def main(): parser = argparse.ArgumentParser(description='Visualizes a trained StackedLayers model.') parser.add_argument('--name', type = str, default = 'junk', help = 'Name for GitResultsManager results directory (default: junk)') #parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)') parser.add_argument('stackedLayersFilename', type = str, help = 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz') parser.add_argument('command', type = str, default = 'embed', choices = ['visall', 'embed'], nargs='?', help = 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.') args = parser.parse_args() resman.start(args.name, diary = not args.nodiary) saveDir = resman.rundir print 'Loading StackedLayers from %s' % args.stackedLayersFilename sl = loadFromPklGz(args.stackedLayersFilename) print 'Loaded these StackedLayers:' sl.printStatus() if args.command == 'embed': embed() elif args.command == 'visall': sl.visAll(saveDir) else: print 'Unknown command:', args.command resman.stop()
def main(): parser = argparse.ArgumentParser(description='Trains a StackedLayers model.') parser.add_argument('layerFilename', type = str, help = 'File defining layers, something like tica-10-15.layers') parser.add_argument('trainParamsFilename', type = str, help = 'File defining training parameters, something like tica-10-15.trainparams') parser.add_argument('--name', type = str, default = 'junk', help = 'Name for GitResultsManager results directory (default: junk)') parser.add_argument('--load', type = str, default = '', help = ('Load a previously created StackedLayers object. This can ' + 'be used to resume training a previously checkpointed, ' + 'partially trained StackedLayers object (default: none)')) parser.add_argument('--maxlayer', type = int, default = -1, help = ('Maximum layer to train, -1 to train all. Can be used to train' + 'only a subset of layers in an otherwise deeper model. Default: -1.')) parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)') args = parser.parse_args() resman.start(args.name, diary = not args.nodiary) saveDir = resman.rundir layerDefinitions = importFromFile(args.layerFilename, 'layers') trainParams = importFromFile(args.trainParamsFilename, 'trainParams') shutil.copyfile(args.layerFilename, os.path.join(saveDir, 'params.layers')) shutil.copyfile(args.trainParamsFilename, os.path.join(saveDir, 'params.trainparams')) # Experiment: train a few Stacked Layers with different ticas assert not args.load, 'Loading does not make sense here.' sls = [] for ii, hiddenWidth in enumerate((8,12,16,20,24,28,32,36,40)): treatmentDir = os.path.join(saveDir, 'treatment_%02d_%d' % (ii, hiddenWidth)) os.mkdir(treatmentDir) print '\n' * 4 + '*' * 40 print 'Treatment %d, width %d (results in %s)' % (ii, hiddenWidth, treatmentDir) print '*' * 40 assert layerDefinitions[2]['type'] == 'tica' layerDefinitions[2]['hiddenSize'] = (hiddenWidth, hiddenWidth) print 'Creating new StackedLayers object' sl = StackedLayers(layerDefinitions) sl.printStatus() sl.train(trainParams, saveDir = treatmentDir, quick = args.quick, maxlayer = args.maxlayer) sls.append(sl) fileFinal = os.path.join(saveDir, 'multiStackedLayers.pkl.gz') saveToFile(fileFinal, sls) resman.stop()
def main(): resman.start('junk', diary = True) params = {} randomParams = False if randomParams: params['hiddenISize'] = random.choice((2, 4, 6, 8, 10, 15, 20)) params['hiddenJSize'] = params['hiddenISize'] params['neighborhoodSize'] = random.choice((.1, .3, .5, .7, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0)) lambd = exp(random.uniform(log(.0001), log(10))) # Uniform in log space params['lambd'] = round(lambd, 1-int(floor(log10(lambd)))) # Just keep two significant figures params['randSeed'] = int(random.uniform(0,9999)) #params['dataWidth'] = random.choice((2, 4)) # just quick #params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28)) params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20)) # 25 and 28 are incomplete params['nColors'] = random.choice((1, 3)) else: params['hiddenISize'] = 15 params['hiddenJSize'] = params['hiddenISize'] params['neighborhoodSize'] = 1.0 params['lambd'] = .026 params['randSeed'] = 22 #params['dataWidth'] = random.choice((2, 4)) # just quick #params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28)) params['dataWidth'] = 10 params['nColors'] = 3 params['isColor'] = (params['nColors'] == 3) params['imgShape'] = ((params['dataWidth'], params['dataWidth'], 3) if params['isColor'] else (params['dataWidth'], params['dataWidth'])) params['maxFuncCalls'] = 300 params['whiten'] = True # Just false for Space Invaders dataset... params['dataCrop'] = None # Set to None to not crop data... paramsRand = params.copy() paramsRand['dataLoader'] = 'loadRandomData' paramsRand['dataPath'] = ('../data/random/randomu01_train_%02d_50000_%dc.pkl.gz' % (paramsRand['dataWidth'], paramsRand['nColors'])) paramsData = params.copy() #paramsData['dataLoader'] = 'loadAtariData' #paramsData['dataPath'] = ('../data/atari/space_invaders_train_%02d_50000_%dc.pkl.gz' # % (paramsData['dataWidth'], paramsData['nColors'])) paramsData['dataLoader'] = 'loadUpsonData3' paramsData['dataPath'] = ('../data/upson_rovio_3/train_%02d_50000_%dc.pkl.gz' % (paramsData['dataWidth'], paramsData['nColors'])) doRand = False if doRand: #resultsRand = reliablyRunTest((0, resman.rundir, '00000_rand', paramsRand, os.getcwd(), os.getenv('DISPLAY',''))) randResultsDir = os.makedirs(resman.rundir, 'rand') runTest(randResultsDir, paramsRand) #resultsData = reliablyRunTest((0, resman.rundir, '00000_data', paramsData, os.getcwd(), os.getenv('DISPLAY',''))) runTest(resman.rundir, paramsData) resman.stop()
def main(): dirs = [name for name in os.listdir('results') if os.path.isdir(os.path.join('results', name))] print 'last few results:' for dir in sorted(dirs)[-10:]: print ' ' + dir resman.start('junk', diary = False) saveDir = resman.rundir embed() resman.stop()
def main(): resman.start('junk', diary = False) #l1tica = loadFromPklGz('results/130402_033310_44cc757_master_psearchTica_UP/00022_data/tica.pkl.gz') l1tica = loadFromPklGz('results/130406_184751_3d90386_rapidhacks_upson3_1c_l1/tica.pkl.gz') # 1c Upson3 #layer1Whitener = loadFromPklGz('../data/upson_rovio_2/white/train_10_50000_1c.whitener.pkl.gz') layer1Whitener = loadFromPklGz('../data/upson_rovio_3/white/train_10_50000_1c.whitener.pkl.gz') layerSizePlan = [10, 15, 23, 35, 53, 80, 120, 180] stackedTica = StackedTICA(l1tica, layer1Whitener, '../data/upson_rovio_3/imgfiles/', layerSizePlan = layerSizePlan, isColor = False, saveDir = resman.rundir) if False: print 'JUST DEBUG...' pdb.set_trace() tica = stackedTica.ticas[0] pdb.set_trace() return #data,labels,strings = loadUpsonData3('../data/upson_rovio_3/train_10_50000_1c.pkl.gz') #data = loadFromPklGz('../data/upson_rovio_3/white/train_10_50000_1c.white.pkl.gz') #stackedTica.plotResults(resman.rundir, stackedTica.ticas[0], data, (10,10), (15,15)) #pdb.set_trace() params = {} params['hiddenISize'] = 15 params['hiddenJSize'] = params['hiddenISize'] params['neighborhoodSize'] = 1.0 params['lambd'] = .026 params['randSeed'] = 0 #params['dataWidth'] = 10 #params['nColors'] = 1 #params['isColor'] = (params['nColors'] == 3) #params['imgShape'] = ((params['dataWidth'], params['dataWidth'], 3) # if params['isColor'] else # (params['dataWidth'], params['dataWidth'])) params['maxFuncCalls'] = 3 #params['whiten'] = True # Just false for Space Invaders dataset... params['dataCrop'] = None # Set to None to not crop data... #params['dataCrop'] = 10000 # Set to None to not crop data... stackedTica.learnNextLayer(params) #print 'HACK FOR DEBUG' saveToFile(os.path.join(resman.rundir, 'stackedTica.pkl.gz'), stackedTica) # save learned model resman.stop()
def main(): print('This is not logged') resman.start('demo-GRM-module-run') for ii in range(3): print('This is logged', ii) print('This is logged (to stderr)', ii, file=sys.stderr) sleep(1) with open(resman.rundir + '/output_file_1.txt', 'w') as ff: ff.write('test output to file in results directory\n') resman.stop() print('Run finished')
def main(): dirs = [ name for name in os.listdir('results') if os.path.isdir(os.path.join('results', name)) ] print 'last few results:' for dir in sorted(dirs)[-10:]: print ' ' + dir resman.start('junk', diary=False) saveDir = resman.rundir embed() resman.stop()
def main(): parser = argparse.ArgumentParser( description='Visualizes a trained StackedLayers model.') parser.add_argument( '--name', type=str, default='junk', help='Name for GitResultsManager results directory (default: junk)') #parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help='Disable diary (default: diary is on)') parser.add_argument( 'stackedLayersFilename', type=str, help= 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz' ) parser.add_argument( 'command', type=str, default='embed', choices=['visall', 'embed'], nargs='?', help= 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.' ) args = parser.parse_args() resman.start(args.name, diary=not args.nodiary) saveDir = resman.rundir print 'Loading StackedLayers from %s' % args.stackedLayersFilename sl = loadFromPklGz(args.stackedLayersFilename) print 'Loaded these StackedLayers:' sl.printStatus() if args.command == 'embed': embed() elif args.command == 'visall': sl.visAll(saveDir) else: print 'Unknown command:', args.command resman.stop()
def main(): parser = argparse.ArgumentParser(description='Visualize performance of different human-activity-detection runs using the generated avg_pr.model.c0.1.e0.01.w3 files (or similar)') parser.add_argument('--name', type = str, default = 'junk', help = 'Name for GitResultsManager results directory (default: junk)') parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)') parser.add_argument('avg_pr_model_file', type = str, nargs = '+', help = 'Which avg_pr.model.c0.1.e0.01.w3 (or similar) files to load. Must give at least one.') args = parser.parse_args() resman.start(args.name, diary = not args.nodiary) visPerf(saveDir = resman.rundir, modelFiles = args.avg_pr_model_file) resman.stop()
def main(): parser = argparse.ArgumentParser(description='Makes features for the human activity deteciton dataset. Example usage:\n./humanObjectFeatures.py /path/to/data_obj_feats.txt') parser.add_argument('--name', type = str, default = 'junk', help = 'Name for GitResultsManager results directory (default: junk)') parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)') #parser.add_argument('stackedLayersFilename', type = str, # help = 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz') #parser.add_argument('command', type = str, default = 'embed', choices = ['visall', 'embed'], nargs='?', # help = 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.') parser.add_argument('--stackedlayers', type = str, default = '', help = 'Path to a *.pkl.gz file containing a pickeled StackedLayers object to load (default: None)') parser.add_argument('--fdata', type = str, default = '', help = 'Path to a "..../formatted/data/method_name" directory containing files like 0510175829_1.txt. Needed only if adding truth labels. (default: None)') parser.add_argument('--outfile', type = str, default = 'data_objs_feats_plus.txt', help = 'What to name the output file (default: data_objs_feats_plus.txt)') parser.add_argument('dataDir', type = str, default = 'data', help = 'Where to look for the "by_id" directory') parser.add_argument('data_obj_feats_file', type = str, help = 'Which data_objs_feats.txt file to load') args = parser.parse_args() resman.start(args.name, diary = not args.nodiary) saveDir = resman.rundir #print 'Loading StackedLayers from %s' % args.stackedLayersFilename #sl = loadFromPklGz(args.stackedLayersFilename) #print 'Loaded these StackedLayers:' #sl.printStatus() stackedLayers = None if args.stackedlayers: stackedLayers = loadFromPklGz(args.stackedlayers) makeFeats(dataDir = args.dataDir, featsFilename = args.data_obj_feats_file, saveDir = saveDir, outputFilename = args.outfile, quick = args.quick, stackedLayers = stackedLayers, formattedDataDir = args.fdata, featsBase = True, featsConst = 0, featsRand = 0, featsSL = False, featsTruth = False) resman.stop()
def main(): resman.start('junk', diary = False) stica = loadFromPklGz('results/130407_132841_76b6586_rapidhacks_upson3_1c_l2_first/stackedTica_mod.pkl.gz') layer1Whitener = loadFromPklGz('../data/upson_rovio_3/white/train_10_50000_1c.whitener.pkl.gz') layerSizePlan = [10, 15, 23, 35, 53, 80, 120, 180] visLayer = 1 largeSampleMatrix, labelMatrix, labelStrings = cached(randomSampleMatrixWithLabels, trainFilter, seed = 0, color = False, Nw = layerSizePlan[visLayer], Nsamples = 50000) seed = 0 Nw = layerSizePlan[visLayer-1] # e.g. 10 Nwbig = layerSizePlan[visLayer] # e.g. 15 Nwshift = Nwbig - Nw # e.g. 15 - 10 = 5 Nsamples = 1000 temp = getBigAndSmallerSamples(trainFilter, layer1Whitener, seed, False, Nw, Nwshift, Nsamples) largeSampleMatrix, stackedSmall, stackedSmallWhite, labelMatrix, labelStrings = temp pooled = stica.getRepresentation(largeSampleMatrix) plotTopActivations(pooled, largeSampleMatrix, (Nwbig,Nwbig), resman.rundir, nActivations = 50, nSamples = 20) pl = (pooled.T - pooled.mean(1)).T for ii in range(len(labelStrings)): print 'finding top for', labelStrings[ii] if labelMatrix[ii,:].sum() == 0: print ' skipping, no examples' continue avgActivationForClass = (pl * labelMatrix[ii,:]).mean(1) sortIdx = argsort(avgActivationForClass) topNeurons = sortIdx[-1:-(50+1):-1] plotTopActivations(pooled[topNeurons,:], largeSampleMatrix, (Nwbig,Nwbig), resman.rundir, nActivations = 50, nSamples = 20, prefix = 'topfor_%s' % labelStrings[ii]) resman.stop()
def main(): parser = argparse.ArgumentParser(description='Trains a StackedLayers model.') parser.add_argument('layerFilename', type = str, help = 'File defining layers, something like tica-10-15.layers') parser.add_argument('trainParamsFilename', type = str, help = 'File defining training parameters, something like tica-10-15.trainparams') parser.add_argument('--name', type = str, default = 'junk', help = 'Name for GitResultsManager results directory (default: junk)') parser.add_argument('--load', type = str, default = '', help = ('Load a previously created StackedLayers object. This can ' + 'be used to resume training a previously checkpointed, ' + 'partially trained StackedLayers object (default: none)')) parser.add_argument('--quick', action='store_true', help = 'Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help = 'Disable diary (default: diary is on)') args = parser.parse_args() resman.start(args.name, diary = not args.nodiary) saveDir = resman.rundir layerDefinitions = importFromFile(args.layerFilename, 'layers') trainParams = importFromFile(args.trainParamsFilename, 'trainParams') shutil.copyfile(args.layerFilename, os.path.join(saveDir, 'params.layers')) shutil.copyfile(args.trainParamsFilename, os.path.join(saveDir, 'params.trainparams')) if args.load: print 'Loading StackedLayers object from %s' % args.load sl = loadFromPklGz(args.load) else: print 'Creating new StackedLayers object' sl = StackedLayers(layerDefinitions) sl.printStatus() sl.train(trainParams, saveDir = saveDir, quick = args.quick) resman.stop()
import pdb import os, sys, time from numpy import * from PIL import Image from scipy.optimize.lbfgsb import fmin_l_bfgs_b from rica import RICA from GitResultsManager import resman, fmtSeconds from util.plotting import tile_raster_images from util.dataLoaders import loadFromPklGz, saveToFile if __name__ == '__main__': resman.start('junk', diary = False) data = loadFromPklGz('../data/upson_rovio_2/train_10_50000_1c.pkl.gz') data = data.T # Make into one example per column #data = data[:,:5000] # HACK!!!!!!!!! nFeatures = 100 lambd = .05 neighborhoodSize = 1.5 print '\nChosen TICA parameters' for key in ['nFeatures', 'lambd']: print ' %20s: %s' % (key, locals()[key]) random.seed(0) rica = RICA(imgShape = (10, 10),
def main(): resman.start('junk', diary=False) useIpython = True if useIpython: client = Client(profile='ssh') #client = Client() print 'IPython worker ids:', client.ids balview = client.load_balanced_view() resultsFilename = os.path.join(resman.rundir, 'allResults.pkl.gz') NN = 1000 allResults = [[None, None] for ii in range(NN)] experiments = [] cwd = os.getcwd() disp = os.environ['DISPLAY'] for ii in range(NN): params = {} random.seed(ii) params['hiddenISize'] = random.choice((2, 4, 6, 8, 10, 15, 20)) params['hiddenJSize'] = params['hiddenISize'] params['neighborhoodSize'] = random.choice( (.1, .3, .5, .7, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0)) lambd = exp(random.uniform(log(.0001), log(10))) # Uniform in log space params['lambd'] = round( lambd, 1 - int(floor(log10(lambd)))) # Just keep two significant figures params['randSeed'] = ii params['maxFuncCalls'] = 300 #params['dataWidth'] = random.choice((2, 4)) # just quick #params['dataWidth'] = random.choice((2, 3, 4, 6, 10, 15, 20, 25, 28)) params['dataWidth'] = random.choice( (2, 3, 4, 6, 10, 15, 20)) # 25 and 28 are incomplete params['nColors'] = random.choice((1, 3)) params['isColor'] = (params['nColors'] == 3) params['imgShape'] = ((params['dataWidth'], params['dataWidth'], 3) if params['isColor'] else (params['dataWidth'], params['dataWidth'])) params['whiten'] = False # Just false for Space Invaders dataset... params['dataCrop'] = None # Set to None to not crop data... paramsRand = params.copy() paramsRand['dataLoader'] = 'loadRandomData' paramsRand['dataPath'] = ( '../data/random/randomu01_train_%02d_50000_%dc.pkl.gz' % (paramsRand['dataWidth'], paramsRand['nColors'])) paramsData = params.copy() paramsData['dataLoader'] = 'loadAtariData' paramsData['dataPath'] = ( '../data/atari/space_invaders_train_%02d_50000_%dc.pkl.gz' % (paramsData['dataWidth'], paramsData['nColors'])) #paramsData['dataLoader'] = 'loadUpsonData' #paramsData['dataPath'] = ('../data/upson_rovio_2/train_%02d_50000_%dc.pkl.gz' # % (paramsData['dataWidth'], paramsData['nColors'])) if not useIpython: resultsRand = reliablyRunTest(resman.rundir, '%05d_rand' % ii, paramsRand) allResults[ii][0] = {'params': paramsRand, 'results': resultsRand} tmpFilename = os.path.join(resman.rundir, '.tmp.%f.pkl.gz' % time.time()) saveToFile(tmpFilename, allResults) os.rename(tmpFilename, resultsFilename) resultsData = reliablyRunTest(resman.rundir, '%05d_data' % ii, paramsData) allResults[ii][1] = {'params': paramsData, 'results': resultsData} tmpFilename = os.path.join(resman.rundir, '.tmp.%f.pkl.gz' % time.time()) saveToFile(tmpFilename, allResults) os.rename(tmpFilename, resultsFilename) else: experiments.append(((ii, 0), resman.rundir, '%05d_rand' % ii, paramsRand, cwd, disp)) experiments.append(((ii, 1), resman.rundir, '%05d_data' % ii, paramsData, cwd, disp)) # Start all jobs jobMap = balview.map_async(reliablyRunTest, experiments, ordered=False) #jobMap = balview.map_async(reliablyRunTest, range(10), ordered = False) for ii, returnValues in enumerate(jobMap): testId, params, results = returnValues print ii, 'Job', testId, 'finished.' allResults[testId[0]][testId[1]] = { 'params': params, 'results': results } tmpFilename = os.path.join(resman.rundir, '.tmp.%f.pkl.gz' % time.time()) saveToFile(tmpFilename, allResults) os.rename(tmpFilename, resultsFilename) #pdb.set_trace() print 'Finished all jobs.' resman.stop()
image_data_raw[:, (img_dim + 1) * ii:(img_dim + 1) * ii + img_dim] = tile_raster_images(X=samples, img_shape=(img_dim, img_dim), tile_shape=(n_samples, 1), tile_spacing=(1, 1)) image = Image.fromarray(image_data) image.save(os.path.join(output_dir, 'samples.png')) if plotRawAlso: image = Image.fromarray(image_data) image.save(os.path.join(output_dir, 'samplesRaw.png')) saveToFile(os.path.join(output_dir, 'rbm.pkl.gz'), rbm) return rbm, meanCosts if __name__ == '__main__': resman.start('junk', diary=True) datasets = load_mnist_data('../data/mnist.pkl.gz', shared=False) print 'done loading.' test_rbm(datasets=datasets, training_epochs=45, n_hidden=500, learning_rate=.002, output_dir=resman.rundir, quickHack=False) resman.stop()
def main(): parser = argparse.ArgumentParser( description='Trains a StackedLayers model.') parser.add_argument( 'layerFilename', type=str, help='File defining layers, something like tica-10-15.layers') parser.add_argument( 'trainParamsFilename', type=str, help= 'File defining training parameters, something like tica-10-15.trainparams' ) parser.add_argument( '--name', type=str, default='junk', help='Name for GitResultsManager results directory (default: junk)') parser.add_argument( '--load', type=str, default='', help=('Load a previously created StackedLayers object. This can ' + 'be used to resume training a previously checkpointed, ' + 'partially trained StackedLayers object (default: none)')) parser.add_argument( '--maxlayer', type=int, default=-1, help=( 'Maximum layer to train, -1 to train all. Can be used to train' + 'only a subset of layers in an otherwise deeper model. Default: -1.' )) parser.add_argument('--quick', action='store_true', help='Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help='Disable diary (default: diary is on)') args = parser.parse_args() resman.start(args.name, diary=not args.nodiary) saveDir = resman.rundir layerDefinitions = importFromFile(args.layerFilename, 'layers') trainParams = importFromFile(args.trainParamsFilename, 'trainParams') shutil.copyfile(args.layerFilename, os.path.join(saveDir, 'params.layers')) shutil.copyfile(args.trainParamsFilename, os.path.join(saveDir, 'params.trainparams')) # Experiment: train a few Stacked Layers with different ticas assert not args.load, 'Loading does not make sense here.' sls = [] for ii, hiddenWidth in enumerate((8, 12, 16, 20, 24, 28, 32, 36, 40)): treatmentDir = os.path.join(saveDir, 'treatment_%02d_%d' % (ii, hiddenWidth)) os.mkdir(treatmentDir) print '\n' * 4 + '*' * 40 print 'Treatment %d, width %d (results in %s)' % (ii, hiddenWidth, treatmentDir) print '*' * 40 assert layerDefinitions[2]['type'] == 'tica' layerDefinitions[2]['hiddenSize'] = (hiddenWidth, hiddenWidth) print 'Creating new StackedLayers object' sl = StackedLayers(layerDefinitions) sl.printStatus() sl.train(trainParams, saveDir=treatmentDir, quick=args.quick, maxlayer=args.maxlayer) sls.append(sl) fileFinal = os.path.join(saveDir, 'multiStackedLayers.pkl.gz') saveToFile(fileFinal, sls) resman.stop()
from numpy import * from PIL import Image from GitResultsManager import resman from tica import TICA from visualize import plotImageData, plotCov, printDataStats, plotImageRicaWW, plotRicaActivations, plotRicaReconstructions from util.dataLoaders import loadAtariData, saveToFile from util.dataPrep import PCAWhiteningDataNormalizer from util.misc import pt, pc '''Refactored version''' if __name__ == '__main__': resman.start('spaceinv_paramsearch', diary = True) saveDir = resman.rundir dataCrop = None ######################### # Parameters ######################### hiddenISize = 20 hiddenJSize = 20 neighborhoodParams = ('gaussian', 2.0, 0, 0) lambd = 1.5 epsilon = 1e-5 maxFuncCalls = 500 randSeed = 0 #dataCrop = 1000
def main(): parser = argparse.ArgumentParser( description= 'Makes features for the human activity deteciton dataset. Example usage:\n./humanObjectFeatures.py /path/to/data_obj_feats.txt' ) parser.add_argument( '--name', type=str, default='junk', help='Name for GitResultsManager results directory (default: junk)') parser.add_argument('--quick', action='store_true', help='Enable quick mode (default: off)') parser.add_argument('--nodiary', action='store_true', help='Disable diary (default: diary is on)') #parser.add_argument('stackedLayersFilename', type = str, # help = 'File where a StackedLayers model was stored, something like stackedLayers.pkl.gz') #parser.add_argument('command', type = str, default = 'embed', choices = ['visall', 'embed'], nargs='?', # help = 'What to do: one of {visall (save all plots), embed (drop into shell)}. Default: embed.') parser.add_argument( '--stackedlayers', type=str, default='', help= 'Path to a *.pkl.gz file containing a pickeled StackedLayers object to load (default: None)' ) parser.add_argument( '--fdata', type=str, default='', help= 'Path to a "..../formatted/data/method_name" directory containing files like 0510175829_1.txt. Needed only if adding truth labels. (default: None)' ) parser.add_argument( '--outfile', type=str, default='data_objs_feats_plus.txt', help='What to name the output file (default: data_objs_feats_plus.txt)' ) parser.add_argument('dataDir', type=str, default='data', help='Where to look for the "by_id" directory') parser.add_argument('data_obj_feats_file', type=str, help='Which data_objs_feats.txt file to load') args = parser.parse_args() resman.start(args.name, diary=not args.nodiary) saveDir = resman.rundir #print 'Loading StackedLayers from %s' % args.stackedLayersFilename #sl = loadFromPklGz(args.stackedLayersFilename) #print 'Loaded these StackedLayers:' #sl.printStatus() stackedLayers = None if args.stackedlayers: stackedLayers = loadFromPklGz(args.stackedlayers) makeFeats(dataDir=args.dataDir, featsFilename=args.data_obj_feats_file, saveDir=saveDir, outputFilename=args.outfile, quick=args.quick, stackedLayers=stackedLayers, formattedDataDir=args.fdata, featsBase=True, featsConst=0, featsRand=0, featsSL=False, featsTruth=False) resman.stop()
default='junk', help='Name of run for ResultsManager. Default: junk') parser.add_argument('--data', metavar='filename', type=str, help='Data filename to load') parser.add_argument('--rbm', metavar='filename', type=str, help='RBM to load from .pkl.gz file') parser.add_argument( '--plotEvery', metavar='steps', type=int, default=1, help= 'How many Gibbs sampling steps to take between each plot. Default: 1') args = parser.parse_args() if not args.data and not args.rbm: parser.error('Must specify --data or --rbm.') resman.start(args.name, diary=True) main(args.data if args.data else None, args.rbm if args.rbm else None, rundir=resman.rundir, plotEvery=args.plotEvery) resman.stop()
#! /usr/bin/env ipythonpl ''' Research code Jason Yosinski ''' from ica import testIca from rbm.utils import load_mnist_data from GitResultsManager import resman if __name__ == '__main__': '''Demonstrate ICA on the MNIST data set.''' resman.start('junk', diary = True) datasets = load_mnist_data('../data/mnist.pkl.gz', shared = False) testIca(datasets = datasets, savedir = resman.rundir, # comment out to show plots instead of saving smallImgHack = False, quickHack = False, ) resman.stop()