/usr/include/ITK-4.5/itkMultilayerNeuralNetworkBase.h is in libinsighttoolkit4-dev 4.5.0-3.
This file is owned by root:root, with mode 0o644.
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*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef __itkMultilayerNeuralNetworkBase_h
#define __itkMultilayerNeuralNetworkBase_h
#include "itkNeuralNetworkObject.h"
namespace itk
{
namespace Statistics
{
/** \class MultilayerNeuralNetworkBase
* \brief This is the itkMultilayerNeuralNetworkBase class.
*
* \ingroup ITKNeuralNetworks
*/
template<typename TMeasurementVector, typename TTargetVector,typename TLearningLayer=LayerBase<TMeasurementVector, TTargetVector> >
class MultilayerNeuralNetworkBase : public NeuralNetworkObject<TMeasurementVector, TTargetVector>
{
public:
typedef MultilayerNeuralNetworkBase Self;
typedef NeuralNetworkObject<TMeasurementVector, TTargetVector>
Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
itkTypeMacro(MultilayerNeuralNetworkBase, NeuralNetworkObject);
/** New macro for creation of through a Smart Pointer. */
itkNewMacro( Self );
typedef typename Superclass::ValueType ValueType;
typedef typename Superclass::MeasurementVectorType MeasurementVectorType;
typedef typename Superclass::TargetVectorType TargetVectorType;
typedef typename Superclass::NetworkOutputType NetworkOutputType;
typedef typename Superclass::LayerInterfaceType LayerInterfaceType;
typedef TLearningLayer LearningLayerType;
typedef LearningFunctionBase<typename TLearningLayer::LayerInterfaceType, TTargetVector>
LearningFunctionInterfaceType;
typedef std::vector<typename LayerInterfaceType::WeightSetInterfaceType::Pointer>
WeightVectorType;
typedef std::vector<typename LayerInterfaceType::Pointer>
LayerVectorType;
typedef TransferFunctionBase<ValueType> TransferFunctionInterfaceType;
typedef InputFunctionBase<ValueType*, ValueType> InputFunctionInterfaceType;
//#define __USE_OLD_INTERFACE Comment out to ensure that new interface works
#ifdef __USE_OLD_INTERFACE
itkSetMacro(NumOfLayers, int);
itkGetConstReferenceMacro(NumOfLayers, int);
itkSetMacro(NumOfWeightSets, int);
itkGetConstReferenceMacro(NumOfWeightSets, int);
#else
int GetNumOfLayers(void) const
{
return m_Layers.size();
}
int GetNumOfWeightSets(void) const
{
return m_Weights.size();
}
#endif
void AddLayer(LayerInterfaceType *);
LayerInterfaceType * GetLayer(int layer_id);
const LayerInterfaceType * GetLayer(int layer_id) const;
void AddWeightSet(typename LayerInterfaceType::WeightSetInterfaceType*);
typename LayerInterfaceType::WeightSetInterfaceType* GetWeightSet(unsigned int id)
{
return m_Weights[id].GetPointer();
}
#ifdef __USE_OLD_INTERFACE
const typename LayerInterfaceType::WeightSetInterfaceType* GetWeightSet(unsigned int id) const;
#endif
void SetLearningFunction(LearningFunctionInterfaceType* f);
virtual NetworkOutputType GenerateOutput(TMeasurementVector samplevector);
virtual void BackwardPropagate(NetworkOutputType errors);
virtual void UpdateWeights(ValueType);
void SetLearningRule(LearningFunctionInterfaceType*);
void SetLearningRate(ValueType learningrate);
void InitializeWeights();
protected:
MultilayerNeuralNetworkBase();
~MultilayerNeuralNetworkBase();
LayerVectorType m_Layers;
WeightVectorType m_Weights;
typename LearningFunctionInterfaceType::Pointer m_LearningFunction;
ValueType m_LearningRate;
//#define __USE_OLD_INTERFACE Comment out to ensure that new interface works
#ifdef __USE_OLD_INTERFACE
//These are completely redundant variables that can be more reliably queried from
// m_Layers->size() and m_Weights->size();
int m_NumOfLayers;
int m_NumOfWeightSets;
#endif
/** Method to print the object. */
virtual void PrintSelf( std::ostream& os, Indent indent ) const;
};
} // end namespace Statistics
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkMultilayerNeuralNetworkBase.hxx"
#endif
#endif
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