Taiyo Mineo / SRNN

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Dependents:   WeatherPredictor

SRNN.cpp@2:d623e7ef4dca, 2015-02-15 (annotated)

Committer:
yukari_hinata
Date:
Sun Feb 15 09:27:31 2015 +0000
Revision:
2:d623e7ef4dca
Parent:
1:da597cb284a2
Child:
4:9d94330f380a
modified.

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 2:d623e7ef4dca 54 delete [] sample; delete [] sample_maxmin;
yukari_hinata 2:d623e7ef4dca 55 delete [] predict_signal; delete [] Win_mid;
yukari_hinata 2:d623e7ef4dca 56 delete [] Wmid_out; 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 1:da597cb284a2 60 /* utilに移動するべき */
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 1:da597cb284a2 65 for (int n=0; n<dim; n++)
yukari_hinata 1:da597cb284a2 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 1:da597cb284a2 72 float *norm_input = new float[this->dim_signal];
yukari_hinata 2:d623e7ef4dca 73
yukari_hinata 1:da597cb284a2 74 // output signal
yukari_hinata 1:da597cb284a2 75 float* out_signal = new float[dim_signal];
yukari_hinata 1:da597cb284a2 76 // value of network in input->hidden layer
yukari_hinata 1:da597cb284a2 77 float* in_mid_net = new float[num_mid_neuron];
yukari_hinata 1:da597cb284a2 78 // value of network in hidden->output layer
yukari_hinata 1:da597cb284a2 79 float* mid_out_net = new float[dim_signal];
yukari_hinata 0:0d42047e140c 80
yukari_hinata 1:da597cb284a2 81 /* Calcurate output signal */
yukari_hinata 1:da597cb284a2 82 for (int i_predict = 0; i_predict < len_predict; i_predict++) {
yukari_hinata 0:0d42047e140c 83
yukari_hinata 1:da597cb284a2 84 // normalize signal
yukari_hinata 1:da597cb284a2 85 for (int n=0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 86 if (i_predict == 0) {
yukari_hinata 1:da597cb284a2 87 // First : given input
yukari_hinata 1:da597cb284a2 88 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 89 } else {
yukari_hinata 1:da597cb284a2 90 // Second~ : previous output
yukari_hinata 1:da597cb284a2 91 norm_input[n] = out_signal[n];
yukari_hinata 1:da597cb284a2 92 }
yukari_hinata 1:da597cb284a2 93 }
yukari_hinata 0:0d42047e140c 94
yukari_hinata 1:da597cb284a2 95 // Get input signal
yukari_hinata 1:da597cb284a2 96 memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal);
yukari_hinata 1:da597cb284a2 97 // 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 98 for (int d = 0; d < num_mid_neuron; d++) {
yukari_hinata 1:da597cb284a2 99 expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
yukari_hinata 1:da597cb284a2 100 }
yukari_hinata 1:da597cb284a2 101 // Bias fixed at 1.
yukari_hinata 1:da597cb284a2 102 expand_in_signal[dim_signal + num_mid_neuron] = 1;
yukari_hinata 1:da597cb284a2 103
yukari_hinata 1:da597cb284a2 104 // 入力->中間層の出力信号和計算
yukari_hinata 1:da597cb284a2 105 multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 106 // 中間層の出力信号計算
yukari_hinata 1:da597cb284a2 107 sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron);
yukari_hinata 1:da597cb284a2 108 expand_mid_signal[num_mid_neuron] = 1;
yukari_hinata 0:0d42047e140c 109
yukari_hinata 1:da597cb284a2 110 // 中間->出力層の出力信号和計算
yukari_hinata 1:da597cb284a2 111 multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 112 // 出力層の出力信号計算
yukari_hinata 1:da597cb284a2 113 sigmoid_vec(mid_out_net, out_signal, dim_signal);
yukari_hinata 0:0d42047e140c 114
yukari_hinata 1:da597cb284a2 115 // expand output signal to origin width.
yukari_hinata 1:da597cb284a2 116 for (int n=0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 117 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 118 }
yukari_hinata 1:da597cb284a2 119
yukari_hinata 1:da597cb284a2 120 }
yukari_hinata 1:da597cb284a2 121
yukari_hinata 1:da597cb284a2 122 // 領域解放
yukari_hinata 1:da597cb284a2 123 delete [] norm_input; delete [] out_signal;
yukari_hinata 1:da597cb284a2 124 delete [] in_mid_net; delete [] mid_out_net;
yukari_hinata 0:0d42047e140c 125
yukari_hinata 0:0d42047e140c 126 }
yukari_hinata 0:0d42047e140c 127
yukari_hinata 0:0d42047e140c 128 /* 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 129 float SRNN::learning(void)
yukari_hinata 0:0d42047e140c 130 {
yukari_hinata 1:da597cb284a2 131 int iteration = 0; // 学習繰り返し回数
yukari_hinata 1:da597cb284a2 132 int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1]
yukari_hinata 1:da597cb284a2 133 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 134 // 係数行列のサイズ
yukari_hinata 1:da597cb284a2 135 int row_in_mid = num_mid_neuron;
yukari_hinata 1:da597cb284a2 136 int col_in_mid = dim_signal + num_mid_neuron + 1;
yukari_hinata 1:da597cb284a2 137 int row_mid_out = dim_signal;
yukari_hinata 1:da597cb284a2 138 int col_mid_out = num_mid_neuron + 1;
yukari_hinata 0:0d42047e140c 139
yukari_hinata 1:da597cb284a2 140 // 行列のアロケート
yukari_hinata 1:da597cb284a2 141 // 係数行列の更新量
yukari_hinata 1:da597cb284a2 142 float* dWin_mid = new float[row_in_mid * col_in_mid];
yukari_hinata 1:da597cb284a2 143 float* dWmid_out = new float[row_mid_out * col_mid_out];
yukari_hinata 1:da597cb284a2 144 // 前回の更新量:慣性項に用いる.
yukari_hinata 1:da597cb284a2 145 float* prevdWin_mid = new float[row_in_mid * col_in_mid];
yukari_hinata 1:da597cb284a2 146 float* prevdWmid_out = new float[row_mid_out * col_mid_out];
yukari_hinata 1:da597cb284a2 147 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 148
yukari_hinata 1:da597cb284a2 149 // 係数行列の初期化
yukari_hinata 1:da597cb284a2 150 for (int i=0; i < row_in_mid; i++)
yukari_hinata 1:da597cb284a2 151 for (int j=0; j < col_in_mid; j++)
yukari_hinata 1:da597cb284a2 152 MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW);
yukari_hinata 0:0d42047e140c 153
yukari_hinata 1:da597cb284a2 154 for (int i=0; i < row_mid_out; i++)
yukari_hinata 1:da597cb284a2 155 for (int j=0; j < col_mid_out; j++)
yukari_hinata 1:da597cb284a2 156 MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW);
yukari_hinata 0:0d42047e140c 157
yukari_hinata 1:da597cb284a2 158 // 信号の正規化:経験上,非常に大切な処理
yukari_hinata 1:da597cb284a2 159 for (int seq=0; seq < len_seqence; seq++) {
yukari_hinata 1:da597cb284a2 160 for (int n=0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 161 MATRIX_AT(norm_sample,dim_signal,seq,n) =
yukari_hinata 1:da597cb284a2 162 normalize_signal(MATRIX_AT(this->sample,dim_signal,seq,n),
yukari_hinata 1:da597cb284a2 163 MATRIX_AT(this->sample_maxmin,2,n,0),
yukari_hinata 1:da597cb284a2 164 MATRIX_AT(this->sample_maxmin,2,n,1));
yukari_hinata 1:da597cb284a2 165 // printf("%f ", MATRIX_AT(norm_sample,dim_signal,seq,n));
yukari_hinata 1:da597cb284a2 166 }
yukari_hinata 1:da597cb284a2 167 // printf("\r\n");
yukari_hinata 0:0d42047e140c 168 }
yukari_hinata 0:0d42047e140c 169
yukari_hinata 1:da597cb284a2 170 // 出力層の信号
yukari_hinata 1:da597cb284a2 171 float* out_signal = new float[dim_signal];
yukari_hinata 1:da597cb284a2 172
yukari_hinata 1:da597cb284a2 173 // 入力層->中間層の信号和
yukari_hinata 1:da597cb284a2 174 float* in_mid_net = new float[num_mid_neuron];
yukari_hinata 1:da597cb284a2 175 // 中間層->出力層の信号和.
yukari_hinata 1:da597cb284a2 176 float* mid_out_net = new float[dim_signal];
yukari_hinata 1:da597cb284a2 177
yukari_hinata 1:da597cb284a2 178 // 誤差信号
yukari_hinata 1:da597cb284a2 179 float* sigma = new float[dim_signal];
yukari_hinata 1:da597cb284a2 180
yukari_hinata 1:da597cb284a2 181 // 前回の二乗誤差値:収束判定に用いる.
yukari_hinata 1:da597cb284a2 182 float prevError;
yukari_hinata 2:d623e7ef4dca 183 squareError = FLT_MAX;
yukari_hinata 1:da597cb284a2 184 /* 学習ループ */
yukari_hinata 1:da597cb284a2 185 while (1) {
yukari_hinata 1:da597cb284a2 186
yukari_hinata 1:da597cb284a2 187 // 終了条件を満たすか確認
yukari_hinata 1:da597cb284a2 188 if (!end_flag) {
yukari_hinata 2:d623e7ef4dca 189 end_flag = !(iteration < maxIteration
yukari_hinata 2:d623e7ef4dca 190 && (iteration <= len_seqence
yukari_hinata 2:d623e7ef4dca 191 || squareError > goalError)
yukari_hinata 1:da597cb284a2 192 );
yukari_hinata 1:da597cb284a2 193 }
yukari_hinata 1:da597cb284a2 194
yukari_hinata 1:da597cb284a2 195 // printf("ite:%d err:%f \r\n", iteration, squareError);
yukari_hinata 1:da597cb284a2 196
yukari_hinata 1:da597cb284a2 197 // 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 198 if (seq == len_seqence && !end_flag) {
yukari_hinata 1:da597cb284a2 199 seq = 0;
yukari_hinata 1:da597cb284a2 200 }
yukari_hinata 1:da597cb284a2 201
yukari_hinata 1:da597cb284a2 202 // 前回の更新量/二乗誤差を保存
yukari_hinata 1:da597cb284a2 203 if (iteration >= 1) {
yukari_hinata 1:da597cb284a2 204 memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid);
yukari_hinata 1:da597cb284a2 205 memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out);
yukari_hinata 1:da597cb284a2 206 prevError = squareError;
yukari_hinata 1:da597cb284a2 207 } else {
yukari_hinata 1:da597cb284a2 208 // 初回は0埋め
yukari_hinata 1:da597cb284a2 209 memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid);
yukari_hinata 1:da597cb284a2 210 memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out);
yukari_hinata 1:da597cb284a2 211 }
yukari_hinata 1:da597cb284a2 212
yukari_hinata 1:da597cb284a2 213 /* 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 214
yukari_hinata 1:da597cb284a2 215 // 入力値を取得
yukari_hinata 1:da597cb284a2 216 memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal);
yukari_hinata 1:da597cb284a2 217 // 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 218 if (iteration == 0) {
yukari_hinata 1:da597cb284a2 219 // 初回は0埋めする
yukari_hinata 1:da597cb284a2 220 memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron);
yukari_hinata 1:da597cb284a2 221 } else {
yukari_hinata 1:da597cb284a2 222 // コンテキスト層 = 前回のコンテキスト層の出力
yukari_hinata 1:da597cb284a2 223 // 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 224 for (int d = 0; d < num_mid_neuron; d++) {
yukari_hinata 1:da597cb284a2 225 expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]);
yukari_hinata 1:da597cb284a2 226 }
yukari_hinata 1:da597cb284a2 227 }
yukari_hinata 1:da597cb284a2 228 // バイアス項は常に1に固定.
yukari_hinata 1:da597cb284a2 229 expand_in_signal[dim_signal + num_mid_neuron] = 1;
yukari_hinata 1:da597cb284a2 230
yukari_hinata 1:da597cb284a2 231 // 入力->中間層の出力信号和計算
yukari_hinata 1:da597cb284a2 232 multiply_mat_vec(Win_mid,
yukari_hinata 1:da597cb284a2 233 expand_in_signal,
yukari_hinata 1:da597cb284a2 234 in_mid_net,
yukari_hinata 1:da597cb284a2 235 num_mid_neuron,
yukari_hinata 1:da597cb284a2 236 dim_signal + num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 237 // 中間層の出力信号計算
yukari_hinata 1:da597cb284a2 238 sigmoid_vec(in_mid_net,
yukari_hinata 1:da597cb284a2 239 expand_mid_signal,
yukari_hinata 1:da597cb284a2 240 num_mid_neuron);
yukari_hinata 1:da597cb284a2 241 expand_mid_signal[num_mid_neuron] = 1;
yukari_hinata 1:da597cb284a2 242 // 中間->出力層の出力信号和計算
yukari_hinata 1:da597cb284a2 243 multiply_mat_vec(Wmid_out,
yukari_hinata 1:da597cb284a2 244 expand_mid_signal,
yukari_hinata 1:da597cb284a2 245 mid_out_net,
yukari_hinata 1:da597cb284a2 246 dim_signal,
yukari_hinata 1:da597cb284a2 247 num_mid_neuron + 1);
yukari_hinata 1:da597cb284a2 248 // 出力層の出力信号計算
yukari_hinata 1:da597cb284a2 249 sigmoid_vec(mid_out_net,
yukari_hinata 1:da597cb284a2 250 out_signal,
yukari_hinata 1:da597cb284a2 251 dim_signal);
yukari_hinata 1:da597cb284a2 252
yukari_hinata 1:da597cb284a2 253
yukari_hinata 1:da597cb284a2 254 for (int i = 0; i < dim_signal; i++) {
yukari_hinata 1:da597cb284a2 255 predict_signal[i] = expand_signal(out_signal[i],
yukari_hinata 1:da597cb284a2 256 MATRIX_AT(sample_maxmin,2,i,0),
yukari_hinata 1:da597cb284a2 257 MATRIX_AT(sample_maxmin,2,i,1));
yukari_hinata 1:da597cb284a2 258 }
yukari_hinata 2:d623e7ef4dca 259 // printf("predict : %f %f %f \r\n", predict_signal[0], predict_signal[1], predict_signal[2]);
yukari_hinata 1:da597cb284a2 260
yukari_hinata 1:da597cb284a2 261 // print_mat(Wmid_out, row_mid_out, col_mid_out);
yukari_hinata 0:0d42047e140c 262
yukari_hinata 1:da597cb284a2 263 // この時点での二乗誤差計算
yukari_hinata 1:da597cb284a2 264 squareError = 0;
yukari_hinata 1:da597cb284a2 265 // 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 266 // ==> つまり,次系列を予測させようとしている.
yukari_hinata 1:da597cb284a2 267 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 268 if (seq < len_seqence - 1) {
yukari_hinata 1:da597cb284a2 269 squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2);
yukari_hinata 1:da597cb284a2 270 } else {
yukari_hinata 1:da597cb284a2 271 squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2);
yukari_hinata 1:da597cb284a2 272 }
yukari_hinata 1:da597cb284a2 273 }
yukari_hinata 1:da597cb284a2 274 squareError /= dim_signal;
yukari_hinata 1:da597cb284a2 275
yukari_hinata 1:da597cb284a2 276 /* 学習の終了 */
yukari_hinata 1:da597cb284a2 277 // 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 278 if (end_flag && (seq == (len_seqence-1))) {
yukari_hinata 1:da597cb284a2 279 // 予測結果をセット.
yukari_hinata 1:da597cb284a2 280 for (int i = 0; i < dim_signal; i++) {
yukari_hinata 1:da597cb284a2 281 predict_signal[i] = expand_signal(out_signal[i],
yukari_hinata 1:da597cb284a2 282 MATRIX_AT(sample_maxmin,2,i,0),
yukari_hinata 1:da597cb284a2 283 MATRIX_AT(sample_maxmin,2,i,1));
yukari_hinata 1:da597cb284a2 284 //printf("%f ", predict_signal[i]);
yukari_hinata 1:da597cb284a2 285 }
yukari_hinata 1:da597cb284a2 286 break;
yukari_hinata 1:da597cb284a2 287 }
yukari_hinata 1:da597cb284a2 288
yukari_hinata 1:da597cb284a2 289 // 収束したと判定したら終了フラグを立てる.
yukari_hinata 1:da597cb284a2 290 if (fabsf(squareError - prevError) < epsilon) {
yukari_hinata 1:da597cb284a2 291 end_flag = 1;
yukari_hinata 1:da597cb284a2 292 }
yukari_hinata 1:da597cb284a2 293
yukari_hinata 1:da597cb284a2 294 /* 学習ステップその2:逆誤差伝搬 */
yukari_hinata 1:da597cb284a2 295 // 誤差信号の計算
yukari_hinata 1:da597cb284a2 296 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 297 if (seq < len_seqence - 1) {
yukari_hinata 1:da597cb284a2 298 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 299 } else {
yukari_hinata 1:da597cb284a2 300 /* 末尾と先頭の誤差を取る (大抵,大きくなる) */
yukari_hinata 1:da597cb284a2 301 sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]);
yukari_hinata 1:da597cb284a2 302 }
yukari_hinata 1:da597cb284a2 303 }
yukari_hinata 1:da597cb284a2 304 // printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]);
yukari_hinata 0:0d42047e140c 305
yukari_hinata 1:da597cb284a2 306 // 出力->中間層の係数の変更量計算
yukari_hinata 1:da597cb284a2 307 for (int n = 0; n < dim_signal; n++) {
yukari_hinata 1:da597cb284a2 308 for (int j = 0; j < num_mid_neuron + 1; j++) {
yukari_hinata 1:da597cb284a2 309 MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j];
yukari_hinata 1:da597cb284a2 310 }
yukari_hinata 1:da597cb284a2 311 }
yukari_hinata 1:da597cb284a2 312
yukari_hinata 1:da597cb284a2 313 // 中間->入力層の係数の変更量計算
yukari_hinata 1:da597cb284a2 314 register float sum_sigma;
yukari_hinata 1:da597cb284a2 315 for (int i = 0; i < num_mid_neuron; i++) {
yukari_hinata 1:da597cb284a2 316 // 誤差信号を逆向きに伝播させる.
yukari_hinata 1:da597cb284a2 317 sum_sigma = 0;
yukari_hinata 1:da597cb284a2 318 for (int k = 0; k < dim_signal; k++) {
yukari_hinata 1:da597cb284a2 319 sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i);
yukari_hinata 1:da597cb284a2 320 }
yukari_hinata 1:da597cb284a2 321 // 中間->入力層の係数の変更量計算
yukari_hinata 1:da597cb284a2 322 for (int j = 0; j < col_in_mid; j++) {
yukari_hinata 1:da597cb284a2 323 MATRIX_AT(dWin_mid,num_mid_neuron,j,i)
yukari_hinata 1:da597cb284a2 324 = sum_sigma * expand_mid_signal[i] *
yukari_hinata 1:da597cb284a2 325 (1 - expand_mid_signal[i]) *
yukari_hinata 1:da597cb284a2 326 expand_in_signal[j];
yukari_hinata 1:da597cb284a2 327 }
yukari_hinata 1:da597cb284a2 328 }
yukari_hinata 0:0d42047e140c 329
yukari_hinata 1:da597cb284a2 330 // 係数更新
yukari_hinata 1:da597cb284a2 331 for (int i = 0; i < row_in_mid; i++) {
yukari_hinata 1:da597cb284a2 332 for (int j = 0; j < col_in_mid; j++) {
yukari_hinata 1:da597cb284a2 333 //printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j));
yukari_hinata 1:da597cb284a2 334 MATRIX_AT(Win_mid,col_in_mid,i,j) =
yukari_hinata 1:da597cb284a2 335 MATRIX_AT(Win_mid,col_in_mid,i,j) -
yukari_hinata 1:da597cb284a2 336 this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) -
yukari_hinata 1:da597cb284a2 337 this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j);
yukari_hinata 1:da597cb284a2 338 // printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j));
yukari_hinata 1:da597cb284a2 339 // 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 340 }
yukari_hinata 1:da597cb284a2 341 //printf("\r\n");
yukari_hinata 1:da597cb284a2 342 }
yukari_hinata 1:da597cb284a2 343 for (int i = 0; i < row_mid_out; i++) {
yukari_hinata 1:da597cb284a2 344 for (int j = 0; j < col_mid_out; j++) {
yukari_hinata 1:da597cb284a2 345 MATRIX_AT(Wmid_out,col_mid_out,i,j)=
yukari_hinata 1:da597cb284a2 346 MATRIX_AT(Wmid_out,col_mid_out,i,j) -
yukari_hinata 1:da597cb284a2 347 this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) -
yukari_hinata 1:da597cb284a2 348 this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j);
yukari_hinata 1:da597cb284a2 349 }
yukari_hinata 1:da597cb284a2 350 }
yukari_hinata 1:da597cb284a2 351
yukari_hinata 1:da597cb284a2 352 // ループ回数/系列のインクリメント
yukari_hinata 1:da597cb284a2 353 iteration += 1;
yukari_hinata 1:da597cb284a2 354 seq += 1;
yukari_hinata 1:da597cb284a2 355
yukari_hinata 0:0d42047e140c 356 }
yukari_hinata 0:0d42047e140c 357
yukari_hinata 1:da597cb284a2 358 delete [] dWin_mid; delete [] dWmid_out;
yukari_hinata 1:da597cb284a2 359 delete [] prevdWin_mid; delete [] prevdWmid_out;
yukari_hinata 1:da597cb284a2 360 delete [] norm_sample; delete [] out_signal;
yukari_hinata 1:da597cb284a2 361 delete [] in_mid_net; delete [] mid_out_net;
yukari_hinata 0:0d42047e140c 362
yukari_hinata 1:da597cb284a2 363 return squareError;
yukari_hinata 0:0d42047e140c 364 }
yukari_hinata 2:d623e7ef4dca 365
yukari_hinata 2:d623e7ef4dca 366 // サンプルの（リ）セット
yukari_hinata 2:d623e7ef4dca 367 void SRNN::set_sample(float* sample_data)
yukari_hinata 2:d623e7ef4dca 368 {
yukari_hinata 2:d623e7ef4dca 369 memcpy(sample, sample_data, sizeof(float) * len_seqence * dim_signal);
yukari_hinata 2:d623e7ef4dca 370 }