def test_convsparsenet(lam_sparse=.1,N=16,perc_var=100.):
from hdl.models import SparseSlowModel
from hdl.learners import SGD
whitenpatches = 1000
#model = ConvWhitenInputModel(imshp=(10,1,32,32),convwhitenfiltershp=(7,7),perc_var=100.)
model = ConvSparseSlowModel(imshp=(10,1,28,28),convwhitenfiltershp=(7,7),N=N,kshp=(7,7),perc_var=perc_var,lam_sparse=lam_sparse)
l = SGD(model=model,datasource='vid075-chunks',display_every=1000,save_every=10000,batchsize=model.imshp[0])
print 'whitenpatches', whitenpatches
print 'model.imshp', model.imshp
print 'model.convwhitenfiltershp', model.convwhitenfiltershp
databatch = l.get_databatch(whitenpatches)
l.model.learn_whitening(databatch)
l.model.setup()
l.learn(iterations=20000)
l.change_target(.5)
l.learn(iterations=5000)
l.change_target(.5)
l.learn(iterations=5000)
#l.learn(iterations=160000)
#l.change_target(.5)
#l.learn(iterations=20000)
#l.change_target(.5)
#l.learn(iterations=20000)
from hdl.display import display_final
display_final(l.model)
def go(l):
databatch = l.get_databatch(whitenpatches)
l.model.learn_whitening(databatch)
l.model.setup()
l.learn(iterations=350000)
l.change_target(.5)
l.learn(iterations=50000)
l.change_target(.5)
l.learn(iterations=50000)
l.change_target(.5)
l.learn(iterations=50000)
display_final(l.model)
def test_convsparsenet_subspace(lam_sparse=1.,lam_slow=1.,N=8,perc_var=100.,stride=(1,1)):
from hdl.models import ConvSparseSlowModel
from hdl.learners import SGD
whitenpatches = 1000
psz = 48
ksz = 16
convwhitenfiltershp=(15,15)
#model = ConvWhitenInputModel(imshp=(10,1,32,32),convwhitenfiltershp=(7,7),perc_var=100.)
model = ConvSparseSlowModel(imshp=(4,1,psz,psz),convwhitenfiltershp=convwhitenfiltershp,N=N,kshp=(ksz,ksz),stride=stride,
sparse_cost='subspacel1',
perc_var=perc_var,
lam_sparse=lam_sparse,
lam_slow=lam_slow,
mask=True,
force_subspace_orthogonal=True)
l = SGD(model=model,datasource='vid075-chunks',display_every=50,save_every=10000,
eta_target_maxupdate=0.5,
batchsize=model.imshp[0])
print 'whitenpatches', whitenpatches
print 'model.imshp', model.imshp
print 'model.convwhitenfiltershp', model.convwhitenfiltershp
databatch = l.get_databatch(whitenpatches)
l.model.learn_whitening(databatch)
l.model.setup()
l.learn(iterations=1000)
l.model.center_basis_functions=False
l.learn(iterations=9000)
l.change_target(.5)
l.learn(iterations=5000)
l.change_target(.5)
l.learn(iterations=5000)
#l.learn(iterations=160000)
#l.change_target(.5)
#l.learn(iterations=20000)
#l.change_target(.5)
#l.learn(iterations=20000)
from hdl.display import display_final
display_final(l.model)
def test_convsparsenet(lam_sparse=.1,N=16,perc_var=100.,stride=1,kshp=(7,7),batchsize=4):
from hdl.learners import SGD
whitenpatches = 1000
if isinstance(kshp,int): kshp = (kshp,kshp)
if isinstance(stride,int): stride = (stride,stride)
imszr = 5*stride[0] + kshp[0] - 1
imszc = 5*stride[1] + kshp[1] - 1
#model = ConvWhitenInputModel(imshp=(10,1,32,32),convwhitenfiltershp=(7,7),perc_var=100.)
model = ConvSparseSlowModel(imshp=(batchsize,1,imszr,imszc),convwhitenfiltershp=(7,7),N=N,kshp=kshp,stride=stride,
perc_var=perc_var,lam_sparse=lam_sparse)
l = SGD(model=model,datasource='vid075-chunks',display_every=50,save_every=10000,batchsize=model.imshp[0])
print 'whitenpatches', whitenpatches
print 'model.imshp', model.imshp
print 'model.convwhitenfiltershp', model.convwhitenfiltershp
databatch = l.get_databatch(whitenpatches)
l.model.learn_whitening(databatch)
l.model.setup()
l.learn(iterations=5000)
l.model.center_basis_functions = False
l.learn(iterations=15000)
l.change_target(.5)
l.learn(iterations=5000)
l.change_target(.5)
l.learn(iterations=5000)
#l.learn(iterations=160000)
#l.change_target(.5)
#l.learn(iterations=20000)
#l.change_target(.5)
#l.learn(iterations=20000)
from hdl.display import display_final
display_final(l.model)
def test_sparsenet_subspace(lam_sparse=1.,lam_slow=1.,N=8,perc_var=100.):
from hdl.models import SparseSlowModel
from hdl.learners import SGD
whitenpatches = 10000
psz = 16
#model = ConvWhitenInputModel(imshp=(10,1,32,32),convwhitenfiltershp=(7,7),perc_var=100.)
model = SparseSlowModel(patch_sz=psz,N=N,
sparse_cost='subspacel1',
perc_var=perc_var,
lam_sparse=lam_sparse,
lam_slow=lam_slow,T=48)
l = SGD(model=model,datasource='vid075-chunks',display_every=100,save_every=10000,batchsize=model.T)
print 'whitenpatches', whitenpatches
databatch = l.get_databatch(whitenpatches)
l.model.learn_whitening(databatch)
l.model.setup()
l.learn(iterations=5000)
l.change_target(.5)
l.learn(iterations=5000)
l.change_target(.5)
l.learn(iterations=5000)
#l.learn(iterations=160000)
#l.change_target(.5)
#l.learn(iterations=20000)
#l.change_target(.5)
#l.learn(iterations=20000)
from hdl.display import display_final
display_final(l.model)
l.model.learn_whitening(databatch)
newA = np.dot(l.model.whitenmatrix,newA)
newA = l.model.normalize_A(newA)
l.model.A.set_value(newA.astype(hdl.models.theano.config.floatX))
l.model.reset_functions()
return l
initial_target = l.eta_target_maxupdate
databatch = l.get_databatch(whitenpatches)
l.model.learn_whitening(databatch)
l.model.setup()
l.eta_target_maxupdate
iterations = 2000
l = learn_loop(l,iterations=iterations)
display_final(l.model,save_string='doubling_0')
doublings = 4
for doubling in range(doublings):
iterations *= 2
l.eta_target_maxupdate = initial_target
print 'Doubling model...'
l = double_patch_sz(l)
display_final(l.model,save_string='doubling_%d_before_learning'%(doubling+1))
l = learn_loop(l,iterations=iterations)
display_final(l.model,save_string='doubling_%d_after_learning'%(doubling+1))
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)
#model_name = 'SparseSlowModel_patchsz064_N2048_NN2048_l2_l1_None_2012-02-05_15-29-08/SparseSlowModel_patchsz064_N2048_NN2048_l2_l1_None.model'
# faces
#model_name = 'SparseSlowModel_patchsz032_N512_NN512_l2_l1_None_2012-02-09_11-40-37/SparseSlowModel_patchsz032_N512_NN512_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None_2012-02-09_11-47-43/SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz032_N512_NN512_l2_l1_None_2012-02-09_16-38-31/SparseSlowModel_patchsz032_N512_NN512_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz032_N512_NN512_l2_l1_None_2012-02-09_19-05-45/SparseSlowModel_patchsz032_N512_NN512_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N768_NN768_l2_l1_None_2012-02-09_19-07-07/SparseSlowModel_patchsz048_N768_NN768_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None_2012-02-09_19-08-50/SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N1024_NN1024_l2_l1_None_2012-02-10_16-23-20/SparseSlowModel_patchsz048_N1024_NN1024_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N1024_NN1024_l2_subspacel1_None_2012-02-10_17-22-55/SparseSlowModel_patchsz048_N1024_NN1024_l2_subspacel1_None.model'
model_name = 'SparseSlowModel_patchsz048_N4096_NN4096_l2_l1_None_2012-03-01_19-15-33/SparseSlowModel_patchsz048_N4096_NN4096_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N512_NN512_l2_subspacel1_None_2012-03-05_11-42-48/SparseSlowModel_patchsz048_N512_NN512_l2_subspacel1_None.model'
#fname = os.path.join(state_dir,model_name)
#m = SparseSlowModel()
#m.load(fname,reset_theano=False)
model_name = 'BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist_2012-03-13_15-24-27/BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist.model'
model_name = 'BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist_2012-03-15_15-56-39/BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist.model'
model_name = 'BinocColorModel_patchsz020_N3200_NN3200_l2_subspacel1_dist_2012-03-18_17-35-13/BinocColorModel_patchsz020_N3200_NN3200_l2_subspacel1_dist.model'
fname = os.path.join(state_dir,model_name)
m = BinocColorModel()
m.load(fname,reset_theano=False)
display_final(m)
print m