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239 lines
8.4 KiB
239 lines
8.4 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import six
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from .initializer import Initializer, Xavier, Constant
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from .regularizer import WeightDecayRegularizer
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__all__ = [
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'ParamAttr',
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'WeightNormParamAttr',
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]
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class ParamAttr(object):
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"""
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Parameter attributes object. To fine-tuning network training process, user
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can set parameter's attributes to control training details. Such as learning rate,
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regularization, trainable, do_model_average and the method to initialize param.
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Args:
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name(str): The parameter's name. Default None.
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initializer(Initializer): The method to initial this parameter. Default None.
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learning_rate(float): The parameter's learning rate. The learning rate when
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optimize is :math:`global\_lr * parameter\_lr * scheduler\_factor`.
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Default 1.0.
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regularizer(WeightDecayRegularizer): Regularization factor. Default None.
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trainable(bool): Whether this parameter is trainable. Default True.
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gradient_clip(BaseGradientClipAttr): The method to clip this parameter's
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gradient. Default None.
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do_model_average(bool): Whether this parameter should do model average
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when model average is enabled. Default True.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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w_param_attrs = fluid.ParamAttr(name="fc_weight",
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learning_rate=0.5,
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regularizer=fluid.regularizer.L2Decay(1.0),
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trainable=True)
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x = fluid.layers.data(name='X', shape=[1], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
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"""
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def __init__(self,
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name=None,
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initializer=None,
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learning_rate=1.0,
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regularizer=None,
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trainable=True,
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gradient_clip=None,
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do_model_average=True):
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self.name = name
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self.initializer = initializer
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self.learning_rate = learning_rate
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self.regularizer = regularizer
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self.trainable = trainable
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self.gradient_clip = gradient_clip
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self.do_model_average = do_model_average
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def _set_default_initializer(self, initializer):
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"""
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Set the default initializer, the initializer should be Constant,
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Uniform, Normal, Xavier, MSRA.
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Args:
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initializer(Initializer): the initializer to set.
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Returns:
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None
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"""
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if initializer is None:
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if self.initializer is None:
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raise ValueError("ParamAttr.initializer is not set")
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return
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if self.initializer is not None:
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return
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self.initializer = initializer
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def _set_default_param_initializer(self):
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"""
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Set the default initializer for the parameter with Xavier.
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Args:
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None.
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Returns:
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None.
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"""
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self._set_default_initializer(Xavier())
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def _set_default_bias_initializer(self):
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"""
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Set the default initializer for the bias with Constant(0.0).
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Args:
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None.
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Returns:
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None.
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"""
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self._set_default_initializer(Constant(0.0))
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@staticmethod
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def _to_attr(arg):
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"""
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Create ParamAttr[s].
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Args:
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arg: Arguments to initialize ParamAttr[s]. arg's type can be
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str, Initializer, float, WeightDecayRegularizer, BaseGradientClipAttr,
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bool, ParamAttr, or a list of above type.
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Returns:
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ParamAttr[s]: ParamAttr[s] initialized with arg.
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Raises:
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arg can not initialize a ParamAttr.
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"""
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if arg is None:
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return ParamAttr()
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elif isinstance(arg, list) or isinstance(arg, tuple):
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return [ParamAttr._to_attr(a) for a in arg]
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elif isinstance(arg, ParamAttr):
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return arg
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elif isinstance(arg, six.string_types):
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return ParamAttr(name=arg)
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elif isinstance(arg, Initializer):
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return ParamAttr(initializer=arg)
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elif isinstance(arg, WeightDecayRegularizer):
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return ParamAttr(regularizer=arg)
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elif isinstance(arg, bool):
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return ParamAttr._to_attr(None) if arg else False
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else:
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raise TypeError("{0} cast to ParamAttr".format(type(arg)))
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def _to_kwargs(self, with_initializer=False):
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"""
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Returns the attributes of this parameter.
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Args:
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with_initializer(bool): Whether to add initializer attr.
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Returns:
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Parameter attributes(map): The attributes of this parameter.
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"""
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kwargs = {
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'name': self.name,
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'optimize_attr': {
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'learning_rate': self.learning_rate
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},
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'regularizer': self.regularizer,
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'trainable': self.trainable,
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'gradient_clip_attr': self.gradient_clip,
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'do_model_average': self.do_model_average
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}
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if with_initializer:
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kwargs['initializer'] = self.initializer
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return kwargs
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class WeightNormParamAttr(ParamAttr):
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"""
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Used for weight Norm. Weight Norm is a reparameterization of the weight vectors
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in a neural network that decouples the magnitude of those weight vectors from
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their direction. Weight Norm has been implemented as discussed in this
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paper: `Weight Normalization: A Simple Reparameterization to Accelerate
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Training of Deep Neural Networks
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<https://arxiv.org/pdf/1602.07868.pdf>`_.
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Args:
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dim(int): Dimension over which to compute the norm. Default None.
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name(str): The parameter's name. Default None.
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initializer(Initializer): The method to initial this parameter. Default None.
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learning_rate(float): The parameter's learning rate. The learning rate when
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optimize is :math:`global\_lr * parameter\_lr * scheduler\_factor`.
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Default 1.0.
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regularizer(WeightDecayRegularizer): Regularization factor. Default None.
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trainable(bool): Whether this parameter is trainable. Default True.
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gradient_clip(BaseGradientClipAttr): The method to clip this parameter's
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gradient. Default None.
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do_model_average(bool): Whether this parameter should do model average.
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Default False.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
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fc = fluid.layers.fc(input=data,
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size=1000,
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param_attr=fluid.WeightNormParamAttr(
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dim=None,
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name='weight_norm_param'))
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"""
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# List to record the parameters reparameterized by weight normalization.
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# If these parameters are treated as Variable rather than Parameter,
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# it can be used to discriminate these parameters and help to serialize
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# these paramters for inference.
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params_with_weight_norm = []
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def __init__(self,
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dim=None,
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name=None,
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initializer=None,
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learning_rate=1.0,
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regularizer=None,
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trainable=True,
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gradient_clip=None,
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do_model_average=False):
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super(WeightNormParamAttr, self).__init__(
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name=name,
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initializer=initializer,
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learning_rate=learning_rate,
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regularizer=regularizer,
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trainable=trainable,
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gradient_clip=gradient_clip,
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do_model_average=do_model_average)
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self.dim = dim
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