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switched convnet to new reader architecture

This commit is contained in:
Frank Seide 2016-08-10 18:18:34 -07:00
Родитель ea3b31fa17
Коммит 6e32e40608
1 изменённых файлов: 27 добавлений и 107 удалений
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134
Examples/Image/Miscellaneous/CIFAR-10/TutorialImage.cntk
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@ -9,6 +9,9 @@ RootDir = "." ; DataDir = "$RootDir$" ; ModelDir = "$RootDir$/Output/Models"
modelPath = "$ModelDir$/01_Convolution"
# image reading pipeline
imageTransforms = ( [ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" ] : [ type = "Transpose" ] )
# Training without BN
TrainConvNet = [
action = "train"
@ -66,57 +69,14 @@ TrainConvNet = [
reader = [
verbosity = 0
randomize = true
deserializers = (
[
type = "ImageDeserializer" ; module = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/train_map.txt"
input = [
features = [
transforms = (
[ type = "Crop" ; cropType = "center" ; cropRatio = 1.0 ; jitterType = "uniRatio" ] :
[
type = "Scale"
width = 32
height = 32
channels = 3
interpolations = "linear"
]:[
type = "Mean"
]:[
type = "Transpose"
]
)
]
labels = [ labelDim = 10 ]
]
deserializers = ([
type = "ImageDeserializer" ; module = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/train_map.txt"
input = [
features = [ transforms = $imageTransforms$ ]
labels = [ labelDim = 10 ]
]
)
]
reader_newer = [
readerType = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/train_map.txt"
randomize = "auto"
prefetch = true
features = [
width = 32
height = 32
channels = 3
#cropType = "random"
#cropRatio = 0.8
#jitterType = "uniRatio"
#interpolations = "linear"
#meanFile = "$DataDir$/cifar-10-batches-py/CIFAR-10_mean.xml"
]
labels = [ labelDim = 10 ]
]
reader_old = [
readerType = "CNTKTextFormatReader"
file = "$DataDir$/Train_cntk_text.txt"
input = [
features = [ dim = 3072 ; format = "dense" ]
labels = [ dim = 10 ; format = "dense" ]
]
])
]
]
@ -179,12 +139,16 @@ TrainConvNetWithBN = [
]
reader = [
readerType = "CNTKTextFormatReader"
file = "$DataDir$/Train_cntk_text.txt"
input = [
features = [ dim = 3072 ; format = "dense" ]
labels = [ dim = 10 ; format = "dense" ]
]
verbosity = 0
randomize = true
deserializers = ([
type = "ImageDeserializer" ; module = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/train_map.txt"
input = [
features = [ transforms = ( [ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" ] : [ type = "Transpose" ] ) ]
labels = [ labelDim = 10 ]
]
])
]
]
@ -193,60 +157,16 @@ Eval = [
action = "eval"
minibatchSize = 16
evalNodeNames = errs:top5Errs # also test top-5 error rate
reader_old = [
readerType = "CNTKTextFormatReader"
file = "$DataDir$/Test_cntk_text.txt"
input = [
features = [ dim = 3072 ; format = "dense" ]
labels = [ dim = 10 ; format = "dense" ]
]
]
reader_newer = [
readerType = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/test_map.txt"
randomize = "auto"
prefetch = true
features = [
width = 32
height = 32
channels = 3
#cropType = "random"
#cropRatio = 0.8
#jitterType = "uniRatio"
#interpolations = "linear"
#meanFile = "$DataDir$/cifar-10-batches-py/CIFAR-10_mean.xml"
]
labels = [
labelDim = 10
]
]
reader = [
verbosity = 0
randomize = true
deserializers = (
[
type = "ImageDeserializer" ; module = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/train_map.txt"
input = [
features = [
transforms = (
[ type = "Crop" ; cropType = "center" ; cropRatio = 1.0 ; jitterType = "uniRatio" ] :
[
type = "Scale"
width = 32
height = 32
channels = 3
interpolations = "linear"
]:[
type = "Mean"
]:[
type = "Transpose"
]
)
]
labels = [ labelDim = 10 ]
]
deserializers = ([
type = "ImageDeserializer" ; module = "ImageReader"
file = "$DataDir$/cifar-10-batches-py/train_map.txt"
input = [
features = [ transforms = $imageTransforms$ ]
labels = [ labelDim = 10 ]
]
)
])
]
]