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47 lines
1.5 KiB
47 lines
1.5 KiB
4 years ago
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# 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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# TODO: define the initializers of Constant in neural network
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from ...fluid.initializer import ConstantInitializer
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__all__ = ['Constant']
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class Constant(ConstantInitializer):
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"""Implement the constant initializer.
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Args:
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value (float32): constant value to initialize the parameter
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Examples:
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.. code-block:: python
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import paddle
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import paddle.nn as nn
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data = paddle.rand([30, 10, 2], dtype='float32')
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linear = nn.Linear(2,
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4,
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weight_attr=nn.initializer.Constant(value=2.0))
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res = linear(data)
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print(linear.weight.numpy())
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#result is [[2. 2. 2. 2.],[2. 2. 2. 2.]]
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"""
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def __init__(self, value=0.0):
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if value is None:
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raise ValueError("value must not be none.")
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super(Constant, self).__init__(value=value, force_cpu=False)
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