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101 lines
4.2 KiB
101 lines
4.2 KiB
# Copyright (c) 2020 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 ...fluid.initializer import NormalInitializer
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from ...fluid.initializer import TruncatedNormalInitializer
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__all__ = ['Normal', 'TruncatedNormal']
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class Normal(NormalInitializer):
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"""The Random Normal (Gaussian) distribution initializer.
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Args:
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mean (float, optional): mean of the normal distribution. The default value is 0.0.
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std (float, optional): standard deviation of the normal distribution. The default value is 1.0.
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name(str, optional): The default value is None. Normally there is no need for user to set this
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property. For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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A parameter initialized by Random Normal (Gaussian) distribution.
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Examples:
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.. code-block:: python
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import paddle
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data = paddle.ones(shape=[3, 1, 2], dtype='float32')
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weight_attr = paddle.framework.ParamAttr(
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name="linear_weight",
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initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0))
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bias_attr = paddle.framework.ParamAttr(
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name="linear_bias",
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initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0))
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linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
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# linear.weight: [[ 2.1973135 -2.2697184]
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# [-1.9104223 -1.0541488]]
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# linear.bias: [ 0.7885926 -0.74719954]
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res = linear(data)
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# res: [[[ 1.0754838 -4.071067 ]]
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# [[ 1.0754838 -4.071067 ]]
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# [[ 1.0754838 -4.071067 ]]]
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"""
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def __init__(self, mean=0.0, std=1.0, name=None):
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assert mean is not None, 'mean should not be None'
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assert std is not None, 'std should not be None'
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super(Normal, self).__init__(loc=mean, scale=std, seed=0)
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class TruncatedNormal(TruncatedNormalInitializer):
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"""The Random TruncatedNormal (Gaussian) distribution initializer.
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Args:
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mean (float, optional): mean of the normal distribution. The default value is 0.0.
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std (float, optional): standard deviation of the normal distribution. The default value is 1.0.
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name(str, optional): The default value is None. Normally there is no need for user to set this
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property. For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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A parameter initialized by Random TruncatedNormal (Gaussian) distribution.
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Examples:
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.. code-block:: python
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import paddle
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data = paddle.ones(shape=[3, 1, 2], dtype='float32')
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weight_attr = paddle.framework.ParamAttr(
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name="linear_weight",
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initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0))
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bias_attr = paddle.framework.ParamAttr(
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name="linear_bias",
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initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0))
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linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
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# linear.weight: [[-1.0981836 1.4140984]
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# [ 3.1390522 -2.8266568]]
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# linear.bias: [-2.1546738 -1.6570673]
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res = linear(data)
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# res: [[[-0.11380529 -3.0696259 ]]
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# [[-0.11380529 -3.0696259 ]]
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# [[-0.11380529 -3.0696259 ]]
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"""
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def __init__(self, mean=0.0, std=1.0, name=None):
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assert mean is not None, 'mean should not be None'
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assert std is not None, 'std should not be None'
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super(TruncatedNormal, self).__init__(loc=mean, scale=std, seed=0)
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