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Diff: SRNN.cpp
- Revision:
- 1:da597cb284a2
- Parent:
- 0:0d42047e140c
- Child:
- 2:d623e7ef4dca
--- a/SRNN.cpp Thu Jan 15 08:22:24 2015 +0000 +++ b/SRNN.cpp Sun Feb 15 04:05:35 2015 +0000 @@ -3,354 +3,366 @@ /* コンストラクタ - 最小の初期化パラメタ * 適宜追加する可能性あり */ -SRNN::SRNN(int dim, +SRNN::SRNN(int dim, int num_mid, int len_seq, + int len_predict, float* input_sample, float* input_sample_maxmin) { - - this->dim_signal = dim; - this->num_mid_neuron = num_mid; // Advice : number of hidden layter shuld be as large as possible. - this->len_seqence = len_seq; - - // sample/sample_maxmin allocation - this->sample = new float[len_seqence * dim_signal]; - this->sample_maxmin = new float[dim_signal * 2]; + + this->dim_signal = dim; + this->num_mid_neuron = num_mid; // Advice : number of hidden layter shuld be as large as possible. + this->len_seqence = len_seq; + this->len_predict = len_predict; + + // sample/sample_maxmin allocation + this->sample = new float[len_seqence * dim_signal]; + this->sample_maxmin = new float[dim_signal * 2]; + + memcpy(this->sample, input_sample, sizeof(float) * len_seqence * dim_signal); + memcpy(this->sample_maxmin, input_sample_maxmin, sizeof(float) * dim_signal * 2); + + this->predict_signal = new float[dim_signal * len_predict]; - memcpy(this->sample, input_sample, sizeof(float) * len_seqence * dim_signal); - memcpy(this->sample_maxmin, input_sample_maxmin, sizeof(float) * dim_signal * 2); - - this->predict_signal = new float[dim_signal]; - - // coffecience matrix allocation - // final +1 for bias - this->Win_mid = new float[num_mid_neuron * (dim_signal + num_mid_neuron + 1)]; - this->Wmid_out = new float[dim_signal * (num_mid_neuron + 1)]; - + // coffecience matrix allocation + // final +1 for bias + this->Win_mid = new float[num_mid_neuron * (dim_signal + num_mid_neuron + 1)]; + this->Wmid_out = new float[dim_signal * (num_mid_neuron + 1)]; + + // input/hidden layer signal allocation + expand_in_signal = new float[dim_signal + num_mid_neuron + 1]; + expand_mid_signal = new float[num_mid_neuron + 1]; - // input/hidden layer signal allocation - expand_in_signal = new float[dim_signal + num_mid_neuron + 1]; - expand_mid_signal = new float[num_mid_neuron + 1]; - - // Parameter settings (Tuning by taiyo) - this->squareError = FLT_MAX; // (large value) - this->maxIteration = 5000; - this->goalError = float(0.001); - this->epsilon = float(0.00001); - this->learnRate = float(0.9); // 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 - this->alpha = float(0.8 * learnRate); - this->alpha_context = float(0.8); - this->width_initW = float(1.0/num_mid_neuron); + // Parameter settings (Tuning by taiyo) + this->squareError = FLT_MAX; // (large value) + this->maxIteration = 5000; + this->goalError = float(0.001); + this->epsilon = float(0.00001); + this->learnRate = float(0.9); // 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 + this->alpha = float(0.8 * learnRate); + this->alpha_context = float(0.8); + this->width_initW = float(1.0/num_mid_neuron); - // random seed decide by time - srand((unsigned int)time(NULL)); - + // random seed decide by time + srand((unsigned int)time(NULL)); + } SRNN::~SRNN(void) { - delete [] sample; delete [] sample_maxmin; + delete [] sample; + delete [] sample_maxmin; delete [] predict_signal; - delete [] Win_mid; delete [] Wmid_out; + delete [] Win_mid; + delete [] Wmid_out; delete [] expand_in_signal; delete [] expand_mid_signal; } -/* utilにいどうするべき */ +/* utilに移動するべき */ void SRNN::sigmoid_vec(float* net, float* out, int dim) { - for (int n=0;n<dim;n++) - out[n] = sigmoid_func(net[n]); + for (int n=0; n<dim; n++) + out[n] = sigmoid_func(net[n]); } /* Predict : predicting next sequence of input */ void SRNN::predict(float* input) { - float *norm_input = new float[this->dim_signal]; + float *norm_input = new float[this->dim_signal]; + + + // output signal + float* out_signal = new float[dim_signal]; + // value of network in input->hidden layer + float* in_mid_net = new float[num_mid_neuron]; + // value of network in hidden->output layer + float* mid_out_net = new float[dim_signal]; - // normalize signal - for (int n=0; n < dim_signal; n++) { - norm_input[n] = - normalize_signal(input[n], - MATRIX_AT(this->sample_maxmin,2,n,0), - MATRIX_AT(this->sample_maxmin,2,n,1)); - } + /* Calcurate output signal */ + for (int i_predict = 0; i_predict < len_predict; i_predict++) { - // output signal - float* out_signal = new float[dim_signal]; - // value of network in input->hidden layer - float* in_mid_net = new float[num_mid_neuron]; - // value of network in hidden->output layer - float* mid_out_net = new float[dim_signal]; + // normalize signal + for (int n=0; n < dim_signal; n++) { + if (i_predict == 0) { + // First : given input + norm_input[n] = normalize_signal(input[n], MATRIX_AT(this->sample_maxmin,2,n,0), MATRIX_AT(this->sample_maxmin,2,n,1)); + } else { + // Second~ : previous output + norm_input[n] = out_signal[n]; + } + } - /* Calcurate output signal */ - // Get input signal - memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal); - // Signal of input layer : 中間層との線形和をシグモイド関数に通す. - for (int d = 0; d < num_mid_neuron; d++) { - expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]); - } - // Bias fixed at 1. - expand_in_signal[dim_signal + num_mid_neuron] = 1; + // Get input signal + memcpy(expand_in_signal, norm_input, sizeof(float) * dim_signal); + // Signal of input layer : 中間層との線形和をシグモイド関数に通す. + for (int d = 0; d < num_mid_neuron; d++) { + expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]); + } + // Bias fixed at 1. + expand_in_signal[dim_signal + num_mid_neuron] = 1; + + // 入力->中間層の出力信号和計算 + multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1); + // 中間層の出力信号計算 + sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron); + expand_mid_signal[num_mid_neuron] = 1; - // 入力->中間層の出力信号和計算 - multiply_mat_vec(Win_mid, expand_in_signal, in_mid_net, num_mid_neuron, dim_signal + num_mid_neuron + 1); - // 中間層の出力信号計算 - sigmoid_vec(in_mid_net, expand_mid_signal, num_mid_neuron); - expand_mid_signal[num_mid_neuron] = 1; + // 中間->出力層の出力信号和計算 + multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1); + // 出力層の出力信号計算 + sigmoid_vec(mid_out_net, out_signal, dim_signal); - // 中間->出力層の出力信号和計算 - multiply_mat_vec(Wmid_out, expand_mid_signal, mid_out_net, dim_signal, num_mid_neuron + 1); - // 出力層の出力信号計算 - sigmoid_vec(mid_out_net, out_signal, dim_signal); - - // expand output signal to origin width. - for (int n=0;n < dim_signal;n++) { - predict_signal[n] = expand_signal(out_signal[n],sample_maxmin[n * 2],sample_maxmin[n * 2 + 1]); - } - - delete [] norm_input; delete [] out_signal; - delete [] in_mid_net; delete [] mid_out_net; + // expand output signal to origin width. + for (int n=0; n < dim_signal; n++) { + predict_signal[i_predict * dim_signal + n] = expand_signal(out_signal[n],sample_maxmin[n * 2],sample_maxmin[n * 2 + 1]); + } + + } + + // 領域解放 + delete [] norm_input; delete [] out_signal; + delete [] in_mid_net; delete [] mid_out_net; } /* 逆誤差伝搬法による学習 局所解?なんのこったよ(すっとぼけ)*/ float SRNN::learning(void) { - int iteration = 0; // 学習繰り返し回数 - int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1] - int end_flag = 0; // 学習終了フラグ.このフラグが成立したら今回のsequenceを最後まで回して終了する. - // 係数行列のサイズ - int row_in_mid = num_mid_neuron; - int col_in_mid = dim_signal + num_mid_neuron + 1; - int row_mid_out = dim_signal; - int col_mid_out = num_mid_neuron + 1; + int iteration = 0; // 学習繰り返し回数 + int seq = 0; // 現在学習中の系列番号[0,...,len_seqence-1] + int end_flag = 0; // 学習終了フラグ.このフラグが成立したら今回のsequenceを最後まで回して終了する. + // 係数行列のサイズ + int row_in_mid = num_mid_neuron; + int col_in_mid = dim_signal + num_mid_neuron + 1; + int row_mid_out = dim_signal; + int col_mid_out = num_mid_neuron + 1; - // 行列のアロケート - // 係数行列の更新量 - float* dWin_mid = new float[row_in_mid * col_in_mid]; - float* dWmid_out = new float[row_mid_out * col_mid_out]; - // 前回の更新量:慣性項に用いる. - float* prevdWin_mid = new float[row_in_mid * col_in_mid]; - float* prevdWmid_out = new float[row_mid_out * col_mid_out]; - float* norm_sample = new float[len_seqence * dim_signal]; // 正規化したサンプル信号; 実際の学習は正規化した信号を用います. - - // 係数行列の初期化 - for (int i=0; i < row_in_mid; i++) - for (int j=0; j < col_in_mid; j++) - MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW); - - for (int i=0; i < row_mid_out; i++) - for (int j=0; j < col_mid_out; j++) - MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW); + // 行列のアロケート + // 係数行列の更新量 + float* dWin_mid = new float[row_in_mid * col_in_mid]; + float* dWmid_out = new float[row_mid_out * col_mid_out]; + // 前回の更新量:慣性項に用いる. + float* prevdWin_mid = new float[row_in_mid * col_in_mid]; + float* prevdWmid_out = new float[row_mid_out * col_mid_out]; + float* norm_sample = new float[len_seqence * dim_signal]; // 正規化したサンプル信号; 実際の学習は正規化した信号を用います. - // 信号の正規化:経験上,非常に大切な処理 - for (int seq=0; seq < len_seqence; seq++) { - for (int n=0; n < dim_signal; n++) { - MATRIX_AT(norm_sample,dim_signal,seq,n) = - normalize_signal(MATRIX_AT(this->sample,dim_signal,seq,n), - MATRIX_AT(this->sample_maxmin,2,n,0), - MATRIX_AT(this->sample_maxmin,2,n,1)); - // printf("%f ", MATRIX_AT(norm_sample,dim_signal,seq,n)); - } - // printf("\r\n"); - } + // 係数行列の初期化 + for (int i=0; i < row_in_mid; i++) + for (int j=0; j < col_in_mid; j++) + MATRIX_AT(Win_mid,col_in_mid,i,j) = uniform_rand(width_initW); - // 出力層の信号 - float* out_signal = new float[dim_signal]; - - // 入力層->中間層の信号和 - float* in_mid_net = new float[num_mid_neuron]; - // 中間層->出力層の信号和. - float* mid_out_net = new float[dim_signal]; + for (int i=0; i < row_mid_out; i++) + for (int j=0; j < col_mid_out; j++) + MATRIX_AT(Wmid_out,col_mid_out,i,j) = uniform_rand(width_initW); - // 誤差信号 - float* sigma = new float[dim_signal]; - - // 前回の二乗誤差値:収束判定に用いる. - float prevError; - - /* 学習ループ */ - while (1) { - - // 終了条件を満たすか確認 - if (!end_flag) { - end_flag = !(iteration < this->maxIteration - && (iteration <= this->len_seqence - || this->squareError > this->goalError) - ); - } - - // printf("ite:%d err:%f \r\n", iteration, squareError); - - // 系列の末尾に到達していたら,最初からリセットする. - if (seq == len_seqence && !end_flag) { - seq = 0; + // 信号の正規化:経験上,非常に大切な処理 + for (int seq=0; seq < len_seqence; seq++) { + for (int n=0; n < dim_signal; n++) { + MATRIX_AT(norm_sample,dim_signal,seq,n) = + normalize_signal(MATRIX_AT(this->sample,dim_signal,seq,n), + MATRIX_AT(this->sample_maxmin,2,n,0), + MATRIX_AT(this->sample_maxmin,2,n,1)); + // printf("%f ", MATRIX_AT(norm_sample,dim_signal,seq,n)); + } + // printf("\r\n"); } - // 前回の更新量/二乗誤差を保存 - if (iteration >= 1) { - memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid); - memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out); - prevError = squareError; - } else { - // 初回は0埋め - memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid); - memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out); - } - - /* 学習ステップその1:ニューラルネットの出力信号を求める */ + // 出力層の信号 + float* out_signal = new float[dim_signal]; + + // 入力層->中間層の信号和 + float* in_mid_net = new float[num_mid_neuron]; + // 中間層->出力層の信号和. + float* mid_out_net = new float[dim_signal]; + + // 誤差信号 + float* sigma = new float[dim_signal]; + + // 前回の二乗誤差値:収束判定に用いる. + float prevError; + + /* 学習ループ */ + while (1) { + + // 終了条件を満たすか確認 + if (!end_flag) { + end_flag = !(iteration < this->maxIteration + && (iteration <= this->len_seqence + || this->squareError > this->goalError) + ); + } + + // printf("ite:%d err:%f \r\n", iteration, squareError); + + // 系列の末尾に到達していたら,最初からリセットする. + if (seq == len_seqence && !end_flag) { + seq = 0; + } + + // 前回の更新量/二乗誤差を保存 + if (iteration >= 1) { + memcpy(prevdWin_mid, dWin_mid, sizeof(float) * row_in_mid * col_in_mid); + memcpy(prevdWmid_out, dWmid_out, sizeof(float) * row_mid_out * col_mid_out); + prevError = squareError; + } else { + // 初回は0埋め + memset(prevdWin_mid, float(0), sizeof(float) * row_in_mid * col_in_mid); + memset(prevdWmid_out, float(0), sizeof(float) * row_mid_out * col_mid_out); + } + + /* 学習ステップその1:ニューラルネットの出力信号を求める */ - // 入力値を取得 - memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal); - // SRNN特有:入力層に中間層のコピーが追加され,中間層に入力される. - if (iteration == 0) { - // 初回は0埋めする - memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron); - } else { - // コンテキスト層 = 前回のコンテキスト層の出力 - // 前回の中間層信号との線形和をシグモイド関数に通す. - for (int d = 0; d < num_mid_neuron; d++) { - expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]); - } - } - // バイアス項は常に1に固定. - expand_in_signal[dim_signal + num_mid_neuron] = 1; + // 入力値を取得 + memcpy(expand_in_signal, &(norm_sample[seq * dim_signal]), sizeof(float) * dim_signal); + // SRNN特有:入力層に中間層のコピーが追加され,中間層に入力される. + if (iteration == 0) { + // 初回は0埋めする + memset(&(expand_in_signal[dim_signal]), float(0), sizeof(float) * num_mid_neuron); + } else { + // コンテキスト層 = 前回のコンテキスト層の出力 + // 前回の中間層信号との線形和をシグモイド関数に通す. + for (int d = 0; d < num_mid_neuron; d++) { + expand_in_signal[dim_signal + d] = sigmoid_func(alpha_context * expand_in_signal[dim_signal + d] + expand_mid_signal[d]); + } + } + // バイアス項は常に1に固定. + expand_in_signal[dim_signal + num_mid_neuron] = 1; + + // 入力->中間層の出力信号和計算 + multiply_mat_vec(Win_mid, + expand_in_signal, + in_mid_net, + num_mid_neuron, + dim_signal + num_mid_neuron + 1); + // 中間層の出力信号計算 + sigmoid_vec(in_mid_net, + expand_mid_signal, + num_mid_neuron); + expand_mid_signal[num_mid_neuron] = 1; + // 中間->出力層の出力信号和計算 + multiply_mat_vec(Wmid_out, + expand_mid_signal, + mid_out_net, + dim_signal, + num_mid_neuron + 1); + // 出力層の出力信号計算 + sigmoid_vec(mid_out_net, + out_signal, + dim_signal); + + + for (int i = 0; i < dim_signal; i++) { + predict_signal[i] = expand_signal(out_signal[i], + MATRIX_AT(sample_maxmin,2,i,0), + MATRIX_AT(sample_maxmin,2,i,1)); + } + printf("predict : %f %f %f \r\n", predict_signal[0], predict_signal[1], predict_signal[2]); + + // print_mat(Wmid_out, row_mid_out, col_mid_out); - // 入力->中間層の出力信号和計算 - multiply_mat_vec(Win_mid, - expand_in_signal, - in_mid_net, - num_mid_neuron, - dim_signal + num_mid_neuron + 1); - // 中間層の出力信号計算 - sigmoid_vec(in_mid_net, - expand_mid_signal, - num_mid_neuron); - expand_mid_signal[num_mid_neuron] = 1; - // 中間->出力層の出力信号和計算 - multiply_mat_vec(Wmid_out, - expand_mid_signal, - mid_out_net, - dim_signal, - num_mid_neuron + 1); - // 出力層の出力信号計算 - sigmoid_vec(mid_out_net, - out_signal, - dim_signal); + // この時点での二乗誤差計算 + squareError = 0; + // 次の系列との誤差を見ている!! ここが注目ポイント + // ==> つまり,次系列を予測させようとしている. + for (int n = 0; n < dim_signal; n++) { + if (seq < len_seqence - 1) { + squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2); + } else { + squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2); + } + } + squareError /= dim_signal; + + /* 学習の終了 */ + // 終了フラグが立ち,かつ系列の最後に達していたら学習終了 + if (end_flag && (seq == (len_seqence-1))) { + // 予測結果をセット. + for (int i = 0; i < dim_signal; i++) { + predict_signal[i] = expand_signal(out_signal[i], + MATRIX_AT(sample_maxmin,2,i,0), + MATRIX_AT(sample_maxmin,2,i,1)); + //printf("%f ", predict_signal[i]); + } + break; + } + + // 収束したと判定したら終了フラグを立てる. + if (fabsf(squareError - prevError) < epsilon) { + end_flag = 1; + } + + /* 学習ステップその2:逆誤差伝搬 */ + // 誤差信号の計算 + for (int n = 0; n < dim_signal; n++) { + if (seq < len_seqence - 1) { + sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample,dim_signal,seq+1,n)) * out_signal[n] * (1 - out_signal[n]); + } else { + /* 末尾と先頭の誤差を取る (大抵,大きくなる) */ + sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]); + } + } + // printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]); - - for (int i = 0; i < dim_signal; i++) { - predict_signal[i] = expand_signal(out_signal[i], - MATRIX_AT(sample_maxmin,2,i,0), - MATRIX_AT(sample_maxmin,2,i,1)); - } - printf("predict : %f %f %f \r\n", predict_signal[0], predict_signal[1], predict_signal[2]); - - // print_mat(Wmid_out, row_mid_out, col_mid_out); - - // この時点での二乗誤差計算 - squareError = 0; - // 次の系列との誤差を見ている!! ここが注目ポイント - // ==> つまり,次系列を予測させようとしている. - for (int n = 0;n < dim_signal;n++) { - if (seq < len_seqence - 1) { - squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,(seq + 1),n)),2); - } else { - squareError += powf((out_signal[n] - MATRIX_AT(norm_sample,dim_signal,0,n)),2); - } - } - squareError /= dim_signal; + // 出力->中間層の係数の変更量計算 + for (int n = 0; n < dim_signal; n++) { + for (int j = 0; j < num_mid_neuron + 1; j++) { + MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j]; + } + } + + // 中間->入力層の係数の変更量計算 + register float sum_sigma; + for (int i = 0; i < num_mid_neuron; i++) { + // 誤差信号を逆向きに伝播させる. + sum_sigma = 0; + for (int k = 0; k < dim_signal; k++) { + sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i); + } + // 中間->入力層の係数の変更量計算 + for (int j = 0; j < col_in_mid; j++) { + MATRIX_AT(dWin_mid,num_mid_neuron,j,i) + = sum_sigma * expand_mid_signal[i] * + (1 - expand_mid_signal[i]) * + expand_in_signal[j]; + } + } - /* 学習の終了 */ - // 終了フラグが立ち,かつ系列の最後に達していたら学習終了 - if (end_flag && (seq == (len_seqence-1))) { - // 予測結果をセット. - for (int i = 0; i < dim_signal; i++) { - predict_signal[i] = expand_signal(out_signal[i], - MATRIX_AT(sample_maxmin,2,i,0), - MATRIX_AT(sample_maxmin,2,i,1)); - //printf("%f ", predict_signal[i]); - } - break; + // 係数更新 + for (int i = 0; i < row_in_mid; i++) { + for (int j = 0; j < col_in_mid; j++) { + //printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j)); + MATRIX_AT(Win_mid,col_in_mid,i,j) = + MATRIX_AT(Win_mid,col_in_mid,i,j) - + this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) - + this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j); + // printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j)); + // printf("dW : %f , prevdW : %f ", MATRIX_AT(dWin_mid,col_in_mid,i,j), MATRIX_AT(prevdWin_mid,col_in_mid,i,j)); + } + //printf("\r\n"); + } + for (int i = 0; i < row_mid_out; i++) { + for (int j = 0; j < col_mid_out; j++) { + MATRIX_AT(Wmid_out,col_mid_out,i,j)= + MATRIX_AT(Wmid_out,col_mid_out,i,j) - + this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) - + this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j); + } + } + + // ループ回数/系列のインクリメント + iteration += 1; + seq += 1; + } - // 収束したと判定したら終了フラグを立てる. - if (fabsf(squareError - prevError) < epsilon) { - end_flag = 1; - } - - /* 学習ステップその2:逆誤差伝搬 */ - // 誤差信号の計算 - for (int n = 0; n < dim_signal; n++) { - if (seq < len_seqence - 1) { - sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample,dim_signal,seq+1,n)) * out_signal[n] * (1 - out_signal[n]); - } else { - /* 末尾と先頭の誤差を取る (大抵,大きくなる) */ - sigma[n] = (out_signal[n] - MATRIX_AT(norm_sample, dim_signal,0,n)) * out_signal[n] * (1 - out_signal[n]); - } - } - // printf("Sigma : %f %f %f \r\n", sigma[0], sigma[1], sigma[2]); - - // 出力->中間層の係数の変更量計算 - for (int n = 0; n < dim_signal; n++) { - for (int j = 0; j < num_mid_neuron + 1; j++) { - MATRIX_AT(dWmid_out,num_mid_neuron,n,j) = sigma[n] * expand_mid_signal[j]; - } - } + delete [] dWin_mid; delete [] dWmid_out; + delete [] prevdWin_mid; delete [] prevdWmid_out; + delete [] norm_sample; delete [] out_signal; + delete [] in_mid_net; delete [] mid_out_net; - // 中間->入力層の係数の変更量計算 - register float sum_sigma; - for (int i = 0; i < num_mid_neuron; i++) { - // 誤差信号を逆向きに伝播させる. - sum_sigma = 0; - for (int k = 0; k < dim_signal; k++) { - sum_sigma += sigma[k] * MATRIX_AT(Wmid_out,num_mid_neuron + 1,k,i); - } - // 中間->入力層の係数の変更量計算 - for (int j = 0; j < col_in_mid; j++) { - MATRIX_AT(dWin_mid,num_mid_neuron,j,i) - = sum_sigma * expand_mid_signal[i] * - (1 - expand_mid_signal[i]) * - expand_in_signal[j]; - } - } - - // 係数更新 - for (int i = 0; i < row_in_mid; i++) { - for (int j = 0; j < col_in_mid; j++) { - //printf("[%f -> ", MATRIX_AT(Win_mid,col_in_mid,i,j)); - MATRIX_AT(Win_mid,col_in_mid,i,j) = - MATRIX_AT(Win_mid,col_in_mid,i,j) - - this->learnRate * MATRIX_AT(dWin_mid,col_in_mid,i,j) - - this->alpha * MATRIX_AT(prevdWin_mid,col_in_mid,i,j); - // printf("%f] ", MATRIX_AT(Win_mid,col_in_mid,i,j)); - // printf("dW : %f , prevdW : %f ", MATRIX_AT(dWin_mid,col_in_mid,i,j), MATRIX_AT(prevdWin_mid,col_in_mid,i,j)); - } - //printf("\r\n"); - } - for (int i = 0; i < row_mid_out; i++) { - for (int j = 0; j < col_mid_out; j++) { - MATRIX_AT(Wmid_out,col_mid_out,i,j)= - MATRIX_AT(Wmid_out,col_mid_out,i,j) - - this->learnRate * MATRIX_AT(dWmid_out,col_mid_out,i,j) - - this->alpha * MATRIX_AT(prevdWmid_out,col_mid_out,i,j); - } - } - - // ループ回数/系列のインクリメント - iteration += 1; - seq += 1; - - } - - delete [] dWin_mid; delete [] dWmid_out; - delete [] prevdWin_mid; delete [] prevdWmid_out; - delete [] norm_sample; delete [] out_signal; - delete [] in_mid_net; delete [] mid_out_net; - - return squareError; + return squareError; }