auto-diff 0.3.2

A neural network library in Rust.
Documentation 
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
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134

use crate::tensor::Tensor;
use super::OpTrait;

/// ELU
pub struct ELU {
}
impl ELU {
    pub fn new() -> ELU {
        ELU {
        }
    }
}
impl OpTrait for ELU {
    fn get_name(&self) -> String {
    }

    /// Forward pass
    fn apply(&mut self, input: &[&Tensor], output: &[&Tensor]) {
        
    }
    
    /// Given the forward input value and backward output_grad,
    /// Update weight gradient.
    /// return backward input gradeint.
    fn grad(&self, input: &[&Tensor], output_grad: &[&Tensor], input_grad: &[&Tensor]) {
    }

    /// access weight values
    fn get_values(&self) -> Vec<&Tensor> {
    }
    fn set_values(&self, v: &[Tensor]) {
    }
    /// access gradient values
    fn get_grads(&self) -> Vec<&Tensor> {
        
    }
}

// Hardshrink
// Hardtanh
// LeakyReLU
// LogSigmoid
// MultiheadAttention
// PReLU

/// ReLU
pub struct ReLU {
}
impl ReLU {
    pub fn new() -> ReLU {
        ReLU {
        }
    }
}
impl OpTrait for ReLU {
    fn get_name(&self) -> String {
        "ReLU".to_string()
    }

    /// Forward pass
    fn apply(&mut self, input: &[&Tensor], output: &[&Tensor]) {
        
    }
    
    /// Given the forward input value and backward output_grad,
    /// Update weight gradient.
    /// return backward input gradeint.
    fn grad(&self, input: &[&Tensor], output_grad: &[&Tensor], input_grad: &[&Tensor]) {
    }

    /// access weight values
    fn get_values(&self) -> Vec<&Tensor> {
    }
    fn set_values(&self, v: &[Tensor]) {
    }
    /// access gradient values
    fn get_grads(&self) -> Vec<&Tensor> {
        
    }
}


// ReLU6
// RReLU
// SELU
// CELU
// Sigmoid
// Softplus
// Softshrink
// Softsign
// Tanh
// Tanhshrink
// Threshold

// Softmin
// Softmax
// Softmax2d
// LogSoftmax
// AdaptiveLogSoftmaxWithLoss


//pub struct ELU {
//}
//impl ELU {
//    pub fn new() -> ELU {
//        ELU {
//        }
//    }
//}
//impl OpTrait for ELU {
//    fn get_name(&self) -> String {
//    }
//
//    /// Forward pass
//    fn apply(&mut self, input: &[&Tensor], output: &[&Tensor]) {
//        
//    }
//    
//    /// Given the forward input value and backward output_grad,
//    /// Update weight gradient.
//    /// return backward input gradeint.
//    fn grad(&self, input: &[&Tensor], output_grad: &[&Tensor], input_grad: &[&Tensor]) {
//    }
//
//    /// access weight values
//    fn get_values(&self) -> Vec<&Tensor> {
//    }
//    fn set_values(&self, v: &[Tensor]) {
//    }
//    /// access gradient values
//    fn get_grads(&self) -> Vec<&Tensor> {
//        
//    }
//}