Struct nncombinator::arr::Arr

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pub struct Arr<T, const N: usize>where
    T: Default + Clone + Send,{ /* private fields */ }

Expand description

Fixed-length one-dimensional array implementation

Implementations

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impl<T, const N: usize> Arr<T, N>where
T: Default + Clone + Send,

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pub fn new() -> Arr<T, N>

Create an instance of Arr

Trait Implementations

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

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

Apply the activation function Read more

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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

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

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

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

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

Apply the activation function Read more

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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

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

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

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

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

Apply the activation function Read more

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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

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

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

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

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

Apply the activation function Read more

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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

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

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

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

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

Apply the activation function Read more

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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

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

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

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

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

Apply the activation function Read more

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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

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

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

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impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Identity<U, DeviceGpu>where
U: UnitValue,
DeviceGpu: Device,

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

Apply the activation function Read more

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

Apply derivatives of the activation function Read more

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

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

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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>,

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

Apply the activation function Read more

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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

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

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

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impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Sigmoid<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 SigmoidForward: Kernel<Args = ActivationForwardArgs>,
 SigmoidBackward: Kernel<Args = ActivationBackwardArgs>,

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

Apply the activation function Read more

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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

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

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

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impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for SoftMax<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 CudaPtr: TryFrom<U, Error = CudaError>,
 SoftMaxForward: Kernel<Args = ActivationForwardArgs>,
 SoftMaxBackward: Kernel<Args = ActivationBackwardArgs>,

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

Apply the activation function Read more

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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

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

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

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impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Swish<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 SwishForward: Kernel<Args = ActivationForwardArgs>,
 SwishBackward: Kernel<Args = ActivationBackwardArgs>,

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

Apply the activation function Read more

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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

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

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

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impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Tanh<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 TanhForward: Kernel<Args = ActivationForwardArgs>,
 TanhBackward: Kernel<Args = ActivationBackwardArgs>,

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

Apply the activation function Read more

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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

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

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

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

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

Apply the activation function Read more

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

Apply derivatives of the activation function Read more

source

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

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

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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,

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

Apply the activation function Read more

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

Apply derivatives of the activation function Read more

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

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

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

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

Apply the activation function Read more

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

Apply derivatives of the activation function Read more

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

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

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

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

Apply the activation function Read more

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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: &L) -> bool

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

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

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

Apply the activation function Read more

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

Apply derivatives of the activation function Read more

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

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

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

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

Apply the activation function Read more

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

Apply derivatives of the activation function Read more

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

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

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impl<'a, T, const N: usize> AsRawMutSlice<'a, T> for Arr<T, N>where
T: Default + Clone + Send,

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fn as_raw_mut_slice(&'a mut self) -> &'a mut [T]ⓘNotable traits for &[u8]`impl Read for &[u8]impl Write for &mut [u8]`

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impl<'a, T, const N: usize> AsRawSlice<T> for Arr<T, N>where
T: Default + Clone + Send,

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fn as_raw_slice(&self) -> &[T]ⓘNotable traits for &[u8]`impl Read for &[u8]impl Write for &mut [u8]`

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impl<U, C, P, D, I, const NI: usize, const NO: usize> Backward<U, &Arr<U, NO>, Result<Arr<U, NI>, TrainingError>> for DiffLinearLayer<U, C, P, D, I, NI, NO>where
 U: Default + Clone + Copy + UnitValue,
 D: Device + DeviceLinear<U, C, NI, NO>,
 C: Index<(usize, usize), Output = U>,
 P: ForwardAll<Input = I, Output = DiffInput<DiffArr<U, NI>, U, NI, NO>> + BackwardAll<U, LossInput = Arr<U, NI>> + PreTrain + Loss,
 I: Debug + Send + Sync,

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fn backward(&mut self, input: &Arr<U, NO>) -> Result<Arr<U, NI>, TrainingError>

Back propagation of errors #Arguments Read more

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impl<U, C, P, D, I, const NI: usize, const NO: usize> Backward<U, &Arr<U, NO>, Result<Arr<U, NI>, TrainingError>> for LinearLayer<U, C, P, D, I, NI, NO>where
 P: ForwardAll<Input = I, Output = Arr<U, NI>> + BackwardAll<U, LossInput = Arr<U, NI>> + PreTrain + Loss,
 U: Default + Clone + Copy + UnitValue,
 D: Device + DeviceLinear<U, C, NI, NO>,
 I: Debug + Send + Sync,

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fn backward(&mut self, input: &Arr<U, NO>) -> Result<Arr<U, NI>, TrainingError>

Back propagation of errors #Arguments Read more

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

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

Apply the activation function Read more

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fn batch_derive(
 &self,
 _: &DeviceCpu,
 _: &VecArr<U, Arr<U, N>>,
 loss: &VecArr<U, Arr<U, N>>,
 _: &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>, DeviceCpu> for ReLu<U, DeviceCpu>where
U: UnitValue,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

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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

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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>, DeviceCpu> for Sigmoid<U, DeviceCpu>where
U: UnitValue,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

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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>, DeviceCpu> for SoftMax<U, DeviceCpu>where
U: UnitValue,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

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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>, DeviceCpu> for Swish<U, DeviceCpu>where
U: UnitValue,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

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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>, DeviceCpu> for Tanh<U, DeviceCpu>where
U: UnitValue,
Vec<Arr<U, N>>: FromParallelIterator<Arr<U, N>>,

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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 Identity<U, DeviceGpu>where
U: UnitValue,
DeviceGpu: Device,

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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>>,
 _: &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

source

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Sigmoid<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 SigmoidForward: Kernel<Args = ActivationForwardArgs>,
 SigmoidBackward: 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

source

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for SoftMax<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 CudaPtr: TryFrom<U, Error = CudaError>,
 SoftMaxForward: Kernel<Args = ActivationForwardArgs>,
 SoftMaxBackward: 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

source

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Swish<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 SwishForward: Kernel<Args = ActivationForwardArgs>,
 SwishBackward: 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

source

impl<U, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu> for Tanh<U, DeviceGpu>where
 U: UnitValue + DataTypeInfo,
 DeviceGpu: Device,
 TanhForward: Kernel<Args = ActivationForwardArgs>,
 TanhBackward: Kernel<Args = ActivationBackwardArgs>,

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

Apply the activation function Read more

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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

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impl<T, const N: usize> Clone for Arr<T, N>where
T: Default + Clone + Send,

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fn clone(&self) -> Self

Returns a copy of the value. Read more

1.0.0 · source

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more

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impl<T: Debug, const N: usize> Debug for Arr<T, N>where
T: Default + Clone + Send,

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more

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impl<T, const N: usize> Deref for Arr<T, N>where
T: Default + Clone + Send,

type Target = Box<[T], Global>

The resulting type after dereferencing.

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fn deref(&self) -> &Self::Target

Dereferences the value.

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impl<T, const N: usize> DerefMut for Arr<T, N>where
T: Default + Clone + Send,

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fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.

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impl<T, const N: usize> Div<T> for Arr<T, N>where
T: Div<T> + Div<Output = T> + Clone + Copy + Default + Send,

type Output = Arr<T, N>

The resulting type after applying the / operator.

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fn div(self, rhs: T) -> Self::Output

Performs the / operation. Read more

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impl<U, C, P, D, I, const NI: usize, const NO: usize> Forward<Arr<U, NI>, Result<Arr<U, NO>, EvaluateError>> for LinearLayer<U, C, P, D, I, NI, NO>where
 P: ForwardAll<Input = I, Output = Arr<U, NI>> + BackwardAll<U, LossInput = Arr<U, NI>> + PreTrain + Loss,
 U: Default + Clone + Copy + Send + UnitValue,
 D: Device + DeviceLinear<U, C, NI, NO>,
 I: Debug + Send + Sync,

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fn forward(&self, input: &Arr<U, NI>) -> Result<Arr<U, NO>, EvaluateError>

Forward propagation implementation Read more

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impl<'data, U, const N: usize> From<ArrView<'data, U, N>> for Arr<U, N>where
U: Default + Clone + Copy + Send,

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fn from(view: ArrView<'data, U, N>) -> Self

Converts to this type from the input type.

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impl<'data, T, const N: usize> IntoParallelRefIterator<'data> for Arr<T, N>where
T: Send + Sync + 'static + Default + Clone,

type Iter = Iter<'data, T>

The type of the parallel iterator that will be returned.

type Item = &'data T

The type of item that the parallel iterator will produce. This will typically be an &'data T reference type. Read more

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fn par_iter(&'data self) -> Self::Iter

Converts self into a parallel iterator. Read more

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impl<T, const N: usize> Mul<T> for Arr<T, N>where
T: Mul<T> + Mul<Output = T> + Clone + Copy + Default + Send,

type Output = Arr<T, N>

The resulting type after applying the * operator.

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fn mul(self, rhs: T) -> Self::Output

Performs the * operation. Read more

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impl<T: PartialEq, const N: usize> PartialEq<Arr<T, N>> for Arr<T, N>where
T: Default + Clone + Send,

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fn eq(&self, other: &Arr<T, N>) -> bool

This method tests for self and other values to be equal, and is used by ==. Read more

1.0.0 · source

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason. Read more

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impl<T, const N: usize> TryFrom<Vec<T, Global>> for Arr<T, N>where
T: Default + Clone + Send,

type Error = SizeMismatchError

The type returned in the event of a conversion error.

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fn try_from(v: Vec<T>) -> Result<Self, Self::Error>

Performs the conversion.

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impl<T: Eq, const N: usize> Eq for Arr<T, N>where
T: Default + Clone + Send,

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impl<T, const N: usize> StructuralEq for Arr<T, N>where
T: Default + Clone + Send,

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impl<T, const N: usize> StructuralPartialEq for Arr<T, N>where
T: Default + Clone + Send,

Auto Trait Implementations

impl<T, const N: usize> RefUnwindSafe for Arr<T, N>where
T: RefUnwindSafe,

impl<T, const N: usize> Send for Arr<T, N>

impl<T, const N: usize> Sync for Arr<T, N>where
T: Sync,

impl<T, const N: usize> Unpin for Arr<T, N>

impl<T, const N: usize> UnwindSafe for Arr<T, N>where
T: UnwindSafe,

Blanket Implementations

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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> ToOwned for Twhere
T: Clone,

type Owned = T

The resulting type after obtaining ownership.

source

fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more

source

fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. 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::arr::Arr

Implementations

impl<T, const N: usize> Arr<T, N>where T: Default + Clone + Send,

pub fn new() -> Arr<T, N>

Trait Implementations

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for Identity<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: &L) -> bool

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>, DeviceCpu<U>> for Sigmoid<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: &L) -> bool

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for SoftMax<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: &L) -> bool

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for Swish<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>, DeviceCpu<U>> for Tanh<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 Identity<U, DeviceGpu<U>>where U: UnitValue<U>, DeviceGpu<U>: Device<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>, _: &Arr<U, N>) -> Result<Arr<U, N>, TrainingError>

fn is_canonical_link<L: LossFunction<U>>(&self, l: &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, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Sigmoid<U, DeviceGpu<U>>where U: UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, SigmoidForward<U>: Kernel<Args = ActivationForwardArgs<U>>, SigmoidBackward<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: &L) -> bool

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: &L) -> bool

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Swish<U, DeviceGpu<U>>where U: UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, SwishForward<U>: Kernel<Args = ActivationForwardArgs<U>>, SwishBackward<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, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Tanh<U, DeviceGpu<U>>where U: UnitValue<U> + DataTypeInfo, DeviceGpu<U>: Device<U>, TanhForward<U>: Kernel<Args = ActivationForwardArgs<U>>, TanhBackward<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 Identity<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, _: &I) -> Result<Arr<U, N>, TrainingError>

fn is_canonical_link<L: LossFunction<U>>(&self, l: &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, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for Sigmoid<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: &L) -> bool

impl<U, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for SoftMax<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: &L) -> bool

impl<U, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for Swish<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, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for Tanh<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<'a, T, const N: usize> AsRawMutSlice<'a, T> for Arr<T, N>where T: Default + Clone + Send,

fn as_raw_mut_slice(&'a mut self) -> &'a mut [T]ⓘNotable traits for &[u8]impl Read for &[u8]impl Write for &mut [u8]

impl<'a, T, const N: usize> AsRawSlice<T> for Arr<T, N>where T: Default + Clone + Send,

fn as_raw_slice(&self) -> &[T]ⓘNotable traits for &[u8]impl Read for &[u8]impl Write for &mut [u8]

impl<T, const N: usize> Arr<T, N>where
T: Default + Clone + Send,

impl<U, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceCpu<U>> for Identity<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>, 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>, DeviceCpu<U>> for Sigmoid<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>, DeviceCpu<U>> for SoftMax<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>, DeviceCpu<U>> for Swish<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>, DeviceCpu<U>> for Tanh<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 Identity<U, DeviceGpu<U>>where
U: UnitValue<U>,
DeviceGpu<U>: Device<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>,
_: &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, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Sigmoid<U, DeviceGpu<U>>where
U: UnitValue<U> + DataTypeInfo,
DeviceGpu<U>: Device<U>,
SigmoidForward<U>: Kernel<Args = ActivationForwardArgs<U>>,
SigmoidBackward<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 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, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Swish<U, DeviceGpu<U>>where
U: UnitValue<U> + DataTypeInfo,
DeviceGpu<U>: Device<U>,
SwishForward<U>: Kernel<Args = ActivationForwardArgs<U>>,
SwishBackward<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, const N: usize> Activation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Tanh<U, DeviceGpu<U>>where
U: UnitValue<U> + DataTypeInfo,
DeviceGpu<U>: Device<U>,
TanhForward<U>: Kernel<Args = ActivationForwardArgs<U>>,
TanhBackward<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 Identity<U, DeviceCpu<U>>where
U: UnitValue<U>,
I: Iterator<Item = U> + Clone,

fn derive(
&self,
_: &DeviceCpu<U>,
_: &I,
loss: &I,
_: &I
) -> 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, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for Sigmoid<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, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for SoftMax<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, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for Swish<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, I, const N: usize> Activation<U, I, Arr<U, N>, DeviceCpu<U>> for Tanh<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<'a, T, const N: usize> AsRawMutSlice<'a, T> for Arr<T, N>where
T: Default + Clone + Send,

fn as_raw_mut_slice(&'a mut self) -> &'a mut [T]ⓘNotable traits for &[u8]`impl Read for &[u8]impl Write for &mut [u8]`

impl<'a, T, const N: usize> AsRawSlice<T> for Arr<T, N>where
T: Default + Clone + Send,

fn as_raw_slice(&self) -> &[T]ⓘNotable traits for &[u8]`impl Read for &[u8]impl Write for &mut [u8]`

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

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

fn batch_derive(
&self,
_: &DeviceCpu<U>,
_: &VecArr<U, Arr<U, N>>,
loss: &VecArr<U, Arr<U, N>>,
_: &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>, 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>, DeviceCpu<U>> for Sigmoid<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>, DeviceCpu<U>> for SoftMax<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>, DeviceCpu<U>> for Swish<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>, DeviceCpu<U>> for Tanh<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 Identity<U, DeviceGpu<U>>where
U: UnitValue<U>,
DeviceGpu<U>: Device<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>>,
_: &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, const N: usize> BatchActivation<U, Arr<U, N>, Arr<U, N>, DeviceGpu<U>> for Sigmoid<U, DeviceGpu<U>>where
U: UnitValue<U> + DataTypeInfo,
DeviceGpu<U>: Device<U>,
SigmoidForward<U>: Kernel<Args = ActivationForwardArgs<U>>,
SigmoidBackward<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>

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