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