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
|
// Copyright (C) 2002 Ronan Collobert (collober@iro.umontreal.ca)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// Torch is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef TANH_INC
#define TANH_INC
#include "GradientMachine.h"
namespace Torch {
/** Tanh layer for #GradientMachine#.
The number of inputs/outputs is the number
of units for this machine.
Formally speaking, $ouputs[i] = tanh(inputs[i])$.
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class Tanh : public GradientMachine
{
public:
/// Create a layer of size #n_units#
Tanh(int n_units);
//-----
virtual int numberOfParams();
virtual void forward(List *inputs);
virtual void backward(List *inputs, real *alpha);
virtual ~Tanh();
};
}
#endif
|
