forked from microsoft/CNTK
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathTrainSimpleTimit.config
156 lines (120 loc) · 3.17 KB
/
TrainSimpleTimit.config
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
command=TimitTrainSimple:TimitTestSimple:TimitWriteSimple
# command=TimitWriteSimple
# deviceId=-1 for CPU, >=0 for GPU devices
DeviceNumber=0
stderr=Demo
precision=float
#######################################
# TRAINING CONFIG (Simple, Fixed LR) #
#######################################
TimitTrainSimple=[
action=train
modelPath=Models/TrainSimple.dnn
# deviceId=-1 for CPU, >=0 for GPU devices
deviceId=$DeviceNumber$
traceLevel=1
# Notation xxx:yyy*n:zzz is equivalent to xxx, then yyy repeated n times,
# then zzz
# example: 10:20*3:5 is equivalent to 10:20:20:20:5
SimpleNetworkBuilder=[
layerSizes=792:512*3:183
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
# epochSize=0 means epochSize is the size of the training set
epochSize=0
minibatchSize=256:1024
learningRatesPerMB=0.8:3.2*14:0.08
momentumPerMB=0.9
dropoutRate=0.0
# maxEpochs=25 # Full experiment
maxEpochs=1 # Small experiment for debugging
]
# Parameter values for the reader
reader=[
# reader to use
readerType=HTKMLFReader
# readMethod=blockRandomize
readMethod=rollingWindow
miniBatchMode=Partial
randomize=Auto
verbosity=1
features=[
dim=792
scpFile=CntkTimit.scp
]
labels=[
mlfFile=TimitLabels.mlf
labelDim=183
labelMappingFile=TimitStateList.txt
]
]
]
#######################################
# TESTING CONFIG (Simple) #
#######################################
TimitTestSimple=[
action=test
modelPath=Models/TrainSimple.dnn
# deviceId=-1 for CPU, >=0 for GPU devices
deviceId=$DeviceNumber$
traceLevel=1
# epochSize=0 means epochSize is the size of the training set
epochSize=0
minibatchSize=256:1024
# Parameter values for the reader
reader=[
# reader to use
readerType=HTKMLFReader
readMethod=rollingWindow
miniBatchMode=Partial
randomize=Auto
verbosity=1
# numMBsToShowResult=10
# evalNodeNames
features=[
dim=792
scpFile=CntkTimit.scp
]
labels=[
mlfFile=TimitLabels.mlf
labelDim=183
labelMappingFile=TimitStateList.txt
]
]
]
#######################################
# OUTPUT RESUTLS (Simple) #
#######################################
TimitWriteSimple=[
action=write
modelPath=Models/TrainSimple.dnn
# deviceId=-1 for CPU, >=0 for GPU devices
deviceId=$DeviceNumber$
outputNodeNames=ScaledLogLikelihood
traceLevel=1
# Parameter values for the reader
reader=[
# reader to use
readerType=HTKMLFReader
features=[
dim=792
scpFile=CntkTimit.scp
]
]
writer=[
wrecords=50000 #Number of records to allocate space for in file
writerType=HTKMLFWriter
ScaledLogLikelihood = [
dim = 183
scpFile=CntkTimitOutput.scp
type=Real # Must be uppercase Real?
]
]
]