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();
}
private static void Main(string[] args)
{
//Performance();
ProcessingDevice.Device = DeviceType.CUDA;
var m0 = NumMath.Random(1000, 1000, 1f);
var arr = new Array<FloatArray2D>(200);
var a = m0 * m0;
Parallel.For(0, 200, i =>
{
Console.WriteLine($"Computing {i}");
arr[i] = m0 * m0;
});
}
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();
}