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]); }