Taiyo Mineo / SRNN

Simple Recurrent Neural Network Predictor

Dependents:   WeatherPredictor

SRNN.cpp@0:0d42047e140c, 2015-01-15 (annotated)

Committer:
yukari_hinata
Date:
Thu Jan 15 08:22:24 2015 +0000
Revision:
0:0d42047e140c
Child:
1:da597cb284a2
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 0:0d42047e140c 6 SRNN::SRNN(int dim,
yukari_hinata 0:0d42047e140c 7 int num_mid,
yukari_hinata 0:0d42047e140c 8 int len_seq,
yukari_hinata 0:0d42047e140c 9 float* input_sample,
yukari_hinata 0:0d42047e140c 10 float* input_sample_maxmin)
yukari_hinata 0:0d42047e140c 11 {
yukari_hinata 0:0d42047e140c 12
yukari_hinata 0:0d42047e140c 13 this->dim_signal = dim;
yukari_hinata 0:0d42047e140c 14 this->num_mid_neuron = num_mid; // Advice : number of hidden layter shuld be as large as possible.
yukari_hinata 0:0d42047e140c 15 this->len_seqence = len_seq;
yukari_hinata 0:0d42047e140c 16
yukari_hinata 0:0d42047e140c 17 // sample/sample_maxmin allocation
yukari_hinata 0:0d42047e140c 18 this->sample = new float[len_seqence * dim_signal];
yukari_hinata 0:0d42047e140c 19 this->sample_maxmin = new float[dim_signal * 2];
yukari_hinata 0:0d42047e140c 20
yukari_hinata 0:0d42047e140c 21 memcpy(this->sample, input_sample, sizeof(float) * len_seqence * dim_signal);
yukari_hinata 0:0d42047e140c 22 memcpy(this->sample_maxmin, input_sample_maxmin, sizeof(float) * dim_signal * 2);
yukari_hinata 0:0d42047e140c 23
yukari_hinata 0:0d42047e140c 24 this->predict_signal = new float[dim_signal];
yukari_hinata 0:0d42047e140c 25
yukari_hinata 0:0d42047e140c 26 // coffecience matrix allocation
yukari_hinata 0:0d42047e140c 27 // final +1 for bias
yukari_hinata 0:0d42047e140c 28 this->Win_mid = new float[num_mid_neuron * (dim_signal + num_mid_neuron + 1)];
yukari_hinata 0:0d42047e140c 29 this->Wmid_out = new float[dim_signal * (num_mid_neuron + 1)];
yukari_hinata 0:0d42047e140c 30
yukari_hinata 0:0d42047e140c 31
yukari_hinata 0:0d42047e140c 32 // input/hidden layer signal allocation
yukari_hinata 0:0d42047e140c 33 expand_in_signal = new float[dim_signal + num_mid_neuron + 1];
yukari_hinata 0:0d42047e140c 34 expand_mid_signal = new float[num_mid_neuron + 1];
yukari_hinata 0:0d42047e140c 35
yukari_hinata 0:0d42047e140c 36 // Parameter settings (Tuning by taiyo)
yukari_hinata 0:0d42047e140c 37 this->squareError = FLT_MAX; // (large value)
yukari_hinata 0:0d42047e140c 38 this->maxIteration = 5000;
yukari_hinata 0:0d42047e140c 39 this->goalError = float(0.001);
yukari_hinata 0:0d42047e140c 40 this->epsilon = float(0.00001);
yukari_hinata 0:0d42047e140c 41 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 0:0d42047e140c 42 this->alpha = float(0.8 * learnRate);
yukari_hinata 0:0d42047e140c 43 this->alpha_context = float(0.8);
yukari_hinata 0:0d42047e140c 44 this->width_initW = float(1.0/num_mid_neuron);
yukari_hinata 0:0d42047e140c 45
yukari_hinata 0:0d42047e140c 46 // random seed decide by time
yukari_hinata 0:0d42047e140c 47 srand((unsigned int)time(NULL));
yukari_hinata 0:0d42047e140c 48
yukari_hinata 0:0d42047e140c 49 }
yukari_hinata 0:0d42047e140c 50
yukari_hinata 0:0d42047e140c 51 SRNN::~SRNN(void)
yukari_hinata 0:0d42047e140c 52 {
yukari_hinata 0:0d42047e140c 53 delete [] sample; delete [] sample_maxmin;
yukari_hinata 0:0d42047e140c 54 delete [] predict_signal;
yukari_hinata 0:0d42047e140c 55 delete [] Win_mid; delete [] Wmid_out;
yukari_hinata 0:0d42047e140c 56 delete [] expand_in_signal;
yukari_hinata 0:0d42047e140c 57 delete [] expand_mid_signal;
yukari_hinata 0:0d42047e140c 58 }
yukari_hinata 0:0d42047e140c 59
yukari_hinata 0:0d42047e140c 60 /* utilにいどうするべき */
yukari_hinata 0:0d42047e140c 61 void SRNN::sigmoid_vec(float* net,
yukari_hinata 0:0d42047e140c 62 float* out,
yukari_hinata 0:0d42047e140c 63 int dim)
yukari_hinata 0:0d42047e140c 64 {
yukari_hinata 0:0d42047e140c 65 for (int n=0;n<dim;n++)
yukari_hinata 0:0d42047e140c 66 out[n] = sigmoid_func(net[n]);
yukari_hinata 0:0d42047e140c 67 }
yukari_hinata 0:0d42047e140c 68
yukari_hinata 0:0d42047e140c 69 /* Predict : predicting next sequence of input */
yukari_hinata 0:0d42047e140c 70 void SRNN::predict(float* input)
yukari_hinata 0:0d42047e140c 71 {
yukari_hinata 0:0d42047e140c 72 float *norm_input = new float[this->dim_signal];
yukari_hinata 0:0d42047e140c 73
yukari_hinata 0:0d42047e140c 74 // normalize signal
yukari_hinata 0:0d42047e140c 75 for (int n=0; n < dim_signal; n++) {
yukari_hinata 0:0d42047e140c 76 norm_input[n] =
yukari_hinata 0:0d42047e140c 77 normalize_signal(input[n],
yukari_hinata 0:0d42047e140c 78 MATRIX_AT(this->sample_maxmin,2,n,0),
yukari_hinata 0:0d42047e140c 79 MATRIX_AT(this->sample_maxmin,2,n,1));
yukari_hinata 0:0d42047e140c 80 }
yukari_hinata 0:0d42047e140c 81
yukari_hinata 0:0d42047e140c 82 // output signal
yukari_hinata 0:0d42047e140c 83 float* out_signal = new float[dim_signal];
yukari_hinata 0:0d42047e140c 84 // value of network in input->hidden layer
yukari_hinata 0:0d42047e140c 85 float* in_mid_net = new float[num_mid_neuron];
yukari_hinata 0:0d42047e140c 86 // value of network in hidden->output layer
yukari_hinata 0:0d42047e140c 87 float* mid_out_net = new float[dim_signal];
yukari_hinata 0:0d42047e140c 88
yukari_hinata 0:0d42047e140c 89 /* Calcurate output signal */
yukari_hinata 0:0d42047e140c 90 // Get input signal
yukari_hinata 0:0d42047e140c 91 memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal);
yukari_hinata 0:0d42047e140c 92 // 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 0:0d42047e140c 93 for (int d = 0; d < num_mid_neuron; d++) {
yukari_hinata 0:0d42047e140c 94 expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
yukari_hinata 0:0d42047e140c 95 }
yukari_hinata 0:0d42047e140c 96 // Bias fixed at 1.
yukari_hinata 0:0d42047e140c 97 expand_in_signal[dim_signal + num_mid_neuron] = 1;
yukari_hinata 0:0d42047e140c 98
yukari_hinata 0:0d42047e140c 99 // 入力->中間層の出力信号和計算
yukari_hinata 0:0d42047e140c 100 multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1);
yukari_hinata 0:0d42047e140c 101 // 中間層の出力信号計算
yukari_hinata 0:0d42047e140c 102 sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron);
yukari_hinata 0:0d42047e140c 103 expand_mid_signal[num_mid_neuron] = 1;
yukari_hinata 0:0d42047e140c 104
yukari_hinata 0:0d42047e140c 105 // 中間->出力層の出力信号和計算
yukari_hinata 0:0d42047e140c 106 multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1);
yukari_hinata 0:0d42047e140c 107 // 出力層の出力信号計算
yukari_hinata 0:0d42047e140c 108 sigmoid_vec(mid_out_net, out_signal, dim_signal);
yukari_hinata 0:0d42047e140c 109
yukari_hinata 0:0d42047e140c 110 // expand output signal to origin width.
yukari_hinata 0:0d42047e140c 111 for (int n=0;n < dim_signal;n++) {
yukari_hinata 0:0d42047e140c 112 predict_signal[n] = expand_signal(out_signal[n],sample_maxmin[n * 2],sample_maxmin[n * 2 + 1]);
yukari_hinata 0:0d42047e140c 113 }
yukari_hinata 0:0d42047e140c 114
yukari_hinata 0:0d42047e140c 115 delete [] norm_input; delete [] out_signal;
yukari_hinata 0:0d42047e140c 116 delete [] in_mid_net; delete [] mid_out_net;
yukari_hinata 0:0d42047e140c 117
yukari_hinata 0:0d42047e140c 118 }
yukari_hinata 0:0d42047e140c 119
yukari_hinata 0:0d42047e140c 120 /* 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 121 float SRNN::learning(void)
yukari_hinata 0:0d42047e140c 122 {
yukari_hinata 0:0d42047e140c 123 int iteration = 0; // 学習繰り返し回数
yukari_hinata 0:0d42047e140c 124 int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1]
yukari_hinata 0:0d42047e140c 125 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 0:0d42047e140c 126 // 係数行列のサイズ
yukari_hinata 0:0d42047e140c 127 int row_in_mid = num_mid_neuron;
yukari_hinata 0:0d42047e140c 128 int col_in_mid = dim_signal + num_mid_neuron + 1;
yukari_hinata 0:0d42047e140c 129 int row_mid_out = dim_signal;
yukari_hinata 0:0d42047e140c 130 int col_mid_out = num_mid_neuron + 1;
yukari_hinata 0:0d42047e140c 131
yukari_hinata 0:0d42047e140c 132 // 行列のアロケート
yukari_hinata 0:0d42047e140c 133 // 係数行列の更新量
yukari_hinata 0:0d42047e140c 134 float* dWin_mid = new float[row_in_mid * col_in_mid];
yukari_hinata 0:0d42047e140c 135 float* dWmid_out = new float[row_mid_out * col_mid_out];
yukari_hinata 0:0d42047e140c 136 // 前回の更新量:慣性項に用いる.
yukari_hinata 0:0d42047e140c 137 float* prevdWin_mid = new float[row_in_mid * col_in_mid];
yukari_hinata 0:0d42047e140c 138 float* prevdWmid_out = new float[row_mid_out * col_mid_out];
yukari_hinata 0:0d42047e140c 139 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 140
yukari_hinata 0:0d42047e140c 141 // 係数行列の初期化
yukari_hinata 0:0d42047e140c 142 for (int i=0; i < row_in_mid; i++)
yukari_hinata 0:0d42047e140c 143 for (int j=0; j < col_in_mid; j++)
yukari_hinata 0:0d42047e140c 144 MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW);
yukari_hinata 0:0d42047e140c 145
yukari_hinata 0:0d42047e140c 146 for (int i=0; i < row_mid_out; i++)
yukari_hinata 0:0d42047e140c 147 for (int j=0; j < col_mid_out; j++)
yukari_hinata 0:0d42047e140c 148 MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW);
yukari_hinata 0:0d42047e140c 149
yukari_hinata 0:0d42047e140c 150 // 信号の正規化:経験上,非常に大切な処理
yukari_hinata 0:0d42047e140c 151 for (int seq=0; seq < len_seqence; seq++) {
yukari_hinata 0:0d42047e140c 152 for (int n=0; n < dim_signal; n++) {
yukari_hinata 0:0d42047e140c 153 MATRIX_AT(norm_sample,dim_signal,seq,n) =
yukari_hinata 0:0d42047e140c 154 normalize_signal(MATRIX_AT(this->sample,dim_signal,seq,n),
yukari_hinata 0:0d42047e140c 155 MATRIX_AT(this->sample_maxmin,2,n,0),
yukari_hinata 0:0d42047e140c 156 MATRIX_AT(this->sample_maxmin,2,n,1));
yukari_hinata 0:0d42047e140c 157 // printf("%f ", MATRIX_AT(norm_sample,dim_signal,seq,n));
yukari_hinata 0:0d42047e140c 158 }
yukari_hinata 0:0d42047e140c 159 // printf("\r\n");
yukari_hinata 0:0d42047e140c 160 }
yukari_hinata 0:0d42047e140c 161
yukari_hinata 0:0d42047e140c 162 // 出力層の信号
yukari_hinata 0:0d42047e140c 163 float* out_signal = new float[dim_signal];
yukari_hinata 0:0d42047e140c 164
yukari_hinata 0:0d42047e140c 165 // 入力層->中間層の信号和
yukari_hinata 0:0d42047e140c 166 float* in_mid_net = new float[num_mid_neuron];
yukari_hinata 0:0d42047e140c 167 // 中間層->出力層の信号和.
yukari_hinata 0:0d42047e140c 168 float* mid_out_net = new float[dim_signal];
yukari_hinata 0:0d42047e140c 169
yukari_hinata 0:0d42047e140c 170 // 誤差信号
yukari_hinata 0:0d42047e140c 171 float* sigma = new float[dim_signal];
yukari_hinata 0:0d42047e140c 172
yukari_hinata 0:0d42047e140c 173 // 前回の二乗誤差値:収束判定に用いる.
yukari_hinata 0:0d42047e140c 174 float prevError;
yukari_hinata 0:0d42047e140c 175
yukari_hinata 0:0d42047e140c 176 /* 学習ループ */
yukari_hinata 0:0d42047e140c 177 while (1) {
yukari_hinata 0:0d42047e140c 178
yukari_hinata 0:0d42047e140c 179 // 終了条件を満たすか確認
yukari_hinata 0:0d42047e140c 180 if (!end_flag) {
yukari_hinata 0:0d42047e140c 181 end_flag = !(iteration < this->maxIteration
yukari_hinata 0:0d42047e140c 182 && (iteration <= this->len_seqence
yukari_hinata 0:0d42047e140c 183 || this->squareError > this->goalError)
yukari_hinata 0:0d42047e140c 184 );
yukari_hinata 0:0d42047e140c 185 }
yukari_hinata 0:0d42047e140c 186
yukari_hinata 0:0d42047e140c 187 // printf("ite:%d err:%f \r\n", iteration, squareError);
yukari_hinata 0:0d42047e140c 188
yukari_hinata 0:0d42047e140c 189 // 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 0:0d42047e140c 190 if (seq == len_seqence && !end_flag) {
yukari_hinata 0:0d42047e140c 191 seq = 0;
yukari_hinata 0:0d42047e140c 192 }
yukari_hinata 0:0d42047e140c 193
yukari_hinata 0:0d42047e140c 194 // 前回の更新量/二乗誤差を保存
yukari_hinata 0:0d42047e140c 195 if (iteration >= 1) {
yukari_hinata 0:0d42047e140c 196 memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid);
yukari_hinata 0:0d42047e140c 197 memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out);
yukari_hinata 0:0d42047e140c 198 prevError = squareError;
yukari_hinata 0:0d42047e140c 199 } else {
yukari_hinata 0:0d42047e140c 200 // 初回は0埋め
yukari_hinata 0:0d42047e140c 201 memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid);
yukari_hinata 0:0d42047e140c 202 memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out);
yukari_hinata 0:0d42047e140c 203 }
yukari_hinata 0:0d42047e140c 204
yukari_hinata 0:0d42047e140c 205 /* 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 206
yukari_hinata 0:0d42047e140c 207 // 入力値を取得
yukari_hinata 0:0d42047e140c 208 memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal);
yukari_hinata 0:0d42047e140c 209 // 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 0:0d42047e140c 210 if (iteration == 0) {
yukari_hinata 0:0d42047e140c 211 // 初回は0埋めする
yukari_hinata 0:0d42047e140c 212 memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron);
yukari_hinata 0:0d42047e140c 213 } else {
yukari_hinata 0:0d42047e140c 214 // コンテキスト層 = 前回のコンテキスト層の出力
yukari_hinata 0:0d42047e140c 215 // 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 0:0d42047e140c 216 for (int d = 0; d < num_mid_neuron; d++) {
yukari_hinata 0:0d42047e140c 217 expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
yukari_hinata 0:0d42047e140c 218 }
yukari_hinata 0:0d42047e140c 219 }
yukari_hinata 0:0d42047e140c 220 // バイアス項は常に1に固定.
yukari_hinata 0:0d42047e140c 221 expand_in_signal[dim_signal + num_mid_neuron] = 1;
yukari_hinata 0:0d42047e140c 222
yukari_hinata 0:0d42047e140c 223 // 入力->中間層の出力信号和計算
yukari_hinata 0:0d42047e140c 224 multiply_mat_vec(Win_mid,
yukari_hinata 0:0d42047e140c 225 expand_in_signal,
yukari_hinata 0:0d42047e140c 226 in_mid_net,
yukari_hinata 0:0d42047e140c 227 num_mid_neuron,
yukari_hinata 0:0d42047e140c 228 dim_signal + num_mid_neuron + 1);
yukari_hinata 0:0d42047e140c 229 // 中間層の出力信号計算
yukari_hinata 0:0d42047e140c 230 sigmoid_vec(in_mid_net,
yukari_hinata 0:0d42047e140c 231 expand_mid_signal,
yukari_hinata 0:0d42047e140c 232 num_mid_neuron);
yukari_hinata 0:0d42047e140c 233 expand_mid_signal[num_mid_neuron] = 1;
yukari_hinata 0:0d42047e140c 234 // 中間->出力層の出力信号和計算
yukari_hinata 0:0d42047e140c 235 multiply_mat_vec(Wmid_out,
yukari_hinata 0:0d42047e140c 236 expand_mid_signal,
yukari_hinata 0:0d42047e140c 237 mid_out_net,
yukari_hinata 0:0d42047e140c 238 dim_signal,
yukari_hinata 0:0d42047e140c 239 num_mid_neuron + 1);
yukari_hinata 0:0d42047e140c 240 // 出力層の出力信号計算
yukari_hinata 0:0d42047e140c 241 sigmoid_vec(mid_out_net,
yukari_hinata 0:0d42047e140c 242 out_signal,
yukari_hinata 0:0d42047e140c 243 dim_signal);
yukari_hinata 0:0d42047e140c 244
yukari_hinata 0:0d42047e140c 245
yukari_hinata 0:0d42047e140c 246 for (int i = 0; i < dim_signal; i++) {
yukari_hinata 0:0d42047e140c 247 predict_signal[i] = expand_signal(out_signal[i],
yukari_hinata 0:0d42047e140c 248 MATRIX_AT(sample_maxmin,2,i,0),
yukari_hinata 0:0d42047e140c 249 MATRIX_AT(sample_maxmin,2,i,1));
yukari_hinata 0:0d42047e140c 250 }
yukari_hinata 0:0d42047e140c 251 printf("predict : %f %f %f \r\n", predict_signal[0], predict_signal[1], predict_signal[2]);
yukari_hinata 0:0d42047e140c 252
yukari_hinata 0:0d42047e140c 253 // print_mat(Wmid_out, row_mid_out, col_mid_out);
yukari_hinata 0:0d42047e140c 254
yukari_hinata 0:0d42047e140c 255 // この時点での二乗誤差計算
yukari_hinata 0:0d42047e140c 256 squareError = 0;
yukari_hinata 0:0d42047e140c 257 // 次の系列との誤差を見ている!! ここが注目ポイント
yukari_hinata 0:0d42047e140c 258 // ==> つまり,次系列を予測させようとしている.
yukari_hinata 0:0d42047e140c 259 for (int n = 0;n < dim_signal;n++) {
yukari_hinata 0:0d42047e140c 260 if (seq < len_seqence - 1) {
yukari_hinata 0:0d42047e140c 261 squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2);
yukari_hinata 0:0d42047e140c 262 } else {
yukari_hinata 0:0d42047e140c 263 squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2);
yukari_hinata 0:0d42047e140c 264 }
yukari_hinata 0:0d42047e140c 265 }
yukari_hinata 0:0d42047e140c 266 squareError /= dim_signal;
yukari_hinata 0:0d42047e140c 267
yukari_hinata 0:0d42047e140c 268 /* 学習の終了 */
yukari_hinata 0:0d42047e140c 269 // 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 0:0d42047e140c 270 if (end_flag && (seq == (len_seqence-1))) {
yukari_hinata 0:0d42047e140c 271 // 予測結果をセット.
yukari_hinata 0:0d42047e140c 272 for (int i = 0; i < dim_signal; i++) {
yukari_hinata 0:0d42047e140c 273 predict_signal[i] = expand_signal(out_signal[i],
yukari_hinata 0:0d42047e140c 274 MATRIX_AT(sample_maxmin,2,i,0),
yukari_hinata 0:0d42047e140c 275 MATRIX_AT(sample_maxmin,2,i,1));
yukari_hinata 0:0d42047e140c 276 //printf("%f ", predict_signal[i]);
yukari_hinata 0:0d42047e140c 277 }
yukari_hinata 0:0d42047e140c 278 break;
yukari_hinata 0:0d42047e140c 279 }
yukari_hinata 0:0d42047e140c 280
yukari_hinata 0:0d42047e140c 281 // 収束したと判定したら終了フラグを立てる.
yukari_hinata 0:0d42047e140c 282 if (fabsf(squareError - prevError) < epsilon) {
yukari_hinata 0:0d42047e140c 283 end_flag = 1;
yukari_hinata 0:0d42047e140c 284 }
yukari_hinata 0:0d42047e140c 285
yukari_hinata 0:0d42047e140c 286 /* 学習ステップその2:逆誤差伝搬 */
yukari_hinata 0:0d42047e140c 287 // 誤差信号の計算
yukari_hinata 0:0d42047e140c 288 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 0:0d42047e140c 289 if (seq < len_seqence - 1) {
yukari_hinata 0:0d42047e140c 290 sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample,dim_signal,seq+1,n)) * out_signal[n] * (1 - out_signal[n]);
yukari_hinata 0:0d42047e140c 291 } else {
yukari_hinata 0:0d42047e140c 292 /* 末尾と先頭の誤差を取る (大抵,大きくなる) */
yukari_hinata 0:0d42047e140c 293 sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]);
yukari_hinata 0:0d42047e140c 294 }
yukari_hinata 0:0d42047e140c 295 }
yukari_hinata 0:0d42047e140c 296 // printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]);
yukari_hinata 0:0d42047e140c 297
yukari_hinata 0:0d42047e140c 298 // 出力->中間層の係数の変更量計算
yukari_hinata 0:0d42047e140c 299 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 0:0d42047e140c 300 for (int j = 0; j < num_mid_neuron + 1; j++) {
yukari_hinata 0:0d42047e140c 301 MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j];
yukari_hinata 0:0d42047e140c 302 }
yukari_hinata 0:0d42047e140c 303 }
yukari_hinata 0:0d42047e140c 304
yukari_hinata 0:0d42047e140c 305 // 中間->入力層の係数の変更量計算
yukari_hinata 0:0d42047e140c 306 register float sum_sigma;
yukari_hinata 0:0d42047e140c 307 for (int i = 0; i < num_mid_neuron; i++) {
yukari_hinata 0:0d42047e140c 308 // 誤差信号を逆向きに伝播させる.
yukari_hinata 0:0d42047e140c 309 sum_sigma = 0;
yukari_hinata 0:0d42047e140c 310 for (int k = 0; k < dim_signal; k++) {
yukari_hinata 0:0d42047e140c 311 sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i);
yukari_hinata 0:0d42047e140c 312 }
yukari_hinata 0:0d42047e140c 313 // 中間->入力層の係数の変更量計算
yukari_hinata 0:0d42047e140c 314 for (int j = 0; j < col_in_mid; j++) {
yukari_hinata 0:0d42047e140c 315 MATRIX_AT(dWin_mid,num_mid_neuron,j,i)
yukari_hinata 0:0d42047e140c 316 = sum_sigma * expand_mid_signal[i] *
yukari_hinata 0:0d42047e140c 317 (1 - expand_mid_signal[i]) *
yukari_hinata 0:0d42047e140c 318 expand_in_signal[j];
yukari_hinata 0:0d42047e140c 319 }
yukari_hinata 0:0d42047e140c 320 }
yukari_hinata 0:0d42047e140c 321
yukari_hinata 0:0d42047e140c 322 // 係数更新
yukari_hinata 0:0d42047e140c 323 for (int i = 0; i < row_in_mid; i++) {
yukari_hinata 0:0d42047e140c 324 for (int j = 0; j < col_in_mid; j++) {
yukari_hinata 0:0d42047e140c 325 //printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j));
yukari_hinata 0:0d42047e140c 326 MATRIX_AT(Win_mid,col_in_mid,i,j) =
yukari_hinata 0:0d42047e140c 327 MATRIX_AT(Win_mid,col_in_mid,i,j) -
yukari_hinata 0:0d42047e140c 328 this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) -
yukari_hinata 0:0d42047e140c 329 this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j);
yukari_hinata 0:0d42047e140c 330 // printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j));
yukari_hinata 0:0d42047e140c 331 // 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 0:0d42047e140c 332 }
yukari_hinata 0:0d42047e140c 333 //printf("\r\n");
yukari_hinata 0:0d42047e140c 334 }
yukari_hinata 0:0d42047e140c 335 for (int i = 0; i < row_mid_out; i++) {
yukari_hinata 0:0d42047e140c 336 for (int j = 0; j < col_mid_out; j++) {
yukari_hinata 0:0d42047e140c 337 MATRIX_AT(Wmid_out,col_mid_out,i,j)=
yukari_hinata 0:0d42047e140c 338 MATRIX_AT(Wmid_out,col_mid_out,i,j) -
yukari_hinata 0:0d42047e140c 339 this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) -
yukari_hinata 0:0d42047e140c 340 this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j);
yukari_hinata 0:0d42047e140c 341 }
yukari_hinata 0:0d42047e140c 342 }
yukari_hinata 0:0d42047e140c 343
yukari_hinata 0:0d42047e140c 344 // ループ回数/系列のインクリメント
yukari_hinata 0:0d42047e140c 345 iteration += 1;
yukari_hinata 0:0d42047e140c 346 seq += 1;
yukari_hinata 0:0d42047e140c 347
yukari_hinata 0:0d42047e140c 348 }
yukari_hinata 0:0d42047e140c 349
yukari_hinata 0:0d42047e140c 350 delete [] dWin_mid; delete [] dWmid_out;
yukari_hinata 0:0d42047e140c 351 delete [] prevdWin_mid; delete [] prevdWmid_out;
yukari_hinata 0:0d42047e140c 352 delete [] norm_sample; delete [] out_signal;
yukari_hinata 0:0d42047e140c 353 delete [] in_mid_net; delete [] mid_out_net;
yukari_hinata 0:0d42047e140c 354
yukari_hinata 0:0d42047e140c 355 return squareError;
yukari_hinata 0:0d42047e140c 356 }