private static void MNISTTrainNetwork(FeedforwardNeuralNetwork fnn, TrainingExample[] trainingExamples, TrainingExample[] testExamples)
{
Console.WriteLine("\nBeginning neural network training procedure.\n");
Console.WriteLine("Enter desired number of epochs.");
string epochsStr = Console.ReadLine();
int epochs = Convert.ToInt32(epochsStr);
do
{
Console.WriteLine("Please wait for training cycle to complete...");
var timer = Stopwatch.StartNew();
fnn.TrainEpochs(trainingExamples, epochs);
Console.WriteLine("Training cycle completed in " + timer.Elapsed + ".");
Console.WriteLine("Test network? Enter Y for yes or anything else for no.");
string inputStr = Console.ReadLine();
char input = Convert.ToChar(inputStr);
if (input == 'Y')
{
MNISTTestNetwork(fnn, testExamples);
}
Console.WriteLine("Continue training? Enter 0 for no or a valid number of epochs for yes.");
epochsStr = Console.ReadLine();
epochs = Convert.ToInt32(epochsStr);
} while (epochs > 0);
}
public static void DecimalBinaryTestNetwork(FeedforwardNeuralNetwork fnn)
{
Matrix inputs = new Matrix(2, 1);
Matrix outputs = fnn.Evaluate(inputs); //test for input = 0
if (outputs[1, 1] > 0.9 && outputs[2, 1] < 0.1 && outputs[3, 1] < 0.1 && outputs[4, 1] < 0.1) {
Console.WriteLine("0 success.");
} else {
Console.WriteLine("0 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
inputs[1, 1] = 0;
inputs[2, 1] = 1;
outputs = fnn.Evaluate(inputs); //test for input = 1
if (outputs[1, 1] < 0.1 && outputs[2, 1] > 0.9 && outputs[3, 1] < 0.1 && outputs[4, 1] < 0.1) {
Console.WriteLine("1 success.");
} else {
Console.WriteLine("1 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
inputs[1, 1] = 1;
inputs[2, 1] = 0;
outputs = fnn.Evaluate(inputs); //test for input = 2
if (outputs[1, 1] < 0.1 && outputs[2, 1] < 0.1 && outputs[3, 1] > 0.9 && outputs[4, 1] < 0.1) {
Console.WriteLine("2 success.");
} else {
Console.WriteLine("2 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
inputs[1, 1] = 1;
inputs[2, 1] = 1;
outputs = fnn.Evaluate(inputs); //test for input = 3
if (outputs[1, 1] < 0.1 && outputs[2, 1] < 0.1 && outputs[3, 1] < 0.1 && outputs[4, 1] > 0.9) {
Console.WriteLine("3 success.");
} else {
Console.WriteLine("3 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
}
public static void DecimalBinaryExample()
{
int[] layers = new int[] { 2, 5, 4 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
DecimalBinaryTestNetwork(fnn);
Matrix[] expectedOutputs = { new Matrix(new float[4, 1] {
{ 1 },
{ 0 },
{ 0 },
{ 0 }
}),
new Matrix(new float[4, 1] {
{ 0 },
{ 1 },
{ 0 },
{ 0 }
}),
new Matrix(new float[4, 1] {
{ 0 },
{ 0 },
{ 1 },
{ 0 }
}),
new Matrix(new float[4, 1] {
{ 0 },
{ 0 },
{ 0 },
{ 1 }
}) };
Matrix[] inputs = { new Matrix(new float[2, 1] {
{ 0 },
{ 0 }
}),
new Matrix(new float[2, 1] {
{ 0 },
{ 1 }
}),
new Matrix(new float[2, 1] {
{ 1 },
{ 0 }
}),
new Matrix(new float[2, 1] {
{ 1 },
{ 1 }
}) };
TrainingExample[] examples = new TrainingExample[4];
for (int i = 0; i < 4; ++i)
{
examples[i] = new TrainingExample(inputs[i], expectedOutputs[i]);
}
fnn.TrainEpochs(examples, 1000);
DecimalBinaryTestNetwork(fnn);
}
public static void DecimalBinaryTestNetwork(FeedforwardNeuralNetwork fnn)
{
Matrix inputs = new Matrix(2, 1);
Matrix outputs = fnn.Evaluate(inputs); //test for input = 0
if (outputs[1, 1] > 0.9 && outputs[2, 1] < 0.1 && outputs[3, 1] < 0.1 && outputs[4, 1] < 0.1)
{
Console.WriteLine("0 success.");
}
else
{
Console.WriteLine("0 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
inputs[1, 1] = 0;
inputs[2, 1] = 1;
outputs = fnn.Evaluate(inputs); //test for input = 1
if (outputs[1, 1] < 0.1 && outputs[2, 1] > 0.9 && outputs[3, 1] < 0.1 && outputs[4, 1] < 0.1)
{
Console.WriteLine("1 success.");
}
else
{
Console.WriteLine("1 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
inputs[1, 1] = 1;
inputs[2, 1] = 0;
outputs = fnn.Evaluate(inputs); //test for input = 2
if (outputs[1, 1] < 0.1 && outputs[2, 1] < 0.1 && outputs[3, 1] > 0.9 && outputs[4, 1] < 0.1)
{
Console.WriteLine("2 success.");
}
else
{
Console.WriteLine("2 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
inputs[1, 1] = 1;
inputs[2, 1] = 1;
outputs = fnn.Evaluate(inputs); //test for input = 3
if (outputs[1, 1] < 0.1 && outputs[2, 1] < 0.1 && outputs[3, 1] < 0.1 && outputs[4, 1] > 0.9)
{
Console.WriteLine("3 success.");
}
else
{
Console.WriteLine("3 fail." + outputs[1, 1] + " " + outputs[2, 1]);
}
}
public static void MNISTTestNetwork(FeedforwardNeuralNetwork fnn, TrainingExample[] testExamples)
{
try {
Console.WriteLine("\nBeginning neural network test procedure.\n");
int numSuccesses = 0;
int numImages = testExamples.Length;
int expectedNum = 0;
for (int r = 0; r < numImages; ++r)
{
for (int i = 1; i <= 10; ++i)
{
if (testExamples[r].expectedOutput[i, 1] == 1)
{
expectedNum = i % 10;
}
}
Matrix actualOutput = fnn.Evaluate(testExamples[r].input);
float largestOutputValue = 0;
int index = 0;
for (int j = 1; j <= 10; ++j)
{
if (actualOutput[j, 1] > largestOutputValue)
{
largestOutputValue = actualOutput[j, 1];
index = j;
}
}
index %= 10;
if (index == expectedNum)
{
++numSuccesses;
}
//Console.WriteLine("Test value: " + expectedNum);
//Console.WriteLine("Network detected: " + (index));
if ((r + 1) % 1000 == 0)
{
Console.WriteLine((r + 1) / 100 + "00 test images processed. Current success percentage: " + (float)numSuccesses / (r + 1) * 100 + "%");
}
}
Console.WriteLine("\nTest regimen completed. Network was correct for " + (float)numSuccesses / numImages * 100 + "% of " + numImages + " images.\n");
} catch (Exception ex) {
Console.WriteLine(ex.ToString());
}
}
public static void MNISTExample()
{
try {
TrainingExample[] testExamples = GetTestExamples();
TrainingExample[] trainingExamples = GetTrainingExamples();
int[] layers = new int[] { 784, 400, 10 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 0.5F, 0.1F);
Console.WriteLine("Neural network constructed.");
MNISTTestNetwork(fnn, testExamples);
MNISTTrainNetwork(fnn, trainingExamples, testExamples);
MNISTTestNetwork(fnn, testExamples);
} catch (Exception ex) {
Console.WriteLine(ex.ToString());
}
}
public static void DecimalBinaryExample()
{
int[] layers = new int[] { 2, 5, 4 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
DecimalBinaryTestNetwork(fnn);
Matrix[] expectedOutputs = { new Matrix(new float[4,1]{ {1},
{0},
{0},
{0}}),
new Matrix(new float[4,1]{ {0},
{1},
{0},
{0}}),
new Matrix(new float[4,1]{ {0},
{0},
{1},
{0}}),
new Matrix(new float[4,1]{ {0},
{0},
{0},
{1}})};
Matrix[] inputs = { new Matrix(new float[2,1]{ {0},
{0}}),
new Matrix(new float[2,1]{ {0},
{1}}),
new Matrix(new float[2,1]{ {1},
{0}}),
new Matrix(new float[2,1]{ {1},
{1}})};
TrainingExample[] examples = new TrainingExample[4];
for (int i = 0; i < 4; ++i) {
examples[i] = new TrainingExample(inputs[i], expectedOutputs[i]);
}
fnn.TrainEpochs(examples, 1000);
DecimalBinaryTestNetwork(fnn);
}
public static void LogicGatesExample()
{
//First layer is input layer
//Initialize a 2-layer ANN
//Two inputs, one output
int[] layers = new int[] { 2, 3, 1 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
//Train this many cycles
int numTrainingEpochs = 10000;
TrainingExample ex1 = new TrainingExample(new Matrix(new float[,]{ { 0 },
{ 0 } }),
new Matrix(new float[,]{ { 0 } }));
TrainingExample ex2 = new TrainingExample(new Matrix(new float[,]{ { 0 },
{ 1 } }),
new Matrix(new float[,]{ { 1 } }));
TrainingExample ex3 = new TrainingExample(new Matrix(new float[,]{ { 1 },
{ 0 } }),
new Matrix(new float[,]{ { 1 } }));
TrainingExample ex4 = new TrainingExample(new Matrix(new float[,]{ { 1 },
{ 1 } }),
new Matrix(new float[,]{ { 0 } }));
TrainingExample[] trainingExamples = { ex1, ex2, ex3, ex4 };
for (int i = 0; i < numTrainingEpochs; ++i) {
//Sets input matrices and output matrices and trains the network accordingly for all combinations
Random rand = new Random();
trainingExamples = trainingExamples.OrderBy(x => rand.Next()).ToArray();
for (int j = 0; j < 4; ++j) {
fnn.TrainIteration(trainingExamples[j].input, trainingExamples[j].expectedOutput, 1 - (1.0F * 0.1F / 4));
}
}
//After training, evaluates the network with respect to all possible inputs
//Prints their outputs to the console to see the result of training
float[,] inputArray = new float[,] { { 0 },
{ 0 } };
Matrix input = new Matrix(inputArray);
Matrix output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[,] { { 0 },
{ 1 } };
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[,] { { 1 },
{ 0 } };
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[,] { { 1 },
{ 1 } };
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
}
private static void MNISTTrainNetwork(FeedforwardNeuralNetwork fnn, TrainingExample[] trainingExamples, TrainingExample[] testExamples)
{
Console.WriteLine("\nBeginning neural network training procedure.\n");
Console.WriteLine("Enter desired number of epochs.");
string epochsStr = Console.ReadLine();
int epochs = Convert.ToInt32(epochsStr);
do {
Console.WriteLine("Please wait for training cycle to complete...");
var timer = Stopwatch.StartNew();
fnn.TrainEpochs(trainingExamples, epochs);
Console.WriteLine("Training cycle completed in " + timer.Elapsed + ".");
Console.WriteLine("Test network? Enter Y for yes or anything else for no.");
string inputStr = Console.ReadLine();
char input = Convert.ToChar(inputStr);
if (input == 'Y') {
MNISTTestNetwork(fnn, testExamples);
}
Console.WriteLine("Continue training? Enter 0 for no or a valid number of epochs for yes.");
epochsStr = Console.ReadLine();
epochs = Convert.ToInt32(epochsStr);
} while (epochs > 0);
}
public static void SineExample()
{
int[] layers = { 1, 3, 1 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
}
public static void MNISTTestNetwork(FeedforwardNeuralNetwork fnn, TrainingExample[] testExamples)
{
try {
Console.WriteLine("\nBeginning neural network test procedure.\n");
int numSuccesses = 0;
int numImages = testExamples.Length;
int expectedNum = 0;
for (int r = 0; r < numImages; ++r) {
for (int i = 1; i <= 10; ++i) {
if (testExamples[r].expectedOutput[i, 1] == 1) {
expectedNum = i % 10;
}
}
Matrix actualOutput = fnn.Evaluate(testExamples[r].input);
float largestOutputValue = 0;
int index = 0;
for (int j = 1; j <= 10; ++j) {
if (actualOutput[j, 1] > largestOutputValue) {
largestOutputValue = actualOutput[j, 1];
index = j;
}
}
index %= 10;
if (index == expectedNum) {
++numSuccesses;
}
//Console.WriteLine("Test value: " + expectedNum);
//Console.WriteLine("Network detected: " + (index));
if ((r + 1) % 1000 == 0) {
Console.WriteLine((r + 1) / 100 + "00 test images processed. Current success percentage: " + (float)numSuccesses/(r+1) * 100 + "%");
}
}
Console.WriteLine("\nTest regimen completed. Network was correct for " + (float)numSuccesses / numImages * 100 + "% of " + numImages + " images.\n");
} catch (Exception ex) {
Console.WriteLine(ex.ToString());
}
}
public static void MNISTExample()
{
try {
TrainingExample[] testExamples = GetTestExamples();
TrainingExample[] trainingExamples = GetTrainingExamples();
int[] layers = new int[] { 784, 400, 10 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 0.5F, 0.1F);
Console.WriteLine("Neural network constructed.");
MNISTTestNetwork(fnn, testExamples);
MNISTTrainNetwork(fnn, trainingExamples, testExamples);
MNISTTestNetwork(fnn, testExamples);
} catch (Exception ex) {
Console.WriteLine(ex.ToString());
}
}
public static void SineExample()
{
int[] layers = { 1, 3, 1 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
}
public static void LogicGatesExample()
{
//First layer is input layer
//Initialize a 2-layer ANN
//Two inputs, one output
int[] layers = new int[] { 2, 3, 1 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
//Train this many cycles
int numTrainingEpochs = 10000;
TrainingExample ex1 = new TrainingExample(new Matrix(new float[, ] {
{ 0 },
{ 0 }
}),
new Matrix(new float[, ] {
{ 0 }
}));
TrainingExample ex2 = new TrainingExample(new Matrix(new float[, ] {
{ 0 },
{ 1 }
}),
new Matrix(new float[, ] {
{ 1 }
}));
TrainingExample ex3 = new TrainingExample(new Matrix(new float[, ] {
{ 1 },
{ 0 }
}),
new Matrix(new float[, ] {
{ 1 }
}));
TrainingExample ex4 = new TrainingExample(new Matrix(new float[, ] {
{ 1 },
{ 1 }
}),
new Matrix(new float[, ] {
{ 0 }
}));
TrainingExample[] trainingExamples = { ex1, ex2, ex3, ex4 };
for (int i = 0; i < numTrainingEpochs; ++i)
{
//Sets input matrices and output matrices and trains the network accordingly for all combinations
Random rand = new Random();
trainingExamples = trainingExamples.OrderBy(x => rand.Next()).ToArray();
for (int j = 0; j < 4; ++j)
{
fnn.TrainIteration(trainingExamples[j].input, trainingExamples[j].expectedOutput, 1 - (1.0F * 0.1F / 4));
}
}
//After training, evaluates the network with respect to all possible inputs
//Prints their outputs to the console to see the result of training
float[,] inputArray = new float[, ] {
{ 0 },
{ 0 }
};
Matrix input = new Matrix(inputArray);
Matrix output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[, ] {
{ 0 },
{ 1 }
};
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[, ] {
{ 1 },
{ 0 }
};
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[, ] {
{ 1 },
{ 1 }
};
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
}
