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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 "TemporalSubSampling.h"
#include "Random.h"
namespace Torch {
TemporalSubSampling::TemporalSubSampling(int input_frame_size, int k_w_, int d_t_)
: GradientMachine(input_frame_size, input_frame_size, 2*input_frame_size)
{
k_w = k_w_;
d_t = d_t_;
weights = params->data[0];
biases = params->data[0] + n_inputs;
der_weights = der_params->data[0];
der_biases = der_params->data[0] + n_inputs;
reset_();
}
void TemporalSubSampling::reset_()
{
real bound = 1./sqrt((real)(k_w));
real *params_ = params->data[0];
for(int i = 0; i < params->n_params; i++)
params_[i] = Random::boundedUniform(-bound, bound);
}
void TemporalSubSampling::reset()
{
reset_();
}
void TemporalSubSampling::forward(Sequence *inputs)
{
if(inputs->n_frames < k_w)
error("TemporalSubSampling: input sequence too small! (n_frames = %d < k_w = %d)", inputs->n_frames, k_w);
int n_output_frames = (inputs->n_frames - k_w) / d_t + 1;
outputs->resize(n_output_frames);
int current_input_frame = 0;
for(int i = 0; i < n_output_frames; i++)
{
real *output_frame_ = outputs->frames[i];
for(int j = 0; j < n_outputs; j++)
output_frame_[j] = biases[j];
// Sur le noyau...
for(int j = 0; j < k_w; j++)
{
// Sur tous les "neurones" de sorties
for(int k = 0; k < n_outputs; k++)
{
real *input_frame_ = inputs->frames[current_input_frame+j];
real sum = 0;
for(int l = 0; l < n_inputs; l++)
sum += input_frame_[l];
output_frame_[k] += weights[k]*sum;
}
}
current_input_frame += d_t;
}
}
void TemporalSubSampling::backward(Sequence *inputs, Sequence *alpha)
{
int n_output_frames = alpha->n_frames;
// NOTE: boucle *necessaire* avec "partial backprop"
int current_input_frame = 0;
for(int i = 0; i < n_output_frames; i++)
{
real *alpha_frame_ = alpha->frames[i];
for(int j = 0; j < n_outputs; j++)
der_biases[j] += alpha_frame_[j];
for(int j = 0; j < k_w; j++)
{
for(int k = 0; k < n_outputs; k++)
{
real *input_frame_ = inputs->frames[current_input_frame+j];
real alpha_ = alpha_frame_[k];
real sum = 0;
for(int l = 0; l < n_inputs; l++)
sum += alpha_*input_frame_[l];
der_weights[k] += sum;
}
}
current_input_frame += d_t;
}
if(partial_backprop)
return;
// NOTE: boucle *non-necessaire* avec "partial backprop"
beta->resize(inputs->n_frames);
for(int i = 0; i < beta->n_frames; i++)
{
real *beta_frame_ = beta->frames[i];
for(int j = 0; j < n_inputs; j++)
beta_frame_[j] = 0;
}
int current_beta_frame = 0;
for(int i = 0; i < n_output_frames; i++)
{
real *alpha_frame_ = alpha->frames[i];
for(int j = 0; j < k_w; j++)
{
for(int k = 0; k < n_outputs; k++)
{
real *beta_frame_ = beta->frames[current_beta_frame+j];
real alpha_mul_weight_ = alpha_frame_[k]*weights[k];
for(int l = 0; l < n_inputs; l++)
beta_frame_[l] += alpha_mul_weight_;
}
}
current_beta_frame += d_t;
}
}
TemporalSubSampling::~TemporalSubSampling()
{
}
}
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