double calculateMSEOnValidationSet(Matrix featuresValidationSet, Matrix labelsValidationSet)
throws Exception {
double sumSquaredError = 0;
for (int instance = 0; instance < featuresValidationSet.rows(); instance++) {
double errorAcrossOutputNodes = 0;
double[] predictedLabel = labelsValidationSet.row(instance); // this is the target
predict(featuresValidationSet.row(instance), labelsValidationSet.row(instance));
for (int col = 0; col < globalStoredOutputNodeFNetValues.length; col++) {
errorAcrossOutputNodes +=
(globalStoredOutputNodeTargetValues[col] - globalStoredOutputNodeFNetValues[col]);
}
sumSquaredError += (errorAcrossOutputNodes * errorAcrossOutputNodes);
}
double MSE =
(sumSquaredError
/ (featuresValidationSet.rows() * globalStoredOutputNodeFNetValues.length));
return MSE;
}
/*
* We incorporate the ability to create an arbitrary network structure.
* We use array of arrays of doubles for each inter-layer matrix
* Thus, between each layer, we need a matrix of weights.
* Num rows * num columns in matrix = nodes in layer below * nodes in layer above
*
* We use the Math library's pow function to raise to exponent: double pow(double base, double exponent)
*
* Hidden Nodes in current Layer (j)
* previous layers nodes[ ]
* Features [ Wij ]
* (i) [ ]
*
* I set up a matrix with dimensions: [ nodes in previous layer ] [ nodes in next layer ]
*
* Since we are traveling through one layer at a time, we need to have another data structure
* that will be outputs for this layer
*
* I use for loops to initialize array of arrays ( allocated necessary memory)
* Please note that: number of layers + 1 = number of weight arrays needed
*/
public void train(Matrix features, Matrix labels) throws Exception {
double[] recentAccuracies = new double[5];
int currentAccuracyIndex = 0;
double currentAccuracy = 0;
Random rand = new Random();
// SHUFFLE labels, features together
features.shuffle(rand, labels);
// need to map 0,1, or 2 to the three dimensional vectors, DO N-OF-K-ENCODING FOR THE
// BACKPROPAGATION
Matrix newNOfKLabelsMatrix = new Matrix();
newNOfKLabelsMatrix.setSize(
labels.rows(), labels.valueCount(0)); // I HARD CODE IN THAT THERE SHOULD BE 3 OUTPUT NODES
for (int row = 0; row < newNOfKLabelsMatrix.rows(); row++) { // for each instance
for (int k = 0; k < labels.valueCount(0); k++) {
if (labels.get(row, 0) == k) {
for (int m = 0; m < labels.valueCount(0); m++) {
newNOfKLabelsMatrix.set(row, m, 0);
}
newNOfKLabelsMatrix.set(row, k, 1);
}
}
}
labels = newNOfKLabelsMatrix;
// IMMEDIATELY SAVE SOME OF THIS, NEVER WILL TRAIN ON THESE
// STICK THESE INTO A VALIDATION SET
// ONCE MSE STARTS TO INCREASE AGAIN ON THE VALIDATION SET, WE'VE GONE TOO FAR
int numRowsToGetIntoTrainingSet = (int) (features.rows() * validationSetPercentageOfData);
Matrix featuresForTrainingTrimmed = new Matrix();
featuresForTrainingTrimmed.setSize(numRowsToGetIntoTrainingSet, features.cols());
Matrix featuresValidationSet = new Matrix();
featuresValidationSet.setSize(features.rows() - numRowsToGetIntoTrainingSet, features.cols());
Matrix labelsForTrainingTrimmed = new Matrix();
labelsForTrainingTrimmed.setSize(numRowsToGetIntoTrainingSet, labels.cols());
Matrix labelsValidationSet = new Matrix();
labelsValidationSet.setSize(features.rows() - numRowsToGetIntoTrainingSet, labels.cols());
// LOOP THROUGH AND PUT MOST OF FEATURES INTO featuresForTrainingTrimmed
for (int row = 0; row < features.rows(); row++) {
for (int col = 0; col < features.cols(); col++) {
if (row < numRowsToGetIntoTrainingSet) {
featuresForTrainingTrimmed.set(row, col, features.get(row, col));
} else {
featuresValidationSet.set(row - numRowsToGetIntoTrainingSet, col, features.get(row, col));
}
}
}
// LOOP THROUGH AND PUT MOST OF FEATURES INTO featuresForTrainingTrimmed
for (int row = 0; row < labels.rows(); row++) {
for (int col = 0; col < labels.cols(); col++) {
if (row < numRowsToGetIntoTrainingSet) {
labelsForTrainingTrimmed.set(row, col, labels.get(row, col));
} else {
labelsValidationSet.set(row - numRowsToGetIntoTrainingSet, col, labels.get(row, col));
}
}
}
features = featuresForTrainingTrimmed;
labels = labelsForTrainingTrimmed;
// LOOP THROUGH AND PUT LEFTOVER PORTION OF FEATURES INTO validationSet
arrayListOfEachLayersWeightMatrices = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
arrayListOfEachLayersWeightMatrices.add(specificLayersWeightMatrix);
}
changeInWeightMatricesForEveryLayer = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
changeInWeightMatricesForEveryLayer.add(specificLayersWeightMatrix);
}
// allocate space/ initialize the previous change in weights that we'll use for momentum
temporaryStashChangeInWeightMatricesForEveryLayer = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
temporaryStashChangeInWeightMatricesForEveryLayer.add(specificLayersWeightMatrix);
}
// ALLOCATE SPACE FOR DELTA ( INTERMEDIATE VALUES THAT WE USE TO UPDATE THE WEIGHTS)
arrayListOfEachLayersDeltaArray = new ArrayList<double[]>();
// EACH LAYER HAS AN ARRAY OF DELTA VALUES
for (int i = 0;
i < numHiddenLayers + 2;
i++) { // each layer // OF COURSE WE COULD HAVE DONE numHiddenLayers + 1, but I want
// consistency with fnet ArrayList
double[] specificLayersDeltaArray;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersDeltaArray = new double[features.cols()]; // INPUTS are the rows
} else if (i == (numHiddenLayers + 1)) {
// specificLayersDeltaArray = new double[ numNodesPerHiddenLayer[ i-1 ] ] ; //[
// numNodesPerHiddenLayer[ labels.cols() ] ] ; // OUTPUTS ARE THE COLUMNS
specificLayersDeltaArray = new double[labels.cols()]; // FIND OUT # NODES AT EACH LEVEL
} else {
specificLayersDeltaArray = new double[numNodesPerHiddenLayer[i - 1]];
}
arrayListOfEachLayersDeltaArray.add(specificLayersDeltaArray);
}
previousChangeInWeightMatricesForEachLayer = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
previousChangeInWeightMatricesForEachLayer.add(specificLayersWeightMatrix);
}
// INITIALIZE ALL OF PREVIOUS DELTA VALUES TO 0 [ THIS IS DONE AUTOMATICALLY, CAN DELETE ALL OF
// THIS CODE ]
// initialize all weights randomly ( small random weights with 0 mean)
double[][] currentLayersWeightMatrix;
for (int i = 0; i < numNodesPerHiddenLayer.length + 1; i++) { // scroll across each layer
currentLayersWeightMatrix = arrayListOfEachLayersWeightMatrices.get(i);
for (int j = 0; j < currentLayersWeightMatrix.length; j++) {
for (int k = 0; k < currentLayersWeightMatrix[j].length; k++) {
currentLayersWeightMatrix[j][k] = (2 * rand.nextDouble()) - 1;
}
}
}
// GO THROUGH AND ADD THE SPECIFIC WEIGHTS
// Initial Weights:
// PUT ALL BIAS WEIGHTS INTO ARRAYLIST (ONE ARRAY FOR EACH LAYER'S BIAS WEIGHTS)
biasWeightsAcrossAllLayers = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
biasWeightsAcrossAllLayers.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
biasWeightsAcrossAllLayers.add(biasArrayForOutputNodesToBeAdded);
}
}
double[] currentBiasLayersWeightArray;
for (int i = 0; i < numNodesPerHiddenLayer.length + 1; i++) { // scroll across each layer
currentBiasLayersWeightArray = biasWeightsAcrossAllLayers.get(i);
for (int j = 0; j < currentBiasLayersWeightArray.length; j++) {
currentBiasLayersWeightArray[j] = (2 * rand.nextDouble()) - 1;
}
}
// We'll need to store the previous bias weights
previousBiasChangeInWeightsAcrossAllLayers = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
previousBiasChangeInWeightsAcrossAllLayers.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
previousBiasChangeInWeightsAcrossAllLayers.add(biasArrayForOutputNodesToBeAdded);
}
}
// temporarily stashed bias weights across all layers
temporarilyStashedChangeInBiasWeightsAcrossAllLayers = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
temporarilyStashedChangeInBiasWeightsAcrossAllLayers.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
temporarilyStashedChangeInBiasWeightsAcrossAllLayers.add(biasArrayForOutputNodesToBeAdded);
}
}
changeInBiasArrayForEveryLayer = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
changeInBiasArrayForEveryLayer.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
changeInBiasArrayForEveryLayer.add(biasArrayForOutputNodesToBeAdded);
}
}
// INITIALIZE BIAS FOR HIDDEN AND OUTPUT NEURONS
// Stochastic weight update
// SOMEHOW GOT TO INITIALIZE ALL OF THIS, ADD BLANKS, SO THAT LATER WE CAN
// storedFNetForEachLayer.set( i, blah );
storedFNetForEachLayer =
new ArrayList<double[]>(); // f_net is the output that is fed into the next layer
for (int i = 0;
i < numHiddenLayers + 2;
i++) { // WE HAVE ONE MORE layer of fnet( consider inputs as fnet)
double[] thisLayersFNetValues;
// COULD DO IF/ELSE STATEMENTS IF WE ARE LOOKING AT INPUTS, OR THEN HIDDEN NODES,
if (i == 0) {
thisLayersFNetValues = new double[features.cols()]; // FIND OUT # NODES AT EACH LEVEL
} else if (i == numHiddenLayers + 1) { // OR IS IT +1
thisLayersFNetValues = new double[labels.cols()]; // FIND OUT # NODES AT EACH LEVEL
} else {
thisLayersFNetValues =
new double[numNodesPerHiddenLayer[i - 1]]; // FIND OUT # NODES AT EACH LEVEL
}
storedFNetForEachLayer.add(thisLayersFNetValues);
}
// -----BEGIN THE TRAINING-----
double netValAtNode = 0;
double fOfNetValAtNode = 0;
for (int epoch = 0;
epoch < 10000;
epoch++) { // For each epoch, cap it at 10000, we want to avoid infinite loop
System.out.println("---Epoch " + epoch + "---");
for (int instance = 0;
instance < features.rows();
instance++) { // later we will swap this Matrix for featuresForTrainingTrimmed
// GO FORWARD
// ---------------------------------------------------------------------------------------------------------------------
// System.out.println("Forward propagating...");
for (int layer = 0;
layer < numHiddenLayers + 2;
layer++) { // HERE LAYER DENOTES HIDDEN LAYER
if (layer == 0) {
storedFNetForEachLayer.set(
layer, Arrays.copyOf(features.row(instance), features.row(0).length));
continue;
}
double[] thisLayersFNetValues =
storedFNetForEachLayer.get(
layer); // make a new array of doubles CAN I PLEASE DELETE THIS LINE OF CODE
for (int node = 0; node < storedFNetForEachLayer.get(layer).length; node++) {
netValAtNode = 0;
// FIND THE CROSS PRODUCT;
// use a for loop to multiply each col of weights vector by each col of
// outputsFromPreviousLayer
for (int colInInputVector = 0;
colInInputVector < storedFNetForEachLayer.get(layer - 1).length;
colInInputVector++) {
netValAtNode +=
(storedFNetForEachLayer.get(layer - 1)[colInInputVector]
* arrayListOfEachLayersWeightMatrices.get(layer - 1)[colInInputVector][node]);
}
netValAtNode += (biasWeightsAcrossAllLayers.get(layer - 1)[node]);
if (netValAtNode < 0) { // make special function
fOfNetValAtNode = (1 / (1 + Math.pow(Math.E, (-1 * netValAtNode))));
} else { // normal
fOfNetValAtNode =
(1
/ (1
+ (1
/ (Math.pow(
Math.E,
(netValAtNode)))))); // if it was positive, then we raise to neg
// exponent
}
thisLayersFNetValues[node] = fOfNetValAtNode; // stick it into the object
}
storedFNetForEachLayer.set(
layer,
thisLayersFNetValues); // or if we are editing object, this is not even necessary
// DOUBLE CHECK
}
// ---NOW FOR THIS INSTANCE, GO
// BACKWARDS-----------------------------------------------------------------------------------------------------------------------
// System.out.println("Back propagating...");
// UPDATE THE WEIGHTS
for (int layer = numHiddenLayers + 1; layer > 0; layer--) { // ACROSS EACH LAYER BACKWARD
if (layer == numHiddenLayers + 1) { // THIS IS AN OUTPUT LAYER
for (int node = 0; node < labels.cols(); node++) {
double deltaArrayForThisLayer[] = arrayListOfEachLayersDeltaArray.get(layer);
deltaArrayForThisLayer[node] =
((labels.get(instance, node) - storedFNetForEachLayer.get(layer)[node])
* (storedFNetForEachLayer.get(layer)[node])
* (1 - (storedFNetForEachLayer.get(layer)[node])));
// should automatically be set since we get the objects address from heap memory, and
// change it
for (int inputToThisNode = 0;
inputToThisNode < numNodesPerHiddenLayer[layer - 2] + 1;
inputToThisNode++) {
double changeInWeightBetweenIJ = 0;
if (inputToThisNode == numNodesPerHiddenLayer[layer - 2]) { // this is a bias node
changeInWeightBetweenIJ =
(learningRate
* 1
* arrayListOfEachLayersDeltaArray
.get(layer)[node]); // NEED TO ADD STUFF FOR MOMENTUM
double[] thisLayersBiasWeights =
changeInBiasArrayForEveryLayer.get(
layer - 1); // NEED TO ADD STUFF FOR MOMENTUM
thisLayersBiasWeights[node] =
(changeInWeightBetweenIJ); // NEED TO ADD STUFF FOR MOMENTUM
} else {
changeInWeightBetweenIJ =
(learningRate
* storedFNetForEachLayer.get(layer - 1)[inputToThisNode]
* arrayListOfEachLayersDeltaArray.get(layer)[node]);
// double[][] thisLayersWeightMatrix =
// arrayListOfEachLayersWeightMatrices.get(layer-1);
// thisLayersWeightMatrix[inputToThisNode][node] += ( changeInWeightBetweenIJ );
double[][] changeInWeightsMatrixForThisLayer =
changeInWeightMatricesForEveryLayer.get(layer - 1);
changeInWeightsMatrixForThisLayer[inputToThisNode][node] =
changeInWeightBetweenIJ;
}
}
}
} else {
for (int node = 0;
node < numNodesPerHiddenLayer[layer - 1] + 1;
node++) { // ACROSS EACH HIDDEN LAYER (ie these are not output nodes)
double deltaArrayForThisLayer[] = arrayListOfEachLayersDeltaArray.get(layer);
if (node == numNodesPerHiddenLayer[layer - 1]) { // this is a bias node
// change in weight = learningRate *
} else { // this is not a bias node
double summedOutgoingWeightsCrossOutputDelta = 0;
for (int outgoingEdgeToOutgoingNode = 0;
outgoingEdgeToOutgoingNode
< arrayListOfEachLayersDeltaArray.get(layer + 1).length;
outgoingEdgeToOutgoingNode++) {
summedOutgoingWeightsCrossOutputDelta +=
(arrayListOfEachLayersDeltaArray.get(layer + 1)[outgoingEdgeToOutgoingNode]
* arrayListOfEachLayersWeightMatrices
.get(layer)[node][outgoingEdgeToOutgoingNode]);
}
deltaArrayForThisLayer[node] =
((summedOutgoingWeightsCrossOutputDelta)
* (storedFNetForEachLayer.get(layer)[node])
* (1 - (storedFNetForEachLayer.get(layer)[node])));
// should automatically be set since we get the objects address from heap memory,
// and change it
if (layer == 1) {
// need a for loop across the neural net's input nodes
for (int inputToTheNeuralNet = 0;
inputToTheNeuralNet < features.cols() + 1;
inputToTheNeuralNet++) {
double changeInWeightBetweenIJ = 0;
if (inputToTheNeuralNet
== features.cols()) { // then we know that this is our bias node
changeInWeightBetweenIJ =
(learningRate
* 1
* arrayListOfEachLayersDeltaArray
.get(layer)[node]); // NEED TO ADD STUFF FOR MOMENTUM
double[] thisLayersBiasWeights =
changeInBiasArrayForEveryLayer.get(
layer - 1); // NEED TO ADD STUFF FOR MOMENTUM
thisLayersBiasWeights[node] =
(changeInWeightBetweenIJ); // NEED TO ADD STUFF FOR MOMENTUM
} else {
changeInWeightBetweenIJ =
(learningRate
* storedFNetForEachLayer.get(layer - 1)[inputToTheNeuralNet]
* arrayListOfEachLayersDeltaArray.get(layer)[node]);
double[][] changeInWeightsMatrixForThisLayer =
changeInWeightMatricesForEveryLayer.get(layer - 1);
changeInWeightsMatrixForThisLayer[inputToTheNeuralNet][node] =
changeInWeightBetweenIJ;
}
}
} else {
for (int inputToThisNode = 0;
inputToThisNode < numNodesPerHiddenLayer[layer - 2] + 1;
inputToThisNode++) {
double changeInWeightBetweenIJ = 0;
if (inputToThisNode
== numNodesPerHiddenLayer[layer - 2]) { // this is a bias node
changeInWeightBetweenIJ =
(learningRate
* 1
* arrayListOfEachLayersDeltaArray
.get(layer)[node]); // NEED TO ADD STUFF FOR MOMENTUM
double[] thisLayersBiasWeights =
changeInBiasArrayForEveryLayer.get(
layer - 1); // NEED TO ADD STUFF FOR MOMENTUM
thisLayersBiasWeights[node] =
(changeInWeightBetweenIJ); // NEED TO ADD STUFF FOR MOMENTUM
} else {
changeInWeightBetweenIJ =
(learningRate
* storedFNetForEachLayer.get(layer - 1)[inputToThisNode]
* arrayListOfEachLayersDeltaArray.get(layer)[node]);
// double[][] thisLayersWeightMatrix =
// arrayListOfEachLayersWeightMatrices.get(layer-1);
// thisLayersWeightMatrix[inputToThisNode][node] += ( changeInWeightBetweenIJ
// );
double[][] changeInWeightsMatrixForThisLayer =
changeInWeightMatricesForEveryLayer.get(layer - 1);
changeInWeightsMatrixForThisLayer[inputToThisNode][node] =
changeInWeightBetweenIJ;
}
}
}
}
}
}
}
// System.out.printf( "e_0=%.17f, e_1=%.17f, e_2=%.17f, e_3=%.17f\n" ,
// arrayListOfEachLayersDeltaArray.get(2)[0], arrayListOfEachLayersDeltaArray.get(1)[0] ,
// arrayListOfEachLayersDeltaArray.get(1)[1] ,
// arrayListOfEachLayersDeltaArray.get(1)[2]);
// System.out.println("Descending Gradient...");
// // PUT TEMPORARILY STASHED INTO PREVIOUS
// // ONLY HERE SHOULD WE PUT IN THE STASHED WEIGHTS INTO THE PREVIOUS-STASH-SPOT
// // PUT STASHED INTO PREVIOUS
//
// // update the bias weights
// GET NEW CHANGE IN WEIGHT THANKS TO MOMENTUM, PLACE IN PREVIOUS SPOT
// should be changeInBiasArrayForEveryLayer not
for (int w = 0; w < previousBiasChangeInWeightsAcrossAllLayers.size(); w++) {
for (int y = 0; y < previousBiasChangeInWeightsAcrossAllLayers.get(w).length; y++) {
double currentChangeInWeightVal = changeInBiasArrayForEveryLayer.get(w)[y];
double[] fullBiasWeightList = biasWeightsAcrossAllLayers.get(w);
double previousXYCoordInBiasWeightMatrix =
previousBiasChangeInWeightsAcrossAllLayers.get(w)[y];
double thisIsTheWeightChangeIncludingMomentum =
(currentChangeInWeightVal + (momentum * previousXYCoordInBiasWeightMatrix));
fullBiasWeightList[y] += thisIsTheWeightChangeIncludingMomentum;
double[] arrayOfPreviousBiases = previousBiasChangeInWeightsAcrossAllLayers.get(w);
arrayOfPreviousBiases[y] = thisIsTheWeightChangeIncludingMomentum;
}
}
// GET NEW CHANGE IN WEIGHT THANKS TO MOMENTUM, PLACE IN PREVIOUS SPOT
// We update the weights ( by adding the changes in weights to the weight matrices) after
// every layer has been processed
for (int w = 0; w < arrayListOfEachLayersWeightMatrices.size(); w++) {
for (int y = 0; y < arrayListOfEachLayersWeightMatrices.get(w).length; y++) {
for (int z = 0; z < arrayListOfEachLayersWeightMatrices.get(w)[y].length; z++) {
double currentXYCoordInMatrix = changeInWeightMatricesForEveryLayer.get(w)[y][z];
double[] fullWeightListForLayer = arrayListOfEachLayersWeightMatrices.get(w)[y];
double previousXYCoordInChangeInWeightMatrix =
previousChangeInWeightMatricesForEachLayer.get(w)[y][z];
double thisIsTheWeightChangeIncludingMomentum =
(currentXYCoordInMatrix + (previousXYCoordInChangeInWeightMatrix * momentum));
fullWeightListForLayer[z] += thisIsTheWeightChangeIncludingMomentum;
double[][] arrayOfPreviousBiases = previousChangeInWeightMatricesForEachLayer.get(w);
arrayOfPreviousBiases[y][z] = thisIsTheWeightChangeIncludingMomentum;
// newWeight(at next round t+1) = learningRate * delta_at_node_we_feed_into * Xi +
// momentum_parameter * change_in_weight_at_t
// momentum goes into the weight updates ( not in the change in weights)
}
}
}
// System.out.printf( "w_0=%.17f, w_1=%.17f, w_2=%.17f, w_3=%.17f, w_4=%.17f,
// w_5=%.17f,\n w_6=%.17f, w_7=%.17f, w_8=%.17f, w_9=%.17f," +
// "w_10=%.17f, w_11=%.17f,\n w_12=%.17f\n" ,
// biasWeightsAcrossAllLayers.get(1)[0],
// arrayListOfEachLayersWeightMatrices.get(1)[0][0] ,
// arrayListOfEachLayersWeightMatrices.get(1)[1][0] ,
// arrayListOfEachLayersWeightMatrices.get(1)[2][0] , biasWeightsAcrossAllLayers.get(0)[0],
// arrayListOfEachLayersWeightMatrices.get(0)[0][0],
// arrayListOfEachLayersWeightMatrices.get(0)[1][0], biasWeightsAcrossAllLayers.get(0)[1],
// arrayListOfEachLayersWeightMatrices.get(0)[0][1],
// arrayListOfEachLayersWeightMatrices.get(0)[1][1],
// arrayListOfEachLayersWeightMatrices.get(0)[0][2],
// biasWeightsAcrossAllLayers.get(0)[2],
// arrayListOfEachLayersWeightMatrices.get(0)[0][2],
// arrayListOfEachLayersWeightMatrices.get(0)[1][2]);
// // ONLY AFTER THIS POINT HAS EVERY LAYER BEEN PROCESSED
}
// if( STOPPING CRITERIA MET ) { // HAVE TO USE THE VALIDATION SET THIS TIME FOR THE STOPPING
// CRITERION
currentAccuracy = calculateMSEOnValidationSet(featuresValidationSet, labelsValidationSet);
// currentAccuracy = calculateMSEOnValidationSet( features , labels ); // On the training set
// now
System.out.println(" Current MSE on epoch # " + epoch + " is: " + currentAccuracy);
currentAccuracyIndex++;
recentAccuracies[currentAccuracyIndex % 5] = currentAccuracy;
double sumAccuracies = 0;
if (currentAccuracyIndex > 5) {
for (int i = 0; i < recentAccuracies.length; i++) {
sumAccuracies +=
Math.abs(recentAccuracies[currentAccuracyIndex % 5] - recentAccuracies[i]);
}
if (sumAccuracies
< 0.01) { // we only stop training when measureAccuracy after 5 epochs does not increase
// by 0.01
break;
}
}
// In theory, it would be wise here to go back to the old best weights because now we're
// already overfitting if the stopping criterion is met
features.shuffle(
rand, labels); // MUST SHUFFLE DATA ROWS AFTER EACH EPOCH,labels is the buddy matrix
}
return;
}