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// Copyright (C) 2002 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 MIXER_INC
#define MIXER_INC
#include "GradientMachine.h"
namespace Torch {
/** Mixer useful for experts mixtures.
Formally speaking, it computes:
$outputs[i] = \sum_j a_j * inputs_j[i]$
where
\begin{itemize}
\item ${a_1,...,a_n}$ are in the table
of the first node of the #inputs# list,
when you call #forward()#.
\item $inputs_j$ are the inputs of the j-th expert.
Therefore, the #inputs# list has the structure
${a, inputs_1, inputs_2, ...}$.
Only $a$ must be alone in one node.
\end{itemize}
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class Mixer : public GradientMachine
{
public:
int n_experts;
//-----
///
Mixer(int n_inputs_, int n_outputs_per_expert);
//-----
virtual int numberOfParams();
virtual void forward(List *inputs);
virtual void backward(List *inputs, real *alpha);
virtual ~Mixer();
};
}
#endif
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