def __init__(self, size=0, **kwargs):
'''
required
size = (int) // local size
'''
super(LRN2D, self).__init__(name=generate_name('norm'), type=kLRN)
# required
assert size != 0, 'local size should be set'
self.layer.lrn_conf.local_size = size
init_values = get_init_values('lrn2d', **kwargs)
setval(self.layer.lrn_conf, **init_values)
def __init__(self, size=0, **kwargs):
'''
required
size = (int) // local size
'''
super(LRN2D, self).__init__(name=generate_name('norm'), type=kLRN)
# required
assert size != 0, 'local size should be set'
self.layer.lrn_conf.local_size = size
init_values = get_init_values('lrn2d', **kwargs)
setval(self.layer.lrn_conf, **init_values)
def __init__(self, **kwargs):
'''
optional
**kwargs
name = (string) // parameter name
lr = (float) // learning rate multiplier
wd = (float) // weight decay multiplier
init = (string) // init type {'constant','uniform','gaussian'}
value = (int) // value for 'constant'
scale = (float) // [low=-scale, high=scale] for 'uniform'
low = (float) // low value for 'uniform'
high = (float) // high value for 'uniform'
mean = (float) // mean for 'gaussian'
std = (float) // std for 'gaussian'
'''
fields = {
'lr_scale': kwargs['lr'] if 'lr' in kwargs else 1,
'wd_scale': kwargs['wd'] if 'wd' in kwargs else 1
}
self.param = Message('Param', **fields).proto
if not 'name' in kwargs:
setval(self.param, name=generate_name('param', 1))
else:
pname = kwargs['name']
# parameter name for RBM
if 'level' in kwargs:
pname += str(kwargs['level'])
if pname[0] == 'b':
pname += '2'
setval(self.param, name=pname)
if 'share_from' in kwargs:
setval(self.param, share_from=kwargs['share_from'])
if 'init' in kwargs:
init_values = get_init_values(kwargs['init'], **kwargs)
if not kwargs['init'] == 'none':
pgen = Message('ParamGen',
type=enumInitMethod(kwargs['init']),
**init_values)
del kwargs['init']
setval(self.param, init=pgen.proto)
else: # default: uniform
pgen = Message('ParamGen', type=enumInitMethod('uniform'))
setval(self.param, init=pgen.proto)
def __init__(self, **kwargs):
'''
optional
**kwargs
name = (string) // parameter name
lr = (float) // learning rate multiplier
wd = (float) // weight decay multiplier
init = (string) // init type {'constant','uniform','gaussian'}
value = (int) // value for 'constant'
scale = (float) // [low=-scale, high=scale] for 'uniform'
low = (float) // low value for 'uniform'
high = (float) // high value for 'uniform'
mean = (float) // mean for 'gaussian'
std = (float) // std for 'gaussian'
'''
fields = {'lr_scale' : kwargs['lr'] if 'lr' in kwargs else 1,
'wd_scale' : kwargs['wd'] if 'wd' in kwargs else 1
}
self.param = Message('Param', **fields).proto
if not 'name' in kwargs:
setval(self.param, name=generate_name('param', 1))
else:
pname = kwargs['name']
# parameter name for RBM
if 'level' in kwargs:
pname += str(kwargs['level'])
if pname[0] == 'b':
pname += '2'
setval(self.param, name=pname)
if 'share_from' in kwargs:
setval(self.param, share_from=kwargs['share_from'])
if 'init' in kwargs:
init_values = get_init_values(kwargs['init'], **kwargs)
if not kwargs['init'] == 'none':
pgen = Message('ParamGen', type=enumInitMethod(kwargs['init']),
**init_values)
del kwargs['init']
setval(self.param, init=pgen.proto)
else: # default: uniform
pgen = Message('ParamGen', type=enumInitMethod('uniform'))
setval(self.param, init=pgen.proto)
