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91 lines
2.8 KiB
91 lines
2.8 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 numpy as np
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import warnings
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"""
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Class of all kinds of Average.
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All Averages are accomplished via Python totally.
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They do not change Paddle's Program, nor do anything to
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modify NN model's configuration. They are completely
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wrappers of Python functions.
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"""
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__all__ = ["WeightedAverage"]
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def _is_number_(var):
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return isinstance(var, int) or isinstance(var, float) or (isinstance(
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var, np.ndarray) and var.shape == (1, ))
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def _is_number_or_matrix_(var):
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return _is_number_(var) or isinstance(var, np.ndarray)
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class WeightedAverage(object):
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"""
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Calculate weighted average.
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The average calculating is accomplished via Python totally.
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They do not change Paddle's Program, nor do anything to
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modify NN model's configuration. They are completely
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wrappers of Python functions.
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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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avg = fluid.average.WeightedAverage()
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avg.add(value=2.0, weight=1)
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avg.add(value=4.0, weight=2)
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avg.eval()
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# The result is 3.333333333.
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# For (2.0 * 1 + 4.0 * 2) / (1 + 2) = 3.333333333
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"""
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def __init__(self):
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warnings.warn(
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"The %s is deprecated, please use fluid.metrics.Accuracy instead." %
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(self.__class__.__name__), Warning)
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self.reset()
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def reset(self):
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self.numerator = None
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self.denominator = None
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def add(self, value, weight):
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if not _is_number_or_matrix_(value):
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raise ValueError(
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"The 'value' must be a number(int, float) or a numpy ndarray.")
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if not _is_number_(weight):
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raise ValueError("The 'weight' must be a number(int, float).")
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if self.numerator is None or self.denominator is None:
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self.numerator = value * weight
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self.denominator = weight
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else:
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self.numerator += value * weight
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self.denominator += weight
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def eval(self):
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if self.numerator is None or self.denominator is None:
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raise ValueError(
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"There is no data to be averaged in WeightedAverage.")
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return self.numerator / self.denominator
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