Skip to content
Snippets Groups Projects
Commit f0b2c8dd authored by Cédric Traizet's avatar Cédric Traizet
Browse files

refactoring of the ae model seems to work, denoising ae is not implemented yet

parent b2fc854c
No related branches found
No related tags found
1 merge request!4Dimensionality reduction algorithms
Hide whitespace changes
Inline Side-by-side
include/AutoencoderModel.h
+ 2
− 4
View file @ f0b2c8dd
......@@ -86,8 +86,8 @@ public:
template <class T>
void TrainOneLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int, unsigned int,double, double, shark::Data<shark::RealVector> &, std::ostream&);
template <class T>
void TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int, unsigned int,double, double,double, shark::Data<shark::RealVector> &, std::ostream&);
template <class T, class Autoencoder>
void TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion,Autoencoder &, unsigned int, unsigned int,double, double,double, shark::Data<shark::RealVector> &, std::ostream&);
protected:
AutoencoderModel();
......@@ -102,8 +102,6 @@ private:
/** Network attributes */
//std::vector<AutoencoderType> m_net;
NetworkType m_net;
itk::Array<unsigned int> m_NumberOfHiddenNeurons;
/** Training parameters */
unsigned int m_NumberOfIterations; // stop the training after a fixed number of iterations
......
include/AutoencoderModel.txx
+ 101
− 30
View file @ f0b2c8dd
......@@ -48,12 +48,64 @@ void AutoencoderModel<TInputValue,NeuronType>::Train()
ofs << "learning curve" << std::endl;
}
/// Initialization of the feed forward neural network
std::vector<size_t> layers;
layers.push_back(shark::dataDimension(inputSamples));
for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
{
layers.push_back(m_NumberOfHiddenNeurons[i]);
std::cout << m_NumberOfHiddenNeurons.Size() << std::endl;
}
// another loop for the decoder should be added, for now i just add the output layer size
std::cout << "i?" << static_cast<int>(m_NumberOfHiddenNeurons.Size()-1) << std::endl;
for (unsigned int i = std::max(0,static_cast<int>(m_NumberOfHiddenNeurons.Size()-1)) ; i > 0; --i)
{
std::cout << i << std::endl;
layers.push_back(m_NumberOfHiddenNeurons[i-1]);
}
layers.push_back(shark::dataDimension(inputSamples));
m_net.setStructure(layers);
shark::initRandomNormal(m_net,0.1);
/// Training of the first Autoencoder (first and last layer of the FF network)
if (m_Epsilon > 0){
shark::TrainingProgress<> criterion(5,m_Epsilon);
if (m_Noise[0] != 0) // Shark doesn't allow to train a layer using a sparsity term AND a noisy input. (shark::SparseAutoencoderError takes an autoen
{
TrainOneLayer(criterion,0 , m_NumberOfHiddenNeurons[0],m_Noise[0],m_Regularization[0], inputSamples,ofs);
}
else
{
OutAutoencoderType net;
TrainOneSparseLayer( criterion, net , 0 , m_NumberOfHiddenNeurons[0],m_Rho[0],m_Beta[0],m_Regularization[0],inputSamples, ofs);
}
criterion.reset();
}
else {
shark::MaxIterations<> criterion(m_NumberOfIterations);
if (m_Noise[0] != 0) // Shark doesn't allow to train a layer using a sparsity term AND a noisy input. (shark::SparseAutoencoderError takes an autoen
{
TrainOneLayer(criterion,0, m_NumberOfHiddenNeurons[0],m_Noise[0],m_Regularization[0], inputSamples, ofs);
}
else
{
OutAutoencoderType net;
TrainOneSparseLayer(criterion, net, 0, m_NumberOfHiddenNeurons[0],m_Rho[0],m_Beta[0],m_Regularization[0], inputSamples, ofs);
}
criterion.reset();
}
/// Training of the other autoencoders
if (m_Epsilon > 0){
shark::TrainingProgress<> criterion(5,m_Epsilon);
for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
for (unsigned int i = 1 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
{
if (m_Noise[i] != 0) // Shark doesn't allow to train a layer using a sparsity term AND a noisy input. (shark::SparseAutoencoderError takes an autoen
{
......@@ -61,7 +113,8 @@ void AutoencoderModel<TInputValue,NeuronType>::Train()
}
else
{
TrainOneSparseLayer( criterion,i , m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i],inputSamples, ofs);
AutoencoderType net;
TrainOneSparseLayer( criterion, net , i , m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i],inputSamples, ofs);
}
criterion.reset();
}
......@@ -71,7 +124,7 @@ void AutoencoderModel<TInputValue,NeuronType>::Train()
else {
shark::MaxIterations<> criterion(m_NumberOfIterations);
for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
for (unsigned int i = 1 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
{
if (m_Noise[i] != 0) // Shark doesn't allow to train a layer using a sparsity term AND a noisy input. (shark::SparseAutoencoderError takes an autoen
{
......@@ -79,7 +132,8 @@ void AutoencoderModel<TInputValue,NeuronType>::Train()
}
else
{
TrainOneSparseLayer(criterion,i, m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i], inputSamples, ofs);
AutoencoderType net;
TrainOneSparseLayer(criterion, net, i, m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i], inputSamples, ofs);
}
criterion.reset();
}
......@@ -95,8 +149,8 @@ void AutoencoderModel<TInputValue,NeuronType>::TrainOneLayer(shark::AbstractStop
std::size_t inputs = dataDimension(samples);
net.setStructure(inputs, nbneuron);
//initRandomUniform(net,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));
initRandomUniform(net,-1,1);
initRandomUniform(net,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));
//initRandomUniform(net,-1,1);
shark::ImpulseNoiseModel noise(noise_strength,0.0); //set an input pixel with probability m_Noise to 0
shark::ConcatenatedModel<shark::RealVector,shark::RealVector> model = noise>> net;
shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);//labels identical to inputs
......@@ -136,15 +190,15 @@ void AutoencoderModel<TInputValue,NeuronType>::TrainOneLayer(shark::AbstractStop
template <class TInputValue, class NeuronType>
template <class T>
void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int layer_index, unsigned int nbneuron,double rho,double beta, double regularization, shark::Data<shark::RealVector> &samples, std::ostream& File)
template <class T, class Autoencoder>
void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion, Autoencoder & net, unsigned int layer_index, unsigned int nbneuron,double rho,double beta, double regularization, shark::Data<shark::RealVector> &samples, std::ostream& File)
{
AutoencoderType net;
//AutoencoderType net;
std::size_t inputs = dataDimension(samples);
net.setStructure(inputs, nbneuron);
//initRandomUniform(net,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));
initRandomUniform(net,-1,1);
initRandomUniform(net,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));
//initRandomUniform(net,-1,1);
shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);//labels identical to inputs
shark::SquaredLoss<shark::RealVector> loss;
shark::SparseAutoencoderError error(trainSet,&net, &loss, rho, beta);
......@@ -172,9 +226,12 @@ void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(shark::Abstra
}
net.setParameterVector(optimizer.solution().point);
//m_net.push_back(net);
m_net.setLayer(layer_index,net.encoderMatrix(),net.hiddenBias());
m_net.setLayer( m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index,net.decoderMatrix(),net.outputBias());
m_net.setLayer(layer_index,net.encoderMatrix(),net.hiddenBias()); // Copy the encoder in the FF neural network
m_net.setLayer( m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index,net.decoderMatrix(),net.outputBias()); // Copy the decoder in the FF neural network
samples = net.encode(samples);
std::cout << "numero zero " << m_net.layerMatrix(layer_index) << "i " << layer_index <<std::endl <<std::endl;
std::cout << "numero uno " << m_net.layerMatrix(m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index) << "i " << m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index << std::endl <<std::endl;
}
template <class TInputValue, class NeuronType>
......@@ -202,6 +259,7 @@ bool AutoencoderModel<TInputValue,NeuronType>::CanWriteFile(const std::string &
template <class TInputValue, class NeuronType>
void AutoencoderModel<TInputValue,NeuronType>::Save(const std::string & filename, const std::string & name)
{
std::cout << "saving model ..." << std::endl;
std::ofstream ofs(filename);
ofs << m_net.name() << std::endl; // the first line of the model file contains a key
boost::archive::polymorphic_text_oarchive oa(ofs);
......@@ -271,13 +329,23 @@ void AutoencoderModel<TInputValue,NeuronType>::Load(const std::string & filename
//m_net.read(ia);
ia >> m_net;
ifs.close();
m_NumberOfHiddenNeurons.SetSize(m_net.size());
for (int i=0; i<m_net.size(); i++){
// This gives us the dimension if we keep the encoder and decoder
size_t feature_layer_index = m_net.layerMatrices().size()/2;
this->m_Dimension = m_net.layerMatrix(feature_layer_index).size1(); // number of neurons in the feature layer (first dimension of the first decoder weight matrix)
std::cout << this->m_Dimension << std::endl;
/* This might not be needed
int number_of_layers = m_net.layerMatrices().size()/2;
std::cout << "number of layers" << number_of_layers << std::endl;
m_NumberOfHiddenNeurons.SetSize(number_of_layers);
for (int i=0; i<number_of_layers; i++){
m_NumberOfHiddenNeurons[i] = m_net[i].numberOfHiddenNeurons();
}
this->m_Dimension = m_NumberOfHiddenNeurons[m_net.size()-1];
this->m_Dimension = m_NumberOfHiddenNeurons[number_of_layers-1];
*/
}
......@@ -285,7 +353,7 @@ template <class TInputValue, class NeuronType>
typename AutoencoderModel<TInputValue,NeuronType>::TargetSampleType
AutoencoderModel<TInputValue,NeuronType>::DoPredict(const InputSampleType & value, ConfidenceValueType * quality) const
{
/*
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
{
......@@ -296,18 +364,20 @@ AutoencoderModel<TInputValue,NeuronType>::DoPredict(const InputSampleType & valu
features.push_back(samples);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
data = m_net.evalLayer( m_net.layerMatrices().size()/2 ,data); // features layer for a network containing the encoder and decoder part
/*
for (int i=0; i<m_net.size(); i++){ // loop over all autoencoders in m_net
data = m_net[i].encode(data);
}
}*/
TargetSampleType target;
target.SetSize(m_NumberOfHiddenNeurons[m_net.size()-1]);
target.SetSize(this->m_Dimension);
for(unsigned int a = 0; a < m_NumberOfHiddenNeurons[m_net.size()-1]; ++a){
for(unsigned int a = 0; a < this->m_Dimension; ++a){
target[a]=data.element(0)[a];
}
return target;
*/
}
......@@ -315,27 +385,28 @@ template <class TInputValue, class NeuronType>
void AutoencoderModel<TInputValue,NeuronType>
::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
{
/*
std::vector<shark::RealVector> features;
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
/*
for (auto net :m_net ){ // loop over all autoencoders in m_net
data = net.encode(data);
}
*/
data = m_net.evalLayer( m_net.layerMatrices().size()/2 ,data); // features layer for a network containing the encoder and decoder part
unsigned int id = startIndex;
target.SetSize(m_NumberOfHiddenNeurons[m_net.size()-1]);
target.SetSize(this->m_Dimension);
for(const auto& p : data.elements())
{
for(unsigned int a = 0; a < m_NumberOfHiddenNeurons[m_net.size()-1]; ++a){
for(unsigned int a = 0; a < this->m_Dimension; ++a){
target[a]=p[a];
}
targets->SetMeasurementVector(id,target);
++id;
}
*/
}
} // namespace otb
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment