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SRNN.cpp
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SRNN.cpp
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#include "SRNN.hpp"
/* コンストラクタ - 最小の初期化パラメタ
* 適宜追加する可能性あり
*/
SRNN::SRNN(int dim,
int num_mid,
int len_seq,
float* input_sample,
float* input_sample_maxmin)
{
this->dim_signal = dim;
this->num_mid_neuron = num_mid; // Advice : number of hidden layter shuld be as large as possible.
this->len_seqence = len_seq;
// sample/sample_maxmin allocation
this->sample = new float[len_seqence * dim_signal];
this->sample_maxmin = new float[dim_signal * 2];
memcpy(this->sample, input_sample, sizeof(float) * len_seqence * dim_signal);
memcpy(this->sample_maxmin, input_sample_maxmin, sizeof(float) * dim_signal * 2);
this->predict_signal = new float[dim_signal];
// coffecience matrix allocation
// final +1 for bias
this->Win_mid = new float[num_mid_neuron * (dim_signal + num_mid_neuron + 1)];
this->Wmid_out = new float[dim_signal * (num_mid_neuron + 1)];
// input/hidden layer signal allocation
expand_in_signal = new float[dim_signal + num_mid_neuron + 1];
expand_mid_signal = new float[num_mid_neuron + 1];
// Parameter settings (Tuning by taiyo)
this->squareError = FLT_MAX; // (large value)
this->maxIteration = 5000;
this->goalError = float(0.001);
this->epsilon = float(0.00001);
this->learnRate = float(0.1);
this->alpha = float(0.8 * learnRate);
this->alpha_context = float(0.8);
this->width_initW = float(1.0/num_mid_neuron);
// random seed decide by time
srand((unsigned int)time(NULL));
}
SRNN::~SRNN(void)
{
delete [] sample; delete [] sample_maxmin;
delete [] predict_signal;
delete [] Win_mid; delete [] Wmid_out;
delete [] expand_in_signal;
delete [] expand_mid_signal;
}
/* utilにいどうするべき */
void SRNN::sigmoid_vec(float* net,
float* out,
int dim)
{
for (int n=0;n<dim;n++)
out[n] = sigmoid_func(net[n]);
}
/* Predict : predicting next sequence of input */
void SRNN::predict(float* input)
{
float *norm_input = new float[this->dim_signal];
// normalize signal
for (int n=0; n < dim_signal; n++) {
norm_input[n] =
normalize_signal(input[n],
MATRIX_AT(this->sample_maxmin,2,n,0),
MATRIX_AT(this->sample_maxmin,2,n,1));
}
// output signal
float* out_signal = new float[dim_signal];
// value of network in input->hidden layer
float* in_mid_net = new float[num_mid_neuron];
// value of network in hidden->output layer
float* mid_out_net = new float[dim_signal];
/* Calcurate output signal */
// Get input signal
memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal);
// Signal of input layer : 中間層との線形和をシグモイド関数に通す.
for (int d = 0; d < num_mid_neuron; d++) {
expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
}
// Bias fixed at 1.
expand_in_signal[dim_signal + num_mid_neuron] = 1;
// 入力->中間層の出力信号和計算
multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1);
// 中間層の出力信号計算
sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron);
expand_mid_signal[num_mid_neuron] = 1;
// 中間->出力層の出力信号和計算
multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1);
// 出力層の出力信号計算
sigmoid_vec(mid_out_net, out_signal, dim_signal);
// expand output signal to origin width.
for (int n=0;n < dim_signal;n++) {
predict_signal[n] = expand_signal(out_signal[n],sample_maxmin[n * 2],sample_maxmin[n * 2 + 1]);
}
delete [] norm_input; delete [] out_signal;
delete [] in_mid_net; delete [] mid_out_net;
}
/* 逆誤差伝搬法による学習 局所解?なんのこったよ(すっとぼけ)*/
float SRNN::learning(void)
{
int iteration = 0; // 学習繰り返し回数
int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1]
int end_flag = 0; // 学習終了フラグ.このフラグが成立したら今回のsequenceを最後まで回して終了する.
// 係数行列のサイズ
int row_in_mid = num_mid_neuron;
int col_in_mid = dim_signal + num_mid_neuron + 1;
int row_mid_out = dim_signal;
int col_mid_out = num_mid_neuron + 1;
// 行列のアロケート
// 係数行列の更新量
float* dWin_mid = new float[row_in_mid * col_in_mid];
float* dWmid_out = new float[row_mid_out * col_mid_out];
// 前回の更新量:慣性項に用いる.
float* prevdWin_mid = new float[row_in_mid * col_in_mid];
float* prevdWmid_out = new float[row_mid_out * col_mid_out];
float* norm_sample = new float[len_seqence * dim_signal]; // 正規化したサンプル信号; 実際の学習は正規化した信号を用います.
// 係数行列の初期化
for (int i=0; i < row_in_mid; i++)
for (int j=0; j < col_in_mid; j++)
MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW);
for (int i=0; i < row_mid_out; i++)
for (int j=0; j < col_mid_out; j++)
MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW);
// 信号の正規化:経験上,非常に大切な処理
for (int seq=0; seq < len_seqence; seq++) {
for (int n=0; n < dim_signal; n++) {
MATRIX_AT(norm_sample,dim_signal,seq,n) =
normalize_signal(MATRIX_AT(this->sample,dim_signal,seq,n),
MATRIX_AT(this->sample_maxmin,2,n,0),
MATRIX_AT(this->sample_maxmin,2,n,1));
// printf("%f ", MATRIX_AT(norm_sample,dim_signal,seq,n));
}
// printf("\r\n");
}
// 出力層の信号
float* out_signal = new float[dim_signal];
// 入力層->中間層の信号和
float* in_mid_net = new float[num_mid_neuron];
// 中間層->出力層の信号和.
float* mid_out_net = new float[dim_signal];
// 誤差信号
float* sigma = new float[dim_signal];
// 前回の二乗誤差値:収束判定に用いる.
float prevError;
/* 学習ループ */
while (1) {
// 終了条件を満たすか確認
if (!end_flag) {
end_flag = !(iteration < this->maxIteration
&& (iteration <= this->len_seqence
|| this->squareError > this->goalError)
);
}
// printf("ite:%d err:%f \r\n", iteration, squareError);
// 系列の末尾に到達していたら,最初からリセットする.
if (seq == len_seqence && !end_flag) {
seq = 0;
}
// 前回の更新量/二乗誤差を保存
if (iteration >= 1) {
memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid);
memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out);
prevError = squareError;
} else {
// 初回は0埋め
memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid);
memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out);
}
/* 学習ステップその1:ニューラルネットの出力信号を求める */
// 入力値を取得
memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal);
// SRNN特有:入力層に中間層のコピーが追加され,中間層に入力される.
if (iteration == 0) {
// 初回は0埋めする
memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron);
} else {
// コンテキスト層 = 前回のコンテキスト層の出力
// 前回の中間層信号との線形和をシグモイド関数に通す.
for (int d = 0; d < num_mid_neuron; d++) {
expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
}
}
// バイアス項は常に1に固定.
expand_in_signal[dim_signal + num_mid_neuron] = 1;
// 入力->中間層の出力信号和計算
multiply_mat_vec(Win_mid,
expand_in_signal,
in_mid_net,
num_mid_neuron,
dim_signal + num_mid_neuron + 1);
// 中間層の出力信号計算
sigmoid_vec(in_mid_net,
expand_mid_signal,
num_mid_neuron);
expand_mid_signal[num_mid_neuron] = 1;
// 中間->出力層の出力信号和計算
multiply_mat_vec(Wmid_out,
expand_mid_signal,
mid_out_net,
dim_signal,
num_mid_neuron + 1);
// 出力層の出力信号計算
sigmoid_vec(mid_out_net,
out_signal,
dim_signal);
for (int i = 0; i < dim_signal; i++) {
predict_signal[i] = expand_signal(out_signal[i],
MATRIX_AT(sample_maxmin,2,i,0),
MATRIX_AT(sample_maxmin,2,i,1));
}
printf("predict : %f %f %f \r\n", predict_signal[0], predict_signal[1], predict_signal[2]);
// print_mat(Wmid_out, row_mid_out, col_mid_out);
// この時点での二乗誤差計算
squareError = 0;
// 次の系列との誤差を見ている!! ここが注目ポイント
// ==> つまり,次系列を予測させようとしている.
for (int n = 0;n < dim_signal;n++) {
if (seq < len_seqence - 1) {
squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2);
} else {
squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2);
}
}
squareError /= dim_signal;
/* 学習の終了 */
// 終了フラグが立ち,かつ系列の最後に達していたら学習終了
if (end_flag && (seq == (len_seqence-1))) {
// 予測結果をセット.
for (int i = 0; i < dim_signal; i++) {
predict_signal[i] = expand_signal(out_signal[i],
MATRIX_AT(sample_maxmin,2,i,0),
MATRIX_AT(sample_maxmin,2,i,1));
//printf("%f ", predict_signal[i]);
}
break;
}
// 収束したと判定したら終了フラグを立てる.
if (fabsf(squareError - prevError) < epsilon) {
end_flag = 1;
}
/* 学習ステップその2:逆誤差伝搬 */
// 誤差信号の計算
for (int n = 0; n < dim_signal; n++) {
if (seq < len_seqence - 1) {
sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample,dim_signal,seq+1,n)) * out_signal[n] * (1 - out_signal[n]);
} else {
/* 末尾と先頭の誤差を取る (大抵,大きくなる) */
sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]);
}
}
// printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]);
// 出力->中間層の係数の変更量計算
for (int n = 0; n < dim_signal; n++) {
for (int j = 0; j < num_mid_neuron + 1; j++) {
MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j];
}
}
// 中間->入力層の係数の変更量計算
register float sum_sigma;
for (int i = 0; i < num_mid_neuron; i++) {
// 誤差信号を逆向きに伝播させる.
sum_sigma = 0;
for (int k = 0; k < dim_signal; k++) {
sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i);
}
// 中間->入力層の係数の変更量計算
for (int j = 0; j < col_in_mid; j++) {
MATRIX_AT(dWin_mid,num_mid_neuron,j,i)
= sum_sigma * expand_mid_signal[i] *
(1 - expand_mid_signal[i]) *
expand_in_signal[j];
}
}
// 係数更新
for (int i = 0; i < row_in_mid; i++) {
for (int j = 0; j < col_in_mid; j++) {
//printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j));
MATRIX_AT(Win_mid,col_in_mid,i,j) =
MATRIX_AT(Win_mid,col_in_mid,i,j) -
this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) -
this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j);
// printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j));
// printf("dW : %f , prevdW : %f ", MATRIX_AT(dWin_mid,col_in_mid,i,j), MATRIX_AT(prevdWin_mid,col_in_mid,i,j));
}
//printf("\r\n");
}
for (int i = 0; i < row_mid_out; i++) {
for (int j = 0; j < col_mid_out; j++) {
MATRIX_AT(Wmid_out,col_mid_out,i,j)=
MATRIX_AT(Wmid_out,col_mid_out,i,j) -
this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) -
this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j);
}
}
// ループ回数/系列のインクリメント
iteration += 1;
seq += 1;
}
delete [] dWin_mid; delete [] dWmid_out;
delete [] prevdWin_mid; delete [] prevdWmid_out;
delete [] norm_sample; delete [] out_signal;
delete [] in_mid_net; delete [] mid_out_net;
return squareError;
}