingError>

impl<U, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for ReLu<U, DeviceCpu<U>>where
U: UnitValue<U>,
I: Iterator<Item = U> + Clone,

fn derive(
&self,
_: &DeviceCpu<U>,
_: &I,
loss: &I,
u: &I
) -> Result<Arr<U, N>, TrainingError>

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for ReLu<U, DeviceCpu<U>>where
U: UnitValue<U>,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

fn batch_apply(
&self,
device: &DeviceCpu<U>,
input: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

fn batch_derive(
&self,
device: &DeviceCpu<U>,
o: &VecArr<U, Arr<U, N>>,
loss: &VecArr<U, Arr<U, N>>,
u: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for ReLu<U, DeviceGpu<U>>where
U: UnitValue<U> + DataTypeInfo,
DeviceGpu<U>: Device<U>,
ReLuForward<U>: Kernel<Args = ActivationForwardArgs<U>>,
ReLuBackward<U>: Kernel<Args = ActivationBackwardArgs<U>>,

fn batch_apply(
&self,
_: &DeviceGpu<U>,
input: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

fn batch_derive(
&self,
_: &DeviceGpu<U>,
_: &VecArr<U, Arr<U, N>>,
loss: &VecArr<U, Arr<U, N>>,
u: &VecArr<U, Arr<U, N>>
) -> Result<VecArr<U, Arr<U, N>>, TrainingError>

impl<U, D> RefUnwindSafe for ReLu<U, D>where
D: RefUnwindSafe,
U: RefUnwindSafe,

impl<U, D> Send for ReLu<U, D>where
D: Send,

impl<U, D> Sync for ReLu<U, D>where
D: Sync,

impl<U, D> Unpin for ReLu<U, D>where
D: Unpin,
U: Unpin,

impl<U, D> UnwindSafe for ReLu<U, D>where
D: UnwindSafe,
U: UnwindSafe,

impl<T> Any for Twhere
T: 'static + ?Sized,

impl<T> Borrow<T> for Twhere
T: ?Sized,

impl<T> BorrowMut<T> for Twhere
T: ?Sized,

impl<T, U> Into<U> for Twhere
U: From<T>,

impl<T, U> TryFrom<U> for Twhere
U: Into<T>,

impl<T, U> TryInto<U> for Twhere
U: TryFrom<T>,