1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92

//! Applies the nonlinear Log-Sigmoid function.
//!
//! Non-linearity activation function: y = (1 + e^(-x))^(-1)
//!
//! A classic choice in neural networks.
//! But you might consider using ReLu as an alternative.
//!
//! ReLu, compared to Sigmoid
//!
//! * reduces the likelyhood of vanishing gradients
//! * increases the likelyhood of a more beneficial sparse representation
//! * can be computed faster
//! * is therefore the most popular activation function in DNNs as of this
//! writing (2015).

use crate::co::{IBackend, SharedTensor};
use crate::conn;
use crate::layer::*;
use crate::util::ArcLock;

#[derive(Debug, Clone)]
#[allow(missing_copy_implementations)]
/// Sigmoid Activation Layer
pub struct Sigmoid;

//
// Sigmoid + SigmoidPointwise
//
impl<B: IBackend + conn::Sigmoid<f32> + conn::SigmoidPointwise<f32>> ILayer<B> for Sigmoid {
    impl_ilayer_activation!();

    fn compute_in_place(&self) -> bool {
        true
    }

    fn reshape(
        &mut self,
        backend: ::std::rc::Rc<B>,
        input_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
        input_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
        weights_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
        weights_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
        output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
        output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
    ) {
        if let Some(inp) = input_data.get(0) {
            let read_inp = inp.read().unwrap();
            let input_desc = read_inp.desc();
            input_gradient[0].write().unwrap().resize(input_desc).unwrap();
            output_data[0].write().unwrap().resize(input_desc).unwrap();
            output_gradient[0].write().unwrap().resize(input_desc).unwrap();
        }
    }
}

impl<B: IBackend + conn::Sigmoid<f32> + conn::SigmoidPointwise<f32>> ComputeOutput<f32, B> for Sigmoid {
    fn compute_output(
        &self,
        backend: &B,
        _weights: &[&SharedTensor<f32>],
        input_data: &[&SharedTensor<f32>],
        output_data: &mut [&mut SharedTensor<f32>],
    ) {
        match input_data.get(0) {
            Some(input) => backend.sigmoid(input, output_data[0]).unwrap(),
            None => backend.sigmoid_pointwise(output_data[0]).unwrap(),
        }
    }
}

impl<B: IBackend + conn::Sigmoid<f32> + conn::SigmoidPointwise<f32>> ComputeInputGradient<f32, B> for Sigmoid {
    fn compute_input_gradient(
        &self,
        backend: &B,
        weights_data: &[&SharedTensor<f32>],
        output_data: &[&SharedTensor<f32>],
        output_gradients: &[&SharedTensor<f32>],
        input_data: &[&SharedTensor<f32>],
        input_gradients: &mut [&mut SharedTensor<f32>],
    ) {
        match output_data.get(0) {
            Some(_) => backend
                .sigmoid_grad(output_data[0], output_gradients[0], input_data[0], input_gradients[0])
                .unwrap(),
            None => backend
                .sigmoid_pointwise_grad(input_data[0], input_gradients[0])
                .unwrap(),
        }
    }
}

impl<B: IBackend + conn::Sigmoid<f32> + conn::SigmoidPointwise<f32>> ComputeParametersGradient<f32, B> for Sigmoid {}