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// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//
// This file is part of Torch 3.1.
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
// IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
// THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "Linear.h"
#include "Random.h"
namespace Torch {
Linear::Linear(int n_inputs_, int n_outputs_) : GradientMachine(n_inputs_, n_outputs_, (n_inputs_+1)*n_outputs_)
{
addROption("weight decay", &weight_decay, 0, "weight decay");
weights = params->data[0];
bias = params->data[0]+n_inputs*n_outputs;
der_weights = der_params->data[0];
der_bias = der_params->data[0]+n_inputs*n_outputs;
reset_();
}
void Linear::reset()
{
reset_();
}
void Linear::reset_()
{
// Note: just to be compatible with "Torch II Dev"
real *weights_ = weights;
real bound = 1./sqrt((real)n_inputs);
for(int i = 0; i < n_outputs; i++)
{
for(int j = 0; j < n_inputs; j++)
weights_[j] = Random::boundedUniform(-bound, bound);
weights_ += n_inputs;
bias[i] = Random::boundedUniform(-bound, bound);
}
}
void Linear::frameForward(int t, real *f_inputs, real *f_outputs)
{
real *weights_ = weights;
for(int i = 0; i < n_outputs; i++)
{
real out = bias[i];
for(int j = 0; j < n_inputs; j++)
out += weights_[j] * f_inputs[j];
weights_ += n_inputs;
f_outputs[i] = out;
}
}
void Linear::frameBackward(int t, real *f_inputs, real *beta_, real *f_outputs, real *alpha_)
{
if(!partial_backprop)
{
for(int i = 0; i < n_inputs; i++)
beta_[i] = 0;
real *weights_ = weights;
for(int i = 0; i < n_outputs; i++)
{
real z = alpha_[i];
for(int j = 0; j < n_inputs; j++)
beta_[j] += z * weights_[j];
weights_ += n_inputs;
}
}
real *der_weights_ = der_weights;
for(int i = 0; i < n_outputs; i++)
{
real z = alpha_[i];
for(int j = 0; j < n_inputs; j++)
der_weights_[j] += z * f_inputs[j];
der_weights_ += n_inputs;
der_bias[i] += z;
}
if(weight_decay != 0)
{
real *src_ = params->data[0];
real *dest_ = der_params->data[0];
// Note: pas de weight decay sur les biais.
for(int i = 0; i < n_inputs*n_outputs; i++)
dest_[i] += weight_decay * src_[i];
}
}
Linear::~Linear()
{
}
}
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