63 lines
1.9 KiB
63 lines
1.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/utils/ThreadLocal.h"
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namespace paddle {
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/**
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* @brief A layer for data normalization
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* - Input: One and only one input layer is accepted. The input layer must
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* be DataLayer with dense data type.
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* - Output: The normalization of the input data
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*
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* Reference:
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* LA Shalabi, Z Shaaban, B Kasasbeh. Data mining: A preprocessing engine
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*
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* Three data normalization methoeds are considered
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* - z-score: y = (x-mean)/std
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* - min-max: y = (x-min)/(max-min)
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* - decimal-scaling: y = x/10^j, where j is the smallest integer such that
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*max(|y|)<1
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*/
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class DataNormLayer : public Layer {
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public:
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enum NormalizationStrategy { kZScore = 0, kMinMax = 1, kDecimalScaling = 2 };
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explicit DataNormLayer(const LayerConfig& config) : Layer(config) {}
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~DataNormLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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protected:
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int mode_;
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std::unique_ptr<Weight> weight_;
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MatrixPtr min_;
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MatrixPtr rangeReciprocal_; // 1/(max-min)
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MatrixPtr mean_;
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MatrixPtr stdReciprocal_; // 1/std
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MatrixPtr decimalReciprocal_; // 1/10^j
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};
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} // namespace paddle
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