public void RNN_Unit_Test_1()
{
var input = new Supervised.NeuralNetwork.Neuron()
{
ActivationFunction = new Math.Functions.Ident(),
Input = 1.0
};
// hh = 0.00845734
var gru = new Supervised.NeuralNetwork.Recurrent.RecurrentNeuron()
{
ActivationFunction = new Math.Functions.Tanh(),
MemoryGate = new Math.Functions.Logistic(),
ResetGate = new Math.Functions.Logistic(),
H = 0,
Rb = 0.0,
Zb = 0.0,
Rh = 0.00822019,
Rx = -0.00808389,
Zh = 0.00486728,
Zx = -0.0040537
};
// Z should equal approx. = 0.49898658
// R should equal approx. = 0.49797904
// htP should equal approx. = 0.00406561 /
// H should equal approx. = 0.00202869
Supervised.NeuralNetwork.Edge.Create(input, gru, 0.00845734);
double output = gru.Evaluate();
Almost.Equal(0.00422846, output, 0.002, "First pass");
gru.Output = 1.5;
double output2 = gru.Evaluate();
Almost.Equal(0.00739980, output2, 0.002, "Second pass");
}
public void RNN_Unit_Test_1()
{
var input = new Supervised.NeuralNetwork.Neuron()
{
ActivationFunction = new Math.Functions.Ident(),
Input = 1.0
};
// hh = 0.00845734
var gru = new Supervised.NeuralNetwork.Recurrent.RecurrentNeuron()
{
ActivationFunction = new Math.Functions.Tanh(),
UpdateGate = new Math.Functions.Logistic(),
ResetGate = new Math.Functions.Logistic(),
H = 0,
Rb = 0.0,
Zb = 0.0,
Rh = 0.00822019,
Rx = -0.00808389,
Zh = 0.00486728,
Zx = -0.0040537
};
// Z should equal approx. = 0.49898658
// R should equal approx. = 0.49797904
// htP should equal approx. = 0.00406561 /
// H should equal approx. = 0.00202869
Supervised.NeuralNetwork.Edge.Create(input, gru, 0.00845734);
double output = gru.Evaluate();
Almost.Equal(0.00422846, output, 0.002, "First pass");
gru.Output = 1.5;
double output2 = gru.Evaluate();
Almost.Equal(0.00739980, output2, 0.002, "Second pass");
}
public void RNN_Unit_Test_2()
{
var input = new Supervised.NeuralNetwork.Neuron()
{
ActivationFunction = new Math.Functions.Ident()
};
input.Input = 10.0;
var gru = new Supervised.NeuralNetwork.Recurrent.RecurrentNeuron()
{
ActivationFunction = new Math.Functions.Tanh(),
UpdateGate = new Math.Functions.Logistic(),
ResetGate = new Math.Functions.Logistic(),
H = 0.0543,
Rb = 1.5,
Zb = -1.5,
Rh = -0.00111453,
Rx = 0.00112138,
Zh = 0.00899571,
Zx = 0.00999628,
Hh = 0.00423760
};
Supervised.NeuralNetwork.Edge.Create(input, gru, 1.0);
double output = gru.Evaluate();
Almost.Equal(0.24144243, output, 0.00001, "1: Hidden state");
Almost.Equal(0.81923206, gru.R, 0.00001, "1: Reset value");
Almost.Equal(0.19788773, gru.Z, 0.00001, "1: Update value");
input.Input = 20.0;
double output2 = gru.Evaluate();
Almost.Equal(0.40416687, output2, 0.00001, "Second pass");
Almost.Equal(0.82085611, gru.R, 0.00001, "2: Reset value");
Almost.Equal(0.21451824, gru.Z, 0.00001, "2: Update value");
}
public void RNN_Unit_Test_2()
{
var input = new Supervised.NeuralNetwork.Neuron()
{
ActivationFunction = new Math.Functions.Ident(),
Output = 10.0
};
var gru = new Supervised.NeuralNetwork.Recurrent.RecurrentNeuron()
{
ActivationFunction = new Math.Functions.Tanh(),
MemoryGate = new Math.Functions.Logistic(),
ResetGate = new Math.Functions.Logistic(),
H = 0.0543,
Rb = 1.5,
Zb = -1.5,
Rh = -0.00111453,
Rx = 0.00112138,
Zh = 0.00899571,
Zx = 0.00999628,
};
Supervised.NeuralNetwork.Edge.Create(input, gru, 1.0);
double output = gru.Evaluate();
//Assert.Equal(0.24144242, output, 0.002, "First pass");
Almost.Equal(0.18775503, output, 0.002, "First pass");
input.Output = 20.0;
double output2 = gru.Evaluate();
//Assert.Equal(0.40416686, output2, 0.002, "Second pass");
Almost.Equal(0.30399969, output2, 0.002, "Second pass");
}
public void RNN_Unit_Test_2()
{
var input = new Supervised.NeuralNetwork.Neuron()
{
ActivationFunction = new Math.Functions.Ident(),
Output = 10.0
};
var gru = new Supervised.NeuralNetwork.Recurrent.RecurrentNeuron()
{
ActivationFunction = new Math.Functions.Tanh(),
MemoryGate = new Math.Functions.Logistic(),
ResetGate = new Math.Functions.Logistic(),
H = 0.0543,
Rb = 1.5,
Zb = -1.5,
Rh = -0.00111453,
Rx = 0.00112138,
Zh = 0.00899571,
Zx = 0.00999628,
};
Supervised.NeuralNetwork.Edge.Create(input, gru, 1.0);
double output = gru.Evaluate();
//Assert.Equal(0.24144242, output, 0.002, "First pass");
Almost.Equal(0.18775503, output, 0.002, "First pass");
input.Output = 20.0;
double output2 = gru.Evaluate();
//Assert.Equal(0.40416686, output2, 0.002, "Second pass");
Almost.Equal(0.30399969, output2, 0.002, "Second pass");
}
