def _get_weights(self, var_name, shape, init=tl.initializers.random_normal()):
""" Get trainable variables. """
weight = get_variable_with_initializer(scope_name=self.name, var_name=var_name, shape=shape, init=init)
if self._weights is None:
self._weights = list()
self._weights.append(weight) # Add into the weight collection
return weight
def _get_weights(self, var_name, shape, init=tl.initializers.random_normal(), trainable=True):
""" Get trainable variables. """
weight = get_variable_with_initializer(scope_name=self.name, var_name=var_name, shape=shape, init=init)
if trainable is True:
if self._trainable_weights is None:
self._trainable_weights = list()
self._trainable_weights.append(weight)
else:
if self._nontrainable_weights is None:
self._nontrainable_weights = list()
self._nontrainable_weights.append(weight)
return weight
def _get_weights(self,
var_name,
shape,
init=tl.initializers.random_normal()):
""" Get trainable variables. """
weight = get_variable_with_initializer(scope_name=self.name,
var_name=var_name,
shape=shape,
init=init)
if self._weights is None:
self._weights = list()
self._weights.append(weight) # Add into the weight collection
return weight
def _get_weights(self,
var_name,
shape,
init=tl.initializers.random_normal()):
weight = get_variable_with_initializer(scope_name=self.name,
var_name=var_name,
shape=shape,
init=init)
if self._weights is None:
self._weights = list()
self._weights.append(weight) # Add into the weight collection
# self.__setattr__(var_name, weight) # FIXME: prefer to remove this line, the weights should be manually defined as members of the Layer
return weight
