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// Copyright (C) 2002 Samy Bengio (bengio@idiap.ch)
// and Ronan Collobert (collober@iro.umontreal.ca)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// Torch is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef MLP_INC
#define MLP_INC
#include "ConnectedMachine.h"
#include "Linear.h"
#include "SumMachine.h"
#include "Softmax.h"
#include "LogSoftmax.h"
#include "Sigmoid.h"
#include "Tanh.h"
#include "SparseLinear.h"
namespace Torch {
/** This class is a simple interface to the #ConnectedMachine# class that
ca be used to build the well-known Multi Layer Perceptron type of
neural networks. It contains a layer of #Linear# followed by a layer
of #Tanh#, followed by a layer of #Linear# and optionally
\begin{itemize}
\item a layer of softmax
\item or a layer of sigmoid
\item or a layer of log-softmax
\item or a layer of tanh
\end{itemize}
Optionally, it also contains a direct connection from the inputs
to the linear layer, and if you want, you can choose sparse inputs.
Options:
\begin{tabular}{lcll}
"inputs to outputs" & bool & connections from inputs to outputs & [false]\\
"weight decay" & real & the weight decay & [0]\\
"softmax outputs" & bool & softmax outputs & [false]\\
"sigmoid outputs" & bool & sigmoid outputs & [false]\\
"log-softmax outputs" & bool & log-softmax outputs & [false]\\
"tanh outputs" & bool & tanh outputs & [false]\\
"sparse inputs" & bool & sparse inputs (to use with SparseDataSet) & [false]
\end{tabular}
@author Samy Bengio (bengio@idiap.ch)
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class MLP : public ConnectedMachine
{
public:
/// the first #Linear# layer
Linear *hidden_layer;
/// the first #Linear# layer for sparse mode
SparseLinear *sparse_hidden_layer;
/// the first #Tanh# layer
Tanh *hidden_tanh_layer;
/// the second #Linear# layer
Linear *outputs_layer;
/// the second #Linear# layer for sparse mode, if there is no hidden units
SparseLinear *sparse_outputs_layer;
/// the optional second #Softmax# layer
Softmax *outputs_softmax_layer;
/// the optional second #Sigmoid# layer
Sigmoid *outputs_sigmoid_layer;
/// the optional second #Softmax# layer
LogSoftmax *outputs_log_softmax_layer;
/// the optional second #Tanh# layer
Tanh *outputs_tanh_layer;
/// the number of hidden units
int n_hidden;
/// the number of inputs
int n_inputs;
/// the number of outputs
int n_outputs;
/// if this is false, add a #Tanh# layer
/** Flags (in order of priority if several are true)
to know what will be the output.
*/
//@{
bool is_softmax_outputs;
bool is_sigmoid_outputs;
bool is_log_softmax_outputs;
bool is_tanh_outputs;
//@}
/// To know if the inputs are sparse
bool is_sparse_inputs;
/// if this is true, add a direct connection from inputs to #Linear#
bool inputs_to_outputs;
/// the eventual weight_decay
real weight_decay;
/// the direct #Linear# layer
Linear* add_layer;
/// if #inputs_to_outputs# is true, we also need a #SumMachine#
SumMachine* sum_layer;
MLP(int n_inputs_, int n_hidden, int n_outputs_);
virtual void init();
virtual ~MLP();
};
}
#endif
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