public RecursiveNeuralNetwork(int inputSize, float learningRate, float std) { w = NumMath.Random(inputSize, inputSize + inputSize, std); wScore = NumMath.Random(1, inputSize, std); b = NumMath.Repeat(inputSize, 1); this.learningRate = learningRate; }
public RecurrentUnity(int input, int hidden, float learning_rate, float std) { input_size = input; hidden_size = hidden; this.learning_rate = learning_rate; Wxt = NumMath.Random(hidden_size, input_size, std); Wtt = NumMath.Random(hidden_size, hidden_size, std); bt = NumMath.Repeat(hidden_size, 0); ResetAdagradParams(); }
public RecursiveNeuralNetworkWithContext(int inputSize, float learningRate, float std) { int hSize = 20; wpl = NumMath.Random(inputSize, inputSize, std); wpr = NumMath.Random(inputSize, inputSize, std); wC = NumMath.Random(inputSize, inputSize, std); wHP = NumMath.Random(hSize, inputSize, std); wHC = NumMath.Random(hSize, inputSize, std); wS = NumMath.Random(1, hSize, 1e-10f); bC = NumMath.Repeat(inputSize, 1f / (float)inputSize); bH = NumMath.Repeat(hSize, 1f / (float)hSize); bP = NumMath.Repeat(inputSize, 1f / (float)inputSize); mwpl = NumMath.Array(inputSize, inputSize); mwpr = NumMath.Array(inputSize, inputSize); mwC = NumMath.Array(inputSize, inputSize); mwHP = NumMath.Array(hSize, inputSize); mwHC = NumMath.Array(hSize, inputSize); mwS = NumMath.Array(1, hSize); mbC = NumMath.Array(inputSize); mbH = NumMath.Array(hSize); mbP = NumMath.Array(inputSize); Adam_m_wpl = NumMath.Array(inputSize, inputSize); Adam_m_wpr = NumMath.Array(inputSize, inputSize); Adam_m_wC = NumMath.Array(inputSize, inputSize); Adam_m_wHP = NumMath.Array(hSize, inputSize); Adam_m_wHC = NumMath.Array(hSize, inputSize); Adam_m_ws = NumMath.Array(1, hSize); Adam_m_bC = NumMath.Array(inputSize); Adam_m_bH = NumMath.Array(hSize); Adam_m_bP = NumMath.Array(inputSize); Adam_v_wpl = NumMath.Array(inputSize, inputSize); Adam_v_wpr = NumMath.Array(inputSize, inputSize); Adam_v_wC = NumMath.Array(inputSize, inputSize); Adam_v_wHP = NumMath.Array(hSize, inputSize); Adam_v_wHC = NumMath.Array(hSize, inputSize); Adam_v_ws = NumMath.Array(1, hSize); Adam_v_bC = NumMath.Array(inputSize); Adam_v_bH = NumMath.Array(hSize); Adam_v_bP = NumMath.Array(inputSize); _learningRate = learningRate; }
public RecurrentNeuralNetwork(int input, int output, int hidden, float learning_rate, float std) { input_size = input; output_size = output; hidden_size = hidden; this.learning_rate = learning_rate; Wxt = NumMath.Random(hidden_size, input_size, std); Wtt = NumMath.Random(hidden_size, hidden_size, std); Why = NumMath.Random(output_size, hidden_size, std); bt = NumMath.Repeat(hidden_size, 0); by = NumMath.Repeat(output_size, 0); ResetAdagradParams(); }
public RecursiveNeuralUnity(int inputSize, float learningRate, float std) { wpl = NumMath.Random(inputSize, inputSize, std); wpr = NumMath.Random(inputSize, inputSize, std); wpr = NumMath.Random(inputSize, inputSize, std); wDeep = NumMath.Random(inputSize, inputSize, std); b = NumMath.Repeat(inputSize, 1); mwpl = NumMath.Random(inputSize, inputSize, 0); mwpr = NumMath.Random(inputSize, inputSize, 0); mwpr = NumMath.Random(inputSize, inputSize, 0); mwDeep = NumMath.Random(inputSize, inputSize, 0); mb = NumMath.Repeat(inputSize, 0); this.learningRate = learningRate; }
public LSTM(int input, int hidden, float learning_rate, float std) { input_size = input; hidden_size = hidden; this.learning_rate = learning_rate; Wf = NumMath.Random(hidden_size, input_size + hidden_size, std) + .5f; Wi = NumMath.Random(hidden_size, input_size + hidden_size, std) + .5f; Wc = NumMath.Random(hidden_size, input_size + hidden_size, std); Wo = NumMath.Random(hidden_size, input_size + hidden_size, std) + .5f; Bf = NumMath.Repeat(hidden_size, 0); Bi = NumMath.Repeat(hidden_size, 0); Bc = NumMath.Repeat(hidden_size, 0); Bo = NumMath.Repeat(hidden_size, 0); ResetAdagradParams(); }