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
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
|
#The Nerv Layer Package#
Part of the [Nerv](../README.md) toolkit.
##Description##
__nerv.Layer__ is the base class and most of its methods are abstract.
###Class hierarchy and their members###
* __nerv.Layer__.
* `table dim_in` It specifies the dimensions of the inputs.
* `table dim_out` It specifies the dimensions of the outputs.
* `string id` ID of this layer.
* `table gconf` Stores the `global_conf`.
* __nerv.AffineLayer__ inherits __nerv.Layer__, both `#dim_in` and `#dim_out` are 1.
* `MatrixParam ltp` The liner transform parameter.
* `BiasParam bp` The bias parameter.
* __nerv.BiasLayer__ inherits __nerv.Layer__, both `#dim_in` nad `#dim_out` are 1.
* `BiasParam bias` The bias parameter.
* __nerv.SigmoidLayer__ inherits __nerv.Layer__, both `#dim_in` and `#dim_out` are 1.
* __nerv.SoftmaxCELayer__ inherits __nerv.Layer__, `#dim_in` is 2 and `#dim_out` is 0. `input[1]` is the input to the softmax layer, `input[2]` is the reference distribution.
* `float total_ce` Records the accumlated cross entropy value.
* `int total_frams` Records how many frames have passed.
* `bool compressed` The reference distribution can be a one-hot format. This feature is enabled by `layer_conf.compressed`.
##Methods##
* __void Layer.\_\_init(Layer self, string id, table global_conf, table layer_conf)__
Abstract method.
The constructing method should assign `id` to `self.id` and `global_conf` to `self.gconf`, `layer_conf.dim_in` to `self.dim_in`, `layer_conf.dim_out` to `self.dim_out`. `dim_in` and `dim_out` are a list specifies the dimensions of the inputs and outputs. Also, `layer_conf` will include the parameters, which should also be properly saved.
* __void Layer.init(Layer self)__
Abstract method.
Initialization method, in this method the layer should do some self-checking and allocate space for intermediate results.
* __void Layer.update(Layer self, table bp_err, table input, table output)__
Abstract method.
`bp_err[i]` should be the error on `output[i]`. In this method the parameters of `self` is updated.
* __void Layer.propagate(Layer self, table input, table output)__
Abstract method.
Given `input` and the current parameters, propagate and store the result in `output`.
* __void Layer.back_propagate(Layer self, Matrix next_bp_err, Matrix bp_err, Matrix input, Matrix output)__
Abstract method.
Calculate the error on the inputs and store them in `next_bp_err`.
* __void Layer.check_dim_len(int len_in, int len_out)__
Check whether `#self.dim_in == len_in` and `#self.dim_out == len_out`, if violated, an error will be posted.
* __void Layer.get_params(Layer self)__
Abstract method.
The layer should return a list containing its parameters.
##Examples##
* a basic example using __Nerv__ layers to a linear classification.
```
require 'math'
require 'layer.affine'
require 'layer.softmax_ce'
--[[Example using layers, a simple two-classification problem]]--
function calculate_accurate(networkO, labelM)
sum = 0
for i = 0, networkO:nrow() - 1, 1 do
if (labelM[i][0] == 1 and networkO[i][0] >= 0.5) then
sum = sum + 1
end
if (labelM[i][1] == 1 and networkO[i][1] >= 0.5) then
sum = sum + 1
end
end
return sum
end
--[[begin global setting and data generation]]--
global_conf = {lrate = 10,
wcost = 1e-6,
momentum = 0.9,
cumat_type = nerv.CuMatrixFloat}
input_dim = 5
data_num = 100
ansV = nerv.CuMatrixFloat(input_dim, 1)
for i = 0, input_dim - 1, 1 do
ansV[i][0] = math.random() - 0.5
end
ansB = math.random() - 0.5
print('displaying ansV')
print(ansV)
print('displaying ansB(bias)')
print(ansB)
dataM = nerv.CuMatrixFloat(data_num, input_dim)
for i = 0, data_num - 1, 1 do
for j = 0, input_dim - 1, 1 do
dataM[i][j] = math.random() * 2 - 1
end
end
refM = nerv.CuMatrixFloat(data_num, 1)
refM:fill(ansB)
refM:mul(dataM, ansV, 1, 1) --refM = dataM * ansV + ansB
labelM = nerv.CuMatrixFloat(data_num, 2)
for i = 0, data_num - 1, 1 do
if (refM[i][0] > 0) then
labelM[i][0] = 1
labelM[i][1] = 0
else
labelM[i][0] = 0
labelM[i][1] = 1
end
end
--[[global setting and data generation end]]--
--[[begin network building]]--
--parameters
affineL_ltp = nerv.LinearTransParam('AffineL_ltp', global_conf)
affineL_ltp.trans = nerv.CuMatrixFloat(input_dim, 2)
for i = 0, input_dim - 1, 1 do
for j = 0, 1, 1 do
affineL_ltp.trans[i][j] = math.random() - 0.5
end
end
affineL_bp = nerv.BiasParam('AffineL_bp', global_conf)
affineL_bp.trans = nerv.CuMatrixFloat(1, 2)
for j = 0, 1, 1 do
affineL_bp.trans[j] = math.random() - 0.5
end
--layers
affineL = nerv.AffineLayer('AffineL', global_conf, {['ltp'] = affineL_ltp,
['bp'] = affineL_bp,
dim_in = {input_dim},
dim_out = {2}})
softmaxL = nerv.SoftmaxCELayer('softmaxL', global_conf, {dim_in = {2, 2},
dim_out = {}})
print('layers initializing...')
affineL:init()
softmaxL:init()
--[[network building end]]--
--[[begin space allocation]]--
print('network input&output&error space allocation...')
affineI = {dataM} --input to the network is data
affineO = {nerv.CuMatrixFloat(data_num, 2)}
softmaxI = {affineO[1], labelM}
softmaxO = {nerv.CuMatrixFloat(data_num, 2)}
affineE = {nerv.CuMatrixFloat(data_num, 2)}
--[[space allocation end]]--
--[[begin training]]--
ce_last = 0
for l = 0, 10, 1 do
affineL:propagate(affineI, affineO)
softmaxL:propagate(softmaxI, softmaxO)
softmaxO[1]:softmax(softmaxI[1])
softmaxL:back_propagate(affineE, nil, softmaxI, softmaxO)
affineL:update(affineE, affineI, affineO)
if (l % 5 == 0) then
nerv.utils.printf("training iteration %d finished\n", l)
nerv.utils.printf("cross entropy: %.8f\n", softmaxL.total_ce - ce_last)
ce_last = softmaxL.total_ce
nerv.utils.printf("accurate labels: %d\n", calculate_accurate(softmaxO[1], labelM))
nerv.utils.printf("total frames processed: %.8f\n", softmaxL.total_frames)
end
end
--[[end training]]--
```
|