fix norm api doc, test=develop (#27652)
* fix norm api doc, test=develop * fix error message, test=develop * fix api norm, test=develop * add adagrad, test=develop * fix bug, test=develop * fix bug, test=develop * add spetral_norm, test=develop * fix adagrad, test=develop * merge , test=developmy_2.0rc
zhang wenhui
4 years ago
committed by
GitHub
parent
3eb106da6d
commit
7a58431c0a
@ -0,0 +1,41 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest
|
||||
import numpy as np
|
||||
import paddle
|
||||
import paddle.fluid.core as core
|
||||
from paddle.fluid.op import Operator
|
||||
from op_test import OpTest
|
||||
import math
|
||||
|
||||
|
||||
class TestAdagradOpV2(unittest.TestCase):
|
||||
def test_v20_coverage(self):
|
||||
paddle.disable_static()
|
||||
inp = paddle.rand(shape=[10, 10])
|
||||
linear = paddle.nn.Linear(10, 10)
|
||||
out = linear(inp)
|
||||
loss = paddle.mean(out)
|
||||
adagrad = paddle.optimizer.Adagrad(
|
||||
learning_rate=0.1, parameters=linear.parameters())
|
||||
out.backward()
|
||||
adagrad.step()
|
||||
adagrad.clear_grad()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@ -0,0 +1,136 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from .optimizer import Optimizer
|
||||
from ..fluid import core
|
||||
from ..fluid import framework
|
||||
from ..fluid.framework import Variable
|
||||
|
||||
__all__ = ["Adagrad"]
|
||||
|
||||
|
||||
class Adagrad(Optimizer):
|
||||
"""
|
||||
The Adaptive Gradient optimizer (Adagrad for short) use an optimization described
|
||||
in paper: `Adaptive Subgradient Methods for Online Learning and
|
||||
Stochastic Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
|
||||
|
||||
The parameter ``param_out`` update rule with gradient ``grad``:
|
||||
|
||||
.. math::
|
||||
|
||||
moment\_out &= moment + grad * grad
|
||||
|
||||
param\_out &= param - \\frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
|
||||
|
||||
|
||||
The original paper does not have the ``epsilon`` attribute. It is added here
|
||||
in our implementation as also proposed `Per-parameter adaptive learning rate
|
||||
methods <http://cs231n.github.io/neural-networks-3/#ada>`_
|
||||
for numerical stability to avoid the division by zero error.
|
||||
|
||||
Args:
|
||||
learning_rate (float|Tensor): The learning rate used to update ``Parameter``.
|
||||
It can be a float value or a ``Variable`` with a float type.
|
||||
epsilon (float, optional): A small float value for numerical stability.
|
||||
The default value is 1e-06.
|
||||
parameters (list, optional): List of ``Tensor`` to update to minimize ``loss``. \
|
||||
This parameter is required in dygraph mode. \
|
||||
The default value is None in static mode, at this time all parameters will be updated.
|
||||
weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
|
||||
It canbe a float value as coeff of L2 regularization or \
|
||||
:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
|
||||
If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
|
||||
the regularization setting here in optimizer will be ignored for this parameter. \
|
||||
Otherwise, the regularization setting here in optimizer will take effect. \
|
||||
Default None, meaning there is no regularization.
|
||||
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
|
||||
some derived class of ``GradientClipBase`` . There are three cliping strategies,
|
||||
ClipGradByGlobalNorm, ClipGradByNorm and ClipGradByValue. Default None,
|
||||
meaning there is no gradient clipping.
|
||||
name (str, optional): Normally there is no need for user to set this property.
|
||||
For more information, please refer to :ref:`api_guide_Name`.
|
||||
The default value is None.
|
||||
initial_accumulator_value (float, optional): Initial value for moment accumulator.
|
||||
The default value is 0.0.
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
|
||||
import paddle
|
||||
import numpy as np
|
||||
|
||||
paddle.disable_static()
|
||||
inp = paddle.rand(shape=[10, 10])
|
||||
linear = paddle.nn.Linear(10, 10)
|
||||
out = linear(inp)
|
||||
loss = paddle.mean(out)
|
||||
adagrad = paddle.optimizer.Adagrad(learning_rate=0.1,
|
||||
parameters=linear.parameters())
|
||||
out.backward()
|
||||
adagrad.step()
|
||||
adagrad.clear_grad()
|
||||
|
||||
"""
|
||||
_moment_acc_str = "moment"
|
||||
|
||||
def __init__(self,
|
||||
learning_rate,
|
||||
epsilon=1.0e-6,
|
||||
parameters=None,
|
||||
weight_decay=None,
|
||||
grad_clip=None,
|
||||
name=None,
|
||||
initial_accumulator_value=0.0):
|
||||
assert learning_rate is not None
|
||||
assert epsilon is not None
|
||||
super(Adagrad, self).__init__(
|
||||
learning_rate=learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=weight_decay,
|
||||
grad_clip=grad_clip,
|
||||
name=name)
|
||||
self.type = "adagrad"
|
||||
self._epsilon = epsilon
|
||||
self.initial_accumulator_value = initial_accumulator_value
|
||||
|
||||
def _create_accumulators(self, block, parameters):
|
||||
assert isinstance(block, framework.Block)
|
||||
|
||||
for p in parameters:
|
||||
self._add_accumulator(
|
||||
self._moment_acc_str,
|
||||
p,
|
||||
fill_value=self.initial_accumulator_value)
|
||||
|
||||
def _append_optimize_op(self, block, param_and_grad):
|
||||
assert isinstance(block, framework.Block)
|
||||
|
||||
moment_acc = self._get_accumulator(self._moment_acc_str,
|
||||
param_and_grad[0])
|
||||
# Create the adagrad optimizer op
|
||||
adagrad_op = block.append_op(
|
||||
type=self.type,
|
||||
inputs={
|
||||
"Param": param_and_grad[0],
|
||||
"Grad": param_and_grad[1],
|
||||
"Moment": moment_acc,
|
||||
"LearningRate": self._create_param_lr(param_and_grad)
|
||||
},
|
||||
outputs={"ParamOut": param_and_grad[0],
|
||||
"MomentOut": moment_acc},
|
||||
attrs={"epsilon": self._epsilon},
|
||||
stop_gradient=True)
|
||||
|
||||
return adagrad_op
|
||||
Loading…
Reference in new issue