Taiyo Mineo
Simple Recurrent Neural Network Predictor
SRNN.cpp@4:9d94330f380a, 2015-02-18 (annotated)
- Committer:
- yukari_hinata
- Date:
- Wed Feb 18 15:01:17 2015 +0000
- Revision:
- 4:9d94330f380a
- Parent:
- 2:d623e7ef4dca
- Child:
- 5:026d42b4455f
Who changed what in which revision?
| User | Revision | Line number | New 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 | 4:9d94330f380a | 54 | delete [] sample; |
| yukari_hinata | 4:9d94330f380a | 55 | delete [] sample_maxmin; |
| yukari_hinata | 4:9d94330f380a | 56 | delete [] predict_signal; |
| yukari_hinata | 4:9d94330f380a | 57 | delete [] Win_mid; |
| yukari_hinata | 4:9d94330f380a | 58 | delete [] Wmid_out; |
| yukari_hinata | 4:9d94330f380a | 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 | 4:9d94330f380a | 76 | |
| yukari_hinata | 1:da597cb284a2 | 77 | // output signal |
| yukari_hinata | 1:da597cb284a2 | 78 | float* out_signal = new float[dim_signal]; |
| yukari_hinata | 1:da597cb284a2 | 79 | // value of network in input->hidden layer |
| yukari_hinata | 1:da597cb284a2 | 80 | float* in_mid_net = new float[num_mid_neuron]; |
| yukari_hinata | 1:da597cb284a2 | 81 | // value of network in hidden->output layer |
| yukari_hinata | 1:da597cb284a2 | 82 | float* mid_out_net = new float[dim_signal]; |
| yukari_hinata | 0:0d42047e140c | 83 | |
| yukari_hinata | 1:da597cb284a2 | 84 | /* Calcurate output signal */ |
| yukari_hinata | 1:da597cb284a2 | 85 | for (int i_predict = 0; i_predict < len_predict; i_predict++) { |
| yukari_hinata | 0:0d42047e140c | 86 | |
| yukari_hinata | 1:da597cb284a2 | 87 | // normalize signal |
| yukari_hinata | 1:da597cb284a2 | 88 | for (int n=0; n < dim_signal; n++) { |
| yukari_hinata | 1:da597cb284a2 | 89 | if (i_predict == 0) { |
| yukari_hinata | 1:da597cb284a2 | 90 | // First : given input |
| yukari_hinata | 1:da597cb284a2 | 91 | 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 | 92 | } else { |
| yukari_hinata | 1:da597cb284a2 | 93 | // Second~ : previous output |
| yukari_hinata | 1:da597cb284a2 | 94 | norm_input[n] = out_signal[n]; |
| yukari_hinata | 1:da597cb284a2 | 95 | } |
| yukari_hinata | 1:da597cb284a2 | 96 | } |
| yukari_hinata | 0:0d42047e140c | 97 | |
| yukari_hinata | 1:da597cb284a2 | 98 | // Get input signal |
| yukari_hinata | 1:da597cb284a2 | 99 | memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal); |
| yukari_hinata | 1:da597cb284a2 | 100 | // 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 | 101 | for (int d = 0; d < num_mid_neuron; d++) { |
| yukari_hinata | 1:da597cb284a2 | 102 | expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]); |
| yukari_hinata | 1:da597cb284a2 | 103 | } |
| yukari_hinata | 1:da597cb284a2 | 104 | // Bias fixed at 1. |
| yukari_hinata | 1:da597cb284a2 | 105 | expand_in_signal[dim_signal + num_mid_neuron] = 1; |
| yukari_hinata | 1:da597cb284a2 | 106 | |
| yukari_hinata | 1:da597cb284a2 | 107 | // 入力->中間層の出力信号和計算 |
| yukari_hinata | 1:da597cb284a2 | 108 | multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1); |
| yukari_hinata | 1:da597cb284a2 | 109 | // 中間層の出力信号計算 |
| yukari_hinata | 1:da597cb284a2 | 110 | sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron); |
| yukari_hinata | 1:da597cb284a2 | 111 | expand_mid_signal[num_mid_neuron] = 1; |
| yukari_hinata | 0:0d42047e140c | 112 | |
| yukari_hinata | 1:da597cb284a2 | 113 | // 中間->出力層の出力信号和計算 |
| yukari_hinata | 1:da597cb284a2 | 114 | multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1); |
| yukari_hinata | 1:da597cb284a2 | 115 | // 出力層の出力信号計算 |
| yukari_hinata | 1:da597cb284a2 | 116 | sigmoid_vec(mid_out_net, out_signal, dim_signal); |
| yukari_hinata | 0:0d42047e140c | 117 | |
| yukari_hinata | 1:da597cb284a2 | 118 | // expand output signal to origin width. |
| yukari_hinata | 1:da597cb284a2 | 119 | for (int n=0; n < dim_signal; n++) { |
| yukari_hinata | 1:da597cb284a2 | 120 | 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 | 121 | } |
| yukari_hinata | 4:9d94330f380a | 122 | |
| yukari_hinata | 1:da597cb284a2 | 123 | } |
| yukari_hinata | 4:9d94330f380a | 124 | |
| yukari_hinata | 1:da597cb284a2 | 125 | // 領域解放 |
| yukari_hinata | 4:9d94330f380a | 126 | delete [] norm_input; |
| yukari_hinata | 4:9d94330f380a | 127 | delete [] out_signal; |
| yukari_hinata | 4:9d94330f380a | 128 | delete [] in_mid_net; |
| yukari_hinata | 4:9d94330f380a | 129 | delete [] mid_out_net; |
| yukari_hinata | 0:0d42047e140c | 130 | |
| yukari_hinata | 0:0d42047e140c | 131 | } |
| yukari_hinata | 0:0d42047e140c | 132 | |
| yukari_hinata | 0:0d42047e140c | 133 | /* 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 | 134 | float SRNN::learning(void) |
| yukari_hinata | 0:0d42047e140c | 135 | { |
| yukari_hinata | 1:da597cb284a2 | 136 | int iteration = 0; // 学習繰り返し回数 |
| yukari_hinata | 1:da597cb284a2 | 137 | int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1] |
| yukari_hinata | 1:da597cb284a2 | 138 | 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 | 139 | // 係数行列のサイズ |
| yukari_hinata | 1:da597cb284a2 | 140 | int row_in_mid = num_mid_neuron; |
| yukari_hinata | 1:da597cb284a2 | 141 | int col_in_mid = dim_signal + num_mid_neuron + 1; |
| yukari_hinata | 1:da597cb284a2 | 142 | int row_mid_out = dim_signal; |
| yukari_hinata | 1:da597cb284a2 | 143 | int col_mid_out = num_mid_neuron + 1; |
| yukari_hinata | 0:0d42047e140c | 144 | |
| yukari_hinata | 1:da597cb284a2 | 145 | // 行列のアロケート |
| yukari_hinata | 1:da597cb284a2 | 146 | // 係数行列の更新量 |
| yukari_hinata | 1:da597cb284a2 | 147 | float* dWin_mid = new float[row_in_mid * col_in_mid]; |
| yukari_hinata | 1:da597cb284a2 | 148 | float* dWmid_out = new float[row_mid_out * col_mid_out]; |
| yukari_hinata | 4:9d94330f380a | 149 | |
| yukari_hinata | 1:da597cb284a2 | 150 | // 前回の更新量:慣性項に用いる. |
| yukari_hinata | 1:da597cb284a2 | 151 | float* prevdWin_mid = new float[row_in_mid * col_in_mid]; |
| yukari_hinata | 1:da597cb284a2 | 152 | float* prevdWmid_out = new float[row_mid_out * col_mid_out]; |
| yukari_hinata | 4:9d94330f380a | 153 | |
| yukari_hinata | 1:da597cb284a2 | 154 | 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 | 155 | |
| yukari_hinata | 4:9d94330f380a | 156 | // 出力層の信号 |
| yukari_hinata | 4:9d94330f380a | 157 | float* out_signal = new float[dim_signal]; |
| yukari_hinata | 4:9d94330f380a | 158 | |
| yukari_hinata | 4:9d94330f380a | 159 | // 入力層->中間層の信号和 |
| yukari_hinata | 4:9d94330f380a | 160 | float* in_mid_net = new float[num_mid_neuron]; |
| yukari_hinata | 4:9d94330f380a | 161 | // 中間層->出力層の信号和. |
| yukari_hinata | 4:9d94330f380a | 162 | float* mid_out_net = new float[dim_signal]; |
| yukari_hinata | 4:9d94330f380a | 163 | |
| yukari_hinata | 4:9d94330f380a | 164 | // 誤差信号 |
| yukari_hinata | 4:9d94330f380a | 165 | float* sigma = new float[dim_signal]; |
| yukari_hinata | 4:9d94330f380a | 166 | |
| yukari_hinata | 1:da597cb284a2 | 167 | // 係数行列の初期化 |
| yukari_hinata | 1:da597cb284a2 | 168 | for (int i=0; i < row_in_mid; i++) |
| yukari_hinata | 1:da597cb284a2 | 169 | for (int j=0; j < col_in_mid; j++) |
| yukari_hinata | 1:da597cb284a2 | 170 | MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW); |
| yukari_hinata | 0:0d42047e140c | 171 | |
| yukari_hinata | 1:da597cb284a2 | 172 | for (int i=0; i < row_mid_out; i++) |
| yukari_hinata | 1:da597cb284a2 | 173 | for (int j=0; j < col_mid_out; j++) |
| yukari_hinata | 1:da597cb284a2 | 174 | MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW); |
| yukari_hinata | 0:0d42047e140c | 175 | |
| yukari_hinata | 1:da597cb284a2 | 176 | // 信号の正規化:経験上,非常に大切な処理 |
| yukari_hinata | 4:9d94330f380a | 177 | for (int i_seq=0; i_seq < len_seqence; i_seq++) { |
| yukari_hinata | 4:9d94330f380a | 178 | for (int dim_n=0; dim_n < dim_signal; dim_n++) { |
| yukari_hinata | 4:9d94330f380a | 179 | MATRIX_AT(norm_sample,dim_signal,i_seq,dim_n) = |
| yukari_hinata | 4:9d94330f380a | 180 | normalize_signal(MATRIX_AT(this->sample,dim_signal,i_seq,dim_n), |
| yukari_hinata | 4:9d94330f380a | 181 | MATRIX_AT(this->sample_maxmin,2,dim_n,0), |
| yukari_hinata | 4:9d94330f380a | 182 | MATRIX_AT(this->sample_maxmin,2,dim_n,1)); |
| yukari_hinata | 4:9d94330f380a | 183 | // printf("%f ", MATRIX_AT(norm_sample,dim_signal,i_seq,dim_n)); |
| yukari_hinata | 1:da597cb284a2 | 184 | } |
| yukari_hinata | 4:9d94330f380a | 185 | //printf("\r\n"); |
| yukari_hinata | 0:0d42047e140c | 186 | } |
| yukari_hinata | 0:0d42047e140c | 187 | |
| yukari_hinata | 1:da597cb284a2 | 188 | // 前回の二乗誤差値:収束判定に用いる. |
| yukari_hinata | 1:da597cb284a2 | 189 | float prevError; |
| yukari_hinata | 2:d623e7ef4dca | 190 | squareError = FLT_MAX; |
| yukari_hinata | 1:da597cb284a2 | 191 | /* 学習ループ */ |
| yukari_hinata | 1:da597cb284a2 | 192 | while (1) { |
| yukari_hinata | 1:da597cb284a2 | 193 | |
| yukari_hinata | 1:da597cb284a2 | 194 | // 終了条件を満たすか確認 |
| yukari_hinata | 1:da597cb284a2 | 195 | if (!end_flag) { |
| yukari_hinata | 2:d623e7ef4dca | 196 | end_flag = !(iteration < maxIteration |
| yukari_hinata | 2:d623e7ef4dca | 197 | && (iteration <= len_seqence |
| yukari_hinata | 2:d623e7ef4dca | 198 | || squareError > goalError) |
| yukari_hinata | 1:da597cb284a2 | 199 | ); |
| yukari_hinata | 1:da597cb284a2 | 200 | } |
| yukari_hinata | 1:da597cb284a2 | 201 | |
| yukari_hinata | 1:da597cb284a2 | 202 | // printf("ite:%d err:%f \r\n", iteration, squareError); |
| yukari_hinata | 1:da597cb284a2 | 203 | |
| yukari_hinata | 1:da597cb284a2 | 204 | // 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 | 205 | if (seq == len_seqence && !end_flag) { |
| yukari_hinata | 1:da597cb284a2 | 206 | seq = 0; |
| yukari_hinata | 1:da597cb284a2 | 207 | } |
| yukari_hinata | 1:da597cb284a2 | 208 | |
| yukari_hinata | 1:da597cb284a2 | 209 | // 前回の更新量/二乗誤差を保存 |
| yukari_hinata | 1:da597cb284a2 | 210 | if (iteration >= 1) { |
| yukari_hinata | 1:da597cb284a2 | 211 | memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid); |
| yukari_hinata | 1:da597cb284a2 | 212 | memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out); |
| yukari_hinata | 1:da597cb284a2 | 213 | prevError = squareError; |
| yukari_hinata | 1:da597cb284a2 | 214 | } else { |
| yukari_hinata | 1:da597cb284a2 | 215 | // 初回は0埋め |
| yukari_hinata | 1:da597cb284a2 | 216 | memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid); |
| yukari_hinata | 1:da597cb284a2 | 217 | memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out); |
| yukari_hinata | 1:da597cb284a2 | 218 | } |
| yukari_hinata | 1:da597cb284a2 | 219 | |
| yukari_hinata | 1:da597cb284a2 | 220 | /* 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 | 221 | |
| yukari_hinata | 1:da597cb284a2 | 222 | // 入力値を取得 |
| yukari_hinata | 1:da597cb284a2 | 223 | memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal); |
| yukari_hinata | 1:da597cb284a2 | 224 | // 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 | 225 | if (iteration == 0) { |
| yukari_hinata | 1:da597cb284a2 | 226 | // 初回は0埋めする |
| yukari_hinata | 1:da597cb284a2 | 227 | memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron); |
| yukari_hinata | 1:da597cb284a2 | 228 | } else { |
| yukari_hinata | 1:da597cb284a2 | 229 | // コンテキスト層 = 前回のコンテキスト層の出力 |
| yukari_hinata | 1:da597cb284a2 | 230 | // 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 | 4:9d94330f380a | 231 | for (int d_in = 0; d_in < num_mid_neuron; d_in++) { |
| yukari_hinata | 4:9d94330f380a | 232 | expand_in_signal[dim_signal + d_in] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d_in] + expand_mid_signal[d_in]); |
| yukari_hinata | 1:da597cb284a2 | 233 | } |
| yukari_hinata | 1:da597cb284a2 | 234 | } |
| yukari_hinata | 4:9d94330f380a | 235 | |
| yukari_hinata | 4:9d94330f380a | 236 | // printf("%d matrix calc start. \r\n", iteration); |
| yukari_hinata | 4:9d94330f380a | 237 | |
| yukari_hinata | 1:da597cb284a2 | 238 | // バイアス項は常に1に固定. |
| yukari_hinata | 1:da597cb284a2 | 239 | expand_in_signal[dim_signal + num_mid_neuron] = 1; |
| yukari_hinata | 4:9d94330f380a | 240 | // printf(" in bias OK \r\n"); |
| yukari_hinata | 1:da597cb284a2 | 241 | // 入力->中間層の出力信号和計算 |
| yukari_hinata | 1:da597cb284a2 | 242 | multiply_mat_vec(Win_mid, |
| yukari_hinata | 1:da597cb284a2 | 243 | expand_in_signal, |
| yukari_hinata | 1:da597cb284a2 | 244 | in_mid_net, |
| yukari_hinata | 1:da597cb284a2 | 245 | num_mid_neuron, |
| yukari_hinata | 1:da597cb284a2 | 246 | dim_signal + num_mid_neuron + 1); |
| yukari_hinata | 4:9d94330f380a | 247 | // printf(" in->mid OK \r\n"); |
| yukari_hinata | 1:da597cb284a2 | 248 | // 中間層の出力信号計算 |
| yukari_hinata | 1:da597cb284a2 | 249 | sigmoid_vec(in_mid_net, |
| yukari_hinata | 1:da597cb284a2 | 250 | expand_mid_signal, |
| yukari_hinata | 1:da597cb284a2 | 251 | num_mid_neuron); |
| yukari_hinata | 4:9d94330f380a | 252 | // printf(" mid sigmoid OK \r\n"); |
| yukari_hinata | 1:da597cb284a2 | 253 | expand_mid_signal[num_mid_neuron] = 1; |
| yukari_hinata | 4:9d94330f380a | 254 | // printf(" mid bias OK \r\n"); |
| yukari_hinata | 1:da597cb284a2 | 255 | // 中間->出力層の出力信号和計算 |
| yukari_hinata | 1:da597cb284a2 | 256 | multiply_mat_vec(Wmid_out, |
| yukari_hinata | 1:da597cb284a2 | 257 | expand_mid_signal, |
| yukari_hinata | 1:da597cb284a2 | 258 | mid_out_net, |
| yukari_hinata | 1:da597cb284a2 | 259 | dim_signal, |
| yukari_hinata | 1:da597cb284a2 | 260 | num_mid_neuron + 1); |
| yukari_hinata | 4:9d94330f380a | 261 | // printf(" mid->out OK \r\n"); |
| yukari_hinata | 1:da597cb284a2 | 262 | // 出力層の出力信号計算 |
| yukari_hinata | 1:da597cb284a2 | 263 | sigmoid_vec(mid_out_net, |
| yukari_hinata | 1:da597cb284a2 | 264 | out_signal, |
| yukari_hinata | 1:da597cb284a2 | 265 | dim_signal); |
| yukari_hinata | 4:9d94330f380a | 266 | // printf(" out sigmoid OK \r\n"); |
| yukari_hinata | 1:da597cb284a2 | 267 | |
| yukari_hinata | 4:9d94330f380a | 268 | for (int i_dim = 0; i_dim < dim_signal; i_dim++) { |
| yukari_hinata | 4:9d94330f380a | 269 | predict_signal[i_dim] = expand_signal(out_signal[i_dim], |
| yukari_hinata | 4:9d94330f380a | 270 | MATRIX_AT(sample_maxmin,2,i_dim,0), |
| yukari_hinata | 4:9d94330f380a | 271 | MATRIX_AT(sample_maxmin,2,i_dim,1)); |
| yukari_hinata | 1:da597cb284a2 | 272 | } |
| yukari_hinata | 4:9d94330f380a | 273 | // printf("%d predict : %f %f %f \r\n", iteration, predict_signal[0], predict_signal[1], predict_signal[2]); |
| yukari_hinata | 1:da597cb284a2 | 274 | |
| yukari_hinata | 1:da597cb284a2 | 275 | // print_mat(Wmid_out, row_mid_out, col_mid_out); |
| yukari_hinata | 0:0d42047e140c | 276 | |
| yukari_hinata | 1:da597cb284a2 | 277 | // この時点での二乗誤差計算 |
| yukari_hinata | 1:da597cb284a2 | 278 | squareError = 0; |
| yukari_hinata | 1:da597cb284a2 | 279 | // 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 | 280 | // ==> つまり,次系列を予測させようとしている. |
| yukari_hinata | 1:da597cb284a2 | 281 | for (int n = 0; n < dim_signal; n++) { |
| yukari_hinata | 1:da597cb284a2 | 282 | if (seq < len_seqence - 1) { |
| yukari_hinata | 1:da597cb284a2 | 283 | squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2); |
| yukari_hinata | 1:da597cb284a2 | 284 | } else { |
| yukari_hinata | 1:da597cb284a2 | 285 | squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2); |
| yukari_hinata | 1:da597cb284a2 | 286 | } |
| yukari_hinata | 1:da597cb284a2 | 287 | } |
| yukari_hinata | 1:da597cb284a2 | 288 | squareError /= dim_signal; |
| yukari_hinata | 1:da597cb284a2 | 289 | |
| yukari_hinata | 1:da597cb284a2 | 290 | /* 学習の終了 */ |
| yukari_hinata | 1:da597cb284a2 | 291 | // 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 | 292 | if (end_flag && (seq == (len_seqence-1))) { |
| yukari_hinata | 1:da597cb284a2 | 293 | // 予測結果をセット. |
| yukari_hinata | 1:da597cb284a2 | 294 | for (int i = 0; i < dim_signal; i++) { |
| yukari_hinata | 1:da597cb284a2 | 295 | predict_signal[i] = expand_signal(out_signal[i], |
| yukari_hinata | 1:da597cb284a2 | 296 | MATRIX_AT(sample_maxmin,2,i,0), |
| yukari_hinata | 1:da597cb284a2 | 297 | MATRIX_AT(sample_maxmin,2,i,1)); |
| yukari_hinata | 4:9d94330f380a | 298 | // printf("%f ", predict_signal[i]); |
| yukari_hinata | 1:da597cb284a2 | 299 | } |
| yukari_hinata | 1:da597cb284a2 | 300 | break; |
| yukari_hinata | 1:da597cb284a2 | 301 | } |
| yukari_hinata | 1:da597cb284a2 | 302 | |
| yukari_hinata | 1:da597cb284a2 | 303 | // 収束したと判定したら終了フラグを立てる. |
| yukari_hinata | 1:da597cb284a2 | 304 | if (fabsf(squareError - prevError) < epsilon) { |
| yukari_hinata | 1:da597cb284a2 | 305 | end_flag = 1; |
| yukari_hinata | 1:da597cb284a2 | 306 | } |
| yukari_hinata | 1:da597cb284a2 | 307 | |
| yukari_hinata | 1:da597cb284a2 | 308 | /* 学習ステップその2:逆誤差伝搬 */ |
| yukari_hinata | 1:da597cb284a2 | 309 | // 誤差信号の計算 |
| yukari_hinata | 1:da597cb284a2 | 310 | for (int n = 0; n < dim_signal; n++) { |
| yukari_hinata | 1:da597cb284a2 | 311 | if (seq < len_seqence - 1) { |
| yukari_hinata | 1:da597cb284a2 | 312 | 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 | 313 | } else { |
| yukari_hinata | 1:da597cb284a2 | 314 | /* 末尾と先頭の誤差を取る (大抵,大きくなる) */ |
| yukari_hinata | 1:da597cb284a2 | 315 | sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]); |
| yukari_hinata | 1:da597cb284a2 | 316 | } |
| yukari_hinata | 1:da597cb284a2 | 317 | } |
| yukari_hinata | 1:da597cb284a2 | 318 | // printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]); |
| yukari_hinata | 0:0d42047e140c | 319 | |
| yukari_hinata | 1:da597cb284a2 | 320 | // 出力->中間層の係数の変更量計算 |
| yukari_hinata | 1:da597cb284a2 | 321 | for (int n = 0; n < dim_signal; n++) { |
| yukari_hinata | 1:da597cb284a2 | 322 | for (int j = 0; j < num_mid_neuron + 1; j++) { |
| yukari_hinata | 1:da597cb284a2 | 323 | MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j]; |
| yukari_hinata | 1:da597cb284a2 | 324 | } |
| yukari_hinata | 1:da597cb284a2 | 325 | } |
| yukari_hinata | 1:da597cb284a2 | 326 | |
| yukari_hinata | 1:da597cb284a2 | 327 | // 中間->入力層の係数の変更量計算 |
| yukari_hinata | 4:9d94330f380a | 328 | float sum_sigma; |
| yukari_hinata | 1:da597cb284a2 | 329 | for (int i = 0; i < num_mid_neuron; i++) { |
| yukari_hinata | 1:da597cb284a2 | 330 | // 誤差信号を逆向きに伝播させる. |
| yukari_hinata | 1:da597cb284a2 | 331 | sum_sigma = 0; |
| yukari_hinata | 1:da597cb284a2 | 332 | for (int k = 0; k < dim_signal; k++) { |
| yukari_hinata | 1:da597cb284a2 | 333 | sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i); |
| yukari_hinata | 1:da597cb284a2 | 334 | } |
| yukari_hinata | 1:da597cb284a2 | 335 | // 中間->入力層の係数の変更量計算 |
| yukari_hinata | 1:da597cb284a2 | 336 | for (int j = 0; j < col_in_mid; j++) { |
| yukari_hinata | 1:da597cb284a2 | 337 | MATRIX_AT(dWin_mid,num_mid_neuron,j,i) |
| yukari_hinata | 1:da597cb284a2 | 338 | = sum_sigma * expand_mid_signal[i] * |
| yukari_hinata | 1:da597cb284a2 | 339 | (1 - expand_mid_signal[i]) * |
| yukari_hinata | 1:da597cb284a2 | 340 | expand_in_signal[j]; |
| yukari_hinata | 1:da597cb284a2 | 341 | } |
| yukari_hinata | 1:da597cb284a2 | 342 | } |
| yukari_hinata | 0:0d42047e140c | 343 | |
| yukari_hinata | 1:da597cb284a2 | 344 | // 係数更新 |
| yukari_hinata | 1:da597cb284a2 | 345 | for (int i = 0; i < row_in_mid; i++) { |
| yukari_hinata | 1:da597cb284a2 | 346 | for (int j = 0; j < col_in_mid; j++) { |
| yukari_hinata | 1:da597cb284a2 | 347 | //printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j)); |
| yukari_hinata | 1:da597cb284a2 | 348 | MATRIX_AT(Win_mid,col_in_mid,i,j) = |
| yukari_hinata | 1:da597cb284a2 | 349 | MATRIX_AT(Win_mid,col_in_mid,i,j) - |
| yukari_hinata | 1:da597cb284a2 | 350 | this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) - |
| yukari_hinata | 1:da597cb284a2 | 351 | this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j); |
| yukari_hinata | 1:da597cb284a2 | 352 | // printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j)); |
| yukari_hinata | 1:da597cb284a2 | 353 | // 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 | 354 | } |
| yukari_hinata | 1:da597cb284a2 | 355 | //printf("\r\n"); |
| yukari_hinata | 1:da597cb284a2 | 356 | } |
| yukari_hinata | 1:da597cb284a2 | 357 | for (int i = 0; i < row_mid_out; i++) { |
| yukari_hinata | 1:da597cb284a2 | 358 | for (int j = 0; j < col_mid_out; j++) { |
| yukari_hinata | 1:da597cb284a2 | 359 | MATRIX_AT(Wmid_out,col_mid_out,i,j)= |
| yukari_hinata | 1:da597cb284a2 | 360 | MATRIX_AT(Wmid_out,col_mid_out,i,j) - |
| yukari_hinata | 1:da597cb284a2 | 361 | this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) - |
| yukari_hinata | 1:da597cb284a2 | 362 | this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j); |
| yukari_hinata | 1:da597cb284a2 | 363 | } |
| yukari_hinata | 1:da597cb284a2 | 364 | } |
| yukari_hinata | 1:da597cb284a2 | 365 | |
| yukari_hinata | 1:da597cb284a2 | 366 | // ループ回数/系列のインクリメント |
| yukari_hinata | 1:da597cb284a2 | 367 | iteration += 1; |
| yukari_hinata | 1:da597cb284a2 | 368 | seq += 1; |
| yukari_hinata | 1:da597cb284a2 | 369 | |
| yukari_hinata | 0:0d42047e140c | 370 | } |
| yukari_hinata | 0:0d42047e140c | 371 | |
| yukari_hinata | 4:9d94330f380a | 372 | delete [] dWin_mid; |
| yukari_hinata | 4:9d94330f380a | 373 | delete [] dWmid_out; |
| yukari_hinata | 4:9d94330f380a | 374 | delete [] prevdWin_mid; |
| yukari_hinata | 4:9d94330f380a | 375 | delete [] prevdWmid_out; |
| yukari_hinata | 4:9d94330f380a | 376 | delete [] norm_sample; |
| yukari_hinata | 4:9d94330f380a | 377 | delete [] out_signal; |
| yukari_hinata | 4:9d94330f380a | 378 | delete [] in_mid_net; |
| yukari_hinata | 4:9d94330f380a | 379 | delete [] mid_out_net; |
| yukari_hinata | 4:9d94330f380a | 380 | delete [] sigma; |
| yukari_hinata | 0:0d42047e140c | 381 | |
| yukari_hinata | 1:da597cb284a2 | 382 | return squareError; |
| yukari_hinata | 0:0d42047e140c | 383 | } |
| yukari_hinata | 2:d623e7ef4dca | 384 | |
| yukari_hinata | 2:d623e7ef4dca | 385 | // サンプルの（リ）セット |
| yukari_hinata | 2:d623e7ef4dca | 386 | void SRNN::set_sample(float* sample_data) |
| yukari_hinata | 2:d623e7ef4dca | 387 | { |
| yukari_hinata | 2:d623e7ef4dca | 388 | memcpy(sample, sample_data, sizeof(float) * len_seqence * dim_signal); |
| yukari_hinata | 2:d623e7ef4dca | 389 | } |