forked from maddin79/darch
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathDESCRIPTION
More file actions
95 lines (95 loc) · 2.64 KB
/
Copy pathDESCRIPTION
File metadata and controls
95 lines (95 loc) · 2.64 KB
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
Package: darch
Type: Package
Title: Package for Deep Architectures and Restricted Boltzmann Machines
Version: 0.12.9000
Date: 2016-07-20
Author: Martin Drees [aut, cre, cph],
Johannes Rueckert [ctb],
Christoph M. Friedrich [ctb],
Geoffrey Hinton [cph],
Ruslan Salakhutdinov [cph],
Carl Edward Rasmussen [cph],
Maintainer: Martin Drees <[email protected]>
Description: The darch package is built on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets). This package is for generating neural networks with many layers (deep
architectures) and train them with the method introduced by the publications
"A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
Y. W. Teh (2006) <DOI:10.1162/neco.2006.18.7.1527>) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov (2006) <DOI:10.1126/science.1127647>). This method includes a
pre training with the contrastive divergence method published by G.E Hinton
(2002) <DOI:10.1162/089976602760128018> and a fine tuning with common known
training algorithms like backpropagation or conjugate gradients.
Additionally, supervised fine-tuning can be enhanced with maxout and
dropout, two recently developed techniques to improve fine-tuning for deep
learning.
License: GPL (>= 2) | file LICENSE
URL: https://github.com/maddin79/darch
BugReports: https://github.com/maddin79/darch/issues
Depends:
R (>= 3.0.0)
Imports:
stats,
methods,
ggplot2,
reshape2,
futile.logger (>= 1.4.1),
caret,
Rcpp (>= 0.12.3)
LinkingTo: Rcpp
Suggests:
foreach,
doRNG,
NeuralNetTools,
gputools,
testthat,
plyr (>= 1.8.3.9000)
Collate:
'RcppExports.R'
'autosave.R'
'net.Class.R'
'darch.Class.R'
'backpropagation.R'
'benchmark.R'
'bootstrap.R'
'caret.R'
'compat.R'
'config.R'
'darch.Add.R'
'darch.Getter.R'
'dataset.R'
'darch.Learn.R'
'darch.R'
'darch.Setter.R'
'darchUnitFunctions.R'
'dropout.R'
'errorFunctions.R'
'rbm.Class.R'
'generateRBMs.R'
'generateWeightsFunctions.R'
'loadDArch.R'
'log.R'
'makeStartEndPoints.R'
'minimize.R'
'minimizeAutoencoder.R'
'minimizeClassifier.R'
'mnist.R'
'momentum.R'
'net.Getter.R'
'newDArch.R'
'params.R'
'plot.R'
'predict.R'
'print.R'
'rbm.Learn.R'
'rbm.Reset.R'
'rbmUnitFunctions.R'
'rbmUpdate.R'
'rpropagation.R'
'runDArch.R'
'saveDArch.R'
'test.R'
'util.R'
'weightUpdateFunctions.R'
RoxygenNote: 5.0.1