49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# Copyright (c) 2021 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 absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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from ..utils import get_param_attr_dict
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class FC(nn.Layer):
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def __init__(self, embedding_size, class_num, **kwargs):
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super(FC, self).__init__()
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self.embedding_size = embedding_size
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self.class_num = class_num
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weight_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.XavierNormal())
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if 'weight_attr' in kwargs:
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weight_attr = get_param_attr_dict(kwargs['weight_attr'])
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bias_attr = None
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if 'bias_attr' in kwargs:
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bias_attr = get_param_attr_dict(kwargs['bias_attr'])
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self.fc = nn.Linear(
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self.embedding_size,
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self.class_num,
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weight_attr=weight_attr,
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bias_attr=bias_attr)
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def forward(self, input, label=None):
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out = self.fc(input)
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return out
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