def setup(self, X):
self._model = ConvWhitenInputModel(imshp=self.imshp,
convwhitenfiltershp=self.whitenfiltershp, perc_var=self.whiten_perc_var)
self._learner = SGD(model=self._model, datasource='X', save_every=10000, display_every=1000, input_data=X)
self._setup_complete = True
def setup(self,X):
self._model=SparseSlowModel(patch_sz=None,D=X.shape[1],N=self.n_atoms,T=self.batchsize,
sparse_cost='l1',slow_cost=None,lam_sparse=self.alpha,perc_var=99.0)
self._learner = SGD(model=self._model,datasource='X',eta_target_maxupdate=.001,
batchsize=self.batchsize,save_every=10000,display_every=1000,input_data=X)
self._setup_complete = True
def setup(self,X):
self._model=ConvSparseSlowModel(imshp=self.imshp,kshp=self.kshp,T=self.batchsize,stride=self.strides,
sparse_cost=self.sparse_cost,slow_cost=self.slow_cost,lam_sparse=self.alpha,mask=self.mask,
whiten=self.whiten,convwhitenfiltershp=self.whitenfiltershp,perc_var=self.whiten_perc_var)
self._learner = SGD(model=self._model,datasource='X',eta_target_maxupdate=self.start_eta_target_maxupdate,
batchsize=self.batchsize,save_every=10000,display_every=1000,input_data=X)
self._setup_complete = True
class ConvWhitening(object):
def __init__(self, imshp, whitenfiltershp=(7, 7), whiten_perc_var=100.,
whitenpatches=1000,**kargs):
self.imshp = imshp
assert len(self.imshp) == 4
self.whitenfiltershp = whitenfiltershp
self.whiten_perc_var = whiten_perc_var
self.whitenpatches = whitenpatches
self._setup_complete = False
self._learning_complete = False
@property
def components_(self):
if self._learning_complete:
return self._model.convwhitenfilter.reshape((self._model.nchannels**2,-1)).T
else:
return
def setup(self, X):
self._model = ConvWhitenInputModel(imshp=self.imshp,
convwhitenfiltershp=self.whitenfiltershp, perc_var=self.whiten_perc_var)
self._learner = SGD(model=self._model, datasource='X', save_every=10000, display_every=1000, input_data=X)
self._setup_complete = True
def fit(self, X, y=None):
if not self._setup_complete: self.setup(X)
databatch = self._learner.get_databatch(self.whitenpatches)
self._learner.model.learn_whitening(databatch)
display_final(self._model)
self._learning_complete = True
return self
def transform(self, X, y=None):
return self._model(X.T).T
class SparseCoding(object):
def __init__(self,n_atoms, alpha=.1, max_iter=10000, batchsize=32, whitenpatches=40000):
self.n_atoms = n_atoms
self.alpha = alpha
self.max_iter = max_iter
self.batchsize = batchsize
self.whitenpatches = whitenpatches
self._setup_complete = False
self._learning_complete = False
@property
def components_(self):
if self._learning_complete:
return np.dot(self._model.dewhitenmatrix,self._model.A.get_value()).T
else:
return
def setup(self,X):
self._model=SparseSlowModel(patch_sz=None,D=X.shape[1],N=self.n_atoms,T=self.batchsize,
sparse_cost='l1',slow_cost=None,lam_sparse=self.alpha,perc_var=99.0)
self._learner = SGD(model=self._model,datasource='X',eta_target_maxupdate=.001,
batchsize=self.batchsize,save_every=10000,display_every=1000,input_data=X)
self._setup_complete = True
def get_learning_schedule(self):
train_epochs = 8
anneal_epochs = 2
iter_chunk = int(np.ceil(self.max_iter*(1./(train_epochs+anneal_epochs))))
train_iterations = iter_chunk
anneal_iterations = iter_chunk
train_list = [{'iterations':train_iterations} for epoch in range(train_epochs)]
anneal_list = [{'iterations':anneal_iterations,'change_target':.5} for epoch in range(anneal_epochs)]
return train_list + anneal_list
def fit(self,X,y=None):
if not self._setup_complete: self.setup(X)
databatch = self._learner.get_databatch(self.whitenpatches)
self._learner.model.learn_whitening(databatch)
self._learner.model.setup()
sched_list = self.get_learning_schedule()
for sdict in sched_list:
if sdict.has_key('change_target'):
self._learner.change_target(sdict['change_target'])
if sdict.has_key('batchsize'):
self._learner.batchsize = sdict['batchsize']
if sdict.has_key('iterations'):
self._learner.learn(iterations=sdict['iterations'])
else:
self._learner.learn()
display_final(self._model)
self._learning_complete = True
return self
def transform(self,X,y=None,output_function='infer',chunk_size=1000):
return self._model(X.T,output_function=output_function,chunk_size=chunk_size).T
class ConvSparseCoding(object):
def __init__(self,imshp, kshp, alpha=1., whiten=True,whitenfiltershp=(7,7),whiten_perc_var=100.,max_iter=10000, batchsize=4, whitenpatches=1000,
start_eta_target_maxupdate=.05,strides=(1,1),mask=True,
sparse_cost='l1',slow_cost=None,**kargs):
self.imshp = imshp
if len(self.imshp) == 3:
self.imshp = (batchsize, self.imshp[0], self.imshp[1], self.imshp[2])
else:
self.imshp = (batchsize, self.imshp[1], self.imshp[2], self.imshp[3])
self.kshp = kshp
self.whiten = whiten
self.whitenfiltershp = whitenfiltershp
self.whiten_perc_var = whiten_perc_var
self.n_atoms = kshp[0]
self.alpha = alpha
self.max_iter = max_iter
self.batchsize = batchsize
self.strides = strides
self.mask = mask
self.sparse_cost = sparse_cost
self.slow_cost = slow_cost
self.whitenpatches = whitenpatches
self.start_eta_target_maxupdate = start_eta_target_maxupdate
self._setup_complete = False
self._learning_complete = False
self.stop_center_basis_functions_epoch = kargs.get('stop_center_basis_functions_epoch',1)
@property
def components_(self):
if self._learning_complete:
return self._model.A.get_value().T
else:
return
def setup(self,X):
self._model=ConvSparseSlowModel(imshp=self.imshp,kshp=self.kshp,T=self.batchsize,stride=self.strides,
sparse_cost=self.sparse_cost,slow_cost=self.slow_cost,lam_sparse=self.alpha,mask=self.mask,
whiten=self.whiten,convwhitenfiltershp=self.whitenfiltershp,perc_var=self.whiten_perc_var)
self._learner = SGD(model=self._model,datasource='X',eta_target_maxupdate=self.start_eta_target_maxupdate,
batchsize=self.batchsize,save_every=10000,display_every=1000,input_data=X)
self._setup_complete = True
def get_learning_schedule(self):
train_epochs = 8
anneal_epochs = 2
iter_chunk = int(np.ceil(self.max_iter*(1./(train_epochs+anneal_epochs))))
train_iterations = iter_chunk
anneal_iterations = iter_chunk
train_list = [{'iterations':train_iterations} for epoch in range(train_epochs)]
anneal_list = [{'iterations':anneal_iterations,'change_target':.5} for epoch in range(anneal_epochs)]
return train_list + anneal_list
def fit(self,X,y=None):
if not self._setup_complete: self.setup(X)
databatch = self._learner.get_databatch(self.whitenpatches)
self._learner.model.learn_whitening(databatch)
self._learner.model.setup()
sched_list = self.get_learning_schedule()
for sind, sdict in enumerate(sched_list):
if sdict.has_key('change_target'):
self._learner.change_target(sdict['change_target'])
if sdict.has_key('batchsize'):
self._learner.batchsize = sdict['batchsize']
if sind == self.stop_center_basis_functions_epoch:
self._model.center_basis_functions = False
if sdict.has_key('iterations'):
self._learner.learn(iterations=sdict['iterations'])
else:
self._learner.learn()
display_final(self._model)
self._learning_complete = True
return self
def transform(self,X,y=None,output_function='infer',chunk_size=1000):
return self._model(X.T,output_function=output_function,chunk_size=chunk_size).T
