from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles(outputFolder='config.n5to20.s10.i100.100IPerSPerN',
neuronBounds=[5, 20],
sortBounds=10,
nInits=100,
nNeuronsPerJob=1,
nInitsPerJob=100,
nSortsPerJob=1,
compress=True)
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(
nSorts=50,
nBoxes=10,
nTrain=6,
nValid=4,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve(
'crossValid',
crossValid=crossValid,
).save(True)
from TuningTools.PreProc import *
#ppCol = PreProcCollection( PreProcChain( MapStd() ) )
ppCol = PreProcChain(Norm1())
place = PreProcArchieve('ppFile', ppCol=ppCol).save()
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles( outputFolder = 'config_citest0',
neuronBounds = [2,2],
sortBounds = 10,
nInits = 2,
nNeuronsPerJob = 1,
nInitsPerJob = 2,
nSortsPerJob = 10,
compress = True )
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(nSorts = 50,
nBoxes = 10,
nTrain = 6,
nValid = 4,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve( 'crossValid_citest0',
crossValid = crossValid,
).save( True )
from TuningTools.PreProc import *
#ppCol = PreProcCollection( PreProcChain( MapStd() ) )
ppCol = PreProcChain( Norm1() )
from TuningTools.TuningJob import fixPPCol
ppCol = fixPPCol(ppCol)
Conv2D(16, kernel_size=(3, 3), activation='relu',
input_shape=(10, 10, 1))) # 8X8
model.add(Conv2D(32, (3, 3), activation='relu')) # 6X6
model.add(Flatten())
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu'))
model.add(Dense(1))
model.add(Activation('tanh'))
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles(
outputFolder='config',
sortBounds=10,
nInits=1,
#neuronBounds = [1,6],
nNeuronsPerJob=1,
models=model,
nInitsPerJob=1,
nSortsPerJob=10,
prefix='job',
compress=True)
#from TuningTools import TuningJobConfigArchieve
#with TuningJobConfigArchieve('config/job_slim.hn0001.s0000.i0000.pic.gz') as (n,s,i,m):
# print m
#from TuningTools.CrossValid import CrossValid, CrossValidArchieve
#crossValid = CrossValid(nSorts = 10,
# nBoxes = 10,
# nTrain = 9,
# nValid = 1,
#!/usr/bin/env python
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles(outputFolder='config.nn5to6_sorts5_5by5_inits5_5by5',
neuronBounds=[5, 6],
sortBounds=5,
nInits=5,
nNeuronsPerJob=2,
nInitsPerJob=5,
nSortsPerJob=5,
compress=True)
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(
nSorts=5,
nBoxes=10,
nTrain=6,
nValid=4,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve(
'crossValid_5sorts',
crossValid=crossValid,
).save(True)
from TuningTools.PreProc import *
ppCol = PreProcCollection(PreProcChain(MapStd()))
place = PreProcArchieve('ppMapStd', ppCol=ppCol).save()
nEt = 2
nEta = 2
nSorts = 2
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles(outputFolder='config_citest0',
neuronBounds=[2, 2],
sortBounds=2,
nInits=1,
nNeuronsPerJob=1,
nInitsPerJob=1,
nSortsPerJob=2,
compress=True)
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(
nSorts=nSorts,
nBoxes=5,
nTrain=3,
nValid=2,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve(
'crossValid_citest0',
crossValid=crossValid,
).save(True)
from TuningTools.PreProc import *
#ppCol = PreProcCollection( PreProcChain( MapStd() ) )
#!/usr/bin/env python
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles( outputFolder = 'config.nn5to6_sorts5_5by5_inits5_5by5',
neuronBounds = [5, 6],
sortBounds = 5,
nInits = 5,
nNeuronsPerJob = 2,
nInitsPerJob = 5,
nSortsPerJob = 5,
compress = True )
from TuningTools.CrossValid import CrossValid, CrossValidArchieve
crossValid = CrossValid(nSorts = 5,
nBoxes = 10,
nTrain = 6,
nValid = 4,
#nTest=args.nTest,
#seed=args.seed,
#level=args.output_level
)
place = CrossValidArchieve( 'crossValid_5sorts',
crossValid = crossValid,
).save( True )
from TuningTools.PreProc import *
ppCol = PreProcCollection( PreProcChain( MapStd() ) )
place = PreProcArchieve( 'ppMapStd', ppCol = ppCol ).save()
from TuningTools.CreateTuningJobFiles import createTuningJobFiles
createTuningJobFiles(outputFolder='configs.n5to10.jk.inits_20by20',
neuronBounds=[5, 10],
sortBounds=10,
nInits=100,
nNeuronsPerJob=1,
nInitsPerJob=20,
nSortsPerJob=1,
prefix='job_config_slim',
compress=True)
#from TuningTools.CrossValid import CrossValid, CrossValidArchieve
#crossValid = CrossValid(nSorts = 10,
# nBoxes = 10,
# nTrain = 9,
# nValid = 1,
# )
#place = CrossValidArchieve( 'crossValid',
# crossValid = crossValid,
# ).save( True )
#from TuningTools.PreProc import *
#ppCol = PreProcChain( Norm1() )
#from TuningTools.TuningJob import fixPPCol
#ppCol = fixPPCol(ppCol)
#place = PreProcArchieve( 'ppFile', ppCol = ppCol ).save()
