def _create_slot_var(self, var, name):
"""Create slot variable for the given Variable
Typical usage is to create variables to hold moving average
of the given Variable
Parameters
----------
var : theano.SharedVariable
Variable of which size and dtype are used to create slot.
name : str
The name of slot.
Returns
-------
Variable
Wrapped Variable of the resulting slot variable.
"""
value = var.get_value(borrow=True)
var_name = var.name.split(':')[0]
name = '/'.join([var_name, name])
var = make_variable(
name=name, shape=value.shape, dtype=value.dtype,
initializer=fetch_initializer('ConstantInitializer')(0),
broadcastable=var.broadcastable)
self._create_parameter_slot(name, var, train=False, serialize=True)
return var.unwrap()
def _instantiate_parameters(self, input_shape):
dim, fmt = len(input_shape), luchador.get_nn_conv_format()
channel = 1 if dim == 2 or fmt == 'NCHW' else 3
self._axes = tuple(i for i in range(dim) if not i == channel)
shape = tuple(input_shape[i] for i in range(dim) if i == channel)
const_init = fetch_initializer('ConstantInitializer')
if self.get_parameter_variable('mean') is None:
mean = wrapper.make_variable(
name='mean', shape=shape,
initializer=const_init(0), trainable=False)
self.set_parameter_variables(mean=mean)
if self.get_parameter_variable('var') is None:
var = wrapper.make_variable(
name='var', shape=shape,
initializer=const_init(1), trainable=False)
self.set_parameter_variables(var=var)
if self.get_parameter_variable('scale') is None:
scale = wrapper.make_variable(
name='scale', shape=shape, trainable=True,
initializer=const_init(self.args['scale']))
self.set_parameter_variables(scale=scale)
if self.get_parameter_variable('offset') is None:
offset = wrapper.make_variable(
name='offset', shape=shape, trainable=True,
initializer=const_init(self.args['offset']))
self.set_parameter_variables(offset=offset)
def _create_slot_var(self, var, name):
"""Create slot variable for the given Variable
Typical usage is to create variables to hold moving average
of the given Variable
Parameters
----------
var : theano.SharedVariable
Variable of which size and dtype are used to create slot.
name : str
The name of slot.
Returns
-------
Variable
Wrapped Variable of the resulting slot variable.
"""
value = var.get_value(borrow=True)
var_name = var.name.split(':')[0]
name = '/'.join([var_name, name])
var = make_variable(
name=name,
shape=value.shape,
dtype=value.dtype,
initializer=fetch_initializer('ConstantInitializer')(0),
broadcastable=var.broadcastable)
self._create_parameter_slot(name, var, train=False, serialize=True)
return var.unwrap()
def _get_alpha(self):
_alpha = self.args['alpha']
initializer = fetch_initializer('ConstantInitializer')(value=_alpha)
alpha = make_variable(name='alpha', shape=[], initializer=initializer)
self._create_parameter_slot(
'alpha', val=alpha, train=True, serialize=True)
return alpha.unwrap()
def _create_slot(self, initial_value, name):
"""Create slot variable independant to gradients and parameters
Example use is beta parameters in Adam and Adamax optimizer.
Only scalar type is supported.
Parameters
----------
initial_value : number
Initial value of the resulting slot
name : str
The name of slot.
Returns
-------
Variable
Wrapped Variable of the resulting slot variable.
"""
init = fetch_initializer('ConstantInitializer')(initial_value)
var = make_variable(name=name,
shape=[],
broadcastable=True,
initializer=init)
self._create_parameter_slot(name, var, train=False, serialize=True)
return var.unwrap()
def _get_bias_init(config):
config = config or {
'typename': 'ConstantInitializer',
'args': {
'value': 0.1
}
}
return fetch_initializer(config['typename'])(**config.get('args', {}))
def _create_slot(self, initial_value, name):
"""Create slot variable independant to gradients and parameters
Example use is beta parameter in Adamax optimizer.
Currently only scalar type is supported.
"""
init = fetch_initializer('ConstantInitializer')(initial_value)
var = make_variable(name=name, shape=[], initializer=init)
self._create_parameter_slot(
name=name, val=var, train=False, serialize=True)
return var.unwrap()
def _create_slot(self, initial_value, name):
"""Create slot variable independant to gradients and parameters
Example use is beta parameter in Adamax optimizer.
Currently only scalar type is supported.
"""
init = fetch_initializer('ConstantInitializer')(initial_value)
var = make_variable(name=name, shape=[], initializer=init)
self._create_parameter_slot(name=name,
val=var,
train=False,
serialize=True)
return var.unwrap()
def _create_slot_var(self, var, slot_name):
"""Create slot variable for the given Variable
Typical usage is to create variables to hold moving average
of the given Variable
"""
var_name = var.name.split(':')[0]
name = '/'.join([var_name, slot_name])
slot_var = make_variable(
name=name, shape=var.get_shape(), dtype=var.dtype,
initializer=fetch_initializer('ConstantInitializer')(0))
self._create_parameter_slot(
name=name, val=slot_var, train=False, serialize=True)
return slot_var.unwrap()
def _create_slot_var(self, var, slot_name):
"""Create slot variable for the given Variable
Typical usage is to create variables to hold moving average
of the given Variable
"""
var_name = var.name.split(':')[0]
name = '/'.join([var_name, slot_name])
slot_var = make_variable(
name=name,
shape=var.get_shape(),
dtype=var.dtype,
initializer=fetch_initializer('ConstantInitializer')(0))
self._create_parameter_slot(name=name,
val=slot_var,
train=False,
serialize=True)
return slot_var.unwrap()
def _instantiate_parameters(self, input_shape, dtype):
dim = len(input_shape)
shape = tuple(input_shape[i] for i in range(dim) if i == 1)
self._axes = tuple(i for i in range(dim) if not i == 1)
self._pattern = tuple((0 if i == 1 else 'x') for i in range(dim))
_LG.debug(' Shape: %s', shape)
_LG.debug(' Axes: %s', self._axes)
_LG.debug(' Pattern: %s', self._pattern)
const_init = fetch_initializer('ConstantInitializer')
if self._parameter_variables['mean'] is None:
mean = wrapper.make_variable(name='mean',
shape=shape,
trainable=False,
initializer=const_init(0),
dtype=dtype)
self.set_parameter_variables(mean=mean)
if self._parameter_variables['var'] is None:
var = wrapper.make_variable(name='var',
shape=shape,
trainable=False,
initializer=const_init(1),
dtype=dtype)
self.set_parameter_variables(var=var)
if self._parameter_variables['scale'] is None:
scale_val = self.args['scale']
scale = wrapper.make_variable(name='scale',
shape=shape,
trainable=True,
initializer=const_init(scale_val),
dtype=dtype)
self.set_parameter_variables(scale=scale)
if self._parameter_variables['offset'] is None:
offset_val = self.args['offset']
offset = wrapper.make_variable(name='offset',
shape=shape,
trainable=True,
initializer=const_init(offset_val),
dtype=dtype)
self.set_parameter_variables(offset=offset)
def _instantiate_parameters(self, input_shape):
dim, fmt = len(input_shape), luchador.get_nn_conv_format()
channel = 1 if dim == 2 or fmt == 'NCHW' else 3
self._axes = tuple(i for i in range(dim) if not i == channel)
shape = tuple(input_shape[i] for i in range(dim) if i == channel)
const_init = fetch_initializer('ConstantInitializer')
if self.get_parameter_variable('mean') is None:
mean = wrapper.make_variable(name='mean',
shape=shape,
initializer=const_init(0),
trainable=False)
self.set_parameter_variables(mean=mean)
if self.get_parameter_variable('var') is None:
var = wrapper.make_variable(name='var',
shape=shape,
initializer=const_init(1),
trainable=False)
self.set_parameter_variables(var=var)
if self.get_parameter_variable('scale') is None:
scale = wrapper.make_variable(name='scale',
shape=shape,
trainable=True,
initializer=const_init(
self.args['scale']))
self.set_parameter_variables(scale=scale)
if self.get_parameter_variable('offset') is None:
offset = wrapper.make_variable(name='offset',
shape=shape,
trainable=True,
initializer=const_init(
self.args['offset']))
self.set_parameter_variables(offset=offset)
def _create_slot(self, initial_value, name):
"""Create slot variable independant to gradients and parameters
Example use is beta parameters in Adam and Adamax optimizer.
Only scalar type is supported.
Parameters
----------
initial_value : number
Initial value of the resulting slot
name : str
The name of slot.
Returns
-------
Variable
Wrapped Variable of the resulting slot variable.
"""
init = fetch_initializer('ConstantInitializer')(initial_value)
var = make_variable(
name=name, shape=[], broadcastable=True, initializer=init)
self._create_parameter_slot(name, var, train=False, serialize=True)
return var.unwrap()
def _instantiate_parameters(self, input_shape, dtype):
dim = len(input_shape)
shape = tuple(input_shape[i] for i in range(dim) if i == 1)
self._axes = tuple(i for i in range(dim) if not i == 1)
self._pattern = tuple((0 if i == 1 else 'x') for i in range(dim))
_LG.debug(' Shape: %s', shape)
_LG.debug(' Axes: %s', self._axes)
_LG.debug(' Pattern: %s', self._pattern)
const_init = fetch_initializer('ConstantInitializer')
if self._parameter_variables['mean'] is None:
mean = wrapper.make_variable(
name='mean', shape=shape, trainable=False,
initializer=const_init(0), dtype=dtype)
self.set_parameter_variables(mean=mean)
if self._parameter_variables['var'] is None:
var = wrapper.make_variable(
name='var', shape=shape, trainable=False,
initializer=const_init(1), dtype=dtype)
self.set_parameter_variables(var=var)
if self._parameter_variables['scale'] is None:
scale_val = self.args['scale']
scale = wrapper.make_variable(
name='scale', shape=shape, trainable=True,
initializer=const_init(scale_val), dtype=dtype)
self.set_parameter_variables(scale=scale)
if self._parameter_variables['offset'] is None:
offset_val = self.args['offset']
offset = wrapper.make_variable(
name='offset', shape=shape, trainable=True,
initializer=const_init(offset_val), dtype=dtype)
self.set_parameter_variables(offset=offset)
def _get_weight_init(config):
config = config or {'typename': 'XavierInitializer'}
return fetch_initializer(config['typename'])(**config.get('args', {}))
def _get_filter_init(config):
"""Make filter initializer. Default to Xavier"""
config = config or {'typename': 'XavierInitializer'}
return fetch_initializer(
config['typename'])(**config.get('args', {}))
def _get_bias_init(config):
config = config or {
'typename': 'ConstantInitializer', 'args': {'value': 0.1}}
return fetch_initializer(config['typename'])(**config.get('args', {}))
def _get_weight_init(config):
config = config or {'typename': 'XavierInitializer'}
return fetch_initializer(config['typename'])(**config.get('args', {}))
def _get_bias_init(config):
"""Make bias initializer. Default to Constant (0.1)"""
config = config or {
'typename': 'ConstantInitializer', 'args': {'value': 0.1}}
return fetch_initializer(
config['typename'])(**config.get('args', {}))
