神经网络结构参数
2014-09-11 09:13
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在使用神经网络的时候，都需要输入每一个网络层的输入和输出，以及每个网络层的激活函数，为了方便后续根据不同的需要，选择合适的输入输出神经元个数和每一层的激活函数，因此，考虑将神经网络的结构参数单独写成一个类的形式，这样也方便以后进行拓展。
根据神经网络的结构特点，每一个神经网络层只要知道输入神经元个数，输出神经元个数，以及激活函数即可，因此，神经网络结构参数上面的几个参数。但是，对于如何设计一个好的结构来获得这些参数呢，目前，我的想法是使用一个字典类型的容器来储存这些数据，将每一层的名称作为键值来储存。
具体实现代码如下：
neuralnetworksparams.h
neuralnetworksparams.cpp
Test_NeuralNetworkParams.cpp
github地址：https://github.com/shengno/NeuralNetworksParams
版权所有，欢迎转载，转载请注明出处，谢谢
根据神经网络的结构特点，每一个神经网络层只要知道输入神经元个数，输出神经元个数，以及激活函数即可，因此，神经网络结构参数上面的几个参数。但是，对于如何设计一个好的结构来获得这些参数呢，目前，我的想法是使用一个字典类型的容器来储存这些数据，将每一层的名称作为键值来储存。
具体实现代码如下：
neuralnetworksparams.h
/*M///////////////////////////////////////////////////////////////////////////
// Copyright (c) 2014, sheng
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
//M*/
#ifndef NEURALNETWORKSPARAMS_H
#define NEURALNETWORKSPARAMS_H
#include <map>
#include <string>
class NeuralNetworksParams
{
public:
NeuralNetworksParams();
NeuralNetworksParams(const NeuralNetworksParams& RObject);
~NeuralNetworksParams();
bool AddInputlayerParams(int NumberOfInputUnits,
int TypeOfActivationFunction);
int GetNumberOfInputUnits();
int GetAFTypeOfInputlayer();
bool AddOutputlayerParams(int NumberOfOutputUnits,
int TypeOfActivationFunction);
int GetNumberOfOutputUnits();
int GetAFTypeOfOutputlayer();
bool AddHiddenlayerParams(int NumberOfUnits,
int TypeOfActivationFunction);
int GetNumberOfHiddenUnits(int IndexOfHiddenlayer);
int GetAFTypeOfHiddenlayer(int IndexOfHiddenlayer);
int GetNumberOfHiddenlayers() const;
private:
std::map<std::string, int> Params;
int NumberOfHiddenlayers;
static const std::string INPUTLAYER;
static const std::string OUTPUTLAYER;
static const std::string HIDDENLAYERPREFIX;
static const std::string ACTIVATIONFUNCTIONPREFIX;
bool Insert(const std::string &Key, int Value);
bool IsExist(const std::string& Key);
int GetValueByKey(const std::string& Key);
std::string GetKeyOfInputLayer() const;
std::string GetKeyOfInputLayerAFType() const;
std::string GetKeyOfOutputLayer() const;
std::string GetKeyOfOutputLayerAFType() const;
std::string GetKeyOfHiddenlayer(int Index) const;
std::string GetKeyOfHiddenlayerAFType(int Index) const;
};
#endif // NEURALNETWORKSPARAMS_Hneuralnetworksparams.cpp
/*M///////////////////////////////////////////////////////////////////////////
// Copyright (c) 2014, sheng
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
//M*/
#include "neuralnetworksparams.h"
#include "IntToString.h"
const std::string NeuralNetworksParams:: INPUTLAYER = "INPUTLAYER";
const std::string NeuralNetworksParams::OUTPUTLAYER = "OUTPUTLAYER";
const std::string NeuralNetworksParams::HIDDENLAYERPREFIX = "HIDDENLAYER_";
const std::string NeuralNetworksParams::ACTIVATIONFUNCTIONPREFIX = "ACTIVATIONFUNCTION_";
/**
* @brief NeuralNetworksParams::NeuralNetworksParams The default constructor.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
NeuralNetworksParams::NeuralNetworksParams() : Params(), NumberOfHiddenlayers(0)
{
}
/**
* @brief NeuralNetworksParams::NeuralNetworksParams The copy constructor
* @param RObject The object which is to be copyed.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
NeuralNetworksParams::NeuralNetworksParams(const NeuralNetworksParams &RObject) :
Params(RObject.Params), NumberOfHiddenlayers(RObject.NumberOfHiddenlayers)
{
}
/**
* @brief NeuralNetworksParams::~NeuralNetworksParams The destructor.
*
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
NeuralNetworksParams::~NeuralNetworksParams()
{
Params.clear();
}
/**
* @brief NeuralNetworksParams::AddInputlayerParams Add the inputlayer params
* @param NumberOfInputUnits The number of the input units
* @param TypeOfActivationFunction The type of the activation function of the
* intput layer
* @return true if the opartion is successed,
* false otherwise
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
bool NeuralNetworksParams::AddInputlayerParams(int NumberOfInputUnits,
int TypeOfActivationFunction)
{
if (NumberOfInputUnits < 1)
{
return false;
}
bool Result = false;
// insert the number of the input units into the params
std::string Key = GetKeyOfInputLayer();
Result = Insert(Key, NumberOfInputUnits);
// insert the typf of the AF of the input layer
Key = GetKeyOfInputLayerAFType();
Result = Result && Insert(Key, TypeOfActivationFunction);
return Result;
}
/**
* @brief NeuralNetworksParams::GetNumberOfInputUnits Get the number of the
* input layer
*
* @return The number of the input layer,
* return -1 if the input layer is not exist.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetNumberOfInputUnits()
{
std::string Key = GetKeyOfInputLayer();
if (IsExist(Key))
{
return Params[Key];
}
return -1;
}
/**
* @brief NeuralNetworksParams::GetAFTypeOfInputlayer Get the type of the
* activation function of the input layer
* @return The type of the activation function of the input layer
* return -1 if the activation function is not exist.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetAFTypeOfInputlayer()
{
std::string Key = GetKeyOfInputLayerAFType();
if (IsExist(Key))
{
return Params[Key];
}
return -1;
}
/**
* @brief NeuralNetworksParams::AddOutputlayerParams Add the params of the
* output layer
* @param NumberOfOutputUnits The number of the output layer
* @param TypeOfActivationFunction The type of the activation function of the
* output layer
* @return true if the operation is successed,
* false otherwise
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
bool NeuralNetworksParams::AddOutputlayerParams(int NumberOfOutputUnits,
int TypeOfActivationFunction)
{
if (NumberOfOutputUnits < 1)
{
return false;
}
bool Result = false;
// insert the number of the output layer
std::string Key = GetKeyOfOutputLayer();
Result = Insert(Key, NumberOfOutputUnits);
// insert the type of the actinvation function of the outpue layer
Key = GetKeyOfOutputLayerAFType();
Result = Result && Insert(Key, TypeOfActivationFunction);
return Result;
}
/**
* @brief NeuralNetworksParams::GetNumberOfOutputUnits Get the number of the
* output units
* @return The number of the output units,
* return -1 if the output layer is not exist.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetNumberOfOutputUnits()
{
std::string Key = GetKeyOfOutputLayer();
return GetValueByKey(Key);
}
/**
* @brief NeuralNetworksParams::GetAFTypeOfOutputlayer Get the tyep of the
* activation function of the output layer.
* @return The type of the activation function of the output layer.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetAFTypeOfOutputlayer()
{
std::string Key = GetKeyOfOutputLayerAFType();
return GetValueByKey(Key);
}
/**
* @brief NeuralNetworksParams::AddHiddenlayerParams Add the params of the
* hidden layer.
* @param NumberOfUnits The number of the units in the hidden layer
* @param TypeOfActivationFunction The type of the activation function of the
* hidden layer
* @return true if the operation is successed,
* false otherwise.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
bool NeuralNetworksParams::AddHiddenlayerParams(int NumberOfUnits,
int TypeOfActivationFunction)
{
if (NumberOfUnits < 1)
{
return false;
}
bool Result = false;
// insert the number of the units of the hidden layer
std::string Key = GetKeyOfHiddenlayer(NumberOfHiddenlayers);
Result = Insert(Key, NumberOfUnits);
// insert the type of the activation function of the hidden layer
Key = GetKeyOfHiddenlayerAFType(NumberOfHiddenlayers);
Result = Result && Insert(Key, TypeOfActivationFunction);
if (Result)
{
NumberOfHiddenlayers++;
}
return Result;
}
/**
* @brief NeuralNetworksParams::GetNumberOfHiddenUnits Get the number of the
* units of the No.Index layer
* @param IndexOfHiddenlayer The 0-base index of the hidden layer
* @return The number of the No.Index hidden layer
* return -1 if the hidden layer is not exist.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetNumberOfHiddenUnits(int IndexOfHiddenlayer)
{
if ((IndexOfHiddenlayer < 0) || (IndexOfHiddenlayer > NumberOfHiddenlayers))
{
return -1;
}
// get the number of the units of the hidden layer
std::string Key = GetKeyOfHiddenlayer(IndexOfHiddenlayer);
return GetValueByKey(Key);
}
/**
* @brief NeuralNetworksParams::GetAFTypeOfHiddenlayer Get the type of the
* activation function of the No.Index hidden layer
* @param IndexOfHiddenlayer The index of the hidden layer
* @return The type of the activaiton function of the hidden layer
* return -1 if the operation is failed.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetAFTypeOfHiddenlayer(int IndexOfHiddenlayer)
{
if ((IndexOfHiddenlayer < 0) || (IndexOfHiddenlayer > NumberOfHiddenlayers))
{
return -1;
}
// get the type of the activation function of the hidden layer
std::string Key = GetKeyOfHiddenlayerAFType(IndexOfHiddenlayer);
return GetValueByKey(Key);
}
/**
* @brief NeuralNetworksParams::GetNumberOfHiddenlayers Get the number of the
* hidden layer
* @return The number of the hidden layer;
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetNumberOfHiddenlayers() const
{
return NumberOfHiddenlayers;
}
/**
* @brief NeuralNetworksParams::Insert Insert the element to the params
* @param Key The key of the elememt
* @param Value The value of the elememt
* @return true if the operation is successed
* false otherwise
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
bool NeuralNetworksParams::Insert(const std::string &Key, int Value)
{
// return false if the value is negative.
if (Value < 0)
{
return false;
}
Params[Key] = Value;
return true;
}
/**
* @brief NeuralNetworksParams::IsExist check if the element of the given key is
* exist.
* @param Key The key of the element.
* @return true if the element is exist.
* false otherwise
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
bool NeuralNetworksParams::IsExist(const std::string &Key)
{
// find the element
std::map<std::string, int>::iterator Ite = Params.find(Key);
// return true when the element is exist in the params
if (Ite != Params.end())
{
return true;
}
return false;
}
/**
* @brief NeuralNetworksParams::GetValueByKey Get the value of the element of
* the given key
* @param Key The key of the element
* @return The value of the element,
* return -1 if the element is not exist.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
int NeuralNetworksParams::GetValueByKey(const std::string &Key)
{
// return the value if the element is exist.
if (IsExist(Key))
{
return Params[Key];
}
return -1;
}
/**
* @brief NeuralNetworksParams::GetKeyOfInputLayer Get the key of the input
* layer.
* @return The key of the input layer
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
std::string NeuralNetworksParams::GetKeyOfInputLayer() const
{
return INPUTLAYER;
}
/**
* @brief NeuralNetworksParams::GetKeyOfInputLayerAFType Get the key of the
* activation function of the input layer
* @return The key of the AF type of the input layer
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
std::string NeuralNetworksParams::GetKeyOfInputLayerAFType() const
{
std::string Result = INPUTLAYER + ACTIVATIONFUNCTIONPREFIX;
return Result;
}
/**
* @brief NeuralNetworksParams::GetKeyOfOutputLayer Get the key of the output
* layer.
* @return The key of the output layer.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
std::string NeuralNetworksParams::GetKeyOfOutputLayer() const
{
return OUTPUTLAYER;
}
/**
* @brief NeuralNetworksParams::GetKeyOfOutputLayerAFType Get the key of the
* type of the activation function of the output layer.
* @return The key of the type of the AF of the output layer.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
std::string NeuralNetworksParams::GetKeyOfOutputLayerAFType() const
{
std::string Result = OUTPUTLAYER + ACTIVATIONFUNCTIONPREFIX;
return Result;
}
/**
* @brief NeuralNetworksParams::GetKeyOfHiddenlayer Get the key of the No.Index
* hidden layer.
* @param Index The 0-base index of the hidden layer.
* @return The key of the No.Index hidden layer
*
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
std::string NeuralNetworksParams::GetKeyOfHiddenlayer(int Index) const
{
std::string Result = IntToString(Index);
Result = HIDDENLAYERPREFIX + Result;
return Result;
}
/**
* @brief NeuralNetworksParams::GetKeyOfHiddenlayerAFType Get the key of the
* type of the activation function of the No.Index layer.
* @param Index The 0-base index of the hidden layer
* @return The key of the type of the No.Index hidden layer.
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
std::string NeuralNetworksParams::GetKeyOfHiddenlayerAFType(int Index) const
{
std::string Result = IntToString(Index);
Result = HIDDENLAYERPREFIX + ACTIVATIONFUNCTIONPREFIX + Result;
return Result;
}Test_NeuralNetworkParams.cpp
/*M///////////////////////////////////////////////////////////////////////////
// Copyright (c) 2014, sheng
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
//M*/
#include "neuralnetworksparams.h"
#include "TypeDefinition.h"
#include <iostream>
/**
* @brief Test_NeuralNetworksParams
*
* @author sheng
* @date 2014-09-10
* @version 0.1
*
* @history
* <author> <date> <version> <description>
* sheng 2014-09-10 0.1 build the module
*
*/
void Test_NeuralNetworksParams()
{
//
NeuralNetworksParams Params;
Params.AddInputlayerParams(10, LINEAR);
Params.AddHiddenlayerParams(20, SIGMOID);
Params.AddHiddenlayerParams(50, TANH);
Params.AddOutputlayerParams(2, RELU);
std::cout << "The number of units of input layer is " <<
Params.GetNumberOfInputUnits() << std::endl;
std::cout << "The type of the input layer is " <<
Params.GetAFTypeOfInputlayer() << std::endl;
std::cout << "The number of units of outpue layer is " <<
Params.GetNumberOfOutputUnits() << std::endl;
std::cout << "The type of the output layer is " <<
Params.GetAFTypeOfOutputlayer() << std::endl;
std::cout << "The number of units of 0 hidden layer is " <<
Params.GetNumberOfHiddenUnits(0) << std::endl;
std::cout << "The type of the 0 hidden layer is " <<
Params.GetAFTypeOfHiddenlayer(0) << std::endl;
std::cout << "The number of units of 1 hidden layer is " <<
Params.GetNumberOfHiddenUnits(1) << std::endl;
std::cout << "The type of the 1 hidden layer is " <<
Params.GetAFTypeOfHiddenlayer(1) << std::endl;
std::cout << "The number of units of 2 hidden layer is " <<
Params.GetNumberOfHiddenUnits(2) << std::endl;
std::cout << "The type of the 2 hidden layer is " <<
Params.GetAFTypeOfHiddenlayer(2) << std::endl;
std::cout << "The number of the hidden layer is " <<
Params.GetNumberOfHiddenlayers() << std::endl;
//
NeuralNetworksParams Params1;
Params1.AddInputlayerParams(0, LINEAR);
Params1.AddHiddenlayerParams(0, SIGMOID);
Params1.AddHiddenlayerParams(0, TANH);
Params1.AddOutputlayerParams(0, RELU);
std::cout << "The number of units of input layer is " <<
Params1.GetNumberOfInputUnits() << std::endl;
std::cout << "The type of the input layer is " <<
Params1.GetAFTypeOfInputlayer() << std::endl;
std::cout << "The number of units of outpue layer is " <<
Params1.GetNumberOfOutputUnits() << std::endl;
std::cout << "The type of the output layer is " <<
Params1.GetAFTypeOfOutputlayer() << std::endl;
std::cout << "The number of units of 0 hidden layer is " <<
Params1.GetNumberOfHiddenUnits(0) << std::endl;
std::cout << "The type of the 0 hidden layer is " <<
Params1.GetAFTypeOfHiddenlayer(0) << std::endl;
std::cout << "The number of units of 1 hidden layer is " <<
Params1.GetNumberOfHiddenUnits(1) << std::endl;
std::cout << "The type of the 1 hidden layer is " <<
Params1.GetAFTypeOfHiddenlayer(1) << std::endl;
std::cout << "The number of units of 2 hidden layer is " <<
Params1.GetNumberOfHiddenUnits(2) << std::endl;
std::cout << "The type of the 2 hidden layer is " <<
Params1.GetAFTypeOfHiddenlayer(2) << std::endl;
std::cout << "The number of the hidden layer is " <<
Params1.GetNumberOfHiddenlayers() << std::endl;
}github地址：https://github.com/shengno/NeuralNetworksParams
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