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61 lines
2.5 KiB
61 lines
2.5 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 UniformInitializer
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__all__ = ['Uniform']
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class Uniform(UniformInitializer):
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"""The random uniform distribution initializer.
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Args:
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low (float, optional): lower boundary of the uniform distribution. The default value is -1.0.
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high (float, optional): upper boundary of the uniform 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 uniform 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.Uniform(low=-0.5, high=0.5))
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bias_attr = paddle.framework.ParamAttr(
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name="linear_bias",
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initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5))
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linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
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# linear.weight: [[-0.46245047 0.05260676]
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# [ 0.38054508 0.29169726]]
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# linear.bias: [-0.2734719 0.23939109]
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res = linear(data)
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# res: [[[-0.3553773 0.5836951]]
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# [[-0.3553773 0.5836951]]
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# [[-0.3553773 0.5836951]]]
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"""
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def __init__(self, low=-1.0, high=1.0, name=None):
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assert low is not None, 'low should not be None'
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assert high is not None, 'high should not be None'
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assert high >= low, 'high should greater or equal than low'
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super(Uniform, self).__init__(
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low=low, high=high, seed=0, diag_num=0, diag_step=0, diag_val=1.0)
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