Taiyo Mineo / SRNN

Important changes to repositories hosted on mbed.com

Mbed hosted mercurial repositories are deprecated and are due to be permanently deleted in July 2026.

To keep a copy of this software download the repository Zip archive or clone locally using Mercurial.

It is also possible to export all your personal repositories from the account settings page.

Dependents:   WeatherPredictor

SRNN.cpp@1:da597cb284a2, 2015-02-15 (annotated)

Committer:
yukari_hinata
Date:
Sun Feb 15 04:05:35 2015 +0000
Revision:
1:da597cb284a2
Parent:
0:0d42047e140c
Child:
2:d623e7ef4dca
first commit

Who changed what in which revision?

UserRevisionLine numberNew contents of line
yukari_hinata 0:0d42047e140c 1 #include "SRNN.hpp"
yukari_hinata 0:0d42047e140c 2
yukari_hinata 0:0d42047e140c 3 /* コンストラクタ - 最小の初期化パラメタ
yukari_hinata 0:0d42047e140c 4 * 適宜追加する可能性あり
yukari_hinata 0:0d42047e140c 5 */
yukari_hinata 1:da597cb284a2 6 SRNN::SRNN(int dim,
yukari_hinata 0:0d42047e140c 7 int num_mid,
yukari_hinata 0:0d42047e140c 8 int len_seq,
yukari_hinata 1:da597cb284a2 9 int len_predict,
yukari_hinata 0:0d42047e140c 10 float* input_sample,
yukari_hinata 0:0d42047e140c 11 float* input_sample_maxmin)
yukari_hinata 0:0d42047e140c 12 {
yukari_hinata 1:da597cb284a2 13
yukari_hinata 1:da597cb284a2 14 this->dim_signal = dim;
yukari_hinata 1:da597cb284a2 15 this->num_mid_neuron = num_mid; // Advice : number of hidden layter shuld be as large as possible.
yukari_hinata 1:da597cb284a2 16 this->len_seqence = len_seq;
yukari_hinata 1:da597cb284a2 17 this->len_predict = len_predict;
yukari_hinata 1:da597cb284a2 18
yukari_hinata 1:da597cb284a2 19 // sample/sample_maxmin allocation
yukari_hinata 1:da597cb284a2 20 this->sample = new float[len_seqence * dim_signal];
yukari_hinata 1:da597cb284a2 21 this->sample_maxmin = new float[dim_signal * 2];
yukari_hinata 1:da597cb284a2 22
yukari_hinata 1:da597cb284a2 23 memcpy(this->sample, input_sample, sizeof(float) * len_seqence * dim_signal);
yukari_hinata 1:da597cb284a2 24 memcpy(this->sample_maxmin, input_sample_maxmin, sizeof(float) * dim_signal * 2);
yukari_hinata 1:da597cb284a2 25
yukari_hinata 1:da597cb284a2 26 this->predict_signal = new float[dim_signal * len_predict];
yukari_hinata 0:0d42047e140c 27
yukari_hinata 1:da597cb284a2 28 // coffecience matrix allocation
yukari_hinata 1:da597cb284a2 29 // final +1 for bias
yukari_hinata 1:da597cb284a2 30 this->Win_mid = new float[num_mid_neuron * (dim_signal + num_mid_neuron + 1)];
yukari_hinata 1:da597cb284a2 31 this->Wmid_out = new float[dim_signal * (num_mid_neuron + 1)];
yukari_hinata 1:da597cb284a2 32
yukari_hinata 1:da597cb284a2 33 // input/hidden layer signal allocation
yukari_hinata 1:da597cb284a2 34 expand_in_signal = new float[dim_signal + num_mid_neuron + 1];
yukari_hinata 1:da597cb284a2 35 expand_mid_signal = new float[num_mid_neuron + 1];
yukari_hinata 0:0d42047e140c 36
yukari_hinata 1:da597cb284a2 37 // Parameter settings (Tuning by taiyo)
yukari_hinata 1:da597cb284a2 38 this->squareError = FLT_MAX; // (large value)
yukari_hinata 1:da597cb284a2 39 this->maxIteration = 5000;
yukari_hinata 1:da597cb284a2 40 this->goalError = float(0.001);
yukari_hinata 1:da597cb284a2 41 this->epsilon = float(0.00001);
yukari_hinata 1:da597cb284a2 42 this->learnRate = float(0.9); // 敏感に反応できるように, 高めに設定した. 時系列データなので, サンプルの時間間隔によって変えるべき
yukari_hinata 1:da597cb284a2 43 this->alpha = float(0.8 * learnRate);
yukari_hinata 1:da597cb284a2 44 this->alpha_context = float(0.8);
yukari_hinata 1:da597cb284a2 45 this->width_initW = float(1.0/num_mid_neuron);
yukari_hinata 0:0d42047e140c 46
yukari_hinata 1:da597cb284a2 47 // random seed decide by time
yukari_hinata 1:da597cb284a2 48 srand((unsigned int)time(NULL));
yukari_hinata 1:da597cb284a2 49
yukari_hinata 0:0d42047e140c 50 }
yukari_hinata 0:0d42047e140c 51
yukari_hinata 0:0d42047e140c 52 SRNN::~SRNN(void)
yukari_hinata 0:0d42047e140c 53 {
yukari_hinata 1:da597cb284a2 54 delete [] sample;
yukari_hinata 1:da597cb284a2 55 delete [] sample_maxmin;
yukari_hinata 0:0d42047e140c 56 delete [] predict_signal;
yukari_hinata 1:da597cb284a2 57 delete [] Win_mid;
yukari_hinata 1:da597cb284a2 58 delete [] Wmid_out;
yukari_hinata 0:0d42047e140c 59 delete [] expand_in_signal;
yukari_hinata 0:0d42047e140c 60 delete [] expand_mid_signal;
yukari_hinata 0:0d42047e140c 61 }
yukari_hinata 0:0d42047e140c 62
yukari_hinata 1:da597cb284a2 63 /* utilに移動するべき */
yukari_hinata 0:0d42047e140c 64 void SRNN::sigmoid_vec(float* net,
yukari_hinata 0:0d42047e140c 65 float* out,
yukari_hinata 0:0d42047e140c 66 int dim)
yukari_hinata 0:0d42047e140c 67 {
yukari_hinata 1:da597cb284a2 68 for (int n=0; n<dim; n++)
yukari_hinata 1:da597cb284a2 69 out[n] = sigmoid_func(net[n]);
yukari_hinata 0:0d42047e140c 70 }
yukari_hinata 0:0d42047e140c 71
yukari_hinata 0:0d42047e140c 72 /* Predict : predicting next sequence of input */
yukari_hinata 0:0d42047e140c 73 void SRNN::predict(float* input)
yukari_hinata 0:0d42047e140c 74 {
yukari_hinata 1:da597cb284a2 75 float *norm_input = new float[this->dim_signal];
yukari_hinata 1:da597cb284a2 76
yukari_hinata 1:da597cb284a2 77
yukari_hinata 1:da597cb284a2 78 // output signal
yukari_hinata 1:da597cb284a2 79 float* out_signal = new float[dim_signal];
yukari_hinata 1:da597cb284a2 80 // value of network in input->hidden layer
yukari_hinata 1:da597cb284a2 81 float* in_mid_net = new float[num_mid_neuron];
yukari_hinata 1:da597cb284a2 82 // value of network in hidden->output layer
yukari_hinata 1:da597cb284a2 83 float* mid_out_net = new float[dim_signal];
yukari_hinata 0:0d42047e140c 84
yukari_hinata 1:da597cb284a2 85 /* Calcurate output signal */
yukari_hinata 1:da597cb284a2 86 for (int i_predict = 0; i_predict < len_predict; i_predict++) {
yukari_hinata 0:0d42047e140c 87
yukari_hinata 1:da597cb284a2 88 // normalize signal
yukari_hinata 1:da597cb284a2 89 for (int n=0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 90 if (i_predict == 0) {
yukari_hinata 1:da597cb284a2 91 // First : given input
yukari_hinata 1:da597cb284a2 92 norm_input[n] = normalize_signal(input[n], MATRIX_AT(this->sample_maxmin,2,n,0), MATRIX_AT(this->sample_maxmin,2,n,1));
yukari_hinata 1:da597cb284a2 93 } else {
yukari_hinata 1:da597cb284a2 94 // Second~ : previous output
yukari_hinata 1:da597cb284a2 95 norm_input[n] = out_signal[n];
yukari_hinata 1:da597cb284a2 96 }
yukari_hinata 1:da597cb284a2 97 }
yukari_hinata 0:0d42047e140c 98
yukari_hinata 1:da597cb284a2 99 // Get input signal
yukari_hinata 1:da597cb284a2 100 memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal);
yukari_hinata 1:da597cb284a2 101 // Signal of input layer : 中間層との線形和をシグモイド関数に通す.
yukari_hinata 1:da597cb284a2 102 for (int d = 0; d < num_mid_neuron; d++) {
yukari_hinata 1:da597cb284a2 103 expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
yukari_hinata 1:da597cb284a2 104 }
yukari_hinata 1:da597cb284a2 105 // Bias fixed at 1.
yukari_hinata 1:da597cb284a2 106 expand_in_signal[dim_signal + num_mid_neuron] = 1;
yukari_hinata 1:da597cb284a2 107
yukari_hinata 1:da597cb284a2 108 // 入力->中間層の出力信号和計算
yukari_hinata 1:da597cb284a2 109 multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 110 // 中間層の出力信号計算
yukari_hinata 1:da597cb284a2 111 sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron);
yukari_hinata 1:da597cb284a2 112 expand_mid_signal[num_mid_neuron] = 1;
yukari_hinata 0:0d42047e140c 113
yukari_hinata 1:da597cb284a2 114 // 中間->出力層の出力信号和計算
yukari_hinata 1:da597cb284a2 115 multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 116 // 出力層の出力信号計算
yukari_hinata 1:da597cb284a2 117 sigmoid_vec(mid_out_net, out_signal, dim_signal);
yukari_hinata 0:0d42047e140c 118
yukari_hinata 1:da597cb284a2 119 // expand output signal to origin width.
yukari_hinata 1:da597cb284a2 120 for (int n=0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 121 predict_signal[i_predict * dim_signal + n] = expand_signal(out_signal[n],sample_maxmin[n * 2],sample_maxmin[n * 2 + 1]);
yukari_hinata 1:da597cb284a2 122 }
yukari_hinata 1:da597cb284a2 123
yukari_hinata 1:da597cb284a2 124 }
yukari_hinata 1:da597cb284a2 125
yukari_hinata 1:da597cb284a2 126 // 領域解放
yukari_hinata 1:da597cb284a2 127 delete [] norm_input; delete [] out_signal;
yukari_hinata 1:da597cb284a2 128 delete [] in_mid_net; delete [] mid_out_net;
yukari_hinata 0:0d42047e140c 129
yukari_hinata 0:0d42047e140c 130 }
yukari_hinata 0:0d42047e140c 131
yukari_hinata 0:0d42047e140c 132 /* 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）*/
yukari_hinata 0:0d42047e140c 133 float SRNN::learning(void)
yukari_hinata 0:0d42047e140c 134 {
yukari_hinata 1:da597cb284a2 135 int iteration = 0; // 学習繰り返し回数
yukari_hinata 1:da597cb284a2 136 int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1]
yukari_hinata 1:da597cb284a2 137 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.
yukari_hinata 1:da597cb284a2 138 // 係数行列のサイズ
yukari_hinata 1:da597cb284a2 139 int row_in_mid = num_mid_neuron;
yukari_hinata 1:da597cb284a2 140 int col_in_mid = dim_signal + num_mid_neuron + 1;
yukari_hinata 1:da597cb284a2 141 int row_mid_out = dim_signal;
yukari_hinata 1:da597cb284a2 142 int col_mid_out = num_mid_neuron + 1;
yukari_hinata 0:0d42047e140c 143
yukari_hinata 1:da597cb284a2 144 // 行列のアロケート
yukari_hinata 1:da597cb284a2 145 // 係数行列の更新量
yukari_hinata 1:da597cb284a2 146 float* dWin_mid = new float[row_in_mid * col_in_mid];
yukari_hinata 1:da597cb284a2 147 float* dWmid_out = new float[row_mid_out * col_mid_out];
yukari_hinata 1:da597cb284a2 148 // 前回の更新量:慣性項に用いる.
yukari_hinata 1:da597cb284a2 149 float* prevdWin_mid = new float[row_in_mid * col_in_mid];
yukari_hinata 1:da597cb284a2 150 float* prevdWmid_out = new float[row_mid_out * col_mid_out];
yukari_hinata 1:da597cb284a2 151 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.
yukari_hinata 0:0d42047e140c 152
yukari_hinata 1:da597cb284a2 153 // 係数行列の初期化
yukari_hinata 1:da597cb284a2 154 for (int i=0; i < row_in_mid; i++)
yukari_hinata 1:da597cb284a2 155 for (int j=0; j < col_in_mid; j++)
yukari_hinata 1:da597cb284a2 156 MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW);
yukari_hinata 0:0d42047e140c 157
yukari_hinata 1:da597cb284a2 158 for (int i=0; i < row_mid_out; i++)
yukari_hinata 1:da597cb284a2 159 for (int j=0; j < col_mid_out; j++)
yukari_hinata 1:da597cb284a2 160 MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW);
yukari_hinata 0:0d42047e140c 161
yukari_hinata 1:da597cb284a2 162 // 信号の正規化:経験上,非常に大切な処理
yukari_hinata 1:da597cb284a2 163 for (int seq=0; seq < len_seqence; seq++) {
yukari_hinata 1:da597cb284a2 164 for (int n=0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 165 MATRIX_AT(norm_sample,dim_signal,seq,n) =
yukari_hinata 1:da597cb284a2 166 normalize_signal(MATRIX_AT(this->sample,dim_signal,seq,n),
yukari_hinata 1:da597cb284a2 167 MATRIX_AT(this->sample_maxmin,2,n,0),
yukari_hinata 1:da597cb284a2 168 MATRIX_AT(this->sample_maxmin,2,n,1));
yukari_hinata 1:da597cb284a2 169 // printf("%f ", MATRIX_AT(norm_sample,dim_signal,seq,n));
yukari_hinata 1:da597cb284a2 170 }
yukari_hinata 1:da597cb284a2 171 // printf("\r\n");
yukari_hinata 0:0d42047e140c 172 }
yukari_hinata 0:0d42047e140c 173
yukari_hinata 1:da597cb284a2 174 // 出力層の信号
yukari_hinata 1:da597cb284a2 175 float* out_signal = new float[dim_signal];
yukari_hinata 1:da597cb284a2 176
yukari_hinata 1:da597cb284a2 177 // 入力層->中間層の信号和
yukari_hinata 1:da597cb284a2 178 float* in_mid_net = new float[num_mid_neuron];
yukari_hinata 1:da597cb284a2 179 // 中間層->出力層の信号和.
yukari_hinata 1:da597cb284a2 180 float* mid_out_net = new float[dim_signal];
yukari_hinata 1:da597cb284a2 181
yukari_hinata 1:da597cb284a2 182 // 誤差信号
yukari_hinata 1:da597cb284a2 183 float* sigma = new float[dim_signal];
yukari_hinata 1:da597cb284a2 184
yukari_hinata 1:da597cb284a2 185 // 前回の二乗誤差値:収束判定に用いる.
yukari_hinata 1:da597cb284a2 186 float prevError;
yukari_hinata 1:da597cb284a2 187
yukari_hinata 1:da597cb284a2 188 /* 学習ループ */
yukari_hinata 1:da597cb284a2 189 while (1) {
yukari_hinata 1:da597cb284a2 190
yukari_hinata 1:da597cb284a2 191 // 終了条件を満たすか確認
yukari_hinata 1:da597cb284a2 192 if (!end_flag) {
yukari_hinata 1:da597cb284a2 193 end_flag = !(iteration < this->maxIteration
yukari_hinata 1:da597cb284a2 194 && (iteration <= this->len_seqence
yukari_hinata 1:da597cb284a2 195 || this->squareError > this->goalError)
yukari_hinata 1:da597cb284a2 196 );
yukari_hinata 1:da597cb284a2 197 }
yukari_hinata 1:da597cb284a2 198
yukari_hinata 1:da597cb284a2 199 // printf("ite:%d err:%f \r\n", iteration, squareError);
yukari_hinata 1:da597cb284a2 200
yukari_hinata 1:da597cb284a2 201 // 系列の末尾に到達していたら,最初からリセットする.
yukari_hinata 1:da597cb284a2 202 if (seq == len_seqence && !end_flag) {
yukari_hinata 1:da597cb284a2 203 seq = 0;
yukari_hinata 1:da597cb284a2 204 }
yukari_hinata 1:da597cb284a2 205
yukari_hinata 1:da597cb284a2 206 // 前回の更新量/二乗誤差を保存
yukari_hinata 1:da597cb284a2 207 if (iteration >= 1) {
yukari_hinata 1:da597cb284a2 208 memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid);
yukari_hinata 1:da597cb284a2 209 memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out);
yukari_hinata 1:da597cb284a2 210 prevError = squareError;
yukari_hinata 1:da597cb284a2 211 } else {
yukari_hinata 1:da597cb284a2 212 // 初回は0埋め
yukari_hinata 1:da597cb284a2 213 memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid);
yukari_hinata 1:da597cb284a2 214 memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out);
yukari_hinata 1:da597cb284a2 215 }
yukari_hinata 1:da597cb284a2 216
yukari_hinata 1:da597cb284a2 217 /* 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 */
yukari_hinata 0:0d42047e140c 218
yukari_hinata 1:da597cb284a2 219 // 入力値を取得
yukari_hinata 1:da597cb284a2 220 memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal);
yukari_hinata 1:da597cb284a2 221 // 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.
yukari_hinata 1:da597cb284a2 222 if (iteration == 0) {
yukari_hinata 1:da597cb284a2 223 // 初回は0埋めする
yukari_hinata 1:da597cb284a2 224 memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron);
yukari_hinata 1:da597cb284a2 225 } else {
yukari_hinata 1:da597cb284a2 226 // コンテキスト層 = 前回のコンテキスト層の出力
yukari_hinata 1:da597cb284a2 227 // 前回の中間層信号との線形和をシグモイド関数に通す.
yukari_hinata 1:da597cb284a2 228 for (int d = 0; d < num_mid_neuron; d++) {
yukari_hinata 1:da597cb284a2 229 expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
yukari_hinata 1:da597cb284a2 230 }
yukari_hinata 1:da597cb284a2 231 }
yukari_hinata 1:da597cb284a2 232 // バイアス項は常に1に固定.
yukari_hinata 1:da597cb284a2 233 expand_in_signal[dim_signal + num_mid_neuron] = 1;
yukari_hinata 1:da597cb284a2 234
yukari_hinata 1:da597cb284a2 235 // 入力->中間層の出力信号和計算
yukari_hinata 1:da597cb284a2 236 multiply_mat_vec(Win_mid,
yukari_hinata 1:da597cb284a2 237 expand_in_signal,
yukari_hinata 1:da597cb284a2 238 in_mid_net,
yukari_hinata 1:da597cb284a2 239 num_mid_neuron,
yukari_hinata 1:da597cb284a2 240 dim_signal + num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 241 // 中間層の出力信号計算
yukari_hinata 1:da597cb284a2 242 sigmoid_vec(in_mid_net,
yukari_hinata 1:da597cb284a2 243 expand_mid_signal,
yukari_hinata 1:da597cb284a2 244 num_mid_neuron);
yukari_hinata 1:da597cb284a2 245 expand_mid_signal[num_mid_neuron] = 1;
yukari_hinata 1:da597cb284a2 246 // 中間->出力層の出力信号和計算
yukari_hinata 1:da597cb284a2 247 multiply_mat_vec(Wmid_out,
yukari_hinata 1:da597cb284a2 248 expand_mid_signal,
yukari_hinata 1:da597cb284a2 249 mid_out_net,
yukari_hinata 1:da597cb284a2 250 dim_signal,
yukari_hinata 1:da597cb284a2 251 num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 252 // 出力層の出力信号計算
yukari_hinata 1:da597cb284a2 253 sigmoid_vec(mid_out_net,
yukari_hinata 1:da597cb284a2 254 out_signal,
yukari_hinata 1:da597cb284a2 255 dim_signal);
yukari_hinata 1:da597cb284a2 256
yukari_hinata 1:da597cb284a2 257
yukari_hinata 1:da597cb284a2 258 for (int i = 0; i < dim_signal; i++) {
yukari_hinata 1:da597cb284a2 259 predict_signal[i] = expand_signal(out_signal[i],
yukari_hinata 1:da597cb284a2 260 MATRIX_AT(sample_maxmin,2,i,0),
yukari_hinata 1:da597cb284a2 261 MATRIX_AT(sample_maxmin,2,i,1));
yukari_hinata 1:da597cb284a2 262 }
yukari_hinata 1:da597cb284a2 263 printf("predict : %f %f %f \r\n", predict_signal[0], predict_signal[1], predict_signal[2]);
yukari_hinata 1:da597cb284a2 264
yukari_hinata 1:da597cb284a2 265 // print_mat(Wmid_out, row_mid_out, col_mid_out);
yukari_hinata 0:0d42047e140c 266
yukari_hinata 1:da597cb284a2 267 // この時点での二乗誤差計算
yukari_hinata 1:da597cb284a2 268 squareError = 0;
yukari_hinata 1:da597cb284a2 269 // 次の系列との誤差を見ている!! ここが注目ポイント
yukari_hinata 1:da597cb284a2 270 // ==> つまり,次系列を予測させようとしている.
yukari_hinata 1:da597cb284a2 271 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 272 if (seq < len_seqence - 1) {
yukari_hinata 1:da597cb284a2 273 squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2);
yukari_hinata 1:da597cb284a2 274 } else {
yukari_hinata 1:da597cb284a2 275 squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2);
yukari_hinata 1:da597cb284a2 276 }
yukari_hinata 1:da597cb284a2 277 }
yukari_hinata 1:da597cb284a2 278 squareError /= dim_signal;
yukari_hinata 1:da597cb284a2 279
yukari_hinata 1:da597cb284a2 280 /* 学習の終了 */
yukari_hinata 1:da597cb284a2 281 // 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
yukari_hinata 1:da597cb284a2 282 if (end_flag && (seq == (len_seqence-1))) {
yukari_hinata 1:da597cb284a2 283 // 予測結果をセット.
yukari_hinata 1:da597cb284a2 284 for (int i = 0; i < dim_signal; i++) {
yukari_hinata 1:da597cb284a2 285 predict_signal[i] = expand_signal(out_signal[i],
yukari_hinata 1:da597cb284a2 286 MATRIX_AT(sample_maxmin,2,i,0),
yukari_hinata 1:da597cb284a2 287 MATRIX_AT(sample_maxmin,2,i,1));
yukari_hinata 1:da597cb284a2 288 //printf("%f ", predict_signal[i]);
yukari_hinata 1:da597cb284a2 289 }
yukari_hinata 1:da597cb284a2 290 break;
yukari_hinata 1:da597cb284a2 291 }
yukari_hinata 1:da597cb284a2 292
yukari_hinata 1:da597cb284a2 293 // 収束したと判定したら終了フラグを立てる.
yukari_hinata 1:da597cb284a2 294 if (fabsf(squareError - prevError) < epsilon) {
yukari_hinata 1:da597cb284a2 295 end_flag = 1;
yukari_hinata 1:da597cb284a2 296 }
yukari_hinata 1:da597cb284a2 297
yukari_hinata 1:da597cb284a2 298 /* 学習ステップその2:逆誤差伝搬 */
yukari_hinata 1:da597cb284a2 299 // 誤差信号の計算
yukari_hinata 1:da597cb284a2 300 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 301 if (seq < len_seqence - 1) {
yukari_hinata 1:da597cb284a2 302 sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample,dim_signal,seq+1,n)) * out_signal[n] * (1 - out_signal[n]);
yukari_hinata 1:da597cb284a2 303 } else {
yukari_hinata 1:da597cb284a2 304 /* 末尾と先頭の誤差を取る (大抵,大きくなる) */
yukari_hinata 1:da597cb284a2 305 sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]);
yukari_hinata 1:da597cb284a2 306 }
yukari_hinata 1:da597cb284a2 307 }
yukari_hinata 1:da597cb284a2 308 // printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]);
yukari_hinata 0:0d42047e140c 309
yukari_hinata 1:da597cb284a2 310 // 出力->中間層の係数の変更量計算
yukari_hinata 1:da597cb284a2 311 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 312 for (int j = 0; j < num_mid_neuron + 1; j++) {
yukari_hinata 1:da597cb284a2 313 MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j];
yukari_hinata 1:da597cb284a2 314 }
yukari_hinata 1:da597cb284a2 315 }
yukari_hinata 1:da597cb284a2 316
yukari_hinata 1:da597cb284a2 317 // 中間->入力層の係数の変更量計算
yukari_hinata 1:da597cb284a2 318 register float sum_sigma;
yukari_hinata 1:da597cb284a2 319 for (int i = 0; i < num_mid_neuron; i++) {
yukari_hinata 1:da597cb284a2 320 // 誤差信号を逆向きに伝播させる.
yukari_hinata 1:da597cb284a2 321 sum_sigma = 0;
yukari_hinata 1:da597cb284a2 322 for (int k = 0; k < dim_signal; k++) {
yukari_hinata 1:da597cb284a2 323 sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i);
yukari_hinata 1:da597cb284a2 324 }
yukari_hinata 1:da597cb284a2 325 // 中間->入力層の係数の変更量計算
yukari_hinata 1:da597cb284a2 326 for (int j = 0; j < col_in_mid; j++) {
yukari_hinata 1:da597cb284a2 327 MATRIX_AT(dWin_mid,num_mid_neuron,j,i)
yukari_hinata 1:da597cb284a2 328 = sum_sigma * expand_mid_signal[i] *
yukari_hinata 1:da597cb284a2 329 (1 - expand_mid_signal[i]) *
yukari_hinata 1:da597cb284a2 330 expand_in_signal[j];
yukari_hinata 1:da597cb284a2 331 }
yukari_hinata 1:da597cb284a2 332 }
yukari_hinata 0:0d42047e140c 333
yukari_hinata 1:da597cb284a2 334 // 係数更新
yukari_hinata 1:da597cb284a2 335 for (int i = 0; i < row_in_mid; i++) {
yukari_hinata 1:da597cb284a2 336 for (int j = 0; j < col_in_mid; j++) {
yukari_hinata 1:da597cb284a2 337 //printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j));
yukari_hinata 1:da597cb284a2 338 MATRIX_AT(Win_mid,col_in_mid,i,j) =
yukari_hinata 1:da597cb284a2 339 MATRIX_AT(Win_mid,col_in_mid,i,j) -
yukari_hinata 1:da597cb284a2 340 this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) -
yukari_hinata 1:da597cb284a2 341 this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j);
yukari_hinata 1:da597cb284a2 342 // printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j));
yukari_hinata 1:da597cb284a2 343 // printf("dW : %f , prevdW : %f ", MATRIX_AT(dWin_mid,col_in_mid,i,j), MATRIX_AT(prevdWin_mid,col_in_mid,i,j));
yukari_hinata 1:da597cb284a2 344 }
yukari_hinata 1:da597cb284a2 345 //printf("\r\n");
yukari_hinata 1:da597cb284a2 346 }
yukari_hinata 1:da597cb284a2 347 for (int i = 0; i < row_mid_out; i++) {
yukari_hinata 1:da597cb284a2 348 for (int j = 0; j < col_mid_out; j++) {
yukari_hinata 1:da597cb284a2 349 MATRIX_AT(Wmid_out,col_mid_out,i,j)=
yukari_hinata 1:da597cb284a2 350 MATRIX_AT(Wmid_out,col_mid_out,i,j) -
yukari_hinata 1:da597cb284a2 351 this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) -
yukari_hinata 1:da597cb284a2 352 this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j);
yukari_hinata 1:da597cb284a2 353 }
yukari_hinata 1:da597cb284a2 354 }
yukari_hinata 1:da597cb284a2 355
yukari_hinata 1:da597cb284a2 356 // ループ回数/系列のインクリメント
yukari_hinata 1:da597cb284a2 357 iteration += 1;
yukari_hinata 1:da597cb284a2 358 seq += 1;
yukari_hinata 1:da597cb284a2 359
yukari_hinata 0:0d42047e140c 360 }
yukari_hinata 0:0d42047e140c 361
yukari_hinata 1:da597cb284a2 362 delete [] dWin_mid; delete [] dWmid_out;
yukari_hinata 1:da597cb284a2 363 delete [] prevdWin_mid; delete [] prevdWmid_out;
yukari_hinata 1:da597cb284a2 364 delete [] norm_sample; delete [] out_signal;
yukari_hinata 1:da597cb284a2 365 delete [] in_mid_net; delete [] mid_out_net;
yukari_hinata 0:0d42047e140c 366
yukari_hinata 1:da597cb284a2 367 return squareError;
yukari_hinata 0:0d42047e140c 368 }