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
|
// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//
// This file is part of Torch 3.1.
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
// IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
// THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "TemporalMean.h"
#include "Random.h"
namespace Torch {
TemporalMean::TemporalMean(int input_frame_size)
: GradientMachine(input_frame_size, input_frame_size)
{
outputs->resize(1);
}
void TemporalMean::forward(Sequence *inputs)
{
real *outputs_ = outputs->frames[0];
for(int i = 0; i < n_outputs; i++)
outputs_[i] = 0;
for(int i = 0; i < inputs->n_frames; i++)
{
real *input_frame_ = inputs->frames[i];
for(int j = 0; j < n_outputs; j++)
outputs_[j] += input_frame_[j];
}
real z = 1./((real)inputs->n_frames);
for(int i = 0; i < n_outputs; i++)
outputs_[i] *= z;
}
void TemporalMean::backward(Sequence *inputs, Sequence *alpha)
{
if(partial_backprop)
return;
beta->resize(inputs->n_frames);
real *alpha_ = alpha->frames[0];
real z = 1./((real)inputs->n_frames);
for(int i = 0; i < beta->n_frames; i++)
{
real *beta_frame_ = beta->frames[i];
for(int j = 0; j < n_outputs; j++)
beta_frame_[j] = alpha_[j]*z;
}
}
TemporalMean::~TemporalMean()
{
}
}
|
