reload(hdl) from hdl.models import SparseSlowModel from hdl.parallel_learners import SGD whitenpatches = 160000 l = SGD(model=SparseSlowModel(patch_sz=16,N=2560,T=16,sparse_cost='l1',slow_cost=None,lam_sparse=1.0), datasource='berkeleysegmentation',batchsize=16,save_every=20000,display_every=20000, ipython_profile=profile) databatch = l.get_databatch(whitenpatches) l.model.learn_whitening(databatch) l.model.setup() l.learn(iterations=100) from time import time as now t0 = now() l.learn(iterations=1000) print 'time = ', now() - t0 #l.change_target(.5) #l.learn(iterations=2000) #l.change_target(.5) #l.learn(iterations=2000) # #from hdl.display import display_final #display_final(l.model)
whitenpatches = 400 convwhitenfiltershp=(11,11) perc_var = 99. N = 641 kshp = (16,16) stride = (8,8) imsz = kshp[0]*6 imshp=(2,1,imsz,imsz) print 'Init...' l = SGD(model=ConvSparseSlowModel(imshp=imshp,convwhitenfiltershp=convwhitenfiltershp,perc_var=perc_var,N=N,kshp=kshp,stride=stride, sparse_cost='subspacel1mean',slow_cost=None,lam_sparse=1.,center_basis_functions=False), datasource='berkeleysegmentation',batchsize=imshp[0],save_every=20000,display_every=1000, ipython_profile=profile) print 'Estimate whitening...' databatch = l.get_databatch(whitenpatches) l.model.learn_whitening(databatch) l.model.setup() l.learn(iterations=40000) l.change_target(.5) l.learn(iterations=5000) l.change_target(.5) l.learn(iterations=5000) from hdl.display import display_final display_final(l.model)
datasource='YouTubeFaces_aligned', new_patch_size_fraction=1.5,pixels_from_center=80, batchsize=imshp[0], save_every=20000, display_every=10000, eta_target_maxupdate=.05, ipython_profile=profile) print 'Estimate whitening...' databatch = l.get_databatch(whitenpatches) l.model.learn_whitening(databatch) l.model.setup() print 'kshp:', l.model.kshp print 'imshp:', l.model.imshp print 'featshp:', l.model.featshp l.learn(iterations=160000) l.change_target(.5) l.learn(iterations=5000) l.change_target(.5) l.learn(iterations=5000) from hdl.display import display_final display_final(l.model) l.model.save() #ConvSparseSlowModel_patchsz064_ksz008_nchannels001_stride004_N031_NN031_convl2_subspacel1mean_None_2012-09-21_20-37-38 # took about 10k iterations to see anything.