static void Main(string[] args)
{
Random rnd = new Random();
int numInputs = 2;
double learningRate = 0.5;
List <double> weightInit = new List <double>()
{
rnd.NextDouble(), rnd.NextDouble()
};
// Creates the input to the test neuron
List <Neuron> inputs = new List <Neuron>(numInputs);
for (int i = 0; i < inputs.Capacity; i++)
{
inputs.Add(new Neuron());
}
// Creates an output layer neuron
Neuron neuronTest = new Neuron(inputs,
weightInit,
outputLayer: true);
//Neuron neuronTest = new Neuron(inputs, outputLayer: true);
//neuronTest.RandomizeWeights();
neuronTest.ActiveEvent += OnActivate;
// Our testing sample values
List <List <double> > input = new List <List <double> >()
{
new List <double>()
{
0, 1
},
new List <double>()
{
1, 0
}
};
List <double> expectedOutput = new List <double>()
{
0, 1
};
char key;
Console.WriteLine("For this test, the first set of inputs are: {0} and {1}", input[0][0], input[0][1]);
Console.WriteLine("The second set of inputs are: {0} and {1}", input[1][0], input[1][1]);
Console.WriteLine("The expected outputs will be: {0} and {1}", expectedOutput[0], expectedOutput[1]);
Console.WriteLine("The initial weights are {0} and {1}", neuronTest.Weights[0], neuronTest.Weights[1]);
Console.WriteLine("The initial bias is {0}\n", neuronTest.Bias);
Console.WriteLine("The learning rate is {0}", learningRate);
Console.WriteLine("The momentum is 1");
long iteration = 0;
do
{
Console.WriteLine("\nIteration {0}", iteration++);
// Prepare the test
for (int epoch = 0; epoch < input.Count; epoch++)
{
Console.WriteLine("For this sample, the inputs are: {0} and {1}", input[epoch][0], input[epoch][1]);
activationFlag = true;
for (int i = 0; i < inputs.Count; i++)
{
// Activate each neuron in the input layer.
inputs[i].RawInput = input[epoch][i];
inputs[i].Activate();
}
while (activationFlag)
{
; // Wait for the activation to finish.
}
// Checks and reports the error of the neuron to the console
Console.WriteLine("The output of the neuron is {0} and should be {1}", neuronTest.Activation, expectedOutput[epoch]);
neuronTest.AssignDelta(1, learningRate, expectedOutput[epoch], AdjustValues: false); // Calculates the derivatives of the neuron's error
Console.WriteLine("The delta of the neuron was {0}", neuronTest.Delta);
double prevBias = neuronTest.Bias;
double prevWeight1 = neuronTest.Weights[0];
double prevWeight2 = neuronTest.Weights[1];
neuronTest.AdjustValues(1, learningRate, expectedOutput[epoch]); // Assigns the gradient to the weight and bias.
Console.WriteLine("The Bias was changed by a factor of {0} to {1} from {2}", prevBias - neuronTest.Bias, neuronTest.Bias, prevBias);
Console.WriteLine("The first Weight was changed by a factor of {0} to {1} from {2}", prevWeight1 - neuronTest.Weights[0], neuronTest.Weights[0], prevWeight1);
Console.WriteLine("The first Weight was changed by a factor of {0} to {1} from {2}", prevWeight2 - neuronTest.Weights[1], neuronTest.Weights[1], prevWeight2);
}
key = Console.ReadKey().KeyChar;
} while (key != 's');
}
static void Main(string[] args)
{
Random rnd = new Random();
int numInputs = 2;
double learningRate = 0.5;
List <double> weightInit1 = new List <double>()
{
rnd.NextDouble(), rnd.NextDouble()
};
List <double> weightInit3 = new List <double>()
{
rnd.NextDouble(), rnd.NextDouble()
};
List <double> weightInit2 = new List <double>()
{
rnd.NextDouble(), rnd.NextDouble()
};
// Creates the input to the test neuron
List <Neuron> inputs = new List <Neuron>(numInputs);
for (int i = 0; i < inputs.Capacity; i++)
{
inputs.Add(new Neuron());
}
// Creates an output layer neuron
Neuron neuronTest1 = new Neuron(inputs,
weightInit1);
Neuron neuronTest3 = new Neuron(inputs,
weightInit3);
Neuron neuronTest2 = new Neuron(new List <Neuron>()
{
neuronTest1, neuronTest3
},
weightInit2,
outputLayer: true);
//Neuron neuronTest = new Neuron(inputs, outputLayer: true);
//neuronTest.RandomizeWeights();
outputID = neuronTest2.ID;
neuronTest2.ActiveEvent += OnActivate;
neuronTest1.ActiveEvent += OnActivate;
// Our testing sample values
List <List <double> > input = new List <List <double> >()
{
new List <double>()
{
0, 0
},
new List <double>()
{
0, 1
},
new List <double>()
{
1, 0
},
new List <double>()
{
1, 1
}
};
List <double> expectedOutput = new List <double>()
{
0, 1, 1, 0
};
char key;
Console.WriteLine("For this test, the first inputs is: {0} and {1}", input[0][0], input[0][1]);
Console.WriteLine("The second inputs is: {0} and {1}", input[1][0], input[1][1]);
Console.WriteLine("The third inputs is: {0} and {1}", input[2][0], input[2][1]);
Console.WriteLine("The fourth inputs is: {0} and {1}", input[3][0], input[3][1]);
Console.WriteLine("The expected outputs will be: {0} and {1} and {2} and {3}", expectedOutput[0], expectedOutput[1], expectedOutput[2], expectedOutput[3]);
Console.WriteLine("The initial weights are {0}:{2}, {3}:{4}, and {1}", neuronTest1.Weights[0], neuronTest2.Weights[0], neuronTest1.Weights[1],
neuronTest3.Weights[0], neuronTest3.Weights[1]);
Console.WriteLine("The initial bias is {0}, {2}, and {1}\n", neuronTest1.Bias, neuronTest2.Bias, neuronTest3.Bias);
Console.WriteLine("The learning rate is {0}", learningRate);
Console.WriteLine("The momentum is 1");
long iteration = 0;
do
{
int currentSet = 0;
while (currentSet++ <= 10000)
{
Console.WriteLine("Iteration {0}", iteration++);
// Prepare the test
for (int epoch = 0; epoch < input.Count; epoch++)
{
//Console.WriteLine("For this sample, the input is: {0} and {1}", input[epoch][0], input[epoch][1]);
activationFlag = true;
for (int i = 0; i < inputs.Count; i++)
{
// Activate each neuron in the input layer.
inputs[i].RawInput = input[epoch][i];
inputs[i].Activate();
}
while (activationFlag)
{
; // Wait for the activation to finish.
}
// Checks and reports the error of the neuron to the console
neuronTest2.AssignDelta(1, learningRate, expectedOutput[epoch], AdjustValues: false); // Calculates the derivatives of the neuron's error
double prevBias2 = neuronTest2.Bias;
double prevWeight2 = neuronTest2.Weights[0];
neuronTest2.AdjustValues(1, learningRate, expectedOutput[epoch]); // Assigns the gradient to the weight and bias.
neuronTest1.AssignDelta(1, learningRate, 0, new List <Neuron>()
{
neuronTest2
}, false); // Calculates the derivatives of the neuron's error
double prevBias1 = neuronTest1.Bias;
double prevWeight1 = neuronTest1.Weights[0];
neuronTest1.AdjustValues(1, learningRate, expectedOutput[epoch]); // Assigns the gradient to the weight and bias.
neuronTest3.AssignDelta(1, learningRate, 0, new List <Neuron>()
{
neuronTest2
}, false); // Calculates the derivatives of the neuron's error
double prevBias3 = neuronTest3.Bias;
double prevWeight3 = neuronTest3.Weights[0];
neuronTest3.AdjustValues(1, learningRate, expectedOutput[epoch]); // Assigns the gradient to the weight and bias.
if (currentSet - 1 == 10000)
{
Console.WriteLine("For this sample, the input is: {0} and {1}", input[epoch][0], input[epoch][1]);
Console.WriteLine("The output of the output neuron is {0} and should be {1}", neuronTest2.Activation, expectedOutput[epoch]);
Console.WriteLine("The output of the first hidden neuron is {0} and should be {1}", neuronTest1.Activation, neuronTest2.Weights[0] * neuronTest2.Delta);
Console.WriteLine("The output of the second hidden neuron is {0} and should be {1}", neuronTest3.Activation, neuronTest2.Weights[1] * neuronTest2.Delta);
}
}
}
key = Console.ReadKey().KeyChar;
} while (key != 's');
}
