public override double[] Perzertron_forward(double[] x) { double[] y = new double[weight_small.GetLength(1)]; for (int i = 0; i < weight_small.GetLength(1); i++) { for (int j = 0; j < weight_small.GetLength(0); j++) { y[i] = y[i] + (x[i] * weight_small[j, i]) + (state_RNN[i] * state_Matrix_RNN[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias0[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Tanh(y[i]); } Set_state_RNN(y); return(y); }
public double[] Perzertron_Forward_First_Step(double[] x) { double[] y = new double[weight_small.GetLength(1)]; for (int i = 0; i < weight_small.GetLength(1); i++) { for (int j = 0; j < weight_small.GetLength(0); j++) { y[i] = y[i] + (x[i] * weight_small[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias0[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Tanh(y[i]); } Set_state_RNN(y); return(y); }
private double[] Candidate_Cell_Gate_3_Forward(double[] x, double[] reset_Gate) { double[] y = new double[weight_3_Small_Candidate_Cell.GetLength(1)]; double[] z = Matrix_work.Vector_Multiplication_Adamar_Shur(reset_Gate, state_GRU); for (int i = 0; i < weight_3_Small_Candidate_Cell.GetLength(1); i++) { for (int j = 0; j < weight_3_Small_Candidate_Cell.GetLength(0); j++) { y[i] = y[i] + (x[i] * weight_3_Small_Candidate_Cell[j, i]) + (z[i] * matrix_GRU_3_Candidate_Cell[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias_3_Candidate_Cell[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Tanh(y[i]); } return(y); }
private double[] Activation_Cell_Tanh(double[] x) { double[] y = new double[x.Length]; for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Tanh(x[i]); } return(y); }
public override double[] Perzertron_forward(double[] x) { double[] y = new double[x.Length]; double[] z; for (int i = 0; i < x.Length; i++) { for (int j = 0; j < x.Length; j++) { y[i] = y[i] + (x[i] * weight_1[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias0[i]; } z = Activation_Func.Softmax(y); return(z); }
private double[] Forget_Gate_3_Forward(double[] x) { double[] y = new double[weight_3_Small_Forget.GetLength(1)]; for (int i = 0; i < weight_3_Small_Forget.GetLength(1); i++) { for (int j = 0; j < weight_3_Small_Forget.GetLength(0); j++) { y[i] = y[i] + (x[i] * weight_3_Small_Forget[j, i]) + (state_LSTM[i] * matrix_LSTM_3_Forget[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias_3_Forget[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Sigmoid(y[i]); } return(y); }
private double[] Candidate_Cell_State_1_Forward(double[] x) { double[] y = new double[weight_1_Small_Candidate_Cell.GetLength(1)]; for (int i = 0; i < weight_1_Small_Candidate_Cell.GetLength(1); i++) { for (int j = 0; j < weight_1_Small_Candidate_Cell.GetLength(0); j++) { y[i] = y[i] + (x[i] * weight_1_Small_Candidate_Cell[j, i]) + (state_LSTM[i] * matrix_LSTM_1_Candidate_Cell[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias_1_Candidate_Cell[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Tanh(y[i]); } return(y); }
private double[] Reset_Gate_2_Forward(double[] x) { double[] y = new double[weight_2_Small_Reset_Gate.GetLength(1)]; for (int i = 0; i < weight_2_Small_Reset_Gate.GetLength(1); i++) { for (int j = 0; j < weight_2_Small_Reset_Gate.GetLength(0); j++) { y[i] = y[i] + (x[i] * weight_2_Small_Reset_Gate[j, i]) + (state_GRU[i] * matrix_GRU_2_Reset_Gate[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias_2_Reset_Gate[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Sigmoid(y[i]); } return(y); }
/// <summary> /// Считать перцептрон вперед /// </summary> /// <param name="x"></param> /// <returns></returns> public virtual double[] Perzertron_forward(double[] x) { double[] y = new double[x.Length]; for (int i = 0; i < x.Length; i++) { for (int j = 0; j < x.Length; j++) { y[i] = y[i] + (x[i] * weight_1[j, i]); } } for (int i = 0; i < x.Length; i++) { y[i] = y[i] + bias0[i]; } for (int i = 0; i < x.Length; i++) { y[i] = Activation_Func.Sigmoid(y[i]); } return(y); }