34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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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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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class KLDivLoss(nn.Layer):
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"""
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Distilling the Knowledge in a Neural Network
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"""
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def __init__(self, temperature=4):
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super(KLDivLoss, self).__init__()
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self.T = temperature
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def forward(self, y_s, y_t):
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p_s = F.log_softmax(y_s / self.T, axis=1)
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p_t = F.softmax(y_t / self.T, axis=1)
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loss = F.kl_div(p_s, p_t, reduction='sum') * (self.T**2) / y_s.shape[0]
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return {"loss_kldiv": loss}
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