单层数据文章来源：https://www.toymoban.com/news/detail-676449.html
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多层管理文章来源地址https://www.toymoban.com/news/detail-676449.html
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 if
return 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到了这里，关于Day 84：网络结构与参数的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
