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()