单层数据
package dl;/*** One layer, support all four layer types. The code mainly initializes, gets,* and sets variables. Essentially no algorithm is implemented.*/
public class CnnLayer {/*** The type of the layer.*/LayerTypeEnum type;/*** The number of out map.*/int outMapNum;/*** The map size.*/Size mapSize;/*** The kernel size.*/Size kernelSize;/*** The scale size.*/Size scaleSize;/*** The index of the class (label) attribute.*/int classNum = -1;/*** Kernel. Dimensions: [front map][out map][width][height].*/private double[][][][] kernel;/*** Bias. The length is outMapNum.*/private double[] bias;/*** Out maps. Dimensions:* [batchSize][outMapNum][mapSize.width][mapSize.height].*/private double[][][][] outMaps;/*** Errors.*/private double[][][][] errors;/*** For batch processing.*/private static int recordInBatch = 0;/************************** The first constructor.** @param paraNum* When the type is CONVOLUTION, it is the out map number. when* the type is OUTPUT, it is the class number.* @param paraSize* When the type is INPUT, it is the map size; when the type is* CONVOLUTION, it is the kernel size; when the type is SAMPLING,* it is the scale size.************************/public CnnLayer(LayerTypeEnum paraType, int paraNum, Size paraSize) {type = paraType;switch (type) {case INPUT:outMapNum = 1;mapSize = paraSize; // No deep copy.break;case CONVOLUTION:outMapNum = paraNum;kernelSize = paraSize;break;case SAMPLING:scaleSize = paraSize;break;case OUTPUT:classNum = paraNum;mapSize = new Size(1, 1);outMapNum = classNum;break;default:System.out.println("Internal error occurred in AbstractLayer.java constructor.");}// Of switch}// Of the first constructor/************************** Initialize the kernel.** @param paraNum* When the type is CONVOLUTION, it is the out map number. when************************/public void initKernel(int paraFrontMapNum) {kernel = new double[paraFrontMapNum][outMapNum][][];for (int i = 0; i < paraFrontMapNum; i++) {for (int j = 0; j < outMapNum; j++) {kernel[i][j] = MathUtils.randomMatrix(kernelSize.width, kernelSize.height, true);} // Of for j} // Of for i}// Of initKernel/************************** Initialize the output kernel. The code is revised to invoke* initKernel(int).************************/public void initOutputKernel(int paraFrontMapNum, Size paraSize) {kernelSize = paraSize;initKernel(paraFrontMapNum);}// Of initOutputKernel/************************** Initialize the bias. No parameter. "int frontMapNum" is claimed however* not used.************************/public void initBias() {bias = MathUtils.randomArray(outMapNum);}// Of initBias/************************** Initialize the errors.** @param paraBatchSize* The batch size.************************/public void initErrors(int paraBatchSize) {errors = new double[paraBatchSize][outMapNum][mapSize.width][mapSize.height];}// Of initErrors/************************** Initialize out maps.** @param paraBatchSize* The batch size.************************/public void initOutMaps(int paraBatchSize) {outMaps = new double[paraBatchSize][outMapNum][mapSize.width][mapSize.height];}// Of initOutMaps/************************** Prepare for a new batch.************************/public static void prepareForNewBatch() {recordInBatch = 0;}// Of prepareForNewBatch/************************** Prepare for a new record.************************/public static void prepareForNewRecord() {recordInBatch++;}// Of prepareForNewRecord/************************** Set one value of outMaps.************************/public void setMapValue(int paraMapNo, int paraX, int paraY, double paraValue) {outMaps[recordInBatch][paraMapNo][paraX][paraY] = paraValue;}// Of setMapValue/************************** Set values of the whole map.************************/public void setMapValue(int paraMapNo, double[][] paraOutMatrix) {outMaps[recordInBatch][paraMapNo] = paraOutMatrix;}// Of setMapValue/************************** Getter.************************/public Size getMapSize() {return mapSize;}// Of getMapSize/************************** Setter.************************/public void setMapSize(Size paraMapSize) {mapSize = paraMapSize;}// Of setMapSize/************************** Getter.************************/public LayerTypeEnum getType() {return type;}// Of getType/************************** Getter.************************/public int getOutMapNum() {return outMapNum;}// Of getOutMapNum/************************** Setter.************************/public void setOutMapNum(int paraOutMapNum) {outMapNum = paraOutMapNum;}// Of setOutMapNum/************************** Getter.************************/public Size getKernelSize() {return kernelSize;}// Of getKernelSize/************************** Getter.************************/public Size getScaleSize() {return scaleSize;}// Of getScaleSize/************************** Getter.************************/public double[][] getMap(int paraIndex) {return outMaps[recordInBatch][paraIndex];}// Of getMap/************************** Getter.************************/public double[][] getKernel(int paraFrontMap, int paraOutMap) {return kernel[paraFrontMap][paraOutMap];}// Of getKernel/************************** Setter. Set one error.************************/public void setError(int paraMapNo, int paraMapX, int paraMapY, double paraValue) {errors[recordInBatch][paraMapNo][paraMapX][paraMapY] = paraValue;}// Of setError/************************** Setter. Set one error matrix.************************/public void setError(int paraMapNo, double[][] paraMatrix) {errors[recordInBatch][paraMapNo] = paraMatrix;}// Of setError/************************** Getter. Get one error matrix.************************/public double[][] getError(int paraMapNo) {return errors[recordInBatch][paraMapNo];}// Of getError/************************** Getter. Get the whole error tensor.************************/public double[][][][] getErrors() {return errors;}// Of getErrors/************************** Setter. Set one kernel.************************/public void setKernel(int paraLastMapNo, int paraMapNo, double[][] paraKernel) {kernel[paraLastMapNo][paraMapNo] = paraKernel;}// Of setKernel/************************** Getter.************************/public double getBias(int paraMapNo) {return bias[paraMapNo];}// Of getBias/************************** Setter.************************/public void setBias(int paraMapNo, double paraValue) {bias[paraMapNo] = paraValue;}// Of setBias/************************** Getter.************************/public double[][][][] getMaps() {return outMaps;}// Of getMaps/************************** Getter.************************/public double[][] getError(int paraRecordId, int paraMapNo) {return errors[paraRecordId][paraMapNo];}// Of getError/************************** Getter.************************/public double[][] getMap(int paraRecordId, int paraMapNo) {return outMaps[paraRecordId][paraMapNo];}// Of getMap/************************** Getter.************************/public int getClassNum() {return classNum;}// Of getClassNum/************************** Getter. Get the whole kernel tensor.************************/public double[][][][] getKernel() {return kernel;} // Of getKernel
}// Of class CnnLayer多层管理
package dl;import java.util.ArrayList;
import java.util.List;/*** CnnLayer builder.*/
public class LayerBuilder {/*** Layers.*/private List<CnnLayer> layers;/************************** The first constructor.************************/public LayerBuilder() {layers = new ArrayList<CnnLayer>();}// Of the first constructor/************************** The second constructor.************************/public LayerBuilder(CnnLayer paraLayer) {this();layers.add(paraLayer);}// Of the second constructor/************************** Add a layer.** @param paraLayer* The new layer.************************/public void addLayer(CnnLayer paraLayer) {layers.add(paraLayer);}// Of addLayer/************************** Get the specified layer.** @param paraIndex* The index of the layer.************************/public CnnLayer getLayer(int paraIndex) throws RuntimeException{if (paraIndex >= layers.size()) {throw new RuntimeException("CnnLayer " + paraIndex + " is out of range: "+ layers.size() + ".");}//Of ifreturn layers.get(paraIndex);}//Of getLayer/************************** Get the output layer.************************/public CnnLayer getOutputLayer() {return layers.get(layers.size() - 1);}//Of getOutputLayer/************************** Get the number of layers.************************/public int getNumLayers() {return layers.size();}//Of getNumLayers
}// Of class LayerBuilder
