concision_neural/layers/layer/
impl_layer.rs

1/*
2    appellation: impl_layer <module>
3    authors: @FL03
4*/
5use crate::layers::LayerBase;
6
7use crate::layers::{Activator, ActivatorGradient, Layer};
8use cnc::{Forward, ParamsBase};
9use ndarray::{Data, Dimension, RawData};
10
11impl<F, S, D> core::ops::Deref for LayerBase<F, S, D>
12where
13    D: Dimension,
14    S: RawData,
15{
16    type Target = ParamsBase<S, D>;
17
18    fn deref(&self) -> &Self::Target {
19        &self.params
20    }
21}
22
23impl<F, S, D> core::ops::DerefMut for LayerBase<F, S, D>
24where
25    D: Dimension,
26    S: RawData,
27{
28    fn deref_mut(&mut self) -> &mut Self::Target {
29        &mut self.params
30    }
31}
32
33impl<A, X, Y, F, S, D> Forward<X> for LayerBase<F, S, D>
34where
35    A: Clone,
36    F: Activator<Y, Output = Y>,
37    D: Dimension,
38    S: Data<Elem = A>,
39    ParamsBase<S, D>: Forward<X, Output = Y>,
40{
41    type Output = Y;
42
43    fn forward(&self, inputs: &X) -> cnc::Result<Self::Output> {
44        let y = self.params().forward(inputs)?;
45
46        Ok(self.rho().activate(y))
47    }
48}
49
50impl<U, V, F, S, D> Activator<U> for LayerBase<F, S, D>
51where
52    F: Activator<U, Output = V>,
53    D: Dimension,
54    S: RawData,
55{
56    type Output = V;
57
58    fn activate(&self, x: U) -> Self::Output {
59        self.rho().activate(x)
60    }
61}
62
63impl<U, F, S, D> ActivatorGradient<U> for LayerBase<F, S, D>
64where
65    F: ActivatorGradient<U>,
66    D: Dimension,
67    S: RawData,
68{
69    type Input = F::Input;
70    type Delta = F::Delta;
71
72    fn activate_gradient(&self, inputs: F::Input) -> F::Delta {
73        self.rho().activate_gradient(inputs)
74    }
75}
76
77impl<A, F, S, D> Layer<S, D> for LayerBase<F, S, D>
78where
79    F: Activator<A, Output = A>,
80    D: Dimension,
81    S: RawData<Elem = A>,
82{
83    type Elem = A;
84    type Rho = F;
85
86    fn rho(&self) -> &Self::Rho {
87        &self.rho
88    }
89
90    fn params(&self) -> &ParamsBase<S, D> {
91        &self.params
92    }
93
94    fn params_mut(&mut self) -> &mut ParamsBase<S, D> {
95        &mut self.params
96    }
97}
concision_neural/layers/layer/impl_layer.rs

concision_neural/layers/layer/
impl_layer.rs
