Aakash-kaushik/mlpack/sparse__autoencoder__impl_8hpp_source.html

 #ifndef MLPACK_METHODS_SPARSE_AUTOENCODER_SPARSE_AUTOENCODER_IMPL_HPP
 #define MLPACK_METHODS_SPARSE_AUTOENCODER_SPARSE_AUTOENCODER_IMPL_HPP

 // In case it hasn't been included yet.
 #include "sparse_autoencoder.hpp"

 namespace mlpack {
 namespace nn {

 template<typename OptimizerType>
 SparseAutoencoder::SparseAutoencoder(const arma::mat& data,
                                      const size_t visibleSize,
                                      const size_t hiddenSize,
                                      double lambda,
                                      double beta,
                                      double rho,
                                      OptimizerType optimizer) :
     visibleSize(visibleSize),
     hiddenSize(hiddenSize),
     lambda(lambda),
     beta(beta),
     rho(rho)
 {
   SparseAutoencoderFunction encoderFunction(data, visibleSize, hiddenSize,
                                             lambda, beta, rho);

   parameters = encoderFunction.GetInitialPoint();

   // Train the model.
   Timer::Start("sparse_autoencoder_optimization");
   const double out = optimizer.Optimize(encoderFunction, parameters);
   Timer::Stop("sparse_autoencoder_optimization");

   Log::Info << "SparseAutoencoder::SparseAutoencoder(): final objective of "
       << "trained model is " << out << "." << std::endl;
 }

 template<typename OptimizerType, typename... CallbackTypes>
 SparseAutoencoder::SparseAutoencoder(const arma::mat& data,
                                      const size_t visibleSize,
                                      const size_t hiddenSize,
                                      double lambda,
                                      double beta,
                                      double rho,
                                      OptimizerType optimizer,
                                      CallbackTypes&&... callbacks) :
     visibleSize(visibleSize),
     hiddenSize(hiddenSize),
     lambda(lambda),
     beta(beta),
     rho(rho)
 {
   SparseAutoencoderFunction encoderFunction(data, visibleSize, hiddenSize,
                                             lambda, beta, rho);

   parameters = encoderFunction.GetInitialPoint();

   // Train the model.
   Timer::Start("sparse_autoencoder_optimization");
   const double out = optimizer.Optimize(encoderFunction, parameters,
       callbacks...);
   Timer::Stop("sparse_autoencoder_optimization");

   Log::Info << "SparseAutoencoder::SparseAutoencoder(): final objective of "
       << "trained model is " << out << "." << std::endl;
 }

 } // namespace nn
 } // namespace mlpack

 #endif
mlpack::Timer::Start
static void Start(const std::string &name)
Start the given timer.
Definition: timers.cpp:28

sparse_autoencoder.hpp

data

mlpack
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: cv.hpp:1

mlpack::nn::SparseAutoencoderFunction
This is a class for the sparse autoencoder objective function.
Definition: sparse_autoencoder_function.hpp:26

mlpack::nn::SparseAutoencoderFunction::GetInitialPoint
const arma::mat & GetInitialPoint() const
Return the initial point for the optimization.
Definition: sparse_autoencoder_function.hpp:86

mlpack::Timer::Stop
static void Stop(const std::string &name)
Stop the given timer.
Definition: timers.cpp:36

mlpack::nn::SparseAutoencoder::SparseAutoencoder
SparseAutoencoder(const arma::mat &data, const size_t visibleSize, const size_t hiddenSize, const double lambda=0.0001, const double beta=3, const double rho=0.01, OptimizerType optimizer=OptimizerType())
Construct the sparse autoencoder model with the given training data.
Definition: sparse_autoencoder_impl.hpp:22

mlpack::Log::Info
static MLPACK_EXPORT util::PrefixedOutStream Info
Prints informational messages if –verbose is specified, prefixed with [INFO ].
Definition: log.hpp:84