diff --git a/README.md b/README.md index 5e6e46b..730ff04 100644 --- a/README.md +++ b/README.md @@ -5,14 +5,14 @@ This is a maven library for neural networks in java 8. 1. [Features](#features) 1.1 [Architecture](#architecture) 1.2 [Supported algorithms](#supported-algorithms) - 1.2.1 [Genetic algorithm](#genetic-algorithm) 1.3 [Rectifiers](#rectifiers) 1.4 [Learning rate descent](#learning-rate-descent) 1.5 [Configuration](#configuration) 2. [Examples](#examples) 2.1 [Constructor of NeuralNetwork](#constructor-of-neuralnetwork) - 2.2 [Supervised learning](#supervised-learning) - 2.3 [Genetic algorithm](#genetic-algorithm) + 2.2 [Methods of NeuralNetwork](#methods-of-neuralnetwork) + 2.3 [Supervised learning](#supervised-learning) + 2.4 [Genetic algorithm](#genetic-algorithm) 3. [Implementation](#implementation) 3.1 [From a Jar file](#from-a-jar-file) 3.2 [From a Maven dependency](#from-a-maven-dependency) @@ -64,7 +64,7 @@ Create a neural network with three input nodes and two output nodes: NeuralNetwork neuralNetwork = new NeuralNetwork(3, 2); -Create a neural network with two output nodes, two hidden layers (4 and 5 nodes) and 6 output nodes: +Create a neural network with two input nodes, two hidden layers (4 and 5 nodes) and 6 output nodes: NeuralNetwork neuralNetwork = new NeuralNetwork(2, 4, 5, 6); @@ -75,41 +75,84 @@ Create a neural network adding hyper parameters (builder pattern may be used as neuralNetwork.setLearningRateDescent(LearningRateDescent.SGD); neuralNetwork.setLearningRate(0.8); neuralNetwork.setMomentum(0.005); + neuralNetwork.setMutationRate(0.5); -### Supervised learning -See below a full test of the neural network with supervised learning: +### Methods of NeuralNetwork +#### Common methods +Use the neural network to predict a value from a given input array. - // prepare input set + double[] in = {1,0.5}; // input values + NeuralNetwork neuralNetwork = new NeuralNetwork(2, 4, 1); + List out = neuralNetwork.predict(in); // prediction + +Get a full overview of the actual contents of a neural network by calling the `toString()` method. + +#### Supervised learning +Fit the neural network model to given input array and the expected output. +The neural network will return a prediction and adjust accordingly. + + double[] in = {1,0.5}; // input + double[] out = {1,0}; // expected output + NeuralNetwork neuralNetwork = new NeuralNetwork(2, 4, 1); + List prediction = neuralNetwork.fit(in, out); // prediction and adjustment of model + +Fit the neural network model sequentially with according input and expected output sets. + + double[] in = {{1,0.5}, {0.2,0.9}; // input set + double[] out = {{1,0}, {0,1}}; // expected output set + NeuralNetwork neuralNetwork = new NeuralNetwork(2, 4, 1, 2000); + neuralNetwork.fit(in, out); // adjustment of model in 2000 iterations + +#### Genetic algorithm +Merge two instances of NeuralNetwork, where as the first instance will be returned modified. +This will work only, if both neural networks are of the same architecture. + + NeuralNetwork neuralNetworkA = new NeuralNetwork(2, 4, 2); + NeuralNetwork neuralNetworkB = new NeuralNetwork(2, 4, 2); + NeuralNetwork result = NeuralNetwork.merge(neuralNetworkA, neuralNetworkB); + +Obtain an identical copy of a NeuralNetwork instance. + + NeuralNetwork master = new NeuralNetwork(2, 4, 2); + NeuralNetwork copy = master.copy(); + +### Supervised learning implementation +See below a full xor test of the neural network with supervised learning: + + // prepare set of expected input values double[][] in = {{0,0}, {1,0}, {0,1}, {1,1}}; + + // prepare corresponding expected output values double[][] out = {{0}, {1}, {1}, {0}}; - // create neural network - NeuralNetwork net = new NeuralNetwork(2, 4, 1); - net.setRectifier(Rectifier.SIGMOID).setLearningRate(0.8).setLearningRateDescent(LearningRateDescent.NONE); + // create and configure neural network + Rectifier rectifier = Rectifier.SIGMOID + NeuralNetwork neuralNetwork = new NeuralNetwork(2, 4, 1); + neuralNetwork.setRectifier(rectifier).setLearningRate(0.8).setLearningRateDescent(LearningRateDescent.NONE); - // train neural network - net.train(in, out, 2000); + // train neural network with the input set, the corresponding expected output set and the training epochs + neuralNetwork.train(in, out, 2000); - // print output values - System.out.println("test with rectifier: " + Rectifier.SIGMOID.getDescription()); - System.out.println("combo 1: " + net.predict(in[0])); - System.out.println("combo 2: " + net.predict(in[1])); - System.out.println("combo 3: " + net.predict(in[2])); - System.out.println("combo 4: " + net.predict(in[3])); + // use the trained NeuralNetwork to predict the output values given to the input set + System.out.println("test with rectifier: " + rectifier.getDescription()); + System.out.println("combo 1: " + neuralNetwork.predict(in[0])); + System.out.println("combo 2: " + neuralNetwork.predict(in[1])); + System.out.println("combo 3: " + neuralNetwork.predict(in[2])); + System.out.println("combo 4: " + neuralNetwork.predict(in[3])); Output: - combo 1: [0.05062227783220413] - combo 2: [0.9461083391423777] - combo 3: [0.9425030935131657] - combo 4: [0.07157249157075309] + combo 1: [0.05062227783220413] // close to 0 + combo 2: [0.9461083391423777] // close to 1 + combo 3: [0.9425030935131657] // close to 1 + combo 4: [0.07157249157075309] // close to 0 -### Genetic algorithm +### Genetic algorithm implementation Step one is to create an own class which extends or implements the required functionality: public class GeneticObjectExample extends GeneticAlgorithmObject { - public GeneticObject(NeuralNetwork neuralNetwork) { + public GeneticObject(NeuralNetwork neuralNetwork) { super(neuralNetwork); } @@ -167,7 +210,7 @@ Add following snippets to your ``pom.xml`` file to import the library: neuralnetwork neural-network-repo - 2.4 + 2.5 @@ -259,3 +302,7 @@ Below an overview of the `neuralnetwork.properties` file. # the reproduction pool is the count of NeuralNetworks chosen for reproduction to be seeded to # the new generation to come. value must not be below 2. genetic_reproduction_pool_size=3 + + # the mutation rate is the percentage of the mutated components of the neural network matrices. + # must have a value between 0.0 and 1.0 + genetic_mutation_rate=0.5 diff --git a/neuralnetwork/neural-network-repo/1.10/neural-network-repo-1.10-javadoc.jar b/neuralnetwork/neural-network-repo/1.10/neural-network-repo-1.10-javadoc.jar deleted file mode 100644 index f2bceb0..0000000 Binary files a/neuralnetwork/neural-network-repo/1.10/neural-network-repo-1.10-javadoc.jar and /dev/null differ diff --git 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neural-network-repo - 1.2 - - - - org.apache.maven.plugins - maven-compiler-plugin - - 1.8 - 1.8 - - - - - - - junit - junit - 4.13 - test - - - - - UTF-8 - file:///C:/Users/Lena Papailiou/git/neuralnetwork/ - - - - - neural-network-repo - ${github.repo} - - - - - \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/1.2/neural-network-repo-1.2.pom.md5 b/neuralnetwork/neural-network-repo/1.2/neural-network-repo-1.2.pom.md5 deleted file mode 100644 index c2a44e1..0000000 --- a/neuralnetwork/neural-network-repo/1.2/neural-network-repo-1.2.pom.md5 +++ /dev/null @@ -1 +0,0 @@ -738dd3a58df4925b1e6a162a28f1b0a1 \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/1.2/neural-network-repo-1.2.pom.sha1 b/neuralnetwork/neural-network-repo/1.2/neural-network-repo-1.2.pom.sha1 deleted file mode 100644 index e87a236..0000000 --- a/neuralnetwork/neural-network-repo/1.2/neural-network-repo-1.2.pom.sha1 +++ /dev/null @@ -1 +0,0 @@ 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a/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom +++ /dev/null @@ -1,51 +0,0 @@ - - - 4.0.0 - - neuralnetwork - neural-network-repo - 1.9 - - - - org.apache.maven.plugins - maven-compiler-plugin - 2.3.2 - - 1.8 - 1.8 - - - - - - - junit - junit - 4.13.1 - test - - - org.jetbrains - annotations - 20.1.0 - compile - - - - - UTF-8 - file:///C:/Users/Lena Papailiou/git/neuralnetwork/ - - - - - neural-network-repo - ${github.repo} - - - - - \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom.md5 b/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom.md5 deleted file mode 100644 index 601e3d3..0000000 --- a/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom.md5 +++ /dev/null @@ -1 +0,0 @@ -617213ca4e6c280dbc00cae6325b984a \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom.sha1 b/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom.sha1 deleted file mode 100644 index dad48a2..0000000 --- a/neuralnetwork/neural-network-repo/1.9/neural-network-repo-1.9.pom.sha1 +++ /dev/null @@ -1 +0,0 @@ -2717c96a198d09bef0e4db5325b3113bb14fbe75 \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar new file mode 100644 index 0000000..bd3577f Binary files /dev/null and b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar differ diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar.md5 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar.md5 new file mode 100644 index 0000000..f73d6a6 --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar.md5 @@ -0,0 +1 @@ +21191d1ae97b751fe82f755eec62391b \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar.sha1 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar.sha1 new file mode 100644 index 0000000..f4463f9 --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-javadoc.jar.sha1 @@ -0,0 +1 @@ +e3c5422f76e2615810049c08335c499a7e8222f3 \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar new file mode 100644 index 0000000..c4597df Binary files /dev/null and b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar differ diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar.md5 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar.md5 new file mode 100644 index 0000000..a57ea04 --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar.md5 @@ -0,0 +1 @@ +0cb42cd6b9df95bc410a81a06c5d8f92 \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar.sha1 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar.sha1 new file mode 100644 index 0000000..d948799 --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5-sources.jar.sha1 @@ -0,0 +1 @@ +5ab8d7d478c61bbe6cff57b24b7d9836aff8071a \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar new file mode 100644 index 0000000..9150943 Binary files /dev/null and b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar differ diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar.md5 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar.md5 new file mode 100644 index 0000000..2ca8b70 --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar.md5 @@ -0,0 +1 @@ +3354b17f6e4579a02c4da42f43adf5aa \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar.sha1 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar.sha1 new file mode 100644 index 0000000..53b987a --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.jar.sha1 @@ -0,0 +1 @@ +ab014ff817f52d2552a9649df431a64f723db26c \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/1.7/neural-network-repo-1.7.pom b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom similarity index 98% rename from neuralnetwork/neural-network-repo/1.7/neural-network-repo-1.7.pom rename to neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom index f9191e0..16c2ef7 100644 --- a/neuralnetwork/neural-network-repo/1.7/neural-network-repo-1.7.pom +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom @@ -6,7 +6,7 @@ neuralnetwork neural-network-repo - 1.7 + 2.5 diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom.md5 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom.md5 new file mode 100644 index 0000000..914467e --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom.md5 @@ -0,0 +1 @@ +2d9e96026f50397933fa4c24868b577f \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom.sha1 b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom.sha1 new file mode 100644 index 0000000..a402e7f --- /dev/null +++ b/neuralnetwork/neural-network-repo/2.5/neural-network-repo-2.5.pom.sha1 @@ -0,0 +1 @@ +32bcf732edc1df8c6ab25eb28ee26ec338a653eb \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/maven-metadata.xml b/neuralnetwork/neural-network-repo/maven-metadata.xml index 3cb5ed3..b958ed6 100644 --- a/neuralnetwork/neural-network-repo/maven-metadata.xml +++ b/neuralnetwork/neural-network-repo/maven-metadata.xml @@ -3,7 +3,7 @@ neuralnetwork neural-network-repo - 2.4 + 2.5 1.2 1.3 @@ -24,7 +24,8 @@ 2.2 2.3 2.4 + 2.5 - 20210128152721 + 20210129201735 diff --git a/neuralnetwork/neural-network-repo/maven-metadata.xml.md5 b/neuralnetwork/neural-network-repo/maven-metadata.xml.md5 index 46d2d4e..ec4909f 100644 --- a/neuralnetwork/neural-network-repo/maven-metadata.xml.md5 +++ b/neuralnetwork/neural-network-repo/maven-metadata.xml.md5 @@ -1 +1 @@ -10a2c9d31dd1e27e9b2a0638df4921f5 \ No newline at end of file +03367ac338ad5043c944ab8e762af009 \ No newline at end of file diff --git a/neuralnetwork/neural-network-repo/maven-metadata.xml.sha1 b/neuralnetwork/neural-network-repo/maven-metadata.xml.sha1 index ea5a772..ea6d2ed 100644 --- a/neuralnetwork/neural-network-repo/maven-metadata.xml.sha1 +++ b/neuralnetwork/neural-network-repo/maven-metadata.xml.sha1 @@ -1 +1 @@ -58af3d57e4bd45d448cd9ec6007b04442b138fe9 \ No newline at end of file +15d9f2d3c9745e7c49c2bf1c573c9bda59c09d42 \ No newline at end of file diff --git a/pom.xml b/pom.xml index 1e7313d..16c2ef7 100644 --- a/pom.xml +++ b/pom.xml @@ -6,7 +6,7 @@ neuralnetwork neural-network-repo - 2.4 + 2.5 diff --git a/src/main/java/neuralnet/Matrix.java b/src/main/java/neuralnet/Matrix.java index 096118a..a9862eb 100644 --- a/src/main/java/neuralnet/Matrix.java +++ b/src/main/java/neuralnet/Matrix.java @@ -188,10 +188,10 @@ void randomize() { } } - void randomize(double factor) { + void randomize(double factor, double mutationRate) { for (int i = 0; i < rows; i++) { for (int j = 0; j < cols; j++) { - if (Math.random() < 0.5) { + if (Math.random() < mutationRate) { data[i][j] = data[i][j] + (Math.random() * 2 - 1) * factor; } } diff --git a/src/main/java/neuralnet/NeuralNetwork.java b/src/main/java/neuralnet/NeuralNetwork.java index ff199d2..2231c43 100644 --- a/src/main/java/neuralnet/NeuralNetwork.java +++ b/src/main/java/neuralnet/NeuralNetwork.java @@ -34,6 +34,7 @@ public class NeuralNetwork implements Serializable { private double learningRate; private double momentum; private int iterationCount; + private double mutationRate; static { URL path = NeuralNetwork.class.getClassLoader().getResource("neuralnetwork.properties"); @@ -76,6 +77,7 @@ public NeuralNetwork(int... layerParams) { try { this.initialLearningRate = Double.parseDouble(PROPERTIES.getProperty("learning_rate")); this.learningRate = initialLearningRate; + this.mutationRate = Double.parseDouble(PROPERTIES.getProperty("genetic_mutation_rate")); if (learningRate < 0 || learningRate > 1) { throw new IllegalArgumentException("Learning rate must be set between 0.0 and 1.0!"); } @@ -83,6 +85,10 @@ public NeuralNetwork(int... layerParams) { if (momentum < 0 || momentum > 1) { throw new IllegalArgumentException("Momentum must be set between 0.0 and 1.0!"); } + this.mutationRate = Double.parseDouble(PROPERTIES.getProperty("genetic_mutation_rate")); + if (mutationRate < 0 || mutationRate > 1) { + throw new IllegalArgumentException("Mutation rate must be set between 0.0 and 1.0!"); + } } catch (NumberFormatException e) { throw new NumberFormatException("Please set a double value for this property!"); } @@ -128,7 +134,7 @@ public List predict(double[] inputNodes) { * @param expectedOutputNodes the expected output nodes as double array * @return the actual output nodes as Double List */ - public List learn(double[] inputNodes, double[] expectedOutputNodes) { + public List fit(double[] inputNodes, double[] expectedOutputNodes) { if (inputNodes == null || expectedOutputNodes == null) { throw new NullPointerException("inputNodes and expectedOutputNodes are required!"); } else if (inputNodes.length != inputLayerNodes) { @@ -174,15 +180,15 @@ public List learn(double[] inputNodes, double[] expectedOutputNodes) { * This method can be used to batch train the neural net with the supervised machine learning approach. * @param inputSet the input set of possible input node values * @param expectedOutputSet the output set of according expected output values - * @param rounds the count of repetitions of the batch training + * @param epochs the count of repetitions of the batch training */ - public void train(double[][] inputSet, double[][] expectedOutputSet, int rounds) { + public void fit(double[][] inputSet, double[][] expectedOutputSet, int epochs) { if (inputSet == null || expectedOutputSet == null) { throw new NullPointerException("inputSet and expectedOutputSet are required!"); } - for (int i = 0; i < rounds; i++) { + for (int i = 0; i < epochs; i++) { int sampleIndex = (int) (Math.random() * inputSet.length); - learn(inputSet[sampleIndex], expectedOutputSet[sampleIndex]); + fit(inputSet[sampleIndex], expectedOutputSet[sampleIndex]); } } @@ -225,13 +231,14 @@ public NeuralNetwork copy() { neuralNetwork.initialLearningRate = this.initialLearningRate; neuralNetwork.learningRate = this.learningRate; neuralNetwork.iterationCount = this.iterationCount; + neuralNetwork.mutationRate = this.mutationRate; return neuralNetwork; } private void randomize(double factor) { for (Layer layer : layers) { - layer.weight.randomize(factor); - layer.bias.randomize(factor); + layer.weight.randomize(factor, mutationRate); + layer.bias.randomize(factor, mutationRate); } } @@ -319,7 +326,7 @@ public void resetLearningRate() { public NeuralNetwork setMomentum(double momentum) { this.momentum = momentum; if (momentum < 0 || momentum > 1) { - throw new IllegalArgumentException("Learning rate must be set between 0.0 and 1.0!"); + throw new IllegalArgumentException("Momentum must be set between 0.0 and 1.0!"); } return this; } @@ -332,6 +339,27 @@ public double getMomentum() { return momentum; } + /** + * Sets mutation rate in percentage for the count of mutated components of the neural network. + * @param mutationRate the mutation rate. Must be between 0.0 and 1.0. + * @return the NeuralNetwork. + */ + public NeuralNetwork setMutationRate(double mutationRate) { + this.mutationRate = mutationRate; + if (mutationRate < 0 || mutationRate > 1) { + throw new IllegalArgumentException("Mutation rate rate must be set between 0.0 and 1.0!"); + } + return this; + } + + /** + * Returns current mutation rate of this NeuralNetwork. Must not match corresponding property. + * @return the mutation rate. + */ + public double getMutationRate() { + return mutationRate; + } + /** * Setter to allow altering properties for the NeuralNetwork configuration. * The set value will not be validated within this method. Please see neuralnetwork.properties @@ -340,7 +368,7 @@ public double getMomentum() { * @param value the value of the property. */ public static void setProperty(String key, String value) { - if (!key.equals("learning_rate") && !key.equals("rectifier") && !key.equals("learning_rate_descent") && !key.equals("learning_decay_momentum") && !key.equals("genetic_reproduction_pool_size")) { + if (!key.equals("learning_rate") && !key.equals("rectifier") && !key.equals("learning_rate_descent") && !key.equals("learning_decay_momentum") && !key.equals("genetic_reproduction_pool_size") && !key.equals("genetic_mutation_rate")) { throw new IllegalArgumentException("Property with key " + key + "is not valid in this context!"); } else if (key.equals("learning_rate")) { if (Double.parseDouble(value) < 0 || Double.parseDouble(value) > 1) { @@ -354,6 +382,10 @@ public static void setProperty(String key, String value) { if (Double.parseDouble(value) < 2) { throw new IllegalArgumentException("Reproduction pool size must be set above 2!"); } + } else if (key.equals("genetic_mutation_rate")) { + if (Double.parseDouble(value) < 0 || Double.parseDouble(value) > 1) { + throw new IllegalArgumentException("Mutation rate must be set between 0.0 and 1.0!"); + } } PROPERTIES.setProperty(key, value); } diff --git a/src/main/resources/neuralnetwork.properties b/src/main/resources/neuralnetwork.properties index 24f7cef..f3104bc 100644 --- a/src/main/resources/neuralnetwork.properties +++ b/src/main/resources/neuralnetwork.properties @@ -25,3 +25,7 @@ learning_decay_momentum=0.01 # the reproduction pool is the count of NeuralNetworks chosen for reproduction to be seeded to # the new generation to come. value must not be below 2. genetic_reproduction_pool_size=3 + +# the mutation rate is the percentage of the mutated components of the neural network matrices. +# must have a value between 0.0 and 1.0 +genetic_mutation_rate=0.5 diff --git a/src/test/java/neuralnet/MatrixTest.java b/src/test/java/neuralnet/MatrixTest.java index 3c46169..86a3f11 100644 --- a/src/test/java/neuralnet/MatrixTest.java +++ b/src/test/java/neuralnet/MatrixTest.java @@ -163,7 +163,7 @@ public void randomizationTest() { Matrix c = a.copy(); b.print(); assertEquals(a, b); - b.randomize(0.9); + b.randomize(0.9, 0.5); b.print(); assertNotEquals(a, b); c.randomize(); diff --git a/src/test/java/neuralnet/NeuralTest.java b/src/test/java/neuralnet/NeuralTest.java index 0259826..35cf7f7 100644 --- a/src/test/java/neuralnet/NeuralTest.java +++ b/src/test/java/neuralnet/NeuralTest.java @@ -21,7 +21,7 @@ public void testXOr() { double[][] in = {{0,0}, {1,0}, {0,1}, {1,1}}; double[][] out = {{0}, {1}, {1}, {0}}; NeuralNetwork net = new NeuralNetwork(2, 15, 15, 1); - net.train(in, out, 4000); + net.fit(in, out, 4000); //assertEquals(net.predict(in[0]), net.predict(in[0]), 0.2); List expected = getStandardizedOutputList(net.predict(in[0])); diff --git a/src/test/java/util/LearningRateDecreaseTest.java b/src/test/java/util/LearningRateDecreaseTest.java index 52ddc2a..5837b75 100644 --- a/src/test/java/util/LearningRateDecreaseTest.java +++ b/src/test/java/util/LearningRateDecreaseTest.java @@ -14,7 +14,7 @@ void descentTest(LearningRateDescent lrd) { double lrinit = 0.8; double lr = 0.8; System.out.println("testing with: " + lrd.getDescription()); - int iter = 110; + int iter = 400; for (int i = 0; i < iter; i++) { lr = lrd.decrease(lrinit, 0.005, i); } diff --git a/src/test/java/util/RectifierAccuracyTest.java b/src/test/java/util/RectifierAccuracyTest.java index 6fedba1..fff61dd 100644 --- a/src/test/java/util/RectifierAccuracyTest.java +++ b/src/test/java/util/RectifierAccuracyTest.java @@ -2,13 +2,8 @@ import neuralnet.NeuralNetwork; import org.junit.Test; -import util.LearningRateDescent; -import util.Rectifier; - -import java.util.List; import static org.junit.Assert.assertEquals; -import static util.NetUtils.getStandardizedOutputList; public class RectifierAccuracyTest { @@ -32,7 +27,7 @@ void testXOr(Rectifier rectifier, double[][] in, double[][] out) { NeuralNetwork net = new NeuralNetwork(2, 4, 1).setLearningRate(0.9).setLearningRateDescent(LearningRateDescent.NONE); net.setRectifier(rectifier); - net.train(in, out, 5000); + net.fit(in, out, 5000); System.out.println("rectifier: " + rectifier.getDescription()); System.out.println(" - error: " + (net.predict(in[0]).get(0) + (1.0-net.predict(in[1]).get(0)) + (1.0-net.predict(in[2]).get(0)) + net.predict(in[3]).get(0))/4.0); @@ -51,7 +46,7 @@ public void xOrTest() { NeuralNetwork net = new NeuralNetwork(2, 4, 1); net.setRectifier(rectifier).setLearningRate(0.8).setLearningRateDescent(LearningRateDescent.NONE); - net.train(in, out, 1000); + net.fit(in, out, 1000); System.out.println("test with rectifier: " + rectifier.getDescription()); System.out.println(" combo 1: " + net.predict(in[0])); diff --git a/src/test/java/util/RectifierSpeedTest.java b/src/test/java/util/RectifierSpeedTest.java index ecb96ef..53cfa5e 100644 --- a/src/test/java/util/RectifierSpeedTest.java +++ b/src/test/java/util/RectifierSpeedTest.java @@ -27,7 +27,7 @@ void testXOr(Rectifier rectifier, double[][] in, double[][] out) { net.setRectifier(rectifier); long startTime = System.currentTimeMillis(); - net.train(in, out, 100000); + net.fit(in, out, 100000); long duration = System.currentTimeMillis() - startTime; System.out.println("rectifier: " + rectifier.getDescription());