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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see fieldDefs = new ArrayList();
for (int i = 0; i < allFields.numAttributes(); i++) {
fieldDefs.add(allFields.attribute(i));
}
m_field = new DerivedFieldMetaInfo(dF, fieldDefs, miningSchema.getTransformationDictionary());
}
protected double getValue(double[] incoming) throws Exception {
return m_field.getDerivedValue(incoming);
}
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("Nueral input (" + getID() + ")\n");
temp.append(m_field);
return temp.toString();
}
}
/**
* Inner class representing a layer in the network.
*/
class NeuralLayer implements Serializable {
/**
* For serialization
*/
private static final long serialVersionUID = -8386042001675763922L;
/** The number of neurons in this layer */
private int m_numNeurons = 0;
/** Activation function (if defined, overrides one in NeuralNetwork) */
private ActivationFunction m_layerActivationFunction = null;
/** Threshold (if defined overrides one in NeuralNetwork) */
private double m_layerThreshold = Double.NaN;
/** Width (if defined overrides one in NeuralNetwork) */
private double m_layerWidth = Double.NaN;
/** Altitude (if defined overrides one in NeuralNetwork) */
private double m_layerAltitude = Double.NaN;
/** Normalization (if defined overrides one in NeuralNetwork) */
private Normalization m_layerNormalization = null;
/** The neurons at this hidden layer */
private Neuron[] m_layerNeurons = null;
/** Stores the output of this layer (for given inputs) */
private HashMap m_layerOutput = new HashMap();
protected NeuralLayer(Element layerE) {
String activationFunction = layerE.getAttribute("activationFunction");
if (activationFunction != null && activationFunction.length() > 0) {
for (ActivationFunction a : ActivationFunction.values()) {
if (a.toString().equals(activationFunction)) {
m_layerActivationFunction = a;
break;
}
}
} else {
// use the network-level activation function
m_layerActivationFunction = m_activationFunction;
}
String threshold = layerE.getAttribute("threshold");
if (threshold != null && threshold.length() > 0) {
m_layerThreshold = Double.parseDouble(threshold);
} else {
// use network-level threshold
m_layerThreshold = m_threshold;
}
String width = layerE.getAttribute("width");
if (width != null && width.length() > 0) {
m_layerWidth = Double.parseDouble(width);
} else {
// use network-level width
m_layerWidth = m_width;
}
String altitude = layerE.getAttribute("altitude");
if (altitude != null && altitude.length() > 0) {
m_layerAltitude = Double.parseDouble(altitude);
} else {
// use network-level altitude
m_layerAltitude = m_altitude;
}
String normMethod = layerE.getAttribute("normalizationMethod");
if (normMethod != null && normMethod.length() > 0) {
for (Normalization n : Normalization.values()) {
if (n.toString().equals(normMethod)) {
m_layerNormalization = n;
break;
}
}
} else {
// use network-level normalization method
m_layerNormalization = m_normalizationMethod;
}
NodeList neuronL = layerE.getElementsByTagName("Neuron");
m_numNeurons = neuronL.getLength();
m_layerNeurons = new Neuron[m_numNeurons];
for (int i = 0; i < neuronL.getLength(); i++) {
Node neuronN = neuronL.item(i);
if (neuronN.getNodeType() == Node.ELEMENT_NODE) {
m_layerNeurons[i] = new Neuron((Element)neuronN, this);
}
}
}
protected ActivationFunction getActivationFunction() {
return m_layerActivationFunction;
}
protected double getThreshold() {
return m_layerThreshold;
}
protected double getWidth() {
return m_layerWidth;
}
protected double getAltitude() {
return m_layerAltitude;
}
protected Normalization getNormalization() {
return m_layerNormalization;
}
/**
* Compute the output values for this layer.
*
* @param incoming the incoming values
* @return the output values for this layer
* @throws Exception if there is a problem computing the outputs
*/
protected HashMap computeOutput(HashMap incoming)
throws Exception {
m_layerOutput.clear();
double normSum = 0;
for (int i = 0; i < m_layerNeurons.length; i++) {
double neuronOut = m_layerNeurons[i].getValue(incoming);
String neuronID = m_layerNeurons[i].getID();
if (m_layerNormalization == Normalization.SOFTMAX) {
normSum += Math.exp(neuronOut);
} else if (m_layerNormalization == Normalization.SIMPLEMAX) {
normSum += neuronOut;
}
//System.err.println("Inserting ID " + neuronID + " " + neuronOut);
m_layerOutput.put(neuronID, neuronOut);
}
// apply the normalization (if necessary)
if (m_layerNormalization != Normalization.NONE) {
for (int i = 0; i < m_layerNeurons.length; i++) {
double val = m_layerOutput.get(m_layerNeurons[i].getID());
// System.err.println("Normalizing ID " + m_layerNeurons[i].getID() + " " + val);
if (m_layerNormalization == Normalization.SOFTMAX) {
val = Math.exp(val) / normSum;
} else {
val = (val / normSum);
}
m_layerOutput.put(m_layerNeurons[i].getID(), val);
}
}
return m_layerOutput;
}
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("activation: " + getActivationFunction() + "\n");
if (!Double.isNaN(getThreshold())) {
temp.append("threshold: " + getThreshold() + "\n");
}
if (!Double.isNaN(getWidth())) {
temp.append("width: " + getWidth() + "\n");
}
if (!Double.isNaN(getAltitude())) {
temp.append("altitude: " + getAltitude() + "\n");
}
temp.append("normalization: " + m_layerNormalization + "\n");
for (int i = 0; i < m_numNeurons; i++) {
temp.append(m_layerNeurons[i] + "\n");
}
return temp.toString();
}
}
/**
* Inner class encapsulating a Neuron
*/
static class Neuron implements Serializable {
/**
* For serialization
*/
private static final long serialVersionUID = -3817434025682603443L;
/** ID string */
private String m_ID = null;
/** The layer we belong to (for accessing activation function, threshold etc.) */
private NeuralLayer m_layer;
/** The bias */
private double m_bias = 0.0;
/** The width (if defined overrides the one in NeuralLayer or NeuralNetwork) */
private double m_neuronWidth = Double.NaN;
/** The altitude (if defined overrides the one in NeuralLayer or NeuralNetwork) */
private double m_neuronAltitude = Double.NaN;
/** The IDs of the neurons/neural inputs that we are connected to */
private String[] m_connectionIDs = null;
/** The weights corresponding to the connections */
private double[] m_weights = null;
protected Neuron(Element neuronE, NeuralLayer layer) {
m_layer = layer;
m_ID = neuronE.getAttribute("id");
String bias = neuronE.getAttribute("bias");
if (bias != null && bias.length() > 0) {
m_bias = Double.parseDouble(bias);
}
String width = neuronE.getAttribute("width");
if (width != null && width.length() > 0) {
m_neuronWidth = Double.parseDouble(width);
}
String altitude = neuronE.getAttribute("altitude");
if (altitude != null && altitude.length() > 0) {
m_neuronAltitude = Double.parseDouble(altitude);
}
// get the connection details
NodeList conL = neuronE.getElementsByTagName("Con");
m_connectionIDs = new String[conL.getLength()];
m_weights = new double[conL.getLength()];
for (int i = 0; i < conL.getLength(); i++) {
Node conN = conL.item(i);
if (conN.getNodeType() == Node.ELEMENT_NODE) {
Element conE = (Element)conN;
m_connectionIDs[i] = conE.getAttribute("from");
String weight = conE.getAttribute("weight");
m_weights[i] = Double.parseDouble(weight);
}
}
}
protected String getID() {
return m_ID;
}
/**
* Compute the output of this Neuron.
*
* @param incoming a Map of input values. The keys are the IDs
* of incoming connections (either neural inputs or neurons) and
* the values are the output values of the neural input/neuron in
* question.
*
* @return the output of this neuron
* @throws Exception if any of our incoming connection IDs cannot be
* located in the Map
*/
protected double getValue(HashMap incoming) throws Exception {
double z = 0;
double result = Double.NaN;
double width = (Double.isNaN(m_neuronWidth))
? m_layer.getWidth()
: m_neuronWidth;
z = m_bias;
for (int i = 0; i < m_connectionIDs.length; i++) {
Double inVal = incoming.get(m_connectionIDs[i]);
if (inVal == null) {
throw new Exception("[Neuron] unable to find connection "
+ m_connectionIDs[i] + " in input Map!");
}
if (m_layer.getActivationFunction() != ActivationFunction.RADIALBASIS) {
// multiply with weight
double inV = inVal.doubleValue() * m_weights[i];
z += inV;
} else {
// Euclidean distance to the center (stored in m_weights)
double inV = Math.pow((inVal.doubleValue() - m_weights[i]), 2.0);
z += inV;
}
}
// apply the width if necessary
if (m_layer.getActivationFunction() == ActivationFunction.RADIALBASIS) {
z /= (2.0 * (width * width));
}
double threshold = m_layer.getThreshold();
double altitude = (Double.isNaN(m_neuronAltitude))
? m_layer.getAltitude()
: m_neuronAltitude;
double fanIn = m_connectionIDs.length;
result = m_layer.getActivationFunction().eval(z, threshold, altitude, fanIn);
return result;
}
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("Nueron (" + m_ID + ") [bias:" + m_bias);
if (!Double.isNaN(m_neuronWidth)) {
temp.append(" width:" + m_neuronWidth);
}
if (!Double.isNaN(m_neuronAltitude)) {
temp.append(" altitude:" + m_neuronAltitude);
}
temp.append("]\n");
temp.append(" con. (ID:weight): ");
for (int i = 0; i < m_connectionIDs.length; i++) {
temp.append(m_connectionIDs[i] + ":" + Utils.doubleToString(m_weights[i], 2));
if ((i + 1) % 10 == 0 || i == m_connectionIDs.length - 1) {
temp.append("\n ");
} else {
temp.append(", ");
}
}
return temp.toString();
}
}
static class NeuralOutputs implements Serializable {
/**
* For serialization
*/
private static final long serialVersionUID = -233611113950482952L;
/** The neurons we are mapping */
private String[] m_outputNeurons = null;
/**
* In the case of a nominal class, the index of the value
* being predicted by each output neuron
*/
private int[] m_categoricalIndexes = null;
/** The class attribute we are mapping to */
private Attribute m_classAttribute = null;
/** Used when the class is numeric */
private NormContinuous m_regressionMapping = null;
protected NeuralOutputs(Element outputs, MiningSchema miningSchema) throws Exception {
m_classAttribute = miningSchema.getMiningSchemaAsInstances().classAttribute();
int vals = (m_classAttribute.isNumeric())
? 1
: m_classAttribute.numValues();
m_outputNeurons = new String[vals];
m_categoricalIndexes = new int[vals];
NodeList outputL = outputs.getElementsByTagName("NeuralOutput");
if (outputL.getLength() != m_outputNeurons.length) {
throw new Exception("[NeuralOutputs] the number of neural outputs does not match "
+ "the number expected!");
}
for (int i = 0; i < outputL.getLength(); i++) {
Node outputN = outputL.item(i);
if (outputN.getNodeType() == Node.ELEMENT_NODE) {
Element outputE = (Element)outputN;
// get the ID for this output neuron
m_outputNeurons[i] = outputE.getAttribute("outputNeuron");
if (m_classAttribute.isNumeric()) {
// get the single norm continuous
NodeList contL = outputE.getElementsByTagName("NormContinuous");
if (contL.getLength() != 1) {
throw new Exception("[NeuralOutputs] Should be exactly one norm continuous element "
+ "for numeric class!");
}
Node normContNode = contL.item(0);
String attName = ((Element)normContNode).getAttribute("field");
Attribute dummyTargetDef = new Attribute(attName);
ArrayList dummyFieldDefs = new ArrayList();
dummyFieldDefs.add(dummyTargetDef);
m_regressionMapping = new NormContinuous((Element)normContNode,
FieldMetaInfo.Optype.CONTINUOUS, dummyFieldDefs);
break;
} else {
// we just need to grab the categorical value (out of the NormDiscrete element)
// that this output neuron is associated with
NodeList discL = outputE.getElementsByTagName("NormDiscrete");
if (discL.getLength() != 1) {
throw new Exception("[NeuralOutputs] Should be only one norm discrete element "
+ "per derived field/neural output for a nominal class!");
}
Node normDiscNode = discL.item(0);
String attValue = ((Element)normDiscNode).getAttribute("value");
int index = m_classAttribute.indexOfValue(attValue);
if (index < 0) {
throw new Exception("[NeuralOutputs] Can't find specified target value "
+ attValue + " in class attribute " + m_classAttribute.name());
}
m_categoricalIndexes[i] = index;
}
}
}
}
/**
* Compute the output. Either a probability distribution or a single
* value (regression).
*
* @param incoming the values from the last hidden layer
* @param preds the array to fill with predicted values
* @throws Exception if there is a problem computing the output
*/
protected void getOuput(HashMap incoming, double[] preds) throws Exception {
if (preds.length != m_outputNeurons.length) {
throw new Exception("[NeuralOutputs] Incorrect number of predictions requested: "
+ preds.length + "requested, " + m_outputNeurons.length + " expected");
}
for (int i = 0; i < m_outputNeurons.length; i++) {
Double neuronOut = incoming.get(m_outputNeurons[i]);
if (neuronOut == null) {
throw new Exception("[NeuralOutputs] Unable to find output neuron "
+ m_outputNeurons[i] + " in the incoming HashMap!!");
}
if (m_classAttribute.isNumeric()) {
// will be only one output neuron anyway
preds[0] = neuronOut.doubleValue();
preds[0] = m_regressionMapping.getResultInverse(preds);
} else {
// clip at zero
// preds[m_categoricalIndexes[i]] = (neuronOut < 0) ? 0.0 : neuronOut;
preds[m_categoricalIndexes[i]] = neuronOut;
}
}
if (m_classAttribute.isNominal()) {
// check for negative values and adjust
double min = preds[Utils.minIndex(preds)];
if (min < 0) {
for (int i = 0; i < preds.length; i++) {
preds[i] -= min;
}
}
// do a simplemax normalization
Utils.normalize(preds);
}
}
public String toString() {
StringBuffer temp = new StringBuffer();
for (int i = 0; i < m_outputNeurons.length; i++) {
temp.append("Output neuron (" + m_outputNeurons[i] + ")\n");
temp.append("mapping:\n");
if (m_classAttribute.isNumeric()) {
temp.append(m_regressionMapping +"\n");
} else {
temp.append(m_classAttribute.name() + " = "
+ m_classAttribute.value(m_categoricalIndexes[i]) + "\n");
}
}
return temp.toString();
}
}
/**
* Enumerated type for the mining function
*/
enum MiningFunction {
CLASSIFICATION,
REGRESSION;
}
/** The mining function */
protected MiningFunction m_functionType = MiningFunction.CLASSIFICATION;
/**
* Enumerated type for the activation function.
*/
enum ActivationFunction {
THRESHOLD("threshold") {
double eval(double z, double threshold, double altitude, double fanIn) {
if (z > threshold) {
return 1.0;
}
return 0.0;
}
},
LOGISTIC("logistic") {
double eval(double z, double threshold, double altitude, double fanIn) {
return 1.0 / (1.0 + Math.exp(-z));
}
},
TANH("tanh") {
double eval(double z, double threshold, double altitude, double fanIn) {
double a = Math.exp( z );
double b = Math.exp( -z );
return ((a-b)/(a+b));
//return (1.0 - Math.exp(-2.0 * z)) / (1.0 + Math.exp(-2.0 * z));
}
},
IDENTITY("identity") {
double eval(double z, double threshold, double altitude, double fanIn) {
return z;
}
},
EXPONENTIAL("exponential") {
double eval(double z, double threshold, double altitude, double fanIn) {
return Math.exp(z);
}
},
RECIPROCAL("reciprocal") {
double eval(double z, double threshold, double altitude, double fanIn) {
return 1.0 / z;
}
},
SQUARE("square") {
double eval(double z, double threshold, double altitude, double fanIn) {
return z * z;
}
},
GAUSS("gauss") {
double eval(double z, double threshold, double altitude, double fanIn) {
return Math.exp(-(z * z));
}
},
SINE("sine") {
double eval(double z, double threshold, double altitude, double fanIn) {
return Math.sin(z);
}
},
COSINE("cosine") {
double eval(double z, double threshold, double altitude, double fanIn) {
return Math.cos(z);
}
},
ELLICOT("ellicot") {
double eval(double z, double threshold, double altitude, double fanIn) {
return z / (1.0 + Math.abs(z));
}
},
ARCTAN("arctan") {
double eval(double z, double threshold, double altitude, double fanIn) {
return 2.0 * Math.atan(z) / Math.PI;
}
},
RADIALBASIS("radialBasis") {
double eval(double z, double threshold, double altitude, double fanIn) {
return Math.exp(fanIn * Math.log(altitude) - z);
}
};
abstract double eval(double z, double threshold, double altitude, double fanIn);
private final String m_stringVal;
ActivationFunction(String name) {
m_stringVal = name;
}
public String toString() {
return m_stringVal;
}
}
/** The activation function to use */
protected ActivationFunction m_activationFunction = ActivationFunction.ARCTAN;
/**
* Enumerated type for the normalization method
*/
enum Normalization {
NONE ("none"),
SIMPLEMAX ("simplemax"),
SOFTMAX ("softmax");
private final String m_stringVal;
Normalization(String name) {
m_stringVal = name;
}
public String toString() {
return m_stringVal;
}
}
/** The normalization method */
protected Normalization m_normalizationMethod = Normalization.NONE;
/** Threshold activation */
protected double m_threshold = 0.0; // default = 0
/** Width for radial basis */
protected double m_width = Double.NaN; // no default
/** Altitude for radial basis */
protected double m_altitude = 1.0; // default = 1
/** The number of inputs to the network */
protected int m_numberOfInputs = 0;
/** Number of hidden layers in the network */
protected int m_numberOfLayers = 0;
/** The inputs to the network */
protected NeuralInput[] m_inputs = null;
/** A map for storing network input values (computed from an incoming instance) */
protected HashMap m_inputMap = new HashMap();
/** The hidden layers in the network */
protected NeuralLayer[] m_layers = null;
/** The outputs of the network */
protected NeuralOutputs m_outputs = null;
public NeuralNetwork(Element model, Instances dataDictionary,
MiningSchema miningSchema) throws Exception {
super(dataDictionary, miningSchema);
String fn = model.getAttribute("functionName");
if (fn.equals("regression")) {
m_functionType = MiningFunction.REGRESSION;
}
String act = model.getAttribute("activationFunction");
if (act == null || act.length() == 0) {
throw new Exception("[NeuralNetwork] no activation functon defined");
}
// get the activation function
for (ActivationFunction a : ActivationFunction.values()) {
if (a.toString().equals(act)) {
m_activationFunction = a;
break;
}
}
// get the normalization method (if specified)
String norm = model.getAttribute("normalizationMethod");
if (norm != null && norm.length() > 0) {
for (Normalization n : Normalization.values()) {
if (n.toString().equals(norm)) {
m_normalizationMethod = n;
break;
}
}
}
String thresh = model.getAttribute("threshold");
if (thresh != null && thresh.length() > 0) {
m_threshold = Double.parseDouble(thresh);
}
String width = model.getAttribute("width");
if (width != null && width.length() > 0) {
m_width = Double.parseDouble(width);
}
String alt = model.getAttribute("altitude");
if (alt != null && alt.length() > 0) {
m_altitude = Double.parseDouble(alt);
}
// get all the inputs
NodeList inputL = model.getElementsByTagName("NeuralInput");
m_numberOfInputs = inputL.getLength();
m_inputs = new NeuralInput[m_numberOfInputs];
for (int i = 0; i < m_numberOfInputs; i++) {
Node inputN = inputL.item(i);
if (inputN.getNodeType() == Node.ELEMENT_NODE) {
NeuralInput nI = new NeuralInput((Element)inputN, m_miningSchema);
m_inputs[i] = nI;
}
}
// get the layers
NodeList layerL = model.getElementsByTagName("NeuralLayer");
m_numberOfLayers = layerL.getLength();
m_layers = new NeuralLayer[m_numberOfLayers];
for (int i = 0; i < m_numberOfLayers; i++) {
Node layerN = layerL.item(i);
if (layerN.getNodeType() == Node.ELEMENT_NODE) {
NeuralLayer nL = new NeuralLayer((Element)layerN);
m_layers[i] = nL;
}
}
// get the outputs
NodeList outputL = model.getElementsByTagName("NeuralOutputs");
if (outputL.getLength() != 1) {
throw new Exception("[NeuralNetwork] Should be just one NeuralOutputs element defined!");
}
m_outputs = new NeuralOutputs((Element)outputL.item(0), m_miningSchema);
}
/* (non-Javadoc)
* @see weka.core.RevisionHandler#getRevision()
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Classifies the given test instance. The instance has to belong to a
* dataset when it's being classified.
*
* @param inst the instance to be classified
* @return the predicted most likely class for the instance or
* Utils.missingValue() if no prediction is made
* @exception Exception if an error occurred during the prediction
*/
public double[] distributionForInstance(Instance inst) throws Exception {
if (!m_initialized) {
mapToMiningSchema(inst.dataset());
}
double[] preds = null;
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds = new double[1];
} else {
preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute().numValues()];
}
double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);
boolean hasMissing = false;
for (int i = 0; i < incoming.length; i++) {
if (i != m_miningSchema.getFieldsAsInstances().classIndex() &&
Double.isNaN(incoming[i])) {
hasMissing = true;
//System.err.println("Missing value for att : " + m_miningSchema.getFieldsAsInstances().attribute(i).name());
break;
}
}
if (hasMissing) {
if (!m_miningSchema.hasTargetMetaData()) {
String message = "[NeuralNetwork] WARNING: Instance to predict has missing value(s) but "
+ "there is no missing value handling meta data and no "
+ "prior probabilities/default value to fall back to. No "
+ "prediction will be made ("
+ ((m_miningSchema.getFieldsAsInstances().classAttribute().isNominal()
|| m_miningSchema.getFieldsAsInstances().classAttribute().isString())
? "zero probabilities output)."
: "NaN output).");
if (m_log == null) {
System.err.println(message);
} else {
m_log.logMessage(message);
}
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds[0] = Utils.missingValue();
}
return preds;
} else {
// use prior probablilities/default value
TargetMetaInfo targetData = m_miningSchema.getTargetMetaData();
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds[0] = targetData.getDefaultValue();
} else {
Instances miningSchemaI = m_miningSchema.getFieldsAsInstances();
for (int i = 0; i < miningSchemaI.classAttribute().numValues(); i++) {
preds[i] = targetData.getPriorProbability(miningSchemaI.classAttribute().value(i));
}
}
return preds;
}
} else {
// construct the input to the network for this instance
m_inputMap.clear();
for (int i = 0; i < m_inputs.length; i++) {
double networkInVal = m_inputs[i].getValue(incoming);
String ID = m_inputs[i].getID();
m_inputMap.put(ID, networkInVal);
}
// now compute the output of each layer
HashMap layerOut = m_layers[0].computeOutput(m_inputMap);
for (int i = 1; i < m_layers.length; i++) {
layerOut = m_layers[i].computeOutput(layerOut);
}
// now do the output
m_outputs.getOuput(layerOut, preds);
}
return preds;
}
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("PMML version " + getPMMLVersion());
if (!getCreatorApplication().equals("?")) {
temp.append("\nApplication: " + getCreatorApplication());
}
temp.append("\nPMML Model: Neural network");
temp.append("\n\n");
temp.append(m_miningSchema);
temp.append("Inputs:\n");
for (int i = 0; i < m_inputs.length; i++) {
temp.append(m_inputs[i] + "\n");
}
for (int i = 0; i < m_layers.length; i++) {
temp.append("Layer: " + (i+1) + "\n");
temp.append(m_layers[i] + "\n");
}
temp.append("Outputs:\n");
temp.append(m_outputs);
return temp.toString();
}
}
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