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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 SOFTMAX_INC
#define SOFTMAX_INC
#include "GradientMachine.h"
namespace Torch {
/** Softmax layer for #GradientMachine#.
The number of inputs/outputs is the number
of units in the machine.
Formally speaking, $ouputs[i] = 1/a * exp(inputs[i]-shift)$
where $a = \sum_j exp(inputs[j]-shift)$.
Options:
\begin{tabular}{lcll}
"shift" & real & shift to avoid overflow & [0]\\
"compute shift" & bool & compute shift to avoid overflow & [false]
\end{tabular}
(you can have the "shift" you want, if you want, or
you can automatically compute the shift)
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class Softmax : public GradientMachine
{
public:
real shift;
bool calc_shift;
//-----
/// Create a Sigmoid layer with #n_units# units.
Softmax(int n_units);
//-----
virtual int numberOfParams();
virtual void forward(List *inputs);
virtual void backward(List *inputs, real *alpha);
virtual ~Softmax();
};
}
#endif
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