提交 9601c2fc 编写于 作者: Y Yu Yang

Merge branch 'develop' of github.com:baidu/Paddle into feature/add_sum_cost_in_args

......@@ -20,6 +20,4 @@ from layers import *
from networks import *
from optimizers import *
from attrs import *
# This will enable operator overload for LayerOutput
import math as layer_math
import layer_math
......@@ -19,34 +19,34 @@ __all__ = [
def convert_and_compare(x, Type):
"""
Convert x to be the same type as Type and then convert back to
check whether there is a loss of information
:param x: object to be checked
:param Type: target type to check x over
"""
Convert x to be the same type as Type and then convert back to
check whether there is a loss of information
:param x: object to be checked
:param Type: target type to check x over
"""
return type(x)(Type(x)) == x
def is_compatible_with(x, Type):
"""
Check if x has a type compatible with Type
:param x: object to be checked
:param Type: target type to check x over
"""
Check if x has a type compatible with Type
:param x: object to be checked
:param Type: target type to check x over
"""
if type(x) == Type:
return True
try:
if float == Type or int == Type:
# avoid those types that can be converted to float/int but not very
# meaningful and could potentially lead to error
# i.e., str and bool typed value should not be used for initializing float/int variable
# avoid those types that can be converted to float/int but not very
# meaningful and could potentially lead to error
# i.e., str and bool typed value should not be used for initializing float/int variable
if not isinstance(x, str) and not isinstance(x, bool):
return convert_and_compare(x, Type)
elif bool == Type:
# should not use string type to initialize bool variable
# should not use string type to initialize bool variable
if not isinstance(x, str):
return convert_and_compare(x, Type)
else:
......@@ -88,6 +88,10 @@ class ParameterAttribute(object):
:type learning_rate: float or None
:param momentum: The parameter momentum. None means use global value.
:type momentum: float or None
:param gradient_clipping_threshold: gradient clipping threshold. If gradient
value larger than some value, will be
clipped.
:type gradient_clipping_threshold: float
:param sparse_update: Enable sparse update for this parameter. It will
enable both local and remote sparse update.
:type sparse_update: bool
......@@ -104,6 +108,7 @@ class ParameterAttribute(object):
l2_rate=None,
learning_rate=None,
momentum=None,
gradient_clipping_threshold=None,
sparse_update=False):
# initialize strategy.
if is_static:
......@@ -152,6 +157,11 @@ class ParameterAttribute(object):
self.attr['sparse_update'] = True
self.attr['sparse_remote_update'] = True
if gradient_clipping_threshold is not None and \
is_compatible_with(gradient_clipping_threshold, float):
self.attr['gradient_clipping_threshold'] = \
gradient_clipping_threshold
def set_default_parameter_name(self, name):
"""
Set default parameter name. If parameter not set, then will use default
......
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