public void TestAccumulatesWeightShift()
{
Neuron n31 = new Neuron();
BiasNeuron bias2 = new BiasNeuron(); ;
Neuron n21 = new Neuron();
n31.Connect(bias2);
n31.Connect(n21);
InputNeuron input = new InputNeuron();
BiasNeuron bias1 = new BiasNeuron();
n21.Connect(bias1);
n21.Connect(input);
input.Input = 1;
n31.SetAnswer(0.9);
double[] ws = n31.GetWeightShifts();
double acc = ws[1];
Assert.AreEqual(acc, 0);
n31.PropagateBackwards();
ws = n31.GetWeightShifts();
acc = ws[1];
Assert.AreNotEqual(acc, 0);
n31.ApplyTraining(0, 1);
ws = n31.GetWeightShifts();
acc = ws[1];
Assert.AreEqual(acc, 0);
}
public void ActivateNeuron()
{
BiasNeuron bias = new BiasNeuron();
double w0 = 0;
neuron.Connect(bias, w0);
Assert.Throws(typeof(NotConfiguredException),
() => neuron.Activation());
InputNeuron i1 = new InputNeuron();
InputNeuron i2 = new InputNeuron();
InputNeuron i3 = new InputNeuron();
i1.Input = 1;
i2.Input = 1;
i3.Input = 1;
double w1 = 1;
double w2 = 1;
double w3 = 1;
neuron.Connect(i1, w1);
neuron.Connect(i2, w2);
neuron.Connect(i3, w3);
double tx = i1.Input*w1+i2.Input*w2+i3.Input*w3;
double expected_activation = 1/(1 + Math.Pow(Math.E, -tx));
MyAssert.CloseTo(neuron.Activation(), expected_activation);
}
public void LastLayerGivesDeltasAndWeightsToTheOneBefore()
{
Neuron n31 = new Neuron();
Neuron n32 = new Neuron();
BiasNeuron bias2 = new BiasNeuron(); ;
Neuron n21 = new Neuron();
Neuron n22 = new Neuron();
n31.Connect(bias2);
n31.Connect(n21);
n31.Connect(n22);
n32.Connect(bias2);
n32.Connect(n21);
n32.Connect(n22);
InputNeuron input = new InputNeuron();
BiasNeuron bias1 = new BiasNeuron();
n21.Connect(bias1);
n21.Connect(input);
n22.Connect(bias1);
n22.Connect(input);
input.Input = 1;
n31.SetAnswer(0.9);
n32.SetAnswer(0.1);
n31.PropagateBackwards();
n32.PropagateBackwards();
double delta31 = n31.GetDelta();
double delta32 = n32.GetDelta();
double n21_n31 = n31.Weights[1];
double n21_n32 = n32.Weights[1];
n21.PropagateBackwards();
double desired_delta_for_n21 = n21_n31*delta31 + n21_n32*delta32;
Assert.AreEqual(desired_delta_for_n21, n21.GetDelta());
}
private void ConstructLayer(int[] neurons_in_layers_without_bias, int layer)
{
for (int i = 0; i < neurons_in_layers_without_bias[layer] + 1; i++)
{
INeuron nn;
if (i == 0)
{
nn = new BiasNeuron();
}
else
{
if (layer == 0)
{
nn = new InputNeuron();
}
else if (layer == LayerCount - 2)
{
nn = CreateNeuron();
}
else
{
nn = is_combined ? new TanhNeuron() : new Neuron();
}
}
neurons[layer][i] = nn;
ConnectNeuronToLayer(nn, layer);
}
}
private void ConstructOutputLayer(int[] neurons_in_layers_without_bias)
{
int layer = neurons_in_layers_without_bias.Length - 1;
neurons[layer][0] = new BiasNeuron();
for (int i = 1; i < neurons_in_layers_without_bias[layer] + 1; i++)
{
if (layer == 0)
{
neurons[layer][i] = new InputNeuron();
}
else
{
neurons[layer][i] = CreateNeuron();
}
}
}
public void ThrowsIfPropagateTwice()
{
Neuron n31 = new Neuron();
BiasNeuron bias2 = new BiasNeuron(); ;
Neuron n21 = new Neuron();
n31.Connect(bias2);
n31.Connect(n21);
InputNeuron input = new InputNeuron();
BiasNeuron bias1 = new BiasNeuron();
n21.Connect(bias1);
n21.Connect(input);
input.Input = 1;
n31.SetAnswer(1);
n31.PropagateBackwards();
Assert.Throws(typeof (CannotPropagateWithEmptyAcc), () => n31.PropagateBackwards());
}
public void TestXNOR_Manualy()
{
Neuron a3_1 = new Neuron();
BiasNeuron bias_2 = new BiasNeuron();
a3_1.Connect(bias_2);
Neuron a2_1 = new Neuron();
a3_1.Connect(a2_1);
Neuron a2_2 = new Neuron();
a3_1.Connect(a2_2);
BiasNeuron bias_1 = new BiasNeuron();
a2_1.Connect(bias_1);
a2_2.Connect(bias_1);
InputNeuron a1_1 = new InputNeuron();
a2_1.Connect(a1_1);
a2_2.Connect(a1_1);
InputNeuron a1_2 = new InputNeuron();
a2_1.Connect(a1_2);
a2_2.Connect(a1_2);
a3_1.SetWeight(0, -10);
a3_1.SetWeight(1, 20);
a3_1.SetWeight(2, 20);
a2_1.SetWeight(0, -30);
a2_1.SetWeight(1, 20);
a2_1.SetWeight(2, 20);
a2_2.SetWeight(0, 10);
a2_2.SetWeight(1, -20);
a2_2.SetWeight(2, -20);
a1_1.Input = 0;
a1_2.Input = 0;
MyAssert.CloseTo(a3_1.Activation(), 1);
a1_1.Input = 0;
a1_2.Input = 1;
MyAssert.CloseTo(a3_1.Activation(), 0);
a1_1.Input = 1;
a1_2.Input = 0;
MyAssert.CloseTo(a3_1.Activation(), 0);
a1_1.Input = 1;
a1_2.Input = 1;
MyAssert.CloseTo(a3_1.Activation(), 1);
}
public void TanhActivation()
{
TanhNeuron tn = new TanhNeuron();
BiasNeuron bias = new BiasNeuron();
double w0 = 0;
tn.Connect(bias, w0);
Assert.Throws(typeof(NotConfiguredException),
() => tn.Activation());
InputNeuron i1 = new InputNeuron();
InputNeuron i2 = new InputNeuron();
InputNeuron i3 = new InputNeuron();
i1.Input = 1;
i2.Input = 1;
i3.Input = 1;
double w1 = 1;
double w2 = 1;
double w3 = 1;
tn.Connect(i1, w1);
tn.Connect(i2, w2);
tn.Connect(i3, w3);
double z = i1.Input * w1 + i2.Input * w2 + i3.Input * w3;
double expected_activation = (Math.Exp(z) - Math.Exp(-z)) / (Math.Exp(z) + Math.Exp(-z));
MyAssert.CloseTo(tn.Activation(), expected_activation);
}
private void ConstructOutputLayer(int[] neurons_in_layers_without_bias)
{
int layer = neurons_in_layers_without_bias.Length - 1;
neurons[layer][0] = new BiasNeuron();
for (int i = 1; i < neurons_in_layers_without_bias[layer] + 1; i++)
{
if (layer == 0)
{
neurons[layer][i] = new InputNeuron();
}
else
{
neurons[layer][i] = CreateNeuron();
}
}
}
private void ConstructLayer(int[] neurons_in_layers_without_bias, int layer)
{
for (int i = 0; i < neurons_in_layers_without_bias[layer] + 1; i++)
{
INeuron nn;
if (i == 0)
{
nn = new BiasNeuron();
}
else
{
if (layer == 0)
{
nn = new InputNeuron();
}
else if (layer == LayerCount - 2)
{
nn = CreateNeuron();
}
else
{
nn = is_combined ? new TanhNeuron() : new Neuron();
}
}
neurons[layer][i] = nn;
ConnectNeuronToLayer(nn, layer);
}
}
