def __init__(self
# Runner-general
,data_location = './normalized.pkl.gz'
,test_run = False
,cross_val = True
# Classifier-general
,epochs = 1000
,batch_size = 100
,max_layer_sizes = 0
,encdecs_name = ""
,model_name = ""
# Classifier-specific
,encdec_optimizers = ('rmsprop',)
,class_optimizers = ('adadelta',)
,class_losses = ('categorical_crossentropy',)
,drop_rates = (0.0,)
,gauss_base_sigmas = (0.0,)
,gauss_sigma_factors = (1.0,)
,l1s = (0.0,)
,l2s = (0.0,)
,enc_use_drop = False
,enc_use_noise = True
,mod_use_drop = True
,mod_use_noise = False
,catalog_name = ""
,starting_index = 0):
self.data_location = data_location or default_data_location
with gzip.open(data_location, 'rb') as f:
self.data = cPickle.load(f)
if cross_val:
self.cross_val_drops = list(self.data.keys()) if cross_val else []
else:
self.cross_val_drops = []
self.cross_val_index = starting_index if cross_val else -1
self.cross_val_test_data = None
if test_run:
self.epochs = 4
self.batch_size = 1000
else:
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.max_layer_sizes = int(max_layer_sizes)
self.encdecs_name = str(encdecs_name) or str(model_name) or time_str()
self.model_name = str(model_name) or str(encdecs_name) or time_str()
self.encdec_optimizers = map(str, encdec_optimizers)
self.class_optimizers = map(str, class_optimizers)
self.class_losses = map(str, class_losses)
self.drop_rates = map(float, drop_rates)
self.gauss_base_sigmas = map(float, gauss_base_sigmas)
self.gauss_sigma_factors = map(float, gauss_sigma_factors)
self.l1s = map(float, l1s)
self.l2s = map(float, l2s)
self.enc_use_drop = bool(enc_use_drop)
self.enc_use_noise = bool(enc_use_noise)
self.mod_use_drop = bool(mod_use_drop)
self.mod_use_noise = bool(mod_use_noise)
self.cat = DummyCatalogManager if test_run else (named_catalog(catalog_name) if catalog_name else CatalogManager)
def run(self):
while self.cross_val_index < len(self.cross_val_drops):
if self.cross_val_drops:
labels, data = self.split_data(True)
else:
labels, data = self.split_data(False)
gauss_combs = [(0, 1)] if 0 in self.gauss_base_sigmas else []
gauss_combs += [(gbs, gsf) for gbs in self.gauss_base_sigmas
for gsf in self.gauss_sigma_factors if gbs != 0]
for (i, eo) in enumerate(self.encdec_optimizers):
for (j, dr) in enumerate(self.drop_rates):
for (k, (gbs, gsf)) in enumerate(gauss_combs):
for (l, l1) in enumerate(self.l1s):
for (m, l2) in enumerate(self.l2s):
ifxval = ("-x%s" % self.cross_val_index
if self.cross_val_drops else "")
current_name = self.model_name + ifxval + (
"-%s%s%s%s" % (j, k, l, m))
enc_name = self.encdecs_name + ifxval + (
"-%s%s%s%s%s" % (i, j, k, l, m))
pc = Classifier(
data=data,
labels=labels,
batch_size=self.batch_size,
epochs=self.epochs,
model_name=current_name,
model_dir="",
encdecs_name=enc_name,
encdec_optimizer=eo,
# Not used for now, set later
class_optimizer=None,
# idem
class_loss=None,
drop_rate=dr,
gauss_base_sigma=gbs,
gauss_sigma_factor=gsf,
l1=l1,
l2=l2,
catalog_class=self.cat)
# TODO do something with max-layer-sizes here
pc.new_encdecs(compile=True,
use_dropout=self.enc_use_drop,
use_noise=self.enc_use_noise)
pc.pretrain()
pc.cap_data()
for (n,
co) in enumerate(self.class_optimizers):
for (o,
cl) in enumerate(self.class_losses):
suffix = "-%s%s%s%s%s%s%s" % (
i, j, k, l, m, n, o)
current_name = self.model_name + ifxval + suffix
start_time = time_str()
pc.catalog_manager.update(
{
'cross_validation': {
'enabled':
bool(self.cross_val_drops),
'drops':
self.cross_val_drops,
'index_reached':
self.cross_val_index
},
'settings': {
'0-eo':
self.encdec_optimizers,
'1-dr': self.drop_rates,
'2-(gbs,gsf)': gauss_combs,
'3-l1s': self.l1s,
'4-l2s': self.l2s,
'5-co':
self.class_optimizers,
'6-cl': self.class_losses
},
ifxval + suffix: {
'finished': False,
'start_time': start_time
}
}, self.model_name)
pc.model_name = current_name
pc.cls_opt = co
pc.cls_lss = cl
pc.new_model(
fresh=False,
compile=True,
use_dropout=self.mod_use_drop,
use_noise=self.mod_use_noise)
history = pc.finetune()
with gzip.open(
os.path.join(
os.path.dirname(
default_data_location),
current_name +
'.history.pkl.gz'),
'wb') as f:
cPickle.dump(history, f, 2)
pc.catalog_manager.set(
{
'finished':
True,
'test_accuracy':
pc.
_model_info['test_accuracy'],
'start_time':
start_time,
'end_time':
time_str()
}, self.model_name,
ifxval + suffix)
self.cross_val_index += 1
def __init__(
self
# Runner-general
,
data_location='./normalized.pkl.gz',
test_run=False,
cross_val=True
# Classifier-general
,
epochs=1000,
batch_size=100,
max_layer_sizes=0,
encdecs_name="",
model_name=""
# Classifier-specific
,
encdec_optimizers=('rmsprop', ),
class_optimizers=('adadelta', ),
class_losses=('categorical_crossentropy', ),
drop_rates=(0.0, ),
gauss_base_sigmas=(0.0, ),
gauss_sigma_factors=(1.0, ),
l1s=(0.0, ),
l2s=(0.0, ),
enc_use_drop=False,
enc_use_noise=True,
mod_use_drop=True,
mod_use_noise=False,
catalog_name="",
starting_index=0):
self.data_location = data_location or default_data_location
with gzip.open(data_location, 'rb') as f:
self.data = cPickle.load(f)
if cross_val:
self.cross_val_drops = list(self.data.keys()) if cross_val else []
else:
self.cross_val_drops = []
self.cross_val_index = starting_index if cross_val else -1
self.cross_val_test_data = None
if test_run:
self.epochs = 4
self.batch_size = 1000
else:
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.max_layer_sizes = int(max_layer_sizes)
self.encdecs_name = str(encdecs_name) or str(model_name) or time_str()
self.model_name = str(model_name) or str(encdecs_name) or time_str()
self.encdec_optimizers = map(str, encdec_optimizers)
self.class_optimizers = map(str, class_optimizers)
self.class_losses = map(str, class_losses)
self.drop_rates = map(float, drop_rates)
self.gauss_base_sigmas = map(float, gauss_base_sigmas)
self.gauss_sigma_factors = map(float, gauss_sigma_factors)
self.l1s = map(float, l1s)
self.l2s = map(float, l2s)
self.enc_use_drop = bool(enc_use_drop)
self.enc_use_noise = bool(enc_use_noise)
self.mod_use_drop = bool(mod_use_drop)
self.mod_use_noise = bool(mod_use_noise)
self.cat = DummyCatalogManager if test_run else (
named_catalog(catalog_name) if catalog_name else CatalogManager)
def run(self):
while self.cross_val_index < len(self.cross_val_drops):
if self.cross_val_drops:
labels, data = self.split_data(True)
else:
labels, data = self.split_data(False)
gauss_combs = [(0, 1)] if 0 in self.gauss_base_sigmas else []
gauss_combs += [(gbs, gsf)
for gbs in self.gauss_base_sigmas
for gsf in self.gauss_sigma_factors
if gbs != 0]
for (i,eo) in enumerate(self.encdec_optimizers):
for (j,dr) in enumerate(self.drop_rates):
for (k,(gbs,gsf)) in enumerate(gauss_combs):
for (l, l1) in enumerate(self.l1s):
for (m, l2) in enumerate(self.l2s):
ifxval = ("-x%s" % self.cross_val_index if self.cross_val_drops else "")
current_name = self.model_name + ifxval + ("-%s%s%s%s" % (j,k,l,m))
enc_name = self.encdecs_name + ifxval + ("-%s%s%s%s%s" % (i,j,k,l,m))
pc = Classifier(
data = data,
labels = labels,
batch_size = self.batch_size,
epochs = self.epochs,
model_name = current_name,
model_dir = "",
encdecs_name = enc_name,
encdec_optimizer = eo,
# Not used for now, set later
class_optimizer = None,
# idem
class_loss = None,
drop_rate = dr,
gauss_base_sigma = gbs,
gauss_sigma_factor = gsf,
l1 = l1,
l2 = l2,
catalog_class = self.cat
)
# TODO do something with max-layer-sizes here
pc.new_encdecs(compile = True,
use_dropout = self.enc_use_drop,
use_noise = self.enc_use_noise)
pc.pretrain()
pc.cap_data()
for (n,co) in enumerate(self.class_optimizers):
for (o,cl) in enumerate(self.class_losses):
suffix = "-%s%s%s%s%s%s%s" % (i,j,k,l,m,n,o)
current_name = self.model_name + ifxval + suffix
start_time = time_str()
pc.catalog_manager.update({
'cross_validation': {
'enabled': bool(self.cross_val_drops),
'drops': self.cross_val_drops,
'index_reached': self.cross_val_index
},
'settings': {
'0-eo': self.encdec_optimizers,
'1-dr': self.drop_rates,
'2-(gbs,gsf)': gauss_combs,
'3-l1s': self.l1s,
'4-l2s': self.l2s,
'5-co': self.class_optimizers,
'6-cl': self.class_losses
},
ifxval + suffix: {
'finished': False,
'start_time': start_time
}
}, self.model_name)
pc.model_name = current_name
pc.cls_opt = co
pc.cls_lss = cl
pc.new_model(fresh = False,
compile = True,
use_dropout = self.mod_use_drop,
use_noise = self.mod_use_noise)
history = pc.finetune()
with gzip.open(os.path.join(os.path.dirname(default_data_location), current_name + '.history.pkl.gz'), 'wb') as f:
cPickle.dump(history, f, 2)
pc.catalog_manager.set({ 'finished': True,
'test_accuracy': pc._model_info['test_accuracy'],
'start_time': start_time,
'end_time': time_str() }, self.model_name, ifxval + suffix)
self.cross_val_index += 1
