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67 lines
2.0 KiB
67 lines
2.0 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 <string>
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#include <vector>
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#include "paddle/utils/Error.h"
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namespace paddle {
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struct Argument;
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/**
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* @brief Activation function is a function that transforms a set of input
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* signals into an output signals. The purpose of the activation function
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* is to introduce non-liearilty into the network.
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*
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* @note Common activation function are provieded, including linear,
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* sigmoid, softmax, sequence_max, relu, brelu, tanh, stanh,
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* softrelu, abs, square, exponential.
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*
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*/
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class ActivationFunction {
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public:
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static ActivationFunction* create(const std::string& type);
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static std::vector<std::string> getAllRegisteredTypes();
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ActivationFunction() {}
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virtual ~ActivationFunction() {}
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/**
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* @brief Foward propagation
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*
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* act.value <- f(act.value),
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* where f is the activation function.
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* Suppose that before calling forward(), act.value is x and
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* after forward() is called, act.value is y, then y = f(x).
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*
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* Usually, act is Layer::output_
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*/
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virtual Error __must_check forward(Argument& act) = 0;
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/**
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* @brief Backward propagaion
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*
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* x and y are defined in the above comment for forward().
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* - Before calling backward(), act.grad = dE / dy, where E is the error/cost
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* - After backward() returns, act.grad = dE / dx = (dE/dy) * (dy/dx)
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*/
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virtual Error __must_check backward(Argument& act) = 0;
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virtual const std::string& getName() const = 0;
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};
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} // namespace paddle
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